Journal articles: 'Optimal Power Flow (OPF)'

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Oh, HyungSeon. "Distributed optimal power flow." PLOS ONE 16, no. 6 (June 18, 2021): e0251948. http://dx.doi.org/10.1371/journal.pone.0251948.

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Objective The objectives of this paper are to 1) construct a new network model compatible with distributed computation, 2) construct the full optimal power flow (OPF) in a distributed fashion so that an effective, non-inferior solution can be found, and 3) develop a scalable algorithm that guarantees the convergence to a local minimum. Existing challenges Due to the nonconvexity of the problem, the search for a solution to OPF problems is not scalable, which makes the OPF highly limited for the system operation of large-scale real-world power grids—“the curse of dimensionality”. The recent attempts at distributed computation aim for a scalable and efficient algorithm by reducing the computational cost per iteration in exchange of increased communication costs. Motivation A new network model allows for efficient computation without increasing communication costs. With the network model, recent advancements in distributed computation make it possible to develop an efficient and scalable algorithm suitable for large-scale OPF optimizations. Methods We propose a new network model in which all nodes are directly connected to the center node to keep the communication costs manageable. Based on the network model, we suggest a nodal distributed algorithm and direct communication to all nodes through the center node. We demonstrate that the suggested algorithm converges to a local minimum rather than a point, satisfying the first optimality condition. Results The proposed algorithm identifies solutions to OPF problems in various IEEE model systems. The solutions are identical to those using a centrally optimized and heuristic approach. The computation time at each node does not depend on the system size, and Niter does not increase significantly with the system size. Conclusion Our proposed network model is a star network for maintaining the shortest node-to-node distances to allow a linear information exchange. The proposed algorithm guarantees the convergence to a local minimum rather than a maximum or a saddle point, and it maintains computational efficiency for a large-scale OPF, scalable algorithm.

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Lakdja, Fatiha, Fatima Zohra Gherbi, Redouane Berber, and Houari Boudjella. "Optimal TCSC placement for optimal power flow." Journal of Electrical Engineering 63, no. 5 (November 1, 2012): 316–21. http://dx.doi.org/10.2478/v10187-012-0046-2.

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Very few publications have been focused on the mathematical modeling of Flexible Alternating Current Transmission Systems (FACTS) -devices in optimal power flow analysis. A Thyristor Controlled Series Capacitors (TCSC) model has been proposed, and the model has been implemented in a successive QP. The mathematical models for TCSC have been established, and the Optimal Power Flow (OPF) problem with these FACTS-devices is solved by Newtons method. This article employs the Newton- based OPF-TCSC solver of MATLAB Simulator, thus it is essential to understand the development of OPF and the suitability of Newton-based algorithms for solving OPF-TCSC problem. The proposed concept was tested and validated with TCSC in twenty six-bus test system. Result shows that, when TCSC is used to relieve congestion in the system and the investment on TCSC can be recovered, with a new and original idea of integration.

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Mohagheghi, Erfan, Mansour Alramlawi, Aouss Gabash, and Pu Li. "A Survey of Real-Time Optimal Power Flow." Energies 11, no. 11 (November 13, 2018): 3142. http://dx.doi.org/10.3390/en11113142.

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There has been a strong increase of penetration of renewable energies into power systems. However, the renewables pose new challenges for the operation of the networks. Particularly, wind power is intermittently fluctuating, and, therefore, the network operator has to fast update the operations correspondingly. This task should be performed by an online optimization. Therefore, real-time optimal power flow (RT-OPF) has become an attractive topic in recent years. This paper presents an overview of recent studies on RT-OPF under wind energy penetration, offering a critical review of the major advancements in RT-OPF. It describes the challenges in the realization of the RT-OPF and presents available approaches to address these challenges. The paper focuses on a number of topics which are reviewed in chronological order of appearance: offline energy management systems (EMSs) (deterministic and stochastic approaches) and real-time EMSs (constraint satisfaction-based and OPF-based methods). The particular challenges associated with the incorporation of battery storage systems in the networks are explored, and it is concluded that the current research on RT-OPF is not sufficient, and new solution approaches are needed.

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Zheng, Xinhu, Dongliang Duan, Liuqing Yang, and Haonan Wang. "Decomposed Iterative Optimal Power Flow with Automatic Regionalization." Energies 13, no. 18 (September 22, 2020): 4987. http://dx.doi.org/10.3390/en13184987.

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The optimal power flow (OPF) problem plays an important role in power system operation and control. The problem is nonconvex and NP-hard, hence global optimality is not guaranteed and the complexity grows exponentially with the size of the system. Therefore, centralized optimization techniques are not suitable for large-scale systems and an efficient decomposed implementation of OPF is highly demanded. In this paper, we propose a novel and efficient method to decompose the entire system into multiple sub-systems based on automatic regionalization and acquire the OPF solution across sub-systems via a modified MATPOWER solver. The proposed method is implemented in a modified solver and tested on several IEEE Power System Test Cases. The performance is shown to be more appealing compared with the original solver.

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Radosavljevic, Jordan, Miroljub Jevtic, Dardan Klimenta, and Nebojsa Arsic. "Optimal power flow for distribution networks with distributed generation." Serbian Journal of Electrical Engineering 12, no. 2 (2015): 145–70. http://dx.doi.org/10.2298/sjee1502145r.

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This paper presents a genetic algorithm (GA) based approach for the solution of the optimal power flow (OPF) in distribution networks with distributed generation (DG) units, including fuel cells, micro turbines, diesel generators, photovoltaic systems and wind turbines. The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. Due to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties, a probabilisticalgorithm is introduced in the OPF analysis. The Weibull and normal distributions are employed to model the input random variables, namely the wind speed, solar irradiance and load power. The 2m+1 point estimate method and the Gram Charlier expansion theory are used to obtain the statistical moments and the probability density functions (PDFs) of the OPF results. The proposed approach is examined and tested on a modified IEEE 34 node test feeder with integrated five different DG units. The obtained results prove the efficiency of the proposed approach to solve both deterministic and probabilistic OPF problems for different forms of the multi-objective function. As such, it can serve as a useful decision-making supporting tool for distribution network operators.

