Optimal Reactive Power Research Articles

Levy flight elitism-enhanced salp swarm algorithms for solving the ORPD problem

The optimal reactive power dispatch (ORPD) challenge is crucial for ensuring the efficient and reliable operation of power systems, hinging on the effective coordination of multiple devices.As a result, solving the ORPD problem involves tackling a complex nonlinear optimization challenge that demands advanced computational algorithms to determine the optimal solution. This article introduces an enhanced version of the modified algorithm, called L-E-SSA, which is inspired by the coordinated movement of a swarm led by a single member (salp swarm algorithm SSA), incorporating Levy Flight elitism, to effectively solve the ORPD problem. This study aims to reduce power losses (Ploss) and total voltage deviation (TVD) by optimizing the control variables. Numerical simulations are carried out on IEEE (30-bus) and IEEE (118-bus) power systems. To evaluate the performance and effectiveness of the modified algorithm (L-E-SSA). The results indicate that the L-E-SSA algorithm delivered strong performance and secured a competitive ranking through statistical analysis. Simulations demonstrate that the proposed method has the potential to be a highly effective solution for the ORPD problem.

Multi-Timescale Reactive Power Optimization and Regulation Method for Distribution Networks Under a Multi-Source Interaction Environment

In the context of constructing new power systems, distribution networks are increasingly incorporating distributed resources such as distributed photovoltaic (PV) systems, decentralized wind turbines (WTs), and new types of energy storage system (ESS), which may lead to prominent issues such as voltage overruns and reverse heavy overloads in the distribution network. While distributed resources are valuable for voltage regulation, their regulation characteristics vary with their operation means, and the randomness and volatility of renewable power generation will also influence the optimization and regulation of voltage in the distribution network. This paper proposes a multi-timescale reactive power optimization and regulation method for distribution networks in a multi-source interactive environment. Firstly, the voltage regulation characteristics of distributed PV systems, decentralized ESSs, and distributed WTs are analyzed. Based on this analysis, a multi-timescale voltage optimization scheme for distribution networks using the MPC method is proposed, which optimizes the voltage regulation strategies for each distributed resource in a rolling manner. Furthermore, an event-triggered real-time voltage zoning control strategy based on voltage sensitivity is proposed to address the real-time sudden voltage overlimit problems. The modified IEEE 33-node system is used to verify the performance of the proposed method. Simulation results indicate that the issue of voltage overruns at distribution network nodes has been improved, and the intraday rolling optimization yields results are more realistic compared with the day-ahead optimization method.

Optimal Reactive Power Dispatch and Demand Response in Electricity Market Using Multi-Objective Grasshopper Optimization Algorithm

Optimal Reactive Power Dispatch (ORPD) is a power system optimization tool that modifies system control variables such as bus voltage and transformer tap settings, and it compensates devices’ Volt Ampere Reactive (VAR) output. It is used to decrease real power loss, enhance the voltage profile, and promote stability. Furthermore, several issues have been faced in electricity markets, such as price volatility, transmission line congestion, and an increase in the cost of electricity during peak hours. Programs such as demand response (DR) provide system operators with more control over how small customers participate in lowering peak-hour energy prices and demand. This paper presents an extensive study on ORPD methodologies and DR programs for lowering voltage deviation, limiting cost, and minimizing power losses to create effective and economical operations systems. The main objectives of this work are to minimize costs and losses in the system and reduce voltage variation. The Grasshopper Optimization Algorithm (GOA) and Dragonfly Algorithm (DA) have been implemented successfully to solve this problem. The proposed technique has been evaluated by using the IEEE-30 bus system. The results obtained by the implementation of demand response systems show a considerable reduction in costs and load demands that benefit consumers through DR considerations. The results obtained from the GOA and DA are compared with those generated by other researchers and published in the literature to ascertain the algorithm’s efficiency.

