Reinforcement learning for control of flexibility providers in a residential microgrid - Mbuwir, Brida V; Geysen, Davy; Spiessens, Fred; Deconinck, Geert (IET Smart Grid, 2020)
Keywords: Active control; Algorithms; Alternative energy sources; B0240Z Other topics in statistics; B0290F Interpolation and function approximation (numerical analysis); B8110C Power system control; B8120K Distributed power generation; B8250 Solar power stations and photovoltaic power systems; C1140Z Other topics in statistics; C1340B Multivariable control systems; C3340H Control of electric power systems; C4130 Interpolation and function approximation (numerical analysis); C6170K Knowledge engineering techniques; C7410B Power engineering computing; C7420 Control engineering computing; Case Study; control engineering computing; Controllers; Cooperation; Distributed generation; distributed power generation; Efficiency; Electric power systems; Electricity; Electricity consumption; electricity consumption patterns; Energy management; Energy storage; fitted Q‐iteration; Flexibility; FQI; heat pump; Heat pumps; iterative methods; learning (artificial intelligence); Machine learning; model-based optimal controller; model-free reinforcement learning techniques; multi-agent systems; multiagent collaborative microgrid settings; Performance evaluation; photovoltaic power systems; photovoltaic production; policy iteration; power consumption; power engineering computing; power generation control; power generation scheduling; power system planning; Renewable resources; Residential development; Residential energy; residential microgrid; RL techniques; rule-based controller; single-agent stochastic microgrid settings; Smart grid; smart grid paradigm; smart meters; stochastic processes; System dynamics; Water heaters; iterative methods; multi-agent systems; smart grid paradigm; photovoltaic production; power generation control; distributed power generation; power consumption; power generation scheduling; smart meters; heat pump; multiagent collaborative microgrid settings; learning (artificial intelligence); stochastic processes; control engineering computing; fitted Q-iteration; FQI; power system planning; model-based optimal controller; photovoltaic power systems; model-free reinforcement learning techniques; RL techniques; machine learning; power engineering computing; heat pumps; single-agent stochastic microgrid settings; policy iteration; residential microgrid; electricity consumption patterns; PI; rule-based controller