Optimal Power Flow Issue Research Articles

Applications of Multi-Objective OPF Solutions with Optimal Placement of Multiple and Multi-Type FACTS Units to IEEE System: Comparison of Different Approaches

Optimal power flow (OPF) problem and its implications for power system stability and efficiency is investigated in this study. OPF, a restricted optimization query with non-linearity and non-convexity, is one of the most challenging and fascinating problems in the recent power system. Based on these parameters, researchers have been working hard over the past few decades to identify the best solutions to the OPF issue that maintain system stability. This work presents multi-objective OPF solutions utilizing Newton's technique with numerous multi-type FACTS units. First, the GA is applied to identify the perfect size and location of the FACTS units. Next, the generator and FACTS settings are optimized. In this instance, four scenarios are taken into consideration and three OFs are employed to see how the OFs affect the positioning and dimensions of FACTS devices. The OF is suggested to consider the reduction of both generation costs and transmission losses while also optimizing the power transfer capacity of designated corridors. A full analysis relating to the IEEE-30 bus system is presented and analyzed.

Multi-objective RIME algorithm-based techno economic analysis for security constraints load dispatch and power flow including uncertainties model of hybrid power systems

In recent times, the landscape of power systems has undergone significant evolution, particularly with the integration of diverse renewable energy sources (RESs). This advancement presents an invaluable opportunity to enhance energy efficiency in the modern power grid, primarily by bolstering the role of stochastic RESs. The challenge lies in the optimal power flow (OPF), a multifaceted and non-linear optimization challenge that grows more complex with the inclusion of stochastic RESs that aims to optimize the allocation of power system resources to minimize the operational cost while maintaining the stability and security of the system. Addressing this, the current study introduces an innovative optimization approach, the Multi-Objective RIME (MORIME) algorithm. Drawing inspiration from the physical phenomenon of rime-ice, called the RIME, the MORIME seeks to effectively tackle OPF issues. This algorithm enhances solution accuracy by smartly dividing with non-dominated sorting and crowding distance mechanism. The proposed OPF model incorporates three types of RESs: solar photovoltaic, wind and small-scale hydropower units. While uncertainties in wind speed and solar irradiation are managed through Monte Carlo simulations, the small hydro unit is considered a constant power source. The efficacy of the MORIME algorithm is tested on IEEE 30 bus systems and results indicate that the MORIME method identifies the optimal solution for the multi-objective OPF problem while satisfying the power system constraints, thereby proving its effectiveness and superiority over MOWOA, MOGWO, MOALO, MOMRFO and MOAGDE in terms of Hyper Volume (HV) and reciprocal of Pareto Sets Proximity (1/PSP) metrices. The MORIME source code is available at: https://github.com/kanak02/MORIME

Optimal power flow analysis with circulatory system-based optimization algorithm

Optimal power flow (OPF) is a challenging optimization problem with a large number of variables and constraints. To overcome the OPF issue, high-performance optimization algorithms are needed. In this direction, this paper has been centered on the optimization of the OPF with the circulatory system-based optimization (CSBO) algorithm. The performance of the algorithm was evaluated on the IEEE 57- and 118-bus power networks for the optimization of non-convex OPF objectives, i.e., fuel cost, power loss, voltage deviation, and enhancement of voltage stability. The solution quality of CSBO is compared with state-of-the-art metaheuristic algorithms such as Artificial Rabbits Optimization (ARO), African Vultures Optimization Algorithm (AVOA), and Chaos Game Optimization (CGO). Based on the OPF results, it is seen that the best fuel cost and voltage deviation results are calculated to be 41666.2344 $/h and 0.5871 p.u with the CSBO algorithm for the IEEE 57-bus power system. The CSBO algorithm obtained the best objective function results for the IEEE 118-bus power network with a fuel cost of 134934.3140 $/h and a power loss of 16.4688 MW. In conclusion, the present paper reports that the CSBO is a powerful and efficient metaheuristic algorithm to solve the OPF problem.

