Real-time operation of multi-micro-grids using a multi-agent system

Keshta, H.E.; Ali, Ahmed; Saied, E. M.; Bendary, Fahmy

doi:10.1016/j.energy.2019.02.145

Cited by 36 publications

(15 citation statements)

References 14 publications

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“…Step (5): Determine whether the learning process converges, whether the Q-learning reaches the Nash equilibrium and whether the Q value of each agent reaches convergence; or the given number of learning steps or time limits have been reached. If it does not converge, then set k = k + 1 and return to Step (2).…”

Section: Regulation and Control Strategy On Decision Layer Based On Qmentioning

confidence: 99%

“…Therefore, the feasibility of the above method remains to be studied. Based on the above considerations, many studies have introduced multi‐agent [5] and game theory [6, 7] into energy management technology by using multiple agents autonomy, interactivity and distributed computing features to reflect coordinated control between multiple stakeholders; use game theory to analyse the interests of different agents and the possible equilibrium of interests. However, at present, the division of the agent or game subject in the integrated energy system is usually based on the ‘source’/‘load’ as the boundary, and the division method is slightly rough.…”

Section: Introductionmentioning

confidence: 99%

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Research on hierarchical control and optimisation learning method of multi‐energy microgrid considering multi‐agent game

Liu

2020

IET Smart Grid

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Due to the depletion of traditional fossil energy, to improve energy efficiency and build a cost-effective integrated energy system has become an inevitable choice. Aiming at the problems that the traditional centralised scheduling method is difficult to reflect the multi-dimensional interests of different agents in the multi-energy microgrid system, and the application of artificial intelligence technology in integrated energy scheduling still needs further exploration, this manuscript proposed a hierarchical control optimisation learning method with consideration of multi-agent game. Firstly, the multi-energy microgrid was taken as the research object, the microgrid system architecture was analysed, and the multi-agent partition in the system was pursued based on different economic interests. Secondly, for the technical aspects involved in the integrated energy regulation and management, the management layers of the multi-energy microgrid were divided, and the functions of different management layers were analysed. Based on this, the regulation functions were realised by considering the Nash Q-learning and the artificial intelligence method of Petri-net. Finally, the learning and decision-making ability of the method through practical cases were analysed, and the effectiveness and applicability of the proposed method were explained. This study explores the application of artificial intelligence technology in energy Internet energy management.

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Section: Regulation and Control Strategy On Decision Layer Based On Qmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on hierarchical control and optimisation learning method of multi‐energy microgrid considering multi‐agent game

Liu

2020

IET Smart Grid

View full text Add to dashboard Cite

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“…On the other hand, the output power of the panels is proportional to the temperature and sun radiation. The value of Φ is proportional to the angle of panels, and the output power is also proportional to Φ and the air temperature in this paper is assumed 25 • C. To analyze the frequency response of solar cells and converters, it is formulated using the first-order transmission function according to Equation (11).…”

Section: Solar System Structurementioning

confidence: 99%

“…Despite the faults and disturbances in the operating system, the proposed method minimizes cost and improves system performance. A MAS is proposed by Keshta et al to achieve optimal energy management for voltage regulation and to enhance the stability of a system under different weather conditions and load perturbations for two connected micro‐grids. An efficient hybrid approach is presented by Sureshkumar et al for the power flow management of the hybrid renewable energy system‐connected smart grid system.…”

Section: Introductionmentioning

confidence: 99%

Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis

Mir

Dayyani

Sutikno

et al. 2019

Computational Intelligence

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There are mainly two most essential problems in power networks, load frequency control and power flow management, which are grown recently because of growth in dimension/complication of grids. Present work suggests a controller based on fuzzy systems in which controller design is performed in a supervisory manner over a multiagent system aiming to control the frequency variation as well as generation cost minimization in the entire grid. The designing processes for low‐frequency controller (LFC) and management are mostly performed separately, which results in the disruption of both outputs. This challenge is tackled in this paper by the integration of them in the designing process. Additionally, stability guarantee is in high importance in the power systems, which is neglected in most of the related works. The Gaussian particle swarm optimization (GPSO) algorithm is applied for determining the optimal values of the decision variables, which can also guarantee the stability of the system by adopting a chaotic map by Gaussian function to balance the seeking abilities of particles that promotes the computation effectiveness without affecting the efficiency of the fuzzy controller. Then, the stability situationof the fuzzy + GPSO method is derived that guarantees a suitable global exploration and rapid convergence, with no require to gradients.

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“…In recent years microgrids (MGs) are seen as an effective solution to increase penetration of distributed energy resources (DERs) into power system due to higher efficiency, economic impact, reliability, and better utilization of local resources 1‐4. The MG is defined as a group of DERs compromising energy storage systems (ESSs), responsive loads, and various distributed generations (DGs) such as diesel engines, micro turbines, wind turbines (WTs), photovoltaic panels (PVs), and fuel cells which are interconnected in a specific region of power system 5‐7. MGs can be operated in both grid‐connected and islanded modes, which increases the reliability of power supply 8.…”

Section: Introductionmentioning

confidence: 99%

A stochastic frequency security constrained optimization model for day‐ahead energy and reserve scheduling of islanded multi‐microgrids systems

Sarcheshmeh

Taher

Mazidi

2020

Int Trans Electr Energ Syst

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Summary Small size of microgrids (MGs), low inertia, and small scale of distributed generations, and high intermittent energy delivery of renewable generations, make the frequency security an important issue in the islanded multi‐microgrids (IMMG) systems which should be considered subject to the techno‐economic constraints of MGs. Motivated by this need, this article proposes a two‐stage stochastic optimization model for day‐ahead energy and reserve scheduling of IMMG systems. The objective of the proposed model is to minimize total operation cost of IMMG while the frequency security of system is preserved within a predefined range. To this end, the hierarchical frequency structure of IMMG system including primary, secondary, and tertiary control levels are precisely formulated in mixed‐integer linear programming format which can be efficiently solved by optimization software package. Meanwhile, to promote the proposed model, responsive loads and energy storage systems are considered in the energy and reserve scheduling problem. The proposed model is applied to a typical IMMG system to optimize energy and reserve scheduling over 24‐hr horizon. The obtained results verify that through the proposed model, the operation costs of MGs are minimized while frequency security of the system is preserved in a cost‐effective manner.

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Real-time operation of multi-micro-grids using a multi-agent system

Cited by 36 publications

References 14 publications

Research on hierarchical control and optimisation learning method of multi‐energy microgrid considering multi‐agent game

Research on hierarchical control and optimisation learning method of multi‐energy microgrid considering multi‐agent game

Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis

A stochastic frequency security constrained optimization model for day‐ahead energy and reserve scheduling of islanded multi‐microgrids systems

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