ELECTRICAL energy is continuously lost due to resistance

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1 814 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 25, NO. 3, SEPTEMBER 2010 Analytical Expressions for DG Allocation in Primary Distribution Networks Duong Quoc Hung, Nadarajah Mithulananthan, Member, IEEE, and R. C. Bansal, Senior Member, IEEE Abstract This paper proposes analytical expressions for finding optimal size and power factor of four types of distributed generation (DG) units. DG units are sized to achieve the highest loss reduction in distribution networks. The proposed analytical expressions are based on an improvement to the method that was limited to DG type, which is capable of delivering real power only. Three other types, e.g., DG capable of delivering both real and reactive power, DG capable of delivering real power and absorbing reactive power, and DG capable of delivering reactive power only, can also be identified with their optimal size and location using the proposed method. The method has been tested in three test distribution systems with varying size and complexity and validated using exhaustive method. Results show that the proposed method requires less computation, but can lead optimal solution as verified by the exhaustive load flow method. Index Terms Analytical expressions, distributed generation (DG), loss reduction, optimal location, optimal power factor, optimal size. I. INTRODUCTION ELECTRICAL energy is continuously lost due to resistance in power system networks, and distribution system loss accounts for more compared to transmission system [1]. Moreover, distribution systems are well known for a higher R/X ratio compared to transmission systems and significant voltage drops can result in substantial power and energy losses along distribution feeders. As a result, loss reduction in distribution systems is one of the greatest challenges to many utilities around the world. Reconfiguration and capacitors placement are two major methods for loss reduction in distribution systems. In recent years, penetration of DG into distribution systems has been increasing around the world. Major reasons for this trend are liberalization of electricity markets, constraints on building new transmission and distribution lines and environmental concerns [2], [3]. For instance, a research by the Electric Power Research Institute (EPRI) estimates that DG will be about 25% of the new generation by 2010, while a similar study by the National Gas Foundation shows that this figure could be even higher, account for nearly 30% [3]. Given this trend, if DGs Manuscript received July 30, 2009; revised December 10, 2009; accepted February 8, Date of current version August 20, Paper no. TEC D. Q. Hung is with the Power Company No. 2, Electricity of Vietnam, Ho Chi Minh City 70000, Vietnam ( hungdq73@yahoo.com). N. Mithulananthan and R. C. Bansal are with the School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Qld. 4072, Australia ( mithulan@itee.uq.edu.au; rcbansal@ieee.org). Color versions of one or more of the figures in this paper are available online at Digital Object Identifier /TEC are placed properly and appropriately sized, they could also be considered as an effective way to reduce losses, improve voltage profiles and increase reliability. There is a wide range of terminologies used for distributed generation, such as embedded generation, dispersed generation, or decentralized generation [3]. DG essentially means a small-scale power station different from a traditional or large central power plant. At present, there are several technologies ranging from traditional to nontraditional used in DG application. The former is nonrenewable technologies such as internal combustion engines, combined cycles, combustion turbines, and microturbines. The latter technologies include fuel cells, storage devices, and a number of renewable energy-based technologies such as photovoltaic, biomass, wind, geothermal, ocean, etc. When renewable energy-based DG units are placed for loss reduction, both aspects of sustainable energy, i.e., renewable energy and energy efficiency are addressed. The challenges in DG applications for loss reduction are proper location, appropriate sizes, and operating strategies. Even if the location is fixed due to some other reasons, improper size would increase the losses in the system beyond the losses for case without DG. Optimal sizing and location depend on the type of DG as well. Hence, in this paper, an attempt is made to develop simple analytical expressions for sizing, which can be easily calculated. Rest of the paper is organized as follows: Section II presents a brief summary of literatures on loss reduction techniques. Proposed analytical expressions for finding optimal sizes of various DG types are introduced in Section III. Optimal placement and operating power factor of DG is also addressed in the section. Section IV presents numerical results of