Optimal Electrical-Thermal Scheduling of Virtual Power Plant with Probabilistic Approach, Comprehensive Demand Response and Risk Modeling

Subject Areas : Energy management

Fateme Fattahi Ardakani ¹ , Seyed Babak Mozafari ² , Soodabeh Soleymani Morchehkhorti ³

1 - Department of Electrical Engineering (Power and Control), Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Department of Electrical Engineering (Power and Control), Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Department of Electrical Engineering (Power and Control), Science and Research Branch, Islamic Azad University, Tehran, Iran

Received: 2023-10-31 Accepted : 2024-04-21 Published : 2025-12-22

Keywords: comprehensive demand response, risk, robust model, stochastic, virtual power plant .,

Abstract :

Virtual power plant is a novel approach for the integrated management of conventional and renewable units in order to meet the demand and benefit from participation in the electricity market. In this paper, an optimal model for day ahead scheduling of a virtual power plant is presented. In the present study, distributed renewable generation energy sources, fossil units and electric vehicles are planned in such a way that supplying electrical and thermal loads and maximum profit are achieved. Uncertainties related to wind and solar energy production, load uncertainty and market price are considered in a scenario-based approach. After generating scenarios using the probability density function of random parameters, linear mixed integer programming is used for scenario reduction and scenarios with higher probability are selected for optimal planning. The problem’s model is non-linear intrinsically but the problem is modeled as a linear problem by using appropriate linear models for conventional generators and electric vehicle’s charge profiles and a scenario-based model. Electricity and heat production and photovoltaic-thermal production units are planned simultaneously and optimally to supply thermal and electrical loads. Also, a comprehensive electrical and thermal demand response is provided to improve the system's performance. System risk is modeled by considering an additional uncertainty for the objective function as a robust model. The proposed model is implemented for the modified 33-bus IEEE distribution system for selected scenarios and the results are compared. The results show the proposed model has improved the system’s load factor and profit and reduced load curtailment. Also, the amount of the system's profit is obtained with a lower risk by considering the larger robustness coefficient.

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