Abstract Regenerative electric boiler (REB) can convert electricity into heating, improve the grid-connected space of wind power plant (WPP), and is an effective way of solving the severe problem of abandoned wind power in China. To achieve a synergistically coordinated optimization between the supply side and the user side, incentive-based demand response (IBDR) is implemented for decentralized thermal load while price-based demand response (PBDR) is implemented for the electrical load and centralized thermal load. The study then integrates WPP, REB, conventional gas turbine (CGT) and IBDR into a hybrid energy system (HES). Next, the uncertainty scenarios of wind speed are simulated using the Latin hypercube sampling method, and Kantorovich distance based on the values predicted using auto-regressive and moving average mode. Then a multi-objective optimization model for HES operation is proposed based on the objectives of maximizing economic revenue and minimizing the level of risk. Finally, an industrial park in northern China is selected for sample analysis. The results show: (1) if the power output of WPP is converted into heating, the economic benefits increase significantly, with certain risks attached. The REB can store energy during off-peak periods and dispense energy during peak periods. The smooth net power output curve is preferable for decreased operational risk. (2) PBDR can smooth the demand curves of electrical and thermal load. The peak-to-valley ratios of the electrical load and thermal load decrease by 1.207 and 1.500. (3) IBDR can provide more reserve service for a HES while simultaneously satisfying different load demands and yielding optimal operation results with the implementation of PBDR. The peak-to-valley ratios of the net power curves are 1.242 (IBDR alone) and 1.214 (IBDR and PBDR). Overall, the proposed optimization model could give full play to the coupling effect of centralized thermal load and decentralized thermal load, which would provide feasible tools for decision-makers to create an optimal operation plan.