Sustainable energy systems: Role of optimization modeling techniques in power generation and supply—A review

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Renewable and Sustainable Energy Reviews 15 (2011) 3480–3500

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Renewable and Sustainable Energy Reviews

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ustainable energy systems: Role of optimization modeling techniques in powereneration and supply—A review

qeel Ahmed Bazmia,b, Gholamreza Zahedia,∗

Process Systems Engineering Centre (PROSPECT), Chemical Engineering Department, Faculty of Chemical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor Bahru,alaysiaBiomass Conversion Research center (BCRC), Department of Chemical Engineering, COMSATS Institute of Information Technology, Lahore, Pakistan

r t i c l e i n f o

rticle history:eceived 10 December 2010ccepted 30 May 2011vailable online 18 July 2011

eywords:ower generation and supply, Optimizationnd modeling, Electricity generationechnologies, Sustainable energy systems

a b s t r a c t

Electricity is conceivably the most multipurpose energy carrier in modern global economy, and thereforeprimarily linked to human and economic development. Energy sector reform is critical to sustainableenergy development and includes reviewing and reforming subsidies, establishing credible regulatoryframeworks, developing policy environments through regulatory interventions, and creating market-based approaches. Energy security has recently become an important policy driver and privatization ofthe electricity sector has secured energy supply and provided cheaper energy services in some countriesin the short term, but has led to contrary effects elsewhere due to increasing competition, resulting indeferred investments in plant and infrastructure due to longer-term uncertainties. On the other handglobal dependence on fossil fuels has led to the release of over 1100 GtCO2 into the atmosphere sincethe mid-19th century. Currently, energy-related GHG emissions, mainly from fossil fuel combustion forheat supply, electricity generation and transport, account for around 70% of total emissions including car-bon dioxide, methane and some traces of nitrous oxide. This multitude of aspects play a role in societaldebate in comparing electricity generating and supply options, such as cost, GHG emissions, radiologi-cal and toxicological exposure, occupational health and safety, employment, domestic energy security,and social impressions. Energy systems engineering provides a methodological scientific framework toarrive at realistic integrated solutions to complex energy problems, by adopting a holistic, systems-based
approach, especially at decision making and planning stage. Modeling and optimization found widespreadapplications in the study of physical and chemical systems, production planning and scheduling systems,location and transportation problems, resource allocation in financial systems, and engineering design.This article reviews the literature on power and supply sector developments and analyzes the role ofmodeling and optimization in this sector as well as the future prospective of optimization modeling as atool for sustainable energy systems.
© 2011 Elsevier Ltd. All rights reserved.

ontents

1. Introduction and background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34812. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3481

2.1. Current state of power generation technologies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34812.2. Decentralized systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34832.3. Optimization modeling studies related to power generation and supply techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3487

2.3.1. Power supply and distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34872.3.2. Power plant operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34902.3.3. Building energy consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3490
2.3.4. Industrial energy consumption . . . . . . . . . . . . . . . . . . . . . . . . . . .2.3.5. Power plants and carbon dioxide capture and storage (C2.3.6. Renewable energy mix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
∗ Corresponding author. Tel.: +607 553583; fax: +607 5566177.E-mail address: [email protected] (G. Zahedi).

364-0321/$ – see front matter © 2011 Elsevier Ltd. All rights reserved.oi:10.1016/j.rser.2011.05.003

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3491CS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3491. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3491

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A.A. Bazmi, G. Zahedi / Renewable and Sustainable Energy Reviews 15 (2011) 3480–3500 3481

2.4. Impact of optimization modeling in power sector development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34912.5. Future prospective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3495

3. Conclusion and outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3495Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3495References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3495

. Introduction and background

There has been an enormous increase in the demand for energyince the middle of the last century as a result of industrial devel-pment and population growth. Consequently, the development ofew and renewable sources of energy has become a matter of pri-rity in many countries all over the world. Electricity is conceivablyhe most multipurpose energy carrier in our modern global econ-my, and it is therefore primarily linked to human and economicevelopment. Electricity growth has overtaken that of any otheruel, leading to ever-increasing shares in the overall mix. This trends expected to continue throughout the following decades, witharge parts of the world population in developing countries appeal-ng connected to power grids. Electricity deserves precise attention

ith regard to its contribution to global greenhouse gas emissions,hich is reflected in the continuing development of low-carbon

echnologies for power generation. A multitude of features play aole in societal debate in comparing electricity generating options,uch as cost, gas emissions, radiological and toxicological exposure,reenhouse, occupational health and safety, employment, domes-ic energy security, and social impressions. Decision-makers will ineneral weight these aspects differently, and similarly the literatureeals with these issues in inconsistent ways.

Attempts to quantify the varied concerns of electricity gener-tion in one end-point indicator in order to aid decision-makingre anxious with problems, among which uncertainty and the dis-ounting are perhaps the two most extremely challenging [1]. Theormation of public perception is further complicated by the facthat media and political campaigns often comment more rapidlynd decisively on contentious issues, thus reaching the public moreffectively than sources of less biased factual information. Forxample nuclear energy is often portrayed and hence perceiveds an invisible danger under the control of a few, and associatedith military use, suppression of information, and high accident

isk [2,3]. On the other hand of the spectrum, large hydroelec-ric dams are associated with the forceful resettlement of largeumbers of people, and the destruction of archaeological heritagend biodiversity [4]. The concept of sustainable development isvolved for a liveable future where human needs are met whileeeping the balance with nature. Driving the global energy systemnto a sustainable path has arisen as a major concern and policybjective.

It is becoming gradually accepted that current energy systems,etworks encompassing everything from primary energy sourceso final energy services, are becoming unsustainable. Driven pri-

arily by concerns over urban air quality, global warming causedy greenhouse gas (GHG) emissions and dependence on deplet-

ng fossil fuel reserves, a transition to alternative energy systems iseceiving serious attention. Such a tradition will certainly involveeeting the growing energy demand of the future with greater

fficiency as well as using more renewable energy sources (such asind, solar, biomass, etc.). While many technical options exist foreveloping a future sustainable and less environmentally damagingnergy supply, they are often treated separately driven by their ownechnical communities and political groups. Energy systems engi-

are efficient and effective in solving energy systems engineer-ing problems, especially at decision making and planning stage.Based on this, multi-objective optimization and optimization underuncertainty produces further in-depth analyses and allows a deci-sion maker to make the final decision from many aspects of view.The aim of this study is to update existing status of optimizationmodeling role in world energy assessments with information pub-lished during the past decade, focusing on electricity-generatingtechnologies and the distribution or supply systems and to envisagethe importance of optimization techniques for future develop-ments in power sector.

2. Discussion

2.1. Current state of power generation technologies

A mix of options to lower the energy per unit of GDP and car-bon intensity of energy systems will be needed to achieve a trulysustainable energy future in a decarbonized world. Energy relatedGHG emissions are a by-product of the conversion and delivery sec-tor which includes extraction/refining, electricity generation anddirect transport of energy carriers in pipelines, wires, ships, etc., aswell as the energy end-use sectors i.e. transport, buildings, industry,agriculture, forestry and waste. Fig. 1 elaborates complex inter-actions between primary energy sources and energy carriers tomeet societal needs for energy services as used by the transport,buildings, industry and primary industry sectors.

Electricity is one of the driving forces of the economic devel-opment of societies. At the start of the 21st century, world facessignificant energy challenges. The concept of sustainable develop-ment is evolved for a liveable future where human needs are metwhile keeping the balance with nature. Initially, DC power sys-tems were popular in the 1870s and 1880s. Small systems weresold to factories around the world, both in urban areas, and remoteundeveloped areas for industrial/mining use. Thomas Edison, andWerner von Siemens lead the largest efforts to electrify the world.DC systems powered factories and small downtown areas, but didnot reach 95% of residents. It became clear that to make real thedream of to supplying whole cities with electric power you wouldneed to generate the power in one place (like a large river withgreat hydro-power potential) and transmit it to the city. This wasdone by several major advancements [6]:

Alternating current: Developed first in Italy and Germany, it quicklyproved to be the best method for harnessing electric power.American engineers like Elihu Thomson at GE and others atWestinghouse developed more advanced AC generators as theyengaged in fierce competition.Three phase power: Three phase AC power was first devel-oped in Germany by August Haselwander in 1887 and madeits major world debut in 1891 at the Lauffen-Frankfurt demon-stration [International Electro-Technical Exhibition] (built byDolivo-Dobrowolsky and Oskar von Miller). Mill Creek 1 in Cal-ifornia proved to be the first commercial use of three phasepower [2].

eering provides a methodological scientific framework to arrivet realistic integrated solutions to complex energy problems, bydopting a holistic, systems-based approach. Superstructure basedodeling strategy, along with MILP and MINLP solution algorithms

Transformers: Transformers control voltage and are a very impor-tant part of the system. Rudimentary transformers were firstdeveloped in Austro-Hungary and England, with the first fullydeveloped design coming from William Stanley in Massachusetts.

3482 A.A. Bazmi, G. Zahedi / Renewable and Sustainable Energy Reviews 15 (2011) 3480–3500

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Fig. 1. Complex interactions between primary energy source

Electricity was originally generated at remote hydroelectricams or by burning fossil fuels in the city centers, delivering elec-ricity to nearby buildings and recycling the waste heat to maketeam to heat the same buildings, while rural houses had no accesso power. Over time, coal plants grew in size, facing pressure toocate far from population because of their pollution. Transmission

ires carried the electricity many miles to users with a 10–15%oss [7]. Because it is not practical to transmit waste heat over longistances, the heat was vented. There was no good technology avail-ble for clean, local generation, so the wasted heat was a trade-offor cleaner air in the cities. Eventually a huge grid was developednd the power industry built all new generation in remote areas, farrom users. All plants were specially designed and built on site, cre-ting economies of scale. It cost less per unit of generation to buildarge plants than to build smaller plants. These conditions prevailedrom 1910 through 1960, and everyone in the power industry andovernment came to assume that remote, central generation wasptimal, that it would deliver power at the lowest cost versus otherlternatives.

Lenzen [8] reviewed eight power sector related technologies asescribed in subsequent text. Seven of these are generating tech-ologies: hydro-, nuclear, wind, photovoltaic, concentrating solar,eothermal and biomass power. The remaining technology is car-

able 1urrent state of development of electricity-generating technologies, adopted from [8].

Technology Annualgeneration(TWhel/y)

Capacityfactor l(%)

MitigationPotential(GtCO2)

Energyrequirement(kWhth/kW

Coal 7755 70–90 – 2.6–3.5Oil 1096 60–90 – 2.6–3.5Gas 3807 ≈60 – 2–3Carbon capture and storage – n.a. 150–250 2–2.5 + 0.3–1

Nuclear fission 2793 86k >180 0.12p

Large hydro 3121 41 200–300 0.1

Small hydro ≈250 ≈50 ≈100 n.a.Wind 260 24.5 ≈450–500 0.05Solar-photovoltaic 12 15 25–200 0.4/1–0.8/1Concentrating Solar ≈1 20–40 25–200 0.3Geothermal 60 70–90 25–500 n.a.Biomass 240 60 ≈100 2.3–4.2

nergy carriers to meet societal needs for energy services [5].

bon capture and storage. This selection is fairly representative fortechnologies that are important in terms of their potential capac-ity to contribute to a low-carbon world economy. Currently, onlynuclear and hydropower generate significant low-carbon portionsof global electricity. Table 1 shows a comparison among these tech-nologies in terms of annual generation, CO2 emission, generationcost and major barriers in deployment.

