Optimal Power Flow Management Research Articles

Optimal Power Flow Management for a Solar PV-Powered Soldier-Level Pico-Grid

Users ought to decide how to operate and manage power systems in order to achieve various goals. As a result, many strategies have been developed to aid in this regard. Optimal power flow management is one such strategy that assists users in properly operating and managing the supply and demand of power in an optimal way under specified constraints. However, in-depth research on optimal power flow management is yet to be explored when it comes to the supply and demand of power for the bulk of standalone renewable energy systems such as solar photovoltaics, especially when it comes to specific applications such as powering military soldier-level portable electronic devices. This paper presents an optimal power flow management modelling and optimisation approach for solar-powered soldier-level portable electronic devices. The OPTI toolbox in MATLAB is used to solve the formulated nonlinear optimal power flow management problem using SCIP as the solver. A globally optimal solution was arrived at in a case study in which the objective function was to minimise the difference between the power supplied to the portable electronic device electronics and the respective portable electronic device power demands. This ensured that the demand for solar-powered soldier-level portable electronic devices is met at all times in spite of the prohibitive case scenarios’ circumstances under the given constraints. This resolute approach underscores the importance placed on satisfying the demand needs of the specific devices while navigating and addressing the limitations posed by the existing conditions or constraints. Soldiers and the solar photovoltaic user fraternity at large will benefit from this work as they will be guided on how to optimally manage their power systems’ supply and demand scenarios. The model developed herein is applicable to any demand profile and any number of portable electronic device and is adaptable to any geographical location receiving any amount of solar radiation.

Optimal Power Flow Management in Microgrids using Distributed Energy Resources

The effective and stable functioning of microgrids using distributed energy resources (DERs) is greatly dependent on ideal power stream management. To get the optimal power flow in microgrids, this study provides an inquiry into the use of cutting-edge optimization techniques. To fulfil load demand and consider DER requirements, system running costs must be kept to a minimum. The microgrid integrates a variety of distributed energy resources, including solar photovoltaic systems, wind turbines, and energy storage systems. The research examines several optimization strategies, such as mixed-integer programming, evolutionary algorithms, and linear programming, to decide on the best DER dispatch and scheduling. The findings give important information on how to efficiently use and manage distributed energy resources to raise the performance and resilience of microgrids.

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 multi-objective thermal exchange optimization model for solving optimal power flow problems in hybrid power systems

This study addresses the challenges associated with optimal power flow (OPF) management in hybrid power systems incorporating diverse energy sources, particularly focusing on the unpredictability of renewable energy sources (RESs). A novel analytics approach is introduced using Multi-Objective Thermal Exchange Optimization (MOTEO). MOTEO is based on modeling energy transfer grounded on Newton’s Law of Cooling. The model integrates innovative non-dominated sorting and crowing distance strategies to effectively solve the multi-objective optimization problem. The proposed hybrid OPF model incorporates four primary types of energy resources: thermal, wind, solar, and small-hydro, offering a holistic approach to power management in hybrid systems. Our model’s practical applicability and efficiency are validated through rigorous testing on a modified IEEE 30-Bus system, benchmarked against other contemporary optimization methodologies. The results demonstrate that the MOTEO model successfully identifies optimal solutions for the multi-objective optimal power flow (MOOPF) problem while maintaining compliance with stringent power system constraints. This novel contribution enhances the field of analytics by providing a robust and efficient model to handle the complex problem of OPF in hybrid systems, thereby ensuring increased system reliability.