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He, Xuan Hu, Wei Wang, Ying Nan Wang, Jun Kong, Jing Geng, and Sheng Bin Fan. "Fuzzy Optimal Power Flow with Multi-Objective Based on Artificial Bee Colony Algorithm in Power System." Applied Mechanics and Materials 448-453 (October 2013): 2473–77. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.2473.

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According to the problem of optimal power flow (OPF), the optimization objectives including minimization of total fuel cost for generating units, minimization of emission for atmospheric pollutants, minimization of active power losses and minimization of voltages deviations are established. The paper uses fuzzy membership functions instead of multi-objective functions to form fuzzy optimal power flow in the optimal power flow calculation process. The novel artificial bee colony (ABC) algorithm is proposed to solve OPF problem with multi-objective. The proposed approach is applied to the OPF problem on IEEE30 test systems. And the simulation results verify the effectiveness of the proposed method.

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Rugthaicharoencheep, Nattachote, Manat Boonthienthong, and Aroon Charlangsut. "Optimal Reactive Power Control in Power System with Particle Swarm Optimization Technique." Applied Mechanics and Materials 891 (May 2019): 246–52. http://dx.doi.org/10.4028/www.scientific.net/amm.891.246.

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This paper considers an application of Newton's optimal power flow to the solution of the secondary voltage/reactive power control in power system. This procedure is based on the sensitivity theory applied to the determination of zones for the secondary voltage/ reactive power control and corresponding reduced set of regulating sources, whose reactive outputs represent control variables in the optimal power flow program. PSO is applied to solve the OPF problem for optimal power flow the optimal power flow program output becomes a schedule to be used by operators in the process of OPF-PSO (Optimal Power Flow - Particle swarm optimization) PSO applied to optimal reactive power dispatch is evaluated on an IEEE 30-bus power system. The optimization strategy is general and can be used to solve other power system optimization problems as well.

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Kubba, Hassan Abdullah, and Mounir Thamer Esmieel. "Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems." Journal of Engineering 24, no. 3 (March 1, 2018): 84. http://dx.doi.org/10.31026/j.eng.2018.03.07.

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Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.

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Srikun, Isaree. "Multi-Objective Optimal Power Flow Solutions Using Differential Search Algorithm." Advanced Materials Research 1077 (December 2014): 241–45. http://dx.doi.org/10.4028/www.scientific.net/amr.1077.241.

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This paper presents a Differential Search Algorithm for solving a multi-objective Optimal Power Flow (OPF) in support of power system operation and control . The multi-objective OPF was formulated for tackling with total generation cost and environmental impacts simultaneously. The proposed method was applied to the standard IEEE 30-bus test system. The results show that solving the multi-objective OPF problem by the Differential Search Algorithm is more effective than other swarm intelligence methods in the literature.

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Ji, Cong, Zhi Nong Wei, Guo Qiang Sun, and Yong Hui Sun. "AC-DC Decoupling Algorithm of Optimal Power Flow with HVDC System." Applied Mechanics and Materials 457-458 (October 2013): 1107–12. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.1107.

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To calculate optimal power flow (OPF) with high voltage direct current (HVDC) system, AC-DC system variables are used to be optimized together. Since there are many HVDC variables, more formula derivation and coding of Jacobi matrix and Hessian Matrix are needed in the calculation. Considering DC system characteristic of OPF with HVDC system, a method of alternative iteration of AC-DC system is presented in this paper, which could take advantage of the existing AC system OPF program and extend to calculate OPF with HVDC system. Simulation and comparison results indicate that, the OPF calculating method of alternative iteration of AC-DC system presented in this paper has good convergence, strong adaptability and high accuracy

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Long, Duong Thanh. "OPTIMAL POWER FLOW WITH TCSC DEVICE USING CUCKOO OPTIMIZATION ALGORITHM." Vietnam Journal of Science and Technology 54, no. 3A (March 20, 2018): 52. http://dx.doi.org/10.15625/2525-2518/54/3a/11958.

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Optimal Power Flow (OPF) problem is an optimization tool through which secure and economic operating conditions of power system is obtained. In recent years, Flexible AC Transmission System (FACTS) devices, have led to the development of controllers that provide controllability and flexibility for power transmission. Series FACTS devices such as Thyristor controlled series compensators (TCSC), with its ability to directly control the power flow can be very effective to power system security. Thus, integration TCSC in the OPF is one of important current problems and is a suitable method for better utilization of the existing system. This paper is applied Cuckoo Optimization Algorithm (COA) for the solution of the OPF problem of power system equipped with TCSC. The proposed approach has been examined and tested on the IEEE 30-bus system. The results presented in this paper demonstrate the potential of COA algorithm and show its effectiveness for solving the OPF problem with TCSC devices over the other evolutionary optimization techniques.

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Khan, Baseem, and Pawan Singh. "Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis." Journal of Engineering 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/9539506.

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The exhaustive knowledge of optimal power flow (OPF) methods is critical for proper system operation and planning, since OPF methods are utilized for finding the optimal state of any system under system constraint conditions, such as loss minimization, reactive power limits, thermal limits of transmission lines, and reactive power optimization. Incorporating renewable energy sources optimized the power flow of system under different constraints. This work presents a comprehensive study of optimal power flows methods with conventional and renewable energy constraints. Additionally, this work presents a progress of optimal power flow solution from its beginning to its present form. Authors classify the optimal power flow methods under different constraints condition of conventional and renewable energy sources. The current and future applications of optimal power flow programs in smart system planning, operations, sensitivity calculation, and control are presented. This study will help the engineers and researchers to optimize power flow with conventional and renewable energy sources.

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Hoang Bao Huy, Truong, and Vo Ngoc Dieu. "A search group algorithm for optimal power flow in power systems." Science and Technology Development Journal 20, K9 (April 15, 2019): 15–22. http://dx.doi.org/10.32508/stdj.v20ik9.1672.

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Economic operation of the electric energy generating system is one of the common problems in power system. This paper presents a new metaheuristic optimization method, the Search Group Algorithm (SGA) for solving optimal power flow (OPF) problem. The proposed method is tested for 11 different cases on the IEEE 30-bus and IEEE-118 bus systems, in which the IEEE 30-bus system is tested with different objective functions including quadratic function, valve point effects and multiple fuels. The obtained results are compared with some well-known optimization algorithms to emphasize the effectiveness of the SGA method for solving different OPF problems with complicated functions.

