- 标题
- 摘要
- 关键词
- 实验方案
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[IEEE 2018 IEEE Power & Energy Society General Meeting (PESGM) - Portland, OR, USA (2018.8.5-2018.8.10)] 2018 IEEE Power & Energy Society General Meeting (PESGM) - Study of Impact of Cloud Distribution on Multiple Interconnected Solar PV Plants Generation and System Strength
摘要： Dependence of solar power generation on solar irradiance results in sudden and dramatic variations in power generation following significant changes in cloud distribution over a solar PV plant. Currently, this phenomenon is being one of the most challenging issues in resource planning and maintaining the reliability of modern power grids with high penetration of solar power. The dramatic variation of solar power generation has a direct impact on system strength at the Points of Interconnection (POIs). Hence, the power quality of the system is compromised, especially because solar PV plants are usually interconnected to distribution systems and near load zones. In this paper, an Artificial Neural Network (ANN) based approach is developed to forecast the clouds distribution for the estimation of sudden and dramatic variations in the solar irradiance. This estimate is used to evaluate the system strength in terms of voltage stability at each POI. We apply newly developed methodology to measure the system strength known as Site-Dependent Short Circuit Ratio (SDSCR), which provides more accurate results of system strength evaluation. The validity and effectiveness of the developed approach is confirmed through comparing its results versus the cloud distribution data provided by weather satellites.
关键词： Artificial neural network,renewable energy,system strength,voltage stability,short circuit ratio
更新于2025-09-23 15:22:29
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Technological development of multi-energy complementary system based on solar PVs and MGT
摘要： The complementary micro-energy network system consisting of solar photovoltaic power generation (solar PVs) and micro-gas turbine (MGT), which not only improves the absorption rate and reliability of photovoltaic power, but also has the advantages of low emission, high efficiency, and good fuel adaptability, has become one of the most promising distributed power systems in the field of micro grid. According to the development of current technology and the demand of actual work, this research described the domestic and foreign development of micro-energy network system based on solar PVs and MGT. Moreover, it analyzed the challenges and future development regarding the micro-energy network system in planning and design, energy utilization optimization and dispatching management, and system maintenance, respectively. Furthermore, it predicted the future development of the key technology of the multi-energy complementary system. These results will be beneficial for the progress of this field both in theory and practice.
关键词： solar photovoltaic power generation,micro gas turbine,micro-energy network,multi-energy complementary system,renewable energy
更新于2025-09-23 15:21:21
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Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Sams??
摘要： This paper focuses on the Model Predictive Control (MPC) based energy scheduling of a smart microgrid equipped with non-controllable (i.e., with fixed power profile) and controllable (i.e., with flexible and programmable operation) electrical appliances, as well as photovoltaic (PV) panels, and a battery energy storage system (BESS). The proposed control strategy aims at a simultaneous optimal planning of the controllable loads, the shared resources (i.e., the storage system charge/discharge and renewable energy usage), and the energy exchange with the grid. The control scheme relies on an iterative finite horizon on-line optimization, implementing a mixed integer linear programming energy scheduling algorithm to maximize the self-supply with solar energy and/or minimize the daily cost of energy bought from the grid under time-varying energy pricing. At each time step, the resulting optimization problem is solved providing the optimal operations of controllable loads, the optimal amount of energy to be bought/sold from/to the grid, and the optimal charging/discharging profile for the BESS. The proposed energy scheduling approach is applied to the demand side management control of the marina of Ballen, Sams? (Denmark), where a smart microgrid is currently being implemented as a demonstrator in the Horizon2020 European research project SMILE. Simulations considering the marina electric consumption (340 boat sockets, a service building equipped with a sauna and a wastewater pumping station, and the harbour master’s office equipped with a heat pump), PV production (60kWp), and the BESS (237 kWh capacity) based on a public real dataset are carried out on a one year time series with a 1 h resolution. Simulations indicate that the proposed approach allows 90% exploitation of the production of the PV plant. Furthermore, results are compared to a na?ve control approach. The MPC based energy scheduling improves the self-supply by 1.6% compared to the na?ve control. Optimization of the business economy using the MPC approach, instead, yields to 8.2% savings in the yearly energy cost with respect to the na?ve approach.
