Abstract
This paper analyzes the management of a large number of distributed battery energy storage systems (BESSs) by a energy utility in order to provide some market services. A heuristic algorithm based on two parts is proposed for this task. The first part, the aggregation, combines the abilities and behavior of the fleet of BESS into a virtual power plant (VPP) by a concise but flexible model. This VPP can be used by the utility as they are used to with traditional power plants. The second part, the disaggregation, distributes VPP control schedules back to the individual BESS by a greedy first-fit decreasing heuristic.The management of a fleet of BESS can also be modeled as a mathematical linear optimization program. The proposed heuristic is compared to and evaluated against this global optimization regarding computational performance and quality of results. It is shown, that the heuristic provides a remarkable speedup when applied to larger number of units. With it, it is possible to handle a group of at least 100,000 individual BESS. Further, the quality of the results are shown. First, the solution of the heuristic is compared to the optimal one of the mathematical program. Second, the methods are both applied and compared in a realistic case study.
Highlights
In most deregulated electricity market environments, the active management of distributed energy resources, here referred to as active demand and supply (ADS) units, is commonly regarded as becoming increasingly important to manage
There are some types of ADS units, which can be managed in a similar way as traditional power plants
In this paper, a methodology was proposed in order to allow the managing of a large number of Battery energy storage system (BESS) by an energy utility
Summary
In most deregulated electricity market environments, the active management of distributed energy resources, here referred to as active demand and supply (ADS) units, is commonly regarded as becoming increasingly important to manage. Two important information are lost due to the aggregation: First, about the distribution of charging/discharging power with the energy content of the BESS (heterogeneity of the fleet) and second, about the distribution of the SoC of the individual BESS Both elements affect the capabilities of the VPP. That the more homogeneous the individual BESS are, the more randomly the schedule of the VPP to follow is, and, the “smarter” the disaggregation is performed, the less influence a higher or lower VPP SoC has on the maximum charging or discharging power As it will be shown, the performance of the proposed aggregation scheme depends significantly on the choice of this function. The VPP charging ability is nearly zero, the VPP is with 0.1% almost fully depleted
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