Robust optimal scheduling of CHP-based microgrids in presence of wind and photovoltaic generation units: An IGDT approach. (March 2022)

• Record Type: Journal Article
• Authors: Komeili, Madad; Nazarian, Peyman; Safari, Amin; Moradlou, Majid
• Is Part Of: Sustainable cities and society. Volume 78(2022)
• Publication Date: 2022-03
• Subjects: Energy management -- CHP-based microgrid -- Day-ahead scheduling -- IGDT approach; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2021.103566
• Languages: English
• ISSNs: 2210-6707

Abstract

Abstract: Application of combined heat and power (CHP) units beside the distributed energy resources (DERs) persuades the power systems via the formation of multi-carrier microgrids (MGs). This paper presents the day-ahead scheduling of multi-carrier MGs. Renewable distributed generators are known as undeniable parts of modern power systems, which may increase the chanciness of the system. Information gap decision theory (IGDT) is applied to address the uncertainties of renewable sources. Moreover, a scenario-based stochastic approach is used to model the uncertainty of electricity prices in this method. Indeed a hybrid stochastic-IGDT-based optimization problem is proposed for optimal energy management of multi-carrier MGs. According to this method, the operation of multi-carrier MGs would be robust against uncertainties and contain a minimum profit. The proposed problem has been formulated as mixed-integer linear programming (MILP). Finally, the proposed energy management has been implemented into a sample multi-carrier microgrid. The results showed that how the MG operator's risk-averse character can change her/his decision-making. Highlights: Robust Optimal Scheduling of CHP-based Microgrids. Proposing an IGDT-based approach to handle uncertainties of renewable generations. Proposing a single level model for the IGDT method to obtain a global optimum. Considering demand response programs for both thermal and electrical loads.