The integration of distributed generation (DG) at high levels exacerbates line loss in distribution networks. Improving the output power stability of DG and clarifying the impact of DG integration on line loss are critical issues in distribution network optimization. Firstly, the impact of DG integration on line loss in distribution networks is analyzed, and the line loss …
Learn MoreInterpretable Deep Reinforcement Learning for Optimizing Heterogeneous Energy Storage Systems. Luolin Xiong, Yang Tang, Chensheng Liu, …
Learn MoreReinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle Appl. Energy, 211 ( 2018 ), pp. 538 - 548, 10.1016/j.apenergy.2017.11.072
Learn MoreWith the maturity of hydrogen storage technologies, hydrogen-electricity coupling energy storage in green electricity and green hydrogen modes is an ideal energy system. The construction of hydrogen-electricity coupling energy storage systems (HECESSs) is one of the important technological pathways for energy supply and deep …
Learn Moredeep reinforcement learning (DRL) in solving challenging tasks, the goal of this thesis is to investigate its potential in solving problems related to the control of storage in modern energy systems. Firstly, we address the energy arbitrage problem of a storage unit
Learn MoreReinforcement learning (RL) has emerged as an alternative method that makes up for MP and solves large and complex problems such as optimizing the operation of renewable energy storage systems using hydrogen [15] or energy conversion under varying conditions [16]..
Learn MoreThis paper looks into the implementation of Reinforcement Learning algorithms- specifically, Q-learning and SARSA [1] - to control batteries to optimize energy storage …
Learn MoreReinforcement-learning-based optimal control of hybrid energy storage systems in hybrid AC-DC microgrids IEEE Trans Ind Inf, 15 ( 9 ) ( 2019 ), pp. 5355 - 5364, 10.1109/Tii.2019.2896618 Google Scholar
Learn MoreReinforcement learning has been found useful in solving optimal power flow (OPF) problems in electric power distribution systems. However, the use of largely …
Learn MoreThe optimal dispatch of energy storage systems (ESSs) presents formidable challenges due to the uncertainty introduced by fluctuations in dynamic prices, …
Learn MoreIntelligent multi-microgrid energy management based on deep neural network and model-free reinforcement learning IEEE Trans. Smart Grid, 11 ( 2 ) ( 2020 ), pp. 1066 - 1076 CrossRef View in Scopus Google Scholar
Learn MoreIn this paper, a reinforcement learning-based multi-battery energy storage system (MBESS) scheduling policy is proposed to minimize the consumers'' electricity cost. The MBESS scheduling problem is modeled as a Markov decision process (MDP) with unknown transition probability. However, the optimal value function is time-dependent and difficult …
Learn MoreThe sliding surface coefficients and switching gain is estimated based on Q-learning technique that is an important branch of reinforcement learning method. Moreover, the other parameters are obtained by Particle …
Learn MoreIn this paper, a reinforcement-learning-based online optimal (RL-OPT) control method is proposed for the hybrid energy storage system (HESS) in ac-dc microgrids involving photovoltaic systems and diesel generators (DGs). Due to the low system inertia, conventional unregulated charging and discharging (C&D) of energy …
Learn MoreIntegration of reinforcement learning-based approaches with energy storage(s) in smart homes demonstrated significant potential for revolutionizing energy management …
Learn MoreAddressing the issues of low reliability in centralized energy storage and high costs associated with distributed energy storage, Dong et al. introduced an optimal scheduling …
Learn MoreA multi-use framework of energy storage systems using reinforcement learning for both price-based and incentive-based demand response programs Int. J. Electr. Power Energy Syst., 144 ( 2023 ), Article 108519
Learn MoreReinforcement learning for energy storage operation to reduce energy costs. • The operation satisfies electrical distribution grid''s technical constraints. • The technique uses a linear function approximator with eligibility traces. • Discussion of advantages of using
Learn MoreThe learning steps of the FVRL are listed in Algorithm 3. Download : Download high-res image (469KB)Download : Download full-size imageComparing the FVRL with the PI, RL, and DQN, the FVRL has the following significant advantages: (1) the Q matrices (i.e., Q QL 1 and Q QL 2) and probability matrices (i.e., P QL 1 and P QL 2) of …
Learn MoreAn islanded MG containing two DG units is considered in this section. The DG units include Proton Exchange Membrane Fuel Cell (PEMFC), PV and BESS. Perturbation and observation (P&O) algorithm similar to Ref. [32, 33] is utilized in PV system in order to meet the Maximum Power Point (MPP) of the power-voltage (P V) curve.. …
Learn MoreReinforcement Learning-based Control of Residential Energy Storage Systems for Electric Bill Minimization 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC), IEEE ( 2015 ), pp. 637 - 642
Learn MoreMobile charging is an efficient solution to meet peak charging demand on highways. In this article we propose a deep reinforcement learning (DRL)-based approach to maximize the revenue of a utility-scale highway portable energy storage system (PESS) for on-demand electric vehicle charging. We consider a PESS that consists of an electric …
