Optimal energy management strategies of microgrids

Tran, Quoc Tuan; Luu, Ngọc An

doi:10.1109/ssci.2016.7849851

Cited by 8 publications

(8 citation statements)

References 11 publications

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“…The objectives of optimal power management algorithms for GT-MGs is to minimize running cost and to maximize profit from energy sales while supplying the demand on the microgrid side [18], [7], [36]. For a battery behind meter systems, the essential variables to solve the problem include the DG power output, the load profile, the measured battery storage system (BSS) instantaneous state of charge (SoC) and the forecasted day-ahead grid tariff profile.…”

Section: Mathematical Formulationsmentioning

confidence: 99%

Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review

Arwa

Folly

2020

IEEE Access

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The utility grids are undergoing several upgrades. Distributed generators that are supplied by intermittent renewable energy sources (RES) are being connected to the grids. As RES generations get cheaper, more customers are opting for peer-to-peer energy interchanges through the smart metering infrastructure. Consequently, power management in grid-tied RES-based microgrids has become a challenging task. This paper reviews the applications of reinforcement learning (RL) algorithms in managing power in grid-tide microgrids. Unlike other optimization methods such as numerical and soft computing techniques, RL does not require an accurate model of the optimization environment in order to arrive at an optimal solution. In this paper, various challenges associated with the control of power in grid-tied microgrids are described. The application of RL techniques in addressing those challenges is reviewed critically. This review identifies the need to improve and scale multi-agent RL methods to enable seamless distributed power dispatch among interconnected microgrids. Finally, the paper gives directions for future research, e.g., the hybridization of intrinsic and extrinsic reward schemes, the use of transfer learning to improve the learning outcomes of RL in complex power systems environments and the deployment of priority-based experience replay in post-disaster microgrid power flow control. INDEX TERMS Electric vehicle charging station, energy management, Markov Decision Process, microgrid, reinforcement learning.

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Section: Mathematical Formulationsmentioning

confidence: 99%

Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review

Arwa

Folly

2020

IEEE Access

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“…In this case, the objective function is to minimize the total cash flow (TCF) which is the operational cost during the studied period. The TCF, as expressed in [38], is defined in terms of paid cash (PC) and received cash (RC). In this study, the PC is designated positive while the RC as negative.…”

Section: Scenario I-mathematical Equationsmentioning

confidence: 99%

“…The computation of battery replacement cost, C BR requires the knowledge of battery change in state of health (DSOH) as well as the aging coefficient of (z) [38]. This is expressed as follows:…”

Section: Scenario I-mathematical Equationsmentioning

confidence: 99%

Wind Farm-Battery Energy Storage Assessment in Grid-Connected Microgrids

Rehman¹,

Salman²,

Alhems³

2020

Energy Engineering

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Renewable energy has received much attention in the last few decades and more investment is being attracted across the world to boost its contribution towards the existing energy mix. In the Kingdom of Saudi Arabia (KSA), many studies have been conducted on the potential of renewable energy sources (RES), such as wind, solar, and geothermal. Many of these studies have revealed that the Kingdom is blessed with an abundance of RES with wind energy being the best after solar. This paper presents an analysis of windfarm distributed generation (WFDG) for energy management strategy in the Eastern Province of KSA. The study investigates the possibility of partially sharing the load of the area from a wind farm of 100 MW installed capacity. Furthermore, the effect of supplementing the WFDG with diesel generators is explored. Based on the historical wind resources and the load data, the cost of electricity is analyzed using a dynamic pricing system. Simulations are carried out using quadratically constrained program available in the General Algebraic Modeling System to obtain the operational cost of the WFDG needed to achieve the target. The dispatch of the battery energy storage system is analyzed during the 24 hours' simulation period. Results showed the overall quantitative responses of the microgrid on the demand, power exchange, and dynamic pricing across all scenarios. Additionally, the study showed that it is crucial to consider the operation of a microgrid at different sites independently. The performance analysis showed that a loss of power supply probability of less than 1% is achievable in all scenarios.

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“…It is therefore necessary to develop new strategies of control and management for microgrid. Various projects have been carried out in an effort to alleviate these challenges [2,[12][13][14][15][16][17].…”

Section: Microgrid Developmentmentioning

confidence: 99%

“…Another solution in consideration is battery energy storage system (BESS) associated to photovoltaic power generation (PV) [14][15][16][17]. Fig.…”

Section: Photovoltaic Production Assosiated With Storage Solutionsmentioning

confidence: 99%

Integration of PV Systems into Grid: From Impact Analysis to Solutions

Tran

Pham

Parent

et al. 2018

2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Syst

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The integration PV systems into grid, due to the intermittent characteristics, can have several impacts on the network functionalities such as the stability, the protection and introduces new management and planning challenges. Theses impacts are more complicated for an islanded grid or weak grid. A better knowledge of the induced constraints becomes necessary in order to determine the PV penetration level, to assess impacts of PV generation on distribution network and to assess technical and economic opportunities provided by PV generation. This paper presents analyses on the impacts of PV systems integration to the grid: variation of voltage and frequency, voltage unbalance, etc. Several solutions, based on intelligent control, protection and energy storage in order to reduce these impacts, to maximize the ancillary services contributed by PV systems and to maintain the grid stability are proposed.

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Optimal energy management strategies of microgrids

Cited by 8 publications

References 11 publications

Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review

Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review

Wind Farm-Battery Energy Storage Assessment in Grid-Connected Microgrids

Integration of PV Systems into Grid: From Impact Analysis to Solutions

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