Solving Power System Research Articles

Solving power system economic emission dispatch problem under complex constraints via dimension differential learn butterfly optimization algorithm with FDC-based

The economic emission dispatch (EED) aims to minimize the fuel and pollutant emission costs of generator units under various complex constraints. Optimizing the EED problem is of crucial importance for alleviating the current energy and environmental pressures. In this work, nearly all known complex constraints in the EED problem, including the valve-point effect, transmission line power loss, prohibited operating zones, and ramp-rate limits, are taken into account, and an enhanced version of butterfly optimization algorithm (FDCDLBOA) is proposed to solve it. First, a new adaptive fragrance is employed to optimize the instability caused by target differences and improve the convergence performance. Second, the proposed dimension differential learning strategy evolves the position of individuals with the help of superior dimensional information in the population, and this extensive learning exchange can balance global and local search, maintain diversity, and get rid of local optima. Third, the Fitness-Distance-Constraint (FDC) guide selection method is employed for the first time to handle the complex constraints of EED problems, enhancing the ability of individuals to bypass the infeasible search areas. After evaluating the proposed FDCDLBOA on CEC 2022 test suite, it is applied to solve 8 EED cases, encompassing small-, medium- and large-scale systems. Notably, the 280-generator case is the first large-scale test to exceed 200 generators. Compared with 9 representative algorithms, FDCDLBOA performs outstandingly in terms of robustness, improvement index (IF), mean constraint violation (MV), feasibility rate (FR) and Quade multiple comparison, among which IF, MV, FR, and Quade are all employed for evaluating the EED problem for the first time. The presented results confirm that the proposed method effectively enhances the robustness of high-quality solutions and the ability to handle complex constraints, demonstrating strong competitiveness and potential in solving the EED problem.

A hybrid quantum–classical algorithm for mixed-integer optimization in power systems

Mixed Integer Linear Programming (MILP) can be considered the backbone of the modern power system optimization process, with a large application spectrum, from Unit Commitment and Optimal Transmission Switching to verifying Neural Networks for power system applications. The main issue of these formulations is the computational complexity of the solution algorithms, as they are considered NP-Hard problems. Quantum computing has been tested as a potential solution towards reducing the computational burden imposed by these problems, providing promising results, motivating the can be used to speedup the solution of MILPs. In this work, we present a general framework for solving power system optimization problems with a Quantum Computer (QC), which leverages mathematical tools and QCs’ sampling ability to provide accelerated solutions. Our guiding applications are the optimal transmission switching and the verification of neural networks trained to solve a DC Optimal Power Flow. Specifically, using an accelerated version of Benders Decomposition , we split a given MILP into an Integer Master Problem and a linear Subproblem and solve it through a hybrid “quantum–classical” approach, getting the best of both worlds. We provide 2 use cases, and benchmark the developed framework against other classical and hybrid methodologies, to demonstrate the opportunities and challenges of hybrid quantum–classical algorithms for power system mixed integer optimization problems.

Hierarchical deep reinforcement learning for self-adaptive economic dispatch

It is challenging to accurately model the overall uncertainty of the power system when it is connected to large-scale intermittent generation sources such as wind and photovoltaic generation due to the inherent volatility, uncertainty, and indivisibility of renewable energy. Deep reinforcement learning (DRL) algorithms are introduced as a solution to avoid modeling the complex uncertainties and to adapt the fluctuation of uncertainty by interacting with the environment and using feedback to continuously improve their strategies. However, the large-scale nature and uncertainty of the system lead to the sparse reward problem and high-dimensional space issue in DRL. A hierarchical deep reinforcement learning (HDRL) scheme is designed to decompose the process of solving this problem into two stages, using the reinforcement learning (RL) agent in the global stage and the heuristic algorithm in the local stage to find optimal dispatching decisions for power systems under uncertainty. Simulation studies have shown that the proposed HDRL scheme is efficient in solving power system economic dispatch problems under both deterministic and uncertain scenarios thanks to its adaptation system uncertainty, and coping with the volatility of uncertain factors while significantly improving the speed of online decision-making.

