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Optimal Allocation of Energy Storage System for Risk Mitigation of DISCOs With High Renewable Penetrations Yu Zheng, Student Member, IEEE, Zhao Yang Dong, Senior Member, IEEE, Feng Ji Luo, Student Member, IEEE, Ke Meng, Member, IEEE, Jing Qiu, Student Member, IEEE, and Kit Po Wong, Fellow, IEEE

Abstract—Along with the increasing penetration of renewable energy, distribution system power flow may be significantly altered in terms of direction and magnitude. This will make delivering reliable power, on demand, a major challenge. In this paper, a novel battery energy storage system (BESS) based energy acquisition model is proposed for the operation of distribution companies (DISCOs) in regulating price or locational marginal price (LMP) mechanisms, while considering energy provision options within DISCO controlled areas. Based on this new model, a new battery operation strategy is proposed for better utilization of energy storage system (ESS) and mitigation operational risk from price volatility. Meanwhile, optimal sizing and siting decisions for BESS is obtained through a cost-benefit analysis method, which aims at maximizing the DISCO’s profit from energy transactions, system planning and operation cost savings. The proposed energy acquisition model and ESS control strategy are verified on a modified IEEE 15-bus distribution network, and risk mitigation is also quantified in two different markets. The promising results show that the capacity requirement for ESS can be reduced and the operational risk can also be mitigated. Index Terms—Control strategy, distribution system, electricity markets, energy storage system, renewable energy.

Rest capacity of the battery for charging and discharging at node (MWh). Current magnitude and limit through line (A). Bus number. Project benefit through a year ($). Number of bus. Real-time load, output of renewable energy, and net demand of the distribution system (MW). Maximum and minimum output of renewable energy at node (MW). Forecasted day-ahead power consumption (MW). Mean power difference between real-time and forecast (MW).

NOMENCLATURE Set of buses and lines. BG

Energy stored in battery and rating energy capability of battery at node (MWh).

Susceptance and conductance and of the line. Total cost function of the battery.

Charging reference for ESS at node (MW). Maximum discharging/charging capacity of ESS at node (MW). Reactive power demand of node (MVar).

Discount rate (%/year). Specific day in a year.

Sum of the power distributed from the substation (MVA).

Manuscript received January 29, 2013; revised July 06, 2013; accepted July 16, 2013. This work was supported in part by an ARC Grant LP110200957 and Fujian regional science and technology major projects, China, 2013H41010151. Paper no. TPWRS-00039-2013. Y. Zheng, F. J. Luo, K. Meng, and J. Qiu are with the Center for Intelligent Electricity Networks, The University of Newcastle, Newcastle, NSW, Australia (e-mail: [email protected]; [email protected]; [email protected]). Z. Y. Dong is with the School of Electrical and Information Engineering, The University of Sydney, Sydney NSW 2006, Australia (e-mail: zydong@ieee. org). K. P. Wong is with the School of Electrical, Electronics and Computer Engineering, The University of Western Australia, Perth 6009, Australia (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TPWRS.2013.2278850 0885-8950 © 2013 IEEE

Rating dispatch power and reverse power of the distribution station (MVA). State-of-charge of battery at node . Maximum and minimum state-of-charge of battery. Specific hour in a day. Time interval. Type of the energy storage system. Minimum and maximum voltage magnitude limit (V).

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IEEE TRANSACTIONS ON POWER SYSTEMS

Voltage magnitude of node (V). Day-ahead electricity price ($/MWh). Real-time electricity price in regulating price market and LMP market ($/MWh). Congest price in LMP market ($/MWh). Average day-ahead electricity price ($/MWh). Fixed retail electricity price ($/MWh). Total, day-ahead, and real-time energy purchasing cost ($). Charging loss (%) and leakage loss factors of ESS (%/month). I. INTRODUCTION