关键词： Energy management,Renewable energy,On-line scheduling,Microgrid,Optimization algorithm,Demand side management,Model predictive control,Energy storage
更新于2025-09-23 15:21:01
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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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Optimal Operation Control of Microgrid Connected Photovoltaic-Diesel Generator Backup System Under Time of Use Tariff
摘要： With the exponentially increasing demand of electrical energy, many developing countries are struggling to provide electricity to the end-users. This challenge has mainly strained traditional power systems. To mitigate against this strain, governments are diversifying and liberalizing the energy market to meet future energy demand. In addition, end-users have several alternatives to reduce electricity cost at demand side. This can be realized either by using ef?cient devices or incorporating renewable energy sources while scheduling their powers economically. Within this framework, microgrids are considered as ef?cient power systems to exploit renewable energy sources with demand side management program. Nowadays, microgrids which can harness photovoltaic solar source at low cost are becoming an attractive option for reduction in electricity cost at demand side. To ensure uninterrupted power supply, diesel generators are often used as backup energy systems in most large-scale or industrial applications in Kenya. Therefore, this paper proposes a constrained optimal operation control strategy of microgrid connected photovoltaic with diesel generator backup system related to commercial and industrial (C&I) setups in Kenya. Particularly, the objective function simultaneously aims at reducing energy purchased from utility grid and the fuel consumption cost of the conventional diesel generator. The constraints related to control variables are taken in the context of C&I in Kenya. The optimal operation control is carried out using FMINCON interior-point algorithm, and two scenarios are analysed. The ?rst scenario is carried out by considering the intermittent mode from 7:00 hrs to 18:00 hrs, while the second scenario is considered in the intermittent connected mode. A case study is done based on the daily load pro?le of the Engineering workshops at Jomo Kenyatta University of Agriculture and Technology (JKUAT) located at ?1.099? latitude and 37.014? longitude. The optimal operation control has shown great bene?ts in terms of energy saving, cost saving as well as daily revenue. The daily energy saving is increased up to 52.1%, the daily cost saving is 20%, and daily energy sold is found to be 142.4 kWh which can generate daily income of $17.
关键词： Renewable energy,Levelized cost of energy,Optimization algorithm,Demand side management,Open-loop optimal control
更新于2025-09-23 15:21:01
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Optimal Energy Management in a Standalone Microgrid, with Photovoltaic Generation, Short-Term Storage, and Hydrogen Production
摘要： This paper addresses the energy management of a standalone renewable energy system. The system is configured as a microgrid, including photovoltaic generation, a lead-acid battery as a short term energy storage system, hydrogen production, and several loads. In this microgrid, an energy management strategy has been incorporated that pursues several objectives. On the one hand, it aims to minimize the amount of energy cycled in the battery, in order to reduce the associated losses and battery size. On the other hand, it seeks to take advantage of the long-term surplus energy, producing hydrogen and extracting it from the system, to be used in a fuel cell hybrid electric vehicle. A crucial factor in this approach is to accommodate the energy consumption to the energy demand and to achieve this, a model predictive control (MPC) scheme is proposed. In this context, proper models for solar estimation, hydrogen production, and battery energy storage will be presented. Moreover, the controller is capable of advancing or delaying the deferrable loads from its prescheduled time. As a result, a stable and efficient supply with a relatively small battery is obtained. Finally, the proposed control scheme has been validated on a real case scenario.
关键词： model predictive control,standalone renewable energy systems,hydrogen,fuel cells,solar photovoltaic energy,deferrable loads,demand side management
更新于2025-09-23 15:21:01
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Evaluation of electrical efficiency of photovoltaic thermal solar collector
摘要： In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.