application of developed analytical expression in three test systems, interesting observations along with discussions. Finally, the major contributions and conclusions are summarized in Section V. II. LOSS REDUCTION TECHNIQUES There have been many studies on the reconfiguration of distribution systems for loss reduction. A switch exchange algorithm was proposed in [4]. In [5], an approximate power-flow technique was developed for analyzing loss reduction from network reconfiguration. In [6], Fan et al. formulated the reconfiguration problem as a linear programming problem and applied a single-loop optimization method to solve network reconfiguration. Other techniques such as the genetic algorithm (GA) [7], simulated annealing (SA) [8], improved Tabu Search (TS) [9], and ant colony search (ACS) algorithm [10] have been used for the purpose of network reconfiguration for reducing losses. For optimal capacitor placement for loss reduction, a well known Golden Rule, or 2/3 rule is presented in [11]. This /$ IEEE

2 HUNG et al.: ANALYTICAL EXPRESSIONS FOR DG ALLOCATION IN PRIMARY DISTRIBUTION NETWORKS 815 method would yield good solutions in system where the loads are uniformly distributed. Many researchers have applied other techniques such as dynamic programming [12], fuzzy expert system [13], TS [14], and GA [15] for finding best locations for capacitors to reduce losses. In distribution systems, DG can deliver a portion of real and/or reactive power so that the feeder current is reduced and voltage profile can be improved with reduction in losses. However, studies indicate that poor selection of location and size would lead to higher losses than the losses without DGs [16], [17]. A technique for DG placement using 2/3 rule has been presented in [17]. Although the 2/3 rule is simple and easy to apply, this technique may not be effective in distribution with not uniformly distributed loads. Besides, if a DG is capable of delivering real and reactive power, applying the method that was developed for capacitor placement may not work. In [18], an analytical approach has been presented to identify appropriate location to place single DG in radial as well as loop systems to minimize losses. But, in this approach, optimal sizing is not considered. GA was applied to determine the size and location of DG in [19] and [20]. Though GA is suitable for multiobjective problems and can lead to a near optimal solution, they demand computational time. Recently, an analytical approach based on exact loss formula was presented to find the optimal size and location of DG [16]. In this method, the load flow is required to be conducted only twice. The first load-flow calculation is needed to calculate the loss of base case. The second load-flow solution is required to find the minimum total loss after DG placement. The technique requires less computation. However, the analytical approach can be applied to DG capable of delivering only real power. Most of the approaches presented so far model DG as a machine that is capable of delivering only real power. However, there are other types of DG being integrated into distribution systems. This paper develops a comprehensive formula by improving the analytical (IA) method proposed in [16] to find the optimal sizes, optimal locations of various types of DG. The paper also presents the importance of operating DGs that are capable of delivering both real and reactive power at the proper power factor to achieve minimum losses. III. PROPOSED METHODOLOGY A. Problem Formulation The total real power loss in power systems is represented by (1), popularly known as exact loss formula [21] P L = [α ij (P i P j + Q i Q j )+β ij (Q i P j P i Q j )] (1) i=1 where α ij = r ij cos (δ i δ j ), β ij = r ij sin (δ i δ j ); V i V j V i V j V i δ i the complex voltage at the bus ith; r ij + jx ij = Z ij the ijth element of [Zbus] impedance matrix; P i and P j the active power injections at the ith and jth buses, respectively; Q i and Q j N the reactive power injections at the ith and jth buses, respectively; the number of buses. B. Types of DG DG can be classified into four major types based on their terminal characteristics in terms of real and reactive power delivering capability as follows: 1) Type 1: DG capable of injecting P only. 2) Type 2: DG capable of injecting Q only. 3) Type 3: DG capable of injecting both P and Q. 4) Type 4: DG capable of injecting P but consuming Q. Photovoltaic, micro turbines, fuel cells, which are integrated to the main grid with the help of converters/inverters are good examples of Type 1. Type 2 could be synchronous compensators such as gas turbines. DG units that are based on synchronous machine (cogeneration, gas turbine, etc.) fall in Type 3. Type 4 is mainly induction generators that are used in wind farms. C. Sizing at Various Locations Assuming a = (sign)tan(cos 1 (PF DG )), the reactive power output of DG is expressed by (2) Q DGi = ap DGi (2) in which sign = +1: DG