- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) - Aalborg, Denmark (2018.10.29-2018.10.31)] 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) - Distributed Cooperative Energy Management in Smart Microgrids with Solar Energy Prediction
摘要： Smart Microgrid (SMG), integrated with renewable energy, energy storage system and advanced bidirectional communication network, has been envisioned to improve efficiency and reliability of power delivery. However, the stochastic nature of renewable energy and privacy concerns due to intensive bidirectional data exchange make the traditional energy management system (EMS) perform poorly. In order to improve operational efficiency and customers’ satisfaction, we propose a distributed cooperative energy management system (DCEMS). We adopt recurrent neural network with long short-term memory to predict the solar energy generation with high accuracy. We then solve the underlying economic dispatch problem with distributed scalable Alternating Direction Method of Multipliers (ADMM) algorithm to avoid single point of failure problem and preserve customers’ privacy. In the first stage, each SMG optimizes its operation decision vector in a centralized manner based on one-day ahead solar energy generation prediction. In the second stage, all SMGs share their energy exchange information with directly connected neighboring SMGs to cooperatively optimize the global operation cost. The proposed DCEMS is deployed in our distributed SMGs emulation platform and its performance is compared with other approaches. The results show that the proposed DCEMS outperforms heuristic rule-based EMS by more than 30%. It can also protect customers’ privacy and avoid single point of failure without degrading performance too much compared to centralized EMS.
关键词： Information prediction,Microgrid emulation platform,Distributed algorithms,Energy management system,Demand-side management
更新于2025-09-23 15:22:29
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Design, Simulation and Fabrication of a High Gain Low Sidelobe Level Waveguide Slot Array Antenna at X-band with Zero Beam Tilts in Both Azimuth and Elevation Directions
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2025-09-23 15:21:01
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Smart charging of electric vehicles considering photovoltaic power production and electricity consumption: a review
摘要： Photovoltaics (PV) and electric vehicles (EVs) are two emerging technologies often considered as cornerstones in the energy and transportation systems of future sustainable cities. They both have to be integrated into the power systems and be operated together with already existing loads and generators and, often, into buildings, where they potentially impact the overall energy performance of the buildings. Thus, a high penetration of both PV and EVs poses new challenges for cities. With a potentially large increase in PV and EV penetration, understanding of the synergies between PV, EVs and existing electricity consumption is required. Thus, a high penetration of both PV and EVs poses new challenges. Understanding of the synergies between PV, EVs and existing electricity consumption is therefore required. Recent research has shown that smart charging of EVs could improve the synergy between PV, EVs and electricity consumption, leading to both technical and economic advantages. Considering the growing interest in this field, this review paper summarizes state-of-the-art studies of smart charging considering PV power production and electricity consumption. The main aspects of smart charging reviewed are objectives, configurations, algorithms and mathematical models. In order to achieve certain objectives, smart charging schemes can be based on optimization or rule based algorithms. The smart charging schemes also vary in terms of control configuration, i.e., centralized and distributed, and depend on spatial configuration, i.e., houses, workplaces and charging stations. Various charging objectives, such as increasing PV utilization and reducing peak loads and charging cost, are reviewed in this paper. The different charging control configurations, i.e., centralized and distributed, along with various spatial configurations, e.g., houses and workplaces, are also discussed. After that, the commonly employed optimization techniques and rule-based algorithms for smart charging are reviewed. Further research should focus on finding optimal trade-offs between simplicity and performance of smart charging schemes in terms of control configuration, charging algorithms, as well as the inclusion of PV power and load forecast in order to make the schemes suitable for practical implementations.
关键词： electric vehicles,Photovoltaics,energy management system,smart charging,charging optimization,electricity consumption
更新于2025-09-23 15:21:01
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A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas
摘要： This paper proposes a novel photovoltaic-pumped hydro storage microgrid design, which is more cost-effective than photovoltaic-battery systems. Existing irrigation infrastructure is modified in order to store energy at a low cost. This energy storage system pumps water from the bottom of a water well to a reservoir at ground level to store surplus energy in the form of gravitational potential energy. This stored water can be released back to the well through a turbine to generate clean electricity when it is needed, or it can be used for irrigation. This microgrid needs a complex management system that takes into account energy generation, energy demand, water demand, energy tariff, and system losses to determine pump power, turbine flow rate, as well as irrigation times. The proposed energy management system considers the current and future state of the system and compares cost-saving and feed-in income for each decision by using two forecasting methods and a multi-level optimisation algorithm. The performance of the management system is experimentally verified on a real pump and turbine. The objective of this study is not only to manage pump power and turbine flow rate, but also to manage irrigation times and water volume. The results show that adding irrigation and water management assist the energy management system in using stored water more efficiently. As a result, electricity costs are reduced by more than 31% compared to existing management methods. The proposed system is simulated in MATLAB to calculate annual electricity costs. The payback period and lifetime benefit of the proposed storage are calculated to investigate the economic aspects of the system.
关键词： Pumped hydro storage system,Microgrid,Energy management system,Energy storage system,Solar photovoltaic system,Renewable energy
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE International Conference on Engineering Veracruz (ICEV) - Boca del Rio, Veracruz, Mexico (2019.10.14-2019.10.17)] 2019 IEEE International Conference on Engineering Veracruz (ICEV) - Comparative of wind systems vs photovoltaic for the implementation in the electric network of Veracruz Port
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： mixed integer linear programming (MILP),fuzzy logic control (FLC),Continuous relaxation (CR),residential appliance,home energy management system (HEMS)
更新于2025-09-19 17:13:59
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[IEEE 2019 Photonics North (PN) - Quebec City, QC, Canada (2019.5.21-2019.5.23)] 2019 Photonics North (PN) - Sapphire fiber Bragg grating coupled with graded-index fiber lens
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： mixed integer linear programming (MILP),fuzzy logic control (FLC),Continuous relaxation (CR),residential appliance,home energy management system (HEMS)
更新于2025-09-19 17:13:59
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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 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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[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