Distribution LMP-based Transactive Day-ahead Market with Variable Renewable Generation
For distribution system operators, this work addresses the challenge of pricing and scheduling under VRE uncertainty, but the approach is incremental as it extends existing DLMP frameworks with a new uncertainty handling method.
The paper proposes a transactive day-ahead market model for distribution systems that determines distribution locational marginal prices (DLMPs) including components for congestion and voltage violations under high variable renewable energy (VRE) penetration. A data-driven probability efficient point method is introduced to handle VRE uncertainties, and simulations on a 69-node system show the impact of peak load, VRE penetration, and battery storage on DLMPs.
The largescale penetration of variable renewable energy (VRE) and their generation uncertainties poses a major challenge for the distribution system operator (DSO) to efficiently determine the day-ahead real and reactive power distribution locational marginal prices (DLMPs) and their underlying components. In this paper, we propose a DLMP-based transactive day-ahead market (DAM) model, that in addition to energy and losses, determines prices for creating congestions and voltage violations under peak-load and large-scale stochastic VRE penetration conditions. To account for the VRE uncertainties and the effect of their large-scale penetration on the DLMP components and distributed energy resources' (DERs) schedules, we propose a novel data-driven probability efficient point (PEP) method that computes the optimal total VRE generation at different confidence (risk) levels to incorporate in the proposed transactive DAM model. We perform a wide range of simulation studies on a modified IEEE 69-node system to validate the proposed methods and demonstrate the effect of peak load conditions, large-scale VRE penetration, and inclusion of battery energy storage systems (BESS) on the resulting positive or negative real and reactive power DLMPs and their components.