将生命周期原则纳入家庭能源管理系统:光伏-电池-电动汽车的最佳负载调度

IF 4.6 4区 化学 Q2 ELECTROCHEMISTRY Batteries Pub Date : 2024-04-19 DOI:10.3390/batteries10040138
Zaid A. Al Muala, Mohammad A. Bany Issa, P. M. Bello Bugallo
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引用次数: 0

摘要

住宅领域的能源管理通过优化使用可再生能源系统(RES)和储能系统(ESS),并根据主电网的状态加以利用,有助于能源系统的调度和安全。这项工作的重点是优化能源流、ESS 参数和基于需求响应 (DR) 计划的能源消耗调度。这项工作的主要目标是最大限度地降低电力成本,同时延长 ESS 的使用寿命,在其整个运行寿命期间获取最大收益,并减少二氧化碳排放。对有效的 ESS 和光伏(PV)能源使用价格进行了建模,并提出了一种有效的能源流管理算法,该算法考虑了 ESS 的生命周期,包括电池、电动汽车(EV)和光伏系统的有效使用,同时降低了能源消耗成本。此外,考虑到安装成本、平准化存储成本(LCOS)、冬季和夏季条件、能源消耗状况和能源价格,还采用了优化技术来获得最佳的 ESS 参数,包括尺寸和放电深度(DOD)。最后,应用优化技术获得最佳能源消耗调度。建议的系统提供了电动汽车、电池、光伏系统、电网和家庭之间能量交换的所有可能性。优化问题使用 MATLAB 中的粒子群优化算法 (PSO) 解决,间隔时间为一分钟。结果显示了所提系统的有效性,与基本方案相比,夏季和冬季的实际成本分别降低了 28.9% 和 17.7%。同样,能源损耗在冬季减少了 26.7%,在夏季减少了 22.3%,电动汽车电池寿命在冬季从 9.2 年延长到 19.1 年,在夏季从 10.4 年延长到 17.7 年。在冬季和夏季情景下,集成系统在运行寿命期间分别提供了 11 600 欧元和 7900 欧元的经济贡献。在夏季和冬季方案中,二氧化碳排放量分别减少了 59.7% 和 46.2%。
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Integrating Life Cycle Principles in Home Energy Management Systems: Optimal Load PV–Battery–Electric Vehicle Scheduling
Energy management in the residential sector contributes to energy system dispatching and security with the optimal use of renewable energy systems (RES) and energy storage systems (ESSs) and by utilizing the main grid based on its state. This work focuses on optimal energy flow, ESS parameters, and energy consumption scheduling based on demand response (DR) programs. The primary goals of the work consist of minimizing electricity costs while simultaneously extending the lifetime of ESSs in conjunction with extracting maximum benefits throughout their operational lifespan and reducing CO2 emissions. Effective ESS and photovoltaic (PV) energy usage prices are modeled and an efficient energy flow management algorithm is presented, which considers the life cycle of the ESSs including batteries, electrical vehicles (EVs) and the efficient use of the PV system while reducing the cost of energy consumption. In addition, an optimization technique is employed to obtain the optimal ESS parameters including the size and depth of discharge (DOD), considering the installation cost, levelized cost of storage (LCOS), winter and summer conditions, energy consumption profile, and energy prices. Finally, an optimization technique is applied to obtain the optimal energy consumption scheduling. The proposed system provides all of the possibilities of exchanging energy between EV, battery, PV system, grid, and home. The optimization problem is solved using the particle swarm optimization algorithm (PSO) in MATLAB with an interval time of one minute. The results show the effectiveness of the proposed system, presenting an actual cost reduction of 28.9% and 17.7% in summer and winter, respectively, compared to a base scenario. Similarly, the energy losses were reduced by 26.7% in winter and 22.3% in summer, and the EV battery lifetime was extended from 9.2 to 19.1 years in the winter scenario and from 10.4 to 17.7 years in the summer scenario. The integrated system provided a financial contribution during the operational lifetime of EUR 11,600 and 7900 in winter and summer scenarios, respectively. The CO2 was reduced by 59.7% and 46.2% in summer and winter scenarios, respectively.
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来源期刊
Batteries
Batteries Energy-Energy Engineering and Power Technology
CiteScore
4.00
自引率
15.00%
发文量
217
审稿时长
7 weeks
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