Carbon capture and storage is seen as a potentially significant CO2mitigation route because it would allow retaining major parts ofcurrent electricity generation infrastructure and build on existingknowledge and practices. Capture technologies are well under-stood but remain to be demonstrated at a large commercial scale,which is not expected before 2020 [8].Nuclear power is seen as a mature technology, with many reactor-years of experience, and modern reactors exhibiting a high degreeof safety. Nuclear power currently contributes 14% of global elec-tricity generation. The majority of nuclear reactors are thermalreactors, and this is expected to remain the case in the mid to long
term. Current average capacity factors of 86% are among the high-est of all technologies and levelised costs are competitive between4 and 7 US¢/kWh. Future Generation-IV reactor designs such asfast reactors and compact liquid metal or salt reactors, as well as
shel)

CO2

emissions(g/kWhel)

Generatingcost(US¢/kWh)

Barriers

900 3–6 Greenhouse gas emissions700 3–6 Resource constraints450 4–6 Fuel price170–280 3–6 + 0–4 Energy penalty, large-scale storage,

late deployment65 3–7 Waste disposal, proliferation, public

acceptance45–200 4–10 Resource potential, social and

environmental impact45 4–20 Resource potential≈65 3–7 Variability and grid integration40/150 – 100/200 10–20 Generating cost50–90 15–25 Generating cost20–140 6–8 Uncertain field capacity35–85 3–9 Efficiency, feedstock availability, cost

tainab
A.A. Bazmi, G. Zahedi / Renewable and Sus
advanced fuel cycles promise advances in reactor fuel utilization,enhanced proliferation resistance, reduction of nuclear waste vol-umes, and passive safety, however no design satisfies all criteria,and deployment is not expected to start before 2030 [8].Hydropower deploys 870 GW and contributes more than 3 PWhannually, or 17% of global electricity generation, and it thereforedominates the renewable technology suite. 90% of this electricityis generated by large hydro dams, with the remainder generatedby small, mostly run-of-river plants. The long-term resource oflarge hydro is limited because most large rivers have already beendammed [8].Wind power is the second strongest growing of all technologiesexamined in this report, with recent annual growth rates of about34%. The technology is mature and simple, and decades of expe-rience exist in a few countries. Due to strong economies of scale,wind turbines have grown to several megawatts per device, andwind farms have now been deployed off-shore. The wind energyindustry is still small but competitive: 120 GW of installed windpower contributes only about 1.5% or 260 TWh to global electricitygeneration at average capacity factors of around 25%, and levelisedcosts between 3 and 7 US¢/kWh, including variability cost [8].Photovoltaic power is the strongest growing of all technologiesexamined so far, with recent annual growth rates of around 40%.One of the largest markets was remote power supplies, in partic-ular for developing-country communities that are not connectedto electricity grids, but this has changed during recent years asdeveloped countries have embarked on rebated residential-roofdeployment programs. Photovoltaic modules are deployed dis-persed at small scale, which makes it difficult to ascertain globallyinstalled capacity, which is estimated at about 9 GW. Assuming anaverage capacity factor of 15%, global generation is 12 TWh [8].Concentrating solar power sometimes also referred to assolar–thermal power, was strongly pursued in the 1980s and1990s, but renewed interest has emerged recently. At present only0.4 GW are operating at large-scale plant levels, generating some1 TWh annually, using mostly parabolic troughs, but also tower,dish and Fresnel designs. Concentrating solar plants integrate wellwith conventional thermal plants, for example as fuel savers. Theaverage capacity factor is at least 20%, but can reach beyond 40%when heat transfer fluids with high thermal capacity are used forhourly storage. Combined with storage, the capacity credit of con-centrating solar power is higher than that of photovoltaic power,with sunny locations and high summer peak loads achieving cred-its of more than 80% [8].Geothermal power has been utilized for power generation since1920. Globally it only accounts for 10 GW deployed, but somecountries derive a major proportion of their electricity fromgeothermal reservoirs. Geothermal plant efficiency depends onthe quality of the resource. Low-temperature resources requireone or two flashing processes in order to utilize steam tur-bines. Electricity generation has been growing slowly at about 4%annually, and is currently about 60 TWh at 70% average capacityfactor, but capacity factors up to 90% are considered possi-ble [8]. Geothermal boasts the largest technical potential of alltechnologies, however resource development can be slow dueto a combination of uncertain field capacity and high drillingcost, requiring a step-wise development process, with resultsobtained from a small number of wells before the field is furtherexpanded.Biomass power is secondary to uses of biomass for liquid trans-portation fuels, but it is currently used economically in dedicatedapplications such as pulp and sugar industries. The search for alter-
native sources of energy was largely dormant until the energycrises of the 1970s and early 1980s sparked renewed interest in theissue. Among the alternative energy sources, vegetable oil-basedfuels were reconsidered, with biodiesel in form of esters of sun-
le Energy Reviews 15 (2011) 3480–3500 3483

flower oil to be reported in 1980 [9]. Biomass power is the areaamong these technologies which gained most encouraging atten-tion of researchers these days. A lot of research work has been donein last three decades on biomass utilization to yield transportationfuels. Balat et al. [10] reviewed the biological and thermochemicalmethods that could be used to produce bioethanol and carriedout an analysis of its global production trends. Demirbas [11]briefly reviewed the modern biomass-based transportation fuelssuch as fuels from Fischer–Tropsch synthesis, bioethanol, fattyacid (m)ethylester, biomethanol, and biohydrogen. Inayat et al.[12–14] developed mathematical models of hydrogen produc-tion process via biomass steam gasification using frameworkconsisting of kinetics models for char gasification, methanation,Boudouard, methane reforming, water gas shift and carbonationreactions to represent the gasification and CO2 adsorption in thegasifier implemented in MATLAB to predict the producer gas com-position, Bio-hydrogen yield and thermodynamic efficiency ofprocess, additionally, developed a model for flowsheet of hydro-gen production from empty fruit bunch from oil palm via steamgasification with in situ carbon dioxide capture, that incorporatesthe chemical reaction kinetics, mass and energy balances calcula-tions with parameter analysis on the influence of the temperature,steam/biomass and sorbent/biomass ratios. On the other hand,due to the overwhelming scientific evidence is that the unfet-tered use of fossil fuels is causing the world’s climate to change;biomass power is gaining an increasing interest. Global deploy-ment in biomass power is only around 50 GW generating 1.5%, orsome 240 TWh [8] of electricity. Currently, biomass plants com-bust agricultural and forestry residues, and waste. The long-termpotential of these types of feedstock is lower than that of dedicatedenergy crops, but the latter have preferential usage for biofuels.Dedicated biomass plants are small in size because of locally lim-ited feedstock availability and transportation requirements, andhence suffer from dis-economies of scale. Further technical chal-lenges are in developing gasifier, boiler and turbine designs thatcan handle variable- and low-quality biomass and deal with theresultant pollutant deposits and corrosion. Co-firing is regarded asthe preferred option, but at biomass shares above 10% it leads toefficiency losses and requires structural changes to plant compo-nents such as feeders. Levelised costs are competitive at between 3and 5 US¢/kWh. Capacity factors are lower than those for coal-firedpower plants, at around 60% [8].

Currently world’s energy requirements are mostly fulfilled byfossil fuels. However, the overwhelming scientific evidence is thatthe unfettered use of fossil fuels is causing the world’s climate tochange, with potential catastrophic effect. Until 1960s everyone inthe power industry and government came to assume that remote,central generation was optimal, that it would deliver power at thelowest cost versus other alternatives, and there was an assump-tion that remote, central generation was optimal, that it woulddeliver power at the lowest cost versus other alternatives. Becauseof their high level of integration, are susceptible to disturbancesin the supply chain. In the case of electricity especially, this supplyparadigm is losing some of its appeal. Apart from vulnerability, cen-tralized energy supply systems are losing its attractiveness due to anumber of further annoying factors including the depletion of fos-sil fuels and their climate change impact, the insecurities affectingenergy transportation infrastructure, and the desire of investors tominimize risks through the deployment of smaller-scale, modulargeneration and transmission systems.

2.2. Decentralized systems

Small-scale decentralized systems are emerging as a viablealternative as being less dependent upon centralized energy sup-


Table 2Comparative description of different decentralized technologies [16,17].

Technology Features Suitable Mode

Co-generation The average efficiency of co-generation systems is estimated to be 85%. The important co-generationtechnologies are bagasse co-generation, steam turbine combined heat, gas turbine combined heat

Both GC and SA

Biomass power Producer gas is the consequence of modern use of biomass and its conversion to higher forms of gaseous fuelthrough the process of gasification. For small-scale applications, biomass requirement range from about 5 kg/hup to about 500 kg/h

Both GC and SA

Small and mini-hydro power The small and mini-hydro power generation systems are environmentally benign as it is run of the rivertechnology where the river flow is not impeded; as a result the river flooding problem is eliminated. Thesystem is classified as small-hydro if the system size varies between 2.5 and 25 MW, mini-hydro typically fallsbelow 2 MW, micro-hydro schemes fall below 500 kW and pico-hydro below 10 kW capacity

SA

Solar PV power Efficiency of commercially available solar PV varies between 7 and 17%. Because of its high initial investment,cost of generation per kWh becomes high making it unaffordable

SA

Biogas The gas that is produced through anaerobic digestion of biomass and other wastes like vegetable residues,animal dung, etc. is called biogas. Biogas generally is 60% methane and 40% carbon dioxide

SA

Wind power Similar to PV systems wind energy systems are also site and season specific. Wind energy systems mostlyfew v

GC

poswtcncaesrahdtot(sStSfiadffims

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operate in grid-connected mode, but only in afor water pumping

ly, and can sometimes use multiple energy sources. On the basisf type of energy resources used, decentralized power is also clas-ified as non-renewable and renewable. These classifications alongith an overabundance of technological alternatives have made

he prioritization process of decentralized power quite compli-ated for decision making. Establishing local generation and a localetwork may be cheaper, easier and faster than extending theentral-station network to remote areas of modest load. The ruralreas of many developing and emerging countries are unlikelyver to see the arrival of classical synchronized AC transmis-ion lines. Decentralized local systems, including those using localesources of renewable energy such as wind, solar and biomass,ppear much more feasible [15].There is abundant literature, whichas discussed various approaches that have been used to supportecision making under such complex situations. The implemen-ation of decentralized energy systems depends upon the extentf decentralization. The extent of decentralization also determineshe condition for the system to be operated in either grid-connectedGC) or stand-alone (SA) mode. A number of articles have been pre-ented for both success and failure narratives of implementation ofA as well as GC systems. But most of the articles were appliedo isolated cases. A generalized approach to assess suitability ofA and GC systems at a given location, based on techno-economicnancial-environmental feasibility does not find adequate cover-ge. Table 2 elaborates the important available technologies forecentralized power generation applicable in mode(s) and theireatures. Only biomass based technologies (cogeneration and gasi-cation) are found to be more versatile towards both GC and SAodes and both can serve as combined heat and power (CHP)

ystem.High fossil fuel prices recorded between 2003 and 2008, com-

ined with concerns about the environmental consequences ofHG emissions, have renewed interest in the development of alter-atives to fossil fuels—specifically, nuclear power and renewablenergy sources. A lot of studies have been made in last two decadeso assess and implement decentralized power systems. Recentmportant and valued researches on different aspects of decen-ralized power system are tabulated as Table 3. High fossil fuelrices recorded between 2003 and 2008, combined with concernsbout the environmental consequences of greenhouse gas emis-ions, have renewed interest in the development of alternativeso fossil fuels—specifically, nuclear power and renewable energyources. In the mainstream media, these systems are increasingly
ssociated with the benefits from virtually free, low-carbon andocally available renewable energy resources such as wind andolar power. But in the specific context of the built environment,he emphasis is on decentralized electricity generation associated
illages isolated systems are operated to provide electricity

with heat production. It is therefore important to realize the poten-tial of biomass based technologies in GHG emission reduction indeveloped countries and their role in promoting sustainable ruraldevelopment in developing countries.