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ECENTLY, many countries have placed great pressure on energy industry to incorporate renewable energy into their energy mix in the form of wind, solar, etc. Governments are promoting the construction of renewable energy projects with generous subsidies and with regulatory support. The cumulative installed capacity has increased markedly since the last decade. Although these types of power generation is more environmentally sustainable, large-scale integration of renewable energy in power distribution systems may significantly alter network power flow in terms of direction and magnitude, which will impose direct impacts on power quality, protection settings, and etc., making delivering reliable power, on demand, a major challenge. Therefore, with the advent of renewables, distribution companies (DISCOs) are facing emerging challenges. In this paper, we assume DISCO to be sole system operator and electricity retailer, and introduce a risk mitigation model for the operation of DISCOs. Normally, DISCOs buy energy through bilateral contracts or pool market to meet electrical demand of end-users. If DISCOs possess renewable energy facilities, they will have more choices to acquire energy. Moreover, DISCOs can also improve their response capability towards electricity market by participating in financial bids for sale of excess energy. Therefore, while DISCOs are struggling with the pressure from integrating more renewables, they can encounter new economic opportunities by participating in different electricity markets. Although there have been noteworthy researches underway in formulating more effective operation rules for electricity markets, majority of previous studies placed great emphasis on generation companies (GENCOs). With the further development of electricity markets, increasing research interests have being directed towards DISCOs. In contract markets, DISCOs buy electricity through negotiated agreements; while in pool markets, DISCOs purchase electricity from forward market and spot market [1] to meet their demand. Normally, energy is purchased at variable prices and sold at fixed or multistep prices. However, due to forecast errors, DISCOs are still responsible for compensating the gap between actual demand and supply. With the introduction of renewables, more severe deviations will show in net demand, which will bring risk to DISCOs during energy purchasing and

bidding processes. Therefore, in order to maximize the profits of DISCOs and to reduce the procurement-cost risk that stems from price volatility, a number of energy procurement allocation strategies had been proposed [2], [3]. In this paper, the risk of DISCOs is analyzed in three continuous stages. Firstly, DISCOs sign one-hour forward bilateral contract with GENCOs [4] or make day-ahead biddings against suppliers’ offers [5], [6] to cover majority of estimated demand. Secondly, when under-/ over-consumption occurs in real-time, DISCOs compensate this gap from pool at real-time price [7] or regulating price [5], and penalty cost is incurred [8]. Thirdly, with integration of renewables, DISCOs can sell excess energy back to pool market by deriving optimal contract under arbitrary penalty [9]–[11]. In this study, the purchasing and selling contracts are combined to day-ahead contracts. The main objective of the present work is to develop an energy storage system (ESS) based energy acquisition model for DISCO in either regulating or deregulating model, meanwhile taking into account day-ahead preparation for energy provision options within DISCO controlled area. Energy storage, a fast developing field, is expected to see a massive leap in the future [12]. In current markets, there are many types of commercialized energy storage devices. Compared with other ESSs, battery energy storage system (BESS) is the most cost-effective one designed for power system operation purposes. Due to its attractive features, the potentials of applying BESS into power system were studied extensively; including increasing renewables penetration, leveling demand curve, controlling system frequency, deferring network upgrade, and etc. A number of recent publications focus on how to address the dispatch problems with high renewable penetration using BESS [13]–[18]. Some other investigations concentrated on the applications of BESS in system frequency control [19]–[21] and power flow control [22]. Although the advances in material science and power electronic techniques have facilitated the effective employment of new energy storage facilities, the high cost and control issues still block the wide applications of ESS. In this paper, a novel control strategy for ESS is presented to mitigate the risk faced by DISCOs participating in electricity markets. The proposed control strategy aims to track the forecasted net demand curve and to reduce the energy exchanged at distribution substation, rather than mitigating power output fluctuations by installing ESS at distributed generation (DG) sites. In general, the merits of this design can be summarized in two aspects. Firstly, with this control strategy, the capacity of ESS only depends on the forecast accuracy of demand and output of DG, instead of the errors between peaks and valleys of power generation profiles. This novel strategy can largely reduce the required capacity of ESS, making the whole project becomes more economic and feasible. Secondly, knowing the exact amount of energy to be exchanged between substation and grid, DISCOs can optimize purchase decisions by buying short-term contracts in day-ahead market and adjusting the expected power curve to make profits in real-time market. However, the successful implementation of ESS relies heavily on planning. The optimal siting, sizing, and operation rules of ESS can help to adjust the power flow and reduce the power loss of distribution systems. In this paper, we present a comprehensive analysis of the economic