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Rao, B. Venkateswara, and G. V. Nagesh Kumar. "Multi-Objective Optimal Power Flow using BAT Search Algorithm with Unified Power Flow Controller for Minimization of Real Power Losses." International Journal of Applied Metaheuristic Computing 6, no. 4 (October 2015): 69–88. http://dx.doi.org/10.4018/ijamc.2015100104.

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In this paper a multi objective optimal power flow (OPF) is obtained by using BAT search algorithm (BAT) with Unified power flow controller (UPFC). UPFC is a voltage source converter type Flexible Alternating Current Transmission System (FACTS) device. It is able to control the voltage magnitudes, voltage angles and line impedances individually or simultaneously. UPFC along with BAT algorithm is used to minimize the total real power generation cost, real power losses in OPF control. The BAT algorithm based OPF has been examined and tested on a 5 bus test system and modified IEEE 30 bus system without and with UPFC. The results obtained with BAT algorithm are compared with Differential Evaluation (DE).

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Quyen, Anh Huy, Anh Viet Truong, and Huong Thi Thanh Vi. "HEURISTIC METHODE FOR OPTIMIZING POWER LOAD FLOW ANALYSIS IN ELECTRICAL POWER SYSTEM." Science and Technology Development Journal 13, no. 2 (June 30, 2010): 36–45. http://dx.doi.org/10.32508/stdj.v13i2.2109.

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The primary goal of a generic optimal power load flow problem Is minimizing total fuel costs of generating units in an electrical power system while maintaining the security of the system. This paper presents an algorithm for optimizing power load flow analysis through the application of Newton ’s method and attends to interchange power between the different power systems. Specifically, it will explore the implementation of data structure such as the binary tree in searching OPF variables (controls, states, constraints) in large power system. So the OPF solution is quickly converging. The primary goal of a generic OFF has been tested by simulation method for 6- bus system in Power World environment. The optimal power flow results is shown that total generation fuel cost in the interchange power case is less expensive than in no interchange power case as well as total transmission losses in the power system are smaller.

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Islam, Mohammad Zohrul, Mohammad Lutfi Othman, Noor Izzri Abdul Wahab, Veerapandiyan Veerasamy, Saifur Rahman Opu, Abinaya Inbamani, and Vishalakshi Annamalai. "Marine predators algorithm for solving single-objective optimal power flow." PLOS ONE 16, no. 8 (August 12, 2021): e0256050. http://dx.doi.org/10.1371/journal.pone.0256050.

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This study presents a nature-inspired, and metaheuristic-based Marine predator algorithm (MPA) for solving the optimal power flow (OPF) problem. The significant insight of MPA is the widespread foraging strategy called the Levy walk and Brownian movements in ocean predators, including the optimal encounter rate policy in biological interaction among predators and prey which make the method to solve the real-world engineering problems of OPF. The OPF problem has been extensively used in power system operation, planning, and management over a long time. In this work, the MPA is analyzed to solve the single-objective OPF problem considering the fuel cost, real and reactive power loss, voltage deviation, and voltage stability enhancement index as objective functions. The proposed method is tested on IEEE 30-bus test system and the obtained results by the proposed method are compared with recent literature studies. The acquired results demonstrate that the proposed method is quite competitive among the nature-inspired optimization techniques reported in the literature.

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Budiman, Aris. "Optimal Power Flow Menggunakan Metode Interior Point yang Disempurnakan." Emitor: Jurnal Teknik Elektro 2, no. 2 (April 20, 2018): 47–50. http://dx.doi.org/10.23917/emitor.v2i2.6007.

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Permasalahan OPF terdiri dari banyaknya objective function, an/ara lain economic dispatch, perencanaan VAR, dan juga minimisasi rugi-rugi. Untuk perencanaan VAR perlu mempertimbangkan biaya operasional rugi-rugi, sehingga membentuk sua/u permasalahan perencanaan dan operasiyang simultan.Di tulisan ini secara khusus hanya membahas metode OPF ImprovedQuadratic Interior Point (IQIP). Metode !QIP ini memiliki unjuk kerja yang secara umum lebih baik dibanding me/ode generasi sebelumnya, yaitu EQJP (Extended Quadratic Interior Point), antara lain karena bisa menggunakan titik awal general dan pada beberapa pengujian lebih cepat konvergen dibandingkan EQJP. Hal ini menyebabkan me/ode IQIP mampu menawarkan perbaikan besar di dalam kecepatan,keakuratan, dan konvergensi di dalam pemecahan masalah optimisasi yang multi• objective function dan multi-constraint. Kemampuan memecahkan optimisasi global dari sistem terinterkoneksi dan sis/em terpartisi untuk optimisasi /oka/juga lebih baik dari generasi sebelumnya.Metode ini telah melalui pengujian terhadap sis/em 14 bus, 30 bus, dan 118 busIEEE. Efektivilas dari metode ini telah dieva/uasi dengan cara dibandingkan denganprogram OPF yang berbasis EQIP (Extended Quadratic Interior Point) dan programMINOSyang cukup dikenal di dunia perencanaan sis/em

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Le Dinh, Luong, Dieu Vo Ngoc, and Pandian Vasant. "Artificial Bee Colony Algorithm for Solving Optimal Power Flow Problem." Scientific World Journal 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/159040.

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This paper proposes an artificial bee colony (ABC) algorithm for solving optimal power flow (OPF) problem. The objective of the OPF problem is to minimize total cost of thermal units while satisfying the unit and system constraints such as generator capacity limits, power balance, line flow limits, bus voltages limits, and transformer tap settings limits. The ABC algorithm is an optimization method inspired from the foraging behavior of honey bees. The proposed algorithm has been tested on the IEEE 30-bus, 57-bus, and 118-bus systems. The numerical results have indicated that the proposed algorithm can find high quality solution for the problem in a fast manner via the result comparisons with other methods in the literature. Therefore, the proposed ABC algorithm can be a favorable method for solving the OPF problem.

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Gaing, Zwe-Lee, and Chia-Hung Lin. "Contingency-Constrained Optimal Power Flow Using Simplex-Based Chaotic-PSO Algorithm." Applied Computational Intelligence and Soft Computing 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/942672.

