Zhang et al., 2013 - Google Patents
Optimal energy management of wind-battery hybrid power system with two-scale dynamic programmingZhang et al., 2013
- Document ID
- 4898760391388260910
- Author
- Zhang L
- Li Y
- Publication year
- Publication venue
- IEEE Transactions on sustainable energy
External Links
Snippet
This study is concerned with the optimal energy management for a wind-battery hybrid power system (WBHPS) with local load and grid connection, by including the current and future information on generation, demand, and real-time utility price. When applying typical …
- 230000005611 electricity 0 abstract description 58
Classifications
-
- 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
-
- 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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
-
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhang et al. | Optimal energy management of wind-battery hybrid power system with two-scale dynamic programming | |
| Rafique et al. | Energy management system, generation and demand predictors: a review | |
| Dong et al. | Machine-learning-based real-time economic dispatch in islanding microgrids in a cloud-edge computing environment | |
| Hussain et al. | Optimal operation of hybrid microgrids for enhancing resiliency considering feasible islanding and survivability | |
| Yang et al. | Fluctuation reduction of wind power and sizing of battery energy storage systems in microgrids | |
| Ross et al. | Energy storage system scheduling for an isolated microgrid | |
| Gao et al. | Dynamic load shedding for an islanded microgrid with limited generation resources | |
| CN111697625A (en) | Island micro-grid opportunity constraint energy scheduling method based on distributed robust optimization | |
| Lee et al. | Novel architecture of energy management systems based on deep reinforcement learning in microgrid | |
| Hu et al. | Probabilistic electric vehicle charging demand forecast based on deep learning and machine theory of mind | |
| Squartini et al. | Optimization algorithms for home energy resource scheduling in presence of data uncertainty | |
| Izzatillaev et al. | Short-term load forecasting in grid-connected microgrid | |
| Ramkumar et al. | Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertainty | |
| Zhang et al. | Economical operation strategy of an integrated energy system with wind power and power to gas technology–a DRL‐based approach | |
| Li et al. | Optimal storage sizing of energy storage for peak shaving in presence of uncertainties in distributed energy management systems | |
| Kushwaha et al. | PFR constrained energy storage and interruptible load scheduling under high RE penetration | |
| Leo et al. | Multi agent reinforcement learning based distributed optimization of solar microgrid | |
| Roy et al. | A hybrid RFCRO approach for the energy management of the grid connected microgrid system | |
| Alanis et al. | Neural model with particle swarm optimization Kalman learning for forecasting in smart grids | |
| Zhang et al. | Optimal energy management of hybrid power system with two-scale dynamic programming | |
| Das et al. | Approximate dynamic programming with enhanced off-policy learning for coordinating distributed energy resources | |
| Mishra et al. | Multi‐objective auto‐encoder deep learning‐based stack switching scheme for improved battery life using error prediction of wind‐battery storage microgrid | |
| Zhang et al. | Sizing and operation co‐optimization strategy for flexible traction power supply system | |
| Hu et al. | Safe Deep Reinforcement Learning-Based Real-Time Multi-Energy Management in Combined Heat and Power Microgrids | |
| Krishna et al. | Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm |