Challenges of Large Wind Generation Intermittence and Fluctuation for Future Smart Grids

Libao Shi, Zheng Xu, Yixin Ni Graduate school at Shenzhen, Tsinghua University

Shenzhen China

Liangzhong Yao, Masoud Bazargan ALSTOM Grid Research & Technology Centre

Stafford, ST17 4LX, United Kingdom

Abstract—With introduction of smart grid concept, how to face up to the impacts of grid-connected renewable energy sources, especially like wind power on power systems becomes one of the most important issues in development of smart grid. This paper will discuss the potential challenges the smart grids will confront with respect to large wind generation intermittence and fluctuation from a technical point of view. The context will involve power system planning, reliability evaluation, voltage stability and frequency stability. Technical requirements and solutions in dealing with those challenges imposed by wind generation for future smart grids are also proposed.

Keywords-smart grid; wind power; intermittence; fluctuation

I. INTRODUCTION Currently, with the introduction of smart grid concept [1-3],

many hot discussion topics closely related to the smart grid technologies involving definition, infrastructure design, integration of distributed generation, fusion of advanced IT technique etc., are sweeping the whole world. As a relatively new development in power systems, the smart grid can support better integration of renewable energy sources into the conventional centralized power systems. On the other hand, with increasing penetration level of renewable energy sources especially like wind power [4], the intermittent and fluctuant characteristics of wind generation correspondingly add more uncertainness and technical challenges for future smart grids. Comparing with the conventional power system reliability evaluation, the reliability analysis in the era of smart grid with consideration of wind generation intermittence and fluctuation has to be rediscussed and readdressed. As for the power system dynamic behavior, the intermittence and fluctuation of wind generation will lead to system frequency and voltage fluctuation and make power system dynamic behavior become more complex. In this situation, smart grids MUST fully take into account the possible and potential impacts of wind generation intermittence and fluctuation during the interaction with renewable power generation.

This paper will discuss the potential challenges the smart grids will confront with respect to large wind generation intermittence and fluctuation from a technical point of view.

II. SMART GRID AND RENEWABLE ENERGY INTERACTIONS It is known that the conventional coal fired power plant

based power system is an inefficient and environmentally

wasteful system that is a major emitter of greenhouse gases and not well suited to the rapid development of renewable energy sources like solar and wind power. Many issues including greater recognition of climate change, commitments to reduce carbon emissions and technology innovation etc., closely related to the conventional power grid, are gaining great concerns around the world. In this situation, governments, utilities and academician are rethinking how the power grid should look. Particularly some new activities involving how to keep renewable energy sources, plug-in hybrid electric vehicles, active home energy management and advanced grid monitoring working together well in one integrated system with a new level of intelligence and communication control architecture have to be reconsidered. Unfortunately, the current power grid can not fully fulfill these requirements which will lead to an unprecedented innovation for the well-known power systems.

A new and more intelligent power grid concept, called as ‘Smart Grid’, is introduced that combines advanced IT with renewable energy to significantly improve how electricity is generated, delivered and consumed. At recent study, there has been debate on a definition of a smart grid that addresses the special emphasis desired by each participant. In summary, a smart grid SHOULD incorporate the following features [1-3].

(1) Enabling nationwide use of plug-in hybrid electric vehicles;

(2) Allowing the seamless integration of renewable energy sources like solar and wind power;

(3) Ushering in a new era of consumer choice;

(4) Making large-scale energy storage a reality;

(5) Exploiting the use of green building standards.

As mentioned above, renewable energy source like wind power plays significant role in the future smart grid. With increasing penetration of large scale grid-connected wind farms, some impacts on the existing power grid involving the system operating behavior have to be considered. For instance, the conventional power line technology is dependent on the constant flow of electricity. The wind power is intermittent in supply. In other words, a sudden gust of wind will cause a spike for the power output. Similarly the intermittent and fluctuant characteristics of wind power in nature will change the dynamic operating behavior of power grid to some extent. Therefore the corresponding technical requirements and

This work has also been supported in part by the Research Project of Science and Technology from Shenzhen Government of China (JC200903180528A)

978-1-4577-0365-2/11/$26.00 ©2011 IEEE 1325

solutions in dealing with the potential challenges imposed by wind generation for future smart grids have to be considered as well.

