[Paper Review] Joint Chance-Constrained Economic Dispatch Involving Joint Optimization of Frequency-related Inverter Control and Regulation Reserve Allocation

Ye Tian, Zhengshuo Li+2 more|arXiv (Cornell University)|Mar 7, 2023

Electric Power System Optimization4 citations

TL;DR

This paper proposes a joint chance-constrained economic dispatch model that co-optimizes frequency-related inverter control (virtual inertia and droop coefficients), regulation reserves, and base-point generation for thermal units, dispatchable inverter-based renewables, and energy storage. By introducing a novel mix-SAA (MSAA) method, the model efficiently handles uncertainty and frequency security constraints, significantly reducing computational burden while ensuring system reliability and minimizing operational costs.

ABSTRACT

The issues of uncertainty and frequency security could become significantly serious in power systems with the high penetration of volatile inverter-based renewables (IBRs). These issues make it necessary to consider the uncertainty and frequency-related constraints in the economic dispatch (ED) programs. However, existing ED studies rarely proactively optimize the control parameters of inverter-based resources related to fast regulation (e.g., virtual inertia and droop coefficients) in cooperation with other dispatchable resources to improve the system frequency security and dispatch reliability. This paper first proposes a joint chance-constrained economic dispatch model that jointly optimizes the frequency-related inverter control, the system up/down reserves, and base-point power for the minimal total operational cost. In the proposed model, multiple dispatchable resources including thermal units, dispatchable IBRs and energy storage are considered, and the (virtual) inertias, the regulation reserve allocations and the base-point power are coordinated. To ensure the system reliability, the joint chance-constraint formulation is also adopted. Additionally, since the traditional sample average approximation (SAA) method cost much computational burden, a novel mix-SAA (MSAA) method is proposed to transform the original intractable model into a linear model that can be efficiently solved via commercial solvers. The case studies validated the satisfactory efficacy of the proposed ED model and the efficiency of the MSAA.

Motivation & Objective

• To address the growing challenges of uncertainty and frequency instability in power systems with high inverter-based renewable (IBR) penetration.
• To integrate frequency-related inverter control parameters (virtual inertia, droop coefficients) into economic dispatch for improved system security.
• To jointly optimize regulation reserves, base-point generation, and inverter control settings under uncertainty.
• To reduce computational complexity in solving chance-constrained ED problems, which are typically intractable with traditional methods.

Proposed method

• Formulates a joint chance-constrained economic dispatch model that incorporates frequency security constraints and uncertainty in renewable generation.
• Co-optimizes three control variables: base-point power of thermal units, regulation reserve allocations, and virtual inertia/droop coefficients of dispatchable IBRs and energy storage.
• Applies a novel mixed-sample average approximation (MSAA) method to transform the non-convex, stochastic problem into a tractable linear program.
• Uses a hybrid sampling strategy in MSAA to balance solution accuracy and computational efficiency, avoiding the high cost of full SAA.
• Implements chance constraints to ensure system reliability under uncertain renewable output, with a specified probability threshold for constraint satisfaction.
• Employs commercial solvers (e.g., Gurobi, CPLEX) to efficiently solve the reformulated linear model.

Experimental results

Research questions

• RQ1 How can inverter control parameters such as virtual inertia and droop coefficients be jointly optimized with regulation reserves and base-point generation to enhance frequency security in high-IBR systems?
• RQ2 What is the impact of integrating frequency-related inverter control into economic dispatch on overall system operational cost and reliability?
• RQ3 Can a mixed-sample average approximation (MSAA) method effectively reduce the computational burden of solving joint chance-constrained economic dispatch problems compared to traditional SAA?
• RQ4 How does the proposed model maintain system reliability under high renewable uncertainty while minimizing total operational cost?
• RQ5 What is the trade-off between computational efficiency and solution accuracy in the proposed MSAA-based approach?

Key findings

• The proposed joint chance-constrained model successfully coordinates virtual inertia, droop coefficients, regulation reserves, and base-point generation, leading to improved frequency security and reduced operational costs.
• The MSAA method significantly reduces computational time compared to traditional sample average approximation (SAA), enabling efficient solution of large-scale stochastic ED problems.
• Case studies demonstrate that the model maintains system reliability with a high probability (e.g., 95% confidence level) under uncertain renewable output.
• The joint optimization of inverter control and reserve allocation leads to a measurable reduction in total operational cost compared to conventional ED approaches.
• The MSAA method achieves solution quality close to full SAA while requiring substantially fewer samples, proving effective for real-time or near-real-time dispatch applications.