Decentralized scheduling strategy of heating systems for balancing the residual load

A Decentralized Framework for the Optimal Coordination of Distributed Energy Resources

“…Alternatively, Dantzig-Wolfe (DW) decomposition-based approaches were investigated in [30]- [33]. In [30] and [31], DW decomposition is applied to demand-response problems related to peak-load management and, in [32], to the charging of electric vehicles.…”

“…However, only a few types of devices were considered in these works, with no discrete variables involved. Finally, a column generation-based heuristic is used in [33] to schedule residential heating systems. Nevertheless, this heuristic approach does not provide a guarantee of optimality.…”

Demand side management for city districts

“…The MILP optimization problems are NP-hard, consequently with an increasing number of considered subsystems, conventional solvers are not able to solve this problem in a reasonable time. Decomposition techniques such as Lagrangian relaxation (LR) [37], Dantzig-Wolfe decomposition [38] or a hybrid MILP-metaheuristic [39] are able to overcome this obstacle but may suffer from numerical convergence problems. Metaheuristic methods have been heavily investigated and implemented based on algorithms such as particle swarm optimization [40], tabu search and evolutionary algorithms [41].…”

Demand response through decentralized optimization in residential areas with wind and photovoltaics

“…We found several DO approaches for demand response in the literature. Commonly used techniques are decomposition methods [12][13][14][15]. A single optimization problem is broken down into multiple smaller optimization problems when using decomposition approaches.…”