Two-stage stochastic-robust model for the self-scheduling problem of an aggregator participating in energy and reserve markets

This paper addresses a two-stage stochastic-robust model for the day-ahead self-scheduling problem of an aggrega-tor considering uncertainties.The aggregator,which integrates power and capacity of small-scale prosumers and flex-ible community-owned devices,trades electric energy in the day-ahead(DAM)and real-time energy markets(RTM),and trades reserve capacity and deployment in the reserve capacity(RCM)and reserve deployment markets(RDM).The ability of the aggregator providing reserve service is constrained by the regulations of reserve market rules,including minimum offer/bid size and minimum delivery duration.A combination approach of stochastic program-ming(SP)and robust optimization(RO)is used to model different kinds of uncertainties,including those of market price,power/demand and reserve deployment.The risk management of the aggregator is considered through con-ditional value at risk(CVaR)and fluctuation intervals of the uncertain parameters.Case studies numerically show the economic revenue and the energy-reserve schedule of the aggregator with participation in different markets,reserve regulations,and risk preferences.

Biding strategy of vehicle parking for participating in energy and spinning reserve markets

In this paper a model for suggesting a smart parking that involves a set of electric cars is presented to auction the management ability and correct parking planning in reserve spinning market, secondary energy market and grid. Parking interest under various scenarios is analyzed and its effective results are presented by a valid model. Besides, particle swarm optimization algorithm is used for calculating maximum benefit.