Optimal Power Flow Analysis of Energy Storage for Congestion Relief, Emissions Reduction, and Cost Savings

Transcription

1 1 Optimal Power Flow Analysis of Energy Storage for Congestion Relief, Emissions Reduction, and Cost Savings Zhouxing Hu, Student Member, IEEE, and Ward T. Jewell, Fellow, IEEE Abstract AC optimal power flow (OPF) simulations are used to study the effects of large-scale energy storage systems on a power system. The economic effects are analyzed under several different conditions, and CO 2 emission reductions offered by the use of storage are considered. The modified IEEE 25-bus Reliability Test System with energy storage and solar photovoltaic generation is simulated using commercial OPF software. The total fuel and emissions costs, and the cost of renewable energy, are analyzed. The net cost savings and unit cost savings provided by energy storage are presented as an economic estimate for constructing an energy storage system. Congestion relief appears to be an economically-promising application for currently-available energy storage systems. Index Terms Energy storage, optimal power flow, fuel emission cost. E I. INTRODUCTION LECTRICITY is always treated as non-storable because of the high expense of construction, operation and maintenance of storage units. There exist a number of methods to store electricity, including pumped hydro, batteries, flywheels, electrochemical capacitors, and compressed air. All techniques for storing energy so far do not have remarkable economic advantages which have prohibited them from becoming widespread. However, the trend for energy storage is definitely larger scale, higher efficiency and lower unit cost. Depending on the size, efficiency and unit cost, energy storage can be constructed for generation or transmission and distribution (T&D) applications [1]. Energy storage for T&D applications has a relatively smaller size and lower added cost [1]. According to the technology constraint, pumped hydro systems are usually large-scale and built for generation applications, and battery storage systems are usually smallscale and built for T&D applications. Nowadays, there is increased interest in energy storage because of renewable generation, environmental issues, and smart grid. In order to effectively reduce CO 2 emissions, fossil fuel will be gradually replaced by renewable source to produce This work was supported by the U.S. Department of Energy under grant DOE DE-FG36-8GO88149, and by the Power Systems Engineering Research Center under projects M21 and M24. Z. Hu is with the Department of Electrical Engineering, Wichita State University, Wichita, KS 6728 USA ( zxhu@wichita.edu). W. T. Jewell is with the Department of Electrical Engineering, Wichita State University, Wichita, KS 6728 USA ( ward.jewell@wichita.edu). electrical power. Wind and solar are the most abundant renewable sources for electric generation. Energy converted from wind and solar becomes more and more competitive in power markets due to the improvement of technology. However, the variable nature of renewable energy limits its large-scale penetration in the power system. Additional spinning reserves are always assigned with renewable generators committed to the system to ensure system reliability. The extra cost and emission of reserve units becomes another economic and environmental issue. However, energy storage could be a solution to these issues. Research in [2] provides reliability evaluations for energy storage along with large-scale wind generation, and observes some potential benefits for both the power system operator and wind farm owners. In the near future, smart grid technologies will be a revolution to power system design and operation. The traditional radial distribution system may become networked. More parameters will be monitored and communicated throughout the grid. With information highly shared, charge and discharge of energy storage embedded into the system can be intelligently controlled in order to optimize the system operation. The benefits of energy storage are demonstrated in [3]. Deferral of new transmission capacity and relief of transmission congestion is one of the benefits investigated in this paper. The use of larger storage units to change the unit dispatch in order to reduce fuel costs is also addressed. This paper studies the economic impacts to the system of large-scale energy storage. Alterations of CO 2 emissions, renewable costs, and transmission loss caused by energy storage are presented as well. The specific type and technique of energy storage will not be discussed. The cost per kwh to store electricity for prevailing storage techniques are analyzed in [1]. Some of those results will be referenced in this paper. The goal of this paper is to provide insight into the effects of energy storage on system operating costs and its feasibility for systems with renewable generation, with and without carbon prices. The results are intended to guide further research into the optimal sizing and scheduling of energy storage. II. METHODOLOGY Time-stepped ac optimal power flow (OPF) simulation will be utilized all through this paper. For cases with CO 2 emission considered, a price is assumed for CO 2 emissions, and the CO 2 emission incorporated cost model which has been developed

