Abstract:Home energy optimization control is one of the important branches of home energy management system (HEMS), but the lack of effective intelligent optimization algorithm has restricted its practical application. In this paper, through analyzing the working characteristics of household appliances, household electric equipment was divided into rigid load, simple adjustable load, battery equipment, HVAC (heating, ventilation, and air conditioning) system equipment, etc. The corresponding load model was then established, taken power grid, photovoltaic power generation and energy storage battery as the energy sources of smart home. With electricity cost and comfort level as the optimization indexes, home energy optimization control model was also establish for the coordinated control of the hybrid energy. In addition, a intelligent solving method was put forward based on improved adaptive particle swarm optimization algorithm (APSOA). Thereafter, the optimal time of each electric appliance, the required power of each time in room temperature control system and the charge and discharge power in each period of the storage battery were obtained. According to electricity situation of one day in summer, this paper built the model and simulated it in Matlab. Compared with the results by particle swarm optimization algorithm (PSOA) and genetic algorithm (GA), the model and algorithm have been verified.
徐建军, 王保娥, 闫丽梅, 李战平. 混合能源协同控制的智能家庭能源优化控制策略[J]. 电工技术学报, 2017, 32(12): 214-223.
Xu Jianjun, Wang Bao’e, Yan Limei, Li Zhanping. The Strategy of the Smart Home Energy Optimization Control of the Hybrid Energy Coordinated Control. Transactions of China Electrotechnical Society, 2017, 32(12): 214-223.
[1] Hazas M, Friday A, Scott J. Look back before leaping forward: four decades of domestic energy inquiry[J]. IEEE Pervasive Computing, 2011, 10(1): 13-19. [2] 国家电网公司: 国家电网智能化规划总报告(修订稿)[N]. 2010. [3] 张新昌, 周逢权. 智能电网引领智能家居及能源消费革新[J]. 电力系统保护与控制, 2014, 42(5): 59-67. Zhang Xinchang, Zhou Fengquan. Smart grid leads the journey to innovative smart home and energy consumption patterns[J]. Power System Protection and Control, 2014, 42(5): 59-67. [4] 陈昌松, 段善旭, 蔡涛, 等. 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, et al. Microgrid energy management model based improved genetic arithmetic[J]. Transactions of China Electro- technical Society, 2013, 28(4): 196-201. [5] 张延宇, 曾鹏, 臧传治. 智能电网环境下家庭能源管理系统研究综述[J]. 电力系统保护与控制, 2014, 42(18): 144-154. Zhang Yanyu, Zeng Peng, Zang Chuanzhi. Review of home energy management system in smart grid[J]. Power System Protection and Control, 2014, 42(18): 144-154. [6] 王江波, 费标清, 王越, 等. 户用微电网能量调度日前计划研究[J]. 电测与仪表, 2013, 50(8): 81-86. Wang Jiangbo, Fei Biaoqing, Wang Yue, et al. Research on energy dispatch day-ahead schedule for household microgrid[J]. Electrical Measurement & Instrumentation, 2013, 50(8): 81-86. [7] 张颖, 容展鹏, 张宇雄, 等. 基于微电网的电网需求响应研究[J]. 电力系统保护与控制, 2015, 43(21): 20-26. Zhang Ying, Rong Zhanpeng, Zhang Yuxiong, et al. Study of grid demand response based on micro grid[J]. Power System Protection and Control, 2015, 43(21): 20-26. [8] 王冬容. 电力需求侧响应理论与实证研究[D]. 北京: 华北电力大学, 2011. [9] 徐建军, 高文峰, 袁樱梓, 等. 一种直流电场提高油田采收率法中150V稳压直流电源的电路设计[J]. 