| 改进自适应MOEA/D算法的楼宇负荷优化调度 |
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| 引用本文： | 易灵芝,林佳豪,刘建康,罗显光,李旺.改进自适应MOEA/D算法的楼宇负荷优化调度[J].计算机工程与应用,2022,58(2):295-302. |
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| 作者姓名： | 易灵芝 林佳豪 刘建康 罗显光 李旺 |
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| 作者单位： | 1.湘潭大学 自动化与电子信息学院&湖南省多能源协同控制技术工程研究中心，湖南 湘潭 411105
2.湖南省风电装备与能源变换2011协同创新中心，湖南 湘潭 411101
3.大功率交流传动电力机车系统集成国家重点实验室，湖南 株洲 412001 |
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| 基金项目： | 国家自然科学基金(61572416);湖南省自科基金和大功率交流传动电力机车系统集成国家重点实验室开放课题(2020K0106)。 |
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| 摘 要： | 针对负荷侧用户用电电费、新能源消纳率和用电峰谷差等问题,提出了一种改进的自适应基于分解的多目标进化算法,进行楼宇微电网签约住户可控负荷优化调度;通过分析负荷的用电特性,将用电负荷分为五类并分类建立数学模型、优化目标函数和约束条件;将广义分解与均匀分配相结合产生新的自适应权重向量使算法非支配解更接近真实帕累托前沿;采用历...
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| 关 键 词： | 楼宇微电网 自适应选择策略 自适应权重向量 基于分解的多目标进化算法(MOEA/D) 自动需求响应 |
Improved Adaptive MOEA/D Algorithm for Building Load Optimization Scheduling |
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| YI Lingzhi,LIN Jiahao,LIU Jiankang,LUO Xianguang,LI Wang.Improved Adaptive MOEA/D Algorithm for Building Load Optimization Scheduling[J].Computer Engineering and Applications,2022,58(2):295-302. |
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| Authors: | YI Lingzhi LIN Jiahao LIU Jiankang LUO Xianguang LI Wang |
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| Affiliation: | 1.College of Automation and Electronic Information, Xiangtan University, Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, Xiangtan, Hunan 411105, China
2.Hunan Province Cooperative Innovation Center for Wind Power Equipment and Energy Conversion, Xiangtan, Hunan 411101, China
3.The State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration, Zhuzhou, Hunan 412001, China |
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| Abstract: | Aiming at the problems of load-side users’ electricity bills, new energy consumption rate, and electricity peak-valley difference, an improved adaptive decomposition-based multi-objective evolutionary algorithm is proposed to optimize the controllable load dispatch of contracted households in the building microgrid. By analyzing the power consumption characteristics of the load, the load is divided into five categories, which establish mathematical models, optimize objective functions and constraints. A new adaptive weight vector is generated by combining generalized decomposition with uniform distribution, which makes the non dominated solution closer to the real Pareto front; the idea of historical experience is adopted to realize adaptive selection strategy by counting the contribution rate of SBX and DE crossover operators to external archive and using roulette. The subgeneration points are modified by the characteristic constraint mapping, which indirectly expands the search space of the algorithm and improves the diversity of the population. The convergence and superiority of the improved AWS-MOEA/D algorithm are verified by the test function; the simulation experiment results of the dispatching of the residents in a residential area show that the improved algorithm can save more electricity bills and effectively increase new energy consumption rate after dispatching. |
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| Keywords: | building micro grid adaptive selection strategy adaptive weight vector multi-objective evolutionary algorithm based on decomposition(MOEA/D) automatic demand response |
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