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This paper proposes solving contingency-constrained optimal power flow (CC-OPF) by a simplex-based chaotic particle swarm optimization (SCPSO). The associated objective of CC-OPF with the considered valve-point loading effects of generators is to minimize the total generation cost, to reduce transmission loss, and to improve the bus-voltage profile under normal or postcontingent states. The proposed SCPSO method, which involves the chaotic map and the downhill simplex search, can avoid the premature convergence of PSO and escape local minima. The effectiveness of the proposed method is demonstrated in two power systems with contingency constraints and compared with other stochastic techniques in terms of solution quality and convergence rate. The experimental results show that the SCPSO-based CC-OPF method has suitable mutation schemes, thus showing robustness and effectiveness in solving contingency-constrained OPF problems.

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Yang, Yu De, and Pei Xian Qu. "Optimal Power Flow Considering Generator Number Constraint on Regulation of Active Power Output." Applied Mechanics and Materials 672-674 (October 2014): 1042–47. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1042.

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Operation number constraint of control means isn’t considered to traditional optimal power flow model, and at optimal solution all the control variables often be changed, which causes a tedious dispatching plan and is difficult to operate. In this paper, Optimal Power Flow (OPF) with constraints limiting the number of control actions was discussed, and 0-1 discrete variables of model was transformed to complementary constraint, then modern interior point algorithm was used for solving. Through simulation the relationship between generator number constraint on regulation of the active power output and optimization objective was explored, which could obtain best balance point on two, and close to conventional OPF optimization objective with reducing the number of regulation.

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Taleb, Nadir, Bachir Bentouati, and Saliha Chettih. "Optimal Power Flow Solutions Incorporating Stochastic Solar Power withthe Application Grey Wolf Optimize." Algerian Journal of Renewable Energy and Sustainable Development 03, no. 01 (June 15, 2021): 74–84. http://dx.doi.org/10.46657/ajresd.2021.3.1.8.

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The present paper aims to validate an electrical network study in consisting of conventional fossil fuel generators with the integration of intermittent generation technologies based on renewable energy resources like wind power or solar photovoltaic (PV) are the stochastic power output. By using an optimal power flow (OPF) problem different frameworks are developed for solving that represent various operating requirements, such as minimization of production fuel cost, and preserving generation emission at the lowest levels... etc. The OPF analysis aims to find the optimal solution and is very important for power system operation with satisfying operational constraints, planning and energy management. However, the intermittent combination of solar exacerbates the complexity of the problem. Within the framework of these criteria, this paper is an overview of the application Grey Wolf Optimizer (GWO) algorithm which solves the OPF problem with renewable energy. The algorithm thus combined and constructed gives optimum results satisfying all network constraints. Give an explanation for findings are based thus need to be with the optimum to effectuate of network constraints.

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Choudhary, Kshitij, Rahul Kumar, Dheeresh Upadhyay, and Brijesh Singh. "Optimal Power Flow Based Economic Generation Scheduling in Day-ahead Power Market." International Journal of Applied Power Engineering (IJAPE) 6, no. 3 (December 1, 2017): 123. http://dx.doi.org/10.11591/ijape.v6.i3.pp123-132.

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The present work deals with the economic rescheduling of the generation in an hour-ahead electricity market. The schedules of various generators in a power system have been optimizing according to active power demand bids by various load buses. In this work, various aspects of power system such as congestion management, voltage stabilization and loss minimization have also taken into consideration for the achievement of the goal. The Interior Point (IP) based Optimal Power Flow (OPF) methodology has been used to obtain the optimal generation schedule for economic system operation. The IP based OPF methodology has been tested on a modified IEEE-30 bus system. The obtained test results shows that not only the generation cost is reduced also the performance of power system has been improved using proposed methodology.

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Choudhary, Kshitij, Rahul Kumar, Dheeresh Upadhyay, and Brijesh Singh. "Optimal Power Flow Based Economic Generation Scheduling in Day-ahead Power Market." International Journal of Applied Power Engineering (IJAPE) 6, no. 3 (December 1, 2017): 124. http://dx.doi.org/10.11591/ijape.v6.i3.pp124-134.

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The present work deals with the economic rescheduling of the generation in an hour-ahead electricity market. The schedules of various generators in a power system have been optimizing according to active power demand bids by various load buses. In this work, various aspects of power system such as congestion management, voltage stabilization and loss minimization have also taken into consideration for the achievement of the goal. The interior point (IP) based optimal power flow (OPF) methodology has been used to obtain the optimal generation schedule for economic system operation. The IP based OPF methodology has been tested on a modified IEEE-30 bus system. The obtained test results shows that not only the generation cost is reduced also the performance of power system has been improved using proposed methodology.

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Naderi, Ehsan, Hossein Narimani, Mahdi Pourakbari-Kasmaei, Fernando V. Cerna, Mousa Marzband, and Matti Lehtonen. "State-of-the-Art of Optimal Active and Reactive Power Flow: A Comprehensive Review from Various Standpoints." Processes 9, no. 8 (July 29, 2021): 1319. http://dx.doi.org/10.3390/pr9081319.

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

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Mukherjee, Aparajita, Sourav Paul, and Provas Kumar Roy. "Transient Stability Constrained Optimal Power Flow Using Teaching Learning-Based Optimization." International Journal of Energy Optimization and Engineering 3, no. 4 (October 2014): 55–71. http://dx.doi.org/10.4018/ijeoe.2014100104.

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Transient stability constrained optimal power flow (TSC-OPF) is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. In order to solve the TSC-OPF problem efficiently, a relatively new optimization technique named teaching learning based optimization (TLBO) is proposed in this paper. TLBO algorithm simulates the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The authors have explained in detail, the basic philosophy of this method. In this paper, the authors deal with the comparison of other optimization problems with TLBO in solving TSC-OPF problem. Case studies on IEEE 30-bus system WSCC 3-generator, 9-bus system and New England 10-generator, 39-bus system indicate that the proposed TLBO approach is much more computationally efficient than the other popular methods and is promising to solve TSC-OPF problem.

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Mukherjee, Aparajita, Sourav Paul, and Provas Kumar Roy. "Transient Stability Constrained Optimal Power Flow Using Teaching Learning Based Optimization." International Journal of Energy Optimization and Engineering 4, no. 1 (January 2015): 18–35. http://dx.doi.org/10.4018/ijeoe.2015010102.