World net electricity generation increases by 87% in the Refer-ence case, from 18.8 trillion kWh in 2007 to 25.0 trillion kWh in2020 and 35.2 trillion kWh in 2035 [100]. Renewable energy is thefastest-growing source of electricity generation in the InternationalEnergy Outlook 2010 (IEO2010) Reference case. Table 4 showsthe world net renewable electricity generation by energy source,2007–2035.The mix of primary fuels used to generate electricityhas changed a great deal over the past four decades on a worldwidebasis. Coal continues to be the fuel most widely used for electric-ity generation, although generation from nuclear power increasedrapidly from the 1970s through the 1980s, and natural-gas-firedgeneration grew rapidly in the 1980s and 1990s. The use of oilfor electricity generation has been declining since themid-1970s,when oil prices rose sharply. Total generations from renewableresources increases by 3.0% annually, and the renewable share ofworld electricity generation grew from 18% in 2007 to 23% in 2035.Almost 80% of the increase is in hydroelectric power and windpower. The contribution of wind energy, in particular, has grownswiftly over the past decade, from 18 GW of net installed capac-ity at the end of 2000 to 159 GW at the end of 2009—a trend thatcontinues into the future. Of the 4.5 trillion kWh of new renew-able generation added over the projection period, 2.4 trillion kWh(54%) is attributed to hydroelectric power and 1.2 trillion kWh(26%) to wind. Electricity generation from nuclear power increasesfrom about 2.6 trillion kWh in 2007 to 4.5 trillion kWh in 2035[101].

Wind and solar are intermittent technologies that can be usedonly when resources are available. Once built, the cost of operat-ing wind or solar technologies, when the resource is available, isgenerally much less than the cost of operating conventional renew-able generation. Solar power, for instance, is currently a “niche”source of renewable energy but can be economical where electric-ity prices are especially high, where peak load pricing occurs, orwhere government incentives are available.

Abundant literature is available on issues, problems andprogress in the power sector. Most of the existing literature isconcerned with implications of climate change mitigation policieson energy technologies, prices, and emissions. For instance, theworld moves towards concerted action to stabilize concentrations
of greenhouse gases (GHG) in the earth’s atmosphere, the profileof energy resources and technologies being used. Table 5 elabo-rates the most recent potential researches (among this abundantliterature) in energy and power sector (during last decade).


Table 3Recent important and valued researches of decentralized electricity systems; extracted from [17].

Author(s) Year Study Domain/Emphasis Reference

M. A. Sheikh 2010 Review of RE supply options; solar energy, wind energy, microhydel power, biogas and geothermal energy inPakistan.

[18]

B. Iglinski, W. Kujawski,et al.

2010 Current status and future objectives of wind power, solar power and biomass power in theKujawsko-Pomorskie Voivodeship (Poland).

[19]

F. Chen et al. 2010 Potential to develop various renewable energies, such as solar energy, biomass energy, wind power,geothermal energy, hydropower in Taiwan and the review of the achievements, polices and future plans inthis area.

[20]

A. Kumar et al. 2010 Review of the availability, current status, major achievements and future potentials of renewable energyoptions including biomass, hydropower, wind energy, solar energy and geothermal energy .in India.

[21]

M.A. Eltawil and Z. Zhao 2010 A review study to investigate and emphasize the importance of the grid-connected PV system regarding theintermittent nature of renewable generation, and the characterization of PV generation with regard to gridcode compliance with a critically review on expected potential problems associated with high penetrationlevels and islanding prevention methods of grid tied PV.

[22]

Salas and Olías 2009 Extensive analysis of all the electrical parameters of grid-connected solar inverters for applications below10 kW.

[23]

Carlos and Khang 2009 A generalized framework to assess the factors affecting the successful completion of grid-connected biomassenergy projects validated with real world data of power plants (Thailand).

[24]

Doukas and Karakosta 2009 The economic, environmental and sustainable benefits as well as removal of barriers for satisfactorydissemination of important RES technologies.

[25]

M. Asif 2009 Renewable energy-based electricity supply options such as macro/micro hydro, Biomass in the form of cropresidues and animal waste and municipal solid waste, small wind electric generators and photovoltaics inPakistan.

[26]

B. Ghobadian et al. 2009 Potential and feasibility to develop various renewable energies, such as solar energy, biomass and biogasenergy, wind power and geothermal energy in Iran.

[27]

L. Chen et al. 2009 Feasibility of densified solid biofuels technology for utilizing agro-residues in China. [28]Y. Himri et al. 2009 A review of the use of renewable energy situation and future objectives in Algeria. [29]J. Paska et al. 2009 An overview on the present state and perspectives of using renewable energy sources including hydropower,

solar energy, wind energy biomass and biogas in Poland.[30]

N.T. Nguyen and M.Ha-Duong

2009 An overview about possibilities of generating electricity and reducing carbon emissions in Vietnam, thepotential of all renewable energy sources together for electricity generation, development of Vietnam’s powersector from 1995 to 2005 and official projections out to 2030 also to analyze the optimized integration of alarge array of grid connected renewable energy technologies, i.e. hydro, geothermal, biomass, wind, solar etc.in the power electric generation system. using the IRP model, to meet the challenges of soaring electricitydemand, growing environmental concerns, energy pricing climax, and energy security over the period2010–2030.

[31]

C. Gokcol et al. 2009 An overview on the importance and potential of biomass and its utilization for biomass energy in Turkey. [32]A. Yilanci, I. Dincer, and

H.K. Ozturk2009 An overview of solar hydrogen production methods, their current status up to the present 2009, preliminary

energy and exergy efficiency analyses for Solar-hydrogen/fuel cell hybrid energy systems for stationary (casestudy, Denizli, Turkey).

[33]

Walker 2008 Assessment of the linkage between stand-alone systems and fuel poverty (case study, UK). [34]Purohit 2008 A detailed estimation of small hydro power (SHP) potential in India under CDM. [35]Adhikari et al. 2008 An overview of CDM portfolio in Thailand by cataloguing potential, opportunities and barriers for executing

decentralized sustainable renewable energy projects in the context of CDM.[36]

Lybaek 2008 Assessment of market opportunities in Asian countries for SA biomass CHP (case study, Thailand). [37]U.K. Mirza et al. 2008 Potential of biomass for energy generation in Pakistan. [38]M.R. Nouni et al. 2008 Renewable energy-based decentralized electricity supply options such as micro hydro, dual fuel biomass

gasifier systems, small wind electric generators and photovoltaics in India.[39]

S. Bilgen et al. 2008 Renewable energy potential and utilization in Turkey and Global warming issues. [40]I. Rofiqul et al. 2008 Review of RE supply options; solar energy, wind energy, hydro power, biogas and tidal energy in Bangladesh

with concluding remarks “There is no way other than taking bio and solar energy for reducing environmentaldegradation.”

[41]

S. Sumathi et al. 2008 Potential of oil palm as bio-diesel crop and waste stream as a source to produce vast amounts of bio-gas andother values added products.

[42]

Zoulias and Lymberopoulos 2007 Simulation and optimization of replacement option of conventional technologies with hydrogen technologies,fuel cells in an existing PV-diesel operated in stand-alone mode by using HOMER) tool.

[43]

Kasseris et al. 2007 Optimization model of the wind-fuel cell hybrid system for larger output under strict and lenient gridnetwork restrictions.

[44]

Hiremath et al. 2007 Total potential, installed capacities of decentralized energy systems (case study, India). [45]Purohit and Michaelowa 2007 Feasibility of bagasse cogeneration projects under CDM with a total CER potential up to 26 million. [46]A.M. Omer 2007 Present status of rural energy recourses including solar energy biomass and biogas energy in Sudan. [47]X. Zeng et al. 2007 An overview on the technology status, potential and the future research and development of straw in the

biomass energy portfolio in China.[48]

A.K. Hossain and O. Badr 2007 Biomass energy potential for the planning small- to medium-scale biomass-to-electricity plants in Bangladesh. [49]Holland et al. 2006 Assessment of the critical factors for successful diffusion of standalone systems in rural regions. [50]Gulli 2006 Social-cost benefit analysis of stand-alone combined heat and power (CHP) systems based on both internal

and external system costs.[51]

I.M. Bugaje 2006 Review of RE scenario in Africa using South Africa, Egypt, Nigeria and Mali as case studies with solar energyand wood biomass as major recourses.

[52]

Mahmoud and Ibrik 2006 Computer-based dynamic economic evaluation model with key economic efficiency indicators to assess threesupply options namely solar PV, diesel generators in SA system and grid extension.

[53]

Hiremath et al. 2006 Review on decentralized energy planning models. [54]Jebaraj and Iniyan 2006 Reviews on decentralized energy models. [55]Ravindranath et al. 2006 Assessment of carbon abatement potential of bioenergy technologies (BETs) by comparison with fossil fuel

alternatives.[56]

Bernal-Agustin andDufo-Lopez

2006 Economic analysis on the grid-connected Solar PV system (case study, Spain). [57]


Table 3 (Continued)


Faulin et al. 2006 Potential of RETs in generating local employment (case study, Spain). [58]Fernandez-Infantes et al. 2006 A computer-based decision support system to design the GC PV system based on electrical, environmental and

economic considerations.[59]

Dosiek and Pillay 2005 Design of a horizontal axis wind SA systems by simulation using MATLAB/SIMULINK. [60]Rabah 2005 Practical implementation of a stand-alone solar PV to improve the quality of life of poor (case study, Kenya). [61]Nakata et al. 2005 System configuration and operation of hybrid systems for the supply of heat and power based on a non-linear

programming optimization model and METANet economic modeling system (Japan).[62]

Khan and Iqbal 2005 SA systems hybrid with other both renewable and nonrenewable sources of energy carriers as a potentialsolution to the problems of SA systems like low capacity factors, excess battery costs and limited capacity tostore extra energy.(using HOMER software to optimize and arrive at the right combination of energy systems).

