Medium-Term Multimarket Optimization for Virtual Power Plants: A Stochastic-Based Decision Environment

This paper presents a decision-making tool tailored for a portfolio manager aiming at maximizing its profit by participating in both energy and balancing services markets. The proposed formulation is modular and flexible so as to comply with any portfolio configuration and to follow evolutions of the market regulation policy. Detailed formulations of both medium-term (i.e., typically from one week up to one year-ahead) and short-term (i.e., day-ahead) perspectives are jointly considered and solved using surrogate-based optimization. The objective is to adequately account for the interdependencies and possible conflicting objectives between these time horizons. Then, in order to overcome the resulting computational burden associated with the different sources of uncertainty, an innovative method for generating time and space-dependent scenarios is developed. The approach is based on nonparametric copulas and, in contrast to traditional methods, allows including a large number of uncertain parameters into the formulation. Finally, the procedure is tested and illustrated for a portfolio manager with diversified assets. The case study is developed to emphasize the advantages of the proposed optimization tool in terms of accuracy and computational burden of the proposed models as well as subsequent generated profit.