Consensus-Based Approach to Day-Ahead Joint Local Energy-Reserve Market Design Considering Uncertainties of Renewable Energy

The penetration of distributed renewable generationprovides green energy to the local energy system. However, theuncertainties it brings to the system are becoming more andmore unacceptable, making it difficult to efficiently operate thelocal energy system. In a foreseeable future, there could bean increasing number of flexible resources, such as naturalgas power plants and interruptible demands, which providesolutions to these uncertainties. In this paper, a novel day-aheadenergy market design for the local energy system is proposed.This market allows its participants to trade day-ahead energyin a peer-to-peer approach. In addition, flexible resources canprovide up/down-ward reserves to the system through reservetrading, where the local system operator (LSO) takes chargeof reserve procurement. Then, to operate the market, a marketclearing model is proposed incorporating operating costs of theday-ahead and real time stages. A consensus-based approach isapplied which enables the market to clear in a decentralizedmethod. Finally, since the LSO bears the reserve procurementcosts as well as the operating costs in the real time stage,we then propose a cost reallocation model to transfer theexpenses to the renewables. Case studies demonstrate the validityand efficiency of the proposed market design, including localenergy trading facilitation, high and stable revenue preservation,high computation efficiency and prevention of strategic biddingbehaviors.

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.