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benefits accrued and cost incurred to a DISCO’s investing in ESS, considering it as a key component in distribution network planning. Since the allocation of ESS in distribution system is a nonlinear optimization problem and it cannot be easily handled by traditional optimization approaches, a fuzzy particle swarm optimization (FPSO) algorithm is proposed to solve this problem. This paper is organized as follows, after introduction section; an ESS based energy acquisition model for DISCOs is presented, followed by the overview of prediction methodologies. After that, the explicit ESS model and the corresponding control strategy are proposed for mitigating the operational risk for DISCOs. And then, the proposed FPSO algorithm is presented and discussed. Finally, the proposed energy acquisition model and ESS control strategy are verified on a modified IEEE 15-bus distribution network, and risk mitigation is also quantified in two different markets. Conclusions and further developments are discussed in the last section. II. OPERATION MODEL OF DISCOS A. Energy Acquisition Model of DISCOs With high renewables penetration, DISCOs have the ability to sell excess energy to other market participants through pool market or bilateral contracts. In spot markets, the producers and purchasers bid or negotiate in day-ahead market, and the actual dis-patchable power is balanced in real-time market for each time interval of a day [23]; in contract mechanism, the penalty is determined based on the difference between purchased and consumed energy [8]. Market price fluctuations have strong impacts on the operation modes of ESS, which are essentially profit-driven. Based on accurate forecast, DISCOs can save significant energy purchasing cost and reduce their risk arising from a volatile real-time market by adjusting the operational modes of ESS. With the advent of ESS, the real-time load gap can be compensated to mitigate the high penalty cost or spinning reserve price. In this paper, an ESS based operational model for DISCO is proposed-see Fig. 1. This model considers different DG units within DISCO supply area. It should be noted that, three assumptions are made for this model: 1) DISCO is the sole system operator and also electricity retailor; 2) DG units are owned and operated by DISCOs, which are therefore dis-patchable in both day-ahead and real-time operation; and 3) the day-ahead procurement energy is set according to the forecasted net demand. B. Forecast Toolboxes Forecast plays a key role in the planning and operation of a distribution system. It is a basic task to support the decision of electricity procurement [24], [25] and can facilitate renewable penetrations [26], [27]. In this paper, a series of advanced forecast toolboxes [28] are used, including OptiLoad, OptiWind, and OptiSolar. OptiLoad uses ensemble models to automatically produce forecasts at different time resolutions. Several important factors considered in the forecasting model, including temperature, seasonal weather, public holidays, and historical load data. OptiWind incorporates highly customized

Fig. 1. Proposed operation framework for DISCOs.

numerical weather prediction (NWP) models and the latest statistical regression methods. OptiSolar forecast system is also based on modified NWP models, considering localized characteristics. For price forecast, an advanced self-adaptive radial basis function neural network (RBFNN) based price prediction model is used [29]. For practical applications, these deployed forecast toolboxes should undergo continuous refinement in light of future operational experience. Improvements in forecasts and better use of the predictions when making decisions can facilitate integration of renewable energy into distribution system and mitigate operational risk stemming from price volatility. C. Energy Transaction Risk The fluctuating nature of renewable energy means that the resulting net load profile is very different to that of the underlying load profile alone. The effect of renewable energy is to alter the regular nature of standard demand profile and replace it with a highly variable modified pattern. The net demand can be computed as (1) Note that under contract mechanism, the day-ahead price and quantities are fixed in a negotiated agreement that defines a purchase schedule. While in pool markets, prices and quantities are usually determined by a price clearing procedure. According to the nature of each market, we assume that there is no market power can affect the pool price in deregulated market while the approximate single purchaser model is supposed to suit the contract markets. The energy purchasing cost of a DISCO according to day-ahead scheduling at each time interval can be calculated as (2)



However, because net load cannot be predicted at very high accuracy at all times, especially after the integration of renewable energy, DISCOs need to compensate the gap between the actual load and the expected one at real-time price, and therefore the risk of energy transaction is caused. The risk in real-time market can be expressed as penalty cost (regulating price) or LMP and congestion fee. In LMP mechanism, if the actual load deviates from day-ahead purchasing, the DISCOs should pay charges at real-time price [6]. In this paper, regulating price mechanism and PJM market are analyzed. The total procurement-cost can be denoted as (3)

Fig. 2. Price triggered mechanism for energy procurement adjustment.

and the operation of BESS is constrained to (8)

For regulating price mechanism

(9) (4)

