Optimal load scheduling of energy hub under integrated demand response

With the development of the Energy Internet and the increasing demand for comprehensive energy services, the coordinated and efficient use of electricity, gas, cold, and heat has garnered much attention. Scientific and reasonable optimization of the load in the multi-energy system can realize coordination and complementation of different energy types and achieve energy conservation and cost reduction. Existing studies on optimal load scheduling of multi-energy systems mainly focus on the coordinated scheduling among different energy sources, with less attention paid to load transfer and reduction, as well as the inconvenience of users participating in demand response, which is not conducive to maximizing the potential of demand response in multi-energy systems. Consequently, the main research contents and contributions of this study are as follows: First, the general modeling method of energy hub was proposed, describing the load demand, input and output of energy conversion equipment, charging and discharging of energy storage devices and demand response of the energy hub. Second, the optimal load scheduling model of energy hub with high coupling of electricity, gas, cold, and heat energy was constructed, including combined heat and power ( CHP) unit, gas boiler, electric boiler, absorption chiller, electric chiller, electricity storage device, thermal storage device, and wind turbine. The proposed model considers the time-of-use compensation price of integrated demand response and the inconvenience that integrated demand response brings to the user. The proposed energy hub optimal load scheduling model aims at minimizing the total economic, carbon tax, and demand response implementation costs. Meanwhile, the constraints of power balance, energy storage devices, demand response, and operating power of energy conversion devices are considered. Third, in the experiment, based on whether to use energy storage devices and consider user participation in demand response, four kinds of simulation scheduling experiment scenarios were designed to explore the impact of energy storage devices and demand response on scheduling results. Fourth, the optimization model proposed in this paper is a mixed integer linear programming problem. An efficient optimization solver Gurobi was used to solve the problem, and the global optimal solution was obtained. Simultaneously, the hourly scheduling strategy of electricity, heat, and cold loads was calculated, including the output strategy of production devices and the charging and discharging strategy of energy storage devices. For two scenarios considering the user′s participation in the demand response, the changes before and after the demand response of electricity, heat, and cooling loads were calculated. Finally, the sensitivity analysis was conducted for the parameters of energy conversion devices and energy storage devices in the energy hub, and the influence of 5% variation of each parameter on the total target cost was analyzed to demonstrate the model′s stability. Based on the above analysis, we found that (1) Considering the multi-energy cooperative scheduling, the proposed model dispatches the shiftable and reducible electricity, heat, and cold loads. It can improve the level of multi-energy complementarity in Energy Internet environment and enhance the economy and reliability level of energy hub. In the simulation example, the total target cost is reduced by 27. 65%, carbon emissions are reduced by 22. 76%, and the peak electrical load is reduced by 17. 06%. (2) Energy storage devices can supplement the surplus capacity during low-load to high-load periods to reduce the energy cost and load peak. Therefore, during energy hub planning and construction, the economy and stability of energy system operation can be improved by installing energy storage devices with appropriate capacity. (3) Under the incentive of demand response, users take the initiative to reduce and transfer the load demands in the period of high electricity prices to further reduce the economic cost of energy hub. Accordingly, management decision-makers can effectively reduce the total costs of energy hub systems through demand response. (4) CHP unit and electric chiller device are more economical and environmentally friendly. In the optimal scheduling strategy, they work at maximum power most of the time in the scheduling process, while electric boiler and absorption chiller devices are less used. Therefore, management decision-makers can identify more economical and efficient devices in the energy hub to increase their utilization level, while uneconomical devices can be employed as supplementary or only for emergency use. Concurrently, economical and efficient devices should be selected in energy hub planning and construction.