Aspects Of Power System Operation Research Articles

A novel nature-inspired nutcracker optimizer algorithm for congestion control in power system transmission lines

In the restructured power system, where uncertainties are common, managing congestion becomes a crucial aspect of power system operation and control. Congestion management aims to alleviate the power system transmission line congestion while meeting the system constraints at minimal cost. This research introduces a generation rescheduling method for congestion management in the electricity market, leveraging an innovative nutcracker optimizer algorithm. The nutcracker optimizer algorithm, inspired by nutcrackers’ food accumulation mechanisms, is a recently developed nature-inspired algorithm. The efficacy of this proposed approach is assessed across modified IEEE 30-bus, and IEEE 118-bus test systems, considering the system parameters. The effectiveness of the proposed congestion management with the nutcracker optimizer algorithm is analyzed by comparing its results with those generated by other recent optimization techniques. Results demonstrated that the nutcracker optimizer algorithm surpasses other comparative methods, requiring fewer fitness function evaluations, avoiding local optima, and displaying encouraging convergence traits. Implementing this approach can assist the system operators in swiftly addressing contingencies, ensuring secure and reliable power system operation within a deregulated environment.

A Novel Approach for Dynamic Analysis of Wind-Integrated Multi-Machine Power Systems

Indeed, the rapid expansion of Renewable Energy Sources (RES) in recent years has brought about numerous benefits, including reduced carbon emissions, increased energy independence, and the creation of new economic opportunities. However, integrating these variable and intermittent sources into existing power systems poses several challenges for power system management. Faults in electrical networks are among the key factors and sources of network disturbances. Control and automation strategies are among the key fault clearing techniques responsible for the safe operation of the system. In recent years, the increasing penetration of wind energy in multi-machine power systems has posed unique challenges to power grid stability and reliability. Accurate assessment methodologies are required to ensure the effective integration of wind energy sources while maintaining grid stability. Several researchers have revealed various constraints of control and automation strategies such as a slow dynamic response, the inability to switch the network on and off remotely, a high fault clearing time and loss minimization. It's important to note that the impact of wind energy on system inertia is a complex and dynamic aspect of power system operation. Ongoing research and technological advancements aim to improve the integration of wind and other renewable energy sources while ensuring grid stability and reliability. As the energy transition continues, addressing these technical challenges is crucial for building a sustainable and resilient power system. In this paper, the influence of doubly-fed induction generator (DFIG) penetration is analyzed to examine the transient stability of power system networks. The concept of a Coupling Strength Index (CSI) derived from Network Structural Characteristics Theory sounds intriguing, especially in the context of identifying critical elements susceptible to the impact of a three-phase fault in a network. The investigation involves studying the transient stability of a power system under different conditions, specifically with and without doubly-fed induction generators (DFIGs) connected to a weak bus. Additionally, a three-phase fault is applied at the middle of the identified weakest line for both the IEEE 9 and 39 bus systems. The investigation of generator speed, rotor angle, and electric power during transient stability analysis provides a holistic view of how a power system responds to disturbances. This information is crucial for ensuring the reliability and stability of the system, especially when studying the integration of renewable energy sources and addressing potential challenges associated with faults and weak buses. This paper presents a pioneering non-iterative framework for dynamically assessing wind energy dominated multi-machine power systems. The proposed framework aims to address the shortcomings of traditional iterative methods, providing a more efficient and reliable approach to power system analysis.

Efficient economic operation based on load dispatch of power systems using a leader white shark optimization algorithm

This article proposes the use of a leader white shark optimizer (LWSO) with the aim of improving the exploitation of the conventional white shark optimizer (WSO) and solving the economic operation-based load dispatch (ELD) problem. The ELD problem is a crucial aspect of power system operation, involving the allocation of power generation resources to meet the demand while minimizing operational costs. The proposed approach aims to enhance the performance and efficiency of the WSO by introducing a leadership mechanism within the optimization process, which aids in more effectively navigating the complex ELD solution space. The LWSO achieves increased exploitation by utilizing a leader-based mutation selection throughout each generation of white sharks. The efficacy of the proposed algorithm is tested on 13 engineer benchmarks non-convex optimization problems from CEC 2020 and compared with recent metaheuristic algorithms such as dung beetle optimizer (DBO), conventional WSO, fox optimizer (FOX), and moth-flame optimization (MFO) algorithms. The LWSO is also used to address the ELD problem in different case studies (6 units, 10 units, 11 units, and 40 units), with 20 separate runs using the proposed LWSO and other competitive algorithms being statistically assessed to demonstrate its effectiveness. The results show that the LWSO outperforms other metaheuristic algorithms, achieving the best solution for the benchmarks and the minimum fuel cost for the ELD problem. Additionally, statistical tests are conducted to validate the competitiveness of the LWSO algorithm.

Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement

The transient behavior of Automatic Generation Control (AGC) systems is a critical aspect of power system operation. Therefore, fully extracting the potential of Battery Energy Storage Systems (BESSs) for AGC enhancement is of paramount importance. In light of the challenges posed by diverse resource interconnections and the variability associated, we propose an online optimization scheme that can adapt to changes in an unknown and variable environment. To leverage the synergy between BESSs and Conventional Generators (CGs), we devise a variant of the Area Injection Error (AIE) as a measure to quantify the ramping needs. Based on this measure, we develop a distributed optimization algorithm with adaptive learning rates for the allocation of the ramping reserve. The algorithm restores a larger learning rate for compliance with the ramping needs upon detecting a potentially destabilizing event. We demonstrate the effectiveness and scalability of the proposed scheme through comprehensive case studies. It is shown that the proposed scheme can improve the transient behavior of the AGC system by bridging the gap in ramping capability.

Two-stage low-carbon optimal dispatch of the power system considering demand response to defend large uncertainties and risks

Introduction: In order to promote the consumption of renewable energy, reduce carbon emissions, and take into account the uncertainty of renewable energy output and load fluctuations in the new power system that can affect the normal operation of market mechanism, a two-stage low-carbon optimization scheduling method for power system that considers demand response under multiple uncertainties is proposed in this paper.Methods: Uncertain scene sets are generated through Latin hypercube sampling and heuristic synchronous backpropagation method is used to reduce scenes to obtain typical scenes and their probabilities. Then, a one-stage optimization model is established with the goal of maximizing energy efficiency and corresponding demand response strategies are obtained. Green certificate carbon trading joint mechanism model consisting of tiered green certificate trading and time-sharing tiered carbon trading are established, and the output of two-stage units are optimized with the goal of minimizing comprehensive operating costs.Result: The simulation results show that the carbon emissions are decreased by 251.57 tons, the consumption rate of renewable energy is increased by 8.64%, and the total costs are decreased by 124.0612 million yuan.Discussion: From this, it can be seen that the dual layer low-carbon optimization scheduling strategy for power system considering demand response under multiple uncertainties can effectively reduce the operating costs and carbon emissions of the system, while balancing the economic and environmental aspects of power system operation.

Performance analysis of power system dynamics with facts controllers: Optimal placement and impact of SSSC and STATCOM

This paper explores the impact of integrating Flexible AC Transmission Systems (FACTS) controllers, specifically the Static Synchronous Series Compensator (SSSC) and the Static Synchronous Compensator (STATCOM), on power system performance. The objective of this study is to evaluate how these controllers can enhance various aspects of power system operation, including voltage regulation, power flow stability, and overall system efficiency. The methodology involves simulating power systems with and without the deployment of SSSC and STATCOM and analyzing their effects on voltage profiles, power flow characteristics, and system losses. The findings reveal that both SSSC and STATCOM significantly improve voltage stability and power flow control, leading to reduced system losses and enhanced operational efficiency. This study introduces a novel approach by comparing the performance enhancements provided by SSSC and STATCOM in different operational scenarios, offering valuable insights into their effectiveness. The results underscore the potential of FACTS technology in advancing power system stability and efficiency, making a substantial contribution to the field of power system optimization.

