Multi-Objective Distributed Generation Planning Considering Active Management

Abstract

Abstract:

Considering the active management, a multi-objective distributed generation planning model was proposed, the upper level planning objective of which was to minimize both the investment and operating cost of distributed generation and the power loss, while the lower level planning objective of which was to minimize the curtailment value of distributed generation. It took into account the randomness of intermittent generation, such as wind turbine generation and photovoltaic power generation, as well as the uncertainty of loads. The Latin hypercube sampling-based Monte Carlo simulation was used to sample the wind speed, the illumination intensity and the load. The upper planning model was solved by the non-dominated sorting genetic algorithm while the lower planning model was solved by the prime-dual interior point method. Finally, the Pareto-optimal solutions were obtained, which could avoid the subjective impact of the traditional weighted methods on determining the weights. The feasibility of the model and the effectiveness of the algorithm were proved by the simulation and analysis of a 33-bus distribution system.

Key words: active management, distributed generation, siting and sizing, multi-objective optimization

CLC Number:

TM 727.4

ZHANG Xiang1，CHENG Haozhong1，FANG Chen2，ZHANG Shenxi1. Multi-Objective Distributed Generation Planning Considering Active Management[J]. Journal of Shanghai Jiaotong University, 2014, 48(09): 1231-1238.

References

［1］Joos G, Ooi B T, McGills D, et al. The potential of distributed generation to provide ancillary services［C］∥Proceedings of Power Engineering Society Summer Meeting. Seattle, USA: IEEE, 2000: 17621767.

［2］梁有伟, 胡志坚, 陈允平. 分布式发电及其在电力系统中的应用综述［J］. 电网技术, 2003, 27(12): 7175.

LIANG Youwei, HU Zhijian, CHEN Yunping. A survey of distributed generation and its application in power system ［J］. Power System Technology, 2003, 27(12): 7175.

［3］BayodRujula A A. Future development of the electricity systems with distributed generation［J］. Energy, 2009, 34(3): 337383.

［4］Shafiu A, Bopp T, Chilvers I. Active management and protection of distribution networks with distributed generation［C］∥Proceedings of Power Engineering Society General Meeting. Denver, USA: IEEE, 2004: 10981103.

［5］Currie R A F, Ault G W, Foote C E T. Fundamental research challenges for active management of distribution networks with high of levels of renewable generation［C］∥Proceedings of Universities Power Engineering Conference. Bristol, UK: IEEE, 2004: 10241028.

［6］张节潭, 程浩忠, 欧阳武, 等. 主动管理模式下分布式风电源规划［J］. 电力科学与技术学报, 2009, 24(4): 1218．

ZHANG Jietan, CHENG Haozhong, OUYANG Wu, et al. Siting and sizing of distributed wind generation under active management mode［J］. Journal of Electric Power Science and Technology, 2009, 24(4): 1218.

［7］张节潭, 程浩忠, 姚良忠, 等. 主动管理在含有分布式电源的配电网中的应用［J］. 电力科学与技术学报, 2008, 23(1): 1823．

ZHANG Jietan, CHENG Haozhong, YAO Liangzhong, et al. Application of active management to distribution network with distribution generation［J］. Journal of Electric Power Science and Technology, 2008, 23(1): 1823.

［8］Griffin T, Tomsovic K, Secrest D, et al. Placement of dispersed generation systems for reduced losses［C］∥Proceedings of the 33rd Hawaii International Conference on System Sciences. Maui, USA: IEEE, 2000: 14461454.

［9］叶德意, 何正友, 臧天磊. 基于自适应变异粒子群算法的分布式电源选址与容量确定［J］. 电网技术, 2011, 35(6): 155160.

YE Deyi, HE Zhengyou, ZANG Tianlei. Siting and sizing of distributed generation planning based on adaptive mutation particle swarm optimization algorithm［J］. Power System Technology, 2011, 35(6): 155160.