For LMP mechanism

(5) The proposed method aims to minimize the investment and operational cost of candidate ESSs as well as payments toward purchased power by a DISCO meeting the constraints of distribution system. D. BESS Operation Strategy Suitable storage of energy at appropriate time and locations can help to balance generation with consumption, and to maintain system stability. In the following study, a novel battery operation strategy is proposed for better utilization of ESS and power loss reduction in the distribution system. Battery Charging Issues: The energy changing of BESS, the key issue for battery operation strategy, can be described as

(6) The state-of-charge (SOC) is expressed as follows: (7)

Operation Objectives: In this paper, the BESS operation strategies are the same in two different markets. Charging/discharging signals are triggered according to demand gap, SOC, and maximum power. The overall target of operation strategy is to track the forecasted demand curve which can be implemented by the following charging signals: (10) Furthermore, due to the load following effect, the estimated load curve can be adjusted to pursue profit through price difference. As is shown in Fig. 2, we can adjust the procurement energy according to forecasted electricity price. When price is higher than predetermined threshold, the DISCOS should decrease the procurement amount in day-ahead market. On the contrary, more electricity can be purchased. The total amount is calculated according to the capacity of BESS. A day-ahead purchasing strategy is proposed as shown in (11) at the bottom of the page, where is the modified amount of energy purchased in day-ahead market; is the allowed depth of ESS; and and are the price threshold indexes, when price deviates from the boundaries, this mechanism will be triggered. Charging Amount Allocation: In this paper, BESS are optimally allocated at the distribution system; the charging amount for each BESS should be decided during operation process. Fig. 3 introduces the allocation method. The remained spaces determine the charging task. When charging/discharging

(11)


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on this conclusion, the remaining life of the battery can be calculated as (14) where is the life cycles at certain DOD and the total energy charging and discharging of the battery.

is

III. PROBLEM FORMULATION Fig. 3. Operation strategy BESS through a day.

A. Optimal Allocation of ESS The main benefits of installing ESS in distribution system can be summarized as 1) to maximize the total economic benefits of a DISCO and to reduce the procurement-cost risk that stems from price volatility; 2) to absorb and release energy to balance generation with consumption and to reduce the energy exchanges at the substation; and 3) to minimize total power losses while satisfying distribution system constraints. The objective function can be described as

TABLE I TYPICAL PARAMETERS OF LEAD-ACID AND LI-ION BATTERY

(15) signal is generated, the remained space of ESS for operation is calculated as follows: (12) The amount of power for each battery is allocated as (13)

If the power exceeds the charging or discharging limit, the amount of power will be adjusted to the maximum charging power or discharging power . In order to avoid frequent switching between charging and discharging, which is unfavorable to the electronic devices, a filter is introduced. The filter is designed as a power recognizer, the trigger signal is sent when the load gap exceeds the minimum value, and the small fluctuation would be filtered. In this paper, the lead-acid batteries and lithium-ion batteries are chosen as potential ESS. The energy density, size, cost, and reliability are compared in Table I [30]. The capacity of energy storage station is a major concern. The other index of BESS is produced according to the capacity which is proportional to the ones of standard cell. It should be noted that, the life cycle of ESS in this work is calculated by the total energy usage. According to the impacts of discharge rate on battery life in [31], the total energy charging/discharging capability of battery is remained stable within the reason depth of discharge (DOD). Base

is the total cost function of the BESS, which where includes investment cost, operation cost, maintenance cost, and residual value: (16) From the objective function, we can find that the final result depends on location and capacity of installed ESS in the distribution system. The data of electricity price, system demand, and weather profile are collected from historical database. The objective function is subject to various operating constraints to satisfy the electrical requirements for distribution network, constraints on ESS operation, and constraints on investment resources. These constraints are discussed as follows:

1) Power Balance Constraint See (17) at the bottom of the next page. 2) Voltage Constraint The voltage magnitude of each node must lie within their power quality limitation: (18) 3) Current Constraint Current magnitude of each branch must lower than rating current to ensure the cable thermal stability: (19)



4) Power Source Limit Constraint Dispatch power and reverse power flow through the substation transformer is allowed within the limitation of the transformers’ capacity and protection system constraints: (20) 5) DGs Operation Limit Constraint (21) 6) ESS system constraints, as formula (8) and (9). B. Fuzzy Particle Swarm Optimization (FPSO)

Fig. 4. Membership function curves.

