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Owing to the increases of energy loads and penetration of renewable energy with variability, it is essential to determine the optimum capacity of the battery energy storage system (BESS) and demand response (DR) within the microgrid (MG). To accomplish the foregoing, this paper proposes an optimal MG operation approach with a hybrid method considering the game theory for a multi-agent system. The hybrid method operation includes both BESS and DR methods. The former is presented to reduce the sum of the MG operation and BESS costs using the game theory, resulting in the optimal capacity of BESS. Similarly, the DR method determines the optimal DR capacity based on the trade-off between the incentive value and capacity. To improve optimization operation, multi-agent guiding particle swarm optimization (MAG-PSO) is implemented by adjusting the best global position and position vector. The results demonstrate that the proposed approach not only affords the most economical decision among agents but also reduces the utilization cost by approximately 8.5%, compared with the base method. Furthermore, it has been revealed that the proposed MAG-PSO algorithm has superiority in terms of solution quality and computational time with respect to other algorithms. Therefore, the optimal hybrid method operation obtains a superior solution with the game theory strategy.
Ji-Won Lee; Mun-Kyeom Kim; Hyung-Joon Kim. A Multi-Agent Based Optimization Model for Microgrid Operation with Hybrid Method Using Game Theory Strategy. Energies 2021, 14, 603 .
AMA StyleJi-Won Lee, Mun-Kyeom Kim, Hyung-Joon Kim. A Multi-Agent Based Optimization Model for Microgrid Operation with Hybrid Method Using Game Theory Strategy. Energies. 2021; 14 (3):603.
Chicago/Turabian StyleJi-Won Lee; Mun-Kyeom Kim; Hyung-Joon Kim. 2021. "A Multi-Agent Based Optimization Model for Microgrid Operation with Hybrid Method Using Game Theory Strategy." Energies 14, no. 3: 603.
As renewable penetration increases in microgrids (MGs), the use of battery energy storage systems (BESSs) has become indispensable for optimal MG operation. Although BESSs are advantageous for economic and stable MG operation, their life degradation should be considered for maximizing cost savings. This paper proposes an optimal BESS scheduling for MGs to solve the stochastic unit commitment problem, considering the uncertainties in renewables and load. Through the proposed BESS scheduling, the life degradation of BESSs is minimized, and MG operation becomes economically feasible. To address the aforementioned uncertainties, a scenario-based method was applied using Monte Carlo simulation and the K-means clustering algorithm for scenario generation and reduction, respectively. By implementing the rainflow-counting algorithm, the BESS charge/discharge state profile was obtained. To formulate the cycle aging stress function and examine the life cycle cost (LCC) of a BESS more realistically, the nonlinear cycle aging stress function was partially linearized. Benders decomposition was adopted for minimizing the BESS cycle aging, total operating cost, and LCC. To this end, the general problem was divided into a master problem and subproblems to consider uncertainties and optimize the BESS charging/discharging scheduling problem via parallel processing. To demonstrate the effectiveness and benefits of the proposed BESS optimal scheduling in MG operation, different case studies were analyzed. The simulation results confirmed the superiority and improved performance of the proposed scheduling.
Yong-Rae Lee; Hyung-Joon Kim; Mun-Kyeom Kim. Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids. Energies 2021, 14, 470 .
AMA StyleYong-Rae Lee, Hyung-Joon Kim, Mun-Kyeom Kim. Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids. Energies. 2021; 14 (2):470.
Chicago/Turabian StyleYong-Rae Lee; Hyung-Joon Kim; Mun-Kyeom Kim. 2021. "Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids." Energies 14, no. 2: 470.
This study proposes a two-stage stochastic p-robust optimal energy trading management for microgrid, including photovoltaic, wind turbine, diesel engine, and micro turbine. To achieve optimal energy management for an microgrid, a hybrid demand response, which combines improved incentive-based and price-based demand responses, is incorporated to reduce peak period load while ensuring the reliability of the microgrid. A multi-scenario tree method is used to generate scenarios for uncertain parameters such as wind turbine, photovoltaic, loads, and market-clearing prices, where each probability density function has been discretized by certain intervals. Then, using a scenario reduction technique, a differential evolution clustering, a set of reduced scenarios can be obtained. The proposed energy management combines a Gaussian-based regularized particle swarm optimization with a fuzzy clustering technique to solve the optimization problem and determine the best compromise solution according to cost-effectiveness and reliability. The effectiveness of the proposed approach has been analyzed for a typical microgrid test system, and then the results demonstrate that the robustness can be improved substantially while guaranteeing the economical operation of microgrid. Therefore, the proposed energy trading management determines the most reasonable solution in terms of economic and reliability issues for the microgrid operator.
