- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 44th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz) - Paris, France (2019.9.1-2019.9.6)] 2019 44th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz) - Flexible control of broadband terahertz radiations from laser plasmas
摘要： This paper presents a novel energy-management method for a microgrid that includes renewable energy, diesel generators, battery storage, and various loads. We assume that the microgrid takes part in a pool market and responds actively to the electricity price to maximize its pro?t by scheduling its controllable resources. To address various uncertainties, a risk-constrained scenario-based stochastic programming framework is proposed using the conditional value at risk method. The designed model is solved by two levels of stochastic optimization methods. One level of optimization is to submit optimal hourly bids to the day-ahead market under the forecast data. The other level of optimization is to determine the optimal scheduling using the scenario-based stochastic data of the uncertain resources. The proposed energy management system is not only bene?cial for the microgrid and customers, but also applies the microgrid aggregator and virtual power plant. The results are shown to prove the validity of the proposed framework.
关键词： microgrid,Controllable load,risk management,smart grid,stochastic optimization,electricity market,renewable energy,energy management
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE Milan PowerTech - Milan, Italy (2019.6.23-2019.6.27)] 2019 IEEE Milan PowerTech - Mitigation Analysis of MV Distribution Network Constraints Thanks to a Self-Consumption Policy For Photovoltaic Distributed Units
摘要： Self-consumption of homemade electricity from renewable energy in encouraged by the European commission to enforce their penetration in state energy portfolios. By using electrical energy storage systems and demand response, this technique can lower the overall cost of the renewable energy transfer in the electrical system and so increase it. To develop self- consumption efficiently, various energy policies can be imagined to create economic support schemes for motivating consumers to make active decisions in this way and mobilizing financial resources for an energy transition toward more RES in the future. Germany has experimented earlier this policy orientation while France is only beginning to consider self-consumption. The goal of this paper is to compare German and French policies in order to evaluate the benefits of PV production self-consumption. So economic national frameworks are recalled and then a 100kW PV producer is considered. With one-year time data series and an energy model, different operation conditions are simulated and times of return on investment are compared. Then, benefits for the electrical network operation are studied. The benchmark distribution network model from CIGRé is used to quantify and compare technical constraint occurrences.
关键词： energy management system.,self-consumption,electrical constraints,renewable energy integration,electrical storage
更新于2025-09-16 10:30:52
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Cooperative Management for PV/ESS-Enabled Electric-Vehicle Charging Stations: A Multi-Agent Deep Reinforcement Learning Approach
摘要： This paper proposes a novel multi-agent deep reinforcement learning method for the energy management of distributed electric vehicle charging stations with a solar photovoltaic system and energy storage system. In the literature, the conventional method is to calculate the optimal electric vehicle charging schedule in a centralized manner. However, in general, the centralized approach is not realistic under certain environments where the system operators for multiple electric vehicle charging stations handle dynamically varying data, such as the status of the energy storage system and electric vehicle-related information. Therefore, this paper proposes a method that can compute the scheduling solutions of multiple electric vehicle charging stations in a distributed manner while handling run-time time-varying dynamic data. As shown in the data-intensive performance evaluation, it can be observed that the proposed method achieves a desirable performance in terms of reducing the operation costs of electric vehicle charging stations.
关键词： solar photovoltaic system,energy management,electric vehicle charging stations,multi-agent deep reinforcement learning,energy storage system
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE International Conference on Modern Electrical and Energy Systems (MEES) - Kremenchuk, Ukraine (2019.9.23-2019.9.25)] 2019 IEEE International Conference on Modern Electrical and Energy Systems (MEES) - Economic Efficiency of a Photovoltaic Power Plants
摘要： This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are sustainable, green, and environmentally friendly renewable energy systems (RESs), e.g., wind and solar; however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage and thermal generation were used to overcome these challenges. Using the I-DEMS to schedule dispatches allowed the RESs and energy storage devices to be utilized to their maximum in order to supply the critical load at all times. Based on the microgrid’s system states, the I-DEMS generates energy dispatch control signals, while a forward-looking network evaluates the dispatched control signals over time. Typical results are presented for varying generation and load profiles, and the performance of I-DEMS is compared with that of a decision tree approach-based DEMS (D-DEMS). The robust performance of the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions.
