Multiobjective Intelligent Energy Management for a Microgrid

By Aymen Chaouachi, Rashad M. Kamel, Ridha Andoulsi, Ken Nagasaka. IEEE transactions on industrial electronics (1982. Print), 2013

618 citations recorded by the source corpus.

In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using artificial intelligence techniques jointly with linear-programming-based multiobjective optimization. The proposed 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 and load demand (LD). An artificial neural network 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. In this paper, 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 literature microgrid energy management approaches based on opportunity charging and Heuristic Flowchart (HF) battery management.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering, Energy Engineering and Power Technology

View Paper: https://www.semanticscholar.org/paper/82ec4b9f1dcbf4c9a6b0aeb4a324cec05a45b10d

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Citations in the corpus

A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings (2014, background): “In recent years the attention is shifting to new spaces for the integration of generation and demand which emphasize the need for prediction models (most of them based on artificial intelligence) for demand and generation forecasting that enable operation as shown by Chaouachi et al. (2013) [121].”
Accelerating the discovery of materials for clean energy in the era of smart automation (2018, methodology): “Machine learning and data-mining-assisted methods, such as those used for multi-battery systems optimization, state-of-charge monitoring, remaining-useful-life prognostics and smart load balancing, also offer great promise for maximizing the utilization and cycle life of Li-ion cells 164–167 .”
Decentralized Energy Management System for Networked Microgrids in Grid-Connected and Islanded Modes (2016, background): “[8] presented a generalized formulation for the EMS of an MG considering the uncertainties related to the forecasted RES-based DG outputs.”
Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review (2019, methodology): “Reference [180] utilized those two methods for an intelligent energy management system that could predict PV generation 24 hours ahead.”
Distributed Energy Trading in Microgrids: A Game-Theoretic Model and Its Equilibrium Analysis (2015, background): “Various forms of demand and supply problems have been studied in literature, aiming for the optimal operation of a microgrid taking into account load demand forecasting, prediction for power generation, and/or energy storage capacities [22]– [28].”
A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems (2015, background): “The management and operation of the future power system and its components—particularly active power distribution grids and microgrids [20]—require new and advanced control functions.”
MOD-DR: Microgrid optimal dispatch with demand response (2017, methodology): “[21] proposed a MG energy management approach using LP with predicted renewable energy resources and load demand.”
Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources (2014, background): “Most published works focus on deterministic microgrid operations [2]–[6].”