Reactive Power Dispatch Research Articles

In-Line Distributed Dispatch of Active and Reactive Power Based on ADMM and Consensus Considering Battery Degradation in Microgrids

This work presents a distributed in-line strategy to manage an isolated microgrid by optimizing active and reactive power dispatch. The proposed objective function leads to minimize the operation costs and addresses some technical requirements such as diminishing power losses and voltage deviation. Additionally, the strategy deals with temporal multi-scale goals, i.e., robustness to demand disturbances and variation of renewable resources (a short-term objective), and preservation of the health of battery-based storage systems (a long-term objective). The technique uses alternating directions method of multipliers (ADMM), accelerated consensus, and a novel battery degradation model (Quadratic AH-Throughput model). We test the proposed solution in a case study that includes renewable resources and lead-acid and lithium batteries. To obtain the results of the case study, we employ a co-simulation scheme that uses Matlab and DIgSILENT. Finally, the performance of the method is compared with a centralized optimization technique.

Scenario-based method to solve Optimal Reactive Power Dispatch using modified ant lion optimizer considering uncertainties in load, solar, and wind power

Scenario-based method to solve Optimal Reactive Power Dispatch using modified ant lion optimizer considering uncertainties in load, solar, and wind power

A Bi-Level Techno-Economic Optimal Reactive Power Dispatch Considering Wind and Solar Power Integration

A Bi-Level Techno-Economic Optimal Reactive Power Dispatch Considering Wind and Solar Power Integration

Multi-timescale Affinely Adjustable Robust Reactive Power Dispatch of Distribution Networks Integrated with High Penetration of PV

Photovoltaic (PV) power generation has highly penetrated in distribution networks, providing clean and sustainable energy. However, its uncertain and intermittent power outputs significantly impair network operation, leading to unexpected power loss and voltage fluctuation. To address the uncertainties, this paper proposes a multi-timescale affinely adjustable robust reactive power dispatch (MTAAR-RPD) method to reduce the network power losses as well as alleviate voltage deviations and fluctuations. The MTAAR-RPD aims to coordinate on-load tap changers (OLTCs), capacitor banks (CBs), and PV inverters through a three-stage structure which covers multiple timescales of “hour-minute-second”. The first stage schedules CBs and OLTCs hourly while the second stage dispatches the base reactive power outputs of PV inverter every 15 min. The third stage affinely adjusts the inverter reactive power output based on an optimized Q-P droop controller in real time. The three stages are coordinately optimized by an affinely adjustable robust optimization method. A solution algorithm based on a cutting plane algorithm is developed to solve the optimization problem effectively. The proposed method is verified through theoretical analysis and numerical simulations.

Optimum Reactive Power Dispatch Solution using Hybrid Particle Swarm Optimization and Pathfinder Algorithm

Optimum reactive power dispatch (ORPD) significantly impacts the operation and control of electrical power systems (EPS) due to its undeniable benefit in the economic operation and reliability of the systems. ORPD is a sub-problem of optimal power flow (OPF). The main aim is to reduce/minimize the real power loss. Among the swarm intelligence (SI) metaheuristic algorithms is particle swarm optimization (PSO), which has fast convergence speed and gives the optimum solution to a particular problem by moving the swarm in the intensification (exploitation) search space. Also, the pathfinder algorithm (PFA) mimics the collective movement of the swarms with a leading member. Therefore, combining the fast convergence of PSO with PFA to form a hybrid technique is considered a viable approach in this study to avoid decreasing PFA searchability when the dimension of the problem increases. In this article, a hybrid algorithm based on a particle swarm optimization and pathfinder algorithm (HPSO-PFA) is proposed for the first time to study the combination of the control variables (generator voltage, transformer tap, and sizing of reactive compensation to obtain the objective function (total real power loss). The proposed method is tested on the IEEE 30 and 118 bus systems. The losses were reduced to 16.14262 MW and 107.2913 MW for the IEEE 30 and 118 test systems. Furthermore, the percentage (%) reduction for the IEEE 30 and 118 test systems are 9.8% and 19.25%, respectively. The result demonstrates the performance of HPSO-PFA gives a better solution than the other algorithms.

