Power System Problems Research Articles

Radial Power Distribution System Fault Classification Model Based on ANFIS

The classification of problems in power systems plays an extremely important part and has evolved into a necessity that is of the utmost importance to the operation of energy grids. For the purpose of fault classification in IEEE 13 node radial distribution systems, this paper makes use of both an Artificial Neural Network (ANN) and a Neural Fuzzy adaptive Inference System (ANFIS). Simulations of the suggested models are carried out in MATLAB/SIMULINK, and fault currents from all three phases are analyzed in order to extract statistical characteristics. Input data vectors include the standard deviation and correlation factors between the currents of any two phases, while output data vectors include the different sorts of faults. The findings demonstrate that the devised method is appropriate for the classification of all symmetrical and unsymmetrical faults.

Multi-agent deep reinforcement learning-based optimal energy management for grid-connected multiple energy carrier microgrids

Multi-agent deep reinforcement learning (MA-DRL) method provides a groundbreaking approach to tackling computational problems in power systems, particularly for distributed energy resources that have been widely adopted to advance energy sustainability. This paper presents a novel optimal energy management based on proposed MA-DRL method. This method employs deep neural network to learn strategy based on stacked-denoising auto-encoders and multi-agent deep deterministic policy gradient learning capability. The MA-DRL method is adopted to find the optimal strategy of the optimal energy management problem under the Markov decision process framework. This method aims to coordinate multiple energies and achieve optimal operation over a variety of hourly dispatches while taking into account the distinct properties of electric and thermal energies. The primary challenge of the planning and operation of multiple energy carrier microgrids (MECMs) is determining the optimal interaction between renewable energy resources, energy storage systems, power-to-thermal conversion systems, and upstream power grid in order to improve overall energy utilization efficiency. The presented robust method can adaptively derive the optimal operation for MECMs through centralized learning and decentralized implementation. The optimization problem is employed in this study to concurrently reduce the total emissions and the operating costs while considering engineering design constraints. Finally, to demonstrate the efficiency of the proposed method, it is verified on an integrated modified IEEE 33-bus and 8-node gas systems.

Review of Metaheuristic Optimization Algorithms for Power Systems Problems

Metaheuristic optimization algorithms are tools based on mathematical concepts that are used to solve complicated optimization issues. These algorithms are intended to locate or develop a sufficiently good solution to an optimization issue, particularly when information is sparse or inaccurate or computer capability is restricted. Power systems play a crucial role in promoting environmental sustainability by reducing greenhouse gas emissions and supporting renewable energy sources. Using metaheuristics to optimize the performance of modern power systems is an attractive topic. This research paper investigates the applicability of several metaheuristic optimization algorithms to power system challenges. Firstly, this paper reviews the fundamental concepts of metaheuristic optimization algorithms. Then, six problems regarding the power systems are presented and discussed. These problems are optimizing the power flow in transmission and distribution networks, optimizing the reactive power dispatching, optimizing the combined economic and emission dispatching, optimal Volt/Var controlling in the distribution power systems, and optimizing the size and placement of DGs. A list of several used metaheuristic optimization algorithms is presented and discussed. The relevant results approved the ability of the metaheuristic optimization algorithm to solve the power system problems effectively. This, in particular, explains their wide deployment in this field.

Peak shaving and short-term economic operation of hydro-wind-PV hybrid system considering the uncertainty of wind and PV power

With uncertain wind and PV power integrated into the grid, the difficulty of peak shaving is exacerbated. Therefore, the peak shaving operation of hydropower has become one of the most important problems in power system. In this paper, an optimal operation strategy of hydro-unit level coordinated peak shaving and economic operation in hydro-wind-PV hybrid system under uncertain conditions of wind and PV power is proposed. Firstly, the uncertainty of wind and PV power is described based on Latin hypercube sampling and scenario reduction methods. Secondly, a two-layer optimization model considering wind and PV uncertainty is established. And then, the synchronous peak shaving strategy and the improved lambda flow iteration strategy are proposed to solve the model. A case study is performed with the HWPHS in the Yarlung Zangbo River basin of China. The results show that (1) The residual load of power system fluctuates steadily under the condition of wind and PV uncertainty, and the peak shaving rate is 95.53%. (2) Compared with the DP algorithm, the proposed algorithm can increase the economic efficiency by 0.89% when solving the lower-layer model. (3) The V3 shaped mapping function proved to be superior to other functions.

Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-ω

The renewable energy sources (RESs)-based economic dispatch problem (EDP) is of vital importance for modern power systems. Environmental pollution, climatic degradation, and rapidly growing prices of continuously depleting fossil fuels have encouraged researchers to consider mechanisms for RES implementation and optimal operations. This paper presents a quasi-oppositional population-based global particle swarm optimizer with inertial weights (QPGPSO-ω) to solve environment friendly EDPs. The optimization technique is applied to solve the EDP under different scenarios including cases where only renewable energy sources (RESs) are used and the cases where combined emission–economic dispatch (CEED) problem is taken into account. The scenario for RESs includes a combination of six wind, five solar PV, and four biofuel systems for power generation. EDPs are considered without any constraints, and the variability of resources is depicted over time, along with the regional load-sharing dispatch (RLSD). The case of CEED considers ten thermal units with the valve point loading (VPL) effect and transmission losses. The results obtained by the proposed QPGPSO-ω algorithm are better than the reported results employing other optimization methods. This is shown by the lower costs achieved up to USD 8026.1439 for the case of only RES-based EDPs, USD 1346.8 for the case of RES-based EDPs with RLSD, and USD 111,533.59 for the case of CEED. Thus, the proposed QPGPSO-ω algorithm was effective in solving the various adopted power dispatch problems in power system.

Projection-based distributed economic dispatch algorithm considering communication delays under switching topologies

In this paper, a projection-based distributed algorithm is proposed to solve the economic dispatch problem of power system with communication delays under switching topologies. The algorithm is suitable for economic dispatch problem with strongly convex cost function, and ensures that the output power of each generator is always kept within its output limit. In addition, the topology is only required to be jointly connected. Firstly, the relationship between the equilibrium point and the optimal solution of the algorithm is analyzed, and then a common Lyapunov function is introduced for a set of switching topologies. According to the Lyapunov stability theory and Cauchy convergence criterion, LMIs-based sufficient conditions for the convergence of the algorithm are obtained. Finally, the effectiveness of the algorithm under different cases are verified by MATLAB simulation results.

Transition and Issues of Substation Protection Relay Systems for Power Distribution

In June 2023, the Protection Relay System Technical Committee planned a special issue labeled as “Protection Relay System Technology in Power Distribution Stations”. Protection relay systems play a vital role in the stable operation of power systems. In recent years, they have also been responsible for solving various problems in power systems, such as the realization of mass introduction of renewable energy, expansion of the operational capacity of power distribution facilities, and rationalization of facility configuration. The following topics were gathered and discussed as a result of the call for this special issue.: (1) Proposals for distribution line protection methods (short circuit protection, ground fault protection), (2) Case studies using IEDs (IEC 61850), N-1 inter trip scheme, bus bar protection relays, and comprehensive retrofits, (3) Optimizing accident recovery during distribution line accidents, (4) Voltage adjustment methods using storage battery capacity operation, (5) Bank Recorder and switchboard configuration when dealing with reverse power flow. A total of 10 papers were submitted. This commentary touches on the contents of the technical reports as an aid to understanding these papers. It also outlines the history of distribution substation protection relays and the latest technology in distribution substation protection relay systems.

