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.
Highlights
1.1 MotivationAccording to a report by the Intergovernmental Panel on Climate Change (IPCC), global warming must be permanently limited to 1.5 ◦C in order to minimize the risk of irreversible consequences for the environment [1]
According to the IEEE Computational Intelligence Society the field of CI can be defined as the “theory, design, application and development of biologically and linguistically motivated computational paradigms” which are organized in three main pillars: neural networks, fuzzy systems and evolutionary computation [3]
As previously mentioned we propose in this paper an abstract control methodology for a virtual power plants (VPP) that can be applied to any given VPP structure and target objectives
Summary
PPP̃iii,,,mmmaianxx(((ttt))) TAAhccettuuoaarlletmmicaianxliimmmuauxmmimppouowmweeprrolliwimmeirittlooimff sisutubob-f-ppslluaabnn-ttpiilaaatnt tttiiimmaeet time point point t point t t. Ci(t) Specific CO2 emissions of sub-plant i at time point t ki(t) Control coefficient of sub plant i at time point t. Pi(t) Power output of sub-plant i at time point t ( ) Step size vector for a VPP at time point t ( ) Control coefficient vector for a VPP at time point t. M(t) Total CO2 emissions of a VPP at time point t NM(t) Error function for the CO2 emissions objective Fi(t) Flexibility potential of sub-plant i at time point t. WO Weighting factor for the operating reserve objective WM Weighting factor for the CO2 emissions objective WF Weighting factor for the flexibility objective E(t) Error function to be optimized by the control methodology. ACO symbols ekl Directed edge leading from node k to node l in the graph kl Pheromone value for the edge from node k to node l pkl Probability for choosing the edge from node k to node l Probability calculation constant Sk List of all edges starting from node k Pheromone constant Evaporation constant
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