Solution of OPF Using GA Variant With Inclusion of FACTS Devices

Optimal power flow (OPF) is a tool used for minimization through that secured and cost-effective power systems is obtained with the inclusion of FACTS devices for existing power systems that enhance the power transfer ability of the power system, which reduces congestion. The current work proposed a solution to the OPF issue in power systems using genetic algorithm (GA). The proposed technique is implemented on an IEEE 30 bus system by considering the minimization of fuel cost and L-index functions. The results at the end of this paper pre-sent the efficacy of GA algorithm to solve OPF issues in power systems and FACTS devices while comparing it to other algorithms presented previous re-search.

FACTS placement for reactive power planning with weak node constraints using an improved symbiotic search algorithm

In this paper, an economically feasible and reliable operation of the IEEE 57 bus system for Optimal Power Flow (OPF) is proposed. The Improved Symbiotic Organisms Search (ISOS) algorithm is proposed for effective reactive power planning as an OPF issue. Further, the optimal position of Flexible AC Transmission Systems (FACTS) is taken into consideration, by including the existing system controlling variables like reactive power generators output, transformer tapping and capacitors connected at shunt. The objective of the work is two-fold; i.e., to reduce the energy loss and to enhance the voltage profile within the prescribed limit by ensuring the economic operation and investment cost of FACTS in the system. In this work, two FACTS devices like Static Var Compensator (SVC) and Thyristor-Controlled Series Controller (TCSC) have been taken into consideration. Voltage sensitivity indicator and reactive power flow are two tools that are utilised in order to locate weak nodes for the implementation of FACTS. Finally, the performance of the ISOS algorithm is compared with that of three other state- of-the -art optimization techniques, such as, Symbiotic Organisms Search (SOS), Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO). A Non-parametric statistical analysis is also performed to investigate the dominance of the ISOS algorithm over others.

Integrating renewable energy and V2G uncertainty into optimal power flow: A gradient bald eagle search optimization algorithm with local escaping operator

AbstractHere, a new approach is proposed for solving the optimal power flow (OPF) problem in transmission networks using a Gradient Bald Eagle Search Algorithm (GBES) with a Local Escaping Operator (LEO). The method takes into account uncertainty of the renewable energy sources (wind energy and photovoltaic systems) and Vehicle‐to‐Grid (V2G) in the stochastic OPF problem. To improve the efficiency of the proposed technique and enhance its local exploitation capability, the LEO method's selection features are utilized. Monte Carlo methods are employed to estimate the generation costs of the renewable sources and PEVs and study their feasibility. The uncertainty of the renewable sources and PEVs is represented by Weibull, lognormal, and normal probability distribution functions (PDFs). The GBES approach is experimentally compared with well‐known meta‐heuristics using twenty‐three different test functions, and the results indicate its superiority over BES and other recently developed algorithms. Furthermore, the proposed method's effectiveness is evaluated using IEEE 30‐bus test system under various scenarios, and the simulation results demonstrate that it can effectively address OPF issues considering renewable energy sources and V2G, providing superior optimal solutions compared to other algorithms.

Optimal Power Flow with Stochastic Renewable Energy Using Three Mixture Component Distribution Functions

The growing usage of renewable energy sources, such as solar and wind energy, has increased the electrical system’s unpredictability. The stochastic behavior of these sources must be considered to obtain significantly more accurate conclusions in the analysis of power systems. To depict renewable energy systems, the three-component mixture distribution (TCMD) is introduced in this study. The mixture distribution (MD) is created by combining the Weibull and Gamma distributions. The results show that TCMD is better than original distributions in simulating wind speed and solar irradiance by reducing the error between real data and the distribution curve. Additionally, this study examines the optimal power flow (OPF) in electrical networks using the two stochastic models of solar and wind energy. The parameters of the probability distribution function (PDF) are optimized using the Mayfly algorithm (MA), which also solves single- and multi-objective OPF issues. Then, to prove the accuracy of the MA method in solving the OPF problem, single- and multi-objective OPF is applied on a standard IEEE-30 bus system to minimize fuel cost, power loss, thermal unit emissions, and voltage security index (VSI), and results are compared with other metaheuristic methods. The outcomes show that the MA technique is dependable and effective in overseeing this challenging problem. Additionally, the suggested OPF MA-based is studied in the OPF problem while accounting for the uncertainty in the models of the wind and solar systems and taking the emissions, VSI, power loss, and fuel cost into consideration in the objective function. The significance of the work lies in the application of a unique optimization technique to a hybrid electrical system using TCMD stochastic model using actual wind and solar data. The proposed MA method could be valuable to system operators as a decision-making aid when dealing with hybrid power systems.