Retracted: “An efficient control strategy for optimal power flow management from a renewable energy source to a generalized three-phase microgrid system: A hybrid squirrel search algorithm with whale optimization algorithm approach”

Retracted: “An efficient control strategy for optimal power flow management from a renewable energy source to a generalized three-phase microgrid system: A hybrid squirrel search algorithm with whale optimization algorithm approach”

A DC–DC Converter Topology for Optimal Utilization of Hybrid Renewable Energy Resources Using Hybrid Technique

This article proposes a DC–DC converter for the optimal use of hybrid renewable energy sources (HRES) with the hybrid control approach using the proposed technology. HRES-like photovoltaic (PV), wind turbine (WT), and battery energy storage technology are examined (BESS). The proposed hybrid control technique is a hybrid wrapper of the Marine Predators Algorithm (MPA) and recalling-enhanced recurrent neural network (RERNN). The biological interaction of MPA is improved using the crossover (C) and mutation (M) method, which is later referred to as the combined CM-MPA (C2MPA) technique. The proposed system is called as C2MPA-RERNN technique. The C2MPA approach is used in the proposed method to evaluate the exact control signals system and to build up a control signals database for offline use according to power variation of source and load sides. The collected dataset is utilized to run the RERNN technique online, allowing the control method to run in less time. Based on the error value of active and reactive power, the C2MPA-RERNN determines optimal gain parameters of a proportional integral (PI) controller. The real and reactive power values are input algorithms. The system error is diminished, and optimal power flow management is connected to tune the PI parameters. The proposed system is tested using the MATLAB/Simulink platform. The gain parameters like Kp and Ki of the proposed system refer 2.5 and 150, respectively.

Energy Management Strategy for Optimal Sizing and Siting of PVDG-BES Systems under Fixed and Intermittent Load Consumption Profile

Advances in PV technology have given rise to the increasing integration of PV-based distributed generation (PVDG) systems into distribution systems to mitigate the dependence on one power source and alleviate the global warming caused by traditional power plants. However, high power output coming from intermittent PVDG can create reverse power flow, which can cause an increase in system power losses and a distortion in the voltage profile. Therefore, the appropriate placement and sizing of a PVDG coupled with an energy storage system (ESS) to stock power during off-peak hours and to inject it during peak hours are necessary. Within this context, a new methodology based on an optimal power flow management strategy for the optimal allocation and sizing of PVDG systems coupled with battery energy storage (PVDG-BES) systems is proposed in this paper. To do this, this problem is formulated as an optimization problem where total real power losses are considered as the objective function. Thereafter, a new optimization technique combining a genetic algorithm with various chaotic maps is used to find the optimal PVDG-BES placement and size. To test the robustness and applicability of the proposed methodology, various benchmark functions and the IEEE 14-bus distribution network under fixed and intermittent load profiles are used. The simulation results prove that obtaining the optimal size and placement of the PVDG-BES system based on an optimal energy management strategy (EMS) presents better performance in terms of power losses reduction and voltage profile amelioration. In fact, the total system losses are reduced by 20.14% when EMS is applied compared with the case before integrating PVDG-BES.

Multithreaded Multiswarm Model for Intelligent Economic Prosumer Load Dispatch for Battery Supported DC Microgrid

Economic load dispatch should be given special care even when the primary responsibility of any demand response model is to provide a consistent supply to the load. Demand can be satisfied by the utility grid as well as self-sustaining user sources. If a user generates excess power after meeting demand, the user can pool it and transfer it to the grid or neighboring consumers. This is referred to as the prosumer model, in which the user serves as both a producer and a consumer. Furthermore, some of the surplus power may be stored in energy storage devices. A sophisticated mathematical model is required to estimate how much power should be generated, pooled, pulled from the grid, gathered from close users, supplied to nearby customers, and so on. This paper tries to present a smart economic load dispatch model for a demand response system that combines a multithreaded swarm model with a reward-based reinforcement system to assure optimal source selection and power flow management. To identify the optimum cost-effective power sharing model among a user, the grid, and neighboring users, the system uses particle swarm optimization (PSO) and artificial bee colony (ABC) optimization. Both models have benefits and drawbacks, and not all models work well with all data input. Using two models at the same time consumes a significant amount of time and computational power. As a consequence, for each data input, an upper bound confidante (UBC) model is used in parallel to select the best economical swarm model based on a semisupervised reinforcement model. A weighted Boruvka’s algorithm based on transmission line cost and transmission loss is being used to construct an optimum economic power sharing model, which is backed by swarm models. The efficiency of each model is evaluated using the same data for both models, and error analysis is performed. It was discovered that each model performs differently for various data, and creating a reinforced multithreaded model helps to increase accuracy, reduce computing time, and improve efficiency.