injecting reactive power; sign = 1: DG consuming reactive power; PF DG is the power factor of DG. The active and reactive power injected at bus i, where the DG located, are given by (3) and (4), respectively, P i = P DGi P D i (3) Q i = Q DGi Q D i = ap DGi Q D i. (4) From (1), (3), and (4), the active power loss can be rewritten as [ ] αij [(P DGi P D i )P j +(ap DGi Q D i )Q j ] P L =. i=1 +β ij [(ap DGi Q D i )P j (P DGi P D i )Q j ] (5) The total active power loss of the system is minimum if the partial derivative of (5) with respect to the active power injection from DG at bus i becomes zero. After simplification and rearrangement, (5) can be written as P L =2 [α ij (P j + aq j )+β ij (ap j Q j )] = 0. (6) P DGi Equation (6) can be rewritten as α ii (P i + aq i )+β ii (ap i Q i )+ + a (α ij P j β ij Q j ) (α ij Q j + β ij P j )=0 (7)

3 816 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 25, NO. 3, SEPTEMBER 2010 let X i = Y i = n (α ij P j β ij Q j ) n (α ij Q j + β ij P j ). j 1 From (3), (4), (7), and (8), (9) can be developed α ii ( PDGi P D i + a 2 P DGi aq D i ) (8) + β ii (Q D i ap D i )+X i + ay i =0. (9) From (9), the optimal size of DG at each bus i for minimizing loss can be written as P DGi = α ii (P D i + aq D i )+β ii (ap D i Q D i ) X i ay i a 2. α ii + α ii (10) The power factor of DG depends on operating conditions and type of DG. When the power factor of DG is given, the optimal size of DG at each bus i for minimizing losses can be found in the following way. 1) Type 1 DG: For Type 1 DG, power factor is at unity, i.e., PF DG = 1, a = 0. From (10), the optimal size of DG at each bus i for minimizing losses can be given by reduced equation (11) P DGi = P D i 1 α ii β iiq D i + (α ij P j β ij Q j ). (11) 2) Type 2 DG: Assuming PF DG = 0 and a =,from(2)to (10), the optimal size of DG at each bus i for minimizing losses is given by reduced equation (12) Q DGi = Q D i + 1 α ii β iip D i (α ij Q j + β ij P j ). (12) 3) Type 3 DG: Assuming 0 < PF DG < 1, sign =+1 and a is a constant, the optimal size of DG at each bus i for the minimum loss is given by (10) and (4), respectively. 4) Type 4 DG: Assuming 0 < PF DG < 1, sign = 1 and a is a constant, the optimal size of DG at each bus i for the minimum loss is given by (10) and (4), respectively. D. Optimal Location For optimal location, the method proposed in [16] is used. Based on this method, first the optimal sizes at various locations have been calculated for different types of DG and the losses were calculated with optimal sizes for each case. The case with minimum losses is selected as the optimal location for each type of DG. Based on this method proposed in [16], one can avoid exhaustive computation and save time, especially for large-scale Fig. 1. Simple distribution system with DG. distribution systems as trend of loss reduction can be captured with α and β coefficients from the bases case. E. Optimal Power Factor Consider a simple distribution system with two buses, a source, a load and DG connected through a transmission line as shown in Fig. 1. The power factor of the single load (PF D 2 ) is given by (13) PF D 2 = P D 2 P 2 D 2 + Q 2 D 2. (13) It can be proved that at the minimum loss occur when power factor of DG is equal to the power factor of load as given by (14). PF D 2 =PF DG2 = P DG2 P 2 DG2 + Q 2 DG2. (14) In practice, a complex distribution system includes a few sources, many buses, many lines and loads. The power factors of loads are different. If each load is supplied by each local DG, at which the power factor of each DG is equal to that of each load, there is no current in the lines. The total line power loss is zero. The transmission lines are also unnecessary. However, that is unrealistic since the capital investment cost for DG is too high. Therefore, the number of installed DGs should be limited. To find the optimal power factor of DG for a radial complex distribution system, fast and repeated methods are proposed. It is interesting to note that in all the three test systems the optimal power factor of DG (Type 3) placed for loss reduction found to be closer to the power factor of combined load of respective system. 1) Fast Approach: Power factor of combined total load of the system (PF D ) can be expressed by (13). In this condition, the total active and reactive power of the load demand are expressed as P D = P D i (15) Q D = i=1 Q D i. (16) i=1 The possible minimum total loss can be achieved if the power factor of DG (PF DG ) is quickly selected to be equal to that of the total load (PF D ). That can be expressed by (17) PF DG =PF D. (17)

