TY - JOUR
T1 - Market-Driven Coordination of Price-Responsive Demand Utilizing the Dantzig-Wolfe Decomposition Approach
AU - Ricardo Alves
AU - Bruno Costa
AU - Helena Martins
AU - Nuno Faria
JF - Journal of Smart Grids, Energy Analytics and Optimization
JO - J Smart Grids Energy Anal Optim
Y1 - 2025
VL - 4
IS - 1
SP - 104
N2 - The growing integration of Distributed Generation (DG) and Demand Response (DR) resources into electrical grids has highlighted the need for innovative solutions to coordinate Price-Responsive Devices (PRDs). These devices operate as autonomous entities seeking to adjust their usage schedules in response to current electricity prices to maximize their individual benefits. Here, we introduce an innovative technique grounded in the Dantzig-Wolfe (DW) Decomposition to coordinate these autonomous PRDs within a decentralized framework. By leveraging the inherent parallel structure of DW, we treat the independent optimization routines of the PRDs as separate subproblems. A central entity—typically the utility or system operator—gathers the consumption proposals (bids) from these devices, resolves the master problem in the DW structure, and generates an appropriate price signal in response. This method achieves rapid convergence because the numerous subproblems—potentially numbering in the millions across many PRDs—can be addressed in parallel. Moreover, drawing on DW principles, if the individual device subproblems remain convex, the process converges to the globally optimal solution (corresponding to a Nash Equilibrium) after a modest number of rounds. The effectiveness of this framework was tested through a case study involving 200 participating homes, each with two load categories: Electric Vehicles (EVs), representing shiftable/interruptible loads, and Electric Water Heaters (EWHs), serving as thermostatically controlled appliances (TCLs). Simulations revealed that convergence to the optimum leads to lower overall generation expenses and reduced consumer bills (calculated via marginal generation costs), alongside a substantial drop in the aggregate demand’s Peak-to-Average Ratio (PAR).
UR - https://isespub.com/m503482892
ER -