Reactive Power Optimization Model Research Articles

A Method for Online Reactive Power Optimization Problem in Regional Power Grid

The automatic voltage control system in regional power grid is a significant measure to improve the system voltage and the power quality. It relies on online reactive power optimization. This paper proposes a novel algorithm aimed to build an online reactive power optimization model, which uses a hybrid algorithm combined the primal-dual interior point method and branch and bound method. Dealing with the discrete variables and continuous variables separately, the solution speed and accelerate convergence problem is improved rapidly. Finally, the efficiency of the proposed method is verified by numerical simulation in a regional power grid system.

Probabilistic Reactive Power Optimization of Distribution Network Considering Stochastic Wind Speed and Load

Based on the probabilistic loss model of distribution network and the improved hybrid particle swarm algorithm, a reactive power optimization algorithm is presented, which encompasses the effects of stochastic wind speed and load. Firstly, with the control vector dimension’s length augmented and with the probabilistic loss method built, the reactive power optimization model is presented. Secondly, with the Niche operations embedded into the original PSO, an improved hybrid PSO algorithm is presented. Lastly, the corresponding software system program is developed in VC++ language and on basis of SQL SERVER platform. While this software system being supplied into a case, the experimental data have proved that this algorithm possesses more adaptability. At the same time, compared with the RTS algorithm, the calculating process is speeded.

Power System Reactive Power Optimization Based on Fuzzy Formulation and Interior Point Filter Algorithm

Considering the soft constraint characteristics of voltage constraints, the Interior-Point Filter Algorithm is applied to solve the formulation of fuzzy model for the power system reactive power optimization with a large number of equality and inequality constraints. Based on the primal-dual interior-point algorithm, the algorithm maintains an updating “filter” at each iteration in order to decide whether to admit correction of iteration point which can avoid effectively oscillation due to the conflict between the decrease of objective function and the satisfaction of constraints and ensure the global convergence. Moreover, the “filter” improves computational efficiency because it filters the unnecessary iteration points. The calculation results of a practical power system indicate that the algorithm can effectively deal with the large number of inequality constraints of the fuzzy model of reactive power optimization and satisfy the requirement of online calculation which realizes to decrease the network loss and maintain specified margins of voltage.

Reactive Power Optimization in Distribution Network with Wind Farm

The traditional methods to adjust voltage in distribution network reactive power optimization is discretization，and it is difficult to realize the continuous voltage adjustment. A reactive power optimization model and algorithm in distribution network with wind farm is proposed. The network loss，deviation of voltage and stability of voltage are taken into account in the multi-objective reactive power optimization model. The quantum particle swarm optimization（QPSO）algorithm has been used to solve the reactive power optimization problem. The algorithm described particle state by wave function, not only increase the diversity of population，but also avoid premature convergence. The comparison of the simulation result between QPSO and PSO on the modified IEEE 33-bus system demonstrated the effectiveness and advantage of quantum particle swarm optimization.

Multi-Objective Reactive Power Optimization Based On The Fuzzy Adaptive Particle Swarm Algorithm

To research the problem on the multi-objective reactive power optimization, to utilize the theory of multiobjective fuzzy optimization to change the multi-objective optimization into the single-objective optimization, and to adopt the fuzzy adaptive particle swarm algorithm to carry out solutions. Comprehensively considering the security and economical efficiency of the system, as well as the condition of the operation constraints, to propose a comprehensive and practical multi-objective reactive power optimization model. To consider the multi-objective reactive power optimization model of the voltage stability index can optimize the economic benefit and safety benefit of the system. Applying the theory of multi-objective fuzzy optimization combined with the adaptive particle swarm optimization algorithm to the problem of the multi-objective reactive power optimization could solve the problem of the different dimensional multi-objective optimization in a better way. After adopting the fuzzy adaptive particle swarm algorithm, the superiorities, such as achieving the global optimal solution, reducing the computational complexity, and improving the computational efficiency,are displayed.

An OPF based approach for assessing the minimal reactive power support for generators in deregulated power systems

Reactive power support is an important ancillary service for secure and reliable operation in power markets. It has recently been recognized that the reactive power support for a generator has two components: one for supporting its own real power transmission and the other for supplying reactive demand, improving system security, and controlling system voltage; and that only the second part should receive financial compensation in competitive power markets. This makes the problem of separating these two components a new focus of current research. An OPF based reactive power optimization model along with a power flow tracing based method is proposed in this paper to tackle this problem. The methodology is tested on four test systems. Detailed analysis of the results of the 39-bus test system is reported.

Analysis of Reactive Power Support of Generators Using Power Flow Tracing Methods

With the emergence of competitive electricity power markets, reactive power ancillary services and reactive power markets have attracted attention from researchers and system operators all over the world. Reactive power is an important system support service for secure and reliable operation of power systems. Improper management of reactive power can also hinder the operational efficiency of other power markets. It has been recognized that the reactive power of a generator has several roles, namely, supplying reactive demand, maintaining system security and supporting its real power transmission. It is rational that the minimal reactive power used to support its real power transmission should not receive financial compensation in power markets. Hence, this component of reactive power can be regarded as the minimal reactive power support of a generator. A reactive power optimization model along with a power flow tracing based method is proposed in this article to tackle this problem. The validity and rationality of the approach are verified using a simple meshed 5-bus system.

Reactive power optimization using successive quadratic programming method

This paper presents a reactive power optimization model that is based on successive quadratic programming (SQP) methods. Mathematical formulation and unified algorithm suppose different objective functions (OF) of reactive power optimization, depending on type and purposes of current reactive power control or planning problem. A bicriterion reactive power optimization model, that represents compromise between economical and security objective functions, is proposed. An efficient algorithm for approximation of initial problem by quadratic programming problem is described. The quadratic programming problem (QP) is solved on the basis of the Newton type quadratic programming method. A modified successive quadratic programming method was developed, that provides reliable convergence of the SQP method.