Coordinated active-reactive power optimization considering photovoltaic abandon based on second order cone programming in active distribution networks

On the basis of predecessors’ coordination optimization of active and reactive power in distribution network, For the necessity of the optimal operation in the distribution network, part of power generated from photovoltaic (PV) cannot be sold to users, and cannot enjoy subsidies. Similarly, the network loss in the power transmission will also bring a certain economic loss. This paper comprehensively considers the economic loss caused by the network loss and PV abandon of the distribution system, and establishes a model to minimize the economic loss. To solve this problem efficiently, the method of DistFlow equation and mixed integer second order cone programming (MISOCP) is used to solve the problem, in this method, the original mixed integer nonlinear programming non-convex problem is transformed into a convex problem, which makes the optimization problem easy to solve. The modified IEEE 33 and IEEE 69 distribution networks are tested by the above method. The optimized results are able to meet the target and have very small relaxation gaps, and the voltage level is also optimized. This coordinated optimization approach helps to optimize the economic operation for active distribution networks with PVs.

Implicit-integral dynamic optimization based on spatial partitioning and temporal segmentation for the power jumps of renewable energy sources

With the widespread development of renewable energy sources, the increasing short-term power volatility poses a significant challenge to the normal operation of power systems. This paper presents a two-timescale adaptive dynamic optimization (TADO) strategy based on implicit trapezoidal integral to address dynamic state fluctuations due to renewable power jumps. Firstly, a static optimization model is established utilizing the model predictive control (MPC) to obtain the optimal states on a longer temporal scale, considering the uncertain renewable power jumps. Next, provide a partitioning method based on modularity to divide the power system into several independent subsystems with constant tie-line power. Using an implicit trapezoidal integral algorithm, each subsystem's dynamic reactive power optimization model is constructed with an appropriate discrete time granularity, accounting for the electromechanical transient processes due to renewable power jumps. Then, to improve the solving efficiency of the dynamic optimization model, a new solution method is proposed based on temporal segmentation. The whole process's optimal state trajectory is achieved by splicing all sub-optimization results. Finally, case studies conducted on the IEEE 9-bus power system and IEEE 39-bus power system validate the feasibility of the proposed strategy.

Optimal Reactive Power Dispatch Using Artificial Gorilla Troops Optimizer Considering Voltage Stability

The power system has been expanded to supply and fulfil the consumers’ requirements for reliability, affordability, and power quality. Power loss reduction and voltage stability enhancement are important points and have been considered interesting subjects for researchers and utilities. Furthermore, reactive power plays an important role in power system stability, security, and voltage improvement, and it is known as reactive power dispatch (RPD). In this paper, a newly developed meta-heuristic optimization technique that inspired the gorilla troop’s social intelligence in nature is applied. It is named Artificial Gorilla Troop Optimization (GTO). In addition, GTO is utilized to solve the optimal reactive power dispatch (ORPD) problem, whose real active power and voltage deviation reduction are the objective functions of this study. Generator voltage, transformer tap-changers, and reactive power compensators are the controlled variables that are optimized for achieving the minimum real power loss and bus voltage deviation. To illustrate the efficiency and performance of the proposed algorithm, IEEE 14-bus and 30-bus systems are employed. Moreover, the obtained results are compared with those obtained with other three already existing optimization algorithms, including the genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA). Obviously, the proposed approach can prove the optimal values of controlled variables in solving the ORPD problem by giving the minimum real power loss and voltage deviation than those from compared techniques with less computation time.

