论文标题
一种可扩展的方法,用于使用基于总体的元启发式学计划分布式能源资源
A Scalable Method for Scheduling Distributed Energy Resources using Parallelized Population-based Metaheuristics
论文作者
论文摘要
近年来,分布式可再生能源资源越来越多地集成到现有的电力电网中。由于可再生能源的不确定性质,网络运营商面临着平衡负载和发电的新挑战。为了满足新的要求,可以使用智能分布式能源工厂,以提供虚拟发电厂,例如需求侧管理或灵活的生成。但是,计算此类分布式能源资源的单位承诺的适当时间表是一个复杂的优化问题,如果考虑大量分布式能源资源,通常对于标准优化算法来说通常太复杂了。为了解决这种复杂的优化任务,基于人群的元启发式学 - 例如进化算法 - 代表强大的替代方案。诚然,进化算法确实需要大量的计算能力来及时解决此类问题。对于此性能问题的一种有希望的解决方案是对替代解决方案的通常耗时评估的并行化。在本文中,提出了一种新的通用且高度可扩展的并行方法,用于使用元启发式算法进行分布式能量资源的单位承诺。它基于微服务,容器虚拟化和发布/订阅消息传递范式,用于调度分布式能源。通过在大数据环境中对三种不同的分布式能源资源调度方案进行并行的优化来评估所提出解决方案的可伸缩性和适用性。新方法提供集群或云并行性,并能够处理大量的分布式能源。新提出的方法的应用可为扩大优化速度提高性能。
Recent years have seen an increasing integration of distributed renewable energy resources into existing electric power grids. Due to the uncertain nature of renewable energy resources, network operators are faced with new challenges in balancing load and generation. In order to meet the new requirements, intelligent distributed energy resource plants can be used which provide as virtual power plants e.g. demand side management or flexible generation. However, the calculation of an adequate schedule for the unit commitment of such distributed energy resources is a complex optimization problem which is typically too complex for standard optimization algorithms if large numbers of distributed energy resources are considered. For solving such complex optimization tasks, population-based metaheuristics -- as e.g. evolutionary algorithms -- represent powerful alternatives. Admittedly, evolutionary algorithms do require lots of computational power for solving such problems in a timely manner. One promising solution for this performance problem is the parallelization of the usually time-consuming evaluation of alternative solutions. In the present paper, a new generic and highly scalable parallel method for unit commitment of distributed energy resources using metaheuristic algorithms is presented. It is based on microservices, container virtualization and the publish/subscribe messaging paradigm for scheduling distributed energy resources. Scalability and applicability of the proposed solution are evaluated by performing parallelized optimizations in a big data environment for three distinct distributed energy resource scheduling scenarios. The new method provides cluster or cloud parallelizability and is able to deal with a comparably large number of distributed energy resources. The application of the new proposed method results in very good performance for scaling up optimization speed.