Multi objective optimal reactive power dispatch for enrichment of power system behavior using modified ant lion optimizer

Multi objective optimal reactive power dispatch for enrichment of power system behavior using modified ant lion optimizer

A Multiobjective Artificial-Hummingbird-Algorithm-Based Framework for Optimal Reactive Power Dispatch Considering Renewable Energy Sources

This paper proposes a new artificial hummingbird algorithm (AHA)-based framework to investigate the optimal reactive power dispatch (ORPD) problem which is a critical problem in the capacity of power systems. This paper aims to improve the performance of power systems by minimizing two distinct objective functions namely active power loss in the transmission network and total voltage deviation at the load buses subjected to various constraints within multiobjective framework. The proposed AHA-based framework maps the inherent flight and foraging capabilities exhibited by hummingbirds in nature to determine the best settings for the control variables (i.e., voltages at generation buses, the tap positions of on-load tap-changing transformers (OLTCs) and the size of switchable shunt VAR compensators) to minimize the overall objective functions. A multiobjective optimal reactive power dispatch framework (MO-ORPD) considering renewable energy sources (RES) and load uncertainties is also proposed to minimize the individual objectives simultaneously. The competency and robustness of the proposed AHA-based framework is validated and tested on IEEE 14 bus and IEEE 39 bus test systems to solve the ORPD problem. Eventually, the results are compared with other well-known optimization techniques in the literature. Box plots and statistical tests using SPSS are performed and validated to justify the effectiveness of the proposed framework.

Applying Deep Learning-based concepts for the detection of device misconfigurations in power systems

The electrical energy system is undergoing major changes due to the necessity for more sustainable energy generation and the following increased integration of novel grid-connected devices, such as inverters or electric vehicle supply equipment. To operate reliably in novel circumstances, as created by the decentralization of generation, power systems usually need grid supportive functions provided by these devices. These functions include control mechanisms such as reactive power dispatch used for voltage control or active power reduction depending on the voltage. As the main contribution of this work, an approach for the development of the detection of misconfigured (e.g., wrongly parameterized control curve) grid devices using solely operational data is proposed. By generating and analyzing operational data of power distribution grids, a Deep Learning-based approach is applied to the detection problem given. An end-to-end framework is used to synthesize and process the data as well as to apply machine learning techniques to it. The results offer insights into the applicability and possible ways to improve the proposed solution and how it could be employed by grid operators. The findings show that DL methods, in contrast to traditional machine learning, can be used for the problem at hand and that the framework developed offers the necessary tools to fine-tune and scale the solution for broader usage.

Optimal Reactive Power Dispatch Using Improved Chaotic PSO Algorithm with the Wingbeat Frequency

Abstract The importance of reactive power to the economy and security of power systems cannot be overemphasized. For instance, Transmission losses increase when reactive power is unevenly distributed on transmission network; and power quality is affected as well. The cheapest way of reducing these transmission lines losses is via reactive power dispatch approach. This study therefore proposes an Improved Chaotic Particle Swarm Optimization algorithm (ICPSO) with the primary aim of reducing real power transmission line losses while adhering to system constraints. Although the traditional PSO has a fast convergence speed, it falls easily into local optimum and it is slow at the later stage of convergence. The ICPSO is proposed in this research to overcome these shortcomings. The approach combines PSO with chaotic map which increases particles’ diversity, allowing particles to explore the search region more; and a wingbeat frequency component which helps to sustain the rate of convergence of particles. MATPOWER 7.1 in MATLAB 2019a environment was utilized for the implementation. The purported algorithm was examined on IEEE14 and IEEE30 Test Beds respectively. When tried out on IEEE14 Test bed, real power loss was reduced from 13.393 MW to 12.260 MW; whereas real power transmission line loss was brought down from 17.557 MW to 15.977 MW when tried out on IEEE30 Test Bed. In terms of reducing real power transmission lines losses, the simulation results show that the proposed approach performs better when compared with other algorithms.

Hierarchical Optimal Reactive Power Dispatch for Active Distribution Network with Multi-microgrids

Hierarchical Optimal Reactive Power Dispatch for Active Distribution Network with Multi-microgrids

A Simplified Sine Cosine Algorithm for the Solution of Optimal Reactive Power Dispatch

In this article, a simplified sine cosine algorithm (SSCA) is applied to solve the optimal reactive power dispatch (ORPD) issues by estimating the control variables. This algorithm uses sine cosine functions and generates number of random solutions to obtain the best solution by fluctuating inwards or outwards. The SSCA is implemented in the ORPD problem to find the best control variables to achieve minimum power loss and maximum net savings. Furthermore, the efficacy of SSCA is validated with other recently used algorithms considering three case studies, i.e., IEEE-30, -57, and -118 bus test system. The results show that the SSCA approach finds more precise and superior ORPD solutions. A comparison among SSCA and other methods proves the robustness of SSCA to attain the solution with faster convergence. The statistical analysis is performed to justify the effectiveness of SSCA by yielding minimum operating cost and maximum net savings as compared to other techniques considered in this study.