A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach

AbstractThe uncertainty of distributed energy resources (DERs) and loads in distribution networks poses challenges for reactive power optimization and control timeliness. The computational limitations of the traditional algorithms and the development of artificial intelligence (AI) based technologies have promoted the advancement of hybrid model‐data‐driven algorithms. This article proposes a two‐stage reactive power optimization method for distributed networks (DNs) based on a hybrid model‐data‐driven approach. In the first stage, based on the topology and line parameters of the DN, as well as forecasts of loads and renewable energy outputs, a mixed‐integer second‐order cone programming (MISOCP) algorithm is used to control the on‐load tap changer (OLTC) positions on an hourly day‐ahead basis. In the second stage, leveraging deep learning technology, the real‐time reactive power output of photovoltaics (PV) and wind power units is controlled at a 5‐min time scale throughout the day. Specifically, using traditional solvers, the global optimal reactive power output for PV and wind power units is determined first, corresponding to various load and renewable energy output scenarios. Then, neural networks are trained to map node power to the optimal reactive power outputs of renewable energy units, capturing the complex physical relationships. For the second stage, a transformer network framework with a self‐attention mechanism and multi‐head attention for deep learning training is applied to uncover the intrinsic and physical spatial relationships among high‐dimensional features. The proposed method is tested on a modified IEEE 33‐bus system with multiple distributed renewable energy sources. The case study results demonstrate that the proposed hybrid model‐data‐driven algorithm effectively coordinates day‐ahead and real‐time controls of various devices, achieving real‐time model‐free optimization throughout the day. Compared to traditional deep neural networks (DNNs) and convolutional neural networks (CNNs), the transformer network provides superior reactive power optimization results.

Research on Reactive Power Optimization of Synchronous Condensers in HVDC Transmission Based on Reactive Power Conversion Factor

With the rapid development of high-voltage direct current (HVDC) transmission systems, the coupling between AC and DC grids is becoming increasingly close. Voltage disturbances in the grid can easily cause commutation failures in the DC system, threatening its safe and stable operation. The new generation of synchronous condensers (SCs) and modified synchronous condenser units are powerful reactive power support devices widely used in large-capacity DC transmission systems. To maximize the voltage support and commutation failure suppression of SCs, this paper proposes improvements in the initial operating state of SCs, using the Shanxi–Wuhan HVDC receiving end in the Hubei power grid as an example, to better support the HVDC commutation process. Additionally, a reactive power output optimization strategy for SCs is proposed, considering the reactive power equivalent factor of electrical connections between grid nodes. This strategy determines the optimal reactive power output limit of SCs near the converter station to suppress DC commutation failures. Simulation results show that this strategy effectively utilizes the dynamic support capabilities of SCs, prevents DC commutation failures, improves HVDC transmission capacity, and enhances the safety and stability of the receiving end power grid, providing theoretical guidance for reactive power output control.

Enhancing microgrid performance: Optimal proactive reactive power dispatch using photovoltaic active power forecasts

AbstractMicrogrids with substantial incorporation of distributed renewable energy sources face challenges such as magnitude voltage rise, frequency variations, and power quality issues. A novel method is proposed to managing and controlling reactive power within microgrids with high integration of photovoltaic panels. The proactive dispatch is carried out for a few minutes in advance, using power forecast and the inverters of the photovoltaic installations as reactive energy providers. The goal is to stabilise voltage levels and reduce overall energy losses. Optimising reactive power poses a non‐convex problem. The presented method tackles this challenge by employing Conic approximations and Wirtinger calculus techniques to convert and frame the issue within a convex framework, simplifying its resolution. It primarily concentrates on fine‐tuning reactive power output from photovoltaic inverters to minimise overall losses and maintain stable magnitude voltage levels. To enable the reactive power dispatch, a Markov switching forecasting model that uses on‐site data, including sky imagery and other external variables, predicting active power generation every 6 minutes. The proposed technique is evaluated in a CIGRE test network, showing an average loss reduction between 14% and 66% for sunny and cloudy conditions with different levels of PV penetration, this while maintaining the steady state voltages in range.