化工自动化及仪表, 2013, 40(10): 1259-1262. Xu Jianjun, Gao Wenfeng, Yuan Yingzi, et al. Circuit design of 150V (DC) power for oil recovery via DC electric field[J]. Control and Instruments in Chemical Industry, 2013, 40(10): 1259-1262. [10] 曾博, 欧阳邵杰, 张建华, 等. 考虑复杂预想场景下光伏高效利用的微电网综合规划[J]. 中国电机工程学报, 2014, 34(25): 4259-4269. Zeng Bo, Ouyang Shaojie, Zhang Jianhua, et al. Integrated planning of micro-grid for efficient utilization of photovoltaic generation considering complicated operation scenarios[J]. Proceedings of the CSEE, 2014, 34(25): 4259-4269. [11] 赵书强, 王明雨, 胡永强, 等. 基于不确定理论的光伏出力预测研究[J]. 电工技术学报, 2015, 30(16): 213-220. Zhao Shuqiang, Wang Mingyu, Hu Yongqiang, et al. Research on the prediction of PV output based on uncertainty theory[J]. Transactions of China Electro- technical Society, 2015, 30(16): 213-220. [12] 闫丽梅, 谢明霞, 徐建军, 等. 含分布式电源的配电网潮流改进算法[J]. 电力系统保护与控制, 2013, 41(5): 17-22. Yan Limei, Xie Mingxia, Xu Jianjun, et al. Improved power flow calculation of distribution network with DG[J]. Power System Protection and Control, 2013, 41(5): 17-22. [13] 刘星平, 李世军, 于浩明, 等. 住宅小区内电动汽车有序充电优化模式[J]. 电工技术学报, 2015, 30(20): 238-245. Liu Xingping, Li Shijun, Yu Haoming, et al. Coordinated charging optimization mode of electric vehicles in the residential area[J]. Transactions of China Electrotechnical Society, 2015, 30(20): 238-245. [14] 刘保平, 杨洪明. 电动汽车及可中断负荷参与电网的调频控制[J]. 电气技术, 2013, 14(5): 9-13. Liu Baoping, Yang Hongming. PEVs and interrup- tible loads for frequency regulation[J]. Electrical Engineering, 2013, 14(5): 9-13. [15] Lu N. An evaluation of the HVAC load potential for providing load balancing service[J]. IEEE Transa- ctions on Smart Grid, 2012, 3(3): 1263-1270. [16] 沈加健, 杨根科, 潘常春. 智能家庭混合能源管理的动态建模与优化[J]. 微型电脑应用, 2012, 28(4): 9-14. Shen Jiajian, Yang Genke, Pan Changchun. Dynamic model and optimization of hybrid energy manage- ment in smart home[J]. Microcomputer Applications, 2012, 28(4): 9-14. [17] 郭思琪, 袁越, 张新松, 等. 多时间尺度协调控制的独立微网能量管理策略[J]. 电工技术学报, 2014, 29(2): 122-129. Guo Siqi, Yuan Yue, Zhang Xinsong, et al. Energy management strategy of isolated microgrid based on multi-time scale coordinated control[J]. Transactions of China Electrotechnical Society, 2014, 29(2): 122-129. [18] 吴伟坡. 基于实时电价的智能家庭能源优化控制[D]. 上海: 上海交通大学, 2013. [19] 周磊, 李扬. 分时电价环境下基于家居能量管理系统的家居负荷建模与优化运行[J]. 电网技术, 2015, 39(2): 367-374. Zhou Lei, Li Yang. Modeling and optimal dispatch for residential load based on home energy manage- ment system under time-of-use pricing[J]. Power System Technology, 2015, 39(2): 367-374. [20] 闫丽梅, 祝玉松, 徐建军, 等. 基于分数阶微积分理论的线路模型建模方法[J]. 电工技术学报, 2014, 29(9): 260-268. Yan Limei, Zhu Yusong, Xu Jianjun, et al. Trans- mission lines modeling method based on fractional order calculus theory[J]. Transactions of China Electrotechnical Society, 2014, 29(9): 260-268. [21] Shao S, Pipattanasomporn M, Rahman S. Develop- ment of physical-based demand response-enabled residential load models[J]. IEEE Transactions on Power Systems, 2013, 28(2): 607-614. [22] Charles S, Jeffrey D, Xiao Dongyi. The residential heat balance method for heating and cooling load calculations[J]. Ashrae Transactions, 2005, 111(1): 308-319. [23] 徐卫星. 基于改进粒子群算法的分布式电源优化配置[J]. 电气技术, 2015, 16(12): 71-75. Xu Weixing. Optimal allocation of distributed generation based on improved PSO[J]. Electrical Engineering, 2015, 16(12): 71-75. [24] 高飞. MATLAB智能算法超级学习手册[M]. 北京: 人民邮电出版社, 2014. [25] 史峰, 王辉, 郁磊, 等. MATLAB智能算法30个案例分析[M]. 北京: 北京航空航天大学出版社, 2011.