Since the panning of ESS in distribution system is a mixed integer nonlinear optimization problem which cannot be easily solved by conventional optimization tools, a new algorithm is proposed. The traditional PSO algorithm mimics the sociological behavior of bird flocking [32]. Each individual represents a potential solution for the optimization problem. The core idea of PSO is the exchange of information among the velocity, global best, local best, and current particles, but its performance is sensitive to its control parameters, including inertia weight and learning factors. It is very difficult to establish the analytically mathematical model to describe the dynamical relationship of the control parameters with the instant searching performance, thus in PSO, these control variables are often set as constants. In the FPSO, fuzzy control approach [33], [34] is employed to adaptively adjust these control parameters. The core idea is to integrate fuzzy logic into heuristic algorithm. Original PSO Algorithm: In original PSO, each individual is called a particle in the search space, which represents a solution for the given problem. Each particle has position and velocity. During evolution, the velocity of each particle is updated by exchanging information among velocities of itself, historical local best particle, and global best particle. Once the velocity is updated, the position is updated. This process can be expressed as

(22) (23) where and are the current position and velocity of the th particle; is the local best position in the memory of the th particle; is the global best position up to now; is the uniform distributed random number within [0,1); and , , and are the three control parameters of the th particle.

Inputs and Outputs: In FPSO, after each iteration, the three control parameters are updated by fuzzy controller. The inputs of the controller are the fitness value difference between the th particle and its local best particle , as well as the performance difference between current particle and global best particle . The outputs are the three new variables. and can be normalized as (24), where is the global worst particle. All the five parameters are considered as fuzzy variables, and their values are assigned as membership grades in three fuzzy subsets: large (L), medium (M), and small (S): (24) Membership Functions: Gaussian curve membership function is chosen, whose definition is in the form of (25) For this ESS allocation problem, we determine parameters of the membership functions by experiments. is set t0 be the same for all membership functions. For and , the membership functions are same, with and , 0.5, and 1 for , respectively; for inertia weight, and , 0.7, and 0.95; for learning factors, and , 1, and 1.9. The shapes of the membership functions are shown as Fig. 4. Fuzzy Rules: The IF/THEN fuzzy rules are established to formulate the conditional statements of the fuzzy logic—see Table II.

(17)


TABLE II FUZZY RULES OF FPSO

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TABLE III PARAMETERS USED FOR SIMULATION

Fig. 6. Optimization process of different algorithm. Fig. 5. Modified IEEE 15-bus distribution radial system.

TABLE IV ESS ALLOCATION OPTIMIZATION RESULT UNDER REGULATED MARKET

The fuzzy rules in Table II reflect the philosophy of fuzzy controller. Firstly, the smaller the performance difference between the particle and the existing best solutions ( and ), the more trust of the particle on its current flying inertia weight to avoid premature; otherwise, the particle chooses to more trust and ; secondly, if is large, the particle choose to more trust ; otherwise, the particle prefers to more trust its own memory . IV. CASE STUDY One modified IEEE 11-kV, 15-bus distribution radial system is used to verify the proposed ESS allocation approach. The benchmark system consists of fourteen loads and five wind units. The two 850-kW wind machines are installed at the bus 11 and the other one 800-kW wind turbines located at bus 9. The solar farm is constructed at bus 6. The one-line diagram of this distribution system is shown in Fig. 5. The historical data for NWP model was downloaded from RDA website [35]. The electricity price data was obtained from PJM [36]. Key parameters are shown in Table III. Simulation was performed without BESS through contract purchasing mechanism, the penalty cost due to energy gap is $98 686 and the cost of energy loss is $62 952. Subsequently, the optimization is performed with three different optimization algorithms, considering two types of BESS, respectively. The optimization results are provided in Fig. 6 and Table IV. The proposed fuzzy-PSO algorithm outperforms other algorithms in solving mixed integer nonlinear optimization problem. As is shown in Fig. 7, benefits and cost are compared for two optimal plans. Although the lead-acid ESS is a more economical choice due to its relatively low cost, the shortcomings are

TABLE V ESS ALLOCATION OPTIMIZATION RESULT UNDER PJM MARKET

obvious that the rather low charge power limits the effect of risk mitigation and the weight is much heavier than Lithium-ion battery. Along with development of battery technologies, the cost gap between these two types of ESS will get smaller. Moreover, some projects require the m. All these requirements will lead to the widespread of Lithium-ion battery.