Educational Interface of Load Flow Methods Using Matlab Graphic User’s Interface Software

this paper introduces a new created programapplication built based on MATLAB programing languageto solve power system networks at steady state problems,which include power flow studies with significant approach.The load flow solution uses iterative techniques that arenamely, Newton-Raphson, Gauss-Seidel and Fast Decouple.After obtaining their results, line changes like lineswitching, tap changer and bus changing such as busdeletion or any specific information at the same bus can beeasily achieved. Moreover, the users can enter their ownsystem, and save it to apply the pervious analysis. Thisfriendly interactive program designed to be simple for theusers with accurate results and it can be applied for anynetwork.

Energy Management of Microgrid Considering Demand Response Using Honey Badger Optimizer

Recently, Microgrids (MGs) have received great attention for solving power system problems, due to their low environmental effects and their economic benefits. This paper proposes a new application of an effective metaheuristic optimization method, namely, Honey Badger Algorithm (HBA), to solve energy management for optimal dispatch of the grid connected MG incorporating Demand Response programs (DRP). Honey badger algorithm is used to solve an incentive DRP, with the aim of minimizing the total cost, which includes conventional generators fuel cost and the cost of power transaction with the main grid considering the load demand. In this paper, two case studies are conducted using HBA and simulation results are compared with those obtained by other algorithms (particle swarm optimization and JAYA algorithm). First case consists of three diesel generators, a PV generator and a wind generator. To prove the scalability of the HBA, the second case, which is much larger, is tested. Simulation results for both case studies obtained by PSO, JAYA, and HBA are deeply discussed. The results show the HBA's effectiveness in solving the energy management with DR problem for MG compared with other well-known optimization techniques.

A novel solution for the optimal reactive power dispatch problem using an artificial neural network integrated with the firefly optimization algorithm

This article applies a novel intelligence technique to solve power system issues faced daily. Compensation for reactive power is a significant issue faced by power system operators in research. The solution can be obtained by handling a multi-objective task and multiconstraints by reducing the active power loss and minimizing the voltage deviation at the load end. The novelty of the research focuses on integrating artificial neural network techniques with the firefly algorithm, a novel optimization algorithm for attaining an objective function. The Levenberg–Marquardt back-propagation algorithm is most suited for proper tuning of the control variables. The objective of this research can be attained by appropriately tuning the control variables connected with the IEEE test bus systems, which helps to maximally improve the voltage profile. Existing research studies have focused on reactive power management, which is attained by solving optimal reactive power flow problems employing nature-inspired approach techniques such as the symbiotic organism search algorithm, the cuckoo search algorithm, the black hole algorithm, the krill herd algorithm, and whale optimization. The evolving strategy, the firefly algorithm (FFA), minimizes the multiconstraint functions more competently and effectively than any conventional algorithm. To showcase the strength of the firefly algorithm incorporating AI, it is examined on standard IEEE test bus systems, namely, the 14-, 30-, and 58-bus networks. The obtained results quantify the effectiveness of the proposed methodology, that is, the artificial intelligence technique implementing the firefly algorithm gives better results than conventional methods.

Optimal allocation of flexible AC transmission system (FACTS) for wind turbines integrated power system

AbstractThe importance of flexible alternating current transmission system (FACTS) devices is increasing day by day as they provide compensation for power systems. The installation of such devices solves many issues regarding the stability and power transferability of power systems. However, for optimizing the performance of FACTS devices, the location and sizes of FACTS devices must be selected very carefully. On the other side, the utilization of optimization techniques is increasing daily, as it is capable of solving nonlinear, random problems by its stochastic nature. As a result, it became a reliable tactic for solving stochastic problems almost in every aspect of our lives. Therefore, it is adopted for solving power system problems, like, FACTS devices allocation, distributed generators allocation, determining component's parameters, and more. In this paper, the optimal size and location of several FACTS devices are selected to achieve the following single objective functions separately: fuel cost minimization and power losses minimization, then combining the mentioned objective function into one multiobjective function for gross cost minimization. The power system optimized is the Institute of Electrical and Electronics Engineers 30‐bus standard system, and the FACTS devices installed are static VAR compensator, thyristor control series compensator, and thyristor‐controlled phase shifter, while the optimization is performed using different conventional and recent optimization algorithms, including a newly developed algorithm, typically Leader Walrus Optimization Algorithm “LWaOA,” making a comparison among such algorithms to investigate the algorithm that possesses the best performance. The results of the optimization processes show the superiority of LWaOA.