Optimal battery sizing for a grid-tied solar photovoltaic system supplying a residential load: A case study under South African solar irradiance

Owing to the global increasing need for clean renewable energy, solar photovoltaic (PV) generation technology has gained more attention. The utilization of a grid-tied solar PV rooftop system may minimize the electricity bills of residential consumers. Battery storage proved to be the most expensive component of a solar PV system. Hence, optimal battery sizing for a grid-tied PV solar system is of fundamental importance to maximize investment returns. This study aims to determine the optimal battery size for the proposed non-interactive grid-tied solar PV-battery system when exposed to South African solar irradiance. The proposed system is investigated for supplying the residential load under the time-of-use (TOU) pricing strategy. Hence, the optimal power flow control model has been developed and utilized to determine the optimal battery size. Optimal power flow management has been achieved through the use MATLAB optimization solver called linprog. Different battery sizes have been analyzed for the selected 4.2-kW solar PV array that supplies a residential load having a peak demand of 4.2-kW. The optimization results indicated that the optimal battery size is 18.3% of the residential load demand, in the context of South African solar irradiance and the TOU tariff scheme. When the selected PV array size matches the peak load demand, the selection of a battery size greater than 18.3% proved to minimize the economic returns as a result of the inflexible electricity cost savings. For PV array size greater than the peak load demand, the optimal battery storage size increases by 11.5% of the daily load energy consumption per kW upsizing.

Optimal control pulses establishment for the power flow management in hybrid renewable energy sources using BCRFA controller

These works are introduced to avoid electrical problems in literary works. An intelligent hybrid control technique for optimal power flow management (OPFM) in grid-associated hybrid renewable energy sources (HRES) with energy storage is proposed in this dissertation. For stabilizing power fluctuations, the intelligent controller is proposed. The proposed intelligent controller is joint performance of Big Bang-Big Crunch (BB-BC) and random forest algorithm known as BCRFA controller. In the proposed controller, the BB-BC method reproduces the evaluation process to establish the exact control signals (ECS) system depending on power variations with source, load side. With this appropriate control, HRES can significantly improve the dynamic safety of the power system. Finally, the proposed system is executed on MATLAB/Simulink work site. The efficiency of the BCRFA system compares the existing system. The result of the comparison reveals that HRES power flow using the proposed system is direct efficiently compared with the existing systems. The efficiency of the various techniques and the proposed technique has been analysed in three case studies. In Case 1, the PV, wind, battery and FC gives the efficiency using proposed technique is 95.3617%, 99.1235%, 98.1264% and 99.4677%. In Case 2, the photovoltaic (PV), wind, battery and fuel cell (FC) give the efficiency using the proposed technique is 91.4365%, 93.6537%, 98.5151% and 97.1562%. In Case 3, the PV, wind, battery and FC give the efficiency using the proposed technique is 92.5479%, 91.2369%, 97.5631% and 90.1245%.

Energy Bill Reduction by Optimizing Both Active and Reactive Power in an Electrical Microgrid

The aim of this paper is to develop tools for the Optimal Power Flow Management Control (OPFMC) in a microgrid (MG). It consists of a photovoltaic system, an energy storage system, a gas turbine, and a main grid (energy exchange). Unlike the conventional approaches, which are limited to the distribution of the active power, this paper relies on an electrical modeling system in order to coordinate and to optimize both the active and the reactive power flow using discrete controls. The proposed optimal power management strategy has two objectives: (i) it aims at forecasting, over a time horizon of 24-hours, the optimal distribution of the active and the reactive power required for each power source connected to the microgrid. The proposed management incorporates the consumption forecasts, the weather, and the tariffs; (ii) it aims at reducing the solicitation of the main grid while taking into consideration the requested reactive power. This is done by using dynamic programming based on Bellman algorithm. Such management has been tested by simulation in a Matlab/Simulink environment and its performance has been compared to the conventional management strategy.

