Multicriteria optimal operation of a microgrid considering risk analysis, renewable resources, and model predictive control

• Published in: Optimal control applications & methods 2020-01, Vol.41 (1), p.94-106
• Main Authors: Zafra‐Cabeza, Ascensión, Velarde, Pablo, Maestre, José M.
• Format: Article
• Language: English
• Subjects: Algorithms; Computer simulation; Controllers; Distributed generation; Electric power generation; mixed integer programming; model predictive control; Multiple criterion; Power dispatch; Predictive control; Renewable resources; Resource management; Risk analysis; Risk management; risk mitigation; smart grid
• ISSN: 0143-2087; 1099-1514
• DOI: 10.1002/oca.2525

Summary This paper proposes an optimal power dispatch by taking into account risk management and renewable resources. In particular, it examines how control engineering and risk management techniques can be applied in the field of power systems through their use in the design of risk‐based model predictive controllers. To this end, this paper proposes a two‐layer control scheme for microgrid management where both levels are based on model predictive control (MPC): the higher level is devoted to risk management while the lower layer is dedicated to power dispatching. In particular, the high‐level controller is based on a risk‐based approach where potential risks have been identified and evaluated. Mitigation actions are the decision variables to be optimized to reduce the consequences of risks and costs. The MPC‐based algorithm decides the appropriate frequency of mitigation actions such as changes in references, constraints, and insurance contracting, by relying on a model that includes integer variables, identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. On the other hand, the low‐level controller drives the plant to suitable values to satisfy demands. A series of simulations on a nonlinear model of a real laboratory‐scale power plant located in the facilities of the University of Seville are conducted under varying conditions to demonstrate the effectiveness of the algorithm when risks are explicitly considered.