Nowadays, to address the different demands of consumers, i.e., electricity, heat and gas, the concept of energy hub (EH) is emerged. Integrating multiple energy sources under the concept of energy ...hub can improve the efficiency and reliability of the system; moreover, efficiency increasing cause to decrease in environmental emission. Environmental emission is a very important issue from the global warming point of view that should be considered in the researches. In this regard, green hydrogen can be used as a clean energy carrier that can be stored for a long time without degradation. To this end, this paper focuses on the concept of hydrogen-based smart micro energy hub (SMEH) considering integrated demand response (IDR) and fuel cell-based hydrogen storage system (HSS). IDR is introduced to control consumers’ electrical and heat demand patterns. Additionally, HSS not only can convert power from renewable energy sources (RESs) to hydrogen (P2H) in a low electricity price period and vice versa (H2P) in a high electricity price period but also can supply the hydrogen-based industry. The aim of the proposed model is to minimize the total energy cost of hydrogen-based energy hub integrated with IDR and HSS under a robust optimization (RO) approach considering the uncertainty of electricity price. The proposed optimization model provides day-ahead scheduling for SMEH, which would improve the operation of the energy system. Finally, simulation and numerical results are provided to confirm the effectiveness of the proposed model. According to the obtained results, considered technologies cause to decrease in operation cost up to 7.8% and improve the system robustness against electricity market uncertainty up to 30%.
Coupling different energy infrastructures, i.e. the concept of energy hub (EH), is an efficient approach to the optimal operation of both electrical and natural gas systems. This paper optimizes the ...risk-constrained scheduling of a wind-integrated smart multi-carrier energy hub (SMEH) and evaluates its operation in combination with compressed air energy storage (CAES) system, an electrical demand response (EDR) program, and a thermal demand response (TDR) program. The proposed SMEH consists of combined heat and power (CHP) units, a CAES system, a thermal storage system, boiler units, and an electrical heat pump (EHP) system. The penetration of wind power generation and application of the CAES system make a dependable condition to the optimal scheduling of the SMEH. The wind turbine generation and electrical and thermal demands are modeled as a scenario-based stochastic problem using the Monte Carlo simulation method. A proper scenario-reduction algorithm is also used to reduce the computational burden. Moreover, the conditional value-at-risk (CVaR) algorithm is merged with the proposed model to propitiate the risk of the high costs relevant to worst scenarios as a proper risk evaluation method. Finally, the proposed system is applied to a studied case to demonstrate the applicability and appropriateness of the proposed method.
•The CVaR method is implemented to model the potential risk of SMEH scheduling cost.•EDR and TDR programs are applied to reduce the SMEH operation cost.•CAES is considered as a solution to reduce the volatility problem of wind generation.•CAES provides more freedom in decision making for optimal scheduling of the SMEH.•A stochastic model is used to model the uncertain wind generation and demands.
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