A novel chaotic flower pollination algorithm for function optimization and constrained optimal power flow considering renewable energy sources

This study presents an improved chaotic flower pollination algorithm (CFPA) with a view to handle the optimal power flow (OPF) problem integrating a hybrid wind and solar power and generate the optimal settings of generator power, bus voltages, shunt reactive power, and tap setting transformers. In spite of the benefits of FPA, it encounters two problems like other evolutionary algorithms: entrapment in local optima and slow convergence speed. Thus, to deal with these drawbacks and enhance the FPA searching accuracy, a hybrid optimization approach CFPA which combines the stochastic algorithm FPA that simulates the flowering plants process with the chaos methodology is applied in this manuscript. Therefore, owing to the various nonlinear constraints in OPF issue, a constraint handling technique named superiority of feasible solutions (SF) is embedded into CFPA. To confirm the performance of the chaotic FPA, a set of different well-known benchmark functions were employed for ten diverse chaotic maps, and then the best map is tested on IEEE 30-bus and IEEE 57-bus test systems incorporating the renewable energy sources (RESs). The obtained results are analyzed statistically using non-parametric Wilcoxon rank-sum test in view of evaluating their significance compared to the outcomes of the state-of-the-art meta-heuristic algorithms such as ant bee colony (ABC), grasshopper optimization algorithm (GOA), and dragonfly algorithm (DA). From this study, it may be established that the suggested CFPA algorithm outperforms its meta-heuristic competitors in most benchmark test cases. Additionally, the experimental results regarding the OPF problem demonstrate that the integration of RESs decreases the total cost by 12.77% and 33.11% for the two systems, respectively. Thus, combining FPA with chaotic sequences is able to accelerate the convergence and provide better accuracy to find optimal solutions. Furthermore, CFPA (especially with the Sinusoidal map) is challenging in solving complex real-world problems.

Optimal Power Flow Incorporating Thyristor-Controlled Series Capacitors Using the Gorilla Troops Algorithm

The optimal power flow issue (OPFI) can be solved in this work using the recently developed algorithm, gorilla troops algorithm (GTA). The goal of OPFI is to reduce numerous functions such as minimizing fuel costs, emissions, and power losses and improving the voltage stability related to electric power networks (EPNs). The GTA is inspired by gorillas’ social habits, which include migration to a strange region, migration toward a specified spot, traveling to other gorillas, competing for adult females, and escorting the silverback. The developed GTA is tested with and without the inclusion of the Thyristor-Controlled Series Capacitor (TCSC) devices in the system. The proposed GTA is applied on a practical Egyptian West Delta-EPN (WD-EPN) and the standard IEEE 57-bus EPN and with and without the inclusion of the TCSC devices to appraise the GTA algorithm’s performance in the OPFI. In addition, the proposed GTA is applied on a large-scale IEEE 118 bus system with higher outperformance compared to particle swarm optimization. The results illustrate that the fuel costs, emissions, voltage stability, and power losses are reduced for the standard IEEE 57-bus EPN with and without TCSC devices by a percentage of (18.847% and 18.818%), (59% and 58.97%), (13.405% and 11.507%), and (64.337% and 65.178%), respectively, while fuel costs, emissions, voltage stability, and power losses are reduced for WD-EPN with and without TCSC devices by a percentage of (8.547%, 8.565%), (13.641%, 13.6%), and (61.949%, 61.954%), respectively. A comparison study is conducted to demonstrate the GTA’s effectiveness when compared with other recently developed algorithms such as improved Salp Swarm Algorithm, quasi-reflection jellyfish search, Salp Swarm Algorithm, improved heap-based algorithm, bat search algorithm, social network search algorithm, electromagnetic field optimization, and other well-known algorithms as well. According to the comparison with these algorithms, the GTA demonstrates the best results among the attained results.