An Efficient Hybrid Technique for Power Flow Management in Smart Grid with Renewable Energy Resources

ABSTRACT In this paper, an optimal power flow management of a hybrid renewable energy source (HRES) with a hybrid approach is proposed. Here, the proposed method is the consolidation of Improved Bear Smell Search (IBSS) and Sparrow Search Algorithm (SSA); hence, it is named as IB4SA technique. Here, the IBSS generates the voltage source inverter control signals based on the power exchange variety between the source side and load side. In the proposed work, the Bear Smell Search (BSS) is incorporated by crossover and mutation function, therefore it is named as IBSS. The multi-objective function is designed by the grid needed active with reactive power varieties generated based on the available source power. SSA process guarantees the detection of online control signals using a parallel implementation against active with reactive power varieties. The control method based on the proposed approach improves the control parameters of the power controller under power flow variations. The power flow management of the smart-grid system is controlled using the proposed technique based on variations in the parameters of the source and load side. The proposed technique is responsible for controlling energy sources using both renewable energy sources and energy storage devices, in order to generate the power requirement by the grid. The proposed approach is executed in MATLAB/Simulink work site and the performance is analyzed with the existing approaches. The statistical analysis for Case 1, 2, and 3 using proposed as well as existing approaches are analyzed. In Case 1, using the proposed technique the parameter of with mean represents 0.528, median represents 0.512 and standard deviation represents 0.033. In Case 2, using the proposed technique the parameter of with mean implies 0.671, median implies 0.656, and standard deviation implies 0.026.

An efficient optimal power flow management based microgrid in hybrid renewable energy system using hybrid technique

In this paper, the optimal power flow management-based microgrid in hybrid renewable energy sources with hybrid proposed technique is presented. The photovoltaic, wind turbine, fuel cell and battery are also presented. The proposed technique is the combined execution of both spotted hyena optimization and elephant herding optimization. Spotted hyena optimization is utilized to optimize the combination of controller parameters based on the voltage variation. In the proposed technique, the spotted hyena optimization combined with elephant herding optimization plays out the assessment procedure to establish the exact control signals for the system and builds up the control signals for offline way in light of the power variety between source side and load side. The objective function is defined by the system data subject to equality and inequality constraints such as real and reactive power limits, power loss limit, and power balance of the system and so on. The constraint is the availability of the renewable energy sources and power demand from the load side in which the battery is used only for lighting load. By utilizing the proposed method, the power flow constraints are restored into secure limits with the reduced cost. At that point, the proposed model is executed in the Matrix Laboratory/Simulink working platform and the execution is assessed with the existing techniques. In this article, the performance analysis of proposed and existing techniques such as elephant herding optimization, particle swarm optimization, and bat algorithm are evaluated. Furthermore, the statistical analysis is also performed. The result reveals that the power flow of the hybrid renewable energy sources by the proposed method is effectively managed when compared with existing techniques.

Optimal Power Flow Management System for a Power Network with Stochastic Renewable Energy Resources Using Golden Ratio Optimization Method

An optimal operation system is a potential solution to increase the energy efficiency of a power network equipped with stochastic Renewable Energy Sources (RES). In this article, an Optimal Power Flow (OPF) problem has been formulated as a single and multi-objective problems for a conventional power generation and renewable sources connected to a power network. The objective functions reflect the minimization of fuel cost, gas emission, power loss, voltage deviation and improving the system stability. Considering the volatile renewable generation behaviour and uncertainty in the power prediction of wind and solar power output as a nonlinear optimization problem, this paper uses a Weibull and lognormal probability distribution functions to estimate the power output of renewable generation. Then, a new Golden Ratio Optimization Method (GROM) algorithm has been developed to solve the OPF problem for a power network incorporating with stochastic RES. The proposed GROM algorithm aims to improve the reliability, environmental and energy performance of the power network system (IEEE 30-bus system). Three different scenarios, using different RES locations, are presented and the results of the proposed GROM algorithm is compared to six heuristic search methods from the literature. The comparisons indicate that the GROM algorithm successfully reduce fuel costs, gas emission and improve the voltage stability and outperforms each of the presented six heuristic search methods.

