Multi-agent Autonomous Decision Making in Smart Micro-Grids’ Energy Management: A Decentralized Approach

Ghorbani, Sajad; Rahmani, Rasoul; Unland, Rainer

doi:10.1007/978-3-319-64798-2_14

Cited by 15 publications

(15 citation statements)

References 19 publications

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“…e myopic strategy, as it was demonstrated earlier in this paper, is an example strategy of this type of approaches. Papers using this method [8,10,11,15,21,24,[35][36][37] proved the effectiveness of such strategies through simulations. More advanced techniques can be incorporated with rule-based approaches for higher efficiency.…”

Section: Algorithmsmentioning

confidence: 96%

“…Controllable loads on the other hand are divided into deferrable and powerlevel controlled loads. Deferrable loads have flexible starting-time operation (e.g., washing machine), while power-level controlled loads can vary their power around the nominal power of operation (e.g., water heater) [12,27,37]. (iii) Low, medium, and high priority loads: priorities are set according to customer preferences defining the order of which loads are fed with energy.…”

Section: Load Scheduling Load Scheduling Is a Part Of The Demand Sidmentioning

confidence: 99%

“…More advanced techniques can be incorporated with rule-based approaches for higher efficiency. We cite as examples artificial neural networks (ANN) [33], fuzzy logic [34], and the adoption of multiagent systems (MAS) [35][36][37]44]. e use of the latter technique has garnered much interest lately.…”

Section: Algorithmsmentioning

confidence: 99%

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Energy Management Strategies for Smart Green MicroGrid Systems: A Systematic Literature Review

Essayeh

Fenni

Dahmouni

et al. 2021

Journal of Electrical and Computer Engineering

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Having neither precise definition nor a commonly accepted scope, the term “MicroGrid” tends to be used differently across researchers and practitioners alike. The management of energy usage within a microgrid is one of the topics that was handled from numerous perspectives. This study presents systematic literature review (SLR) of research on architectures and energy management techniques for microgrids, providing an aggregated up-to-date catalogue of solutions suggested by the scientific community. The SLR incorporated 45 papers selected according to inclusion/exclusion criteria and defined a priori. The selection process was based on an automated search and covered three known digital libraries. The extraction process covers three main questions. (i) The architectures of microgrids including their components, their bus configuration, and the adopted utility grid policy. (ii) The employed methods to ensure an optimal usage of energy under uncertainty. (iii) The confronted challenges and constraints of the suggested strategies. The findings of this SLR indicate a great diversity of methods and a rich background. Finally, the SLR suggests that future research should take into account the uncertainty aspect relating to energy management rather than the direct use of historical data as it is commonly done in most research papers. A sensitivity analysis should be provided in the latter case.

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Section: Algorithmsmentioning

confidence: 96%

Section: Load Scheduling Load Scheduling Is a Part Of The Demand Sidmentioning

confidence: 99%

See 1 more Smart Citation

Energy Management Strategies for Smart Green MicroGrid Systems: A Systematic Literature Review

Essayeh

Fenni

Dahmouni

et al. 2021

Journal of Electrical and Computer Engineering

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“…Due to its feature of solving complex problems in a more computationally efficient manner compared to a single-agent system, many researchers have used it to solve energy management problems [36]. A MAS-based system was considered in a grid-connected microgrid for optimal operation [37]. Additionally, a MAS-based intelligent EMS for the islanded microgrid is designed in reference [38] to balance the energy among the generators, batteries and loads.…”

Section: The Assessment Of the Energy Management System For Hresmentioning

confidence: 99%

Control Strategy of a Hybrid Renewable Energy System Based on Reinforcement Learning Approach for an Isolated Microgrid