[63]

Pelet et al. 2005 Multi-objective evolutionary programming technique to rationalize the design of energy systems for remotelocations.

[64]

Santarelli and Pellegrino 2005 Mathematical optimization model to minimize the total investment cost of hydrogen based stand-alonesystem to supply electricity to residential users, integrated with renewable energy systems like solar PV andmicro-hydro.

[65]

Kamel and Dahl 2005 Economic assessment of hybrid solar–wind systems against the diesel using NREL’s renewable energysimulation tool called HOMER (hybrid optimization model for electric renewables).

[66]

Jeong et al. 2005 A fuzzy logic algorithm as a strategy for effective load management resulting an improved resilience andsystem operation efficiency of a hybrid fuel-cell and battery stand-alone system.

[67]

Silveira 2005 The potential of CDM in promoting bio-energy technologies to promote sustainable development indeveloping countries.

[68]

Santarelli et al. 2004 Design methodology of a stand-alone system, by integrating renewable energy systems, based on energyanalysis, electricity management and hydrogen management (case study, Italy).

[69]

Hoogwijk et al. 2004 Some of the facts about geographical, technical and economic potential of wind across the globe. [70]Lindenberger et al. 2004 Analyses of modernization options for a local energy system, based on both demand reduction and

supply-related measures as an extension of the optimization model called deco (dynamic energy, emission,and cost optimization).

[71]

Kishore et al. 2004 The potential role of biomass in global climate change mitigation and the extent of commercialization andmainstreaming of biomass energy technologies within the framework of clean development mechanism(CDM). A case study

[72]

Beck and Martinot 2004 Policies and key barriers for diffusion of SA systems and GC systems like unfavorable pricing rules, privateownership, and lack of locational pricing leading to undervaluation of GC systems.

[73]

Bakos and Tsagas 2003 Techno-economic assessment for technical feasibility and economic viability of a hybrid solar/windinstallation for residential electrification and heat (case study, Greece).

[74]

J. Chang, D.Y.C. Leung et al. 2003 An overview on the research and development of renewable energy, such as solar, biomass, geothermal, oceanand wind energy in China.

[75]

Kumar et al. 2003 Power costs and optimum size of a stand-alone biomass energy plant based on agricultural residues, wholeforest residues, and residues of lumber activities (case study, Canada).

[76]

Kaldellis 2003 Financial analysis of grid-connected wind energy systems (of the entire Greek state). [77]Atikol and Guven 2003 Sizing of the grid-connected cogeneration systems based on electrical load and thermal load in textile

industries (case study, Turkey).[78]

Dasappa et al. 2003 Isolated biomass gasifiers being used to provide low temperature and high temperature thermal requirementsof industries.

[79]

Ro and Rahman 2003 A computer model tested controller system to improve the system stability of fuel cell GC systems in powerdistribution network.

[80]

Kolhe et al. 2002 Economic viability of a stand-alone solar PV system along with a diesel-powered system. [81]Chakrabarti and

Chakrabarti2002 Feasibility study for solar energy based SPV stand-alone system based on socio-economic and environmental

aspects (case study, India).[82]

Martinot 2002 An extensive discussion on the policies, strategies and lessons learnt from the GEF (Global environmentalFacility) project on the status of grid-based renewable energy systems in developing countries.

[83]

Manolakos et al. 2001 Simulation based software tool for optimizing the design of a hybrid energy system consisting of wind and PVto supply electricity and water for a remote island village.

[84]

Gupta 2000 Policy approach in India for grid based RETs. [85]Stone et al. 2000 Investment, operational costs and impact of rural electrification project initiatives (case study, India). [86]Bates and Wilshaw 1999 Status of solar PV power systems, governmental policies towards renewable and key market barriers for the

successful and quick diffusion of solar PV power systems.[87]

Ackermann et al. 1999 Simulation based validated economic optimization tool to evaluate different options for distributedgeneration, and improve power quality of an embedded wind generation system in weak grid conditions.

[88]

Meurer et al. 1999 Generation of measurement performance data of an autonomous SA hybrid renewable energy system (RES) tooptimize the energy output and operational reliability with the aid of simulation programs.

[89]

Vosen and Keller 1999 Optimization and simulation model for a SA solar powered battery-hydrogen hybrid system for fluctuatingdemand and supply scenarios using two storage algorithms for with or without prior knowledge about thefuture demand.

[90]

Rana et al. 1998 Optimal RE mix for specific energy demand. [91]Sidrach-de-Cardona and

Lopez1998 Generalized model to evaluate energy losses and the performance of (a 2 kW) grid-connected photovoltaic

system at different regions, climate conditions and irradiation (case study, Spain).[92]

Gabler and Luther 1998 Development and validation of simulation and optimization model for a wind–solar hybrid SA system tooptimize the design of converters and storage devices so as to minimize the energy payback time.

[93]

Ravindranath and Hall 1995 System configuration, operational details, and costing of a biogas unit (case study, India). [94]Ravindranath 1993 Biomass Gasification as environmentally sound technology for decentralizes electricity. [95]Ramakumar et al. 1992 A knowledge based approach for the design of integrated renewable energy systems (IRES). [96]Joshi et al. 1992 Development of a linear mathematical model to optimize the energy mix of different energy source-end-use

conversion devices to supply energy to villages (case study, India).[97]

Siyambalapitiya et al. 1991 Importance of the pre-evaluation of techno-economic-social parameters of the grid-connected ruralelectrification systems.

[98]

Reddy et al. 1990 Choice of technology for quality energy services (cost comparison). [99]


Table 4World net renewable electricity generation by energy source, 2007-2035 (Billion kWh) [100].

Region 2007 2015 2020 2025 2030 2035 Average annual percentchange, 2007–2035

Hydropower 2999 3689 4166 4591 5034 5418 2.1Wind 165 682 902 1115 1234 1355 7.8Geothermal 57 98 108 119 142 160 3.7Solar 6 95 126 140 153 165 12.7Other 235 394 515 653 773 874 4.8

66

2a

wttateoctfo

rwdes

teS

iriis

Total 3462 4958 5817

.3. Optimization modeling studies related to power generationnd supply techniques

Over the second half of the 20th century, optimization foundidespread applications in the study of physical and chemical sys-

ems, production planning and scheduling systems, location andransportation problems, resource allocation in financial systems,nd engineering design. A large number of problems in produc-ion planning and scheduling, location, transportation, finance, andngineering design require that decisions be made in the presencef uncertainty. The optimization under uncertainty includes thelassical recourse-based stochastic programming, robust stochas-ic programming, probabilistic (chance-constraint) programming,uzzy programming, and stochastic dynamic programming. Theseptimization techniques are briefly reviewed by Sahinidis [145].

During the course of 21st century, energy systems will beequired to meet several important goals, including conformanceith the environmental, economic, and social goals of sustainableevelopment. The existence of multiple goals, multiple stockhold-rs, and numerous available technologies lend itself to the use of aystem approach to solving energy system problems.

Energy systems engineering provides a methodological scien-ific framework to arrive at realistic integrated solutions to complexnergy problems, by adopting a holistic, system-based approach.uch an integrated approach features:

A superstructure representation where alternatives in terms ofenergy technologies, raw materials and possible routes towardselectricity and hydrogen, among others, are captured.A mixed-integer optimization model which allows for the devel-opment of a single mathematical model to represent all possibleenergy system alternatives within the superstructure, along withappropriate solution algorithms (MILP, MINLP, etc.).A multi-objective optimization approach to simultaneously addressand quantify the trade-offs among competing objectives, suchas profitability, environmental impacts, energy consumption, andsystem operability.An optimization under uncertainty strategy to analyze the impactof technological uncertainties over a long-term horizon on theprofit/energy consumption/environmental impacts of an energysystem.Artificial intelligence (AI) techniques are applied for modeling, iden-tification, optimization, prediction, forecasting and control ofcomplex systems like Adaptive Control, Robust Pattern Detection,Optimization, Scheduling and Complex Mapping. AI is commonlydefined as the science and engineering of making intelligentmachines, especially intelligent computer programs.

AI-based systems are being developed and deployed worldwiden a wide variety of applications, mainly because of their symbolic
easoning, flexibility and explanation capabilities. AI has been usedn different sectors, such as engineering, economics, medicine, mil-tary, marine, etc. Mellita and Kalogirou [146] used AI techniques toolve problems in photovoltaic systems application including fore-
18 7336 7972 3.0

casting and modeling of meteorological data-, sizing of photovoltaicsystems and modeling-, simulation, and control of photovoltaicsystems and highlighted the potential of AI as design tool in pho-tovoltaic systems. Nowicka-Zagrajeka et al. [147] addressed theissue of modeling and forecasting electricity loads applying a two-step procedure to a series of system-wide loads from the Californiapower market using ANN approach. Chaudry et al. [148] developeda multi-time period combined gas and electricity network opti-mization model which takes into account the varying nature of gasflows, network support facilities such as gas storage and the powerramping characteristics of electricity generation units.

2.3.1. Power supply and distributionDuring the last decade several new concepts of energy planning

and management such as decentralized planning, energy conser-vation through improved technologies, waste recycling, integratedenergy planning, introduction of renewable energy sources andenergy forecasting have emerged. Recent trends in electric util-ity restructuring have included increasing competition in an openelectricity supply marketplace, which has sharpened attention tokeeping operation and maintenance costs for infrastructure as lowas possible. Some research literature suggests that one side-effectof restructuring has been a reduced willingness on the part ofsome utilities to invest in environmental protection beyond whatis absolutely required by law and regulation [149]. Within theelectricity sector, network planning is closely related to genera-tion planning. In recent context, where centralized energy supplysystems are losing its attractiveness due to a number of furtherannoying factors including the depletion of fossil fuels and theirclimate change impact, the actual operation of the generating unitsno longer depends on state-or utility-based centralized procedures,but rather on decentralized decisions of generation firms whosegoals are to maximize their own profits. All firms compete to pro-vide generation services at a price set by the market, as a result ofthe interaction of all of them and the demand. As a result, electric-ity firms are exposed to significantly higher risks and their needfor suitable decision-support models has greatly increased. Hence,a new area of highly interesting research for the electrical industryhas opened up. Numerous publications give evidence of extensiveeffort by the research community to develop electricity marketmodels adapted to the new competitive context.

