The uncertainties related to renewable energy resources (RERs) and energy markets have a direct impact on the scheduling of hybrid microgrids and stand as a challenge in renewable-based systems. Due to this, many mathematical methods are employed for modeling uncertainties, but applying a suitable approach is important for reaching accurate results. This paper presents the chance-constrained programming technique to model the fluctuations in renewable outputs and electricity market prices to effectively deliberate the probabilistic nature of them. In this respect, the transactive energy concept is used to provide the energy sharing possibility for hybrid microgrids with a high portion of renewables for clean electricity generation. The interaction between the electrical and gas network as a result of using the gas-fired devices in each microgrid, is also included in this study. To test the effectiveness of the suggested framework, the IEEE-10 bus case study with five commercial hybrid microgrids is selected and the scheduling of microgrids is carried out in the interconnected electricity and gas networks. The different impacts of the proposed method are analyzed by considering two cases: optimal scheduling of hybrid microgrids without uncertainty modeling (Case I) and with it (Case II). In Case I and II, numerical results indicated $22 378.067 expected operation cost for Case II in comparison with $26 014.359 for Case I, which proves the effectiveness of the proposed model in probabilistic modeling of the system as well as achieving the economic benefits for hybrid microgrids. Highlights: Optimal chance-constrained DA scheduling of hybrid microgrids is effectively conducted. The CCP method is used for probabilistic evaluation of the problem in the presence of RERs. Transactive energy technology is employed for managing energy trading in the system. LHS and FFS methods are applied for scenario generation and reduction, respectively. The interactions between electricity and gas networks are effectively modeled.
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment
- Nuclear Energy and Engineering
- Fuel Technology
- Energy Engineering and Power Technology