关键词： microgrid,Adaptive dynamic programming,reinforcement learning,evolutionary computing,dynamic energy management system (DEMS),renewable energy,neural networks
更新于2025-09-16 10:30:52
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Reinforcement Learning-Based Energy Management of Smart Home with Rooftop Solar Photovoltaic System, Energy Storage System, and Home Appliances
摘要： This paper presents a data-driven approach that leverages reinforcement learning to manage the optimal energy consumption of a smart home with a rooftop solar photovoltaic system, energy storage system, and smart home appliances. Compared to existing model-based optimization methods for home energy management systems, the novelty of the proposed approach is as follows: (1) a model-free Q-learning method is applied to energy consumption scheduling for an individual controllable home appliance (air conditioner or washing machine), as well as the energy storage system charging and discharging, and (2) the prediction of the indoor temperature using an artificial neural network assists the proposed Q-learning algorithm in learning the relationship between the indoor temperature and energy consumption of the air conditioner accurately. The proposed Q-learning home energy management algorithm, integrated with the artificial neural network model, reduces the consumer electricity bill within the preferred comfort level (such as the indoor temperature) and the appliance operation characteristics. The simulations illustrate a single home with a solar photovoltaic system, an air conditioner, a washing machine, and an energy storage system with the time-of-use pricing. The results show that the relative electricity bill reduction of the proposed algorithm over the existing optimization approach is 14%.
关键词： reinforcement learning,smart grid,artificial neural network,smart home,consumer comfort,home energy management system
更新于2025-09-16 10:30:52
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Accounting for the Dependence of Coil Sensitivity on Sample Thickness and Lift-Off in Inductively Coupled Photoconductance Measurements
摘要： This paper presents the results of three control strategies of managed energy services with home energy management system (HEMS)-integrated devices. The HEMS controls and monitors three types of managed devices: 1) heating; 2) task-speci?c; and 3) energy storage devices. Three approaches are proposed for the rolling optimization by the HEMS, namely, mixed integer linear programming (MILP), continuous relaxation (CR), and fuzzy logic controller (FLC). The CR approach is identi?ed to reduce the computational complexity of the MILP by changing the MILP into an LP solution. Three types of FLC control approaches are proposed, namely, heat-related FLC, task-related FLC, and FLC for the battery. Each control strategy is evaluated against cost optimization, computational resource, and practical implementation. The ?ndings in this paper show that all three algorithmic control strategies successfully perform cost optimization, even with inaccurate forecasting information.
关键词： fuzzy logic control (FLC),home energy management system (HEMS),mixed integer linear programming (MILP),Continuous relaxation (CR),residential appliance
更新于2025-09-16 10:30:52
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Return of Interest Planning for Photovoltaics Connected with Energy Storage System by Considering Maximum Power Demand
摘要： In this study, a general building of medium size with an Energy Storage Systems (ESS)-connected Photovoltaic (PV) system (energy storage system that is connected to a photovoltaic system) was chosen to develop a tool for a better economic evaluation of its installation and use. The newly obtained results, from the revised economic evaluation algorithm that was proposed in this study, showed the e?ective return of investment period (ROI) would be 8.62 to 12.77 years. The ratio of maximum power demand to contract demand and the falling cost of PVs and ESS was the factors that could a?ect the ROI. While using the cost scenario of PVs and ESS from 2019 to 2024, as estimated by the experts, the ROI was signi?cantly improved. The ROI was estimated to be between 4.26 to 8.56 years by the year 2024 when the cost scenario was considered. However, this result is obtained by controlling the ratio of maximum power demand to contract demand. Continued favorable government policies concerning renewable energy would be crucial in expanding the supply and investment in renewable energy resources, until the required ROI is attained.