A new voltage sensitivity-based distributed feedback online optimization for voltage control in active distribution networks

Rising sustainability, environmental, and decarbonization considerations have prompted a recent increase in the integration of inverter-based distributed energy resources (DER), such as photovoltaics (PV), into the power grid. This trend raises concerns about potential voltage violations within distribution systems, emphasizing the growing relevance of effective voltage regulation. Utilizing smart inverters for active and reactive power control proves highly efficient in addressing voltage rise issues within active distribution networks (ADN). This work proposes a distributed feedback online optimization (FOO) strategy for voltage regulation in ADNs. The FOO strategy utilizes online recursive least squares (RLS) sensitivity-based feedback, relying on local measurements to iteratively compute optimal reactive power set points for DERs, ensuring bus voltage regulation to within acceptable limits. The distributed control algorithm employs a primal-dual update approach, enabling coordinated information exchange between neighboring controllers. This online feedback-based control offers a promising solution for real-time coordinated voltage control in distribution grids, particularly with the increasing integration of inverter-based DERs at the grid's edge. The approach demonstrates robustness under varying conditions, communication dynamics, and topology changes, validated using real distribution feeders with actual solar PV production and consumption data. Advancements in sensing, communication systems, and smart grid tools further enhance the feasibility and scalability of this strategy for evolving power systems.

Using Advanced Metering Infrastructure Data from MV/LV Substations to Minimize Reactive Energy Supply Cost to Final Consumers

This article presents an original methodology to determine the optimal level of reactive energy transmission to low-voltage consumers supplied from MV/LV substations that guarantees the lowest total costs of reactive energy transmission through the DSO network and its generation in receiving installations within the reactive power compensation process. The average value of the optimal factor tgφ to be maintained by customers depends on the efficiency of the network, the characteristics of the load, and the market costs of energy losses due to the transmission of reactive energy through the network that are covered by the DSO and the costs of reactive energy generation in receiving installations. The results presented for real MV/LV substations operating in the Polish distribution network demonstrate the application of annual measurements of active and reactive energy consumed and generated registered by AMI systems to calculate the optimal reactive power compensation level. They can be applied to verify the permissible levels of reactive energy compensation applied by the DSOs until now within the yearly tariffs for customers.

Optimal reactive power dispatch with renewable energy sources using hybrid whale and sine cosine optimization algorithm

The optimization of reactive power dispatch entails the complex challenge of controlling and managing the flow of reactive power in power networks to maintain desired voltage levels across many buses. Nowadays, there is a rising preference for employing renewable energy sources rather than traditional thermal generators. This change presents both challenges and possibilities for power system operators and managers. This paper addresses the Optimal Reactive Power Dispatch (ORPD) problem by presenting a novel approach that incorporates solar and wind power plants into existing power networks using the Hybrid Whale and Sine Cosine Optimisation Algorithm (HWSCOA). Solar and wind power plants are established at bus 5 and bus 8 respectively to replace traditional thermal generators in a specific case study using the IEEE 30-bus system. To handle uncertainties associated with load demand changes and the intermittent nature of renewable energy generation, the study employs probability density functions and a variety of scenarios. The primary goal is to minimize power losses in transmission cables while also lowering voltage changes throughout the network. To address uncertainty in load demands and renewable energy output, a scenario-based methodology is used, generating 30 different scenarios to cover all conceivable outcomes. By presenting the ORPD challenge as an optimization problem, the study hopes to achieve considerable reductions in power losses and voltage variations from nominal levels. The findings of this study reveal encouraging results, including significant reductions in power losses and optimized voltage stability even under shifting conditions.

Data-Driven Distributionally Robust Optimization for Day-Ahead Operation Planning of a Smart Transformer-Based Meshed Hybrid AC/DC Microgrid Considering the Optimal Reactive Power Dispatch

Data-Driven Distributionally Robust Optimization for Day-Ahead Operation Planning of a Smart Transformer-Based Meshed Hybrid AC/DC Microgrid Considering the Optimal Reactive Power Dispatch

Multi-objective Reactive Power Optimization of a Distribution Network based on Improved Quantum-behaved Particle Swarm Optimization

