Optimal Reactive Power Flow Research Articles

Decentralised Voltage Regulation through Optimal Reactive Power Flow in Distribution Networks with Dispersed Generation

The global capacity for renewable electricity generation has surged, with distributed photovoltaic generation being the primary driver. The increasing penetration of non-programmable renewable Distributed Energy Resources (DERs) presents challenges for properly managing distribution networks, requiring advanced voltage regulation techniques. This paper proposes an innovative decentralised voltage strategy that considers DERs, particularly inverter-based ones, as autonomous regulators in compliance with the state-of-the-art European technical standards and grid codes. The proposed method uses an optimal reactive power flow that minimises voltage deviations along all the medium voltage nodes; to check the algorithm’s performance, it has been applied to a small-scale test network and on a real Italian medium-voltage distribution network, and compared with a fully centralised ORPF. The results show that the proposed decentralised autonomous strategy effectively improves voltage profiles in both case studies, reducing voltage deviation by a few percentage points; these results are further confirmed through an analysis conducted over several days to observe how seasons affect the results.

Optimal Reactive Power Flow of AC-DC Power System with Shunt Capacitors Using Backtracking Search Algorithm

In this paper, it is proposed that a two-terminal high voltage direct current (HVDC) be integrated into the power system. Line-commutated converter (LCC)-HVDC is used because of its ability to reduce line losses, which improves overall system efficiency. Shunt capacitors also aid in voltage maintenance by compensating for the reactive power demand. In essence, limiting voltage drops in electrical networks promotes a more efficient power transmission and distribution by lowering resistive losses. In power system investigations, it was discovered that the HVDC link and SCB exist separately. So, for the first time, the backtracking search algorithm (BSA) is used to solve the optimal reactive power flow (ORPF) of a power system with a HVDC link and shunt capacitor banks (SCB). Although BSA simulations on a modified IEEE 30 bus yielded successful results, ABC was also utilized for comparing the outcomes of different methods. Overall, three separate cases of the modified IEEE 30 bus system were examined. When the acquired results are compared to other methods, the suggested algorithm is found to be better at concerning effectiveness as well as performance.

Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation

This paper develops a data-driven framework to synthesize local Volt/Var control strategies for distributed energy resources (DERs) in power distribution grids (DGs). Aiming to improve DG operational efficiency, as quantified by a generic optimal reactive power flow (ORPF) problem, we propose a two-stage approach. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> stage involves learning the manifold of optimal operating points determined by an ORPF instance. To synthesize local Volt/Var controllers, the learning task is partitioned into learning local surrogates (one per DER) of the optimal manifold with voltage input and reactive power output. Since these surrogates characterize efficient DG operating points, in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">second</i> stage, we develop local control schemes that steer the DG to these operating points. We identify the conditions on the surrogates and control parameters to ensure that the locally acting controllers collectively converge, in a global asymptotic sense, to a DG operating point agreeing with the local surrogates. We use neural networks to model the surrogates and enforce the identified conditions in the training phase. AC power flow simulations on the IEEE 37-bus network empirically bolster the theoretical stability guarantees obtained under linearized power flow assumptions. The tests further highlight the optimality improvement compared to prevalent benchmark methods.

Bi-objective optimal active and reactive power flow management in grid-connected AC/DC hybrid microgrids using metaheuristic–PSO

Abstract In the context of evolving energy needs and environmental concerns, efficient management of distributed energy resources within microgrids has gained prominence. This paper addresses the optimization of power flow management in a hybrid AC/DC microgrid through an energy management system driven by particle swarm optimization. Unlike traditional approaches that focus solely on active power distribution, our energy management system optimizes both active and reactive power allocation among sources. By leveraging 24-hour-ahead forecasting data encompassing load predictions, tariff rates and weather conditions, our strategy ensures an economically and environmentally optimized microgrid operation. Our proposed energy management system has dual objectives: minimizing costs and reducing greenhouse gas emissions. Through optimized operation of polluting sources and efficient utilization of the energy storage system, our approach achieved significant cost savings of ~15% compared with the genetic algorithm counterpart. This was largely attributed to the streamlined operation of the gas turbine system, which reduced fuel consumption and associated expenses. Moreover, particle swarm optimization maintained the efficiency of the gas turbine by operating at ~80% of its nominal power, effectively lowering greenhouse gas emissions. The effectiveness of our proposed strategy is validated through simulations conducted using the MATLAB® software environment.