A survey on voltage stability indices for power system transmission and distribution systems

Voltage stability is a critical aspect of power system operation, ensuring the reliable and efficient delivery of electrical energy to consumers. Recently, voltage stability in the power system has received much attention. The primary cause of voltage instability is the lack of real and reactive power generation to cope with the continuous demand increment. Maintaining voltage stability while planning, controlling, and assessing the system’s security is a difficult task for power system engineers. From knowledge of past incidents, a lack of reactive power is identified as the primary cause of voltage instability, which may further lead to the total collapse of the system. The importance of voltage stability assessment is essential to maintain the integrity of power systems. It helps prevent voltage instability, which can lead to cascading failures and widespread outages. This paper uses various parameters, including load demand, generator capacity, and system impedance, to develop voltage stability indices (VSIs). The paper mainly focuses on the idea of VSIs that have been developed in order to monitor and control the criticality of the power system. The voltage stability indices for transmission and distributed systems, as well as their subclasses, are thoroughly reviewed in this study. Furthermore, traditional voltage stability methods, as well as various software tools for monitoring the voltage stability problems, are also discussed. In addition, the development of VSIs and its related concepts are clearly described in this paper. This comprehensive survey provides a decent groundwork for future work in this area, and assists professionals in selecting the optimal VSI for various applications. Moreover, it provides a concise overview of methods and the importance of voltage stability assessment in both transmission and distribution systems.

Application of data‐driven methods in power systems analysis and control

AbstractThe increasing integration of variable renewable energy resources through power electronics has brought about substantial changes in the structure and dynamics of modern power systems. In response to these transformations, there has been a surge in the development of tools and algorithms leveraging real‐time computational power to enhance system operation and stability. Data‐driven methods have emerged as practical approaches for extracting reliable representations from non‐linear system data, enabling the identification of dynamics and system parameters essential for analysing stability and ensuring reliable operation. This study provides a comprehensive review of recent contributions in the literature concerning the application of data‐driven identification, analysis, and control methods in various aspects of power system operation. Specifically, the focus is on frequency support, power oscillation detection, and damping, which play crucial roles in maintaining grid stability. By discussing the challenges posed by parametric uncertainties, load and source variability, and reduced system inertia, this review sheds light on the opportunities for future research endeavours.

ENHANCEMENT OF LOAD FREQUENCY CONTROL OF MULTI AREA TIE LINE INTERCONNECTED POWER SYSTEM BASED ON INTEGRAL CONTROLLER TECHNIQUE AND POLE PLACEMENT DESIGNE BASED ON STATE SPACE MODELING

Load Frequency Control (LFC) is an important essential aspect of power system operation that ensures the frequency constraints at normal condition. This article deals with the Load Frequency Control of multi region area for tie-line interconnected power system and present a control system to improve the frequency deviation response for a specific changing in the load. The control system that using in this article are the integral controller and the pole-placement design based on state space modeling to improve the performance of frequency response and get a robust stability.

Transformer-based deep learning model for forced oscillation localization

Accurately locating Forced Oscillations (FOs) source(s) in a large-scale power system is a challenging task, and an important aspect of power system operation. In this paper, a complementary use of Deep Learning (DL)-based and Dissipating Energy Flow (DEF)-based methods are proposed to localize forced oscillation source(s) using data from Phasor Measurement Units (PMUs), by tracing the forced oscillations source(s) on the branch level in the power system network. The robustness, effectiveness and speed of the proposed approach is demonstrated in a WECC 240-bus test system, with high renewable integration in the system. Several simulated cases were tested, including non-gaussian noise, partially observable system, and operational topology variations in the system which correspond to real-world challenges. Timely localization of forced oscillation at an early stage provides the opportunity for taking remedial reaction. The results show that without the information of system operational topology, the proposed method can achieve high localization accuracy in only 0.33 s.

A novel optimization method for wind power investment considering economic and security concerns

This paper proposes a novel optimization method for wind power investment to find the optimal location and sizing of multiple wind farms considering both the economic and security aspects of power system operation and planning. The proposed approach maintains the system's security against transient instabilities while improving the voltage profile in the network and minimizing the cost resulting from the investment of wind farms and their operation with thermal units. The transient stability assessment is performed for the power system, considering the uncertainties due to its wind power generation. To model these uncertainties, Taguchi's orthogonal array testing method is utilized. Using Taguchi's method, all the uncertainties in an optimization problem are modeled with only a few representative testing scenarios, and thus, it provides computation efficacy. Moreover, an enhanced hybrid algorithm combining the particle swarm and gray-wolf optimization methods is developed to obtain efficient results in solving the problems formulated. The proposed wind power investment approach is implemented on the New England 39-bus test system, and the results show its effectiveness in providing a reliable and economic wind investment strategy for both investors and operators in the long-term operation and planning of the power system.

