Optimal VAR Research Articles

Centralized Volt–Var Optimization Strategy Considering Malicious Attack on Distributed Energy Resources Control

The adoption of information and communication technology based centralized volt–var control (VVC) leads to an optimal operation of a distribution feeder. However, it also poses a challenge that an adversary can tamper with the metered data and, thus, can render the VVC action ineffective. Distribution system state estimation (DSSE) acts as a backbone of centralized VVC. Distributed energy resources (DER) injection measurements constitute leverage measurements from a DSSE point of view. This paper proposes two solutions as a volt–var optimization DSSE malicious attack mitigating strategy when the DER injection measurements are compromised. The first solution is based on local voltage regulation controller set-points. The other solution effectively employs historical data or forecast information. The concept is based on a cumulant-based probabilistic optimal power flow with the objective of minimizing the expectation of total power losses. The effectiveness of the approach is performed on the 95-bus U.K. generic distribution system and validated against Monte Carlo simulations.

A Review of Reactive Power Optimization in Distribution Network

The reactive power (VAR) optimization in distribution network is one of important measures for loss reduction, voltage profile improvement and power factor correction. This paper classifies the VAR optimization into VAR configuration optimization and VAR operation optimization based on different implementation phases. Meantime, the VAR optimal operation is sorted by static VAR optimization and dynamic VAR optimization. Afterward the article summarizes the research situation of VAR optimization. Eventually the paper presents the future work of VAR optimization according to the research status.

Symbiotic Organisms Search Technique for SVC Installation in Voltage Control

<p>Increasing demand experienced by electric utilities in many parts of the world involving developing country is a normal phenomenon. This can be due to the urbanization process of a system network, which may lead to possible voltage decay at the receiving buses if no proper offline study is conducted. Unplanned load increment can push the system to operate closes to its instability point. Various compensation schemes have been popularly invented and proposed in power system operation and planning. This would require offline studies, prior to real system implementation. This paper presents the implementation of Symbiotic Organisms Search (SOS) algorithm for solving optimal static VAr compensator (SVC) installation problem in power transmission systems. In this study, SOS was employed to perform voltage control study in a transmission system under several scenarios via the SVC installation scheme. This realizes the feasibility of SOS applications in addressing the compensating scheme for the voltage control study. Minimum and maximum bound of the voltage at all buses have been considered as the inequality constraints as one of the aspects. A validation process conducted on IEEE 26-Bus RTS realizes the feasibility of SOS in performing compensation scheme without violating system stability. Results obtained from the optimization process demonstrated that the proposed SOS optimization algorithm has successfully reduced the total voltage deviation index and improve the voltage profile in the test system. Comparative studies have been performed with respect to the established evolutionary programming (EP) and artificial immune system (AIS) algorithms, resulting in good agreement and has demonstrated its superiority. Results from this study could be beneficial to the power system community in the planning and operation departments in terms of giving offline information prior to real system implementation of the corresponding power system utility.</p>

Optimal placement and sizing of distributed generation‐based wind energy considering optimal self VAR control

The impact of distributed generation (DG) units on the voltage stability has become a challenging issue especially when squirrel cage induction generator (SCIG)-based wind DGs are utilised. Optimisation methods are tools which can be used to place and size the DG units in the distribution system, so as to utilise these units optimally within certain constraints. This study aims to optimally size and allocate advanced wind energy based DG technology with innovative reactive power capability, reduced capital cost, and improved energy capture capability to improve voltage stability. Therefore, a new combination of SCIG and doubly-fed induction generator (DFIG) based DG configuration is proposed. In this configuration, the reactive power absorbed by SCIG is supplied by DFIG, and therefore, the combined system operates at unity power factor, which makes it feasible to comply with the IEEE 1547 standard. A methodology is proposed to optimally size and allocate the DG system with an objective function to improve the voltage profile considering numerous technical and economic constraints. The performance of the proposed DG configuration is compared with DGs that utilise SCIG with a parallel reactive power compensation. IEEE 30-bus test system is used to demonstrate the effectiveness of the proposed methodology.