A novel solution for the optimal reactive power dispatch problem using an artificial neural network integrated with the firefly optimization algorithm

This article applies a novel intelligence technique to solve power system issues faced daily. Compensation for reactive power is a significant issue faced by power system operators in research. The solution can be obtained by handling a multi-objective task and multiconstraints by reducing the active power loss and minimizing the voltage deviation at the load end. The novelty of the research focuses on integrating artificial neural network techniques with the firefly algorithm, a novel optimization algorithm for attaining an objective function. The Levenberg–Marquardt back-propagation algorithm is most suited for proper tuning of the control variables. The objective of this research can be attained by appropriately tuning the control variables connected with the IEEE test bus systems, which helps to maximally improve the voltage profile. Existing research studies have focused on reactive power management, which is attained by solving optimal reactive power flow problems employing nature-inspired approach techniques such as the symbiotic organism search algorithm, the cuckoo search algorithm, the black hole algorithm, the krill herd algorithm, and whale optimization. The evolving strategy, the firefly algorithm (FFA), minimizes the multiconstraint functions more competently and effectively than any conventional algorithm. To showcase the strength of the firefly algorithm incorporating AI, it is examined on standard IEEE test bus systems, namely, the 14-, 30-, and 58-bus networks. The obtained results quantify the effectiveness of the proposed methodology, that is, the artificial intelligence technique implementing the firefly algorithm gives better results than conventional methods.

Improved adaptive gaining-sharing knowledge algorithm with FDB-based guiding mechanism for optimization of optimal reactive power flow problem

Improved adaptive gaining-sharing knowledge algorithm with FDB-based guiding mechanism for optimization of optimal reactive power flow problem

Optimal Integration of D-STATCOMs in Radial and Meshed Distribution Networks Using a MATLAB-GAMS Interface

This paper proposes an interconnection of the MATLAB and GAMS software interfaces, which were designed based on a master-slave methodology, to solve the mixed-integer nonlinear programming (MINLP) model problem associated with the problem regarding the optimal location and sizing of static distribution compensators (D-STATCOMs) in meshed and radial distribution networks, considering the problem of optimal reactive power flow compensation and the fact that the networks have commercial, industrial, and residential loads for a daily operation scenario. The objective of this study is to reduce the annual investment and operating costs associated with energy losses and the installation costs of D-STATCOMs. This objective function is based on the classical energy budget and the capacity constraints of the device. In the master stage, MATLAB software is used to program a discrete version of the sine-cosine algorithm (DSCA), which determines the locations where the D-STATCOMs will be installed. In the slave stage, using the BONMIN solver of the GAMS software and the known locations of the D-STATCOMs, the MINLP model representing the problem under study is solved to find the value of the objective function and the nominal power of the D-STATCOMs. To validate the effectiveness of the proposed master-slave optimizer, the 33-node IEEE test system with both radial and meshed topologies is used. With this test system, numerical comparisons were made with the exact solution of the MINLP model, using different solvers in the GAMS software, the genetic-convex strategy, and the discrete-continuous versions of the Chu and Beasley genetic algorithm and the salp swarm optimization algorithm. The numerical results show that DSCA-BONMIN achieves a global solution to the problem under study, making the proposed method an effective tool for decision-making in distribution companies.