A Modified Whale Optimizer for Single- and Multi-Objective OPF Frameworks

This paper is concerned with an imperative operational problem, called the optimal power flow (OPF), which has several technical and economic points of view with respect the environmental concerns. This paper proposes a multiple-objective optimizer NSWOA (non-dominated sorting whale optimization algorithm) for resolving single-objective OPFs, as well as multi-objective frameworks. With a variety of technical and economic power system objectives, the OPF can be formulated. These objectives are treated as single- and multi-objective OPF issues that are deployed with the aid of the proposed NSWOA to solve these OPF formulations. The proposed algorithm modifies the Pareto ranking and analyzes the optimum compromise solution based on the optimal Euclidian distances. This proposed strategy ensures high convergence speed and improves search capabilities. To achieve this study, an IEEE 30-bus (sixth-generation unit system) is investigated, with eight scenarios studied that highlight technical and environmental operational needs. When compared to previous optimization approaches, the suggested NSWOA achieves considerable techno-economic improvements. Additionally, the statical analyses are carried out for 20 separate runs. This analysis proves the high robustness of the proposed NSWOA at low levels of standard deviation.

Optimal Power Flow Analysis Based on Hybrid Gradient-Based Optimizer with Moth–Flame Optimization Algorithm Considering Optimal Placement and Sizing of FACTS/Wind Power

Optimal power flow (OPF) is one of the most significant electric power network control and management issues. Adding unreliable and intermittent renewable energy sources to the electrical grid increase and complicates the OPF issue, which calls for using modern optimization techniques to solve this issue. This work presents the optimal location and size of some FACTS devices in a hybrid power system containing stochastic wind and traditional thermal power plants considering OPF. The FACTS devices used are thyristor-controlled series compensator (TCSC), thyristor-controlled phase shifter (TCPS), and static var compensator (SVC). This optimal location and size of FACTS devices was determined by introducing a multi-objective function containing reserve costs for overestimation and penalty costs for underestimating intermittent renewable sources besides active power losses. The uncertainty in the wind power output is predicted using Weibull probability density functions. This multi-objective function is optimized using a hybrid technique, gradient-based optimizer (GBO), and moth–flame optimization algorithm (MFO).

System security enhancement using hybrid HUA‐GPC approach under transmission line(s) and/or generator(s) outage conditions

AbstractIn this manuscript, a hybrid HUA‐GPC strategy with unified power flow controller (UPFC) is proposed to enhance that security of power system under the condition of transmission and/or generator failure. The proposed hybrid strategy is joint implementation of Human urbanization algorithm (HUA) and Giza Pyramids Construction (GPC) and commonly named as HUA‐GPC approach. GPC approach is utilized to develop that search behavior of HUA. The main aim of the proposal is the operation and control of electrical system is to satisfy the demand continuously in the absence of failures. A new severity function is created with transmission line loads and bus voltage magnitude deviations. The proposed function and generation fuel cost are evaluated in contingency conditions of transmission line (s) and/or generator (s). System safety in contingency conditions is evaluated with optimal power flow issue. To improve system safety in contingency conditions in the presence of UPFC, it is essential to recognize an optimal position to install this device. To recognize an optimal UPFC position, in this proposal, a HUA‐GPC technique depending on line overload sensitivity index (LOSI) is evolved. The LOSI is assessed for every transmission lines in contingencies. An energy injection model based on UPFC voltage source, incorporation process and optimal location recognition approach depending on line overload sensitivity indices is proposed. The constraints are the constraints of equality and inequality. The proposed approach is then performed on the MATLAB/Simulink work platform and the efficiency is compared with existing strategies. The quality of the solution for the proposed system is analyzed in terms of best aptitude, worst aptitude, mean aptitude, SD, coefficient of variation and the error of best is 1110936.01, 1097382.12, 1107656.56, 1051.98, 0.075, and 0.82.

An analysis of optimal power flow strategies for a power network incorporating stochastic renewable energy resources

An optimal power management solution is a potential tool to produce cost-effective and environmentally friendly power supply using renewable energy sources (RESs) for the electrical power network. Therefore, the article introduces a novel optimization algorithm inspired by the vitality, namely, Manta Ray Foraging Optimization (MRFO), to figure out both multi- and single-objective problems of optimal power flow (OPF) incorporating stochastic RES. The OPF problems are designed by considering four different objective functions: transmission power loss, emission index, fuel operational costs, and voltage deviation. The stochastic and volatile nature of RES increases the complexity of the OPF issue. In this study, a new MRFO algorithm and some modern metaheuristic algorithms were used to settle the issue of OPF, enhance the energy efficiency, and environmental and cost performance of the power network. The test cases, with and without RES, different RES locations on the network, increase in the load, and outages of some transmission lines, are considered by addressing the challenge of the proposed OPF. These cases are tested with bus systems as 30 and 118, and the outcome from the suggested MRFO is compared with six metaheuristic optimization algorithms. Moreover, OPF challenges are successfully settled by the MRFO algorithm and outperform the proposed metaheuristic optimization methods.