Solving Bilevel Optimal Bidding Problems Using Deep Convolutional Neural Networks

Current state-of-the-art solution techniques for solving bilevel optimization problems either assume strong problem regularity criteria or are computationally intractable. In this article, we address power system problems of bilevel structure, commonly arising after the deregulation of the power industry. Such problems are predominantly solved by converting the lower level problem into a set of equivalent constraints using the Karush–Kuhn–Tucker optimality conditions at an expense of binary variables. Furthermore, in case the lower level problem is nonconvex, the strong duality does not hold rendering the single-level reduction techniques inapplicable. To overcome this, we propose an effective numerical scheme based on bypassing the lower level completely using an approximation function that replicates the relevant lower level effect on the upper level. The approximation function is constructed by training a deep convolutional neural network. The numerical procedure is run iteratively to enhance the accuracy. As a case study, the proposed method is applied to a price-maker energy storage optimal bidding problem that considers an ac power flow-based market clearing in the lower level. The results indicate that greater actual profits are achieved as compared to the less accurate dc market representation.

Transmission line fault analysis using actuating relays and Atmega 328P controller

<span lang="EN-US">Since transmission lines account for 80–86% of power system problems, protecting transmission lines is a critical concern. This research proposes a method to identify and categorize various short circuit defects on transmission lines. Various operational and fault circumstances on the transmission of high voltage lines, including L-L fault, single phase to earth fault, and double line fault, double line Three fault scenarios triple line fault, triple line to ground fault, and triple line are simulated using MATLAB software. These flaws damage the linked devices to the power grid. The main motto of this work is to analyze various kinds of faults and also to detect the type of the fault in transmission line. In order to simulate and evaluate the various faults, a 100 km transmission line model was created. A sim power system block library had a fault block, and the harmonic content of the various faults was studied using the FFT tool, the effect of faults was observed. Based on the analysis, a hardware model is designed to identify the faults in transmission line using actuating relays and Atmega 328P microcontroller and display the fault type and parameters like fault impedance, and distance at which fault occurred.</span>

A modified artificial bee colony algorithm based on a non-dominated sorting genetic approach for combined economic-emission load dispatch problem

The multi-objective optimization algorithms (MOO) are used to obtain the best compromising solutions when two or more objective functions need to be optimized simultaneously. The convergence and diversity are critical factors to consider while solving the MOO problems because they determine the possibility of obtaining an evenly distributed Pareto front. This paper proposes a hybrid optimization algorithm to solve a multi-objective economic emission dispatch (MOEED) problem of electrical power systems. The electrical power generated by consuming fossil fuels is very costly and also burning of these fuels contributes to global warming. Hence, electrical power is generated at the least cost and emission. The MOEED problems have been solved in the past by using swarm intelligence and evolutionary algorithms. However, the solutions reported in the literature, are either inferior or the constraints are violated. The algorithm proposed in this paper is an integration of an Artificial Bee Colony optimization algorithm and a Non-Dominated Sorting Genetic Algorithm-II. The effectiveness of the proposed algorithm is evaluated by applying it to three test systems having 6, 10, and 40 coal-based generators. Additionally, various multi-criteria decision-making algorithms are used to identify the best non-dominated solutions obtained by the proposed algorithm and compared with previously reported results. The best fuel costs obtained by the proposed approach, for a 6-unit test system with and without transmission losses are found to be 605.9983 and 600.11140 $/h respectively. While the best emission values, for this test system, with and without transmission losses are found to be 0.1941 and 0.1942 tons/h. Moreover, the best fuel costs obtained by the proposed approach, for 10 and 40-unit test systems are found to be 111181.9871 and 121369.0838 $/h respectively. Furthermore, the best emission values for these test systems are found to be 3932.24322 and 176682.264 tons/h respectively. All these results are obtained without constraint violations and within 10–600 iterations.

Aspects to Consider in the Evaluation of Photovoltaic System Projects to Avoid Problems in Power Systems and Electric Motors

This paper analyzes the problems in electrical power systems with high penetration of photovoltaic systems, which must be considered in evaluating new projects with these generation sources. The study analyzes the characteristics of photovoltaic systems, their generated effects, and proposals for technological improvements. The features of electric motors and their affectation when they are fed from networks with power quality problems are also described. As a result, the variability in energy production, the difficulties in frequency regulation, the increase in harmonics, and the instability in generating reactive power, are the main problems caused by the massive use of photovoltaic systems. Regarding electric motors, it is shown that harmonics and voltage regulation are the power quality problems that most affect their operation. None of the studies conducted analyzes the direct influence that photovoltaic systems can have on the process of electric motors. Considering the growth prospects for using photovoltaic systems and the importance of electric motors, it is suggested that the scientific community develop research focused on this problem.

