GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

A framework for stochastic estimation of electric vehicle charging behavior for risk assessment of distribution networks

Power systems are being transformed to enhance the sustainability.This paper contributes to the knowledge regarding the operational process of future power networks by developing a realistic and stochastic charging model of electric vehicles(EVs).Large-scale integration of EVs into residential distribution networks(RDNs)is an evolving issue of paramount significance for utility operators.Unbalanced voltages prevent effective and reliable operation of RDNs.Diversified EV loads require a stochastic approach to predict EVs charging demand,consequently,a probabilistic model is developed to account several realistic aspects comprising charging time,battery capacity,driving mileage,state-of-charge,traveling frequency,charging power,and time-of-use mechanism under peak and off-peak charging strategies.An attempt is made to examine risks associated with RDNs by applying a stochastic model of EVs charging pattern.The output of EV stochastic model obtained from Monte-Carlo simulations is utilized to evaluate the power quality parameters of RDNs.The equipment capability of RDNs must be evaluated to determine the potential overloads.Performance specifications of RDNs including voltage unbalance factor,voltage behavior,domestic transformer limits and feeder losses are assessed in context to EV charging scenarios with various charging power levels at different penetration levels.Moreover,the impact assessment of EVs on RDNs is found to majorly rely on the type and location of a power network.