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- 摘要
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- 实验方案
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Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources
摘要： Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the incorporation of energy resources (RESs) i.e., solar energy. For this purpose, a real-time electricity price (RTP), energy demand, user preferences and renewable energy parameters are taken as an inputs and genetic algorithm (GA) has been used to manage and schedule residential load with the objective of cost, user discomfort, and peak-to-average ratio (PAR) reduction. Initially, RTP is used to reduce the energy consumption cost. However, to minimize the cost along with reducing the peaks, a combined pricing model, i.e., RTP with inclining block rate (IBR) has been used which incorporates user preferences and RES to optimally schedule load demand. User comfort and cost reduction are contradictory objectives, and difficult to maximize, simultaneously. Considering this trade-off, a combined pricing scheme is modelled in such a way that users are given priority to achieve their objective as per their requirements. To validate and analyze the performance of the proposed algorithm, we first propose mathematical models of all utilized loads, and then multi-objective optimization problem has been formulated. Furthermore, analytical results regarding the objective function and the associated constraints have also been provided to validate simulation results. Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort.
关键词： inclining block rate,real-time pricing,renewable energy sources,appliances scheduling,demand response,genetic algorithm,demand side management
更新于2025-09-23 15:23:52
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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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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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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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Self-emitting blue and red EuOX (X = F, Cl, Br, I) materials: band structure, charge transfer energy, and emission energy
摘要： For microgrids with renewable energy sources, the main goal is to optimize the energy usage in a particular area based on the prediction of energy consumption and production. However, prediction error cannot be evaded and it causes various problems to operate microgrid system. To solve these problems, we are going to propose two-stage energy operation model in a local microgrid system. In addition, by applying cooperative game theory with the Shapley-value algorithm, revenue and payment are determined in real-time period based on the actual contribution of individual prosumer. Numerical anaylsis shows that our suggested approach can reduce peak load more than 16% and reduce the total charge over 5% compared with the case of non-cooperative energy market.
关键词： photovoltaic voltage,Demand side management,Cooperative game theory,Shapley value,Karush Kuhn Tucker conditions
更新于2025-09-19 17:15:36
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[IEEE 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS) - Marrakech, Morocco (2019.10.28-2019.10.30)] 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS) - Feasibility study of Grid connected Photovoltaic system for electricity generation with peak demand deficit
摘要： Due to great interest in the development of renewable energy technologies in Morocco, this paper studied the performance of a solar photovoltaic grid-connected system located in Rabat, Morocco. Firstly a demand side management strategy has been developed to maximize energy efficiency of the system using a load shifting day-ahead approach. In the second part, an examination of resulted load requirement using a simulation model based on HOMER software is carried out. The analysis of the results has shown that the proposed model is an effective and feasible tool of electricity generation.
关键词： Smart Grid,Optimal generation,Load shifting,Renewable energy,Demand Side Management
更新于2025-09-16 10:30:52
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Demand-Side Management of Air-Source Heat Pump and Photovoltaic Systems for Heating Applications in the Italian Context
摘要： Matching demand pro?le and solar irradiance availability is necessary to meet space heating and domestic hot water needs by means of an air-source heat pump and photovoltaic system in a single-family house. Demand-side management, with smart control of the water storage set-point, is a simple but effective technique. Several studies in the literature pursue demand-side matching and self-consumption goals through system adjustments based on the model predictive control. This study proposes a rule-based control strategy, based on instantaneous photovoltaic (PV) power production, with the purpose of enhancing the self-consumption. This strategy exploits the building’s thermal capacitance as a virtual battery, and the thermal storage capacity of the system by running the heat pump to its limit when PV surplus power is available, and by eventually using an electric heater in order to reach higher temperatures. Results of annual dynamic simulations of a building and its heating system show that the proposed rule-based control strategy is able to reduce signi?cantly the energy exchanges between the system and the grid. Despite the enlarged renewable energy share, economic analysis points out the pursuit of the self-consumption goal may lead to a diminution of the economic advantage in the Italian context (Italian weather data and the electric power pricing scheme).
关键词： net-metering,demand-side management,photovoltaic,self-consumption,air-source heat pump
更新于2025-09-11 14:15:04
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[IEEE 2018 53rd International Universities Power Engineering Conference (UPEC) - Glasgow, United Kingdom (2018.9.4-2018.9.7)] 2018 53rd International Universities Power Engineering Conference (UPEC) - Load and PV Generation Forecast Based Cost Optimization for Nanogrids with PV and Battery
摘要： Power system resiliency and robustness became major concerns of the system operators and researchers after the introduction of the smart grid concept. The improvements in the battery storage systems (BSS) and the photovoltaic (PV) systems encourage power systems operators to enable the use of those systems in resiliency and robustness studies. Utilization of those systems not only contributes to the robustness of the power systems but also decrease the operational costs. There are several methods in literature to operate the grid systems with partitions of PV and BSS in the most economical way. Although these methods are straightforward and work fine, they can not guarantee the most economical result on a daily basis. In this paper, deep learning based PV generation and load forecasts are used to improve the results of optimization in terms of economic aspects in nano-grid applications. In the considered system, there are loads, PV generation units, BSS and grid connection. Bi-directional power flow is permitted between the main grid and the nano-grid system. The forecasting methodologies and used optimization algorithms will be explained in this paper.
关键词： demand-side management,smart grids,mathematical programming,recurrent neural networks,forecasting
更新于2025-09-04 15:30:14