Optimal Reactive Power Dispatch by Success History Based Adaptive Differential Evolution Salp Swarm Algorithm

In this study, a novel hybrid algorithm success history-based adaptive differential evolution salp swarm algorithm (SHADE-SSA) is proposed to solve two different cases of IEEE 30 bus reactive power dispatch problems integrated with thermal generators, wind farms and solar photovoltaic plants. Real power loss minimization and voltage deviation minimization are considered as main objectives in the present work. The performance and robustness of the proposed hybrid SHADE-SSA algorithm are compared with the results of five different metaheuristic algorithms for the same test system and consider the same control variables and constraints. The results of the simulation of the proposed algorithm conform to the effective choice for the solution of optimal reactive power dispatch problems of power systems.

Many-objective optimization of real and reactive power dispatch problems

Power dispatch is one of the basic functions in power systems operation and planning activities. It involves security, performance, and economic aspects, while maintaining generation and demand balance. The Optimal Power Dispatch (OPD) and the Optimal Reactive Power Dispatch (ORPD) are formulations of the power dispatch that respectively address objectives related to efficiency, and voltage regulation and stability. Under most common approaches, both problems are independently solved, despite their strong connection. This paper addresses the problem of the simultaneous dispatch of real and reactive power considering four conflicting objectives: minimization of network power losses, maximization of system voltage stability, maximization of power factor on transmission network step-down transformers, and minimization of power dispatch economics. The mathematical model is developed based on the current regulatory framework of the Brazilian electricity sector. To solve the problem, an optimization methodology using the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is proposed. This methodology is tested both on IEEE 30-bus and 57-bus systems and on a Brazilian 10351-bus system. For each system, a set of efficient solutions with good convergence and diversity is obtained. We highlight a 6.5% (30.1 MW) reduction of power losses in the Brazilian system, as well as a 2.8% reduction in the power dispatch cost and a increase of 0.6 in the power factor. The methodology showed that the simultaneous dispatch of real and reactive power is a successful approach to improving the performance of power systems in several criteria.

A split Bregman method solving optimal reactive power dispatch for a doubly-fed induction generator-based wind farm

This paper proposes an optimal reactive power control method to maximize wind farm revenue and minimize the total electrical losses of a doubly-fed induction generator (DFIG)-based wind farm. Specifically, the split Bregman method is used to solve the optimal control problem in a distributed manner. That is, the optimization problem is decomposed into sub-problems by the optimal distributed control strategy, and each sub-problem is solved independently in each local controller through the parallel method, which reduces the calculating burden and improves the information privacy. Thus, when a fault occurs, the proposed distributed control strategy can overcome the system fault and improve the reliability and security of the system. Furthermore, an economic financial model of annual revenue is contributed to examine the income impact with or without certified emission reduction (CER) by the clean development mechanism (CDM). Compared with the dual ascent (DA) method, sequential quadratic programming (SQP) method and the proportional dispatch method (PDM), the annual revenue (AR) of the wind farm using the proposed split Bregman method is the highest. Simulation results demonstrate that this method has promising performance in both optimization quality and computational efficiency.

RETRACTION NOTICE: Pareto-based allocations of multi-type flexible AC transmission system devices for optimal reactive power dispatch using Kinetic Gas Molecule Optimization algorithm

RETRACTION NOTICE: Pareto-based allocations of multi-type flexible AC transmission system devices for optimal reactive power dispatch using Kinetic Gas Molecule Optimization algorithm

Reactive power control in photovoltaic systems through (explainable) artificial intelligence

Across the world, efforts to support the energy transition and halt climate change have resulted in significant growth of the number of renewable distributed generators (DGs) installed over the last decade, among which photovoltaic (PV) systems are the fastest growing technology. However, high PV penetration in the electricity grid is known to lead to numerous operational problems such as voltage fluctuations and line congestions, which could be eased by utilizing the reactive power capability of PV systems. To this end, we propose to use artificial neural network (ANN) to predict optimal reactive power dispatch in PV systems by learning approximate input–output mappings from AC optimal power flow (ACOPF) solutions in either a centralized or a decentralized manner. In the case of decentralized control, we leverage Shapley Additive Explanations (SHAP), an explainable artificial intelligence (XAI) technique, to identify non-local grid state measurements which significantly influence the optimal dispatch of each individual system. Both centralized and decentralized ANN-based controllers are evaluated through a case study based on the CIGRE medium-voltage distribution grid and compared to baseline control strategies. Results show that both ANN-based controllers exhibit superior performance, hindering voltage problems and line congestions which are encountered with baseline strategies while recording an energy saving of 0.44% compared to fixed power factor control. By leveraging ANN and SHAP, the proposed decentralized controllers for reactive power control are able to achieve ACOPF-level performance while promoting data privacy and reducing computational burden.

