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A Heuristic Combinatorial Optimization Algorithm for Load-Leveling and Peak Demand Reduction using Energy Storage Systems

机译：利用储能系统的负载均衡和峰值需求减少的启发式组合优化算法

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摘要

A method for applying combinatorial optimization algorithms to Energy Storage System (ESS) scheduling is presented in this paper. Scheduling is essential for the integration of ESS in electrical networks at grid level or at consumer level to achieve the objectives of integration such as constraint management or energy cost reduction and for efficient storage dispatch. It also shows that for a time-of-use (ToU) tariff scheme based on the shape of the demand profile with higher prices tied to peak periods, effective load-leveling, and peak demand reduction always leads to energy cost reduction. While other methods usually require more information such as generation cost curves or ToU tariffs to schedule ESS, the proposed method uses only demand profile information and ESS parameters to achieve load-leveling and peak demand reduction and also considers the entire optimization time horizon. This is done by combining heuristic bin packing and subset sum algorithms with specific modifications to the standard forms and through transformations. A case study is presented in which the algorithm is used to schedule household ESS with repurposed electric vehicle batteries and the results are compared to a demand response scheme on the same setup.
机译：提出了一种将组合优化算法应用于储能系统调度的方法。调度对于将ESS集成到电网级别或用户级别的电网至关重要，以实现诸如约束管理或降低能源成本的集成目标以及有效的存储调度。它还表明，对于基于需求曲线形状的使用时间（ToU）费率计划，较高的价格与高峰时段相关，有效的负载均衡和高峰需求减少总是可以降低能源成本。虽然其他方法通常需要更多信息（例如发电成本曲线或ToU费率）来调度ESS，但所提出的方法仅使用需求概况信息和ESS参数来实现负载均衡和峰值需求减少，并且还考虑了整个优化时间范围。这是通过结合启发式bin打包算法和子集和算法以及对标准格式的特定修改并通过转换来完成的。提出了一个案例研究，其中使用该算法来调度家用ESS和重新使用的电动汽车电池，并将结果与​​相同设置下的需求响应方案进行比较。

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