Locational Marginal Pricing Mechanism for Uncertainty Management Based on Improved Multi-ellipsoidal Uncertainty Set

Abstract

The large-scale integration of renewable energy sources (RESs) brings huge challenges to the power system. A cost-effective reserve deployment and uncertainty pricing mechanism are critical to deal with the uncertainty and variability of RES. To this end, this paper proposes a novel locational marginal pricing mechanism in day-ahead market for managing uncertainties from RES. Firstly, an improved multi-ellipsoidal uncertainty set (IMEUS) considering the temporal correlation and conditional correlation of wind power forecasting is formulated to better capture the uncertainty of wind power. The dimension of each ellipsoidal subset is optimized based on a comprehensive evaluation index to reduce the invalid region without large loss of modeling accuracy, so as to reduce the conservatism. Then, an IMEUS-based robust unit commitment (RUC) model and a robust economic dispatch (RED) model are established for the day-ahead market clearing. Both the reserve cost and ramping constraints are considered in the overall dispatch process. Furthermore, based on the Langrangian function of the RED model, a new locational marginal pricing mechanism is developed. The uncertainty locational marginal price (ULMP) is introduced to charge the RES for its uncertainties and reward the generators who provide reserve to mitigate uncertainties. The new pricing mechanism can provide effective price signals to incentivize the uncertainty management in the day-ahead market. Finally, the effectiveness of the proposed mechanism is verified via numerous simulations on the PJM 5-bus system and IEEE 118-bus system.

Distribution Locational Marginal Pricing Based Equilibrium Optimization Strategy for Data Center Park with Spatial-temporal Demand-side Resources

“…where M j is large enough for constraint (10) and constraint (11) to be relaxed when u Pnet jth = 0 and u Pnet jth = 1, respectively, e.g., M j = P net j max . Then, (6) becomes:…”

“…The DLMPs, including the marginal electric power price, marginal loss price, marginal voltage support price, and marginal congestion price, can promote end-users to manage the demandside resources to reduce electricity costs. However, to make the problem easy to solve, many studies use direct current optimal power flow (DCOPF) and power transfer distribution factor to calculate DLMP [11], [12], which carries great errors because line reactance cannot be ignored in the distribution network.…”

Revisit power system dispatch: Concepts, models, and solutions

“…The common used methods to build the uncertainty sets in the robust dispatch models include box intervals [174] , polyhedral sets [175] , ellipsoidal sets [176] , and discrete sets [177] .…”

Robust optimisation of electricity‐heating integrated energy system based on data‐driven PV interval estimation

“…The box uncertainty set is based on the point prediction with a fluctuation range of 15% of the predicted value [37]. Based on the historical forecast deviation, the maximum fluctuation deviation of loads is determined to be 10% [38], and the uncertainty adjustment parameter Γ PV = 144, Γ L = 108 are set to regulate the conservatism. The day-ahead dispatching costs of IES are shown in Table 7.…”