Power System Problems Research Articles

Multiobjective Optimal Power Flow Using Multiobjective Search Group Algorithm

This paper proposes a new multi-objective method that efficiently solves the multi-objective optimal power flow (MOOPF) problem in power systems. The objective of solving the MOOPF problem is to concurrently optimize the fuel cost, emissions, and active power loss. The proposed multi-objective search group algorithm (MOSGA) is an effective method that combines the merits of the original search group algorithm with fast nondominated sorting, crowding distance, and archive selection strategies to acquire a nondominated set in a single run. The MOSGA is employed on IEEE 30-bus and 57-bus systems to validate its robustness and efficiency. It was found that implementing MOSGA to solve the MOOPF significantly enhanced the performance of power systems in terms of economic, environmental, and technical benefits. As for Case 6, the fuel cost, emissions, and active power loss were reduced by 16.5707%, 52.0605%, and 60.9443%, respectively. The simulation results were analyzed and compared with those of previously reported studies based on the best individual solutions, compromise solutions, and performance indicators. The comparative results confirmed the potential and advantage of MOSGA when solving the MOOPF problem efficiently and MOSGA had high-quality optimal solutions.

Enhancement of Power Quality by mitigating of sag and swell problem in power system using DVR

In the present-day electric power system scenario, the custom of non-linear loads in power systems has ignited the investigation of power quality (PQ) problems in distribution systems like sag and swell. An effective control scheme is projected to enhance the voltage quality of subtle loads via a dynamic voltage restorer (DVR). Active Power Filter is one of the essential filters for mitigating power quality problems, and the controller is one of its integral parts. The simulation has been done using MATLAB/Simulink. The efficiency of the controller is illustrated by the results of the simulation.

A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations

Recently there has been a surge of interest in adopting deep neural networks (DNNs) for solving the optimal power flow (OPF) problem in power systems. Computing optimal generation dispatch decisions using a trained DNN takes significantly less time when compared to conventional optimization solvers. However, a major drawback of existing work is that the machine learning models are trained for a specific system topology. Hence, the DNN predictions are only useful as long as the system topology remains unchanged. Changes to the system topology (initiated by the system operator) would require retraining the DNN, which incurs significant training overhead and requires an extensive amount of training data (corresponding to the new system topology). To overcome this drawback, we propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach. The key idea behind this approach is to find a common initialization vector that enables fast training for any system topology. The developed OPF-predictor is validated through simulations using benchmark IEEE bus systems. The results show that the MTL approach achieves significant training speed-ups and requires only a few gradient steps with a few data samples to achieve high OPF prediction accuracy and outperforms other pretraining techniques.

The Extension of Auxiliary Problem Principle for Solving Dynamic Economic Dispatch Problem in Power Systems

In this paper, we study the convex optimization problem with linear constraint, and the objective function is composed of m separable convex functions. Considering the special case where the objective function is composed of two separable convex functions, the auxiliary problem principle (APP) is an effective parallel distributed algorithm for solving the special case. Inspired by the principle of APP, a natural idea to solve separable convex optimization problem with m ≥ 3 is to extend the method of APP, resulting in the APP‐like algorithm. The convergence of the APP‐like algorithm is not clear yet. In this paper, we give a sufficient condition for the convergence of the APP‐like algorithm. Specifically, the APP algorithm is a special case of the APP‐like algorithm when m = 2. However, simulation results show that the convergence efficiency of the APP‐like algorithm is affected by the selection of penalty parameter. Therefore, we propose an improved APP‐like algorithm in this paper. Simulation results show that the improved APP‐like algorithm is robust to the selection of penalty parameter and that the convergence efficiency of the improved APP‐like algorithm is better when compared with the APP‐like algorithm.

Continuous Switch Model and Heuristics for Mixed- Integer Problems in Power Systems

Continuous Switch Model and Heuristics for Mixed- Integer Problems in Power Systems

Short-Term Load Forecasting Method Based on Feature Preference Strategy and LightGBM-XGboost

Short term load forecasting is one of the important problems in power system. Accurate forecasting results can improve the flexibility of power market and resource utilization efficiency, which is of great significance to the efficient operation of power system.A short-term power load forecasting model based on feature selection and error correction is proposed to address the problems of low accuracy and weak generalization ability of short-term power load forecasting. The coarse set of features is firstly screened by using Maximal Information Coefficient (MIC), and then the fine set of key features affecting load forecasting is screened by using LightGBM and XGboost feature selection algorithms respectively. The corresponding fine set of features and historical loads are input into LightGBM and XGboost models with powerful prediction function for prediction, and use the predicted value to correct the error and complete the load prediction. Taking the actual data of a certain area in Northwest China as an example, the experimental results show that the proposed model has better prediction effect than other models.