Fig. 7. Cost and benefits construction under regulating price market. Fig. 9. SOC of ESS of different time of day.

V. CONCLUSION

Fig. 8. SOC of ESS of a week.

After the optimization under the regulating price market, we repeat the simulation under the deregulated market, the total risk of congestion charge loss is $61 423 which is caused by unbalance demand and unstable real-time price of 5-min interval. We found that the total benefits of risk mitigation cannot cover the cost of the Li-ion battery. Therefore, the optimization is performed for lead-acid BESS only in PJM market. The optimization results are shown in Table V. In order to show the fluctuations of SOC of the three candidate ESSs clearly, we intercept the curves through a week in Fig. 8. We also record and compute the daily SOC of the ESS in Fig. 9. From the figure, we can find that the SOC is always low in the 14:00–23:00 when the energy price is rather high. With this kind of energy storage strategy, DISCOs can make more profits. The required power capacity of ESS in the optimal plan is less than 0.85 MW, which is about 25% of integrated renewable energy. If the BESSs are installed at the renewable site, the required capacity of BESS for is over 30% of the rated renewable energy. From the simulation results above, we can draw a conclusion that the required power capacity of BESS is reduced with the proposed charge strategy and the risk for DISCO is mitigated. We believe that with the widespread installation of ESS, not only the DISCOs can make profit but also the power system spinning reserve can be reduced.

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Yu Zheng (S’12) received the B.E. degree from Shanghai Jiao Tong University, China. He is now pursuing the Ph.D. degree at the Centre for Intelligent Electricity Networks (CIEN), The University of Newcastle, Australia, and he was previously with the Hong Kong Polytechnic University. His research interests include power electronic applied in power system, power system planning, smart grid, and intelligent system applications to power engineering.

Zhao Yang Dong (M’99–SM’06) received the Ph.D. degree from the University of Sydney, Australia, in 1999. He is now a Professor and Head of School at the University of Sydney, Australia. He is immediate Ausgrid Chair Professor and Director of the Centre for Intelligent Electricity Networks (CIEN), the University of Newcastle, Australia. His research interest includes smart grid, power system planning, power system security, load modeling, renewable energy systems, electricity market, and computational intelligence and its application in power engineering. Prof. Dong is an editor of the IEEE TRANSACTIONS ON SMART GRID and IEEE POWER ENGINEERING LETTERS.

Feng Ji Luo (S’10) received the B.S. and M.S. degrees in software engineering from Chongqing University, Chongqing, China, in 2006 and 2009, respectively. Currently, he is pursuing the Ph.D. degree in the University of Newcastle, Australia. His research interests include computational intelligence applications, distributed computing, and power system operation.

Ke Meng (M’10) received the Ph.D. from the University of Queensland, Australia, in 2009. He is currently with the Centre for Intelligent Electricity Networks (CIEN), the University of Newcastle, Australia. His research interest includes pattern recognition, power system stability analysis, wind power, and energy storage.

Jing Qiu (S’12) received the B.Eng. degree in control engineering from Shandong University, China, and the M.Sc. degree in environmental policy and management from the University of Manchester, U.K. He is now pursuing the Ph.D. degree at the Centre for Intelligent Electricity Networks (CIEN), The University of Newcastle, Australia. His areas of interest include electricity market modeling, power system planning, renewable energy and risk management.

Kit Po Wong (M’87–SM’90–F’02) received the M.Sc, Ph.D., and higher doctorate D.Eng. degrees from the University of Manchester, Institute of Science and Technology, Manchester, U.K., in 1972, 1974, and 2001, respectively. Since 1974, he has been with the School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Perth, Australia, where he is currently a Winthrop Professor. He is a Con-Joint Professor of University of Newcastle. His current research interests include power system analysis, planning and operations, and smart grids. Prof. Wong received three Sir John Madsen Medals (1981, 1982, and 1988) from the Institution of Engineers Australia, the 1999 Outstanding Engineer Award from IEEE Power Chapter Western Australia, and the 2000 IEEE Third Millennium Award. He was General Chairman of IEEE/CSEE PowerCon2000 conference. He was an Editor-in-Chief of IEE Proceedings in Generation, Transmission and Distribution. Currently he is serving as Editor-in-Chief for IEEE POWER ENGINEERING LETTERS.