Phan¹,

Lai²

2019

Applied Sciences

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Due to the rising cost of fossil fuels and environmental pollution, renewable energy (RE) resources are currently being used as alternatives. To reduce the high dependence of RE resources on the change of weather conditions, a hybrid renewable energy system (HRES) is introduced in this research, especially for an isolated microgrid. In HRES, solar and wind energies are the primary energy resources while the battery and fuel cells (FCs) are considered as the storage systems that supply energy in case of insufficiency. Moreover, a diesel generator is adopted as a back-up system to fulfill the load demand in the event of a power shortage. This study focuses on the development of HRES with the combination of battery and hydrogen FCs. Three major parts were considered including optimal sizing, maximum power point tracking (MPPT) control, and the energy management system (EMS). Recent developments and achievements in the fields of machine learning (ML) and reinforcement learning (RL) have led to new challenges and opportunities for HRES development. Firstly, the optimal sizing of the hybrid renewable hydrogen energy system was defined based on the Hybrid Optimization Model for Multiple Energy Resources (HOMER) software for the case study in an island in the Philippines. According to the assessment of EMS and MPPT control of HRES, it can be concluded that RL is one of the most emerging optimal control solutions. Finally, a hybrid perturbation and observation (P&O) and Q-learning (h-POQL) MPPT was proposed for a photovoltaic (PV) system. It was conducted and validated through the simulation in MATLAB/Simulink. The results show that it showed better performance in comparison to the P&O method.

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“…However, the impact of demand response programs on the consumer's level of convenience is not considered in the investigated models. Authors in [22,23] addressed this issue by proposing an MAS-based DSM approach for a microgrid, in which the level of consumer convenience is considered as an important factor in decision-making.…”

mentioning

confidence: 99%

An Innovative Stochastic Multi-Agent-Based Energy Management Approach for Microgrids Considering Uncertainties

et al. 2019

Self Cite

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In microgrids a major share of the energy production comes from renewable energy sources such as photovoltaic panels or wind turbines. The intermittent nature of these types of producers along with the fluctuation in energy demand can destabilize the grid if not dealt with properly. This paper presents a multi-agent-based energy management approach for a non-isolated microgrid with solar and wind units and in the presence of demand response, considering uncertainty in generation and load. More specifically, a modified version of the lightning search algorithm, along with the weighted objective function of the current microgrid cost, based on different scenarios for the energy management of the microgrid, is proposed. The probability density functions of the solar and wind power outputs, as well as the demand of the households, have been used to determine the amount of uncertainty and to plan various scenarios. We also used a particle swarm optimization algorithm for the microgrid energy management and compared the optimization results obtained from the two algorithms. The simulation results show that uncertainty in the microgrid normally has a significant effect on the outcomes, and failure to consider it would lead to inaccurate management methods. Moreover, the results confirm the excellent performance of the proposed approach.The major share of the energy production in microgrids usually comes from renewable energy sources such as photovoltaic panels (PV) or wind turbines (WT). The intermittent nature of such units can cause fluctuation and power imbalance in the system. Hence, energy management in microgrids considering the uncertainties in generation/demand has always been of paramount importance to researchers in this field [3-6]. State-of-the-ArtResearch on all aspects of microgrids has become very popular in the recent past. In this review we will concentrate on optimization objectives in microgrids only since this is the topic of this paper.Reference [3] presents a review of existing optimization objectives, tools, and solution approaches used in microgrid energy management. In [6], a summary of various uncertainty quantification methods along with a comparative analysis on utilized communication technologies are presented. An energy management solution based on a Bayesian optimization algorithm (BOA) is proposed by [7] in which the optimization problem is formulated without a closed-form objective function expression, and solved using a BOA-based data-driven framework. In [8], a two-layer predictive energy management system (EMS) for microgrids was proposed. The upper layer reduces the total operational cost and the lower layer try to remove the forecast fluctuations. In [9], an optimal scheduling of a standalone microgrid under system uncertainties is proposed. This model uses a dynamic programming method to solve a single-objective optimization problem to reduce the operation and emission costs.Flexibility and cooperative behavior of multi-agent systems (MASs) make them appropriate candidates for ener...

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Multi-agent Autonomous Decision Making in Smart Micro-Grids’ Energy Management: A Decentralized Approach

Cited by 15 publications

References 19 publications

Energy Management Strategies for Smart Green MicroGrid Systems: A Systematic Literature Review

Energy Management Strategies for Smart Green MicroGrid Systems: A Systematic Literature Review

Control Strategy of a Hybrid Renewable Energy System Based on Reinforcement Learning Approach for an Isolated Microgrid

An Innovative Stochastic Multi-Agent-Based Energy Management Approach for Microgrids Considering Uncertainties

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