Liu et al., 2021 - Google Patents
Deep reinforcement learning for stochastic dynamic microgrid energy managementLiu et al., 2021
- Document ID
- 9464818875099580544
- Author
- Liu L
- Zhu J
- Chen J
- Ye H
- Publication year
- Publication venue
- 2021 IEEE 4th International Electrical and Energy Conference (CIEEC)
External Links
Snippet
This paper proposes a deep reinforcement learning (DRL) based algorithm for the stochastic dynamic microgrid energy management. First, we consider AC power flow constraints which makes the problem non-convex and uncertainties in load, renewable generation and real …
- 230000002787 reinforcement 0 title abstract description 15
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S40/00—Communication or information technology specific aspects supporting electrical power generation, transmission, distribution or end-user application management
- Y04S40/20—Information technology specific aspects
- Y04S40/22—Computer aided design [CAD]; Simulation; Modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/70—Systems integrating technologies related to power network operation and communication or information technologies mediating in the improvement of the carbon footprint of electrical power generation, transmission or distribution, i.e. smart grids as enabling technology in the energy generation sector not used, see subgroups
- Y02E60/76—Computer aided design [CAD]; Simulation; Modelling
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Shuai et al. | Online scheduling of a residential microgrid via Monte-Carlo tree search and a learned model | |
Cao et al. | Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning | |
Wenzhi et al. | Hierarchical energy optimization management of active distribution network with multi-microgrid system | |
Sharma et al. | Grey wolf optimisation for optimal sizing of battery energy storage device to minimise operation cost of microgrid | |
Luo et al. | Short‐term operational planning framework for virtual power plants with high renewable penetrations | |
Bahmani-Firouzi et al. | An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties | |
Quan et al. | Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: A comparative study | |
Shilaja et al. | Internet of medical things-load optimization of power flow based on hybrid enhanced grey wolf optimization and dragonfly algorithm | |
El Bourakadi et al. | Intelligent energy management for micro-grid based on deep learning LSTM prediction model and fuzzy decision-making | |
Abedi et al. | Rolling-horizon optimization integrated with recurrent neural network-driven forecasting for residential battery energy storage operations | |
Liu et al. | Deep reinforcement learning for stochastic dynamic microgrid energy management | |
Shi et al. | Modelling and solutions of coordinated economic dispatch with wind–hydro–thermal complex power source structure | |
Jeyaraj et al. | Computer‐assisted demand‐side energy management in residential smart grid employing novel pooling deep learning algorithm | |
Kong et al. | Real-time pricing method for VPP demand response based on PER-DDPG algorithm | |
Venkatesh et al. | Unit commitment–a fuzzy mixed integer linear programming solution | |
Zhang et al. | Economical operation strategy of an integrated energy system with wind power and power to gas technology–a DRL‐based approach | |
Cheng et al. | Mitigating the impact of photovoltaic power ramps on intraday economic dispatch using reinforcement forecasting | |
Liu et al. | Deep reinforcement learning for real-time economic energy management of microgrid system considering uncertainties | |
Habachi et al. | Solving economic dispatch and unit commitment problem in smart grid system using eagle strategy based crow search algorithm | |
Chakraborty et al. | Profit maximization of retailers with intermittent renewable sources and energy storage systems in deregulated electricity market with modern optimization techniques: A review | |
Mu et al. | Multi-Objective Interval Optimization Dispatch of Microgrid via Deep Reinforcement Learning | |
Aribowo et al. | Long-term electricity load forecasting based on cascade forward backpropagation neural network | |
Gaber et al. | Hourly electricity price prediction applying deep learning for electricity market management | |
Dou et al. | Double‐deck optimal schedule of micro‐grid based on demand‐side response | |
Karagiannopoulos et al. | Decentralized control in active distribution grids via supervised and reinforcement learning |