2 2 in [4] will be built into the OPF simulator. The key function for total fuel and emissions cost is [4]: CO (1) Where is the price of fuel j in $/MBtu, CO is the given CO 2 price in $/ton, is the CO 2 emission factor of fuel j in ton/mbtu,,, and are polynomial heat rate coefficients, and is the real power output of generator i in MW. Fuel prices assumed for coal, gas and oil are average fuel prices of 28 [5]. Fuel price for nuclear is the average nuclear price of 26 [6]. Fuel emission factors for coal, oil and gas are selected from [7]. The total fuel price plus cost of CO 2 emissions for varying CO 2 prices are shown in Table I. When these prices are used in the conventional ac OPF analysis, available units are committed according to the total price and their output characteristics, resulting in reduced CO 2 production. minimizing the total generation costs. Equality and inequality constraints are enforced through changes to system controls. Equality constraints include area MW interchange, bus MW and MVAr power balance, generator voltage setpoints, interface MW limits, and transmission line and transformer limits. Inequality constraints include generator real and reactive power limits, interface MW limits, and transmission line and transformer limits [11]. Fuel Type CO 2 Emission Factor (ton/mbtu) TABLE I CO 2 EMISSION INCORPORATED FUEL PRICE CO 2 Price ($/ton) CO 2 Emission Incorporated Fuel Price ($/MBtu) Coal Gas Oil Three cases of increasing complexity are studied. All consider renewable energy in the form of solar photovoltaic (PV). The first case is simple economic dispatch with no consideration of system losses or transmission limits and a simple 12-hour overnight charging and 12-hour discharge during the day. The case is run with and without prices on carbon. The second case also considers carbon prices and uses the same charge / discharge schedule as case 1, but considers line losses and limits using ac OPF. The third and last case does not consider carbon prices, but uses a difference energy storage schedule to address some of the issues identified by the first two cases. III. MODEL DESCRIPTION The modified IEEE 24-bus Reliability Test System (RTS) [8], Fig. 1 [9] [1], is used for the case study simulations in this paper. Three 1 MW solar photovoltaic (PV) generators are added to bus 25 which is directly connected to bus 8, one of the primary load buses. An energy storage system is added to bus 24. Its size and location are subject to change. The simulation is based on a typical peak-load day which is divided into 24 one-hour periods. The OPF for each hour is solved using commercial ac OPF software PowerWorld Simulator [11]. Linear programming (LP) OPF has been implemented in this simulator. The LP OPF arrives at the optimal solution by iterating between a standard power flow solution and a linear program, then changing system controls to remove any limit violations. Minimum cost is the objective function, Fig. 1. Modified IEEE Reliability Test System [9] [1] A. Generators There are 12 power plants (G1-G1, GR and GS), with a total of 37 generators dispatched in this test system. The total generation capacity is 3,75 MW without considering the capacity credit for the renewable generators. Classified by fuel type, generation capacities for coal, gas, oil, nuclear, hydro and renewable (i.e. solar in this paper) are shown in Fig. 2. An energy storage system (GS) is treated as a generator (i.e. pumped hydro or stationary batteries) with a negative or positive output during its charging or discharging period, respectively. The energy storage is not automatically dispatched by OPF because the OPF does not have the ability to optimize dispatch over a series of hours, or to consider the effects of storage energy during one period and discharging it during another. Instead, storage is scheduled for the entire 24- hour period prior to each simulation. This is common practice for the scheduling of energy storage and any energy-limited

