A Causation-Based Framework for Pricing and Cost Allocation of Energy, Reserves, and Transmission in Modern Power Systems

ArXiv ID: 2505.24159 “View on arXiv”

Authors: Luiza Ribeiro, Alexandre Street, Jose Manuel Arroyo, Rodrigo Moreno

Abstract

The increasing vulnerability of power systems has heightened the need for operating reserves to manage contingencies such as generator outages, line failures, and sudden load variations. Unlike energy costs, driven by consumer demand, operating reserve costs arise from addressing the most critical credible contingencies - prompting the question: how should these costs be allocated through efficient pricing mechanisms? As an alternative to previously reported schemes, this paper presents a new causation-based pricing framework for electricity markets based on contingency-constrained energy and reserve scheduling models. Major salient features include a novel security charge mechanism along with the explicit definition of prices for up-spinning reserves, down-spinning reserves, and transmission services. These features ensure more comprehensive and efficient cost-reflective market operations. Moreover, the proposed nodal pricing scheme yields revenue adequacy and neutrality while promoting reliability incentives for generators based on the cost-causation principle. An additional salient aspect of the proposed framework is the economic incentive for transmission assets, which are remunerated based on their use to deliver energy and reserves across all contingency states. Numerical results from two case studies illustrate the performance of the proposed pricing scheme.

Keywords: Contingency-Constrained Scheduling, Nodal Pricing, Operating Reserves, Electricity Markets, Cost Allocation, Commodities (Electricity)

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper relies heavily on advanced mathematical optimization, Lagrangian duality, and game-theoretic concepts (like the nucleolus) for its pricing framework, indicating high math complexity. However, it presents only theoretical models and two numerical case studies without any real-world data, backtests, or implementation details, resulting in low empirical rigor.

  flowchart TD
    A["Research Goal: Develop cost-reflective<br>pricing for energy, reserves, and<br>transmission based on causation"] --> B["Key Methodology:<br>Contingency-Constrained Scheduling"]
    
    B --> C{"Inputs/Data"}
    C --> C1["Generator Cost Curves"]
    C --> C2["Transmission Constraints"]
    C --> C3["Credible Contingencies"]
    
    C --> D["Computational Process:<br>Security-Constrained Optimization"]
    
    D --> E["Key Findings/Outcomes"]
    E --> E1["Nodal Pricing<br>Revenue Neutral"]
    E --> E2["Security Charge Mechanism<br>for Reserves"]
    E --> E3["Transmission Remuneration<br>Based on Use Across States"]