针对传统能量管理策略经济性差、调峰能力弱的问题,提出一种基于分布式发电的储能系统（ESS）能量管理策略。首先结合Pareto最优理论,建立以降低能源成本与平滑负荷峰谷效应为目标的需求侧优化调度模型。在首次优化过程中,采用具有自适应变异与动态更新惯性权重功能的改进粒子群算法（IPSO）求解最优经济指标。进而以该指标作为约束,对调度区间进行划分,实现目标的完全解耦,并引入惩罚函数获得增广目标函数。最终通过贪婪算法（GA）进行二次优化,可求得在最佳经济效益的前提下最大程度平滑负荷峰谷效应的非支配调度向量。仿真结果表明：IPSO具有较高的收敛速度与精度,所提策略在3种场景下分别降低用电成本18.6%、32.8%、17.3%,负荷峰均较首次优化降低21.2%、10.3%、20.1%,并通过多场景数据集下的仿真验证了结论的普适性。
Abstract
Focused on the poor economy and weak peak shaving performance of traditional energy management strategies, an energy management strategy for energy storage system （ESS） based on distributed generation is proposed. Firstly, a demand-side optimal scheduling model aiming at energy cost diminution and load peak valley effect smooth is established combined with Pareto optimization theory. To obtain the optimal economic indicators, an improved particle swarm algorithm （IPSO） with adaptive variation and dynamic updating of inertia weights is applied during the first optimization process. Then, the scheduling interval is divided by the indicator as a constraint to achieve the complete decoupling of the target, while a penalty function is introduced to acquire an augmented objective function. Finally, the greedy algorithm（GA） is used for secondary optimiation, and the non-dominated scheduling vector which can smooth the load peak-valley effect to the greatest extent under the premise of the best economic benefit can be obtained. The simulation results show that IPSO strategy with high convergence speed and accuracy, can reduce the electricity cost by 18.6%, 32.8% and 17.3%, and gets 21.2%, 10.3% has and 20.1% load peak-to-average rate reduction compared with the first optimization in three scenarios, respectively. The generality and reasonableness of the conclusions verified by the typical data set.
关键词
分布式发电 /
储能 /
能量管理 /
多目标优化 /
Pareto理论
Key words
distributed power generation /
energy storage /
energy management /
multi objective optimization /
Pareto principle
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