Multi-Period Optimal Reactive Power Dispatch Using a Mean-Variance Mapping Optimization Algorithm

Abstract

Optimal reactive power dispatch plays a key role in the safe operation of electric power systems. It consists of the optimal management of the reactive power sources within the system, usually with the aim of reducing system power losses. This paper presents both a new model and a solution approach for the multi-period version of the optimal reactive power dispatch. The main feature of a multi-period approach lies on the incorporation of inter-temporal constraints that allow the number of switching operations in transformer taps and capacitor banks to be limited in order to preserve their lifetime and avoid maintenance cost overruns. The main contribution of the paper is the constraint handling approach which consists of a multiplication of sub-functions which act as penalization and allow simultaneous consideration of both the feasibility and optimality of a given candidate solution. The multi-period optimal reactive power dispatch is an inherently nonconvex and nonlinear problem; therefore, it was solved using the metaheuristic mean-variance mapping optimization algorithm. The IEEE 30-bus and IEEE 57-bus test systems were used to validate the model and solution approach. The results allow concluding that the proposed model guarantees an adequate reactive power management that meets the objective of minimizing power losses and keeping the transformer taps and capacitor bank movements within limits that allow guaranteeing their useful life.

Day-Ahead Operational Planning for DisCos Based on Demand Response Flexibility and Volt/Var Control

“…The main data from both systems are available online at [56]. Lastly, the adopted technical and economic specifications for the DOP [25,37,41] are displayed:…”

“…These metaheuristic algorithms are generally inspired by natural or social phenomena. Various optimization techniques, such as genetic algorithms (GA) [22], artificial hummingbird algorithm [23], sine cosine [24], particle swarm optimization (PSO) [20], mean variance mapping optimization [25], and artificial bee colony [26], among others, have been implemented to solve the DOP problem. In [20], the authors integrate flexible load, CBs, and OLTC to minimize system losses and reduce load peaks, considering a maximum number of daily operations for CBs and OLTC in the problem constraints.…”

Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface

“…In this case, v g i is the voltage magnitude of the ith generator; c j is the tap position of capacitor bank j; r l is the tap position of the lth reactor compensator; v g,max i and v g,min i represent the bounds (upper and lower, respectively) related to voltage variables at generation buses; q g,max k and q g,min k represent upper and lower limits of power generation at bus k; c max Note that the injections of reactive power provided by the capacitor banks as well as the absorption of reactive power of the reactors are a function of the tap positions of these devices [22,41]. Furthermore, the tap modeling can be performed as a continuous variable or by means of discrete stages, using the former results in a nonlinear optimization model, while using the later results in a general MINLP problem [42].…”