Optimal power flow with stochastic solar power using barnacles mating optimizer

This work proposes the implementation of recent evolutionary metaheuristic algorithm namely, barnacles mating optimizer (BMO) to solve the Optimal Power Flow (OPF) issue. BMO is inspired by the mating behaviour of barnacles which happened in two ways: by normal copulation and sperm-cast. The effectiveness of the proposed BMO in solving the OPF is tested on a modified IEEE-30 bus system that is integrated with solar PV farms for five cases viz (1) cost minimization of the power generation that consists of thermal and stochastic solar power generations, (2) power loss minimization, (3) voltage deviation minimization, (4) emission minimization and (5) combined cost and emission minimization of power generations. To demonstrate the effectiveness and the veracity of the solution obtained by BMO, several recent algorithms that have been reported in the literature will be utilized and compared intensively. In the end, the simulation results demonstrate that the BMO can be effectively becoming an alternative solution for the OPF issue in general.

A Probabilistic Multi-Objective Approach for Power Flow Optimization in Hybrid Wind-Based Power Systems Using Grasshopper Optimization Algorithm

This article introduces a grasshopper optimization algorithm (GOA) to efficiently prove its superiority for solving different objectives of optimal power flow (OPF) based on a mixture thermal power plant that incorporates uncertain wind energy (WE) sources. Many practical constraints of generators, valve point effect, multiple fuels, and the various scenarios incorporating several configurations of WEs are considered for both singles along with multi-objectives for the OPF issue. Within the article, the considered method is verified on two common bus experiment systems, i.e. IEEE 30-bus as well as the IEEE 57-bus. Here, the fuel amount minimization and emission minimization are studied as the primary purposes of a GOA-based OPF problem. However, the proposed algorithm yields a reasonable conclusion about the better performance of the wind turbine. Computational results express the effectiveness of the proposed GOA approach for the secure and financially viable of the power system under various uncertainties. The comparison is tabulated with the existing algorithms to provide superior results.

Hybrid algorithm based on MBHS and PSO with Optimal Power Flow Problem for Non-Smooth Cost Functions

Optimal Power Flow (OPF) is the primary apparatus in power framework administrators for both working and arranging stages. It is made to define a couple of expectations dependent on power organizing factors with a couple of confinements. This paper examines the likelihood that utilizations couple of currently developed progressive ways to deal with anticipating answers for OPF issues based on ruler butterfly agreement search (MBHS) calculation which uses Particle swam streamlining (PSO) for ideal settings of OPF issue control factors. The standard IEEE 30-bus with IEEE 57-bus test framework is assessed and examined by the presentation of the proposed methodologies with different useful destinations and furthermore, the correlation is made to this strategy. At long last, the acquired outcomes that are recovered from the connected reproduction accommodate the MBHS and PSO with effective answers for the issue in OPF.

Optimal Power Flow to Manage Voltage Profiles in Interconnected Networks Using Expert Systems

The optimal power flow issue is one of the most important problems faced by dispatching engineers regarding large scale power systems. It is a particular mathematical approach of the global power system optimization problem that aims at determining the least control movements to keep power system at the most desired state. Thus, it represents a flexible and powerful tool, which can address a wide range of planning and operation studies. However, the complexity of optimal power flow increases dramatically with large-scale networks, which often discourages the utilization of this powerful tool in many applications. This paper proposes a new intelligent approach to facilitate the implementation of the optimal power flow calculations to be utilized in various control centres. A main advantage of the proposed intelligent systems with real control centres is the possibility of controlling the system voltage profile in a tracking mode. The simulation results using this intelligent system when applied to the IEEE 30-bus power network emphasize the validity and effectiveness of the proposed technique.