关键词： economic feasibility,maximum power demand per contract demand,ROI,building energy management system (BEMS),ESS connected PV system
更新于2025-09-16 10:30:52
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[IEEE OCEANS 2019 MTS/IEEE SEATTLE - Seattle, WA, USA (2019.10.27-2019.10.31)] OCEANS 2019 MTS/IEEE SEATTLE - Distributed Acoustic Sensing measurement by using seafloor optical fiber cable system off Sanriku for seismic observation
摘要： The energy management system (EMS) at utility control centers collects real-time measurements to monitor current grid conditions. The EMS is also a suite of analytics that synthesizes these measurements to provide the grid operator with information to identify current problems and potential future problems. With evolving grid influences, such as growth of variable renewable generation resources, distributed generation, microgrids, demand response (DR), and customer engagement programs, managing the grid is becoming more challenging. Concurrently, however, there are nascent new technologies and advances in grid management schemes that will improve the ability to manage the future grid operations. These technologies include new subsecond synchrophasor measurements and analytics, advances in high-performance computing, visualization platforms, digital relays, cloud computing, and so on. Advances in grid management schemes include adding more intelligence at the substation and distribution systems, as well as microgrids and wide-area monitoring systems. One key initiative is to develop a predict-and-mitigate paradigm enabling anticipatory vision and timely decisions to mitigate potential problems before they spread to the rest of the grid. The word ‘‘proactive’’ means ‘‘to act now in anticipation of future problems.’’ Proactive grid management opportunities and solutions are described in this paper.
关键词： visualization,simulators,FACTS,phasor measurement units (PMUs),Energy management systems (EMSs),synchrophasors,wide-area monitoring system (WAMS)
更新于2025-09-16 10:30:52
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Optimal Scheduling of Residential Home Appliances by Considering Energy Storage and Stochastically Modelled Photovoltaics in a Grid Exchange Environment Using Hybrid Grey Wolf Genetic Algorithm Optimizer
摘要： The transformation of a conventional power system to a smart grid has been underway over the last few decades. A smart grid provides opportunities to integrate smart homes with renewable energy resources (RERs). Moreover, it encourages the residential consumers to regulate their home energy consumption in an effective way that suits their lifestyle and it also helps to preserve the environment. Keeping in mind the techno-economic reasons for household energy management, active participation of consumers in grid operations is necessary for peak reduction, valley filling, strategic load conservation, and growth. In this context, this paper presents an efficient home energy management system (HEMS) for consumer appliance scheduling in the presence of an energy storage system and photovoltaic generation with the intention to reduce the energy consumption cost determined by the service provider. To study the benefits of a home-to-grid (H2G) energy exchange in HEMS, photovoltaic generation is stochastically modelled by considering an energy storage system. The prime consideration of this paper is to propose a hybrid optimization approach based on heuristic techniques, grey wolf optimization, and a genetic algorithm termed a hybrid grey wolf genetic algorithm to model HEMS for residential consumers with the objectives to reduce energy consumption cost and the peak-to-average ratio. The effectiveness of the proposed scheme is validated through simulations performed for a residential consumer with several domestic appliances and their scheduling preferences by considering real-time pricing and critical peak-pricing tariff signals. Results related to the reduction in the peak-to-average ratio and energy cost demonstrate that the proposed hybrid optimization technique performs well in comparison with different meta-heuristic techniques available in the literature. The findings of the proposed methodology can further be used to calculate the impact of different demand response signals on the operation and reliability of a power system.
关键词： energy storage system,home-to-grid energy exchange,hybrid grey wolf genetic algorithm,home energy management system,photovoltaic generation and smart grid
更新于2025-09-12 10:27:22
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Energy storage and management system design optimization for a photovoltaic integrated low-energy building
摘要： This study aims to analyze and optimize the photovoltaic-battery energy storage (PV-BES) system installed in a low-energy building in China. A novel energy management strategy considering the battery cycling aging, grid relief and local time-of-use pricing is proposed based on TRNSYS. Both single-criterion and multi-criterion optimizations are conducted by comprehensively considering technical, economic and environmental performances of the system based on decision-making strategies including the weighted sum and minimum distance to the utopia point methods. The single-criterion optimizations achieve superior performances in the energy supply, battery storage, utility grid and whole system aspect respectively over the existing scenario of the target building. The multi-criterion optimization considering all performance indicators shows that the PV self-consumption and PV efficiency can be increased by 15.0% and 48.6% while the standard deviation of net grid power, battery cycling aging and CO2 emission can be reduced by 3.4%, 78.5% and 34.7% respectively. The significance and impact of design parameters are further quantified by both local and global sensitivity analyses. This study can provide references for the optimum energy management of PV-BES systems in low-energy buildings and guide the renewable energy and energy storage system design to achieve higher penetration of renewable applications into urban areas.
关键词： Battery energy storage,Energy management,Optimization,Solar photovoltaic,Sensitivity analysis
更新于2025-09-12 10:27:22