4 HUNG et al.: ANALYTICAL EXPRESSIONS FOR DG ALLOCATION IN PRIMARY DISTRIBUTION NETWORKS 817 2) Repeated Approach: In this method, the optimal power factor is found by calculating power factors of DG (change in a small step of 0.01) near to the power factor of combined load. The sizes and locations of DG at various power factors with respect to losses are identified from (14). The losses are compared together. The optimal power factor of DG for which the total loss is at minimum is determined. F. Computational Procedure When power factor of DG is set to be equal to that of combined total loads, computational procedure to find optimal size and location of one of four types of DGs is described in the following. Step 1: Run load flow for the base case. Step 2: Find the base case loss using (1). Step 3: Calculate power factor of DG using (17). Step 4: Find the optimal size of DG for each bus using (4) and (10). Step 5: Place DG with the optimal size obtained in step 4 at each bus, one at a time. Calculate the approximate loss for each case using (1) with the values α and β of base case. Step 6: Locate the optimal bus at which the total loss is minimum corresponding with the optimal size at that bus. Step 7: Run load flow with the optimal size at the optimal location obtained in step 6. Calculate the exact loss using (1) and the values α and β after DG placement. It is noted that when the type and power factor of DG is given; the computational procedure to find the optimal size and location of DG is as described earlier apart from step 3. At this step, the power factor of DG is entered rather than using (17). In exhaustive load flow (ELF) method, optimal sizes, optimal location, and power factor are obtained with a number of load flow solutions. IV. NUMERICAL RESULTS A. Test Systems Three test systems have been used to test and validate the proposed analytical expressions for optimal sizing, placement, and power factor. The first system is 16-bus radial test distribution system with a total real and reactive load of 28.7 MW and 5.9 MVAr, respectively [22]. The second system is 33-bus test radial distribution system with a total real and reactive load of 3.7 MW and 2.3 MVAr, respectively [23]. The last system is 69-bus test radial distribution system with a total real and reactive load of 3.8 MW and 2.69 MVAr, respectively [24]. An analytical software tool has been developed in MATLAB environment to run load flow and calculate losses and optimal sizes of DG. Though the proposed analytical methods can handle four different types of DG, results of Type 3-DG, i.e., DG capable of delivering real and reactive power is presented in this paper. B. Assumptions and Constraints 1) The sizing and location of DG is considered at the peak load only. Fig. 2. Optimal sizes of DG at 0.98 lagging load power factor at various locations with respect to losses for 16-bus system. Fig. 3. Optimal sizes of DG at 0.85 lagging load power factor at various locations with respect to losses for 33-bus system. 2) Maximum active power limit of DG for different test systems is assumed to be equal to the total active load of the system. 3) The lower and upper voltage thresholds for DG with the optimal size, location and power factor are set at pu. C. Simulation Results 1) Sizing Allocation: Figs. 2 4 show optimal sizes and total losses with the optimal sizes of DG one at a time at respective location for 16, 33, and 69 bus test systems, respectively. In all the figures, lower half shows the optimal sizes obtained using proposed method (IA) and ELF method using exact loss formula. Notice in all the cases, optimal sizes of DG obtained from the proposed method is closer to actual optimal size obtained from exhaustive load flow solutions. Similarly, total losses obtained from the method without updating α and β for the purpose of identifying best location and exhaustive methods give a comparable result. Observe that when DG with a large size is placed at any locations near a swing bus, the loss reduction of each system is quite low. In contrast, only a small size of DG is added at the other locations, a higher loss reduction can be achieved. As a result, finding a location at which the total loss at minimum

5 818 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 25, NO. 3, SEPTEMBER 2010 Fig. 4. Optimal sizes of DG at 0.82 lagging load power factor at various locations with respect to losses for 69-bus system. Fig. 6. Plot of optimum sizes and locations of DG at various power factors versus losses for 33-bus test system. Fig. 5. Plot of optimum sizes and locations of DG at various power factors versus losses for 16-bus test system. is important. That can be implemented by the support of IA as presented in the following. 2) Site Selection: Figs. 2 4 also show (upper half) the total losses with the optimal DG at various buses, one at a time using both methodologies. The total losses by IA are slightly higher than the exact losses at by ELF. However, the trend of losses with IA and ELF are completely coinciding and for the purpose of identifying location, a fast method would be sufficient. In other words, IA can find the optimal locations with less computational efforts. In 16-bus, 33-bus, and 69-bus test distribution systems used in the paper, the best locations for DG for minimizing losses found to be buses 9, 6, and 61, respectively. 