Optimal Reactive Power Compensation via D-STATCOMs in Electrical Distribution Systems by Applying the Generalized Normal Distribution Optimizer

This research deals with the problem regarding the optimal siting and sizing of distribution static compensators (D-STATCOMs) via the application of a master–slave optimization technique. The master stage determines the nodes where the D-STATCOMs must be located and their nominal rates by applying the generalized normal distribution optimizer (GNDO) with a discrete–continuous codification. In the slave stage, the successive approximations power flow method is implemented in order to establish the technical feasibility of the solution provided by the master stage, i.e., voltage regulation and device capabilities, among other features. The main goal of the proposed master–slave optimizer is to minimize the expected annual operating costs of the distribution grid, which includes the energy loss and investment costs of the D-STATCOMs. With the purpose of improving the effectiveness of reactive power compensation during the daily operation of the distribution grid, an optimal reactive power flow (ORPF) approach is used that considers the nodes where D-STATCOMs are located as inputs in order to obtain their daily expected dynamical behavior with regard to reactive power injection to obtain additional net profits. The GNDO approach and the power flow method are implemented in the MATLAB programming environment, and the ORPF approach is implemented in the GAMS software using a test feeder comprising 33 nodes with both radial and meshed configurations. A complete comparative analysis with the Salp Swarm Algorithm is presented in order to demonstrate the effectiveness of the proposed two-stage optimization approach in the fixed operation scenario regarding the final objective function values. In addition, different tests considering the possibility of hourly power injection using D-STATCOMs through the ORPF solution demonstrate that additional gains can be obtained in the expected annual operative costs of the grid.

Optimal reactive power control for dual‐active‐bridge converter using improved dual‐phase‐shift modulation strategy for electric vehicle application

AbstractDual active bridge (DAB) based DC‐DC converters are in high demand for offering quick charging capacity to Electric vehicles (EVs). The conventional technique for DAB is based on single‐phase‐shift (SPS), though it provides simple control but suffers from high peaking in the inductor current under varying charging rates for EV and dc‐link voltage ratio. In this paper, initially the expression for optimal reactive power flow into DAB converter under SPS and dual phase‐shift (DPS) control techniques are thoroughly examined. Based on the analytical analysis, an improved dual‐phase‐shift modulation (IDPSM) method for real and reactive power control through a DAB converter is proposed for varying charging rates of an EV and dc‐link voltage ratio. The proposed IDPSM control technique is implemented using the inverse perturb and observe (IPO) method that provides optimum inner phase‐shift between the legs of the H‐bridge to track minimum reactive power or back power flow into the system. This method automatically adjusts the inner and outer phase shift ratios under different loading conditions. Further, the proposed IDPSM scheme enhances the range of ZVS operation even under light load conditions, which is lacking with the conventional control techniques. The Lyapunov theory used to determine the stability of the DAB DC/DC converter. To validate the proposed control scheme, a detailed simulation is carried out using MATLAB/Simulink with a 2 kW system under varying charging rates of an EV. Further, to support the effectiveness of the proposed scheme, a comparison with different phase shift modulation schemes is also presented. Finally, an experimental prototype for DAB is developed to confirm the feasibility of the control technique.

Application of the SSA for Optimal Reactive Power Compensation in Radial and Meshed Distribution Using D-STATCOMs

This paper deals with the problem regarding the optimal placement and sizing of distribution static compensators (D-STATCOMs) in radial and meshed distribution networks. These grids consider industrial, residential, and commercial loads within a daily operation scenario. The optimal reactive power flow compensation problem is formulated through a mixed-integer nonlinear programming (MINLP) model. The objective function is associated with the minimization of the expected energy losses costs for a year of operation by considering the investment costs of D-STATCOMs. To solve the MINLP model, the application of a master–slave optimization approach is proposed, which combines the salp swarm algorithm (SSA) in the master stage and the matricial backward/forward power flow method in the slave stage. The master stage is entrusted with defining the optimal nodal location and sizes of the D-STATCOMs, while the slave stage deals with the power flow solution to determine the expected annual energy losses costs for each combination of nodes and sizes for the D-STATCOMs as provided by the SSA. To validate the effectiveness of the proposed master–slave optimizer, the IEEE 33-bus grid was selected as a test feeder. Numerical comparisons were made against the exact solution of the MINLP model with different solvers in the general algebraic modeling system (GAMS) software. All the simulations of the master–slave approach were implemented in the MATLAB programming environment (version 2021b). Numerical results showed that the SSA can provide multiple possible solutions for the studied problem, with small variations in the final objective function, which makes the proposed approach an efficient tool for decision-making in distribution companies.

