Battery Energy Management in a Microgrid Using Batch Reinforcement Learning

Mbuwir, Brida V.; Ruelens, Frederik; Spiessens, Fred; Deconinck, Geert

doi:10.3390/en10111846

Cited by 142 publications

(103 citation statements)

References 28 publications

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“…Figure depicts the comparisons between efficiencies of the controlled inverter topology and the conventional inverter topologies depicted in previous studies . It is observed that the efficiency of the proposed inverter is higher than that of the literature.…”

Section: Resultsmentioning

confidence: 99%

Performance assessment of stand‐alone transformerless inverters

Khan

Haque

Kurukuru

2019

Int Trans Electr Energ Syst

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Summary There is a significant improvement in the efficiency and reliability of photovoltaic inverter by using transformerless topologies. But, the issues related to voltage regulation and total harmonic distortion (THD) needs to be addressed deeply. This paper aims to study the performance parameters like voltage regulation, efficiency, and THD of highly efficient transformerless inverters. In addition to this, a modified topology is proposed, which exhibits improved performance while operating with controllers, such as fuzzy and neuro‐fuzzy, are implemented. Simulation is performed using MATLAB. A prototype of the proposed transformerless inverter is also developed in the lab along with the modified control. The proposed topology isolates the photovoltaic (PV) array form the load during the zero‐voltage state. When operated in stand‐alone mode with an intelligent controller, the proposed topology provides benefits with low THD, less power loss, improved voltage regulation, enhanced performance, and efficiency. The result shows an error of 1% in the nominal value of output voltage under varying load and ambient conditions. Settling time is observed to be less than 0.1 seconds for both simulation and prototype. THD is observed at 1.9% for simulation and 2.1% for prototype results, which is better than the previous work.

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Section: Resultsmentioning

confidence: 99%

Performance assessment of stand‐alone transformerless inverters

Khan

Haque

Kurukuru

2019

Int Trans Electr Energ Syst

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“…They treat the MG as a black box and find a near-optimal strategy from interactions with it. For instance, Brida et al [18] developed a battery energy management strategy for a MG by using the batch RL technology. Sunyong et al [19] proposed a RL-based EMS for a MG-like smart building to reduce the operating cost.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning

Wang

et al. 2019

Energies

189

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Driven by the recent advances and applications of smart-grid technologies, our electric power grid is undergoing radical modernization. Microgrid (MG) plays an important role in the course of modernization by providing a flexible way to integrate distributed renewable energy resources (RES) into the power grid. However, distributed RES, such as solar and wind, can be highly intermittent and stochastic. These uncertain resources combined with load demand result in random variations in both the supply and the demand sides, which make it difficult to effectively operate a MG. Focusing on this problem, this paper proposed a novel energy management approach for real-time scheduling of an MG considering the uncertainty of the load demand, renewable energy, and electricity price. Unlike the conventional model-based approaches requiring a predictor to estimate the uncertainty, the proposed solution is learning-based and does not require an explicit model of the uncertainty. Specifically, the MG energy management is modeled as a Markov Decision Process (MDP) with an objective of minimizing the daily operating cost. A deep reinforcement learning (DRL) approach is developed to solve the MDP. In the DRL approach, a deep feedforward neural network is designed to approximate the optimal action-value function, and the deep Q-network (DQN) algorithm is used to train the neural network. The proposed approach takes the state of the MG as inputs, and outputs directly the real-time generation schedules. Finally, using real power-grid data from the California Independent System Operator (CAISO), case studies are carried out to demonstrate the effectiveness of the proposed approach.

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“…In contrast to the conventional distributed methods, learning-based methods can be easily adapted with a real-time problem after the off-line training process. In RL, Q-learning is a popular method and is widely used for the optimal operation of microgrids [19][20][21][22][23]. A fitted Q-iteration-based algorithm has been proposed in [19] for a BESS.…”

Section: Introductionmentioning

confidence: 99%

“…In RL, Q-learning is a popular method and is widely used for the optimal operation of microgrids [19][20][21][22][23]. A fitted Q-iteration-based algorithm has been proposed in [19] for a BESS. A data-driven method is utilized in [19] and it uses a state-action value function to optimize a scheduling plan for the BESS in grid-connected mode.…”

Section: Introductionmentioning

confidence: 99%

Q-Learning-Based Operation Strategy for Community Battery Energy Storage System (CBESS) in Microgrid System

2019

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Energy management systems (EMSs) of microgrids (MGs) can be broadly categorized as centralized or decentralized EMSs. The centralized approach may not be suitable for a system having several entities that have their own operation objectives. On the other hand, the use of the decentralized approach leads to an increase in the operation cost due to local optimization. In this paper, both centralized and decentralized approaches are combined for managing the operation of a distributed system, which is comprised of an MG and a community battery storage system (CBESS). The MG is formed by grouping all entities having the same operation objective and is operated under a centralized controller, i.e., a microgrid EMS (MG-EMS). The CBESS is operated by using its local controller with different operation objectives. A Q-learning-based operation strategy is proposed for optimal operation of CBESS in both grid-connected and islanded modes. The objective of CBESS in the grid-connected mode is to maximize its profit while the objective of CBESS in islanded mode is to minimize the load shedding amount in the entire system by cooperating with the MG. A comparison between the Q-learning-based strategy and a conventional centralized-based strategy is presented to show the effectiveness of the proposed strategy. In addition, an adjusted epsilon is also introduced for epsilon-greedy policy to reduce the learning time and improve the operation results.

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Battery Energy Management in a Microgrid Using Batch Reinforcement Learning

Cited by 142 publications

References 28 publications

Performance assessment of stand‐alone transformerless inverters

Performance assessment of stand‐alone transformerless inverters

Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning

Q-Learning-Based Operation Strategy for Community Battery Energy Storage System (CBESS) in Microgrid System

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