Operation and control problems of power systems with distributed generation

Воропай, Н.И.; Efimov, Dmitry

doi:10.1109/pes.2009.5275453

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…In this regard, this work considers an action space consisting of three options, i.e., a ∈ [0, 1,2], where:…”

Section: Action Space (A)mentioning

confidence: 99%

“…The actions in A = [0, 1,2] can be represented in binary form with two contacts, S1 and S2 (0 = open, 1 = closed) for illustration, purposes as shown in Figure 4. This binary representation allows one to take into account the charge/discharge constraint, Equation (3).…”

Section: Microgrid Case Studymentioning

confidence: 99%

“…The liberalization of the electricity market and environmental concerns have introduced new challenges in the design and operation of power grids [1]. As such, climate and energy packages adopted worldwide have resulted in clear objectives for the energy sector.…”

Section: Introductionmentioning

confidence: 99%

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Battery Energy Management in a Microgrid Using Batch Reinforcement Learning

et al. 2017

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Abstract:Motivated by recent developments in batch Reinforcement Learning (RL), this paper contributes to the application of batch RL in energy management in microgrids. We tackle the challenge of finding a closed-loop control policy to optimally schedule the operation of a storage device, in order to maximize self-consumption of local photovoltaic production in a microgrid. In this work, the fitted Q-iteration algorithm, a standard batch RL technique, is used by an RL agent to construct a control policy. The proposed method is data-driven and uses a state-action value function to find an optimal scheduling plan for a battery. The battery's charge and discharge efficiencies, and the nonlinearity in the microgrid due to the inverter's efficiency are taken into account. The proposed approach has been tested by simulation in a residential setting using data from Belgian residential consumers. The developed framework is benchmarked with a model-based technique, and the simulation results show a performance gap of 19%. The simulation results provide insight for developing optimal policies in more realistically-scaled and interconnected microgrids and for including uncertainties in generation and consumption for which white-box models become inaccurate and/or infeasible.

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“…In this regard, this work considers an action space consisting of three options, i.e., a ∈ [0, 1,2], where:…”

Section: Action Space (A)mentioning

confidence: 99%

Section: Microgrid Case Studymentioning

confidence: 99%

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Battery Energy Management in a Microgrid Using Batch Reinforcement Learning

et al. 2017

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“…MGs are small-scale power grids, composed of distributed energy resources (whether renewable or not), loads, and energy storage, which can be connected to the utility-grid and trading energy with it or isolated depending on local generation and storage [1,2]. However, the uncertain and intermittent characteristics of VRE cause technical challenges to the operation and control of MG [3]. The battery energy storage system (BESS) integration into the MG can buffer the volatility in VRE production and minimize spilling of excess energy [4].…”

Section: Introductionmentioning

confidence: 99%

Battery Scheduling Control of a Microgrid Trading with Utility Grid Using Deep Reinforcement Learning

Mahmoud

Tsuji

2023

IEEJ Transactions Elec Engng

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Managing microgrids (MGs) with variable renewable energy (VRE) is challenging because of uncertainties of electricity production, loads, and energy price, so we need flexible control strategies for battery energy storage system (BESS) to handle those challenges. Model-based approaches require precise models of the MG to give accurate results but having an accurate model can be difficult in continually changing environments. We introduced a new day-ahead optimization method to control BESS scheduling and power exchange between the utility grid and an MG with a load, photovoltaics, and BESS with the aim of energy cost minimization. Deep reinforcement learning (DRL) was used for the optimization of sequential actions of BESS over a time horizon. A theoretical optimum scheduling was derived using a linear programming optimization to be compared with the DRL agent. Both no-battery and greedy control algorithms were used as baselines. It was shown that the results of the proposed technique were better than the baselines through numerical simulations using whole year's data.

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“…Distributed generation (DG) has a lot of advantages such as reduced investment costs, flexibility, reliability, peak power shaving and clean power [1][2][3][4][5][6][7][8]. Integrating DG units into the smart grid, however, causes the modern grid to encounter voltage control [9], power loss [10] and optimal placement [11] issues. Concerning the voltage drop issue, the authors in [12] outlined different methods of distributed and decentralized voltage control mentioning their advantages and shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Voltage Regulation and Power Loss Minimization in Radial Distribution Systems via Reactive Power Injection and Distributed Generation Unit Placement

et al. 2018

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Distributed Generation (DG) has become an essential part of the smart grids due to the widespread integration of renewable energy sources. Reactive power compensation is still one of most important research topics in smart grids. DG units can be used for reactive power compensation purposes, therefore we can improve the voltage profile and minimize power losses in order to improve the power quality. In this paper two methods will be used to accomplish the mentioned tasks; the first technique depends on the reactive power demand change of the proposed network loads, whereas the second technique uses an algorithm to control DG units according to the measured voltage values in the feeders to generate the needed reactive power. Both methods were applied to different scenarios of DG unit positions and different reactive power values of loads. The chosen DG unit is made up of a Type-4 wind farm which could be used as a general unit where it is able to control reactive power generation in a wider range separately from active power. The simulation results show that using these two methods, the voltage profile could be improved, power losses reduced and the power factor increased according to the placement of DG units.

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Operation and control problems of power systems with distributed generation

Cited by 7 publications

References 14 publications

Battery Energy Management in a Microgrid Using Batch Reinforcement Learning

Battery Energy Management in a Microgrid Using Batch Reinforcement Learning

Battery Scheduling Control of a Microgrid Trading with Utility Grid Using Deep Reinforcement Learning

Voltage Regulation and Power Loss Minimization in Radial Distribution Systems via Reactive Power Injection and Distributed Generation Unit Placement

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