Background: Reactive power optimization (RPO) is crucial for distribution networks in the context of large-scale renewable distributed generation (RDG) access. Objective: To address the problems caused by the connection of RDG, an RPO model and an improved quantum-behaved particle swarm optimization (IQPSO) algorithm are proposed. Method: In this study, a dynamic S-type function is proposed as the objective function of the minimum active power loss, whereas an exponential function is proposed as the objective function of the minimum voltage deviation to establish an RPO objective function. The operating cost of distribution is considered as the third objective function. To address the RPO problem, a QPSO algorithm based on the ε-greedy strategy is proposed in this paper. ModifiedIEEE33 bus and IEEE69 bus systems were used to evaluate the proposed RPO method in simulations. Results: The simulation results reveal that the IQPSO algorithm obtains a better solution, and the proposed RPO model can considerably reduce active power loss, node voltage deviation, and distribution network operating costs. Conclusion: The RPO model and IQPSO algorithm proposed in this study provide a highperformance method to analyze and optimize reactive power management in distribution network.

Distributed energy storage participates in reactive power optimization strategy research of new distribution system

Abstract We studied the reactive power control strategy of distributed energy storage in distribution systems, improved reactive power support capacity, and enhanced system reliability and new energy carrying capacity. Firstly, the principles and methods of reactive power optimization in distribution networks are studied. Then, the principles and mechanisms of distributed energy storage participating in reactive power control in distribution networks are studied. Finally, the genetic algorithm optimization method for reactive power optimization in distribution networks is studied, including parameter setting, target selection, and genetic strategy improvement, providing support for fine control of reactive power and voltage in distribution systems and efficient operation. Through simulation analysis of the IEEE33 node system, it is shown that distributed energy storage can improve the reactive voltage level of the distribution system and promote the consumption of new energy.

Power efficiency improvement in reactive power dispatch under load uncertainty

Nowadays, there is a significant rise in electricity demand, posing challenges for power grid operators due to inaccurate forecasting, leading to excessive power losses and voltage instability. This paper addresses these issues by focusing on solving optimal reactive power dispatch (ORPD) while considering load demand uncertainty. The main objective of solving ORPD is to reduce power losses by adjusting generator voltage ratings, transformer tap ratio, and shunt capacitors' reactive power. Monte Carlo simulation (MCS) is employed to generate load scenarios using the normal probability density function, while a reduction-based technique is implemented to decrease the number of those scenarios. The improved gray wolf optimization (I-GWO) algorithm is introduced for the first time to address the stochastic ORPD problem. Experimentation is conducted on an IEEE-30 bus system when results are contrasted with conventional gray wolf optimization (GWO) and five other algorithms as stated in the literature. The I-GWO algorithm's performance is assessed with and without considering load demand uncertainty. Through Friedman's statistical tests, a significant decrease of 20.96% in active power losses and 63.06% in the summation of expected power losses is observed. The I-GWO algorithm's results on the ORPD problem demonstrate its effectiveness and robustness.

Neural network‐based integrated reactive power optimization study for power grids containing large‐scale wind power

AbstractThe high uncertainty of wind power output greatly affects the rapid reactive power optimization of power systems. This paper proposes a neural network‐based comprehensive reactive power optimization method for large‐scale wind power grids, effectively addressing the challenges of rapid reactive power optimization in power systems. Firstly, by constructing typical wind‐power‐load scenarios, the generalization ability of the neural network is improved. Then, focusing on the comprehensive reactive power optimization problem after integrating typical wind‐power‐load scenarios into the system, the improved Harris hawks optimization algorithm (HHO) is compared with the particle swarm optimization algorithm and traditional HHO algorithm, highlighting its advantages. Finally, HHO is utilized for solving, thereby constructing a comprehensive reactive power optimization strategy tag set. Furthermore, through deep fitting of the neural network between the power grid operating state and the comprehensive reactive power optimization strategy, the computational complexity and decision‐making time of reactive power optimization are reduced.