Guest Editorial: Medpower 2018 Selected Papers

Guest Editorial: Medpower 2018 Selected Papers

Stochastic multi-objective optimal energy and reactive power dispatch considering cost, loading margin and coordinated reactive power reserve management

In the recent years, modern wind farms (WF) are being increasingly integrated in the power systems. Thanks to the capability curve of doubly-fed induction generator (DFIG) wind turbines, these modern WFs are able to separately supply active and reactive powers. Accordingly, by increasing the penetration of this kind of sources, both voltage stability and reactive power reserve (RPR) management of power system can be affected. This issue makes it important to propose a new methodology for optimal energy and reactive power dispatch in a system with integrated modern WFs in order to effectively take into consideration the capabilities of modern WFs in different aspects of power system operation. Under this perspective, this paper proposes a stochastic multi-objective optimal dispatch (SMO-OD) problem in the presence of modern WFs to make a techno-economic framework for the system operators (SOs). The proposed SMO-OD problem aims at minimizing expected payments of energy and reactive power markets, and maximizing expected RPR and loading margin (LM) in the presence of wind power generation uncertainty. This paper also contributes to the relevant literature by formulating a model which determines the RPR of modern WFs to be included in the management of total RPR of the power system. Also, a coordinated RPR (CRPR) scheme is proposed by using this model. The proposed model benefits from a detailed reactive power output model of the DFIG wind turbines in order to handle real-world power system operation. Two studies are considered in the proposed SMO-OD problem: (i) SMO-OD with uncoordinated RPR provision (SMO-OD-URPR), and (ii) SMO-OD with the CRPR provision (SMO-OD-CRPR). The proposed SMO-OD problem is applied to the IEEE 30-bus test system. Simulation results show that using the proposed model, a proper trade-off is made between technical and economic aspects of power system operation, and considerable LM and RPR are provided. In the proposed SMO-OD-CRPR problem, the value of expected RPR increases compared to SMO-OD-URPR problem, and the DFIG-based WF has more expected RPR to inject to grid under contingency or sudden increased load. Moreover, by increasing the wind penetration level, not only does the value of expected payments of energy and reactive power markets reduce, but also the value of expected LM and RPR increases.

Geographic information system and weather based dynamic line rating for generation scheduling

Transmission line ratings influence the economic and security aspects of power system operation, generation and planning. The use of geographical information systems in power system generation and planning activities is gaining importance. This paper proposes a geographical information system and weather based dynamic line rating (GISWDLR) and investigates its effectiveness in a security constrained unit commitment problem (SCUCP). Here, the dynamic line ratings (DLRs) of conductors at different locations along the transmission line are calculated using the geographic parameters and weather parameters. Thereafter, the minimum value of DLRs at all indentified locations of the transmission line is taken as DLR of the transmission line. Here, binary real coded particle swarm optimization (BRPSO) is employed to solve the SCUCP. The proposed method is validated using the benchmark IEEE 30 bus system. Here, results obtained using the proposed GISWDLR, static line rating (SLR) and conventional UCP are compared. Also, the effectiveness of BRPSO is validated by comparing it with the conventional enhanced priority list based method.

Hierarchical Distributed Mixed-Integer Optimization for Reactive Power Dispatch

Abstract One key aspect of power system operation is the minimization of line losses while maintaining voltages within certain limits. Doing so can be achieved through the use of voltage regulators, tap changing transformers, and shunt capacitors. However, both tap changing transformers and/or shunt capacitors are operated in a discrete manner, i.e. the inputs can only take finitely many discrete values. Thus, the problem of minimizing line losses while maintaining voltage stability can be cast as a mixed-integer optimization problem. For large grids or cases where independent entities have concurrent control of the system, distributed optimization provides a solution. The present paper discusses a hierarchically distributed approach; i.e. the problem is partitioned into local subproblems and solved using a mixed-integer extension of the Augmented Lagrangian based Alternating Direction Inexact Newton (ALADIN) algorithm. We present results for the IEEE 14- and 30-bus systems and compare them with previous approaches, such as particle swarm optimization and genetic algorithms.

