Optimal integration of CCHP with electric Vehicle parking lots in energy hub

灵活性(工程) 汽车工程 可再生能源 储能 光伏系统 电势能 能量(信号处理) 能源消耗 电动汽车 能源会计 热能 工程类 功率(物理) 电气工程 统计 物理 数学 量子力学
作者
Ting Han,Yalin Yan,Benjamin Safar
出处
期刊:Sustainable Energy Technologies and Assessments [Elsevier]
卷期号:58: 103324-103324
标识
DOI:10.1016/j.seta.2023.103324
摘要

Nowadays, due to environmental problems and the lack of fossil fuels, the use of Electric Vehicles (EVs) is extended. These systems need optimal planning and management, like all energy tools. With another look at electrical generation systems, the combined energy production units have also increased dramatically, with only one kind of fuel, it is possible to produce any kind of energy such as electrical, thermal, and cooling energies at the same time. This production type is called the energy hub. The main goal of creating an energy hub is to meet several types of demand at the same time and to increase the flexibility of the system. The Combined Cooling, Heat, and Power (CCHP) unit, which has a higher efficiency ratio to traditional systems, could be referred to as the main component of the energy hub. To reduce the losses of these systems, their planning could be implemented in coordination with the EV parking lot. The energy hub system could provide the mentioned conditions in the presence of renewable energy sources and energy storage units in a coordinated and optimal way. In this paper, the presence of an EV parking lot in an energy hub is investigated that includes a CCHP, a photovoltaic unit in the presence of demand response programs, and its effects on optimizing power consumption to reduce cost. The demand response programs are used to reduce the operating costs of the energy hub. Moreover, the uncertainties of EV consist of entry and exit times and charge level, solar irradiation, and loads value are applied in the model. Finally, the cost of risk by the conditional value–at–risk method is investigated. The simulations are done by determining 3 various case studies for better investigation of the proposed model. The results show that the proposed solution method of the demand response program could decrease the total cost of the energy hub by about 1.90 % in both summer and winter, under risk-averse conditions.
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