Dynamic pricing for responsive demand to increase distribution network efficiency. (1st November 2017)

• Record Type: Journal Article
• Authors: Gu, Chenghong; Yan, Xiaohe; Yan, Zhang; Li, Furong
• Is Part Of: Applied energy. Volume 205(2017)
• Page Start: 236
• Page End: 243
• Publication Date: 2017-11-01
• Subjects: Network congestion -- Investment -- Demand response -- Tariff -- Load curtailment -- Generation curtailment; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.07.102
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Relates system operation to investment varying at different time granularity. Quantifies the cost according to customer contribution to network congestion. Introduces a dynamic pricing scheme to reflect demand response on the two costs. Translates customer impact on networks into effective economic signals. Abstract: This paper designs a novel dynamic tariff scheme for demand response (DR) by considering networks costs through balancing the trade-off between network investment costs and congestion costs. The objective is to actively engage customers in network planning and operation for reducing network costs and finally their electricity bills. System congestion costs are quantified according to generation and load curtailment by assessing their contribution to network congestion. Plus, network investment cost is quantified through examining the needed investment for resolving system congestion. Customers located at various might face the same energy signals but they are differentiated by network cost signals. Once customers conduct DR during system congested periods, the smaller savings from investment and congestion cost are considered as the economic singles for rewarding the response. The innovation is that the method translates network congestion/investment costs into tariffs, where current research is mainly focused on linking customer response to energy prices. A typical UK distribution network is utilised to illustrate the new approach and results show that derived economic signals can effectively benefit end customers for reducing system congestion costs and deferring required network investment.