An Advanced High Dimensional Power System of Optimization Problems Algorithm

New Evolutionary Algorithm-based for High-Dimensional Power System Optimization Problems and Modern power system optimization problems face growing challenges as there are numerous decision variables and unbiased functions. Multi-objective evolutionary algorithms (MOEAs) basically are popularly applied for solving and dealing with such complicated problems in substantial power systems that have several objective functions. However, customary MOEAs works appropriately when the number of objective features comes to be less than three. Generally, the effectiveness of MOEAs begins to decrease apparently with an increase in the number of unbiased functions. This study is devoted to applying an authentic gradable evolutionary algorithm for the purpose of solving power system optimization issues which manifest numerous objective functions. In particular, this seeks an algorithm based upon NSGA-II algorithm dedicated for an efficient solution of multi-objective optimization problems. Here, we attempt to modify NSGA-II algorithm to effectively solve many target optimization problems. A novel effective grouping method is applied in the proposed algorithm in similar to the non-dominant grouping method of NSGA-II algorithm to accelerate decision convergence action in POF. The recommended algorithm is compared with modern numerous objective optimization algorithms following three test problems. The findings reveal that as the number of unbiased functions is noticeably large, the algorithm that we propose is significantly superior to other algorithms.

Multi-functional energy storage system for supporting solar PV plants and host power distribution system

Energy storage systems (ESS) will play a critical role in the ongoing development of the future electrical grid, especially as penetration of renewable energy generation increases. Since the costs of ESS are still high, it is imperative to research diverse control modes of ESS so as to use them in an effective manner, thereby offsetting their high costs. ESS technologies can diminish curtailment of renewable generators and provide much needed storage capabilities for supporting the grid, such as providing voltage regulation, relieving congestion, and improving power quality. This study develops six control modes for a battery ESS (BESS), namely, Current Limiting, Power Limiting, Load Leveling, Voltage Regulation, Power Factor Correction, and Simultaneous Real and Reactive Power Supply. The control modes are verified by simulation using a realistic utility 2.8-MW/5.6-MWh BESS and three solar PV plants connected to a power distribution grid. The study results demonstrate that the BESS functions properly in all the control modes. It can be used in all four quadrants of real and reactive power, i.e., it can provide any combination of real and reactive power amount within its rating for supporting the solar PV farms and the grid. The control modes enable effective operation of the BESS, which is a new tool to solve power system critical operational issues, thereby helping ensure the grid resilient, efficient, and reliable operation.

A successive flow direction enforcing algorithm for optimal operation of variable-impedance FACTS devices

This paper presents a novel successive flow direction enforcing (SFDE) algorithm for solving power system operation models with flexible AC transmission system (FACTS) devices. The power flow control capabilities of FACTS devices can improve the utilization of the existing transmission network. However, the added computational complexity of modeling FACTS devices in power system operation models is one of the primary challenges for FACTS deployment. A prominent component is the nonlinearity introduced by variable-impedance FACTS devices to the linear DC power flow equations. According to previous studies, such nonlinearity can be tackled by preassigning the power flow directions on lines equipped with FACTS to restore the linearity of DC power flow equations. However, this method has the issue of converging to suboptimal solutions in some cases, due to suboptimal flow direction assignment. The algorithm presented in this paper can address the issue of suboptimality, while still achieving computational efficiency improvements. Power system operation models with FACTS are solved iteratively with flow directions enforced, and the predetermined flow directions are adjusted successively based on the results of the previous iteration. Simulation studies confirm the effectiveness of the method in converging to the globally optimal solution within a few iterations in almost all practical cases.