Network Structure-Based Critical Bus Identification for Power System Considering Line Voltage Stability Margin

Voltage security assessment of power system is an important and all-inclusive aspect of power system operation and preventive control actions. Fast and accurate detection of critical components of the power system is one essential approach for preventing the occurrence of voltage collapse phenomenon. Over the years, several approaches for voltage collapse point identification and prevention have been widely studied using the continuous power flow approach, minimum singular value of eigenvalues, Jacobian matrices, and power transfer concept. In this work, critical node (bus) identification based on power system network structure is proposed. In this approach, the power system is treated as a multidimensional graph with several nodes (buses) linked together by the transmission lines. An improved line voltage stability margin estimator which is based on active and reactive power changes in a power system is used as the weight of each transmission line and an adaptation of the degree of centrality approach is used to determine the criticality of the system buses. A comparative analysis with other bus voltage stability indices is presented to test the suitability of the proposed approach using the IEEE 14, 30, 57 and 118 bus test systems.

Analyzing Effect of System Inertia on Grid Frequency Forecasting Usnig Two Stage Neuro-Fuzzy System

Frequency forecasting is an important aspect of power system operation. The system frequency varies with load-generation imbalance. Frequency variation depends upon various parameters including system inertia. System inertia determines the rate of fall of frequency after the disturbance in the grid. Though, inertia of the system is not considered while forecasting the frequency of power system during planning and operation. This leads to significant errors in forecasting. In this paper, the effect of inertia on frequency forecasting is analysed for a particular grid system. In this paper, a parameter equivalent to system inertia is introduced. This parameter is used to forecast the frequency of a typical power grid for any instant of time. The system gives appreciable result with reduced error .

Evaluasi Keandalan Perencanaan Pembangkit Wilayah Jawa-Bali dengan Mempertimbangkan Ketidakpastian Peramalan Beban

Power system reliability is one of important aspects of power system operation. Lack of capacity in the generation system will affect reliability level of the power system. System evaluation needs to be done to know whether the system is still reliable to supply the load that is projected to increase 8.2% per year. Java-Bali system evaluation planning period 2015-2024 is conducted in this paper. Load forecast uncertainty (LFU) is one of the parameters that needs to be considered when conducting system planning. It is because load tends to be uncertain. This research uses two scenarios: without and with load forecast uncertainty. Reliability indexes LOLE and LOLP are used to evaluate the Java-Bali system. All calculations are based on a program using MATLAB. The result shows that reliability indexes LOLE and LOLP are higher with considering LFU. It means reliability decreases, rather than planning without considering LFU. Moreover, Java-Bali system reliability indexes meet the PLN’s standard value with the best value acquired in 2019.

A 34-year simulation of wind generation potential for Ireland and the impact of large-scale atmospheric pressure patterns

To study climate-related aspects of power system operation with large volumes of wind generation, data with sufficiently wide temporal and spatial scope are required. The relative youth of the wind industry means that long-term data from real systems are not available. Here, a detailed aggregated wind power generation model is developed for the Republic of Ireland using MERRA reanalysis wind speed data and verified against measured wind production data for the period 2001–2014. The model is most successful in representing aggregate power output in the middle years of this period, after the total installed capacity had reached around 500 MW. Variability on scales of greater than 6 h is captured well by the model; one additional higher resolution wind dataset was found to improve the representation of higher frequency variability. Finally, the model is used to hindcast hypothetical aggregate wind production over the 34-year period 1980–2013, based on existing installed wind capacity. A relationship is found between several of the production characteristics, including capacity factor, ramping and persistence, and two large-scale atmospheric patterns – the North Atlantic Oscillation and the East Atlantic Pattern.

Quantitative Frequency Security Assessment for Multi-Machine Power System based on COI frequency

The frequency is one of the instruments for measuring the health status of the power system, due to its ability to anticipate any imbalance between generations and loads. If the generated power is adequate for the system load and losses, then, the system will be in a steady state, otherwise frequency deviates from nominal value due to the mismatch between the generation and load. If the frequency continues to deviate from nominal value, the system may collapse. The assessment of frequency stability level becomes an essential aspect of power system operation and also for projecting the ability of the power system to maintain nominal frequency when subjected to any disturbance. In this paper, a method is proposed to evaluate frequency security of multi-machine power system using transient frequency deviation index (TFDI) which is based on Center of Inertia (COI) referred frequency. The proposed method has been tested on the New England 39- bus test system. Results show that the proposed method takes the advantage of TFDI in accumulating the effect of frequency trajectory deviations. These frequency trajectories may be obtained from the time domain simulation or Wide Area Measurement System (WAMS). The results also show the advantages of COI referred frequency in representing the equivalent frequency of the system. The method would provide a reliable and efficient base for load shedding relays adjustment and operation control.