Abstract：This paper presents a microgrid energy management model(MEMM) to optimize the operation of microgrids. As the energy output of photovoltaic(PV) system is intermittent and random, the output of power forecasting model is used as the input of MEMM. Because the charge and discharge management of energy storage system(ESS) is a complex planning issue that needs to be optimized across multiple-time steps, energy equality constraint is applied to optimize operation strategies of ESS. The distributed generation(DG) and ESS are defined as belonging to one unified model in the MEMM so that smart management of ESS, economic load dispatch and operation optimization of distributed generation are simplified into a single-object optimization problem. Two energy management strategies are designed for economic operation of microgrids under the stand-alone and grid-connection conditions and an improved genetic algorithm optimization module is proposed to achieve the two strategies. Application of the proposed energy management model on a small DC microgrid system shows the validity of the method and the computation results can be used to evaluate the economical performance of microgrids.
陈昌松, 段善旭, 蔡涛, 刘邦银. 基于改进遗传算法的微网能量管理模型[J]. 电工技术学报, 2013, 28(4): 196-201.
Chen Changsong, Duan Shanxu, Cai Tao, Liu Bangyin. Microgrid Energy Management Model Based on Improved Genetic Arithmetic. Transactions of China Electrotechnical Society, 2013, 28(4): 196-201.
[1] Chung I, Liu W, Cartes D A, et al. Control methods of inverter-interfaced distributed generators in a microgrid system[J]. IEEE Transactions on Industry Applications, 2010, 46(3): 1078-1088. [2] Xiarnay C, Asano H, Papathanassiou S, et al. Policymaking for microgrids[J]. IEEE Transactions on Energy Conversion, 2008, 6(3): 66-77. [3] Sao C K, Lehn P W. Control and power management of converter fed microgrids[J]. IEEE Transactions on Power Systems, 2008, 23(3): 1088-1098. [4] Katiraei F, Iravani R, Hatziargyriou N D, et al. Microgrids management[J]. IEEE Power and Energy Magazine, 2008, 6(3): 54-65. [5] Jewell W T, Unruh T D. Limits on cloud-induced fluctuation in photovoltaic generation[J]. IEEE Transactions on Energy Conversion, 1999, 5(1): 8-14. [6] Mellit A, Pavan A M. A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy[J]. Solar Energy, 2010, 84(8): 807-821. [7] Sera D, Teodorescu R, Hantschel J, et al. Optimized maximum power point tracker for Fast-Changing environmental conditions[J]. IEEE Transactions on Industrial Electronics, 2008, 55(7): 2629-2637. [8] Tsikalakis A G, Hatziargyriou N D. Centralized control for optimizing microgrids operation[J]. IEEE Transactions on Energy Conversion, 2008, 23(1): 241-248. [9] Chakraborty S, Weiss M D, Simoes M G. Distributed intelligent energy management system for a single-phase high-frequency AC microgrid[J]. IEEE Transactions on Industrial Electronics, 2007, 54(1): 97-109. [10] Marnay C, Venkataramanan G, Stadler M, et al. Optimal technology selection and operation of commercial-building microgrids[J]. IEEE Transactions on Power Systems, 2008, 23(3): 975-982. [11] Bae In Su, Kim Jin O. Reliability evaluation of customers in a microgrid[J]. IEEE Transactions on Power Systems, 2008, 23(3): 1416-1422. [12] 钱科军, 袁越, 石晓丹, 等. 分布式发电的环境效益分析[J]. 中国电机工程学报, 2008, 28(29): 11-15. Qian Kejun, Yuan Yue, Shi Xiaodan, et al. Environmental benefits analysis of distributed generation[J]. Proceedings of the CSEE, 2008, 28(29): 11-15 [13] 陈昌松, 段善旭, 殷进军. 基于神经网路的光伏阵列发电预测模型的设计[J]. 电工技术学报, 2009, 24(9): 153-158. Chen Changsong, Duan Shanxu, Yin Jinjun. Design of photovoltaic array power forecasting model based on neutral network[J]. Transactions of China Electrotechnical Society, 2009, 24(9): 153-158.
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