Robust Stochastic Optimization for Energy Sharing between Multi-Carrier Microgrids using Transactive Energy Management System
Conference: ETG-Kongress 2021 - ETG-Fachtagung
03/18/2021 - 03/19/2021 at Online
Proceedings: ETG-Fb. 163: ETG-Kongress 2021
Pages: 6Language: englishTyp: PDF
Authors:
Zare Oskouei, Morteza (Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran)
Teimourzadeh Baboli, Payam (R&D Energy Division, OFFIS – Institute for IT, Oldenburg, Germany)
Babazadeh, Davood (Institute of Elec. Power & Energy Tech., Hamburg University of Technology, Hamburg, Germany)
Abstract:
Recently, multi-carrier microgrids (MCMGs) have attracted more attention as they provide practical solutions for economical operation and optimal configuration of power systems. MCMGs operators have been looking for economic strategies to deal with the existing challenges in the various energy markets. Therefore, this paper investigates the economic opportunity to minimize the operating cost of each MCMG in the competitive energy markets using transactive energy management (TEM) mechanism as a pioneering energy sharing method. Based on the proposed strategy, all MCMGs can exchange energy with each other to take advantage of existing opportunities in energy markets by reducing the dependency on the main energy networks. Moreover, the role of effective energy conversion facilities, e.g. combined heat and power (CHP) unit, tri-state compressed air energy storage (CAES) system, and photovoltaic (PV) system, as an environmental-friendly and sustainable source of energy, is considered in promoting the economic performance of MCMGs. To perform more comprehensive analysis, the effect of the uncertainty associated with the electricity market price, energy demands, and output power of PV systems on the expected cost is investigated using the robust-stochastic approach. The proposed strategy is formulated as a mixed-integer linear programming (MIP) model and solved using the CPLEX solver in GAMS software. The performance of the proposed strategy is demonstrated through a set of scenarios using various MCMGs.