Ventosa [150] reviewed the electricity generation market mod-eling focusing on a survey of the most relevant publicationsregarding electricity market modeling, identifying three majortrends: optimization models, equilibrium models and simulationmodels and concluded That “the impressive advances registered inthis research field underscore how much interest this matter hasdrawn during the last decade”. Jebaraj and Iniyan [151] presented areview on different types of models such as energy planning mod-
els, energy supply–demand models, forecasting models, renewableenergy models, emission reduction models, optimization modelsand models based on neural network and fuzzy and suggested thatthe neural networks can be used in the energy forecasting and the


Table 5Potential researches in energy and power sector in last decade.


N. Boccard 2010 An overview of the ability of wind power output to serve electricity demand all around theyear, hour by hour, focusing on “capacity credit”, methodology to assess the “social cost” ofwind power and contribution (or lack there of) of wind power generation (WPG) to adequacy,with special analysis of the cost estimates for the six European countries (Germany, Denmark,Spain, France, Portugal and Ireland) on the basis of load and WPG output data.

[102]

J. Clifton and B.J. Boruff 2010 A review of policies designed to stimulate the contribution of renewable sources highlights thecontinued reliance upon fossil fuels to supply current and future electricity needs in Australia.Potential CSP sites are defined in the Wheat belt region of Western Australia throughoverlaying environmental variables and electricity infrastructure on a high resolution gridusing widely available datasets and standard geographical information system (GIS) software.

[103]

Cansino, J.M., et al. 2010 A comprehensive overview of the main tax incentives used in the EU-27 member States topromote green electricity focusing on the European regulation of tax incentives for greenelectricity, the actual share of renewable energy sources in gross electricity consumption,main tax incentives considered in direct taxes, and pigouvian and other taxes.

[104]

I. Purohit and P. Purohit 2010 A technical and economic assessment of concentrating solar power (CSP) technologies in Indiataking two projects namely PS-10 (based on power tower technology) and ANDASOL-1 (basedon parabolic trough collector technology) as reference cases.

[105]

J. Badcock and M. Lenzen 2010 The estimation of the extent of subsidization globally, via selected mechanisms, for a numberof different electricity-generating technologies covering coal-fired, nuclear, wind, solar PV,concentrating solar, geothermal, biomass and hydroelectric power.

[106]

L. Kosnik 2010 An overview on cost-benefit perspective, topographical features for small scale hydropowersites in the US and to determine the cost-effectiveness of developing these sites. Concludingthat while the average cost of developing small scale hydropower is relatively high, there stillremain hundreds of sites on the low end of the cost scale that are cost-effective to developright now.

[107]

U. Arena et al. 2010 A comparison between the most promising design configurations for the industrial applicationof gasification based, biomass-to-energy co-generators in the 100–600 kWe range and thetechno-economic performances of two energy generation devices, a gas engine and anexternally fired gas turbine, have been estimated on the basis of the manufacturer’sspecifications drawing conclusion that the internal combustion engine layout is the solutionthat currently offers the higher reliability and provides the higher internal rate of return forthe investigated range of electrical energy production.

[108]

Gomis-Bellmunt, O., et al. 2010 The evaluation of power generated by variable and constant frequency offshore wind farmsconnected to a single large power converter, the evaluation of the power capture increasewhen employing a variable frequency wind farm connected to a HVDC grid by means of largepower converter proving the grid frequency and voltage for the wind farm, focusing on theenergy capture analysis, other extremely important issues related to variable frequency windfarm engineering.

[109]

M. Thirugnanasambandam et al. 2010 Review on the current status of the solar thermal technologies, performance analyses ofexisting designs (study), mathematical simulation (design) and fabrication of innovativedesigns with suggested improvements and development.

[110]

M.M. Abu-Khader 2009 A comprehensive review on recent advances in nuclear power sector. [111]I. Altmana and T. Johnson 2009 A review of organizational issues, the broad industrial structure of the current bio-power

industry and current organizational mechanisms based on data from the U.S. EnergyInformation Administration.

[112]

M. Bolinger and R. Wiser 2009 An overview of wind power sector growth both globally and specifically in the USdemonstrating recent increases in wind turbine pricing, installed project costs, and windpower prices and the factors to mitigate the impact of rising costs on wind power prices in theUnited States in recent years.

[113]

N. Caldés et al. 2009 The socio-economic impacts of increasing the installed solar thermal energy power capacity inSpain, using an input–output analysis under two different scenarios: (i) based on two solarthermal power plants currently in operation (with 50 and 17 MW of installed capacity); (ii) thecompliance to the Spanish Renewable Energy Plan (PER) 2005–2010 reaching 500 MW by2010.

[114]

C. Chen and E.S. Rubin 2009 The comprehensive overview the plant configurations of IGCC systems with and without CO2

capture, analysis of several factors influencing the performance and cost of IGCC systems withand without CO2 capture, including coal quality and CO2 removal efficiency, additionallyfactors in a probabilistic uncertainty analysis and the potential effects of two advancedtechnologies—an ion transport membrane (ITM) system for oxygen production and anH-frame gas turbine (GT) system for power generation—on the performance and cost of IGCCsystems with CCS.

[115]

Othman, M.R., et al. 2009 A review summarizing the clean development mechanism (CDM) and adoption of CMD forMalaysia and Indonesia, a comparison of energy policies of both countries with advancedindustrialized countries, current status of carbon capture and storage (CCS) technologies, andchoice of coal fired power plants for Malaysia and Indonesia.

[116]

V. Fthenakis et al. 2009 A study to forecast future energy demand levels in three distinct stages (Present to 2020,2020–2050, and 2050–2100) in realizing the development of the SW solar power plant for theUS, and its extrapolation for the deployment level of existing solar technologies,supplemented by other renewable energy sources, to prove the feasibility for solar energy tosupply that energy including (1) PV, (2) PV combined with compressed air energy storage(CAES) power plants, and (3) CSP plants with thermal storage systems with concludingremarks that the it is clearly feasible to replace the present fossil fuel energy infrastructure inthe US with solar power and other renewables, and reduce CO2 emissions to a levelcommensurate with the most aggressive climate-change goals.

[117]

J. Hansson et al. 2009 A review on The European coal-fired power plant infrastructure, technical biomass co-firingpotential and factors influencing the prospects for co-firing.

[118]


Table 5 (Continued)


D.L. Gallup 2009 A review study to highlight some production engineering advances in geothermal technologythat have been made over about the past two decades.

[119]

M.I. Sohel et al. 2009 A theoretical analysis including modeling and simulation of a typical plant using NewZealand’s local weather data taking the Rotokawa binary cycle geothermal plant is as a testcase and compared against other base load options, comparison of improved summer hot-dayperformance to other peak load options as well as policy implications.

[120]

A. Yilanci et al. 2009 A review on solar-hydrogen/fuel cell hybrid energy systems describing solar hydrogenproduction methods, and their current status, and preliminary energy and exergy efficiencyanalyses for a photovoltaic-hydrogen/fuel cell hybrid energy system in Denizli, Turkey withthree different energy demand paths – from photovoltaic panels to the consumer. Minimumand maximum overall energy and exergy efficiencies of the system are calculated based onthese paths.

[121]

Neij, L. 2008 An analytical framework for the analysis of future cost development of new energytechnologies for electricity generation; based on an assessment of available experience curves,complemented with bottom-up analysis of sources of cost reductions and, for sometechnologies, judgmental expert assessments of long-term development paths.

[122]

L. Kosnik 2008 A study of the potential for water power development as one method to reduce US greenhousegas emissions from new small/micro hydropower dams, uprating facilities at existing largehydropower dams, new generating facilities at existing non-hydropower dams, andhydrokinetics as well as the cost-effectiveness of developing these sources of water-basedenergy, concluding that while water power will never be the complete answer toemissions-free energy production, a strong case can be made that it can be a useful part of theanswer.

[123]

D. Driver 2008 A review on materials priorities for energy and power sector and current status includingmaterials for energy conservation, turbine technology, Water power, fuel cell technology,nuclear fission and fusion materials, high-temperature power generation materials, solarenergy—photovoltaics (PVs), wind power and functional materials for energy generation andconservation.

[124]

T. Oliver 2008 A study discussing the current status of the science and technologies for fossil-fuelled powergeneration and outlines likely future technologies, development targets and timescalesfollowed by a description of the scientific and technological developments that are needed tomeet these challenges.

[125]

C. Yin et al. 2008 A review on the state-of-the-art knowledge on grate-fired boilers burning biomass: the keyelements in the firing system and the development, the important combustion mechanism,the recent breakthrough in the technology, the most pressing issues, the current research anddevelopment activities, and the critical future problems to be resolved.

[126]

M. Mueller and R. Wallace 2008 A comprehensive overview on some of the key challenges to be met in the development ofmarine renewable energy technology.

[127]

S. Shanthakumar et al. 2008 A critical review of various flue gas conditioning techniques employed for controlling thesuspended particulate matter (SPM) level in thermal power stations including the in-depthanalysis of data obtained from different thermal power stations of the world.

[128]

C. Di Blasi 2008 A review on chemical kinetics of biomass/char combustion and gasification, criticallyanalyzing the state of the art of rate laws and kinetic constants for the gasification, withcarbon dioxide and steam, and the combustion of chars produced from lignocellulosic fuels,including a brief outline about yields and composition of pyrolysis products, and the roleplayed by various factors, such as heating rate, temperature and pressure of the pyrolysisstage, feedstock and content/composition of the inorganic matter, on char reactivity.

[129]

Som, S. and A. Datta 2008 A comprehensive review pertaining to fundamental studies on thermodynamic irreversibilityand exergy analysis in the processes of combustion of gaseous, liquid and solid fuels,concluding that the important consideration of fuel economy for a combustor of apower-producing unit pertains to the trade-off between the efficient conversion of energyquantity and minimum destruction of energy quality (exergy).

[130]

E.S. Rubin et al. 2007 A Study summarizing and comparing the results of recent studies of the current cost of fossilfuel power systems with and without CO2 capture, including pulverized coal (PC) combustionplants, coal-based integrated gasification combined cycle (IGCC) plants, and natural gascombined cycle (NGCC) plants; a broader range of key assumptions that influence these costcomparisons; and quantify the implications of CCS energy requirements on plant-levelresource requirements and multi-media emissions. A generalized modeling tool is used toestimate and compare the emissions, efficiency, resource requirements and current costs offossil fuel power plants with CCS on a systematic basis.

[131]

K. Damen et al. 2007 A comparative study analyzing the promising electricity and hydrogen production chains withCO2 capture, transport and storage and energy carrier transmission, distribution and end-useto assess (avoided) CO2 emissions, energy production costs and CO2 mitigation costs.

[132]

J. Koornneef, M. Junginger, and A. Faaij 2007 An overview analyzing the development and economical performance of fluidized bedcombustion (FBC) and its derivatives circulating fluidized bed (CFB) and bubbling fluidized bed(BFB) with a descriptive overview given of the technology and the market penetration base ona database comprises technological and economical data on 491 FBC projects.

