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In order to meet the user’s electricity demand and make full use of distributed energy, a hybrid energy system (HES) was proposed and designed, including wind turbines (WTs), photovoltaic (PV) power generation, conventional gas turbines (CGTs), incentive-based demand response (IBDR), combined heat and power (CHP) and regenerative electric (RE) boilers. Then, the collaborative operation problem of HES is discussed. First, the paper describes the HES’ basic structure and presents the output model of power sources and heating sources. Next, the maximum operating income and minimum load fluctuation are taken as the objective function, and a multi-objective model of HES scheduling is proposed. Then an algorithm for solving the model is proposed that comprises two steps: processing the objective functions and constraints into linear equations and determining the optimal weight of the objective functions. The selected simulation system is a microgrid located on an eastern island of China to comparatively analyze the influence of RE-heating storage (RE-HS) and price-based demand response (PBDR) on HES operation in relation to four cases. By analyzing the results, the following three conclusions are drawn: (1) HES can comprehensively utilize a variety of distributed energy sources to meet load demand. In particular, RE technology can convert the abandoned energy of WT and PV into heat during the valley load time, to meet the load demand combined with CHP; (2) The proposed multi-objective scheduling model of HES operation not only considers the maximum operating income but also considers the minimum load fluctuation, thus achieving the optimal balancing operation; (3) RE-HS and PBDR have a synergistic optimization effect, and when RE-HS and PBDR are both applied, an HES can achieve optimal operation results. Overall, the proposed decision method is highly effective and applicable, and decision makers could utilize this method to design an optimal HES operation strategy according to their own actual conditions.

energies

A Multi-objective Scheduling Optimization Model for Hybrid Energy System Connected with Wind-Photovoltaic-Conventional Gas Turbines, CHP Considering Heating Storage Mechanism