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AN OVERVIEW OF LOAD MANAGEMENT

TECHNIQUES IN SMART GRID

Anzar Mahmood, Nadeem Javaid, Muhammad Asghar Khan, Sohail Razzaq

COMSATS Institute of Information Technology, Islamabad, Pakistan

Corresponding author: nadeemjavaid@comsats.edu.pk,

nadeemjavaidqau@gmail.com, www.njavaid.cpm

SUMMARY

Load management (LM) is supposed to have a vital role in future energy management

systems. This article presents overview and comparison of LM techniques along with

related technologies and implementation challenges in smart grid. The article also

covers consumer and utility concerns in context of LM to enhance readers’ intuition

about the topic. Two major categories of LM techniques, incentive based and dynamic

pricing based schemes have been discussed and compared. Most commonly used

incentive based direct load control (DLC) is elaborated in detail. Dynamic pricing

based energy consumption scheduling (ECS) schemes, featuring peak load reduction

and consumers’ energy cost minimization at residential level, are also emphasized.

Furthermore, the article incudes a description of dynamic pricing based home energy

management and associated optimization techniques as well as comparison of the

latest schemes.

Key Words

Smart Grid; Load Management; Demand Side; Dynamic Pricing; Direct Load

Control; Home Energy Systems; Consumption; Scheduling

1. INTRODUCTION

Energy is one of the most important components of human life which is present

ubiquitously and can be rendered as soul of modern machine age. Energy

management is important and interesting focus of researchers since decades.

According to international energy agency (IEA), the total primary energy supply of

the world has been increased to 13,113 million tons of oil equivalent (MTOE) in 2011

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as compared to 6,109 MTOE in 1973 [1].The largest amount of energy is consumed in

buildings which is estimated almost 40% of the world total consumption and has been

doubled in 2010 as compared to 1971 [2]. It is the use of energy by the occupants of

the buildings which is usually expressed as per capita energy consumption and taken

as an index for development and prosperity of a country [3].

Electricity is distributed through existing electro-mechanical grid which has been

serving since nineteenth century. Generation of more power to meet ever increasing

demand, which is expected to be doubled by 2020 [4], has many concerns regarding

the limited fossil fuels

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and high carbon emissions. Carbon emissions deteriorate our environment and put a

question mark over global sustainability.

Renewable energy resources are cleaner form of energy; however, the energy density

and technological maturity of the fossil fuels still dominate their use. It is estimated

that the US energy profile will constitute 33% of renewable resources by 2020 with

high penetration of wind and solar power plants [5]. Integration of the huge amount of

distributed generation, usually consisting of renewable resources, to the power grid

has raised many issues [6]. The concept of virtual utility to integrate different kind of

distributed generation to a central energy (heat and electricity) network has been

proposed in [7]. Unpredictable behaviour of these intermittent energy resources has

adverse effects on power system stability.

In beginning, LM procedures were based on unidirectional communication between

users and utilities [8]. These procedures were mainly implemented by the utilities and

the role of users in LM programs were negligible. However, integration of advanced

communication infrastructure enables bi-directional flow of data and power among

different stakeholders of power system and hence allows more efficient LM involving

both utility and consumers [9].

Need to control the demand in order to shape the load profile was first realized

in1970s [10]. Now it has evolved to the concept of demand side management (DSM)

and is characterized by utility operations and incentives for the consumers in order to

bring power usage at desired level at all times. Major objectives of DSM include:

peak clipping, valley filling, peak shifting and deploying new efficient uses [11].

DSM can help the consumers to lower their payments and utility to minimize the need

of peaking plants. Obviously, the utility desires the shape of the load curve to be

balanced with a reduced peak-to-average ratio (PAR) for all the hours while

consumers want reliable energy supplies at minimum cost. In literature, load

management (LM), demand response (DR) and DSM are found as overlapping

concepts and are used interchangeably [12].

There are two major types of LM schemes: dynamic pricing based and incentive

based. Pricing based programs include real time pricing (RTP), time of use (ToU)

pricing, critical peak pricing (CPP), etc. and incentive based programs include direct

load control (DLC), curtailable services, demand bidding etc. [13]. DLC acts only

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when peak demand crosses the certain limit whereas dynamic pricing is an ongoing

phenomenon. Since it is difficult to respond dynamic pricing schemes manually, the

customers need home energy management systems (HEMS) in order to automatically

respond the price variations through scheduling of their appliances for optimal total

cost. Dynamic energy management can be better implemented in smart grid.

The smart grid is an integration of the advanced information and communication

technologies (ICTs) to existing electro-mechanical power systems [11]. Bi-directional

flow of data and power between utility and end users is one of the main characteristics

of smart grid aimed at demand management in an efficient and dynamic way [5].

This article presents an overview of LM techniques in smart grid, the comparison of

these techniques, related technologies and implementation challenges. Most

commonly used incentive based DLC program along with dynamic pricing based

energy consumption scheduling (ECS) at residential level and associated optimization

techniques are elaborated in detail. Rest of the paper is organized as follows. Smart

grid, LM and development of the

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Related technologies are discussed in section 2. DLC and pricing based ECS are

elaborated in sections 3 and 4 respectively. Comparative discussion of LM techniques

is presented in section 5 followed by LM challenges in section 6. Conclusions are

briefed in section 7.

2. LOAD MANAGEMENT IN SMART GRID

Before going to the core discussion of this article, it seems necessary to discuss some

basic dynamics of power system and smart grid which play important role in LM.

This section describes the smart grid basic dynamics in context of energy

management along with consumers’ and utilities’ concerns and development of some

related technologies. These topics are elaborated in following subsections.

2.1. Power system dynamics and peak load management

Power system has two basic parameters, voltage level and frequency, which should be

monitored continuously to ensure system stability and reliability. Increasing gap

between demand and supply due to insufficient generation or transmission capability

causes overloading of the system. Consequently under frequency and under voltage

conditions affect the system's stability. In order to control these parameters, two

conventional mechanisms are used in power systems.

First is the excitation system along with automatic voltage regulator to control the

generator reactive power and system's voltage. Second is the prime-mover control

which is used for generator active power and frequency control. In addition to

overloading disturbances, faults may also cause the transients and affect system

dynamics. Moreover, peak load severely affects the power system’s economics and

dynamics if not managed efficiently. Peak load management is a major concern of the

electric utilities as peak demand puts stress on system stability, widens the supply-

demand mismatch and causes adverse economic effects [14].

Utilities and consumers are two major stakeholders of power system and their main

concerns are highlighted in Figures 1 and 2, respectively. Intuitively, power quality

and environmental effects should be top priorities in order to ensure global

sustainability. However, priority of the elements depends heavily on individual

consumer or set of consumers and may vary in different geographical areas. Smart

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grid is envisioned to better address these concerns. Energy management in smart grid

is briefed in the following subsection.

3. INCENTIVE BASED DLC

LM programs are divided into two major categories: incentive based and dynamic

pricing based schemes. The most prominent incentive based pricing scheme is DLC

which is discussed in this section.

In DLC, the utility takes over the control and has an authority to shut down or cycle

consumer’s electrical appliances (depending on the contractual terms). Incentive

based LM programs like DLC pay the incentive money to the consumer for the time

which they are asked to reduce or shutdown the load on short notice during peak

period [30], [31].