3 3 generation, such as hydroelectric. The cycle efficiency of GS is assumed to be 1% in most of the study cases unless specified. Fig. 2. Modified IEEE RTS generation capacity (MW) by fuel type Renewable generators (GR) are added to the system in order to study the cost of renewables operating with and without energy storage. Daily output data of solar in the RTS is scaled from data on the ERCOT system [12] (see Fig. 3). The fuel price and cost model for the renewable generators will not be considered. It is assumed that the renewable generators are paid at the LMPs on their bus for each hour. Solar Output (MW) Solar, 3 Hydro, 3 Nuclear, 8 Oil, 8 Gas, 951 Fig. 3 Daily solar generation and load profile Coal, 1274 Solar Total MW Load B. Transmission lines There are 39 transmission lines across the 25-bus network rated at two voltage levels: 138 kv and 23 kv. The MVA limits for all the branches are enforced in the constrained OPF simulation. C. Loads The maximum load in this test system is 2,85 MW. The 24-hour load profile for the IEEE RTS is scaled based on a typical summer day in 28 (see Fig. 3) [1] retrieved from 28 ERCOT Hourly Load Data Archives [13]. 5 Load (MW) IV. CASE STUDIES While solving the OPF for this test system, CO 2 emission cost, transmission losses and transmission line limits are important factors that will affect the cost saving algorithm for scheduling energy storage on the power system. The unit cost benefit per MWh to store electricity is maximized with storage efficiency varying from 7% to 1%. A. Unconstrained Lossless OPF Case with CO 2 Emission Considered In this case, resistance and MVA limits are removed for all the transmission lines in the modified RTS. With these simplifications, the geographical locations of generators and loads will not affect the system OPF results. Without transmission constraints, there will be no congestion cost on any bus. The marginal energy price becomes the locational marginal price (LMP) which is identical for all the 25 buses. An energy storage system with maximum 1 MW charge / discharge rate and 12 MWh capacity is located on bus 24. In this case, the location of the storage will not affect the results. This energy storage system is schedule to charge at 1 MW during first 12-hour light load period and discharge at 1 MW during the remaining 12-hour heavier load period. For simplicity the efficiency is assumed to be 1%. In this case, the basic principle of operating energy storage is charging at a lower price energy and returning it back to the grid to offset some higher price generation. If there is no penalty for CO 2 emission, coal has a much cheaper fuel price than gas and oil, but it has a much higher CO 2 emission rate. In this condition, operating energy storage to reduce the daily fuel cost will increase the daily CO 2 emission. If a CO 2 emission cost ($/ton) is added, both unit dispatch and optimal scheduling of energy storage will be different according to the CO 2 emission price. The OPF simulation results for this simple case show that energy storage reduces total fuel+emission costs at all CO 2 prices and reduces CO 2 emissions when the CO 2 price is higher than 16 $/ton. Fig. 4 plots the changes in total fuel+emission cost, renewable energy cost (paid to the owner based on the bus LMP) and CO 2 emissions compared with the case with no energy storage. Generally, CO 2 emission is reduced by stored energy when the fuel+emission cost of gas is cheaper than that of coal, i.e., when the price of CO 2 emissions is higher than $18/ton. Another observation from this plot is that the highest total fuel+emission cost savings occurs at CO 2 emission price, and the second highest saving occurs at $4/ton CO 2 emission price. The cost reduction by energy storage is almost proportional to the price gap between the fuel+emission cost of the fuel, typically coal and gas. At the equilibrium point of CO 2 price (calculated to be 18 $/ton in Table I), the energy storage is not capable of reducing the fuel+emission cost based on this operating scenario. If assuming that renewable generators are paid at its LMP while generating, renewable costs at all CO 2 prices are slightly reduced by the addition of storage. This is not a generalized conclusion. Energy storage might cause a slight rise in the bus LMP in the morning and a slightly drop in the afternoon. In this case, all the renewable

4 4 generators are solar, which has a greater energy generation during the afternoon. If wind generators are connected to the grid, the renewable cost change will be difficult to predict. Daily Cost Change ($) Fig. 4 Cost change and emission change with energy storage Unit Cost Saving ($/MWh) CO2 Price ($/ton) Fig. 5 Unit cost savings of energy storage at different CO 2 prices Fuel-emission cost change ($) Renewable cost change ($) CO2 emission change (ton) CO2 Price ($/ton) Fig. 6 Added cost (COE) vs. discharge time, 1 MW generation application operating 25 d/year [1] Based on the capacity of energy storage, the unit cost saving per MWh energy stored is defined as the net fuel+emission cost saving divided by the energy charged in a daily cycle. The unit cost saving in this case is shown in Fig. 5. A simple estimate of whether use of storage is