[133]

J. Beer 2007 A review of electric power generation system development with special attention to plantefficiency.

[134]

J. Decarolis and D. Keith 2006 An economic characterization of a wind system in which long-distance electricitytransmission, storage, and gas turbines are used to supplement variable wind power output tomeet a time-varying load.

[135]

R.B. Duffey 2005 Role for nuclear power in the future hydrogen economy and synergy of nuclear with windpower for hydrogen generation.

[136]


Table 5 (Continued)


B. Buhre et al. 2005 A comprehensive review of researches undertaken on Oxy-fuel combustion technology forcoal-fired power generation, the status of the technology development and assessmentsproviding comparisons with other power generation options, and identification of researchneeds in the area.

[137]

A. Khaliq and R. Kumar 2005 The application of finite-time heat-transfer theory to optimize ecologically the power outputof an endo-reversible and regenerative gas-turbine power-cycle for infinitethermal-capacitance rates to and from the reservoirs coupled to the constant temperatureheat-reservoirs using finite time heat-transfer theory to determine the optimum values ofpower output, thermal efficiency and exergetic efficiency under a state of maximumecological-function.

[138]

T. Nakata 2004 A review on the various issues associated with the energy-economic model and its applicationto national energy policies, renewable energy systems, and the global environment.

[139]

A. Sahin 2004 An overview of wind energy history, wind-power meteorology, the energy–climate relations,wind-turbine technology, wind economy, wind–hybrid applications and the 2004status ofinstalled wind energy capacity all over the world.

[140]

Z. En 2004 A review to provide a comprehensive account of solar energy sources and conversion methodswith explanatory background material both from application and research points of view,applications of solar energy in terms of low and high temperature collectors, photovoltaicdevices future electric energy generations based on solar power site-exploitation andtransmission by different means over long distances such as fiber-optic cables and futureperspective use of solar energy in combination with water and as a consequent electrolysisanalysis generation of hydrogen gas.

[141]

T. Tsoutsos et al. 2003 Feasibility on a solar power system based on the stirling dish (SD) technology reviews andcomparison of the available stirling engines in the perspective of a solar stirling system.

[142]

D. Egre and J.C. Milewski 2002 A review illustrating the necessity to evaluate each hydroelectric project in relation to theservices it provides and to compare electricity supply projects on the basis of equivalentservices provided to society.

[143]

J. Werther et al. 2000 An overview of various issues related to the combustion of agricultural residues such as theproblems associated with the properties of the residues such as low bulk density, low ashmelting points, high volatile matter contents and the presence of nitrogen, sulfur, chlorine and

tents

[144]

fr

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2

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sometimes high moisture conof agricultural residues.

uzzy logic for energy allocation. More researches in the area areeviewed in Table 5.

.3.2. Power plant operationSince the early 1990s, multi-variable and multi-objective power

lant optimization has been used extensively by US electric util-ties, as neural-network-based software reached maturity andecame available as commercial products. Reportedly, more than00 units (coal-, oil- and gas-fired) have used such optimizationoftware, of which approximately 280–300 are coal-fired boilers.owever, it also is clear that while many of these units have usedptimization software for some time (after they have been installednd calibrated successfully), they are not using them anymore. Thiss the result of many factors, including the fact that a number ofptimization products are not available in the market because theyave been acquired by competitors.

Nowadays, power systems are being operated more and morelose to their stability limits due to the economic and environ-ental constraints. Static voltage stability has become one of theajor factors that are threatening the operation security of electric

ower system. In order to improve the Loading Margin of the sys-em, the current available methods mainly include reactive power

anagement [152–154], generation rescheduling [155–158] andoad curtailment [159,160]. System operators (SO) generally usehe reactive power management as the first option because of itsow cost, and the load curtailment as the last option because ofts high expense. However, the voltage stability problem can notlways be fully settled by the first option. Furthermore if too manyeactive power facilities were deployed, then a large amount ofunk costs would appear. Therefore, GR, a convenient and effectiveool at hand for SO, is paying more and more attention.

.3.3. Building energy consumptionIn many counties, building energy consumption is very often

esponsible for about 40% of the total final energy demand. In US,

and design considerations of facilities for the combustion

buildings account for nearly 40% of energy use. In EU, about 57%of the total final energy consumption is used for space heating,25% for domestic hot water and 11% for electricity [161]. In China,building energy consumption has been increasing at more than10% a yearduring the past 20 years [162]. The increasing of energyconsumptionleads to more environmental issues. Nowadays, manynational governments have established some regulations andadopted some technologies to improve energy utilization efficiencyand mitigate environmental impact such as an energy-efficienttechnology, combined cooling heating and power (CCHP) systemis broadly identified as an alternativefor the world to meet andsolve energy-related problems, such asincreasing energy demands,increasing energy cost, energy supplysecurity and environmen-tal concerns [163–168]. When CCHP system isused for a building,it is called building cooling heating and power (BCHP) system[169–171]. The complexity of BCHP system structure and opera-tion modeplus the variation building loads complicates the designof BCHP system. Many researchers haveoptimized different BCHPsystems in consideration of differentoptimization objectives likeThe cost optimization method to determine the capacity of BCHPsystem so as to minimizethe capital and operation cost [172–179],even including CO2 emissionscosts [180–182], thermo-economicmethodologies [183,184] and energy, economy and environmenthas also been optimized [185–189].Wu and Wang [163] reviewedCCHP systems, including various technologies, provide an alterna-tivefor the world to meet and solve energy-related problems, suchas energy shortages, energy supply security,emission control, theeconomy and conservation of energyand presented diverseCCHPconfigurations of existing technologies, particularly four typicalsystems of various size ranges.The worldwide status quo of CCHPdevelopment is briefly introduced by dividing the world into four
mainsections: the US, Europe, Asia and the Pacific and rest of theworld. It is concluded that, within decades, promising CCHPtech-nologies can flourish with the cooperative efforts of governments,energy-related enterprises and professionalassociations.

tainab

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The optimization problems of BCHP systems have been statedasinear programming problem [173,181], non-linear program-

ingproblem [190,191], mixed integer programming problem174,177,192] and multi-objective programming problem [187].he classical solution methods to these optimization problemsnclude simplex method [181,193], dynamic programming [194],agrangian relaxation [195,196], sequential quadratic program-ing [179,197], Newton’s method [198,199] and reduced gradientethod [200,201]. Since 1998, an important quantity of researches

ses artificial intelligence methods to optimize BCHP system suchs branch-and-bound algorithm [177,192], genetic algorithm (GA)180,189], evolutionary programming [187,193,202], and particlewarm optimization algorithm [8,203–205].

.3.4. Industrial energy consumptionIn industry, open-cycle gas-turbine power-plants are widely

pplied. Radcenco et al. [206] developed the optimization model forhe performance of an open-cycle simple gas-turbine power-planty incorporating into the power-plant model, and its optimization,he irreversibility due to the various pressure-drops distributedlong the flow path. The analogy between irreversibility of ther-al resistance and the irreversibility fluid-flow resistance was

xploited by Bejan [207] and Radcenco [208], and was further stud-ed by Bejan [209–213]. For the open Brayton cycles, the principlef optimally tuning the fuel-flow rate and subsequent distributionf pressure-drops was used [206].

Uran [214] developed an optimization system for combinedeat and electricity production in the wood-processing industryonstructing thermo-economic optimization model to minimizenergy costs, using a mathematical formula that computes the low-st heat capacity as the cogeneration system starts to be moreayable than a non-cogeneration system. A thermo-economic anal-sis of systems, cogeneration and non-cogeneration, including theayback period of the capital invested in the cogeneration systemnd harmonization of heat production and disposable wood wastes fuel found cogeneration or non-cogeneration optimal.

.3.5. Power plants and carbon dioxide capture and storage (CCS)As generation of carbon dioxide (CO2) greenhouse gas is inher-

nt in the combustion of fossil fuels, effective capture of CO2 fromndustrial and commercial operations is viewed as an importanttrategy which has the potential to achieve a significant reduc-ion in atmospheric CO2 levels. Carbon dioxide capture and storageCCS) is a CO2diminutionoption that can contribute substantially tochieve ambitious CO2 reduction targets. Thiruvenkatachari et al.215] briefly reviewed CO2 capture methods, classified existing andmerging post combustion CO2capture technologies, comparedheir features, then addresses possible future system, and con-luded that further work is required to be fully evaluated for theirotential for large scale CO2 capture from fossil fuel-fired powertations. The electricity sector especially, with large point sources ofO2, offers opportunities to apply CCS at a large scale [216]. Resultsf techno-economic energy models show that power plants com-ined with CCS can indeed compete from a mitigation perspectiveith other non- or low-emitting CO2 technologies such as nuclear

nergy or renewable energy. Necessary pre-conditions are strictlimate policies, a decrease in the cost of CCS, and more specifi-ally, an improvement in the performance of capture technologiesy technological learning [217–219].

Van-den-Broek et al. [220] has extended the concept of tech-ology learning curves to simultaneously consider improvements

n key system performance parameters as well as cost variables
n assessing the future development of power plants with CO2apture. While early publications of learning curves were actuallyased on physical measures such as labor efficiency [221], they areowadays mostly applied to identify cost trends. However, Yeh and

Rubin [222] also used them to assess the efficiency improvementsof pulverized coal-fired power plants. Van-den-Broek et al. [220], intheir investigation, found that “When technology spill over is takeninto account, the new power plants without CO2 capture also stim-ulate the improvement in power plants with CO2 capture as theyconsist largely of similar technologies. For example, the SPC boilergets an additional experience of 1300 GW between 2001 and 2050in both scenarios. Whereas in reference scenario in the study (REF),this is mainly a result from the capacity growth of supercritical pul-verized coal-fired power plants (SPCs) without capture, in carbonconstraint scenario in the study (CCC) it is half from the growth ofSPCs without capture and half with capture. In both the REF and CCCscenarios, the additional experience (including replacement capac-ity) of the gas turbine combined cycle (GTCC) power block is around3100 GW in 2050, but in each scenario this total reflects differentadditional capacities of natural gas combined cycle power plant(NGCC), natural gas combined cycle power plant with post combus-tion carbon dioxide capture (NGCC-CC), and integrated gasificationcombined cycle power plant on coal and biomass (IGCC), integratedgasification combined cycle power plant on coal and biomass withpre-combustion carbon dioxide capture (IGCC-CC) power plants”.

2.3.6. Renewable energy mixRenewable energy technologies produce marketable energy by

converting natural phenomena into useful forms of energy Thesetechnologies use the sun’s energy and its direct and indirect effectson the earth (solar radiation, wind, falling water and various plants,i.e. biomass), gravitational forces (tides), and the heat of the earth’score (geothermal) as the resources from which energy is produced.These resources have massive energy potential, however, they aregenerally diffused and not fully accessible, most of them are inter-mittent, and have distinct regional variability [223]. Nowadays,significant progress is made by improving the collection and con-version efficiencies, lowering the initial and maintenance costs, andincreasing the reliability and applicability. A worldwide researchand development using modeling, optimization and simulationtools in the field of renewable energy resources and systems iscarried out during the last two decades.

