Optimal Energy Management

Abstract

This chapter is composed of two portions: In the first portion, a neural network based distributed secondary control scheme for an autonomous smart microgrid system is proposed. In this scheme, the controller is designed to act dynamically to load changes and the associated optimized gains have been evaluated using differential evolution optimization procedure. The performance comparison between the proposed controller with fixed-gain controller is also performed. The proposed controller is faster than the traditional one. The adaptability and robustness of the controller is also demonstrated with respect to the load sharing and voltage/frequency regulation with the help of MATLAB simulations. In the second portion, a generalized formulation for intelligent energy management of a microgrid is proposed using artificial intelligence techniques jointly with linear-programming-based multiobjective optimization. The multiobjective intelligent energy management aims to minimize the operation cost and the environmental impact of a microgrid, taking into account its preoperational variables as future availability of renewable energies (REs) and load demand (LD). An ANN ensemble is developed to predict 24-h-ahead photovoltaic generation and 1-h-ahead wind power generation and LD. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. A fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results show considerable minimization on operation cost and emission level compared to the literature on microgrid energy management approaches based on opportunity charging and Heuristic Flowchart (HF) battery management. Next, the supervision design with predicted powers flow optimization for DC microgrid based on photovoltaic sources, storage, grid connection, and DC load. The supervision control, designed as a four-layer structure, takes into account forecast of power production and load power demand, storage capability, grid power limitations, grid time-of-use tariffs, optimizes energy cost, and handles instantaneous power balancing in the microgrid. Optimization aims to reduce the microgrid energy cost while meeting all constraints and is carried out by mixed integer linear programming.