Self-learning demand side management for a heterogeneous cluster of devices with binary control actions

Demand side management in a day-ahead wholesale market: A comparison of industrial & social welfare approaches

“…Researches on DSM have addressed the minimization of energy consumption, maximization of customer utility, the minimization of customer discomfort, the stabilization of electricity prices, and multi-objective optimizations from the customer side [33][34][35][36][37][38][39][40]. In addition, there have also been studies on the integration of DSM and renewable uncertainty [41], centralized or distributed demand control algorithms [14,30,[42][43][44][45][46], demand-side storage [47,48], models of customer behavior [49], and prediction of DSM participation potential [50][51][52].…”

LINEAR breakthrough project: Large-scale implementation of smart grid technologies in distribution grids

“…In addition to these coordination mechanisms, the scheduling of a fleet electric vehicles is modeled as a stochastic optimization problem in [31]. A selflearning algorithm for performing demand side management on a more general cluster of devices was developed in [32].…”

Model-free control of thermostatically controlled loads connected to a district heating network

“…Heat storage however, can provide demand flexibility enabling flexibility at the production side through demand response approaches. This flexibility allows operational opportunities for cost reduction, examples being peak shaving/valley filling [5] and energy arbitrage by selling the electricity production of the CHP on the wholesale market [1,2,6]. Well referenced embodiments of local heat storage are Thermostatically Controlled Loads (TCLs) [7] such as a hot water storage tank [8] where the heat is stored directly in the water, but also the building envelope [1,7,9,10] 1 Bert J. Claessens is currently working at REstore and can be contacted at bert.claessens@restore.eu can be used to store heat.…”