feasible is obtained by comparing the unit cost results with the cost to store energy ($/kwh) data provided in Fig. 6 [1], it is clear that none of the prevailing storage techniques provides any economic CO2 Emission Change (ton) advantage in this operating scenario. It is assumed that the size of the storage will not affect the unit cost, and this energy storage is operated 25 days per year at a 12 hour charge / discharge cycle. B. Constrained OPF Case with CO 2 Emission Considered When line limits and losses, and thus transmission congestion, are considered, the geographical locations of all generators and loads, as well as the energy storage system, will affect the OPF results and total system operating cost. In a constrained case, the LMP at each bus will be the summation of its marginal energy price and marginal congestion price. Thus minimizing the energy price will not be the only factor considered while operating an energy storage system. For this case, the same 1 MW / 12 MWh storage system will be operated at bus 24. Based on the conclusions of the previous case, bus 24 is selected because of its highest LMP deviation. Line resistance and limits are included in the OPF solution. Results of the system cost and emission changes from the no-storage case at all CO 2 prices can be observed in Fig. 7. The total fuel+emission cost change and CO 2 emission change are different from the previous case when the CO 2 price rises above 8 $/ton. Energy storage increases the system cost at a CO 2 price between 8 and 18 $/ton and decreases daily CO 2 emissions when the CO 2 price is close to 8. This is caused by the transmission constraints and losses. Most of the coal generators are located remotely from load centers while gas generators are closer. During the increase of the CO 2 price, the fuel+emission cost for all the coal, gas and oil units will increase, but coal increases faster than gas and oil. When the fuel+emission price of the coal is close to gas, the OPF will not dispatch all the coal generators prior to the gas generators considering the transmission loss, constraints, and other costs. Thus, the storage increases the daily output of the gas units and decreases the output of the coal units, resulting in a rise in total system cost and a fall in total CO 2 emissions. Fuel Emission Cost Change ($) CO2 Price ($/ton) Fuel emission cost change ($) Renewable cost change ($) CO2 emission change (ton) Fig. 7 Cost change and emission change with energy storage In this case, renewable cost is increased by energy storage at CO 2 price and is significantly decreased at a $4/ton CO 2 price. At CO 2 price, charging storage causes an average CO2 Emission Change (ton)

5 5 increase of 4 $/MWh of congestion price and 2 $/MWh of energy price on bus 25 during 1 am to 4 am and 8 am to 12 pm (see Fig. 8 and Fig. 9). Although the LMP reduces an average of 2 $/MWh between 4 pm and 8 pm, the critical rise of LMP between 8 am and 12 am causes a gross increase for the daily renewable cost. Several solutions could take care of this problem. The first is to reduce the size of storage and charge less in order to avoid the additional rise of the LMP (see Case C). The second is locating storage closer to load centers (e.g. bus 8) where it will be more unlikely to cause transmission congestion (see Fig. 1). Renewable cost in this case will be decreased by energy storage, and system operating cost will be lower than it was when the storage was located on bus 24. The third option is to split the one large size energy storage unit into several small size units which are distributed throughout the power system [3]. In this case, though, one large-scale energy storage unit is applied on the RTS while the power delivering capability of its transmission lines are restricted by congestion. Although storage reduces fuel+emission cost and renewable cost for certain CO 2 prices, the unit cost saving per MWh energy stored is even lower than for Case A (see Fig. 11). This reduction in the storage benefit is caused by the extra amount of transmission congestion cost and transmission loss that resulted from storing energy. Fig. 8 LMP of bus 25 without energy storage Fig. 9 LMP of bus 25 with energy storage Fig. 1 LMP of bus 25 with energy storage on bus 8 Unit Cost Saving ($/MWh) CO2 Price ($/ton) Fig. 11 Unit cost savings of energy storage at different CO 2 prices C. Constrained OPF without consideration of CO 2 Emissions From previous OPF simulation results, the transmission line connected between bus 16 and 24 (see Fig. 1) is the major constraint during the 24 hour period. It causes 15 out of 25 buses in the whole system to have positive congestion costs during 2 pm and 9 pm. The reason is that the 3 MW hydro generators (G9) are scheduled to operate from 3 pm to 8 pm. Keeping Fig. 1 in mind, during peak hours a large amount