Building Marginal Pattern Library With Unbiased Training Dataset for Enhancing Model-Free Load-ED Mapping

Input-output mapping for a given power system problem, such as loads versus economic dispatch (ED) results, has been demonstrated to be learnable through artificial intelligence (AI) techniques, including neural networks. However, the process of identifying and constructing a comprehensive dataset for the training of such input-output mapping remains a challenge to be solved. Conventionally, load samples are generated by a pre-defined distribution, and then ED is solved based on those load samples to form training datasets, but this paper demonstrates that such dataset generation is biased regarding load-ED mapping. The marginal unit and line congestion (i.e., marginal pattern) exhibit a unique characteristic called “step change” in which the marginal pattern changes when the load goes from one critical loading level (CLL) to another, and there is no change of marginal units within the interval of the two adjacent CLLs. Those loading intervals differ significantly in size. The randomly generated training dataset overfills intervals with large sizes and underfits intervals with small sizes, so it is biased. In this paper, three algorithms are proposed to construct a marginal pattern library to examine this bias according to different computational needs, and an enhancement algorithm is proposed to eliminate the bias in the load-ED training dataset generation. Three illustrative test cases demonstrate the proposed algorithms, and comparative studies are constructed to show the superiority of the enhanced, unbiased training dataset.

An MISOCP-Based Decomposition Approach for the Unit Commitment Problem with AC Power Flows

Unit Commitment (UC) and Optimal Power Flow (OPF) are two fundamental problems in short-term electric power systems planning that are traditionally solved sequentially. The state-of-the-art mostly uses a direct current (DC) approximation of the power flow equations. However, utilizing the DC approach in the UC-level may lead to infeasible or suboptimal generator commitment schedules for the OPF problem. In this paper, we aim to simultaneously solve the UC Problem with alternating current (AC) power flow equations, which combines the challenging nature of both UC and OPF Problems. Due to the highly nonconvex nature of the AC flow equations, we utilize the mixed-integer second-order cone programming (MISOCP) relaxation of the UC Problem as the basis of our solution approach. The MISOCP relaxation is utilized for finding both a lower bound and a candidate generator commitment schedule. Once this schedule is obtained, we solve a multi-period OPF problem to obtain feasible solutions for the UC problem with AC power flows. For smaller instances, we develop two different algorithms that exploit the recent advances in the OPF literature and obtain high-quality feasible solutions with provably small optimality gaps. For solving larger instances, we develop a Lagrangian decomposition based approach that yields promising results.

A Distributed Approach to Economic Dispatch in Power Systems

This paper proposes a distributed dynamics to solve the economic dispatch problem in electric power systems. The approach can be implemented in a fully decentralized manner and it does not require any central control unit. It is assumed that the participating entities can exchange information over a potentially asymmetric communication network. In particular, when the local generation cost functions are quadratic and the communication network is strongly connected, we establish the exponential convergence of the proposed dynamics to the optimal solution of the dispatch problem. We also provide simulation studies on an IEEE benchmark to demonstrate the performance of the proposed dynamics.

Field PMU Test and Calibration Method– Part I: General Framework and Algorithms for PMU Calibrator

Laboratory testing of phasor measurement units (PMUs) guarantees their performance under laboratory conditions. However, many factors may cause PMU measurement problems in actual power systems, resulting in the malfunction of PMU-based applications. Therefore, field PMUs need to be tested and calibrated to ensure their performance and data quality. In this paper (Part I), a general framework for the field PMU test and calibration in different scenarios is proposed. This framework consists of a PMU calibrator and an analysis center, where the PMU calibrator provides the reference values for PMU error analysis. Two steps are implemented to ensure the calibrator accuracy for complex field signals: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\textcircled{1}$</tex> by analyzing the frequency-domain probability distribution of random noise, a Fourier-transform-based signal denoising method is proposed to improve the anti-interference capability of the PMU calibrator; and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\textcircled{2}$</tex> a general synchrophasor estimation method based on complex bandpass filters is presented for accurate synchrophasor estimations in multiple scenarios. Simulation and experimental test results demonstrate that the PMU calibrator has a higher accuracy than that of other calibrator algorithms and is suitable for field PMU test. The analysis center for evaluating the performance of field PMUs and the applications of the proposed field PMU test system are provided in detail in Part II of the next-step research.

