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Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads

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  • Saberi-Beglar, Kasra
  • Zare, Kazem
  • Seyedi, Heresh
  • Marzband, Mousa
  • Nojavan, Sayyad

Abstract

Climate change has detrimental impacts on the environment and sustainability, leading to employing alternative energy systems such as combined cooling, heat and power (CCHP). The concept of integrated energy systems (IES) allows the coordination of several components, such as electric vehicles (EV), to serve various demands simultaneously. This paper focuses on coordinating the CCHP and electric vehicle parking lot (EVPL) integrated with photovoltaic (PV) technology as renewable energy (RE). The residential energy hub (REH) is modeled to integrate these components to meet the demands and minimize REH’s operating costs and carbon emissions. EVPL functions as dynamic electrical storage besides serving EVs. Stochastic programming is used to model RE, EV, loads, and electricity price uncertainties. Demand response (DR) is applied for shiftable electrical loads. The thermodynamic model of heating and cooling loads is developed with flexibility as integrated demand response (IDR) based on the building’s desired temperature. The emission cost model with penalty factors enforces REH to use less-pollutant energy sources. Subsequently, a risk-aversion strategy, namely downside risk constraint (DRC), is implemented to diminish the associated risk as the consequence of uncertainties for the decision-maker. Different constraint level is applied to provide various conservative decision-making strategies for the operator. Summer and winter scenarios with and without DR and flexible thermal loads were used to evaluate the model’s accuracy. The scheduling problem is solved in IEEE 33-bus test system. The results reveal that the DR could reduce the operation cost by 5% in summer and 8% in winter. Moreover, zero risk for summer and winter is gained at the cost of 10.4% and 3.1% increment in operating costs.

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  • Saberi-Beglar, Kasra & Zare, Kazem & Seyedi, Heresh & Marzband, Mousa & Nojavan, Sayyad, 2023. "Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads," Applied Energy, Elsevier, vol. 329(C).
    • Handle: RePEc:eee:appene:v:329:y:2023:i:c:s0306261922015227
    DOI: 10.1016/j.apenergy.2022.120265
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    2. Zhang, Hui & Wang, Jiye & Zhao, Xiongwen & Yang, Jingqi & Bu sinnah, Zainab Ali, 2023. "Modeling a hydrogen-based sustainable multi-carrier energy system using a multi-objective optimization considering embedded joint chance constraints," Energy, Elsevier, vol. 278(C).
    3. Pinciroli, Luca & Baraldi, Piero & Compare, Michele & Zio, Enrico, 2023. "Optimal operation and maintenance of energy storage systems in grid-connected microgrids by deep reinforcement learning," Applied Energy, Elsevier, vol. 352(C).
    4. Song, Yuguang & Xia, Mingchao & Chen, Qifang, 2023. "The robust synchronization control scheme for flexible resources considering the stochastic and delay response process," Applied Energy, Elsevier, vol. 343(C).
    5. Yang, Lijun & Jiang, Yaning & Chong, Zhenxiao, 2023. "Optimal scheduling of electro-thermal system considering refined demand response and source-load-storage cooperative hydrogen production," Renewable Energy, Elsevier, vol. 215(C).
    6. Kamran Taghizad-Tavana & As’ad Alizadeh & Mohsen Ghanbari-Ghalehjoughi & Sayyad Nojavan, 2023. "A Comprehensive Review of Electric Vehicles in Energy Systems: Integration with Renewable Energy Sources, Charging Levels, Different Types, and Standards," Energies, MDPI, vol. 16(2), pages 1-23, January.

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