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

doi:10.35833/MPCE.2020.000824

Home > Archive>Volume 9, Issue 4, 2021 >734-750. DOI:10.35833/MPCE.2020.000824

PDF HTML Export

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

DOI:

10.35833/MPCE.2020.000824

Author:

Affiliation:

Fund Project:

This work was supported in part by the National Key R&D Program of Chi‐ na (No. 2020YFE0200400) and the National Nature Science Foundation of Chi‐ na (No. 51907140).

Article

Figures

Metrics

Reference

Cited by

Materials

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.

表 6 Table 6

表 1 Table 1

表 7 Table 7

表 13 Table 13

表 4 Table 4

表 8 Table 8

表 10 Table 10

表 11 Table 11

表 3 Table 3

图1 Flowchart of proposed model.Fig.1

图2 Modified IEEE 30-bus system.Fig.2

图3 Forecasting values of electrical load as well as wind and solar generation.Fig.3

图4 Comparison of different power flow models in Case 1.Fig.4

图5 Unit commitment solution of Case 2. (a) Unit commitment with overvoltage risks. (b) Unit commitment with voltage limits.Fig.5

图6 Total cost.Fig.6

图7 Spillage of wind and solar generation at different penetration levels.Fig.7

图8 Severity indices of overvoltage for Case 3.Fig.8

图9 Hourly dispatches of H1 and H2.Fig.9

图10 Comparison of power flow from bus 6 to bus 9.Fig.10

图11 Spillage of wind and solar generation as well as LOL in Case 4.Fig.11

图12 Convergence comparison.Fig.12

图 Linearization of quadratic branch flow limits.Fig.

图1 Modeling process of IMEUS.Fig.1

图2 Temporal correlation and conditional correlation of wind power forecasting. (a) Temporal correlation coefficient of forecasting error of wind power. (b) Conditional correlation between forecasting error and forecasting value of wind power.Fig.2