3) Power Factor Selection: Figs. 5 7 show the optimal sizes and locations of DGs at various power factors and total losses by IA method for 16, 33, and 69-bus test systems, respectively. The power factors of DG varied from zero to 1.00, both in leading and lagging operation in small steps of This study is carried out to see the optimal power factor of DG that would give minimum losses. In 16-bus system, buses 8, 9, and 12 found to be good candidate locations for distributed generator placement for minimizing losses. These results are in agreement with the results obtained from previous section site selection. Notice at bus 9, the best location, power factor plays an important role in Fig. 7. Plot of optimum sizes and locations of DG at various power factors versus losses for 69-bus test system. TABLE I POWER FACTORS OF DG AND COMBINED LOADS loss reduction. The range of power factors good for minimizing losses at bus 9 is 0.87 leading to 0.46 lagging with optimal sizes ranging from 7.46 to MVA. Though in reality the sizes will be fixed and power factor can be allowed to vary to observe the impact of DG power factor on loss reduction. If the power factor is fixed due to the limitation of the technology, it is important to select the appropriate size and location to achieve minimum losses. If the technology available for DG to be a Type I, i.e., around unity power factor, the best location is bus 9. On the other hand if DG type is Type II, i.e., zero power factor then the best location is at bus 8, for reducing losses and so on. In this test system, the best power factor found to be closer to unity (i.e., 0.99) and corresponding size and losses are MVA and kw, respectively. Unity power factor means this system needs only

6 HUNG et al.: ANALYTICAL EXPRESSIONS FOR DG ALLOCATION IN PRIMARY DISTRIBUTION NETWORKS 819 TABLE II COMPARISON OF RESULTS OF DIFFERENT TECHNIQUES real power injections to reduce losses. However, this is not the case in other systems as presented below. In 33-bus test system, bus 6 found to be the best location. This result too coincides with the result obtained from the previous section. The range of power factors for that location is 0.89 leading to 0.7 lagging and corresponding optimal sizes are MVA. The best power factor of DG located at 6 is 0.82 lagging with an optimal size of 3.02 MVA. This means that the system needs both real and reactive power injection to reduce losses. However, more real power injection is needed compared to reactive power. The total losses corresponding to DG at the optimal location, optimal size, and best power factor is kw. In 69-bus test system, bus 61 is the obvious location for a range of power factors compared to other locations. The best power factor of DG located at bus 61 is 0.82 lagging with an optimal size of 2.2 MVA. In this case too, both real and reactive power injections are needed to minimize the losses. The total loss corresponding to these optimal settings is kw. Table I shows the optimal power factor of DG for different test systems using IA method with repeated approach and combined load power factor of each system. It is interesting to see that the optimal power factor of DG for minimizing losses is in close agreement with load power factor. This could be a guidance to select the appropriate technological option for minimizing losses in the system, given a choice. However, it is important to notice that optimal power factor depends on the location as well. It should be noted that, in DG case leading power factor means, DG is absorbing reactive power. On the other hand, lagging means DG supplying reactive power and this is just opposite to load power factor conventions. D. Summary Table II shows a summary and comparison of the simulation results for 16, 33, and 69-bus test systems for two approaches for calculating power factor of DG, i.e., fast and repeated approaches. In fast approach, the DG power factor is set at combined load power factor, and in the repeated approach, the best power factor is calculated by checking all the power factors step by step. The base case losses of all the test system too presented to show the loss reduction by distributed generators. The loss reduction by IA and ELF is almost the same. However, ELF can demand excessively computational time since it TABLE III VOLTAGES BEFORE AND AFTER DG runs so many load flows in calculation around the solution obtained for IA method which need only a couple of load flow runs. Hence, IA method requires shorter computational time. This technique is appropriate and useful for large-scale distribution systems. Selection of the power factor of DG equal to that of the load is feasible, and it can lead to an optimal or near-optimal solution as shown in Table II. With DG the total losses can be reduced significantly while satisfying all the power and voltage constraints. When only one DG is considered in terms of the optimal size, location and power factor