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Role of EVs in the optimal operation of multicarrier energy systems

Ghadertootoonchi, A ; Sharif University of Technology | 2023

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-031-35911-8_4
  3. Publisher: Springer Science and Business Media Deutschland GmbH , 2023
  4. Abstract:
  5. Multicarrier energy systems have recently gained considerable attention as such a concept can address the mutual interrelation between energy carriers. Multicarrier energy systems aim to satisfy the different types of energy demands of a consumer while minimizing economic and environmental costs. Different energy conversion units are used in an energy hub to achieve this goal. One of the newly added components to energy hubs is electric vehicles (EVs). EVs could be regarded as a pure consumer, or a flexibility provider for the energy hub, as their batteries can be utilized to store the electricity and release it whenever needed. Therefore, EVs can help reduce the operational cost of the energy hub and increase its flexibility and reliability. In addition, one of the inherent characteristics of EVs is their uncertainty of behavior. The energy consumption of an electric vehicle depends on various parameters ranging from the weather condition to the drivers’ behavior. One must be aware of these stochasticities and consider their effect on the optimum scheduling of the system to optimize an energy hub. For instance, one way to reduce the degree of uncertainty is to forecast the behavior of the EV using machine learning methods and use the results in the optimization process. In this regard, numerous studies have been conducted to reveal the effects of EV integration into multicarrier energy systems. Each of these studies modeled the EVs in a specific way, either deterministic or stochastic. This chapter aims to provide a comprehensive review of these methods and their related assumptions. Then, at the end of this chapter, a case study will be modeled using the Pyomo optimization package, an open-source Advanced Mathematical Programming Language (AMPL) library in Python, solved using the available open-source solvers such as GLPK, CBC, and HiGHS. The described optimization codes for this chapter are available on the authors’ Gitub at Link. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG
  6. Keywords:
  7. Distribution systems ; Electric vehicle ; Energy hub ; Flexibility ; Integrated energy systems ; Multi-carrier energy study
  8. Source: Green Energy and Technology ; Volume Part F1271 , 2023 , Pages 69-117 ; 18653529 (ISSN)
  9. URL: https://link.springer.com/chapter/10.1007/978-3-031-35911-8_4