Promising Grid-Forming VSC Control Schemes Toward Sustainable Power Systems: Comprehensive Review and Perspectives

Driven by environmental and economic aspects, the proliferation of renewable energy sources (RES) has been expanded in power systems worldwide. In this regard, the intermittent generation of such RESs (e.g., photovoltaic and wind farms) can cause several operational and stability problems in such low-inertia power systems. To handle these issues, great interest in the literature has been directed to develop feasible grid-forming voltage source converters (VSC) control schemes that have voltage/frequency regulatory functions. Accordingly, an inclusive review is presented in this paper for these promising grid-forming VSC control schemes which will be the backbone of sustainable converter-dominated power systems. Specifically, control structures, smart grid-support functionalities, stability issues, and fault current mitigations are compared considering the existing grid-forming VSC control schemes. Besides, the applications of grid-forming VSC along with their benefits and drawbacks are investigated for both isolated and bulk power systems. The evaluation aspects are also explored for assessing the performance of the existing VSC control schemes in the grid. Finally, the current challenges that face VSC in forthcoming applications and existing gaps are highlighted while corresponding perspectives for future research studies are defined for stable sustainable low-inertia power systems.

Online Transient Frequency Safety Prediction Machine of Power System Based on Time-Feature Attention Module

Facing the frequency safety problem of renewable energy power systems, this paper proposes a Frequency Safety Prediction Machine (FSPM) based on a one-dimensional convolutional neural network (1D-CNN) and Time-Feature Attention Module (TFAM). To ensure the rapidity of online frequency safety prediction (FSP), the one-dimensional time series data (1D-TSD) at multiple moments are standardized and divided into multiple time channels and transmitted to FSPM at the same time, which speeds up the speed of data flow. The TFAM in FSPM realizes the function of focusing on key moments and key features. Make FSPM pay more attention to the system information at certain critical moments and certain key feature information that can fully represent the fault, thereby improving the prediction accuracy. In addition, FSPM achieves dual prediction of Frequency Danger Level (FDL) and Time Safety Margin (TSM) at the same time. Finally, the test results on the IEEE 39 bus system and the Illinois power grid both containing renewable energy show that the FSPM not only has good performance in both FDL and TSM prediction but also has low model complexity, good noise resistance, and robustness.

Applications of Flower Pollination Algorithm in Electrical Power Systems: A Review

The use of metaheuristic, nature inspired algorithms for solving complex optimization problems with non-linearity and multimodality, has become a popular tool in the field of science and engineering. Presently, there has been much development of such tools, with academics creating new nature-inspired algorithms that could potentially be more efficient and effective. One such algorithm is the Flower Pollination Algorithm (FPA) developed by Xin-She Yang in 2012. This metaheuristic algorithm is modelled after the evolutionary process of flowering plants and has been useful in many fields of science and engineering, particularly in electrical power systems. With the rapid expansion of industries and population growth, engineers are faced with the arduous task of satisfying the growing electrical demand. To improve electrical power systems and reduce running costs, FPA and its variants have been implemented to determine optimal solutions to complex combinatorial optimization problems, with little computational effort, and has proven to be more effective than other popular metaheuristic algorithms, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Owing to its balanced global and local search capabilities, and better convergence characteristics, numerous studies have been conducted to utilize FPA and its variants in real-world optimization problems. Thus, this paper aims to provide a comprehensive review of FPA and its variants to successfully determine optimal results to non-deterministic polynomial-time hard problems in electrical power systems and other fields of science. To accomplish this, an in- depth description of FPA and its parameters that influence its performance are discussed, including the various modifications and hybridizations of the algorithm. In addition, an in- depth review of applications using FPA and its variants in electrical power systems and other fields of science, is provided.