Application of ANFASO for optimal power flow management of MG‐connected system with energy storage

The power flow management scheme for a microgrid (MG)-connected system utilizing a hybrid technique is suggested in this dissertation. An MG-connected system includes photovoltaic, wind turbine, micro turbine and battery storage. Due to the use of this resource, power production is intermittent and unpredictable, as well as unstable, which causes fluctuation of power in hybrid renewable energy system. To ensure the fluctuation of power, an optimal hybrid technique is suggested. The suggested hybrid technique is joint execution on ANFIS and ASOA. ANFIS stands for adaptive neuro fuzzy interference system, and ASOA stands for advanced salp swarm optimization algorithm, thus it is commonly known as the ANFASO method. In the established method, ANFIS is applied to continuously track the MG-connected system's required load. ASOA optimizes the perfect combination of MG in terms of predicted required load. The suggested methodology is used for optimal cost and to increase renewable energy sources (RESs). Constraints are RES accessibility, power demand and the storage elements. Using the MATLAB/Simulink work site, the ANFASO approach is executed and implemented compared with existing methods. The suggested method is compared with genetic algorithm (GA), BFA and the artificial bee colony algorithm (ABC), and the observed elapsed time of ABC is 37.11 seconds, BFA is 36.96 seconds and GA is 38.08 seconds. The elapsed time of the proposed technique was found to be lower (36.47 seconds) compared to existing techniques. Significant improvements regarding utilization of RES and total generation cost accuracy are attainable by utilizing the proposed approach.

RETRACTED: An efficient control strategy for optimal power flow management from a renewable energy source to a generalized three-phase microgrid system: A hybrid squirrel search algorithm with whale optimization algorithm approach

This paper introduces a hybrid approach for power flow management (PFM) of the hybrid renewable energy source (HRES) connected microgrid (MG) system. Here, the proposed approach is the combination of both the squirrel search algorithm (SSA) with whale optimization algorithm (WOA) named as SSAWO. Here, the SSA is developing the control signals of the voltage source inverter subject to the difference of power exchange between the source side and load side. The multi-objective function is shaped by the grid required active and reactive power varieties generated based on the accessible source power. The WOA procedures guarantee to locate the online control signals by utilizing the parallel execution against the active and reactive power varieties. The proposed technique-based control model enhances the control parameters of the power controller in light of the power flow varieties. By using the proposed methodology, the PFM of the MG system is controlled based on the source side and load side parameters variations. In order to render the power demand by the grid, the present system is accountable for dominating the energy sources using, optimally, both renewable energy sources and energy storage devices. Finally, the proposed model is implemented in MATLAB/Simulink working platform and the execution equates with the existing techniques.

Power flow management scheme of hybrid renewable energy source to maximize power transfer capability using I2HOSOA approach

Due to the intermittent nature of renewable sources, the generation of power is varied which is the main problem in renewable energy system. Miss-matching between the power generation and load power causes a deviation from the desired voltage and frequency in power supply. Therefore, a new efficient smart grid system is required for an optimal power flow management. In this paper, a hybrid approach is presented for power flow management of HRES connected smart grid system. The novelty of the proposed approach is the combined execution of IHHO with SOA named as I2HOSOA technique. In the established work, the HHO is integrated by crossover and mutation function, it is known as IHHO. The main contribution of the proposed strategy is to control the power flow based on source and load side parameters variations. In the proposed approach, the control signals of the voltage source are developed by the IHHO based on the variety of power exchange between the source and load side. Similarly, the online control signals are located by the SOA procedure by utilizing the parallel execution against the active and reactive power varieties. The multi-objective function is shaped by the grid required active and reactive power varieties created based on accessible source power. Here, the control parameters of the power controller are enhanced by the proposed technique based control models in light of the power flow varieties. The comparison between established and existing methods is analyzed in terms of reactive current injection, grid code, current amplitude limitation control, active power control, zero active power oscillations, and injection of active and reactive power. Furthermore, the statistical evaluation of established, and existing methods of mean, median, and standard deviation, is evaluated. Finally, the proposed model is executed in MATLAB/Simulink working platform and the execution is compared with the existing techniques.