III. CHALLENGES TO SYSTEM OPERATION

It is known that as increasing of penetration levels of wind power, the high penetration of intermittent wind power (for example, greater than 20 percent of generation meeting load) will affect the power flow dispatch and power system planning significantly. The impacts of intermittence and uncertainties of wind generation on power system capacity planning and reliability evaluation investigated in [5]-[11] would be the potential challenges for the future smart grid. In [5], an integrated power capacity planning model was described to consider the contributions of wind power to system reliability in peak demand periods. The seasonal expected energy production of wind power is used to account for the reliability contribution of wind power. And the Mixed integer programming (MIP) is used to solve the proposed model. In [6], the capacity expansion of regional power system containing intermittence power was discussed. The issue of reactive power capacity considering wind farm was discussed in [7]. A short term power system planning method to cope with the nature of wind generation considering stochastic security of power system is presented in [8]. In [9], the impact of wind farm in power system reliability is discussed and a new procedure for reliability evaluation of power systems with wind farm in HLI is presented. The proposed method is capable of finding both loss of load expectation and loss of energy expectation analytically in the presence of wind power. In [10], a simplified wind speed model that can generate wind speed probability distributions for wind farm sites is presented. The developed wind speed model can be combined with the wind turbine generator characteristics to obtain a wind farm generation model. And a probabilistic method is proposed to illustrate that the N-1 security criterion is not suitable for wind power transmission planning. In [11], the concept of the capacity credit integrated one or more wind farms applying the IEEE-reliability test system is examined. The basic Loss of Load Expectation, Loss of Energy Expectation and Unit Commitment Risk probability indices are employed to evaluate the increase in peak load carrying capability attributable to an added wind facility and the assigned capacity credit.

References [12]-[17] applied analytical methods to discuss power system reliability evaluation considering wind power. K.R. Voorspools in [12] presented an analytical formula, instead of stochastic reliability evaluation, for credit capacity (how much conventional power can be avoided). In [13], analytical approach was combined with a failure-effect to perform probabilistic reliability calculations. Zhao et al. in [14] presented an analytical offshore wind farm reliability model for optimization analysis. Ehsani et al. in [15] presented an analytical method for the reliability assessment of the flat-rated wind turbine generation systems. The power-velocity characteristic of the flat-rated wind turbines is employed to model the operating behavior of the installed wind turbine generators. And the Weibull distribution model is used for wind-power potential estimation. A novel analytical approach of reliability evaluation for wind-diesel hybrid power system

with battery bank for power supply in remote areas is proposed in [16]. The proposed approach is developed on the discrete Weibull wind speed distribution. By employing wind speed frame analysis, an analytical model of wind-diesel hybrid system is developed to handle system outage as a result of component failure and wind speed fluctuation. In [17], an analytical approach is presented for the reliability modeling of large wind farms. The proposed method is capable of finding both annual frequency and average time of load curtailment analytically in the presence of wind power.

References [18]-[26] discussed Monte Carlo simulation based method for power system reliability evaluation considering wind power. A Monte Carlo based production cost simulation model for reliability assessment of power systems with wind power generation is discussed in [18]. The effects of wind forecast error is addressed in the model by applying forecasted value in day-ahead unit commitment and actual value in real-time operation. R. Billinton et al. in [19] investigates the reliability effects on a composite generation and transmission system associated with the addition of large-scale wind energy conversion systems (WECS) using the state sampling Monte Carlo simulation technique. F. Vallée et al. in [20] proposed a two-state probabilistic model for wind generation and to define the equivalent capacity for global wind production. This model is based on the convolution between each single wind park multistates histograms and permits to compute accurate equivalent capacity for the wind production. In [21] F. Vallée et al. discussed the influence of correlations of different wind parks on wind equivalent capacity. The wind equivalent capacities are calculated using a nonsequential Monte Carlo Simulation. Furthermore, two cases are investigated to evaluate the importance of the wind correlation level between parks located in the same geographical region. Rajesh Karki et al., in [22] proposed a simplified wind power generation model for reliability evaluation. The method is further simplified by determining the minimum multistate representation for a wind farm generation model in reliability evaluation The sequential Monte Carlo simulation or a multistate wind farm representation approach is applied. In [23] R. Billinton and W. Wangdee used sequential Monte Carlo simulation approach for bulk electric system, based on an auto-regressive moving average (ARMA) time series wind speed model. Markov Chain was introduced to model a synthetic wind speed time series generator in [24]. Besides, L.F. Wang and C. Singh in [25] introduced population-based intelligent search to reliability evaluation to find the meaningful system states. The proposed solution methodology is also compared with the Monte Carlo simulation through conceptual analyses and numerical simulations In [26], a method of contingency analysis, based on probabilistic risk measure, was presented for power system with high penetration of stochastic generation. The essence of the proposed technique is the use of a probabilistic risk measure for the security assessment of the N subsystems deriving from the application of the N-1 criterion. The Monte Carlo simulation technique is applied to solve the prolem.