Decentralized Disturbance Observer-Based Sliding Mode Load Frequency Control in Multiarea Interconnected Power Systems

The load frequency control (LFC) problem in interconnected multiarea power systems is facing more challenges due to increasing uncertainties caused by the penetration of intermittent renewable energy resources, random changes in load patterns, uncertainties in system parameters and unmodeled system dynamics, leading to a compromised reliability of power systems and increasing the risk of power outages. In responding to this problem, this paper proposes a decentralized disturbance observer-based sliding mode LFC scheme for multiarea interlinked power systems with external disturbances. First, a reduced power system order is constructed by lumping disturbances from tie-line power deviations, load variations and the output power from renewable energy resources. The disturbance observer is then designed to estimate the lumped disturbance, which is further utilized to construct a novel integral-based sliding surface. The necessary and sufficient conditions to determine the tuning parameters of the sliding surface are then formulated in terms of linear matrix inequalities (LMIs), thus guaranteeing that the resultant sliding mode dynamics meet the <i>H</i>_&#x221E; performance requirements. The sliding mode controller is then synthesized to drive the system trajectories onto the predesigned sliding surface in finite time in the presence of a lumped disturbance. From a practical perspective, the merit of the proposed control method minimizes the impact of the lumped disturbance on the system frequency, which has not been considered to date in sliding mode LFC design. Numerical simulations are illustrated to validate the effectiveness of the proposed LFC strategy and verify its advantages over other approaches.

Multi-Objective Optimization Framework for Optimal Power Flow Problem of Hybrid Power Systems Considering Security Constraints

The hybrid model of the power network infrastructure is an essential part of the sophisticated technology of the electrical network. For the traditional Optimal Power Flow (OPF) problem, thermal generators are typically employed to generate electricity. In this case, the amount of fuel required to produce power is constrained, and emissions from the network are frequently neglected. Renewable Energy Sources (RESs) have received increasing attention due to various potential characteristics such as cleanliness, diversity, and renewability. As an outcome, RESs are being integrated into the electrical grid at an increasing rate. The study in this paper proposes a techno-monetary investigation into the single- and multi-objective OPF, coordinating with RESs like wind, PhotoVoltaic (PV), and small hydropower units with hybrid PV. Moreover, the probability density functions of Weibull, Lognormal, and Gumble have been used independently to predict the required power in this paper. This paper considers an equilibrium optimizer for handling the OPF problem. The superior performance of the equilibrium optimizer is further verified with the results of both single- and multi-objective through comparative analysis with state-of-the-art counterparts, and the indications are that the proposed method can find better optimal solutions, use a lower number of generated solutions, and faster convergence with well distributed optimal Pareto front for multi-objective problems. The results are verified by employing a modified IEEE-30 bus hybrid power network, and performance comparisons are made among several well-established algorithms. Simulation findings show that the suggested strategy can achieve a reasonable compromise solution for different objectives.

Small Signal Stability Analysis and Optimize Control of Large-Scale Wind Power Collection System

The frequency security problem of power system is highlighted as wind power penetration increases yearly, the eigenvalue analysis method based on the deterministic model is difficult to accurately evaluate the small signal stability of power system. The Weibull probability distribution is used to describe the uncertainty of wind speed. In this paper, the shape and scale parameters of the Weibull distribution are calculated by using the maximum likelihood approach and combining with the measured data, and the stability of small signal with large-scale wind power access is analyzed by the probability distribution method. Meanwhile, considering that the lack of inertia of the system caused by high-penetration wind power access can easily lead to the problem of frequency stability, it is proposed to take the maximum damping ratio of regional oscillation mode as optimization objective, virtual inertia and pitch angle control parameters as variables, and frequency stability as constraints, an small signal stability optimization model of wind power access to power system is established according to the proposed probabilistic small signal stability model. The sensitivity algorithm is used to obtain the optimal solution for the virtual inertia and pitch angle control parameters, and the validity of the proposed method and model is verified by two-area four-machine examples. Simulation results show that by optimizing the virtual inertia and pitch angle parameters of wind turbines, the small signal stability of the high-penetration wind power access system can be improved under the condition of ensuring sufficient inertia of the system, which provides a certain theoretical basis for the safe and reliable operation of wind power large-scale cluster access power system.