Small-signal modeling and wide-band oscillation analysis with the high proportion of renewable energy integrated through LCC-HVDC

With the high proportion of renewable energy through high-voltage direct current (HVDC) transmission into the grid and the application of high proportion of power electronic equipment, in the modern power system, the traditional electromagnetic conversion equipment represented by synchronous generators is gradually replaced by power electronic equipment, which in a high level has been affecting the dynamic behavior of the power system. In recent years, the wide-band oscillation problem of unknown mechanism continues to appear in power systems, which poses a major threat to the security, stability, and operation of power systems. Therefore, based on the typical oscillation event, this paper constructs a typical small-signal model of the new power system which is composed of synchronous generator, HVDC transmission equipment, and renewable energy generation equipment. The model is suitable for the analysis of wide-band oscillation problem of the new power system with renewable energy as the main body. The comparison of time-domain response with the detailed time-domain model shows that the proposed small-signal model can well reflect the dynamic behavior of the detailed time-domain model. Based on the model, the influence of access distance, access proportion, and operation and control modes of renewable energy integrated through LCC-HVDC on the wide-band oscillation characteristics of the system is analyzed, and the correctness of the analysis is verified by the detailed time-domain model.

Stochastic scheduling of power system in the presence of electric vehicle and renewable sources considering security and economic indexes using an improved mutation particle swarm optimization (M-PSO) algorithm

Increased air pollution and global temperature as well as motor vehicle fuel consumption have depleted fossil fuel resources and increased environmental problems caused by the consumption of such fuels. In addition to methods such as combined heat and power (CHP) technology and distributed generation (DG) of energy at the consumption site, renewable energy sources and EVs are considered suitable methods for achieving this goal, which is prepared by the grid or battery electric energy. Generation uncertainty due to the lack of solar radiation and constant wind blow at different hours of the day is the only challenge for using renewable energies. Moreover, system reliability is a concept that refers to the safe and reliable operation of the system. In general, the wider and more important the system, the more attention that is paid to calculating its reliability in planning and decision making. This study aims to examine the problem of probabilistic power system planning by calculating the power system reliability, evaluating the effect of the presence of these vehicles on security and economic indicators and renewable energy sources, and modeling uncertainties using a Least Squares Generative Adversarial Network (LSGANs) method with generating various scenarios for solar irradiance and wind speed. Furthermore, the Kantorovich distance matrix (KDM) is used to reduce the number of generated scenarios. In the proposed model, the conditional value-at-risk (CVaR) method is implemented to assess and control the risk caused by uncertainties of the proposed problem. Using the power stored in the EV battery is evaluated to cover wind and solar energy source uncertainties.

A developed eagle-strategy supply-demand optimizer for solving economic load dispatch problems

This article proposes a new effective technique, named eagle-strategy supply–demand based optimization algorithm with chaotic (ESCSDO), for achieving global solutions to economic load dispatch (ELD) problems in power systems. This improvement is suggested to enhance the population diversity, the balance between local and global search, and the premature convergence of the original supply–demand based optimization (SDO) algorithm. The proposed ESCSDO algorithm benefits from a strategy named eagle strategy (ES) search strategy with ten chaotic maps. The efficacy of the ESCSDO technique is tested on 23 benchmark functions. Also, the robustness of the ESCSDO algorithm to find precise results in various aspects is examined and proven on ELD problems including four cases, 6, 13, 15, and 40-unit test systems. Finally, the obtained results using the proposed technique are compared with the those obtained by several well-known techniques and results reported in the previous literature. The simulation results present proof of the efficacious performance of the proposed ESCSDO technique in solving great diversity of ELD problems.