Some diverse and most promising researches of optimizationand modeling in energy and power sector are highlighted in Table 6.

2.4. Impact of optimization modeling in power sectordevelopment

Economies and societies have been changing more rapidly thanever throughout the past decades. The accelerating emergenceof new technologies, new knowledge about climate dynamics,changing political and economic constellations, and increasing aca-demic publishing activity mean that assessments of the state ofglobal energy systems are becoming outdated more quickly thanbefore. The beginning of the industrial revolution marked a pro-found change from gradual refinement of low-power systems torapid-intensive systems of all sorts. Along with this acceleration ofevolution came a rapid expansion of the ability of human beingsto multiply their maximum power output through the applicationof technology. Energy systems are complex, often involving com-binations of thermal, mechanical and electrical energy, which areused to achieve one or more of several possible goals, such as elec-tricity generation, climate control of enclosed spaces, propulsion oftransportation vehicles, and so on. Energy systems therefore lendthemselves to the use of a system approach to problem solving.Decision makers of energy systems, in many instances, must take
into account a number of goals (physical, financial or environmen-tal) may be local, regional or global in nature. One if the best knownconcepts at the present time that considers multiple goals for thevarious economic activities that underpin human society, among


Table 6Modeling, optimization and simulation researches in energy and power sector.


L.d.S. Coelho and A.A.P. Santos 2011 A nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) withGaussian activation functions and robust clustering algorithms to model the conditional mean anda parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification tomodel the conditional volatility and to provide multi-step-ahead point and direction-of-changeforecasts of the Spanish electricity pool prices.

[224]

A.M. Foley et al. 2010 A reviews on the changing role of electricity systems modeling in a strategic manner, focusing onthe modeling response to key developments, the move away from monopoly towards liberalizedmarket regimes and the increasing complexity brought about by policy targets for renewableenergy and emissions, providing an overview of electricity systems modeling techniques discussesa number of key proprietary electricity systems models used in the USA and Europe.

[225]

A. Gómez-Barea and B. Leckner 2010 A review on modeling of biomass gasification in bubbling and circulating fluidized bed (FB)gasifiers focusing on Mixing and reactions, Kinetic models, reactor modeling, fluidization models,identifying the need for further investigation and concluding that most of the FB biomassgasification models fit reasonably well experiments selected for validation, despite the variousformulations and input data.

[226]

R.-H. Liang, Y.-K. Chen and Y.-T. Chen 2010 A fuzzy optimization approach for solving the Volt/Var control problem in a distribution systemwith uncertainties with emphasis on wind to find an optimum combination of tap position for themain transformer under load tap changer (ULTC) and on/off status for switched capacitors in a dayto minimize the voltage deviation on the secondary bus of the main transformer, reactive powerflow through the main transformer and real power loss on feeders.

[227]

P. Bhatt, R. Roy and S.P. Ghoshal 2010 A GA/particle swarm intelligence based optimization describing comparative performanceanalysis of the two specific varieties of controller devices for optimal transient performance ofautomatic generation control of an interconnected two-area power system, having multiplethermal–hydro–diesels mixed generating units.

[228]

E. Cayer et al. 2010 A parametric study and optimization performed on a transcritical power cycle using sixperformance indicators: thermal efficiency, specific net output, exergetic efficiency, total UA andsurface of the heat exchangers as well as the relative cost of the system. The independentparameters are the maximum temperature and pressure of the cycle as well as the net poweroutput.

[229]

Ren, H., & Gao, W. 2010 A mixed-integer linear programming (MILP) model developed for the integrated plan andevaluation of distributed energy resources (DER) systems. Given the site’s energy loads, localclimate data, utility tariff structure, and information (both technical and financial) on candidateDER technologies, the model minimizes overall energy cost for a test year by selecting the units toinstall and determining their operating schedules. Furthermore, the economic, energetic andenvironmental effects of the DER system are evaluated.

[230]

L. Jing, P. Gang and J. Jie 2010 A novel design which combines the Organic Rankine Cycle (ORC) with the Compound ParabolicConcentrators (CPC) collectors based on simulation model of the low temperature solar thermalelectric generation in areas of Canberra, Singapore, Bombay, Lhasa, Sacramento and Berlin withHCFC-123 as the working fluid, investigating and optimizing the influences of the CPC collector tiltangle adjustment, the connection between the heat exchangers and the collectors, and the ORCevaporation temperature.

[231]

Azadeh, A., et al. 2010 An innovative model of agent based simulation, based on Ant Colony Optimization (ACO)algorithm is proposed in order to compare three available strategies of clearing wholesaleelectricity markets, uniform, pay-as-bid, and generalized Vickrey rules. The supply side actors ofthe power market are modeled as adaptive agents to learn how to bid strategically to optimizetheir profit through indirect interaction with other actors of the market.

[232]

J.M. Yusta et al. 2010 A mathematical optimization model development to simulate costs and the electricity demand ofa machining process using the generalized reduced gradient approach, to find the optimumproduction schedule that maximizes the industry profit considering the hourly variations of theprice of electricity in the spot market, describing different price scenarios to analyze the impact ofthe spot market prices for electricity on the optimal scheduling of the machining process and onthe industry profit.

[233]

Möst, D., & Keles, D. 2010 An overview and classification of stochastic models dealing with price risks in electricity markets,focusing on various stochastic methods developed in operation research with practical relevanceand applicability, including the concepts of (1) stochastic processes for commodity prices(especially for electricity), (2) scenario generation and reduction, which is important due to theneed for a structured handling of large data amounts; as well as (3) stochastic optimizing modelsfor investment decisions, short- and mid-term power production planning and long-term systemoptimization.

[234]

M.H. Amjadi et al. 2010 A study dealing with estimation of electricity demand of Iran based on economic indicators usingParticle Swarm Optimization (PSO) Algorithm based on Gross Domestic Product (GDP),population, number of customers and average price electricity by developing two differentestimation models: a linear model and a non-linear model.

[235]

S. Porkar et al. 2010 A study introducing a new framework included mathematical optimization model by a newsoftware package interfacing two powerful softwares (MATLAB and GAMS) for obtaining theoptimal distributed generation (DG) capacity sizing and sitting investments with capability tosimulate large distribution system planning, minimizing total system planning costs for DGinvestment, DG operation and maintenance, purchase of power by the distribution companies(DISCOs) from transmission companies and system power losses.

[236]

D. Niu et al. 2010 A novel technique to forecast day-ahead electricity prices based on Self-Organizing Map neuralnetwork (SOM) and Support Vector Machine (SVM) models.

[237]

T. Niknam et al. 2010 A price-based novel approach for daily Volt/Var control in distribution systems using DistributedGeneration (DG) units adopted to determine the optimum active and reactive power dispatch forthe DG units, the reactive power contribution of the capacitor banks, and the tap settings of thetransformers in a day in advance, using fuzzy adaptive particle swarm optimization (FAPSO)method to solve the daily Volt/Var control which is a non-linear mixed-integer problem.

[238]


Table 6 (Continued)


J. Wang et al. 2010 Parametric analysis and exergy analysis to examine the effects of thermodynamic parameters onthe cycle performance and exergy destruction in each component and optimization of thethermodynamic parameters of the supercritical CO2 power cycle with exergy efficiency as anobjective function by means of genetic algorithm (GA) under the given waste heat condition usingan artificial neural network (ANN) with the multi-layer feed-forward network type andback-propagation training to achieve parametric optimization design rapidly.

[239]

J. Sadhukhan et al. 2009 Process simulation and modeling of a cost-effective and cleaner combined heat and power (CHP)generation gasification plant from low-cost, fourth-generation biomass waste feedstocks using theAspen simulator.

[240]

F. Frombo et al. 2009 A GIS-based Environmental Decision Support System (EDSS) to define planning and managementstrategies for the optimal logistics for energy production from woody biomass to solve theoptimization problem relevant to the strategic decision level.

[241]

P.A. Østergaard 2009 A study reviewing a number of possible optimization criteria for the design of energy systemswith large shares of fluctuating renewable energy sources (A case study of Western Denmark).

[242]

A. Ehsani et al. 2009 Development of a procedure (General Algebraic Modeling System-GAMS Rev. 140) for compulsoryprovision of spinning reserve using a risk-constrained cost-based mechanism, focusing onelectrical energy and spinning reserve simultaneously, generators are paid the opportunity costassociated with their reduced energy because compulsion is financially unattractive among themand the transmission system reliability is considered in a simplified manner when computingcomposite system risk.

[243]

A. Hatami et al. 2009 A mathematical method based on mixed-integer stochastic programming to determine theoptimal sale price of electricity to customers and the electricity procurement policy of a retailerfor a specified period.

[244]

A. Yucekaya et al. 2009 A study presenting two particle swarm optimization (PSO) algorithms to determine bid prices andquantities under the rules of a competitive power market, the first method uses a conventionalPSO technique to find solutions and the second method uses a decomposition technique inconjunction with the PSO approach.

[245]

M. Toksari 2009 A study presenting Turkey’s net electricity energy generation and demand based on economicindicators, Forecasting model for electricity energy generation and demand “Ant colonyoptimization electricity energy estimation (ACOEEE) model” is developed by the ant colonyoptimization (ACO) approach using population, gross domestic product (GDP), import andexport.is first proposed.

[246]

Bunn, D., & Day, C. 2009 Detailed computational models of price formation in the England and Wales electricity pool, basedon the data from 1990 to 2001 that provides a benchmark against which to assess generatorconduct, and thereby help to diagnose the separate causes of market structure and marketconduct when actual prices appear to be higher than marginal cost.

[247]

H. Siahkali and M. Vakilian 2009 Development of a new approach for solving the generation scheduling (GS) problem consideringthe reserve requirement, load balance and wind power availability constraints using particleswarm optimization (PSO) method applied to a 12-unit test system (including 10 conventionalthermal generating units and 2 wind farms) to determine the acceleration constants of proposedPSO and the global variant-based passive congregation PSO.

[248]

Chicco, G., & Mancarella, P. 2009 A comprehensive input–output matrix approach aimed at modeling small-scale trigenerationequipment taking into account the interactions among plant components and external energynetworks that maintains the separation among the individual energy vectors, each of which can beassociated to its time-dependent price, providing the basic framework for formulatingoptimization problems concerning management of trigeneration systems within an energymarket context.

[179]

P. Malo 2009 A flexible Copula-MSM (Markov Switching Multifractal) approach for modeling spot and weeklyfutures price dynamics to separately model the dependence structure, while enabling use ofmultifractal stochastic volatility models to characterize fluctuations in marginal returns.

[249]

A.A. Rentizelas et al. 2009 An optimization model for multi-biomass tri-generation energy supply developed employing GISto calculate the transportation cost from all potential biomass collection points to all potentialCHP plant locations. Then, optimization is performed regarding the optimal sizing of the powerplant (defining which kind of energy to produce for the specific area), and biomass collection andharvesting scheduling.