A Mathematical Programming Approach for Allocation and Analysis of TCSC in Power Transmission Systems

In this paper, a mathematical programming approach based on a branch and bound (BB) algorithm is proposed in order to determine the optimal allocation of thyristor-controlled series compensators (TCSC) in electrical power transmission systems, considering line thermal limits. The TCSC modeling is added in the Y-bus matrix, facilitating the computational implementation. BB-Y is the methodology used in this paper aiming to maximize the loading of the system considering a certain quantity of TCSC. In addition, the proposed algorithm is implemented for another test, using a determined number of TCSC, in order to improve the network voltage profile and minimize the total transmission losses. The methodology uses an interior point method to solve the problem relaxed, modeled as a reactive optimal power flow problem. Simulations are performed on the IEEE 118-bus test system, allocating different quantities of TCSC to find the maximum load the system can support. Given a certain number of TCSC, previously defined for a specific load, the TCSC are reallocated considering different objectives. For two TCSC the time for relocation is shorter than 4 seconds. The results also show that from three TCSC onwards, the maximum load increasing is not relevant, being around 0.3%. Therefore, the conclusion is that the algorithm has a low processing time, and conservative solutions allied with the presented methodology can be used in the optimal allocation of TCSC, what the aim is to optimize the systems performance.

Efficient Hybrid Algorithm Solution for Optimal Reactive Power Flow Using the Sensitive Bus Approach

This paper presents the design and application of an efficient hybrid algorithm for solving the Optimal Reactive Power Flow (ORPF) problem. The ORPF is formulated as a nonlinear constrained optimization problem where the active power losses must be minimized. The proposed approach is based on the hybridization of Particle Swarm Optimization (PSO) and Tabu-Search (TS) technique. The proposed PSO-TS approach is used to find the settings of the control variables (i.e. generation bus voltages, transformer taps, and shunt capacitor sizes) which minimize transmission active power losses. The bus locations of the shunt capacitors are identified according to sensitive buses. To show the effectiveness of the proposed method, it is applied to the IEEE 30 bus benchmark test system and is compared with PSO and TS without hybridization, along with some other published approaches. The obtained results reveal the effectiveness of the proposed method in dealing with the highly nonlinear constrained nature of the ORPF problem.

A primal–dual penalty-interior-point method for solving the reactive optimal power flow problem with discrete control variables

The Optimal Reactive Power Flow (ORPF) problem has been used as an important computational tool for power system planning and operation. Its mixed-discrete version (DORPF) is formulated as a non-convex, non-linear optimization problem with discrete and continuous variables, which is aimed at minimizing the transmission losses while meeting the power demand and enforcing operational and physical limits of the system. Although the DORPF problem has been solved by a myriad of methods, some of them present regularization problems associated with poor matrix-conditioning in the optimal solution, some do not provide for rapid infeasibility detection and some do not provide effective ways for handling the discrete nature of the control variables. Although some of these complicating issues have been tackled separately in the literature by various studies, a method that addresses all these issues has not yet been proposed for solving the DORPF problem. In this paper, an integration of optimization approaches is proposed for handling all such complicating issues. The basis of this approach is a primal–dual penalty-interior-point method, which integrates the good properties of penalty methods (e.g. regularization effects on the constraints and rapid infeasibility detection) and interior-point methods (e.g. scalability and good convergence behavior), without suffering from their disadvantages. In the proposed approach, this method is integrated with a sinusoidal penalty function method for handling the discrete nature of the control variables of the DORPF problem, together with a specific inertia correction strategy designed to avoid local maximizers associated with such a penalty function. Numerical tests carried out with the IEEE 30-, 57-, 118- and 300-bus systems focus on showing that all the complicating issues have been addressed by the proposed method. Comparisons with the results obtained by interior-point methods are also provided.