VaR as the CVaR sensitivity: Applications in risk optimization

VaR minimization is a complex problem playing a critical role in many actuarial and financial applications of mathematical programming. The usual methods of convex programming do not apply due to the lack of sub-additivity. The usual methods of differentiable programming do not apply either, due to the lack of continuity. Taking into account that the CVaR may be given as an integral of VaR, one has that VaR becomes a first order mathematical derivative of CVaR. This property will enable us to give accurate approximations in VaR optimization, since the optimization VaR and CVaR will become quite closely related topics. Applications in both finance and insurance will be given.

Dealing with Multiple Solutions in Structural Vector Autoregressive Models

ABSTRACTStructural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior.

Equivalence of Robust VaR and CVaR Optimization

We show that robust optimization of the VaR and CVaR risk measures with a minimum return constraint for distribution ambiguity reduce to the same second order cone program. We use this result to formulate models for robust risk optimization for joint ambiguity in distribution, mean returns and covariance matrices, for ellipsoidal ambiguity sets. We also obtain models for robust VaR and CVaR optimization for polytopic and interval ambiguity sets of the means and covariance. The models unify and/or extend several existing models. We also propose an algorithm and a heuristic for constructing an ellipsoidal ambiguity set from point estimates given by multiple securities analysts, and show how to overcome the well-known conservatism of robust optimization models. Using CDS spread return data from eurozone crisis countries we illustrate that investment strategies using robust optimization models perform well even out-of-sample. Finally, using a controlled experiment we show how the well-known sensitivity of CVaR to mis-specifications of the first four moments of the distribution is alleviated with the robust models.

Chaos embedded krill herd algorithm for optimal VAR dispatch problem of power system

Chaotic krill herd algorithm (KHA) (CKHA) is proposed in this paper to solve the optimal VAR dispatch problem of power system considering either minimization of real power loss or that of absolute value of total voltage deviation or improvements of voltage profile as an objective while satisfying all the equality and the inequality constraints of the power system network. Detailed studies of different chaotic maps are illustrated. Among these, Logistic map is considered in the proposed technique to improve the performance of the basic KHA. The performance of the proposed CKHA is implemented, successfully, on standard IEEE 14- and IEEE 118-bus test power systems in which the control of bus voltages, tap position of transformers and reactive power sources are involved. The results offered by the proposed CKHA are compared to other evolutionary optimization based techniques surfaced in the recent state-of-the-art literature. Simulation results indicate that the proposed CKHA approach yields better optimization efficacy over some other recent popular techniques in terms of results offered, effectiveness, quality of solution and convergence speed.

An ant colony system based control of shunt capacitor banks for bulk electricity consumers

The present work focuses on the formulation of an optimization algorithm based on Ant Colony Search (ACS) technique operational from a Blackfin DSP microcontroller platform. This intelligent algorithm enables switching of appropriate Shunt Capacitor Banks (SCBs) in installations of bulk consumers of electricity to improve their power factor. The application is mainly targeted on cold storages, rolling mills, traction substation and steel plants in India, where SCBs are installed over a long period in an unplanned manner. Unawareness of the impact of energy savings coupled with the fiscal incapability of these users make them unable to install costly power electronic devices for p.f. control ACS based embedded controller is sensitive to unpredictable load changes and selective capacitors switching cause optimal VAR control with minimum stress to the system. Employing the distributed computational model based on ACS becomes an ideal tool for the combinatorial optimization problem to select a best combination within a bank of varying sizes and varying status signifying dynamically changing search space. This low cost device will reduce the penalty billing of consumers by improving the p.f. and improved metered reading. The use of this device will lead to permeation of technology to the strata which needs it most making energy management possible without any human intervention.

Impact of EV penetration on Volt–VAR Optimization of distribution networks using real-time co-simulation monitoring platform

This paper aims to investigate the impact of different Electric Vehicle (EV) penetration on quasi real-time Volt–VAR Optimization (VVO) of smart distribution networks. Recent VVO solutions enable capturing data from Advanced Metering Infrastructure (AMI) in quasi real-time to minimize distribution networks loss costs and perform Conservation Voltage Reduction (CVR) to save energy. The emergence of EVs throughout distribution feeder increases grid complexity and uncertainty levels that could affect AMI-based VVO objectives. Hence, this paper primarily introduces an AMI-based VVO engine, able to minimize grid loss and Volt–VAR control assets operating costs while maximizing CVR benefit. It then presents a real-time co-simulation platform comprised of the VVO engine, grid model in a real-time simulator and monitoring platform, communicating with each other through DNP.3 protocol, to test the precision and performance of AMI-based VVO in presence of different EV penetration levels. Accordingly, 33-node distribution feeder is studied through different EV penetration scenarios. The results show significant changes in AMI-based VVO performance especially in CVR sub-part of VVO according to EV model and type. Thus, this study could lead near future VVO solutions to gain higher levels of accuracy and efficiency considering smart microgrid components such as EV in their models.