Event-triggered model-free adaptive predictive control for multi-area power systems under deception attacks

This paper focuses on the event-triggered model-free adaptive predictive load frequency control (LFC) problem for multi-area power systems under false data injection (FDI) attacks. First, the power system model is assumed to be unknown, and the nonlinear power system is converted into an equivalent linear data model using the input and output data of the powerline system. The FDI attacks on the power system is also modeled, and a Bernoulli stochastic process is used to represent whether the data is transmitted successfully or not. Second, the equivalent linear data model to predicts upcoming events, and a new event-triggered model-free adaptive predictive control scheme is designed via the predictions. And a RBF neural network disturbance estimator is designed to estimate and compensate for the effects caused by disturbances, and then power system stability is analyzed. An event-triggered scheme is also developed in the design of the LFC in order to save communication and computational burden. The results show that the designed control algorithm is data-driven independent of the power system structure and does not require the measurement of any system state signals. Finally, the effectiveness and correctness of the scheme is verified by a three-area numerical example and simulation results.

Design and development of power data service platform based on multi dimension

Abstract With the acceleration of power grid modernization, the scale of power grid is expanding. Running operations are becoming increasingly complex. There is a great demand for the analysis and calculation of the operation status of the power grid, line transformation design, fault analysis and treatment, etc. At the same time, with the construction of modern power grids and the continuous development of the power industry, the security problems of power systems have become increasingly prominent. This paper introduced the background of the power data service platform and then conducted academic research and summary on the research and development and design of power data service platform and multidimensional power data service platform. Then it was summarized with the analysis of international journals of emerging power systems. After establishing the algorithm model, the spark platform algorithm framework various algorithms were proposed to provide a theoretical basis for the R&D (research and development) and design of the multidimensional power data service platform. Then it analyzed the factors related to the R&D and design of power data service platform. At the end of the article, the simulation experiment was carried out, and the experiment was summarized and discussed. The experimental results showed that the average value of the neural network on the three criteria is 78.03 %. Therefore, the stability rate, accuracy rate, and recall rate of such algorithms as neural network are excellent in the research and development and design of power data service platform.

Blockchain-assisted spent electric vehicle battery participation for load frequency control problems in interconnected power systems is resilient, low-carbon, and offers revenues to the operators

Parked electric vehicles (EVs) participation is thought to be a low-carbon and resilient solution in addressing the load frequency control (LFC) problems in interconnected power systems (IPS). But this participation results in multiple issues such as trust, traceability, and transparency (3Ts) for EV owners in terms of financial compensation, battery effects, and range anxiety; early EV batteries end-of-life (EoL) management by recyclers; and burden on critical materials for new EV batteries manufacturing. Hence, to avoid the highlighted issues while addressing LFC, we propose a product life-extension circular business model and blockchainification of non-digital assets inspired spent EV battery (SEVB) buffer storage system. Two area IPS where areas 1 and 2 having a tie-line power agreement, SEVB storage system, particle swarm optimization (PSO)-based controller, and blockchain-based participatory smart contracts (BC-PSC) are modeled. Resilience Performance, Life Cycle Analysis, and Techno-Economic (RePLiCATE) approach is used for realizing the feasibility. Results showed BC-PSC protocols and PSO effectively obtained the controller's optimal parameters under disruptive scenarios ranging from 5 to 20%. The recovery times went from 18.60 to 46.43 sec. SEVB in LFC produces 18.71% less global warming potential than parked EVs. Revenues for SEVB owners were 122.27%, 100.22%, and 66.67% higher than cash spent on charging at off-peak, mid-peak, and on-peak, respectively.