Solution of the Optimal Reactive Power Flow Problem Using a Discrete-Continuous CBGA Implemented in the DigSILENT Programming Language

The problem of the optimal reactive power flow in transmission systems is addressed in this research from the point of view of combinatorial optimization. A discrete-continuous version of the Chu &amp; Beasley genetic algorithm (CBGA) is proposed to model continuous variables such as voltage outputs in generators and reactive power injection in capacitor banks, as well as binary variables such as tap positions in transformers. The minimization of the total power losses is considered as the objective performance indicator. The main contribution in this research corresponds to the implementation of the CBGA in the DigSILENT Programming Language (DPL), which exploits the advantages of the power flow tool at a low computational effort. The solution of the optimal reactive power flow problem in power systems is a key task since the efficiency and secure operation of the whole electrical system depend on the adequate distribution of the reactive power in generators, transformers, shunt compensators, and transmission lines. To provide an efficient optimization tool for academics and power system operators, this paper selects the DigSILENT software, since this is widely used for power systems for industries and researchers. Numerical results in three IEEE test feeders composed of 6, 14, and 39 buses demonstrate the efficiency of the proposed CBGA in the DPL environment from DigSILENT to reduce the total grid power losses (between 21.17% to 37.62% of the benchmark case) considering four simulation scenarios regarding voltage regulation bounds and slack voltage outputs. In addition, the total processing times for the IEEE 6-, 14-, and 39-bus systems were 32.33 s, 49.45 s, and 138.88 s, which confirms the low computational effort of the optimization methods directly implemented in the DPL environment.

Minimizing Power Loss Using Modified Artificial Bee Colony Algorithm

Electrical energy losses are found in any part of the power system. In the power system, it is essential to minimize the real power loss in transmission lines. The voltage deviation at the load buses through controlling the reactive power flow is very important. This ensures the secured operation of power systems regarding voltage stability and the economics of the process due to loss minimization. In this paper, the Modified Artificial Bee Colony (MABC) algorithm is implemented to solve the power system's optimal reactive power flow problem. Generator bus voltages, transformer tap positions, and settings of switched shunt of compensators are used as decision variables to control the reactive power flow. These control variable values are adjusted for loss reduction. MABC algorithm is tested on the standard IEEE-30 bus test system. The results are compared with Firefly algorithm (FA) and Artificial Bee Colony (ABC) algorithm method to prove the effectiveness of the newest algorithm. The power loss results are quite productive, and the algorithm is the most efficient than the other methods such as ABC algorithm and FA algorithm. These results are produced by Matlab 2017b.

State-of-the-Art of Optimal Active and Reactive Power Flow: A Comprehensive Review from Various Standpoints

Optimal power flow (OPF), a mathematical programming problem extending power flow relationships, is one of the essential tools in the operation and control of power grids. To name but a few, the primary goals of OPF are to meet system demand at minimum production cost, minimum emission, and minimum voltage deviation. Being at the heart of power system problems for half a century, the OPF can be split into two significant categories, namely optimal active power flow (OAPF) and optimal reactive power flow (ORPF). The OPF is spontaneously a complicated non-linear and non-convex problem; however, it becomes more complex by considering different constraints and restrictions having to do with real power grids. Furthermore, power system operators in the modern-day power networks implement new limitations to the problem. Consequently, the OPF problem becomes more and more complex which can exacerbate the situation from mathematical and computational standpoints. Thus, it is crucially important to decipher the most appropriate methods to solve different types of OPF problems. Although a copious number of mathematical-based methods have been employed to handle the problem over the years, there exist some counterpoints, which prevent them from being a universal solver for different versions of the OPF problem. To address such issues, innovative alternatives, namely heuristic algorithms, have been introduced by many researchers. Inasmuch as these state-of-the-art algorithms show a significant degree of convenience in dealing with a variety of optimization problems irrespective of their complexities, they have been under the spotlight for more than a decade. This paper provides an extensive review of the latest applications of heuristic-based optimization algorithms so as to solve different versions of the OPF problem. In addition, a comprehensive review of the available methods from various dimensions is presented. Reviewing about 200 works is the most significant characteristic of this paper that adds significant value to its exhaustiveness.

