Optimal operational management of a micro-grid, including high penetration level of renewable energy resources

Abstract

This paper presents a proper scenario-based method for hourly operation management of a Micro-Grid (MG) in a stochastic environment. The proposed method can model the uncertainty of the power produced by wind and solar resources, load demand and electricity market price simultaneously. In order to reduce the computational time of the problem, a Linear Programming (LP) is proposed to reduce and rearrange the number of scenarios and their probabilities respectively. The optimal objective value of the original multi-scenario problem is closely approximated by the optimal objective value of the reduced problem. In fact, the original stochastic model is transformed into a deterministic equivalent using the reduced set of scenarios. Each new obtained scenario is considered as the input for a deterministic problem with a specific probability. In this regard, each problem is formulated as a multi-objective optimization problem to minimize the total operating cost of the MG considering reliability issues. Then, the Shuffled Frog Leaping Algorithm (SFLA) is employed to solve the optimization problem. The SFLA is also compared with conventional heuristic algorithms (i.e., Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)) in terms of capability and superiority. The simulations are conducted on a typical low-voltage grid-connected MG which includes Micro-Turbine (MT), high penetration of Wind Turbine (WT) and Photovoltaic (PV) generation and Energy Storage System (ESS).