关键词： hybrid machine learning model,Renewable energy,photovoltaic-thermal (PV/T),least square support vector machine (LSSVM),adaptive neuro-fuzzy inference system (ANFIS),neural networks (NNs)
更新于2025-09-23 15:21:01
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Performance evaluation of a MPPT controller with model predictive control for a photovoltaic system
摘要： Efficiency has been a major factor in the growth of photovoltaic (PV) systems. Different control techniques have been explored to extract maximum power from PV systems under varying environmental conditions. This paper evaluates the performance of a new improved control technique known as model predictive control (MPC) in power extraction from PV systems. Exploiting the ability of MPC to predict future state of controlled variables, MPC has been implemented for tacking of maximum power point (MPP) of a PV system. Application of MPC for maximum power point tracking (MPPT) has been found to result into faster tracking of MPP under continuously varying atmospheric conditions providing an efficient system. It helps in reducing unwanted oscillations with an increase in tracking speed. A detailed step by step process of designing a model predictive controller has been discussed. Here, MPC has been applied in conjunction with conventional perturb and observe (P&O) method for controlling the dc-dc boost converter switching, harvesting maximum power from a PV array. The results of MPC controller has been compared with two widely used conventional methods of MPPT, viz. incremental conductance method and P&O method. The MPC controller scheme has been designed, implemented and tested in MATLAB/Simulink environment and has also been experimentally validated using a laboratory prototype of a PV system.
关键词： maximum power point tracking (MPPT),prediction model,Model predictive control (MPC),cost function,photovoltaic (PV),renewable energy
更新于2025-09-23 15:21:01
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Regional band-gap tailoring of 1550nm-band InAs quantum dot Intermixing by controlling ion implantation depth
摘要： This paper studies the electric vehicle (EV) charging scheduling problem to match the stochastic wind power. Besides considering the optimality of the expected charging cost, the proposed model innovatively incorporates the matching degree between wind power and EV charging load into the objective function. Fully taking into account the uncertainty and dynamics in wind energy supply and EV charging demand, this stochastic and multistage matching is formulated as a Markov decision process. In order to enhance the computational efficiency, the effort is made in two aspects. Firstly, the problem size is reduced by aggregating EVs according to their remaining parking time. The charging scheduling is carried out on the level of aggregators and the optimality of the original problem is proved to be preserved. Secondly, the simulation-based policy improvement method is developed to obtain an improved charging policy from the base policy. The validation of the proposed model, scalability, and computational efficiency of the proposed methods are systematically investigated via numerical experiments.
关键词： smart grid,simulation-based policy improvement (SBPI),renewable energy,Electric vehicle (EV)
更新于2025-09-23 15:21:01
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Analysis of the Power Quality of a Grid-Connected Photovoltaic System
摘要： The use of Grid-Connected Photovoltaic Systems is increasingly on the rise in Brazil. Faced with this growth, it is necessary to evaluate the impacts of this source in the electric energy systems. Based on this scenario, the present work aims to analyze and quantify the impact of a grid-connected photovoltaic system connection, evaluating the Electric Power Quality indicators based on the levels specified in Module 8 of the Electric Energy Distribution Procedures in the National Electric System. To perform the data collection, an energy analyzer device was installed at the output of the grid-connected system inverter located in the Energy Laboratory of the Federal Rural Semi-Arid University, located in the city of Mossoró-RN. Using the collected data, it was possible to analyze parameters of voltage and current distortion, power factor, active, reactive and apparent power, voltage, frequency variations, and voltage unbalance. It was observed that the criteria analyzed were within the appropriate standards although there were also verified cases that there was an elevation in tension levels. Finally, it is concluded that the impacts caused are relevant within the electric system, and grid connected system performance was satisfactory, although there are still possibilities for improvements.
关键词： Power Quality,Photovoltaic System,Grid-Connected,Renewable Energy,Distributed Generation
更新于2025-09-23 15:21:01