A Reactive Power Reserve Constrained Optimum Reactive Power Dispatch Using Coronavirus Herd Immunity Optimizer

The Optimal Reactive Power Dispatch (ORPD) is a very important study in planning the power system operation. The conventional ORPD formulations focus only on driving the objective functions toward optima and ignore the benefits to retain adequate Reactive Power Reserve (RPR) in the system. Studies show that inadequate RPR can jeopardize the power system voltage security under stressed conditions. In view of this, a Reserve Constrained ORPD (RC-ORPD) is proposed in this paper and solved using the recently developed, Coronavirus Herd Immunity Optimizer (CHIO). For this purpose, the minimum required Effective Reactive Reserve (ERR) is devised and introduced as a constraint in the ORPD problems. Here, the minimization of power loss and L-index are taken as objective functions, and the problem is formulated as a non-linear constrained optimization problem. The proposed problem is tested on standard IEEE 30 bus and IEEE 57 bus systems. To assess the impact of the proposed strategy, the same problems are also solved by relaxing the ERR constraint. Comparisons reveal that the added constraint improves voltage stability linked parameters and offers more RPR in the system without sacrificing much on the optimum solution. Further, the proficiency of CHIO is verified through various case studies and comparisons.

A Reserve Constrained Optimum Reactive Power Dispatch in Wind-Integrated Power Systems Using Coronavirus Herd Immunity Optimizer

The optimal reactive power dispatch (ORPD) plays a significant role in planning stable and secure power system operations. The challenges associated with the stable operation of power system networks increase with the rise in wind energy penetration into the system. To guarantee the secure operation of wind-integrated power system networks, a scenario-based reserve constrained optimal reactive power dispatch (RC-ORPD) approach is proposed in this work. For this, the effect of uncertain wind power penetration on the effective reactive reserve (ERR) and voltage stability indicator (L-index) is assessed. The threshold ERR required to retain in the system is evaluated, and is incorporated as an additional constraint to the conventional ORPD problems. The proposed problem is modeled as a scenario-based non-linear complex optimization problem and is solved using a newly developed meta-heuristic called “Coronavirus Herd Immunity Optimizer (CHIO)”. Programs are developed in MATLAB and tested on the wind-integrated IEEE 30, IEEE 118, and practical Indian 62 bus systems. The anticipated strategy is validated through several case studies and comparisons. The comparison reveals that the newly added constraint improves the voltage stability of the wind-integrated power systems without negotiating much on the numerical values of the objectives. Also, the efficacy of the CHIO is verified through statistical studies and comparisons with other contemporary algorithms.

Design of fractional comprehensive learning PSO strategy for optimal power flow problems

Optimal reactive power dispatch (ORPD) is one of the paramount issue for the researchers during the investigation of power system performance in dynamic load scenarios. In this paper, a new nature inspired computing paradigm based on fractional order comprehensive learning particle swarm optimization (FO-CLPSO) is designed and implemented for solving the reactive power dispatch problems. The objective of the study is to improve the power system efficiency by reducing line losses, enhancing bus voltage profiles and reducing the operating cost of the system for different load factors. The decision variables for the fitness evaluation are the tap changer settings, generator bus voltages, fixed capacitors and flexible AC transmission systems (FACTS). The operation, validity and scalability of the FO-CLPSO are tested on standard IEEE 30 bus and IEEE-57 bus systems. The exploitation and exploration for FO-CLPSO are further extended using different fractional orders for minimization problems in ORPD to critically analyze the performance by comparing with several state of art counterpart methodologies. The stability, consistency, robustness and reliability of FO-CLPSO for the solution of ORPD problems is also substantiated through detailed statistical analyses including the development of empirical cumulative distribution functions, probability plots, box plot illustrations and histograms both for precision and complexity metrics.

Application of Coulomb’s and Franklin’s laws algorithm to solve large-scale optimal reactive power dispatch problems

Application of Coulomb’s and Franklin’s laws algorithm to solve large-scale optimal reactive power dispatch problems