Market-based coordination of price-responsive demand using Dantzig-Wolfe decomposition method

作     者：Foad Najafi Matthias Fripp 

作者机构：Department of Electrical EngineeringUniversity of Hawaii at Manoa2540 Dole StreetHonoluluHawaiiUnited States 

出 版 物：《Energy and AI》 (能源与人工智能(英文))

年 卷 期：2023年第14卷第4期

页      面：329-340页

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Ulupono Initiative and Blue Planet Foundation 

主　　题：Demand-side management(DSM) Distributed optimization Dantzig-Wolfe algorithm Price responsive devices(PRD) Appliance coordination 

摘      要：The increasing penetration of Distributed Generation(DG)and Demand Responsive(DR)loads in power systems has necessitated the development of novel approaches to address the coordination problem of Price Responsive Devices(PRD).These PRDs are treated as self-interested players aiming to optimize their consumption patterns based on prevailing *** this paper,we propose a new algorithm based on the Dantzig-Wolfe(DW)Decomposition method,which tackles the coordination problem of self-interested PRDs in a distributed *** the distributed nature of the DW approach,we model the self-interested algorithms of PRDs as sub-problems within the DW *** coordinator,or grid operator,responsible for collecting the energy consumption information(energy bids)of PRDs,solves the master problem of the DW and determines the price signal *** proposed algorithm exhibits fast convergence as the sub-problems within DW,which could involve a large number of PRDs(potentially millions),can be solved ***,based on the DW theory,if the PRDs’subproblems are convex,reaching the optimal point(equivalent to Nash Equilibrium)is guaranteed within a limited number of *** evaluate the proposed model,we conducted a simulation involving 200 participant households,each equipped with two types of loads:Electric Vehicles(EVs)as examples of inter-ruptible loads,and Electric Water Heaters(EWHs)as examples of Thermostatically Controlled Loads(TCLs).The results demonstrate that when the algorithm converges to the optimal point,both the generation cost and user payment(based on the marginal cost of generation)***,there is a significant reduction in the Peak to Average Ratio(PAR)of the aggregate load.