Case study on optimization scheme design based on load prediction for regional heating system in industrial community. (July 2018)

• Record Type: Journal Article
• Authors: Li, Hao; Su, Pengwei; Yu, Bo; zhao, Jun; Cheng, Ling; An, Qingsong
• Is Part Of: Energy procedia. Volume 145(2018)
• Page Start: 319
• Page End: 326
• Publication Date: 2018-07
• Subjects: Heating load prediction -- Multi-stage dynamic programming -- Linear programming -- Optimization scheme; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2018.04.058
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: With the promotion of the electrical energy substitution, the electric heating in a larger scale tends to aggravate the peak load of grid. Such challenges could be solved by regenerative electric boiler, due to its advantages of transferring electric load to the valley. Furthermore, it is feasible to reduce the cost by combining the heat pumps and regenerative electric boilers. In order to obtain the optimization scheme of such integrated system, firstly, the heating load prediction model for an industrial community was established based on artificial neural network, and then, a fast-solving method was developed for engineering practice in this case study faced to operation optimization of regional heating system, by converting the multi-stage dynamic programming problem into linear programming problem. The time step of calculation loop is set as one-hour, and the start-stop signal of equipment could be obtained for the next 24 hours no more than one minute. A trade-off between calculation cost and accuracy can be achieved through proposed method or model, with a rapid response in design for such industrial community.