simultaneously; the loss reduction for the three systems is achieved at the highest levels of 68.21%, 67.81%, and 89.68%, respectively. Table III shows the minimum and maximum voltages before and after DG by IA for the three test systems. After DG, voltage profiles improve significantly. The voltages at various buses maintain within the acceptable constraints. V. CONCLUSION The paper has proposed analytical expressions for finding optimal size and power factor of different types of distributed generators for minimizing losses in primary distribution systems. Validity of the proposed analytical expressions for finding optimal size are tested and verified on three test distribution systems with varying sizes and complexity using exhaustive load flow solutions. Results show that locations, sizes, and operating power factor of distributed generators are crucial factors in reducing losses. If properly placed, appropriately sized and operated distributed generators can reduce losses significantly. In all the test systems used in this study, operating power factor of DGs for minimizing losses found to be closer to the power factor of combined load in the respective system. This could be a good guidance for operating distributed generators that have the

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Yin, and S. S. H. Lee, Distribution feeder reconfiguration for loss reduction, IEEE Trans. Power Del., vol.3,no.3, pp , Jul [23] M. A. Kashem, V. Ganapathy, G. B. Jasmon, and M. I. Buhari, A novel method for loss minimization in distribution networks, in Proc. Int. Con. Electr. Util. Deregulation Restruct. Power Technol., Proc., Apr. 2000, pp [24] M. E. Baran and F. F. Wu, Optimum sizing of capacitor placed on radial distribution systems, IEEE Trans. Power Del., vol.4,no.1,pp , Jan in distribution systems. Duong Quoc Hung received the M.Eng. degree in energy field of study (specialization on electric power system management) from Asian Institute of Technology, Bangkok, Thailand, in May Since 1999, he has been with the Power Company No. 2, Electricity of Vietnam, Ho Chi Minh City, Vietnam, where he is currently an Electrical Engineer in the Technical Department. His research interests include distribution system design and operations, distributed generators, capacitors, and reconfiguration and application of optimization techniques Nadarajah Mithulananthan (M 02) received the B.Sc. (Eng.) degree from the University of Peradeniya, Peradeniya, Sri Lanka and the M.Eng. degree from the Asian Institute of Technology, Bangkok, Thailand, in May 1993 and August 1997, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Waterloo, Waterloo, ON, Canada, in He was an Electrical Engineer at the Generation Planning Branch, Ceylon electricity Board, and a Researcher at Chulalongkorn University, Bangkok, Thailand. He is currently a Senior Lecturer at the School of Information Technology and Electrical Engineering, The University of Queensland (UQ), Brisbane, Qld., Australia. Prior to joining UQ, he was an Associate Professor at the Asian Institute of Technology. His research interests include integration of renewable energy in power systems and power system stability and dynamics. R. C. Bansal (SM 03) received the M.E. degree from Delhi College of Engineering, Delhi, India, in 1996, the M.B.A. degree from Indira Gandhi National Open University, New Delhi, India, in 1997, and the Ph.D. degree from the Indian Institute of Technology Delhi (IIT Delhi), Delhi, India, in From August 1989 to August 1998, he was with the Civil Construction Wing, All India Radio, for nine years. From June 1999 to December 2005, he was an Assistant Professor in the Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani, India. From February 2006 to June 2008, he was with the School of Electrical and Electronics Engineering Division, School of Engineering and Physics, The University of the South Pacific, Suva, Fiji. He is currently a faculty Member at the School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Qld., Australia. He is the author or coauthor of more than 125 papers published in national/international journals and conference proceedings. His research interests include reactive power control in renewable energy systems and conventional power systems, power system optimization, analysis of induction generators, and artificial intelligence techniques applications in power systems. He is an Editorial Board Member of the IET Renewable Power Generation Journal and Electric Power Components and Systems Energy Sources, Part B: Economics, Planning and Policy. Dr. Bansal is the Editor of IEEE TRANSACTIONS OF ENERGY CONVERSION AND POWER ENGINEERING LETTERS and the Associate Editor of IEEE TRANSACTIONS OF INDUSTRIAL ELECTRONICS. He is a member of the Board of Directors of the International Energy Foundation (IEF), Alberta, Canada. He is also a member of the Institution of Engineers (India) and a Life Member of the Indian Society of Technical Education.