Computational intelligence based optimization of hierarchical virtual power plants

Abstract

In the context of renewable energy sources, virtual power plants (VPP) are regarded as a key technology for an intelligent control of the complex, decentralized, distributed and heterogeneous power generation process. However, an economic and ecological control of a VPP turns out to be a highly critical task: due to the strongly varying characteristics of VPPs, in terms of complexity, technology mix, environmental conditions and target objectives to be optimized during operation, the control of an individual VPP needs to be able to effectively take into account all of those individual constraints. Therefore, we propose in this paper an abstract control methodology for a VPP in combination with computational intelligence (CI) metaheuristics, which is designed to be flexibly applicable for different VPP sizes, target objectives and power plant types. The methodology furthermore provides the possibility to build hierarchical VPPs as they are often demanded by the system operators. To demonstrate the effectiveness of the control methodology, three exemplary optimization targets are considered and applied to different compositions of flat/hierarchical VPPs: the minimization of operating reserve requirements, the minimization of hbox {CO}_{2} emissions and the maximization of the power plant flexibility. Furthermore, the methodology is combined and evaluated with three exemplary CI metaheuristics: simulated annealing (SA), particle swarm optimization (PSO) and ant colony optimization (ACO). To legitimize the use of such advanced CI metaheuristics for the optimization problem, gradient descent optimization (GDO) as a traditional optimization technique is regarded as well. On the basis of concrete example scenarios as well as extensive, aggregated test runs, the results show that the control methodology is capable of efficiently optimizing various compositions of VPPs towards the given objectives.