A multiagent stochastic bi-level model for optimal integration of distributed generators

• Published in: Electric power systems research Vol. 213; p. 108707
• Main Authors: Marquez, Jorge Luis Angarita, Mokryani, Geev, Duque, Carlos
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2022
• Subjects: Coordinated planning; Distributed generation; Multi-agent; Optimal regulation; Stochastic Bi-level programming; Optimal regulation; Multi-agent; Coordinated planning; Distributed generation; Stochastic Bi-level programming
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2022.108707

•A bi-level model is proposed to foster the integration of Distributed Generation in distribution systems.•The volatility of the market price is modeled using the discrete probability density function.•A coordinated planning is proposed to optimize the performance of the system considering all stakeholders.•A robust model of tariff, distribution network and distributed generation is proposed in a robust mix-integer linear model. This paper aims to find an optimal economic signal to foster Distributed Generator (DG) owners to improve the performance of Distribution System. The proposed model considers all agents involved including Distribution Network Operators (DNOs), retailers, consumer, and prosumers. The relationship between all agents, the impact in the tariff and the Policy Maker (PM) role is described and modelled. The specific incentive studied here is the price paid to DG owners for the energy delivered to the network. A coordinated planning approach (CPA) is proposed to find the optimal size and location of DGs in order to minimize the total cost of distribution system using a stochastic bi-level mix-integer linear programming model subject to network constraints taking into account the volatility of the market price. A realistic case study (16-bus UK generic medium voltage distribution system) is used to demonstrate the effectiveness of the proposed method. Additionally, this paper explores the impact of DG integration on network cost and DG energy price on customers’ and prosumers’ payments, modeling tariff capacity and energy charges. The results show that optimal integration of DG can improve the performance of the distribution system up to 15% and reduce consumers’ payments by 13%.