［10］邱晓燕, 夏莉丽, 李兴源. 智能电网建设中分布式电源的规划［J］. 电网技术, 2010, 34(4): 710.

QIU Xiaoyan, XIA Lili, LI Xingyuan. Planning of distributed generation in construction of smart grid［J］. Power System Technology, 2010, 34(4): 710.

［11］张节潭, 程浩忠, 姚良忠, 等. 分布式风电源选址定容规划研究［J］. 中国电机工程学报, 2009, 29(16): 17.

ZHANG Jietan, CHENG Haozhong, YAO Liangzhong, et al. Study on siting and sizing of distributed wind generation［J］. Proceedings of the CSEE, 2009, 29(16): 17.

［12］夏澍, 周明, 李庚银. 分布式电源选址定容的多目标优化算法［J］. 电网技术, 2011, 35(9): 115121.

XIA Shu, ZHOU Ming, LI Gengyin. Multiobjective optimization algorithm for distributed generation locating and sizing［J］. Power System Technology, 2011, 35(9): 115121.

［13］Zou K, Agalgaonkar A P, Muttaqi K M, et al. Distribution system planning with incorporating DG reactive capability and system uncertainties［J］. IEEE Trans on Sustainable Energy, 2012, 3(1): 112123.

［14］吴义纯, 丁明. 基于蒙特卡罗仿真的风力发电系统可靠性评价［J］. 电力自动化设备, 2004, 24(12): 7073.

WU Yichun, DING Ming. Reliability assessment of wind power generation based on Monte Carlo simulation［J］. Electric Power Automation Equipment, 2004, 24(12): 7073.

［15］Liu Z P, Wen F S, Ledwich G. Optimal siting and sizing of distributed generation in distribution system considering uncertainties［J］. IEEE Trans on Power Delivery, 2011, 26(4): 25412551.

［16］Zhang J T, Cheng H Z, Wang C. Technical and economic impacts of active management on distribution network［J］. International Journal of Electrical Power and Energy System, 2009, 31（23）: 130138.

［17］张节潭, 苗淼, 范宏, 等. 含风电场的双层电源规划［J］. 电网技术, 2011, 35(11): 4449.

ZHANG Jietan, MIAO Miao, FAN Hong, et al. Bilevel generation expansion planning with largescale wind farms［J］. Power System Technology, 2011, 35(11): 4449.

［18］徐迅, 陈楷, 龙禹, 等. 考虑环境成本和时序特性的微网多类型分布式电源选址定容规划［J］. 电网技术, 2013, 37(4): 914921.

XU Xun, CHEN Kai, LONG Yu, et al. Optimal site selection and capacity determination of multitypes of distributed generation in microgrid considering environment cost and timing characteristics［J］. Power System Technology, 2013, 37(4): 914921.

［19］张沈习, 程浩忠, 张立波, 等. 含风电机组的配电网多目标无功规划［J］. 电力系统保护与控制, 2013, 41(1): 4046.

ZHANG Shenxi, CHENG Haozhong, ZHANG Libo, et al. Multiobjective reactive power planning in distribution system incorporating with wind turbine generation［J］. Power System Protection and Control, 2013, 41(1): 4046.

［20］于晗, 钟志勇, 黄杰波, 等. 采用拉丁超立方采样的电力系统概率潮流计算方法［J］. 电力系统自动化, 2009, 33(21): 3236.

YU Han, ZHONG Zhiyong, HUANG Jiebo, et al. A probabilistic load flow calculation method with Latin hypercube sampling［J］. Automation of Electric Power System, 2009, 33(21): 3236.

［21］Yu H, Chung C Y, Wong K P, et al. Probabilistic load flow evaluation with hybrid Latin hypercube sampling and cholesky decomposition［J］. IEEE Trans on Power System, 2009, 24(2): 661667.

［22］Baran M E, Wu F F. Network reconfiguration in distribution systems for loss reduction and load balancing［J］. IEEE Trans on Power Delivery, 1989, 4(2): 14011407.

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