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Transient stability constrained optimal power flow (TSC-OPF) is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. In order to solve the TSC-OPF problem efficiently, a relatively new optimization technique named teaching learning based optimization (TLBO) is proposed in this paper. TLBO algorithm simulates the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The authors have explained in detail, the basic philosophy of this method. In this paper, the authors deal with the comparison of other optimization problems with TLBO in solving TSC-OPF problem. Case studies on IEEE 30-bus system WSCC 3-generator, 9-bus system and New England 10-generator, 39-bus system indicate that the proposed TLBO approach is much more computationally efficient than the other popular methods and is promising to solve TSC-OPF problem.

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Lokhande, Netra M., and Debirupa Hore. "Computational Analysis of Different Artificial Intelligence Based Optimization Techniques for Optimal Power Flow and Economic Load Dispatch Problem." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 1 (February 1, 2013): 82–87. http://dx.doi.org/10.24297/ijct.v4i1b.3062.

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The purpose of this paper is to present a computational Analysis of various Artificial Intelligence based optimization Techniques used to solve OPF problems. The various Artificial Intelligence methods such as Genetic Algorithm(GA), Particle Swarm Optimization(PSO), Bacterial Foraging Optimization(BFO), ANN are studied and analyzed in detail. The objective of an Optimal Power Flow (OPF) algorithm is to find steady state operation point which minimizes generation cost and transmission loss etc. or maximizes social welfare, load ability etc. while maintaining an acceptable system performance in terms of limits on generatorsâ€™ real and reactive powers, power flow limits, output of various compensating devices etc. Traditionally, Classical optimization methods were used effectively to solve optimal power flow. But, recently due to the incorporation of FACTS devices and deregulation of power sector the traditional concepts and practices of power systems are superimposed by an economic market management and hence OPF have become more complex. So, in recent years, Artificial Intelligence (AI) methods have been emerged which can solve highly complex OPF problems at faster rate.

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Rahman, Muhammad Affiq Abd, Bazilah Ismail, Kanendra Naidu, and Mohd Khairil Rahmat. "Review on population-based metaheuristic search techniques for optimal power flow." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 1 (July 1, 2019): 373. http://dx.doi.org/10.11591/ijeecs.v15.i1.pp373-381.

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Optimal power flow (OPF) is a non-linear solution which is significantly important in order to analyze the power system operation. The use of optimization algorithm is essential in order to solve OPF problems. The emergence of machine learning presents further techniques which capable to solve the non-linear problem. The performance and the key aspects which enhances the effectiveness of these optimization techniques are compared within several metaheuristic search techniques. This includes the operation of particle swarm optimization (PSO) algorithm, firefly algorithm (FA), artificial bee colony (ABC) algorithm, ant colony optimization (ACO) algorithm and differential evolution (DE) algorithm. This paper reviews on the key elements that need to be considered when selecting metaheuristic techniques to solve OPF problem in power system operation.

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Reddy, S. Surender, and P. R. Bijwe. "Efficiency Improvements in Meta-Heuristic Algorithms to Solve the Optimal Power Flow Problem." International Journal of Emerging Electric Power Systems 17, no. 6 (December 1, 2016): 631–47. http://dx.doi.org/10.1515/ijeeps-2015-0216.

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AbstractThis paper proposes the efficient approaches for solving the Optimal Power Flow (OPF) problem using the meta-heuristic algorithms. Mathematically, OPF is formulated as non-linear equality and inequality constrained optimization problem. The main drawback of meta-heuristic algorithm based OPF is the excessive execution time required due to the large number of power flows needed in the solution process. The proposed efficient approaches uses the lower and upper bounds of objective function values. By using this approach, the number of power flows to be performed are reduced substantially, resulting in the solution speed up. The efficiently generated objective function bounds can result in the faster solutions of meta-heuristic algorithms. The original advantages of meta-heuristic algorithms, such as ability to handle complex non-linearities, discontinuities in the objective function, discrete variables handling, and multi-objective optimization, etc., are still available in the proposed efficient approaches. The proposed OPF formulation includes the active and reactive power generation limits, Valve Point Loading (VPL) and Prohibited Operating Zones (POZs) effects of generating units. The effectiveness of proposed approach is examined on IEEE 30, 118 and 300 bus test systems, and the simulation results confirm the efficiency and superiority of the proposed approaches over the other meta-heuristic algorithms. The proposed efficient approach is generic enough to use with any type of meta-heuristic algorithm based OPF.

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He, Xuanhu, and Wei Wang. "Fuzzy Multiobjective Optimal Power Flow Based on Modified Artificial Bee Colony Algorithm." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/961069.

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This paper presents a modified artificial bee colony (MABC) algorithm to solve optimal power flow (OPF) problem. In the proposed MABC algorithm, the searching operation for new food source of artificial bee colony (ABC) algorithm is replaced with mutation and crossover operation of differential evolution (DE) algorithm to improve exploitation capacity. The OPF objective functions involve minimization of total fuel cost of generating units, minimization of emission of atmospheric pollutants, minimization of active power losses, and minimization of voltage deviations. The fuzzy satisfaction-maximizing method is utilized to convert the multiobjectives problem into single objective problem. The proposed approach is applied to the OPF problem on IEEE 30-bus test system. And the results are compared with those obtained by other heuristic algorithms, which demonstrate that the MABC algorithm not only has a better exploration capacity but also possesses stronger exploitation capacity and can effectively solve the OPF problem.

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Abdollahi, Arsalan, Ali Ghadimi, Mohammad Miveh, Fazel Mohammadi, and Francisco Jurado. "Optimal Power Flow Incorporating FACTS Devices and Stochastic Wind Power Generation Using Krill Herd Algorithm." Electronics 9, no. 6 (June 24, 2020): 1043. http://dx.doi.org/10.3390/electronics9061043.

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This paper deals with investigating the Optimal Power Flow (OPF) solution of power systems considering Flexible AC Transmission Systems (FACTS) devices and wind power generation under uncertainty. The Krill Herd Algorithm (KHA), as a new meta-heuristic approach, is employed to cope with the OPF problem of power systems, incorporating FACTS devices and stochastic wind power generation. The wind power uncertainty is included in the optimization problem using Weibull probability density function modeling to determine the optimal values of decision variables. Various objective functions, including minimization of fuel cost, active power losses across transmission lines, emission, and Combined Economic and Environmental Costs (CEEC), are separately formulated to solve the OPF considering FACTS devices and stochastic wind power generation. The effectiveness of the KHA approach is investigated on modified IEEE-30 bus and IEEE-57 bus test systems and compared with other conventional methods available in the literature.

