MPC-based interval number optimization for electric water heater scheduling in uncertain environments

In this paper,interval number optimization and model predictive control are proposed to handle the uncertain-but-bounded parameters in electric water heater load scheduling.First of all,interval numbers are used to describe uncertain parameters including hot water demand,ambient temperature,and real-time price of electricity.Moreover,the traditional thermal dynamic model of electric water heater is transformed into an interval number model,based on which,the day-ahead load scheduling problem with uncertain parameters is formulated,and solved by interval number optimization.Different tolerance degrees for constraint violation and temperature preferences are also discussed for giving consumers more choices.Furthermore,the model predictive control which incorporates both forecasts and newly updated information is utilized to make and execute electric water heater load schedules on a rolling basis throughout the day.Simulation results demonstrate that interval number optimization either in day-ahead optimization or model predictive control format is robust to the uncertain hot water demand,ambient temperature,and real-time price of electricity,enabling customers to flexibly adjust electric water heater control strategy.

Optimization Method for Electrical Water Heaters Considering Shifting Potentials of Electricity Consumption and Water-use Activities

Electrical water heaters(EWHs)are important can-didates to provide demand-response services.The traditional optimization method for EWHs focuses on the optimization of the electricity consumption,without considering the shifting potential of the wateruse activities.This paper proposes an optimization method for EWHs considering the shifting potentials of both the electricity consumption and wateruse activities.Con-sidering that the wateruse activities could be monolithically shifted,the shifting model of the water-use activities was developed.In addition to the thermodynamic model of the EWH,the optimal scheduling model of the EWH was developed and solved using mixed-integer linear programming.Case studies were performed on a single EWH and aggregate EWHs,demon-strating that the proposed method can shift the water-use activities and therefore increase the load-shifting potential of the EWHs.

Combined peakreductionandself-consumptionusingproximalpolicy optimisation

Residential demand response programs aim to activate demand flexibility at the household level.In recent years,reinforcement learning(RL)has gained significant attention for these type of applications.A major challenge of RL algorithms is data efficiency.New RL algorithms,such as proximal policy optimisation(PPO),have tried to increase data efficiency.Addi tionally,combining RL with transfer learning has been proposed in an effort to mitigate this challenge.In this work,we further improve upon state-of-the-art transfer learning performance by incorporating demand response domain knowledge into the learning pipeline.We evaluate our approach on a demand response use case where peak shaving and self-consumption is incentivised by means of a capacity tariff.We show our adapted version of PPO,combined with transfer learming,reduces cost by 14.51%compared to a regular hysteresis controller and by 6.68%compared to traditional PPO.