of energy flows from the generation area (upper location) to the load area (lower location) and causes significant congestion through bus 24. Additionally, most of the large power plants located remotely from load centers are coal, nuclear, and hydro generators (i.e. G7, G8, G9, G5, G6 and G1 in Fig. 1) which have an advantage on fuel or emission price, or both, and are always dispatched. Consequently, the highest marginal congestion price of $/MWh occurs on bus 24 at 7 pm. Compared to the significant fluctuation of congestion prices, marginal energy prices of the whole system stay between 18.5 $/MWh and 88.1 $/MWh. This energy price variation is almost identical to the previous case. It reflects the unit commitment situation on the system. Without incorporating a CO 2 emission price, a high energy price indicates that more gas generators are dispatched. The variation in LMP on bus 24 with no energy storage on the system, shown in Fig. 12, is primarily caused by the significant rise of the marginal congestion price between 2 pm and 9 pm. In comparison with the congestion price, the marginal energy price has a much smaller change during peak

6 6 hours. In order to maximize the unit system operating cost saving that achieved by installing energy storage, the location, size and schedule of the storage will be selected based on relieving the transmission congestion previously observed on the whole system. Unlike the previous cases, the energy price will not be fully considered in scheduling storage operation. renewable bus, is illustrated in Fig. 15 and Fig. 16. If it is assumed that all the solar generators are paid based on their LMPs, renewable costs (and income of renewable owners) will be reduced as long as the congestion prices drop. Both fuel+emission cost and renewable cost are reduced after installing energy storage, as shown in Fig Energy Storage Output (MW) LMP $/MWh 24 Congestion $/MWh 24 Energy $/MWh -6 eff.=1% eff.=9% eff.=8% eff.=7% Fig. 12 LMP of bus 24 without energy storage Fig. 14 Detailed schedule of energy storage LMP $/MWh 24 Congestion $/MWh 24 Energy $/MWh Fig. 13 LMP of bus 24 with energy storage Through observations of the results of previous cases, and with 1% storage cycle efficiency assumed, an energy storage system with size / capacity of 53 MW / 222 MWh is placed on bus 24. Detailed schedule of energy storage is shown in Fig. 14. The size of the storage is determined by the maximum power output that is need for totally eliminating the congestion cost on bus 24. The schedule for peak-hour operation (discharge) is settled based on removing congestion. The schedule for off-peak operation (charge) is settled based on the demand of the energy for discharging (i.e. 222 MWh at 1% efficiency). It is important to note that energy storage installed on a bus with a higher difference in its daily marginal congestion price will result in a higher system cost saving per unit energy stored. As a result of discharging storage in the afternoon, the congestion cost during peak hours is eliminated on bus 24. The tradeoff is that the congestion cost during off-peak hours increases slightly because of storage charging. Fig. 13 shows the LMP of bus 24 with this operation of the energy storage system. Meanwhile, the LMP change on bus 25, which is the Fig. 15 LMP of bus 25 without energy storage Fig. 16 LMP of bus 25 with energy storage The net reduction of system operating cost does not have much meaning unless divided by energy storage capacity. The result is unit cost saving per MWh energy stored. In this case, the result is 238 $/MWh (i.e..238 $/kwh). Compare with the data provided in Fig. 6, Na/S battery storage (.2 $/kwh) and

7 7 pumped hydro (.8 $/kwh) have economic benefits in this test system. Note that the length of the discharge cycle in the simulation is 6 hr (i.e. from 3 pm to 8 pm), and the cost effect of storage size is ignored. However, it is arbitrary to assume that energy storage has 1% cycle efficiency. Although technologies improve rapidly, the prevailing technique of large scale energy storage have reached efficiency of around 7% to 95% [1]. For generation applications, efficiencies of Na/S battery energy storage and pumped hydro are reported to be 77% and 75% respectively [1]. Energy storage with lower efficiencies (i.e. 9%, 8% and 7%) are also simulated in this case. Without considering renewable cost reduction, fuel cost savings by every MWh of energy stored are roughly $17 (i.e..17 $/kwh) at 77% efficiency (see Fig. 18). Referring to Fig. 6, pumped hydro is the only storage technique that has economic benefit to apply in this case so far. Fig. 17 Fuel+emission cost and renewable cost before (column 1) and after (column 2) installing energy storage Unit Cost Saving ($/MWh) ,4, 1,2, 1,, 8, 6, 4, 2, Fuel-Emission cost ($/day) Renewable cost ($/day) 1% 9% 8% 