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IV. CHALLENGES TO SYSTEM STABILITY AND CONTROL The wind generation fluctuations not only lead to voltage

fluctuations at the bus connected to the wind generator, but also contribute to the load voltage fluctuation, which is also called “flicker problem”. So the influence of wind generation on power system voltage stability is being more and more emphasized by scientists and engineers, particularly in the era of smart grid. The issues addressed in [27-39] would be the potential challenges to system stability and control for the future smart grid. In [27], an accurate voltage stability index (VSI) is needed to quantify the voltage stability margin in a distribution system with wind farms connected. The STATCOM is also included to demonstrate its capability for voltage stability improvement. In [28], a VAR regulation strategy is proposed to solve VAR and voltage stability caused by wind farm integration. Wind farm VAR limitation and power factor limitation are considered in wind farm VAR demand calculation as well. A. A. Tamimi et al. in [29] proposed two methods to determine maximum sizes of multiple new wind farms for maximizing wind penetration levels. In both methods, each new wind farm size is determined using an iterative process where each wind farm's size is incremented by a fixed value and its impact on voltage stability margins (VSMs) is observed. Incremented wind farms which result in less negative impact on VSMs are sized larger until reaching voltage instability. In [30], the impact of wind generation on voltage stability in transmission networks with consideration the intermittent nature of wind generation and their penetration level are investigated. A voltage collapse proximity indicator VCPI, based on network loadability is used to investigate the impact of wind generation on voltage stability. In [31], wind farm reactive power loop was analyzed which contributes to voltage regulation, modifies the location of the eigenvalues of a selected power system, with increasing wind power penetration. Federico Milano et al [32] compared static and dynamic continuation power flow techniques, time domain simulations and quasi-static time domain simulations for voltage stability analysis of networks with high wind power penetration. Three wind turbine models, fixed-speed induction generator (FSIG); doubly-fed induction generator (DFIG); and direct- drive synchronous generator (DDSG) are considered during analysis. In [33], a quasi multiple turbine representation method was introduced to consider every wind turbine’s effect of a wind farm, in the study of flicker contribution of wind power. In [34], voltage fluctuation was studied by probabilistic flow method, with probabilistic factors such as wind generation output, load fluctuation, branch outrage. Furthermore, a three-parameter Weibull distribution model that takes the location parameter into account is proposed to describe wind speed random fluctuation. In [35], load flow calculation and dynamic contingency study are conducted on the base of detail wind turbine model. In the frequency stability analysis, it has been confirmed that with large amount of fluctuation power integrated, the power balance of power system is very easily to be broken, which will lead to frequency deviation and swing. A method of quantifying wind penetration was proposed in [36], based on the amount of fluctuating power that can be filtered by wind turbine generators and thermal plants. For optimal wind power acquisition, the penetration level is estimated to be 49%. In [37], frequency deviation due to wind farms was

studies based on the transfer function of system components. The deviation is estimated by a deterministic method based on the transfer functions of system components The research identifies speed governors as one key component in enabling high wind penetration and there is need to factor their increased wear and tear into the ancillary services cost. In [38] the frequency response of the British transmission system was studied with the conclusion that CCGT plants for frequency service need additional primary response, and there is a potential to reduce first frequency requirement with the wind power. An energy storage system based wind power filtering algorithm was proposed in [39] to diminish the wind power impact on system frequency.

V. POSSIBLE TECHNICAL REQUIREMENTS AND SOLUTIONS As described in II, a smart grid incorporates demand

management, distributed electricity generation and grid management which allows for a wide array of more efficient, ‘greener’ systems to generate and consume electricity. The highlights and components of smart grid provide the following possible technical requirements and solutions to be in front of the potential challenges addressed above.

(1) More effective demand management to reduce electricity consumption in homes, offices and factories. The corresponding demand response, smart meter and variable pricing are actually required.

(2) Smart buildings with smart appliances need to be fully developed.

(3) Application of Green IT equipment and technology mainly including network based power management system, network printers, server virtualization, the procurement of energy-efficient equipment etc.

(4) Application of large-scale energy storage techniques.

(5) Effective bidirection communication technology.

(6) Application of intelligent optimization technology based on advanced IT technology is turning to monitor and control the electrical grid in real time.

VI. CONCLUSIONS In smart grid era, the renewable energy sources, especially

like wind power can be interconnected seamlessly as well as continuing to support the conventional power sources. Naturally, some new issues will arise. Among them, how to consider the possible and potential impact of wind generation intermittence and fluctuation on smart grid becomes very important and imperative. This paper addresses the potential challenges the smart grid may face to in the future in the aspects of power system operation, stability and control. In this situation, the new idea and solutions need to be explored and exploited elaborately. Some possible requirements and solutions involving demand management, smart device development, green IT technology, energy storage and the development of real time intelligent optimization algorithm from technical point of view are given as well.

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