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A Data-Driven Pool Strategy for Price-Makers Under Imperfect Information

Kedi Zheng, Hongye Guo, Qixin Chen

Abstract

This paper studies the pool strategy for price-makers under imperfect information. In this occasion, market participants cannot obtain essential transmission parameters of the power system. Thus, price-makers should estimate the market results with respect to their offer curves using available historical information. The linear programming model of economic dispatch is analyzed with the theory of rim multi-parametric linear programming (rim-MPLP). The characteristics of system patterns (combinations of status flags for generating units and transmission lines) are revealed. A multi-class classification model based on support vector machine (SVM) is trained to map the offer curves to system patterns, which is then integrated into the decision framework of the price-maker. The performance of the proposed method is validated on the IEEE 30-bus system, Illinois synthetic 200-bus system, and South Carolina synthetic 500-bus system.

A Data-Driven Pool Strategy for Price-Makers Under Imperfect Information

Abstract

This paper studies the pool strategy for price-makers under imperfect information. In this occasion, market participants cannot obtain essential transmission parameters of the power system. Thus, price-makers should estimate the market results with respect to their offer curves using available historical information. The linear programming model of economic dispatch is analyzed with the theory of rim multi-parametric linear programming (rim-MPLP). The characteristics of system patterns (combinations of status flags for generating units and transmission lines) are revealed. A multi-class classification model based on support vector machine (SVM) is trained to map the offer curves to system patterns, which is then integrated into the decision framework of the price-maker. The performance of the proposed method is validated on the IEEE 30-bus system, Illinois synthetic 200-bus system, and South Carolina synthetic 500-bus system.

Paper Structure

This paper contains 19 sections, 2 theorems, 32 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Rim-MPLP Analysis for OPF
  3. Learning System Patterns
  4. Learning Probabilities of CRs
  5. Learning of Parametric Functions
  6. Optimization Framework
  7. GenCo's Optimization Problem
  8. Discussion on Available Information
  9. The Overall Framework
  10. Complexity Analysis
  11. Case Study
  12. Comparisons
  13. Classification Results
  14. Bid Results in IEEE 30-Bus System
  15. Bid Results in Illinois 200-Bus System
  16. ...and 4 more sections

Key Result

Proposition 1

The feasible parameter space $\mathcal{C}\times \mathcal{L}$ for (equ:mplp) is covered by convex polytopes, a.k.a. critical regions $\mathcal{R}_k$, $k=1,2,\cdots,K$. (i) Within each critical region, the system primal and dual optimal variable solutions can be expressed as linear-affine functions of

Figures (5)

  • Figure 1: An example of CRs in a small-sized OPF case.
  • Figure 2: Framework of the proposed method.
  • Figure 3: Confusion matrix for the 30-bus block-form lossless case.
  • Figure 5: An example on the change of objective function values in gradient descent.
  • Figure : Axis: M denotes the MPEC method; R denotes the RDC-like method.

Theorems & Definitions (4)

  • Proposition 1
  • proof
  • Proposition 2
  • proof