[Paper Review] An Adjustable Chance-Constrained Approach for Flexible Ramping Capacity Allocation

Zhiwen Wang, Chen Shen+3 more|arXiv (Cornell University)|Aug 1, 2017

Electric Power System Optimization37 references3 citations

TL;DR

This paper proposes an adjustable chance-constrained optimization approach to allocate flexible ramping capacity (FRC) in power systems with high wind penetration. By introducing conditional distributions of wind ramping based on forecast errors and formulating a tractable reformulation of the chance-constrained model, the method balances operational risk and FRC cost, demonstrating effectiveness and computational efficiency in a modified IEEE 118-bus system test case.

ABSTRACT

With the fast growth of wind power penetration, power systems need additional flexibility to cope with wind power ramping. Several electricity markets have established requirements for flexible ramping capacity (FRC) reserves. This paper addresses two crucial issues that have rarely been discussed in the literature: 1) how to characterize wind power ramping under different forecast values and 2) how to achieve a reasonable trade-off between operational risks and FRC costs. Regarding the first issue, this paper proposes a concept of conditional distributions of wind power ramping, which is empirically verified by using simulation and real-world data. For the second issue, this paper develops an adjustable chance-constrained approach to optimally allocate FRC reserves. Equivalent tractable forms of the original problem are devised to improve computational efficiency. Tests carried out on a modified IEEE 118-bus system demonstrate the effectiveness and efficiency of the proposed method.

Motivation & Objective

• To address the lack of systematic characterization of wind power ramping under varying forecast conditions.
• To develop a method that optimally balances operational risk and flexible ramping capacity (FRC) costs in power systems.
• To improve computational efficiency in FRC allocation through tractable reformulations of chance-constrained optimization problems.
• To validate the proposed approach using real-world and simulated wind data in a standard test system.

Proposed method

• Introduces conditional distributions of wind power ramping as a function of forecast errors, empirically validated using simulation and real data.
• Develops an adjustable chance-constrained programming framework to model uncertainty in wind ramping while allowing risk tolerance to be adjusted.
• Derives equivalent convex reformulations of the original non-convex chance-constrained problem to enhance computational tractability.
• Applies the reformulated model to a modified IEEE 118-bus system to evaluate performance and scalability.
• Uses historical and simulated wind data to calibrate ramping distribution models and assess risk exposure under different forecast scenarios.

Experimental results

Research questions

• RQ1 How can wind power ramping be effectively characterized under different forecast conditions?
• RQ2 What is the optimal trade-off between operational risk and FRC cost in power systems with high wind penetration?
• RQ3 How can chance-constrained optimization be made computationally tractable for large-scale FRC allocation problems?
• RQ4 To what extent does the proposed method improve system reliability and cost efficiency compared to conventional approaches?

Key findings

• The proposed conditional distribution model of wind ramping accurately captures forecast-dependent variability, validated with real-world and simulated data.
• The adjustable chance-constrained approach enables systematic risk management by allowing decision-makers to tune risk tolerance levels.
• The reformulated optimization model achieves significant computational efficiency gains compared to the original non-convex formulation.
• Test results on the IEEE 118-bus system confirm the method's effectiveness in reducing FRC costs while maintaining acceptable risk levels.