Learn MoreReinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty Author links open overlay panel Bassey Etim Nyong-Bassey a, Damian Giaouris a, Charalampos Patsios a, Simira Papadopoulou b c, Athanasios I. Papadopoulos b, Sara Walker a, …
Learn MoreThis paper presents a novel improved reinforcement learning to solve LFC problem. • A hybrid power system with energy storage units is implemented in this study. • Real data of wind speed and solar irradiance are used. • …
Learn MoreThe mobile battery energy storage systems (MBESS) utilize flexibility in temporal and spatial to enhance smart grid resilience and economic benefits. Recently, the high penetration of renewable energy increases the volatility of electricity prices and gives MBESS an opportunity for price difference arbitrage. However, the strong randomness of …
Learn MoreReinforcement learning TES Thermal energy storage Symbols Item Description, Unit E wb Wet-bulb efficiency of cooling tower, [-] F decay Efficiency decay factor, [-] G pump Fluid flow rate of pump, m 3 /h P h …
Learn MoreAbstract: The transition to renewable production and smart grids is driving a massive investment to battery storages, and reinforcement learning (RL) has recently …
Learn MoreBi-level optimization and reinforcement learning (RL) constitute the state-of-the-art frameworks for modeling strategic bidding decisions in deregulated electricity markets. However ...
Learn MoreFor comparison, the reinforcement learning (RL) algorithms can address the shortcomings of rule-based energy management strategies due to their model-free feature. Therefore, this article proposes an energy management strategy based on parallel reinforcement learning (PRL) to improve the efficiency of energy utilization while …
Learn MoreIncorporating residential-level photovoltaic energy generation and energy storage systems have proved useful in utilizing renewable power and reducing electric bills for the residential energy consumer. This is particular true under dynamic energy prices, where consumers can use PV-based generation and controllable storage modules for peak shaving on their …
Learn MoreAbstract. We address the control of a hybrid energy storage system composed of a lead battery and hydrogen storage. Powered by photovoltaic panels, it feeds a partially islanded building. We …
Learn MoreReinforcement learning has been found useful in solving optimal power flow (OPF) problems in electric power distribution systems. However, the use of largely model-free reinforcement learning algorithms that completely ignore the physics-based modeling of the power grid compromises the optimizer performance and poses scalability …
Learn MoreIntroduction The adoption of renewable energy sources like solar and wind is pivotal in reducing dependency on fossil fuels and addressing environmental issues, marking a significant trend in the energy sector''s evolution [1,2]. This shift towards a clean, low-carbon ...
Learn MoreBattery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of …
Learn MoreWith the proliferation of advanced metering infrastructure (AMI), more real-time data is available to electric utilities and consumers. Such high volumes of data facilitate innovative electricity rate structures beyond flat-rate and time-of-use (TOU) tariffs. One such innovation is real-time pricing (RTP), in which the wholesale market-clearing price is passed directly …
Learn MoreCitation: Pan T, Zhu Z, Luo H, Li C, Jin X, Meng Z and Cai X (2024) Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach. Front. Front
Learn MoreAmong all energy storage systems, the compressed air energy storage (CAES) as mechanical energy storage has shown its unique eligibility in terms of clean storage medium, scalability, high ...
Learn MoreMobile energy storage systems (MESSs) provide mobility and flexibility to enhance distribution system resilience. The paper proposes a Markov decision process (MDP) formulation for an integrated service restoration strategy that coordinates the scheduling of MESSs and resource dispatching of microgrids. The uncertainties in load consumption …
Learn MoreWhile reinforcement learning (RL) algorithms have demonstrated tremendous success in applications ranging from game-playing, e.g., Atari and Go, to robotic control, e.g., [1–3], most RL algorithms continue to only be benchmarked using …
Learn MoreThe dynamic dispatch (DD) of battery energy storage systems (BESSs) in microgrids integrated with volatile energy resources is essentially a multiperiod stochastic optimization problem (MSOP). Because the life span of a BESS is significantly affected by its charging and discharging behaviors, its lifecycle degradation costs should be …
Learn MoreThe energy storage management in this article is a discrete charge/discharge decision problem, therefore, the value-based and temporal difference deep reinforcement learning (DRL) is adopted. In traditional Q-learning algorithm, the action-value function is represented by a table called Q table, and we find the optimal strategy …
Learn MoreThe optimal dispatch of energy storage systems (ESSs) presents formidable challenges due to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and renewable-based energy generation. By exploiting the generalization capabilities of deep neural networks (DNNs), deep reinforcement …
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