Review of Different Methods for Siting and Sizing Distributed Generator

Distributed generator has been confirmed to be a means by which installation of a new centralized generator can be delayed or postponed in the era of deregulation. It helps to minimize line losses and improve voltage profile. the benefits associated with distributed generator can be greatly harnessed when DG is optimally sited and sized within power system network. In this study, different methods used by previous researchers were extensively reviewed. The summary of the methodologies from previous works were itemized one after the other. This was done under the impact of distributed generator in a network. These methodologies are summaries under three categories namely; analytical, meta-heuristic and hybrid optimization methods. All the methods are based on solving non-linear load flow solution except the Inherent Structural Network Topology. In this review work, the inherent structural network topology is recommended for further studies to solve power system problems.

Identification study of solar cell/module using recent optimization techniques

This paper proposes the application of a novel metaphor-free population optimization based on the mathematics of the Runge Kutta method (RUN) for parameter extraction of a double-diode model of the unknown solar cell and photovoltaic (PV) module parameters. The RUN optimizer is employed to determine the seven unknown parameters of the two-diode model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. Consequently, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. The suggested objective function achieves the closeness degree between the estimated and experimental data. For getting the generic model, applications of the proposed RUN are carried out on two different commercial PV cells. To assess the proposed algorithm, a comprehensive comparison study is employed and compared with several well-matured optimization algorithms reported in the literature. Numerical simulations prove the high precision and fast response of the proposed RUN algorithm for solving multiple PV models. Added to that, the RUN can be considered as a good alternative optimization method for solving power systems optimization problems.

A Simulation Approach to Solve Power System Transmission Problems

The cost of electricity is an indicator of a country's level of development. This industry involves billions of dollars. Hence, the operation of Electric Power Systems constitutes a real problem that consists of a set of stages related to distinct components. In this paper, we propose a novel simulation model to study the state of transmission line systems due to scheduled and unscheduled maintenances. The simulation model is founded in an extension of the Discrete Event System Specification formalism called Routed DEVS. The transmission line system is modeled with a set of discrete-event simulation models that provide a complete state description of components. Such a simulation model can be adapted to describe any transmission line system through a network topology. A case study is presented with aims to show who the simulation works. Our proposal is the starting point to study real-life operations of Electric Power Systems from generation to transmission stages.

Optimal Allocation and Sizing of Multi DG Units Including Different Load Model Using Evolutionary Programming

This paper presents the optimal allocation and sizing of multi distributed generation (DG) units including different load models using evolutionary programming (EP) in solving power system optimization problem. This paper also studies on the effect of multi DG placement in different load model. To optimize the power distribution system, multi DG units were used to reduce losses power distribution system. By using EP, the optimal allocation and sizing of multi-DG was determined in order to obtain maximum benefits from its installation. The propose technique was tested into IEEE 69-bus distribution system. The result shows the placement of DG can reduce power loss 89% to 98%. The placement of multi-DG unit has better performance compare to single DG.

A Multi-Objective Optimal Power Flow Control of Electrical Transmission Networks Using Intelligent Meta-Heuristic Optimization Techniques

Optimal power flow (OPF) is considered one of the most critical challenges that can substantially impact the sustainable performance of power systems. Solving the OPF problem reduces three essential items: operation costs, transmission losses, and voltage drops. An intelligent controller is needed to adjust the power system’s control parameters to solve this problem optimally. However, many constraints must be considered that make the design process of the OPF algorithm exceedingly tricky due to the increased number of limitations and control variables. This paper proposes a multi-objective intelligent control technique based on three different meta-heuristic optimization algorithms: multi-verse optimization (MVO), grasshopper optimization (GOA), and Harris hawks optimization (HHO) to solve the OPF problem. The proposed control techniques were validated by applying them to the IEEE-30 bus system under different operating conditions through MATLAB simulations. The proposed techniques were then compared with the particle swarm optimization (PSO) algorithm, which is very popular in the literature studying how to solving the OPF problem. The obtained results show that the proposed methods are more effective in solving the OPF problem when compared to the commonly used PSO algorithm. The proposed HHO, in particular, shows that it can form a reliable candidate in solving power systems’ optimization problems.