[250]

Louit, D., et al. 2009 A simple model to determine the optimal major (preventive) maintenance actions (MMA) intervalbased on a relative time scale (i.e. time since last major maintenance event) and the combinationof data from different sections of a grid, under a normalization scheme, additionally, extendedmaintenance times and sequential execution of the MMAs resulting in the loss of importantinformation for the characterization of the failure process, with a case study to illustrate theoptimal tree trimming interval around an electricity distribution network.

[251]

J.D. Mondol et al. 2009 Optimizing the economic viability of grid-connected photovoltaic systems investigating effect ofload matching between PV supply and load demand, sizing ratio, electricity buying and sellingprices, utility rate schedule, PV inclination and PV capacity on PV contribution to building load andPV electricity cost.

[252]

H. Yang et al. 2009 An optimal design model for designing hybrid solar–wind systems employing battery banks forcalculating the system optimum configurations and ensuring that the annualized cost of thesystems is minimized while satisfying the custom required loss of power supply probability.

[253]

F.E. Benth and S. Koekebakker 2008 Stochastic modeling of financial electricity contracts traded in many deregulated power marketsthat deliver (either physically or financially) electricity over a specified time period, and isfrequently referred to as swaps since they in effect represent an exchange of fixed for floatingelectricity price, using the Heath–Jarrow–Morton approach to model swap prices.

[254]

N. Ayoub et al. 2008 An optimization model for designing and evaluating Biomass Utilization Networks (BUN)superstructure, in local areas using Generalized Algorithm and application of the proposedmethodology as a case study to a local Japanese area.

[255]

C. Diblasi 2008 A review study on modeling chemical and physical processes of wood and biomass pyrolysisemphasizing on chemical kinetics in relation to primary reactions, described by both one- andmulti-component mechanisms, and secondary reactions of tar cracking and polymerization.

[256]


Table 6 (Continued)


G. Baskar and M. Mohan 2008 A study to explore the application of a proposed improved particle optimization to the securityconstrained economic load dispatch problem with a view to minimize the total fuel cost ofthermal units.

[257]

S. Mariano et al. 2008 A novel method, based on nonlinear programming (NLP), for optimizing power generationefficiency and short-term hydro scheduling (STHS), particularly concerning head-sensitivereservoirs under competitive environment.

[258]

W. Zhang and Y. Liu 2008 A new formulation of multi-objective reactive power and voltage control for power system usingMulti-objective Particle Swarm Optimization with active power loss, voltage deviation and thevoltage stability index of the system as objectives.

[259]

A. Shunmugalatha and S. Slochanal 2008 A study describing the hybrid particle swarm optimization, which incorporates the breeding andsubpopulation process in genetic algorithm into particle swarm optimization to determine theoptimum cost of generation for maximum loadability limit of power system.

[260]

E Smeets et al. 2007 A review of existing databases and outlook studies, in order to develop a bottom-up model, calledthe Quickscan model, to estimate the technical potential of bioenergy crop production in the year2050, based on an evaluation of data and studies on relevant factors such as population growth,per capita food consumption and the efficiency of food production including three types ofbiomass energy sources: dedicated bioenergy crops, agricultural and forestry residues and waste,and forest growth.

[261]

Botterud, A., & Korpas, M. 2007 Dynamic model to formulate the power generation investment problem for a decentralized andprofit-maximizing investor operating in a restructured and competitive power system,investigating how uncertainty influences the optimal timing of investments in new powergeneration capacity, using A real options approach to take long-term uncertainty in load growth,and its influence on future electricity prices, into account in the investment optimization.

[262]

E. Erdogdu 2007 An ARIMA modeling using co-integration analysis and autoregressive integrated moving average,focusing on “energy crisis in Turkey” by both providing an electricity demand estimation andforecast, and comparing the results with official projections.

[263]

A. Rong and R. Lahdelma 2007 A heuristic modeling to investigate the impact of power-ramp constraints on CHP productionplanning and develop a robust heuristic for dealing with the power-ramp constraints based on thesolution to the problem with relaxed ramp-constraints.

[264]

D. Henning et al. 2006 A study to describe an energy system optimization model framework, its application to a localenergy utility and analyses of issues that influence the production of district heating, electricityand steam, such as cogeneration, policy instruments and marginal costs with an overview ofenergy systems analysis and district heating in Sweden.

[265]

P. Chan et al. 2006 A study on selecting electricity contracts for a large-scale chemical production plant, whichrequires electricity importation, under demand uncertainty, focusing on two common types ofelectricity contracts, time zone contract and loading curve contract. A multi-period linearprobabilistic programming model is adopted for the contract selection and optimization, and byusing the probabilistic programming, a solution procedure is proposed that allow users todetermine the best electricity contract according to their desired confident level of theuncertainties.

[266]

Olsina, F., et al. 2006 A simulation model based on system dynamics with extensive discussion on the underlyingmathematical formulations and focusing on replicating the system structure of power marketsand the logic of relationships among system components in order to derive its dynamicalresponse, while the simulations suggest that there might be serious problems to adjust earlyenough the generation capacity necessary to maintain stable reserve margins, and consequently,stable long-term price levels.

[267]

A.C. Caputo et al. 2005 Thermal utilization processes plant cost optimization modeling of biomass utilization for directproduction of electric energy by means of combustion and gasification-conversion processes overa capacity range from 5 to 50 MW taking into account total capital investments, revenues fromenergy sale and total operating costs including logistic costs, economic profitability of bio-energyplants in terms of net present value (NPV), and a mapping of logistic constraints on plantprofitability in the specified capacity range.

[268]

Y. Brar, J. Dhillon and D. Kothari 2005 A multi-objective thermal power generation scheduling problem having four objectives includingthe economic index, an environmental index and security index to be minimized simultaneouslywhile inequality constraints imposed to meet the real and reactive power flow limits on each lineare incorporated as objectives to be minimized.

[269]

A. Rong and R. Lahdelma 2005 A linear programming (LP) model formulating the hourly trigeneration problem with a jointcharacteristic for three energy components to minimize simultaneously the production andpurchase costs of three energy components, as well as CO2 emissions costs, in addition exploringthe structure of the problem to propose the specialized Tri-Commodity Simplex (TCS) algorithm.

[270]

P. Ostergaard 2005 Modeling grid losses and the geographic distribution of electricity generation analyzing thedifferent impacts on the transmission grid in two cases both using scattered load balancing (1)where load balance is sought kept locally in each 150 kV node throughout the transmission systemand (2) where only the overall load balance of the entire system is kept.

[271]

S.-J. Deng and W. Jiang 2005 A class of stochastic mean-reverting models for electricity prices with Levy process-drivenOrnstein–Uhlenbeck (OU) processes being the building blocks.

[272]

E. Thorin et al. 2005 A tool developed for long-term optimization of cogeneration systems in a competitive marketenvironment based on mixed integer linear-programming and Lagrangian relaxation using ageneral approach without heuristics to solve the optimization problem of the unit commitmentproblem and load dispatch.

[273]

E. Castronuovo and J. Lopes 2004 A study exploiting the concept of the combined use of wind power production and hydrostorage/production, through the development of an operational optimization approach applied toa wind generator park with little water storage ability, with the operational strategy to befollowed for the hours ahead by a pump station and an hydraulic generator embedded in awind/hydro pumping facility, using the Portuguese energy remuneration rules.

[274]

J.L. Silveira and C.E. Tuna 2003 A study focusing on thermo-economic analysis of cogeneration plants to produce electric powerand saturated steam presenting a new methodology “the minimum Exergetic Production Cost(EPC)”, based on the Second Law of Thermodynamics.

[275]


Table 6 (Continued)


J. Nowicka-Zagrajeka and R. Weron 2002 Modeling and forecasting electricity loads with ARMA processes applying a two-step procedure toa series of system-wide loads from the California power market.

[276]

A. Williams, M. Pourkashanian and J.M. Jones 2001 A review on computer modeling of combustion of pulverized coal and biomass, 2001 status ofavailable sub-models and understanding of combustion of pulverized coal and biomass from theviewpoint of comport modeling.

[277]

ower

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Fig. 2. Summary of citation of researches on p

he management of energy systems, is “sustainable development”.riving the global energy system into a sustainable path has beenmerged as a major concern and policy objective.

Deregulation in electricity markets requires fast and robust opti-ization tools for a secure and efficient operation of the electric

ower system. In addition, there is the need of integrating and coor-inating operational decisions taken by different utilities acting inhe same market. Superstructure based modeling strategy, alongith MILP and MINLP solution algorithms are efficient and effec-

ive in solving energy systems engineering problems, especially atecision making and planning stage. Based on this, multi-objectiveptimization and optimization under uncertainty produces furthern-depth analyses and allows a decision maker to make the finalecision from many aspects of view.

.5. Future prospective

Energy policy is of great significance to energy systems, espe-ially to the development of renewable or sustainable energy.olicymakers usually need to establish policies based on detailedssessment of competing technologies and huge amounts of sce-ario analyses. Likewise the power supply and distribution alsoeed intellectual decision making. However, this procedure coulde greatly facilitated by superstructure based modeling and opti-ization. The recent advancements in modeling, optimization and

imulation tools open new horizon for researchers to utilize andmplement these techniques and tools to power supply networksnd energy planning and management. Based on this review study,his can be envisaged that there is now more room for research andevelopment activities in power generation and supply sector.

. Conclusion and outlook

Energy security has recently become an important policy drivernd privatization of the electricity sector has secured energy sup-ly and provided cheaper energy services in some countries inhe short term, but has led to contrary effects elsewhere due to

and supply during last decade in this article.

increasing competition, resulting in deferred investments in plantand infrastructure due to longer-term uncertainties. Until 1960s,everyone in the power industry and government came to assumethat remote, central generation was optimal, that it would deliverpower at the lowest cost versus other alternatives. Because of theirhigh level of integration, are susceptible to disturbances in the sup-ply chain. In the case of electricity especially, this supply paradigmis losing some of its appeal. Small-scale decentralized systems areemerging as a viable alternative as being less dependent uponcentralized energy supply, and can sometimes use more than oneenergy source. Researchers envisaged an increasing decentraliza-tion of power supply, expected to make a particular contributionto climate protection. Over abundances of technological alterna-tives have made the prioritization process of decentralized powerquite complicated for decision making. During last two decades, alot of research work has been carried out to cater these challenges.Fig. 2 shows a summary of citation summary of potential researchesrelated to power and supply, clearly indicating the increasing trendin recent years. Recent advancement in optimization modelingprovided an ease to researchers to find optimal and sustainablesolutions of the complex problems associated with power genera-tion and supply scenarios. This review study can be concluded thatthe modeling and optimization are proved as effective and usefultools for problem solving in power and supply sector and especiallyfor policymakers to establish policies based on detailed assessmentof competing technologies and huge amounts of scenario analyses.

Acknowledgement

Authors would like to acknowledge Universiti TeknologiMalaysia for financial support under grant No. PY/2011/00557.

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Sustainable energy systems: Role of optimization modeling techniques in power generation and supply—A review