Optimal Power Flow Management and Control of Grid Connected Photovoltaic-Battery System

Abstract This paper presents optimal power flow management in real time for grid connected photovoltaic (PV)–battery system. The objective is the real time, dynamic and optimal scheduling of battery storage which reduces grid power consumption and achieves the peak load shaving. For this purpose, two strategies are developed; one is rule-based and other is optimization algorithm based. Two strategies are compared, pros and cons of those strategies are established. Though rule-based technique is simple & easy to implement, the optimization technique provides more optimal regulation of battery which provides the lesser electricity cost. Forecasted PV/load powers are used to get optimal value of battery power. In order to handle uncertainties due to forecasted data, fuzzy-based corrective strategy is developed to modify the reference battery power as per the present values of PV/load powers. The proposed analysis is carried out for over one exemplary day. The simulations are presented using typical load/PV data of India and Finland as a part of research collaboration. Experimental results are also carried out using proposed scheme.

Analysis of power management and cost minimization in MG—A hybrid GOAPSNN technique

AbstractThis paper presents a hybrid technique for optimal power flow management and production cost minimization of microgrid (MG)‐connected system with renewable energy sources (RES). The hybrid technique is the joint execution of both the particle swarm optimization (PSO)–aided artificial neural network (ANN) and grasshopper optimization algorithm (GOA) named as GOAPSNN technique. The proposed technique is concerned with the mathematical optimization problems that involve more than one objective function to be optimized. At first, the PSO‐ANN technique predicts the load demand in the MG‐connected system by using the inputs of MG‐connected system like photovoltaic (PV) system, wind turbine (WT), microturbine (MT), diesel engine (DE), and battery. According to the load demand, GOA is utilized to choose an optimal configuration of MG, ie, fuel cost minimization, emission factors, operating, and maintenance cost. The proposed technique is executed in MATLAB/Simulink platform and compared with the existing strategies, such as squirrel optimization with gravitational search–aided neural network (SOGSNN), adaptive neuro‐fuzzy interference system and advanced salp swam optimization algorithm (ANFASO). In this paper, the maximum generated power of PV, WT, MT, and battery is 7.9, 9.8, 15.7, and 4.56 kW, respectively. The state of charge (SoC) of the proposed technique is investigated, which is around 83%. In light of the load demand, the proposed approach has less production cost.

Hierarchical Signal Processing for Tractable Power Flow Management in Electric Grid Networks

Rapid advancements in smart grid technologies have brought about the proliferation of intelligent and actuating power system components such as distributed generation, storage, and smart appliance units. Capitalizing fully on the potential benefits of these systems for sustainable and economical power generation, management, and delivery is currently a significant challenge due to issues of scalability, intermittency, and heterogeneity of the associated networks. In particular, vertically integrated and centralized power system management is no longer tractable for optimally coordinating these diverse devices at large scale while also accounting for the underlying complex physical grid constraints. To address these challenges, we propose a hierarchical signal processing framework for optimal power flow management whereby the cyber-physical network relationships of the modern grid are leveraged to enable intelligent decision-making by individual devices based on local constraints and external information. Decentralized and distributed techniques based on convex optimization and game theoretic constructs are employed for information exchanges and decision-making at each tier of the proposed framework. It is shown via theoretical and simulation studies that our technique allows for the seamless integration of power components into the grid with low computational and communication overhead while maintaining optimal, sustainable, and feasible grid operations.