Techno-economics analysis of battery energy storage system (BESS) design for virtual power plant (VPP)–A case study in Malaysia

Renewable Energy (RE) penetration to power system has been a trend in recent years. Malaysia under the new RE target has a vision to achieve 20% of RE in energy mix by 2025. Flexibility and stability of power system can be a concern due to high penetration of RE in the system. Battery Energy Storage System (BESS) has been identified as one of the possible solutions to mitigate this issue. This paper will discuss the capabilities of this technology to reduce peak demand charge and potential to solve power system issues and the techno-economic analysis for this technology. This covers technical and economic analysis to ensure viability and feasibility of this technology. This is a novel analysis in terms of Malaysian energy market as BESS is considered relatively new technology and still in early stage in terms of commercial deployment.

Solving power systems optimization problems in the presence of renewable energy sources using modified exchange market algorithm

This paper introduces a novel evolutionary algorithm namely modified exchange market algorithm (MEMA) to solve the various problems in the power system operation topics such as combined heat and power economic emission dispatch (CHPEED), and multi-area economic dispatch (MAED) in the presence of renewable energy sources (RESs). The MEMA is a new version of the EMA that has been modified to improve its performance and to find a better solution with high robustness. To scrutiny the applicability of the MEMA, it is employed to solve the different problems with considering RESs. Also, to prove the scalability and evaluate the performance of the proposed method, several large and small scale test systems are used in each case. Also, three different approaches are considered to investigate the impacts of RESs. In the first approach, RESs are assumed to be a negative load, while in the latter case, their cost impacts on the problem are also considered. Finally, in the last approach, the uncertainty of RES and its stochastic nature are considered. In all test systems, the results obtained by the proposed method are compared with the results of other suggested techniques in this era. Also, the robustness of the proposed method is investigated using parametric and non-parametric statistical tests. The comparative and statistical analysis confirm the superiority and robustness of the proposed method over the other methods in all test systems.

Deployment of FACTS Controllers in Solving Power System Network Problems: A Review

Flexible alternating current transmission systems (FACTS) deployments and applications are on the increase in modern day power network systems because of their advantages over conventional ways of power network physical expansion. Therefore, a comprehensive review of FACTS controllers with their various applications is carried out in this paper. Formation of different applications and advantages of FACTS devices into voltage control, power flow control, system stability control, power quality control, and economic benefits as inferred from the literatures is among the vantage points of this presentation. FACTS background alongside different techniques of deployments, leading to various applications and performance of these devices, hitherto organized structurally based on target objectives are also explored, presented and discussed. Summarily, this study provides an overview of the background, topological structures, deployment techniques and cutting-edge utilization of FACTS controllers, with a view to acquainting power players, electrical engineers, network designers as well as researchers, with the trends in the development, status and future direction of FACTS applications. Convincingly, the content of this article will benefit all the stakeholders in the area of FACTS deployments and utilizations.

Solving Power System Economic Dispatch Problem Based on Genetic Algorithm

With the rapid development of the world economy, the demand for power load is also growing rapidly. The power system economic dispatch problem studied in this paper is to reduce the fuel cost of generator units on the premise of satisfying some series of constraints of power system. In this paper, genetic algorithm is adopted as the solution strategy for power system economic dispatch problem, and the penalty function is used to deal with the equality constraint condition in the power system economic dispatch problem. Finally, we use the genetic algorithm to solve the IEEE-9 notes power system economic dispatch problem and give the corresponding simulation results. The simulation results show that the genetic algorithm can solve the power system economic dispatch problem effectively.

Intelligent Multi-Microgrid Energy Management Based on Deep Neural Network and Model-Free Reinforcement Learning

In this paper, an intelligent multi-microgrid (MMG) energy management method is proposed based on deep neural network (DNN) and model-free reinforcement learning (RL) techniques. In the studied problem, multiple microgrids are connected to a main distribution system and they purchase power from the distribution system to maintain local consumption. From the perspective of the distribution system operator (DSO), the target is to decrease the demand-side peak-to-average ratio (PAR), and to maximize the profit from selling energy. To protect user privacy, DSO learns the MMG response by implementing a DNN without direct access to user’s information. Further, the DSO selects its retail pricing strategy via a Monte Carlo method from RL, which optimizes the decision based on prediction. The simulation results from the proposed data-driven deep learning method, as well as comparisons with conventional model-based methods, substantiate the effectiveness of the proposed approach in solving power system problems with partial or uncertain information.