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Ben Hmida, Jalel, Mohammad Javad Morshed, Jim Lee, and Terrence Chambers. "Hybrid Imperialist Competitive and Grey Wolf Algorithm to Solve Multiobjective Optimal Power Flow with Wind and Solar Units." Energies 11, no. 11 (October 24, 2018): 2891. http://dx.doi.org/10.3390/en11112891.

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The optimal power flow (OPF) module optimizes the generation, transmission, and distribution of electric power without disrupting network power flow, operating limits, or constraints. Similarly to any power flow analysis technique, OPF also allows the determination of system’s state of operation, that is, the injected power, current, and voltage throughout the electric power system. In this context, there is a large range of OPF problems and different approaches to solve them. Moreover, the nature of OPF is evolving due to renewable energy integration and recent flexibility in power grids. This paper presents an original hybrid imperialist competitive and grey wolf algorithm (HIC-GWA) to solve twelve different study cases of simple and multiobjective OPF problems for modern power systems, including wind and photovoltaic power generators. The performance capabilities and potential of the proposed metaheuristic are presented, illustrating the applicability of the approach, and analyzed on two test systems: the IEEE 30 bus and IEEE 118 bus power systems. Sensitivity analysis has been performed on this approach to prove the robustness of the method. Obtained results are analyzed and compared with recently published OPF solutions. The proposed metaheuristic is more efficient and provides much better optimal solutions.

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Chen, Yuwei, Ji Xiang, and Yanjun Li. "SOCP Relaxations of Optimal Power Flow Problem Considering Current Margins in Radial Networks." Energies 11, no. 11 (November 15, 2018): 3164. http://dx.doi.org/10.3390/en11113164.

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Optimal power flow (OPF) is a non-linear and non-convex problem that seeks the optimization of a power system operation point to minimize the total generation costs or transmission losses. This study proposes an OPF model considering current margins in radial networks. The objective function of this OPF model has an additional term of current margins of the line besides the traditional transmission losses and generations costs, which contributes to thermal stability margins of power systems. The model is a reformulated bus injection model with clear physical meanings. Second order cone program (SOCP) relaxations for the proposed OPF are made, followed by the over-satisfaction condition guaranteeing the exactness of the SOCP relaxations. A simple 6-node case and several IEEE benchmark systems are studied to illustrate the efficiency of the developed results.

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Schellenberg, A., W. Rosehart, and J. Aguado. "Introduction to Cumulant-Based Probabilistic Optimal Power Flow (P-OPF)." IEEE Transactions on Power Systems 20, no. 2 (May 2005): 1184–86. http://dx.doi.org/10.1109/tpwrs.2005.846188.

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Kouadri, Ramzi, Linda Slimani, Tarek Bouktir, and Ismail Musirin. "Optimal Power Flow Solution for Wind Integrated Power in presence of VSC-HVDC Using Ant Lion Optimization." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (November 1, 2018): 625. http://dx.doi.org/10.11591/ijeecs.v12.i2.pp625-633.

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This paper studies the impact of incorporating wind power generation WPG on the power system on prsence of voltage source converter based high voltage DC (VSC-HVDC). A new meta-heuristic optimization technique are use for solving of the optimal power flow (OPF) problem, this technique optimization namely Ant Lion Optimizer (ALO). The optimization method is the Ant Lion Optimizer (ALO) method for resolve the optimal power flow (OPF) with incorporating of wind power generation on prsence of VSC-HVDC. And we used weibull distribution model of the wind farm. The ALO-OPF method has been examined and tested on standard test systems IEEE 30 bus with objective functions is minimization of cost total of production TPC are contain the sum of thermal and wind generation cost.

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Diep-Thanh, Thang, Quang Nguyen-Phung, and Huy Nguyen-Duc. "Stochastic control for optimal power flow in islanded microgrid." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (April 1, 2019): 1045. http://dx.doi.org/10.11591/ijece.v9i2.pp1045-1057.

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The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.

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Rahul, Jinendra, Yagvalkya Sharma, and Dinesh Birla. "Reduction of Transmission Losses based on Optimal Power Flow using Genetic Algorithm." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 3 (June 30, 2012): 105–7. http://dx.doi.org/10.24297/ijct.v2i3b.2698.

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This paper presents the application of GeneticAlgorithm (GA) for solving optimal power flow problems. It is animportant tool for performance analysis of many power systemsproblems. Optimal power flow (OPF) is of very muchsignificance in power system operation analysis underderegulated environment of electricity industry. The OPFoptimizes a power system operating objective function, whilesatisfying a set of system operating constraints. The basic OPFsolution is obtained with production cost minimization as theobjective function and the optimal settings of the power systemare determined. OPF can also be formulated for reactive poweroptimization, as minimization of system active power losses andimproving the voltage stability in the system. In the presentpaper objective function is to reduce transmission losses usingGA, a IEEE 30-bus test power system is studied for optimalpower flow. It is described in the paper that GA based optimalpower flow can provide optimal solution

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Hardiansyah, Hardiansyah. "A modified ABC algorithm for solving optimal power flow problem." Serbian Journal of Electrical Engineering 17, no. 2 (2020): 199–211. http://dx.doi.org/10.2298/sjee2002199h.

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This paper presents a modified artificial bee colony (MABC) algorithm for solving the optimal power flow (OPF) problem in power system. Artificial bee colony algorithm is a recent population-based optimization method which has been successfully used in many complex problems. A new mutation strategy inspired from the differential evolution (DE) is introduced in order to improve the exploitation process. The new algorithm is implemented to the OPF problem so as to minimize the total generation cost when considering the equality and inequality constraints. In order to validate of the proposed algorithm, it is applied to the standard IEEE 30-bus test system. The results show that the proposed technique provides better solutions than other heuristic techniques reported in literature.

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Lu, Jin Ling, and Qiang Zhang. "Multi-Objective Optimal Power Flow with Transient Stability Constraints." Applied Mechanics and Materials 556-562 (May 2014): 2067–71. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2067.