원-팩터 모형을 이용한 KOSPI200지수 구성종목의 최적 포트폴리오 구성 및 VaR 측정

원-팩터 모형을 이용한 KOSPI200지수 구성종목의 최적 포트폴리오 구성 및 VaR 측정

Optimal VAR control for real power loss minimization using differential evolution algorithm

Abstract Differential evolution algorithm (DEA) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been proved to be a promising evolutionary algorithm for solving the ORPD problem and many engineering problems. However, the success of DEA in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies (mutation strategies) and their associated control parameter values. This paper presents a differential evolution technique with various trial vector generation strategies based on optimal reactive power dispatch for real power loss minimization in power system. The proposed methodology determines control variable settings such as generator terminal voltages, tap positions and the number of shunts compensator to be switched, for real power loss minimization in the transmission systems. The DE method has been examined and tested on the IEEE 14-bus, 30-bus and the equivalent Algerian electric 114-bus power system. The obtained results are compared with two other methods, namely, interior point method (IPM), Particle Swarm Optimization (PSO) and other methods in the literature. The comparison study demonstrates the potential of the proposed approach and shows its effectiveness and robustness to solve the ORPD problem.

Optimal allocation and adaptive VAR control of PV-DG in distribution networks

The development of distributed generation (DG) has brought new challenges to power networks. One of them that catches extensive attention is the voltage regulation problem of distribution networks caused by DG. Optimal allocation of DG in distribution networks is another well-known problem being widely investigated. This paper proposes a new method for the optimal allocation of photovoltaic distributed generation (PV-DG) considering the non-dispatchable characteristics of PV units. An adaptive reactive power control model is introduced in PV-DG allocation as to balance the trade-off between the improvement of voltage quality and the minimization of power loss in a distribution network integrated with PV-DG units. The optimal allocation problem is formulated as a chance-constrained stochastic programming (CCSP) model for dealing with the randomness of solar power energy. A novel algorithm combining the multi-objective particle swarm optimization (MOPSO) with support vector machines (SVM) is proposed to find the Pareto front consisting of a set of possible solutions. The Pareto solutions are further evaluated using the weighted rank sum ratio (WRSR) method to help the decision-maker obtain the desired solution. Simulation results on a 33-bus radial distribution system show that the optimal allocation method can fully take into account the time-variant characteristics and probability distribution of PV-DG, and obtain the best allocation scheme.

Voltage Control and Optimization of Distribution Network with DGs Based on Droop Characteristics

The limitations of both grid-connection control and var optimization for improving the voltage of the distribution network with DGs are pointed out in this paper. Thus, it is proposed that the integration of grid-connection control and var optimization for complementary advantages becomes necessary. A model of var optimization based on genetic algorithm is designed, and analysis of examples is carried out by use of MATLAB. Meanwhile, control models of grid-connected inverters based onP-fdroop control andP-δdroop control are designed, whose characteristics are compared by simulations done on MATLAB/Simulink. Results show the validity of the supposed var optimization technique and grid-connection control technique.

Optimal Reinsurance Under VaR and CTE Risk Measures When Ceded Loss Function is Concave

Cai et al. (2008) explored the optimal reinsurance designs among the class of increasing convex reinsurance treaties under VaR and CTE risk measures. However, reinsurance contracts always involve a limit on the ceded loss function in practice, and thus it may not be enough to confine the analysis to the class of convex functions only. The object of this article is to present an optimal reinsurance policy under VaR and CTE optimization criteria when the ceded loss function is in a class of increasing concave functions and the reinsurance premium is determined by the expected value principle. The outcomes reveal that the optimal form and amount of reinsurance depend on the confidence level p for the risk measure and the safety loading θ for the reinsurance premium. It is shown that under the VaR optimization criterion, the quota-share reinsurance with a policy limit is optima, while the full reinsurance with a policy limit is optima under CTE optimization criterion. Some illustrative examples are provided.

