Multi-period power loss optimization with limited number of switching actions for enhanced continuous power supply

Ionescu, Cristiana; Bulac, Constantin; Capitanescu, Florin; Wehenkel, Louis

doi:10.1109/ichqp.2014.6842830

Cited by 8 publications

(11 citation statements)

References 21 publications

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“…Moreover, research [19] puts forward a novel particle swarm optimization algorithm with quantum behavior (QPSO) to solve reactive power optimization in power system with distributed generation. Ionescu et al [20] presented a mixed integer hybrid differential evolution method to handle multiperiod active power loss minimization. They formulated the task as a mixed-integer nonlinear programming (MINLP) problem, including constraints that specifically limit the number of switching actions between two successive anticipated system states.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Interior Points. Equations (20) and 21are both deterministic nonlinear programming problems. Firstly, the discrete control variables such as switched capacitor/reactors and onload taps changers are relaxed as continuous variables.…”

Section: Relaxed Dynamic Reactive Power Optimization Model Formentioning

confidence: 99%

“…And then the restrictions on the number of times of reactive power control equipment are relaxed, which means we do not consider the limit of the times of actions firstly. The optimal solution obtained by solving the relaxation model is the lower bound of the optimal solution of the original reactive power optimization model shown by (20) and (21). Relaxation model is as follows:…”

Section: Relaxed Dynamic Reactive Power Optimization Model Formentioning

confidence: 99%

“…Variables. The solution of the relaxation model is − − and + − , which are the lower bounds of the original problems (20) and (21). Based on the solution of the relaxation model, a model is established for each discrete device that minimizes the amount of collation error as the objective function with constraints of time coupling.…”

Section: Loss In Increments As Small As the Target Of Discretementioning

confidence: 99%

“…Based on the solution of the relaxation model, a model is established for each discrete device that minimizes the amount of collation error as the objective function with constraints of time coupling. The variables discretization model of problems (20) and 21is essentially a mixed integer quadratic programming:…”

Section: Loss In Increments As Small As the Target Of Discretementioning

confidence: 99%

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A Multistage Solution Approach for Dynamic Reactive Power Optimization Based on Interval Uncertainty

Shen

Liu

2018

Mathematical Problems in Engineering

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In order to solve the uncertainty and randomness of the output of the renewable energy resources and the load fluctuations in the reactive power optimization, this paper presents a novel approach focusing on dealing with the issues aforementioned in dynamic reactive power optimization (DRPO). The DRPO with large amounts of renewable resources can be presented by two determinate large-scale mixed integer nonlinear nonconvex programming problems using the theory of direct interval matching and the selection of the extreme value intervals. However, it has been admitted that the large-scale mixed integer nonlinear nonconvex programming is quite difficult to solve. Therefore, in order to simplify the solution, the heuristic search and variable correction approaches are employed to relax the nonconvex power flow equations to obtain a mixed integer quadratic programming model which can be solved using software packages such as CPLEX and GUROBI. The ultimate solution and the performance of the presented approach are compared to the traditional methods based on the evaluations using IEEE 14-, 118-, and 300-bus systems. The experimental results show the effectiveness of the presented approach, which potentially can be a significant tool in DRPO research.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Section: Relaxed Dynamic Reactive Power Optimization Model Formentioning

confidence: 99%

Section: Relaxed Dynamic Reactive Power Optimization Model Formentioning

confidence: 99%

Section: Loss In Increments As Small As the Target Of Discretementioning

confidence: 99%

Section: Loss In Increments As Small As the Target Of Discretementioning

confidence: 99%

See 3 more Smart Citations

A Multistage Solution Approach for Dynamic Reactive Power Optimization Based on Interval Uncertainty

Shen

Liu

2018

Mathematical Problems in Engineering

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Operating Modes Optimization of Bulk Electrical Power Networks: Structural and Parametrical Methods

Kuchanskyy

Malakhatka

Zaporozhets

2021

Power Systems Research and Operation

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A stochastic search algorithm for voltage and reactive power control with switching costs and ZIP load model

Feinberg

Yuan

2016

Electric Power Systems Research

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In power distribution systems, the goal of the voltage and reactive power control is to maintain acceptable voltage at all points along the distribution feeder under all load conditions. This should be done in the most costefficient manner. The control is usually conducted by switching devices such as shunt capacitors and transformer load tap changers in response to changing load conditions. This paper considers voltage and reactive power control models that explicitly take into account the operational costs of switching devices and the correlation between loads and voltage profiles. In addition to maintaining acceptable voltage at all points along the distribution feeder, the aim is to determine the proper settings of shunt capacitors and transformer load tap changers in order to minimize the energy loss or the total energy consumption over a selected planning horizon. A random search algorithm called approximate stochastic annealing is proposed for optimizing these models. The algorithm searches the optimal control schedule by randomly sampling from a sequence of probability distributions over the space of all possible settings of tap changers and shunt capacitors. The algorithm is shown to be globally convergent and its effectiveness is illustrated by comparing its performance with that of three other commonly used procedures on a 69-bus distribution system and a 24-bus system with distributed generation.

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Multi-period power loss optimization with limited number of switching actions for enhanced continuous power supply

Cited by 8 publications

References 21 publications

A Multistage Solution Approach for Dynamic Reactive Power Optimization Based on Interval Uncertainty

A Multistage Solution Approach for Dynamic Reactive Power Optimization Based on Interval Uncertainty

Operating Modes Optimization of Bulk Electrical Power Networks: Structural and Parametrical Methods

A stochastic search algorithm for voltage and reactive power control with switching costs and ZIP load model

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