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In this paper, the deficiency of the general transient stability constrained OPF model was analyzed,on the basis of which, a multi-objective transient stability constrained OPF model was proposed. The transient stability constraints was to be as a objective function instead of the general constraints,which and power generation cost function were taken weighted sum to be converted into a single-objective optimization model. Multiple optimal solutions under different weights were given based on the original dual interior point method,which can offer a variety of options for operating personnel according to actual needs. In terms of improving the efficiency of the algorithm,The two groups of differentiated rotor equations were eliminated to be one set of equations.And the transient differential equations were dispersed to inequality constraints instead of equality constraints.the dimensions of the correction equation and the scale of the problem solved were significantly reduced. Numerical simulations on the New England 10-generator 39-bus power system, demonstrated the effectiveness and feasibility of the proposed method.

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Gupta, Saket, Narendra Kumar, Laxmi Srivastava, Hasmat Malik, Amjad Anvari-Moghaddam, and Fausto Pedro García Márquez. "A Robust Optimization Approach for Optimal Power Flow Solutions Using Rao Algorithms." Energies 14, no. 17 (September 1, 2021): 5449. http://dx.doi.org/10.3390/en14175449.

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This paper offers three easy-to-use metaphor-less optimization algorithms proposed by Rao to solve the optimal power flow (OPF) problem. Rao algorithms are parameter-less optimization algorithms. As a result, algorithm-specific parameter tuning is not required at all. This quality makes these algorithms simple to use and able to solve various kinds of complex constrained optimization and engineering problems. In this paper, the main aim to solve the OPF problem is to find the optimal values of the control variables in a given electrical network for fuel cost minimization, real power losses minimization, emission cost minimization, voltage profile improvement, and voltage stability enhancement, while all the operating constraints are satisfied. To demonstrate the efficacy of Rao algorithms, these algorithms have been employed in three standard IEEE test systems (30-bus, 57-bus, and 118-bus) to solve the OPF problem. The OPF results of Rao algorithms and the results provided by other swarm intelligence (SI)/evolutionary computing (EC)-based algorithms published in recent literature have been compared. Based on the outcomes, Rao algorithms are found to be robust and superior to their competitors.

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Srikun, Isaree, Lakkana Ruekkasaem, and Pasura Aungkulanon. "A Solution to Multi Objective Optimal Power Flow Using Hybrid Cultural-Based Differential Evolution." Applied Mechanics and Materials 457-458 (October 2013): 1236–40. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.1236.

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This paper presents a hybrid Cultural-based Differential Evolution for solving a multi-objective Optimal Power Flow (OPF) in support of power system operation and control . The multi-objective OPF was formulated for tackling with total generation cost and environmental impacts simultaneously. The proposed method was applied to the standard IEEE 30-bus test system. The results show that solving the multi-objective OPF problem by the Cultural-based Differential Evolution is more effective than other swarm intelligence methods in the literature.

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Chen, Meng Jen, Yu Chi Wu, Wen Shiush Chen, Pei Wei Huang, and Tsung Wei Tsai. "Integration of Power System Real-Time Digital Simulator and Optimal Power Flow." Advanced Materials Research 590 (November 2012): 195–200. http://dx.doi.org/10.4028/www.scientific.net/amr.590.195.

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In this paper, a framework for integrating a real-time digital simulator and EMS-OPF program is proposed and addressed, through two different communication architectures: asynchronous and synchronous. Validation of these communication architectures is carried out by Ethernet UDP/IP (asynchronous) and analog channels of IO card (synchronous). With this framework, both dynamic and steady-state performance of a power system can be studied easily in real-time mode.

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Jiang, Chu-Yang, and Hsiao-Dong Chiang. "Pseudo-Pitchfork Bifurcation of Feasible Regions in Power Systems." International Journal of Bifurcation and Chaos 28, no. 01 (January 2018): 1830002. http://dx.doi.org/10.1142/s0218127418300021.

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Local bifurcations occur in power systems, causing changes in power system dynamic behaviors. These local bifurcations include the saddle-node bifurcation, Hopf bifurcation, and structure-induced bifurcation. This paper presents a new type of bifurcation that can occur in the optimal power flow (OPF) problem. This new type of bifurcation, termed pseudo-pitchfork bifurcation, involves bifurcations of the feasible components of the OPF problem and the disappearance of local optimal power flow solutions. The main features of this special type of bifurcation are demonstrated on several power systems with different loading condition parameters and different constraint parameters. Then the computation consideration and physical meaning of the pseudo-pitchfork bifurcation are roughly discussed. It is also demonstrated that a pseudo-pitchfork bifurcation occurring between feasible components can help us interpret the loss or birth of optimal power flow solutions and can lead to powerful numerical methods for computing high-quality optimal power flow solutions.

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Leon, Luis M., Arturo S. Bretas, and Sergio Rivera. "Quadratically Constrained Quadratic Programming Formulation of Contingency Constrained Optimal Power Flow with Photovoltaic Generation." Energies 13, no. 13 (June 28, 2020): 3310. http://dx.doi.org/10.3390/en13133310.

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Contingency Constrained Optimal Power Flow (CCOPF) differs from traditional Optimal Power Flow (OPF) because its generation dispatch is planned to work with state variables between constraint limits, considering a specific contingency. When it is not desired to have changes in the power dispatch after the contingency occurs, the CCOPF is studied with a preventive perspective, whereas when the contingency occurs and the power dispatch needs to change to operate the system between limits in the post-contingency state, the problem is studied with a corrective perspective. As current power system software tools mainly focus on the traditional OPF problem, having the means to solve CCOPF will benefit power systems planning and operation. This paper presents a Quadratically Constrained Quadratic Programming (QCQP) formulation built within the matpower environment as a solution strategy to the preventive CCOPF. Moreover, an extended OPF model that forces the network to meet all constraints under contingency is proposed as a strategy to find the power dispatch solution for the corrective CCOPF. Validation is made on the IEEE 14-bus test system including photovoltaic generation in one simulation case. It was found that in the QCQP formulation, the power dispatch calculated barely differs in both pre- and post-contingency scenarios while in the OPF extended power network, node voltage values in both pre- and post-contingency scenarios are equal in spite of having different power dispatch for each scenario. This suggests that both the QCQP and the extended OPF formulations proposed, could be implemented in power system software tools in order to solve CCOPF problems from a preventive or corrective perspective.