Analysis of Influence of Vertical Vibration on Natural Heat Convection Coefficients from Horizontal Concentric and Eccentric Annulus

Renewable energy is crucial for reducing emissions and meeting future energy demands. However, due to concerns regarding intermittent supply, integrating RE into a multi-microgrid system might pose various power system problems, for instance, unstable electrical power output. As a result, increased load reactive power demands result in voltage losses during peak load demand. Therefore, it can be minimized by utilizing Flexible Alternating Current Transmission System (FACTS) devices in electrical networks, which are designed to strengthen the stability and control of power transfer and act as a controller for the AC transmission specification, which also provides speed and flexibility for certain applications. By identifying the need to implement solutions that can sustain the electric power quality of a microgrid, this paper presents a review of various method approaches which could be used to evaluate the impact of integrating the multi-microgrid systems with FACTS devices for voltage profile improvement and real power loss reduction in power system. In this paper, a comprehensive study is carried out for optimum multi-microgrid placement, considering the minimization of power losses, enhancement of voltage stability, and improvement of the voltage profile. An attempt has been made to summarize the existing approaches and present a detailed discussion that can help the energy planners decide which objective and planning factors need more attention for optimum locations and capacity for multi-microgrid and FACTS devices.

Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System

Hybrid generating systems in power networks have emerged as a result of the rapid growth of renewable infrastructure and widespread support for green energy. One of the most significant problems in designing and operating an electric power generation system is the efficient scheduling of all power generation facilities to meet the rising power demand. Economic load dispatch (ELD) is a generic procedure in the electrical power system, and the ELD in power system problems involves scheduling the power generating units to reduce cost and satisfy system constraints. Metaheuristic algorithms are gaining popularity for solving constrained ELD issues because of their larger global solution capacity, flexibility, and derivative-free construction. In this research, the ELD problem of integrated renewable resources is solved using a unique solution model based on hybrid optimization. Furthermore, this work considers multiobjectives such as total wind generation cost, total cost function of thermal units, and penalty cost function. The hybrid optimization model optimizes the power generation of thermal power plants within the maximum and minimum limitations. Additionally, the turbines are selected optimally by the hybrid optimization model to ensure the power generation of wind turbines based on the demands. The proposed hybrid optimization is a combination of particle swarm optimization (PSO) and cat swarm optimization (CSO), and the new algorithm is referred to as the particle oriented cat swarm optimization model (POCSO). Finally, the performance of the proposed work is compared to other conventional models. In particular, the cost function of POCSO is 6.25%, 6%, 11.7%, 36%, 27%, and 46.42% better than the cost function of whale optimization algorithm (WOA), elephant herd optimization (EHO), moth-flame optimization (MFO), dragonfly algorithm (DA), sealion optimization (SLnO), CSO, and PSO methods, respectively. Also, for IEEE-30 bus system, the best value of the proposed work is 7.46%, 5.41%, 16.30%, 14.88%, 17.60%, 13.86%, 15.21%, 17.49%, and 4.27% better than that of the PSO, CSO, SLnO, DA, MFO, EHO, WOA, multiagent glowworm swarm optimization (MAGSO), and Harris hawks optimization-based feed-forward neural network (HHO-FNN) methods, respectively.

Optimal Power Flow Using a Hybrid Improved Harris Hawks Optimization Algorithm-Pattern Search Method

The optimal power flow (OPF) problems for effective power system planning and operations are solved using a hybrid Improved Harris Hawks Optimization and Pattern Search (PS) (hIHHO-PS) algorithm in this research. The OPF problem is considered minimization of non-convex active power generation fuel cost (NAPGFC), total active power losses (TPL), shunt capacitor cost (SCC), voltage variation (VD), and maximization of social welfare (SW) and loadability factor (LF) objective functions. The proposed hIHHO-PS algorithm was tested on different benchmark test functions (unimodal (UM), multi-modal (MM), and fixed-dimension MM functions) and IEEE 30 bus test system, and obtained results are validated with existing algorithms. These results show the proposed hIHHO-PS algorithm outperformed the others. Further, to enhance the OPF results, optimally place the multi-line Flexible Alternating Current Transmission System (FACTS) devices (unified power flow controller (UPFC) and generalized unified power flow controller (GUPFC)) using severity index. Furthermore, test the effects of control modes of multi-line UPFC and GUPFC devices on OPF results. These results show the multi-line FACTS devices with control mode enhance the OPF results, especially GUPFC in control mode operation.