Multiobjective Intelligent Energy Management for a Microgrid

Abstract

In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using Artificial Intelligence (AI) techniques jointly with linear programming based multiobjective optimization. The proposed Multiobjective Intelligent Energy Management (MIEM) aims to minimize the operation cost and the environmental impact of a microgrid taking into account its pre-operational variables as future availability of renewable energies and load demand. An artificial Neural Network Ensemble (NNE) is developed to predict 24 hour ahead photovoltaic generation, one hour ahead wind power generation and load demand. 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. In this study, 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 shows considerable minimization on operation cost and emission level comparing to literature microgrid energy management approaches based on opportunity charging and heuristic flowchart battery management. Index Terms-MultiobjectiveIntelligent Energy Management (MIEM), Neural Network Ensemble (NNE), Fuzzy Logic, Shortterm Forecasting, Microgrid.

Financial and environmental benefits of activedistribution networks

“…Although without formal analysis of the failures times, in [30,31] the authors present an initial approach focused in an expected longer battery life due to the smooth operation strategy.…”

A Survey on Smart Agent-Based Microgrids for Resilient/Self-Healing Grids

“…The centralized controller acts as an energy supervisor [66,67] and makes control action decisions based upon measured signals and objective functions, which are communicated to each local controller [15][16][17][68][69][70]. Objective functions may be conflicting; for example, to minimize system operation and maintenance costs and environmental impact (carbon footprint), while maximizing system efficiency may be competing objectives, complicating the achievement of a solution.…”

“…Objective functions may be conflicting; for example, to minimize system operation and maintenance costs and environmental impact (carbon footprint), while maximizing system efficiency may be competing objectives, complicating the achievement of a solution. Often, MO problems do not have a single solution but rather a set of non-dominated solutions, called a Pareto set, which include alternatives representing potential compromises among The centralized controller acts as an energy supervisor [66,67] and makes control action decisions based upon measured signals and objective functions, which are communicated to each local controller [15][16][17][68][69][70]. Objective functions may be conflicting; for example, to minimize system Energies 2017, 10, 620 5 of 25 operation and maintenance costs and environmental impact (carbon footprint), while maximizing system efficiency may be competing objectives, complicating the achievement of a solution.…”

Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization

“…Recently, the fuzzy optimization methods have been applied to microgrid scheduling preliminarily. For instance, the authors in [25] present a fuzzy-logic expert system to handle uncertainties related to the forecasted parameters and the fuzzy operational environment of the microgrid. In the context of multi-objective optimization of the microgrid, the authors in [26] propose a fuzzy decision approach to represent microgrid operators' preferences in compromising between two objectives.…”