Northern goshawk optimization for optimal reactive power compensation in photovoltaic low-voltage radial distribution networks

In recent years, photovoltaic systems (PVs) have been increasingly integrated into radial distribution networks (RDNs). However, network operators experience significant issues because they are sporadic. To reduce the load on the main grid and improve the network performance, capacitor banks (CBs) are frequently employed for power factor correction and VAr compensation. However, a large number of CBs must be switched at once in accordance with variations in load and PV generation to maintain feeder voltage regulation and improve its performance. This study's optimization strategy for CB control considers a variety of goals. Using seasonal load profiles and PV generation patterns, a novel and efficient northern goshawk optimization (NGO) was created to determine the appropriate control of CBs. By resolving the typical CBs allocation problem in EDNs and comparing the findings with those in the literature, the effectiveness of NGO was initially cross-verified. In the second stage, an NGO is used to maximize annual net savings while maintaining the best possible control over CBs. Considering the PVs and CBs of the network, a modified IEEE 33-bus test system was used to evaluate the effectiveness of the suggested methodology.

Optimal active and reactive energy management for a smart microgrid system under the Moroccan grid pricing code

This article presents an innovative active and reactive energy management system (AR-EMS) specifically designed for residential buildings in Morocco, seamlessly integrated with a Smart Microgrid (SMG) and the Electrical Power Grid (EPG) supplier. The considered SMG incorporates a Photovoltaic system (PV) and an Energy Storage System (ESS). In Morocco, the EPG utilizes a Range Tariff System (RTS), where the tariff structure depends on the total energy consumed at the end of each month. Hence, the proposed AR-EMS is designed to computes optimal active and reactive power setpoints of each source at 15-minute intervals for the next 30 days, based on forecasts of energy consumption and PV power production. The AR-EMS consists of two distinct layers: the Long-Term Optimization Layer (LTOL) and the Short-Term Optimization Layer (STOL). The LTOL focuses on optimizing active and reactive power flow over an extended time horizon of 30 days, relying on forecasted data. In contrast, the STOL involves short-term forecasting and energy optimization over the next 24-hour horizon, addressing the LTOL forecasting uncertainties. The proposed strategy emphasizes a multi-objective problem aiming at minimizing both active and reactive energy bill, ESS degradation cost, Peak-to-Average Ratio (PAR) and carbon emissions. The considered problem is solved using a metaheuristic algorithm, specifically the Lexicographic approach within the Particle Swarm Optimization (PSO) method. To validate the effectiveness of the proposed approach, two comparative analyses are conducted. The first involves the impact of time horizons on the energy cost and the management performances within the Moroccan context. The second comparison, highlights the benefits and shortfalls of using simultaneous active and reactive power management, comparing it with a system exclusively managing active power (A-EMS). Both compared strategies are evaluated using the Moroccan tariff RTS and real world data. The comparative analysis highlights the advantages of the proposed AR-EMS, demonstrating benefits in terms of active and reactive energy bill reduction by 58.9% and 83.2%, respectively, and showcases a 49.3% reduction in carbon emissions while improving the EPG power factor by 6% compared to A-EMS, In conclusion, the optimization results are encouraging the Moroccan government to further develop the installation of SMGs.

A coordinated active and reactive power optimization approach for multi-microgrids connected to distribution networks with multi-actor-attention-critic deep reinforcement learning

As a promising approach to managing distributed energy, the use of microgrids has attracted significant attention among those managing continuous connections to distribution networks. However, the barriers of the data sharing among different microgrids, the uncertainty of the distributed renewable sources and loads, and the nonlinear optimization of power flow make traditional model-based optimization methods difficult to be applied. In this paper, a data-driven coordinated active and reactive power optimization method is proposed for distribution networks with multi-microgrids. A multi-agent deep reinforcement learning (MADRL) method is used to protect the data privacy of each microgrids. Moreover, attention mechanism, which pays attention to crucial information, is presented to overcome the problem of slow convergence caused by the dimensionality explosion of the optimized variables. Two types of agents, controlling discrete action and continuous action devices, respectively, are formulated in coordinated optimization, which reduces voltage violations and improves the system operation efficiency. In addition, in order to improve the performance of the online agent model under variable operation conditions, the transfer learning is embedded in the training process of the MADRL. The proposed method is verified on a modified IEEE 33-bus distribution network with nine microgrids.