An efficient NSCE algorithm for multi-objective reactive power system compensation with UPFC

&lt;span id="docs-internal-guid-a50ef6a8-7fff-b6d7-8e58-d434be6097d4"&gt;&lt;span&gt;This paper proposes a novel approach based on the NSCE (elitist non dominated sorting cross entropy), for the optimization of the location and the size of a flexible AC transmission system device (FACTS) namely: unified power flow controller (UPFC) to achieve the optimal reactive power flow (ORPF). In the present work, the main objective is to minimize the real power losses, the cost investment of several UPFC and the deviation voltages using intelligent algorithms. The proposed study is multiobjective, in which, the power generator buses, the control voltages, the ratio tap changer of transformers and the reactive power injections from installed UPFC are considered as control variables. The proposed NSCE algorithm is validated on IEEE 30-bus test system. A comparison with elitist non dominated sorting genetic algorithm (NSGA-II) and a regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) is done and completed with hybridization of them.&lt;/span&gt;&lt;/span&gt;

Optimal reactive power flow of power systems with two-terminal HVDC and multi distributed generations using backtracking search algorithm

This paper deals with optimal reactive power flow (ORPF) of power systems with two-terminal high voltage direct current (HVDC) and multi distributed generations (DGs) using backtracking search algorithm (BSA). For the first time with this study, the power system is modeled closest to the real world and the power loss of both test systems are minimized by BSA, while DGs and HVDC links are existing simultaneously in the power systems. Although simulations on modified IEEE 30-bus and modified IEEE 57-bus test systems have given successful results by using BSA, genetic algorithm (GA) was also used to compare the outcomes of different algorithms. This study was performed for five and three different cases of the modified 30-bus and modified 57-bus test systems, respectively. The obtained results show that although the power loss does not vary much according to the connection points of HVDC link, the optimal replacement of the DGs to the power system reduces the power loss considerably. In addition to this, when the obtained results are compared with those in the literature, it is seen that the proposed algorithm is better than the other algorithms in terms of performance and effectiveness.

Reducing the Number of Control Actions in the Discrete Reactive Optimal Power Flow

This paper proposes a new formulation for the Discrete Reactive Optimal Power Flow problem (DROPF) that aims at reducing the number of control actions of the problem. For such a purpose, additional constraints are introduced in the classical model to the DROPF problem so as to represent specific behaviors of voltage control devices in the electrical system. These behaviors involve the actuation of a control device only when the voltage magnitude limit of the bus controlled by such device is effectively reached. The proposed DROPF model is solved by the branch-and-bound method implemented in the free solver BONMIN. Numerical tests were performed using the IEEE 14, 30 and 118 bus electrical systems and demonstrated the efficiency of the proposed model. The results are compared with the ones obtained by a classical DROPF.

Optimal reactive power flow procedure to set up an effective local voltage control

These days, due to distributed generation increasing, the operation and control of active distribution grid becomes complicated. Thus, their voltage control is the main topic of many research papers recently. While network reinforcement mitigated voltage rise in passive distribution networks, local voltage control, coordinated voltage control and centralized voltage control have been studied in several research papers. However, many of these approaches need plenty of sensors to gather large number of measurements that could cause complexity. This paper presents a new procedure to set up a local voltage control law devoted to properly manage the voltage profile (e.g. minimizing losses on MV feeders and increasing grid hosting capacity), without relying on advanced telecommunication architectures. The final goal of this paper is in a procedure that could be directly adopted in the today in place electric distribution grids. In particular, the contribution proposed is a method to optimize, offline, the Q(V) characteristic curve of the local voltage control for distributed generation units. The approach proposed has been implemented and tested on a real-life distribution grid of an Italian mountain area, where overvoltage problems could seriously bound renewable exploitation.