A Time-Power Series Based Semi-Analytical Approach for Power System Simulation

TL;DR

This paper introduces a semi-analytical power series method for power system simulation that enables efficient online dynamic security assessment by approximating differential equations with power series and adaptive error control.

Contribution

It presents a novel power series-based semi-analytical approach for power system simulation, including an error bound and a dynamic bus method for general DAEs, enhancing online simulation capabilities.

Findings

Effective in 39-bus and 2383-bus systems

Potential for real-time online simulation

Outperforms some existing methods in accuracy and efficiency

Abstract

Time domain simulation is the basis of dynamic security assessment for power systems. Traditionally, numerical integration methods are adopted by simulation software to solve nonlinear power system differential-algebraic equations about any given contingency under a specific operating condition. An alternative approach promising for online simulation is to offline derive a semi-analytical solution (SAS) and then online evaluate the SAS over consecutive time windows regarding the operating condition and contingency until obtaining the simulation result over a desired period. This paper proposes a general semi-analytical approach that derives and evaluates an SAS in the form of power series in time to approximate the solutions of power system differential equations. An error-rate upper bound of the SAS is also proposed to guarantee the reliable use of adaptive time windows for evaluation of the SAS. A dynamic bus method is proposed to extend the semi-analytical approach for solving general power system DAEs by efficiently linking the SASs for dynamic components through the numerical solution of the network algebraic equations. Case studies performed on the New England 39-bus system and the Polish 2383-bus system test the performance of the proposed semi-analytical approach and compare to existing methods. The results show that the SAS based approach has potentials for online simulations.