Decision making of a virtual power plant under uncertainties for bidding in a day-ahead market using point estimate method

Environmental concerns, improvements in renewable energy technologies, governmental incentives for the use of these resources, and increased T&D costs, are the main factors driving the energy sector into a new era, where considerable portions of electrical demand will be met through widespread installation of Distributed Energy Resources (DERs). The Virtual Power Plant (VPP) is a decentralized energy management system tasked to aggregate the capacity of some Distributed Generations (DGs), storage facilities, and Dispatchable Loads (DLs) for the purpose of energy trading and/or providing system support services. Due to the stochastic behavior of the prime sources of some DGs, such as wind speed and temperature, the steady state analysis of the systems with integration of such DG units requires a probabilistic approach. In this paper, a probabilistic Price Based Unit Commitment (PBUC) approach using Point Estimate Method (PEM) is employed to model the uncertainty in market price and generation sources, for optimal bidding of a VPP in a day-ahead electricity market. Also, the uncertainty of stochastic DGs generations is handled through increasing the amount of required reserve. The proposed model allows a VPP to decide on the unit commitment of its DERs, and the optimal sale/purchase bids to the day-ahead market. The proposed optimization algorithm is applied to an 18-buses system.

► We propose a methodology based on probabilistic PBUC for VPP bidding in energy market.
► Possibility of exchanging energy with upstream network via three GSPs is considered.
► The results is analyzed for three cases; with and without observing uncertainties.
► The local operation of DERs will be highly sensitive to the market price variations.
► The choice of the adequate reserve management strategy is crucial for VPP success.