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Khunkitti, Sirote, Apirat Siritaratiwat, Suttichai Premrudeepreechacharn, Rongrit Chatthaworn, and Neville Watson. "A Hybrid DA-PSO Optimization Algorithm for Multiobjective Optimal Power Flow Problems." Energies 11, no. 9 (August 29, 2018): 2270. http://dx.doi.org/10.3390/en11092270.

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In this paper, a hybrid optimization algorithm is proposed to solve multiobjective optimal power flow problems (MO-OPF) in a power system. The hybrid algorithm, named DA-PSO, combines the frameworks of the dragonfly algorithm (DA) and particle swarm optimization (PSO) to find the optimized solutions for the power system. The hybrid algorithm adopts the exploration and exploitation phases of the DA and PSO algorithms, respectively, and was implemented to solve the MO-OPF problem. The objective functions of the OPF were minimization of fuel cost, emissions, and transmission losses. The standard IEEE 30-bus and 57-bus systems were employed to investigate the performance of the proposed algorithm. The simulation results were compared with those in the literature to show the superiority of the proposed algorithm over several other algorithms; however, the time computation of DA-PSO is slower than DA and PSO due to the sequential computation of DA and PSO.

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Pareek, Parikshit, and Hung D. Nguyen. "State-Aware Stochastic Optimal Power Flow." Sustainability 13, no. 14 (July 7, 2021): 7577. http://dx.doi.org/10.3390/su13147577.

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The increase in distributed generation (DG) and variable load mandates system operators to perform decision-making considering uncertainties. This paper introduces a novel state-aware stochastic optimal power flow (SA-SOPF) problem formulation. The proposed SA-SOPF has objective to find a day-ahead base-solution that minimizes the generation cost and expectation of deviations in generation and node voltage set-points during real-time operation. We formulate SA-SOPF for a given affine policy and employ Gaussian process learning to obtain a distributionally robust (DR) affine policy for generation and voltage set-point change in real-time. In simulations, the GP-based affine policy has shown distributional robustness over three different uncertainty distributions for IEEE 14-bus system. The results also depict that the proposed SA-OPF formulation can reduce the expectation in voltage and generation deviation more than 60% in real-time operation with an additional day-ahead scheduling cost of 4.68% only for 14-bus system. For, in a 30-bus system, the reduction in generation and voltage deviation, the expectation is achieved to be greater than 90% for 1.195% extra generation cost. These results are strong indicators of possibility of achieving the day-ahead solution which lead to lower real-time deviation with minimal cost increase.

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Leeprechanon, Nopbhorn, and Prakornchai Phonrattanasak. "Bees Two-Hive Algorithm for Optimal Power Flow." Applied Mechanics and Materials 313-314 (March 2013): 870–75. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.870.

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This paper presents bees two-hive algorithm for solving the optimal power flow (OPF) problem with various constraints. The objective of the proposed technique is to improve the quality solution of the conventional bees algorithm that minimize the total fuel cost subject to operational and physical constraints i.e. energy balance, generation and transmission limits including security constraints. The proposed methodology is tested on the IEEE 30-bus test system. The results obtained using the proposed approach are compared to GA, PSO, BA and other conventional. The comparison of quality solution with other algorithms confirms performance of proposed technique. Simulation results demonstrate that bees two-hive algorithm provides better results than other heuristic techniques.

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Et. al., Vijaya Bhaskar K,. "Modern Swarm Intelligence based Algorithms for Solving Optimal Power Flow Problem in a Regulated Power System Framework." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 1786–93. http://dx.doi.org/10.17762/turcomat.v12i2.1515.

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This paper presents artificial swarm intelligent based algorithms viz., Firefly Algorithm (FFA), Dragonfly Algorithm (DA) and Moth Swarm Algorithm (MSA) to take care of the issues related to optimal power flow (OPF) problem in a power system network. The optimal values of various decision variables obtained by swarm intelligent based algorithms can optimize various objective function of OPF problem. This article is focused with four objectives such as minimization of total fuel cost (TFC) and total active power loss (TAPL); improvisation of total voltage profile (TVD) and voltage stability index (VSI). The effectiveness of various swam intelligent algorithms are investigated on a standard IEEE-30 bus. The performance of distinct algorithms is compared with statistical measures and convergence characteristics.

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Bentouati, Bachir, Lakhdar Chaib, and Saliha Chettih. "Optimal Power Flow using the Moth Flam Optimizer: A Case Study of the Algerian Power System." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 3 (March 1, 2016): 431. http://dx.doi.org/10.11591/ijeecs.v1.i3.pp431-445.

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In this paper, a new technique of optimization known as Moth-Flam Optimizer (MFO) has been proposed to solve the problem of the Optimal Power Flow (OPF) in the interconnected power system, taking into account the set of equality and inequality constraints. The proposed algorithm has been presented to the Algerian power system network for a variety of objectives. The obtained results are compared with recently published algorithms such as; as the Artificial Bee Colony (ABC), and other meta-heuristics. Simulation results clearly reveal the effectiveness and the robustness of the proposed algorithm for solving the OPF problem.

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Chen, Gonggui, Zhengmei Lu, Zhizhong Zhang, and Zhi Sun. "Optimal Power Flow Using an Improved Hybrid Differential Evolution Algorithm." Open Electrical & Electronic Engineering Journal 11, no. 1 (October 31, 2017): 177–92. http://dx.doi.org/10.2174/1874129001711010177.

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Objective: In this paper, an improved hybrid differential evolution (IHDE) algorithm based on differential evolution (DE) algorithm and particle swarm optimization (PSO) has been proposed to solve the optimal power flow (OPF) problem of power system which is a multi-constrained, large-scale and nonlinear optimization problem. Method: In IHDE algorithm, the DE is employed as the main optimizer; and the three factors of PSO, which are inertia, cognition, and society, are used to improve the mutation of DE. Then the learning mechanism and the adaptive control of the parameters are added to the crossover, and the greedy selection considering the value of penalty function is proposed. Furthermore, the replacement mechanism is added to the IHDE for reducing the probability of falling into the local optimum. The performance of this method is tested on the IEEE30-bus and IEEE57-bus systems, and the generator quadratic cost and the transmission real power losses are considered as objective functions. Results: The simulation results demonstrate that IHDE algorithm can solve the OPF problem successfully and obtain the better solution compared with other methods reported in the recent literatures.

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Journal articles on the topic 'Optimal Power Flow (OPF)'

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