7% Storage Efficiency Fig. 18 Unit cost saving of energy storage at different efficiency Another observation is that CO 2 emissions do not increase drastically after storing energy because the energy is not stored based on the marginal energy price. Cheaper coal energy is not dispatched to offset higher-price gas generation. Detailed CO 2 emission data and other results are shown in Table II. Daily OPF Results Coal TABLE II DETAILED OPF RESULTS FOR CASE C No Energy Storage Energy Storage 1% eff. 9% eff. 8% eff. 7% eff Gas Oil Nuclear Hydro Solar CO 2 emission (ton) Fuelemission cost ($) Renewable cost ($) Total energy loss V. CONCLUSION This paper simulated three cases to examine the economic potential of an energy storage system. Large-scale energy storage systems, able to shift a certain amount of daily energy consumed from higher fuel cost generation to lower cost generation, could reduce the system operating cost. With CO 2 emission cost incorporated into the fuel cost, daily CO 2 emission could be reduced by energy storage along with system operating cost. However, present technology, cost and cycle efficiency of storage, transmission limits and power losses in the system are major restrictions for large-scale energy storage becoming economic on the power system. In order to achieve a much higher unit cost saving based on every MWh of electricity being stored, a relatively smaller energy storage system could be used to relieve transmission congestion during peak hours. Pumped hydro storage appears to be economically feasible for this application, but it needs proper geographical location. Na/S battery storage is also close to economically feasible in this test system. Constructing new transmission lines is always an option for relieving congestion on the grid. If congestion only happens during several peak hours or a short period for some days, the load on the new transmission line, and thus its benefits, may be too low to justify its construction. Energy storage could be a better choice in such circumstances. In addition, an energy storage system has many other potential benefits and is flexible so that schedules can be changed to accommodate many kinds of system demand. VI. REFERENCES [1] P. Poonpun and W. T. Jewell, Analysis of the Cost per Kilowatt Hour to Store Electricity, IEEE Transactions on Energy Conversion, vol. 23, no. 2, pp , Jun. 28.

8 8 [2] P. Hu, R. Karki and R. Billinton, Reliability evaluation of generating systems containing wind power and energy storage, IET Gener. Transm. Distrib., Vol. 3, lss. 8, pp , Mar. 29. [3] W. Jewell, P. Gomatom, L. Bam, and R. Kharel, Evaluation of Distributed Electric Energy Storage and Generation, Final Report for PSERC Project T-21. PSERC Publication 4-25, Power Syst. Eng. Res. Center, Jul. 24. [Online]. Available: / [4] M. Shao and W. T. Jewell, CO 2 Emission-Incorporated AC Optimal Power Flow and Its Primary Impacts on Power System Dispatch and Operations, IEEE PES 21 General Meeting, Minneapolis, July 21. [5] Energy Information Administration, [Online]. Available: [6] Energy Information Administration, Table F17: Nuclear Consumption, Price and Expenditure Estimates, 26, 26, [Online]. Available: [7] S. Goodman, M. Walker, Benchmarking air emissions of the 1 largest electric power producers in the United States 24, Apr. 26. [8] Reliability test system task force, The IEEE Reliability Test System A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee, Power Systems, IEEE Trans., vol. 14, no. 3, pp , Aug [9] L. Goel, V. P. Aparna, and P. Wang, A Framework to Implement Supply and Demand Side Contingency Management in Reliability Assessment of Restructured Power System, Power Systems, IEEE Trans., vol. 22, no. 1, pp , Feb. 27. [1] P. Poonpun, Effects of Low Carbon Emission Generation and Energy Storage on Greenhouse Gas Emissions in Electric Power Systems, Ph.D. dissertation, Dept. Elec. Eng., Wichita State University, May 29. [11] PowerWorld Simulator, Software Package, Ver. 14., PowerWorld Corporation, [Online]. Available: [12] ERCOT, Distributed Generation Task Force, Nov. 27, [Online]. Available: [13] ERCOT, Hourly Load Data Archives, [Online]. Available: VII. BIOGRAPHIES Zhouxing Hu (S 8) received the Bachelors degree in Electrical Engineering from Chongqing University, Chongqing, China, in 27, and the M.S. degree in Electrical Engineering from Wichita State University, Wichita, KS, in 21. Currently, he is pursuing Ph.D. degree and working as a Graduate Research Assistant at Wichita State University. Ward T. Jewell (M 77 F 3) teaches electric power systems and electric machinery as a professor of Electrical Engineering at Wichita State University. He is Site Director for the Power System Engineering Research Center (PSerc). Dr. Jewell performs research in electric power quality and advanced energy technologies and has been with