Development and Field Test of Voltage VAR Optimization in the Korean Smart Distribution Management System

This paper is a summary of the development and demonstration of an optimization program, voltage VAR optimization (VVO), in the Korean Smart Distribution Management System (KSDMS). KSDMS was developed to address the lack of receptivity of distributed generators (DGs), standardization and compatibility, and manual failure recovery in the existing Korean automated distribution system. Focusing on the lack of receptivity of DGs, we developed a real-time system analysis and control program. The KSDMS VVO enhances manual system operation of the existing distribution system and provides a solution with all control equipment operated at a system level. The developed VVO is an optimal power flow (OPF) method that resolves violations, minimizes switching costs, and minimizes loss, and its function can vary depending on the operator’s command. The sequential mixed integer linear programming (SMILP) method was adopted to find the solution of the OPF. We tested the precision of the proposed VVO on selected simulated systems and its applicability to actual systems at two substations on the Jeju Island. Running the KSDMS VVO on a regular basis improved system stability, and it also raised no issues regarding its applicability to actual systems.

A Novel Adaptive Elite-Based Particle Swarm Optimization Applied to VAR Optimization in Electric Power Systems

Particle swarm optimization (PSO) has been successfully applied to solve many practical engineering problems. However, more efficient strategies are needed to coordinate global and local searches in the solution space when the studied problem is extremely nonlinear and highly dimensional. This work proposes a novel adaptive elite-based PSO approach. The adaptive elite strategies involve the following two tasks: (1) appending the mean search to the original approach and (2) pruning/cloning particles. The mean search, leading to stable convergence, helps the iterative process coordinate between the global and local searches. The mean of the particles and standard deviation of the distances between pairs of particles are utilized to prune distant particles. The best particle is cloned and it replaces the pruned distant particles in the elite strategy. To evaluate the performance and generality of the proposed method, four benchmark functions were tested by traditional PSO, chaotic PSO, differential evolution, and genetic algorithm. Finally, a realistic loss minimization problem in an electric power system is studied to show the robustness of the proposed method.

Optimal reinsurance with concave ceded loss functions under VaR and CTE risk measures

Most of the studies on optimal reinsurance are from the viewpoint of the insurer and the optimal ceded functions always turn out to be convex. However reinsurance contracts always involve a limit on the ceded loss function in practice, thus it may not be enough to confine the analysis to the class of convex functions only. In this paper, we study the problem of optimal reinsurance under VaR and CTE optimization criteria when the ceded loss functions are in the class of increasing concave functions. By using a simple geometric approach, we prove that under the VaR optimization criterion, the quota-share reinsurance with a policy limit is always optimal, while the full reinsurance with a policy limit is optimal under the CTE optimization criterion. Some illustrative examples are presented.

Loadability of power systems and optimal SVC placement

This paper investigates the loadability of power systems. A voltage stability assessment is performed using the worst-case reactive power margin as an index. In addition, the influence of active power load increment on the voltage stability assessment is also examined. Based on these results, a messy genetic-algorithm optimization scheme is proposed for optimal static VAR compensator (SVC) placement aimed at the voltage stability enhancement of power systems under the most critical operation conditions. The SVC planning is formulated as a multi-objective optimization problem in terms of the maximum worst-case reactive power margin, highest load voltages towards the critical operating points, minimum real power losses, and lowest SVC device costs. During the genetic algorithm search for the optimal solution, the most critical disturbance scenario is estimated using each SVC placement and the configuration of the original power system. The candidate solution thereby obtained is further checked against the N−1 security criterion.

Optimal VAR control for improvements in voltage profiles and for real power loss minimization using Biogeography Based Optimization

In this paper, a Biogeography Based Optimization (BBO) technique is introduced to solve multi-constrained optimal reactive power flow (ORPF) problem in power system. ORPF is a multi-objective nonlinear optimization problem that minimizes the bus voltage deviation and real power loss. The feasibility of the proposed algorithm is demonstrated for IEEE 30-bus system and IEEE 118-bus system. A comparison of simulation results reveals optimization efficacy of the proposed scheme over other well established population based optimization techniques like conventional particle swarm optimization (PSO), general passive congregation PSO (GPAC), local passive congregation PSO (LPAC), coordinated aggregation (CA) and interior point based OPF (IP-OPF).