H.J. Kim; M.K. Kim; J.W. Lee. A two-stage stochastic p-robust optimal energy trading management in microgrid operation considering uncertainty with hybrid demand response. International Journal of Electrical Power & Energy Systems 2020, 124, 106422 .
AMA StyleH.J. Kim, M.K. Kim, J.W. Lee. A two-stage stochastic p-robust optimal energy trading management in microgrid operation considering uncertainty with hybrid demand response. International Journal of Electrical Power & Energy Systems. 2020; 124 ():106422.
Chicago/Turabian StyleH.J. Kim; M.K. Kim; J.W. Lee. 2020. "A two-stage stochastic p-robust optimal energy trading management in microgrid operation considering uncertainty with hybrid demand response." International Journal of Electrical Power & Energy Systems 124, no. : 106422.
This paper proposes an optimal energy management approach for a grid-connected microgrid (MG) by considering the demand response (DR). The multi-objective optimization framework involves minimizing the operating cost and maximizing the utility benefit. The proposed approach combines confidence-based velocity-controlled particle swarm optimization (CVCPSO) (i.e., PSO with an added confidence term and modified inertia weight and acceleration parameters), with a fuzzy-clustering technique to find the best compromise operating solution for the MG operator. Furthermore, a confidence-based incentive DR (CBIDR) strategy was adopted, which pays different incentives in different periods to attract more DR participants during the peak period and thus ensure the reliability of the MG under the peak load. In addition, the peak load shaving factor (PLSF) was employed to show that the reliability of the peak load had improved. The applicability and effectiveness of the proposed approach were verified by conducting simulations at two different scales of MG test systems. The results confirm that the proposed approach not only enhances the MG system peak load reliability, but also facilitates economical operation with better performance in terms of solution quality and diversity.
Hyung-Joon Kim; Mun-Kyeom Kim. Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response. Energies 2019, 12, 4142 .
AMA StyleHyung-Joon Kim, Mun-Kyeom Kim. Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response. Energies. 2019; 12 (21):4142.
Chicago/Turabian StyleHyung-Joon Kim; Mun-Kyeom Kim. 2019. "Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response." Energies 12, no. 21: 4142.
An optimal operation of new distributed energy resources can significantly advance the performance of power systems, including distribution network (DN). However, increased penetration of renewable energy may negatively affect the system performance under certain conditions. From a system operator perspective, the tie-line control strategy may aid in overcoming various problems regarding increased renewable penetration. We propose a bi-level optimization model incorporating an energy band operation scheme to ensure cooperation between DN and microgrid (MG). The bi-level formulation for the cooperation problem consists of the cost minimization of the DN and profit maximization of the MG. The goal of the upper-level is to minimize the operating costs of the DN by accounting for feedback information, including the operating costs of the MG and energy band. The lower-level aims to maximize the MG profit, simultaneously satisfying the reliability and economic targets imposed in the scheduling requirements by the DN system operator. The bi-level optimization model is solved using an advanced method based on the modified non-dominated sorting genetic algorithm II. Based on simulation results using a typical MG and an actual power system, we demonstrate the applicability, effectiveness, and validity of the proposed bi-level optimization model.
Ho-Young Kim; Mun-Kyeom Kim; Hyung-Joon Kim. Optimal Operational Scheduling of Distribution Network with Microgrid via Bi-Level Optimization Model with Energy Band. Applied Sciences 2019, 9, 4219 .
AMA StyleHo-Young Kim, Mun-Kyeom Kim, Hyung-Joon Kim. Optimal Operational Scheduling of Distribution Network with Microgrid via Bi-Level Optimization Model with Energy Band. Applied Sciences. 2019; 9 (20):4219.
Chicago/Turabian StyleHo-Young Kim; Mun-Kyeom Kim; Hyung-Joon Kim. 2019. "Optimal Operational Scheduling of Distribution Network with Microgrid via Bi-Level Optimization Model with Energy Band." Applied Sciences 9, no. 20: 4219.