Computing the Load Margin of Power Systems Using Crow Search Algorithm

Power system operation centers require knowledge of the load level where the system may present instability characteristics. It is common practice to calculate the load margin where the system can collapse in voltage respecting a direction of load growth. However, the increase in load can also cause low damped oscillation modes to appear and cause small-signal stability problems of power systems. This research proposes a Crow Search Algorithm-based method to calculate the load margin of power systems considering small-signal stability and voltage stability requirements. To solve the problem of good initial conditions for the search problem, the Sine Cosine Algorithm was used. The proposed method was evaluated in the IEEE 39-bus system and the results achieved show the effectiveness of the method in determining the load margins quickly.

Optimum generation and VAr scheduling on a multi-objective framework using exchange market algorithm

This paper presents an application of exchange market algorithm (EMA) in solving multi-objective optimisation problems of power systems. This optimisation algorithm is based on the activities of shareholders to maximise their profit in the exchange market. The uniqueness of this algorithm lies in the fact that, it enjoys double exploitation and exploration property unlike several other algorithms. In order to investigate its search capability, the EMA is utilised to solve power systems active and reactive related objectives simultaneously in presence of several nonlinear constraints. Both optimum generation and VAr planning problems are formulated as conventional optimal power flow (OPF) problem. Fuel cost (active related objective), transmission line loss and total voltage deviation (reactive related objectives) are taken as different objective functions. The multi-objective optimisation problem is performed through weighted sum approach. Both fuzzy and equal weight approach is utilised to declare the compromised solution. Programs are developed on MATLAB and simulations are performed on Standard IEEE-30 and IEEE-57 bus systems. The search capability of EMA in solving the multi-objective power system problems are compared with PSO based solutions.

Development of a Chance-Constrained Dual-Objective Fractional Programming for Shandong’s Clean Power Transition

In this study, an inexact mixed-integer interval stochastic fractional model (IMSFP) is developed for Shandong’s sustainable power system management under uncertainties. Shandong has a high proportion of fossil-fuel power, which has resulted in significant greenhouse gas emissions. Future is an essential period for energy structure transition. Developed IMSFP can effectively tackle dual objective, system efficiency represented as output/input ratios, as well as uncertainties described as interval values and probability distributions in the constraints and objectives. The results indicate that the clean power transition and capacity expansion scheme are sensitive to different constraint-violation risk levels. Obtained interval solutions can provide flexible strategies for resource allocation and expansion capacities under multiple complexities. An economic single objective model (IMCLP) is also developed, which aims at minimizing the system cost. The comparative results illustrate that the IMSFP model can better characterize the real-world power system problems through optimizing a ratio between clean energy utilization and system cost. Biomass and wind power would be major developed electricity forms in the future, and solar energy has great development potential. In short, the proposed IMSFP model is advantageous in balancing conflicting dual objectives and reflecting complicated interactions among system efficiency, economic cost, system reliability, and constraint-violation scenarios.

Multiobjective Ant Lion Optimization for Performance Improvement of Modern Distribution Network

Multiobjective ant lion optimization (MALO) is a technique developed to imitate ant foraging behavior. This method has many advantages, including straightforward, scalable, flexible, balanced, and fast response. The MALO technique consists of five stages: ants perform optimization by random walking by updating their position, building traps, inserting ants into traps, capturing prey, and rebuilding traps. MALO has been successfully used to find optimal solutions to power system problems. Computer-assisted operations characterize modern distribution networks to solve complex problems. The complexity of the distribution network problem is owing to the integration of distributed energy resources (DERs). A DER is a renewable energy power plant with a capacity of up to 10 MW that has gained popularity in recent years. In its application, the integration of DERs into the distribution network can cause new problems, namely load imbalances or excessive voltage increases on the buses where the DER is injected. Therefore, good planning is required to place the DER. This study proposes a multiobjective optimization technique based on MALO to determine the optimal DER location and capacity. MALO is a relatively new optimization method that has the potential to improve distribution network performance. Test cases were conducted for an IEEE 33-bus radial power-distribution network. Four scenarios were considered, integrating DER types I, II, III, and IV. In each design, the placement of one DER, two DERs, and three DERs was modeled to optimize the location and capacity. The results of the multiobjective optimization show that the MALO technique can improve the distribution network performance, which is characterized by a significant power loss reduction, an increase in the bus voltage profile, and a balanced load on each feeder.