Showing papers on "Electric power system published in 2022"
TL;DR: In this article, the authors reviewed the history, state-of-the-art and the future of the DL's application in power system frequency analysis and control, and the application status of DL in frequency situation awareness, frequency security and stability assessment, and frequency regulation and control were summarized.
98 citations
TL;DR: In this article , the authors reviewed the history, state-of-the-art and the future of the DL's application in power system frequency analysis and control, and the application status of DL in frequency situation awareness, frequency security and stability assessment, and frequency regulation and control.
98 citations
TL;DR: In this article , an optimal scheduling model for isolated micro-grids by using automated reinforcement learning-based multi-period forecasting of renewable power generation and loads is proposed to reduce the negative impact of the uncertainty of load and renewable energies outputs on microgrid operation.
Abstract: In order to reduce the negative impact of the uncertainty of load and renewable energies outputs on microgrid operation, an optimal scheduling model is proposed for isolated microgrids by using automated reinforcement learning-based multi-period forecasting of renewable power generations and loads. Firstly, a prioritized experience replay automated reinforcement learning (PER-AutoRL) is designed to simplify the deployment of deep reinforcement learning (DRL)-based forecasting model in a customized manner, the single-step multi-period forecasting method based on PER-AutoRL is proposed for the first time to address the error accumulation issue suffered by existing multi-step forecasting methods, then the prediction values obtained by the proposed forecasting method are revised via the error distribution to improve the prediction accuracy; secondly, a scheduling model considering demand response is constructed to minimize the total microgrid operating costs, where the revised forecasting values are used as the dispatch basis, and a spinning reserve chance constraint is set according to the error distribution; finally, by transforming the original scheduling model into a readily solvable mixed integer linear programming via the sequence operation theory (SOT), the transformed model is solved by using CPLEX solver. The simulation results show that compared with the traditional scheduling model without forecasting, this approach manages to significantly reduce the system operating costs by improving the prediction accuracy.
88 citations
TL;DR: An assessment framework is presented that combines all the three methods in a single model to evaluate their synergistic effects on wind integration and network reliability and shows that the proposed combination of methods reduces system dispatch, load curtailment and wind curtailment costs the most.
84 citations
TL;DR: In this article , the authors present an assessment framework that combines all the three methods in a single model to evaluate their synergistic effects on wind integration and network reliability, and show that the proposed combination of methods reduce system dispatch, load curtailment and wind curtailment costs the most when compared to any combinations with fewer methods or using each method in isolation.
68 citations
TL;DR: In this paper, a modified structure of the tilted integral derivative (TID) controller is developed for the load frequency control issue of a multi-area interconnected multi-source power system and a new optimization algorithm known as Archimedes optimization algorithm (AOA) is used to fine-tune the proposed ID-T controller parameters.
61 citations
TL;DR: In this paper , a comprehensive review of recent technical core aspects for LFC based on classical and modern power system which involves the nonlinear model, controller design parameters, soft-computing application, attributes of load forecasting, as well as integration of renewable energy sources (RES) in a deregulated environment.
Abstract: The electrical power system has experienced several changes during the last decade, raised by continuously increasing load demand, rapid depletion in fossil fuels, and newly electrical deregulation policy. In the past, numerous review literatures has been published in the field of Load Frequency Control (LFC), which deals with different and recent control strategies for the successful operation of the power system. Moreover, due to changes in lifestyle, increasing load demand, expansion of industrialization, and environmental issues, Renewable Energy Sources (RES) integration becomes the obvious adaptive choice. Since generation from RES is stochastic in nature and depends upon the weather condition and other aspects at every instant of time. Therefore, high penetration may reflect specific issues regarding voltage instability, frequency stabilization, and reliability obstruction. Hence this issue has been covered effectively in this review work. Furthermore, the work precisely summarized and briefly explained the different scenarios of Energy Storage (ES), micro-grid and Flexible AC Transmission System (FACTS) to explore the possible solutions and future aspects. The merit and demerit of different controllers are also investigated with the help of a comparison's tables and also some other analytical comparisons. The overall study examines an in-depth review of recent technical core aspects for LFC based on classical and modern power system which involves the non-linear model, controller design parameters, soft-computing application, attributes of Load forecasting, as well as integration of RES in a deregulated environment. This effective, comprehensive literature survey is very helpful for researchers to bridge the gap between recent development, implementation, challenges and future trends of RES in LFC.
60 citations
TL;DR: Wang et al. as mentioned in this paper proposed a novel deep learning intelligent system incorporating data augmentation for short-term voltage stability assessment (STVSA) of power systems, which leveraged semi-supervised cluster learning to obtain labeled samples in an original small dataset.
58 citations
TL;DR: This paper proposes a method based on Hybrid Particle Swarm Optimization in order to design a WADC that ensures robustness to power system operating uncertainties, time delays variations on the WadC channels and the permanent failure of the W ADC communication channels.
Abstract: The presence of low-frequency and low-dampened oscillation modes can compromise the operating stability of power systems. Recent research has shown that the use of phasor measurement units data to compose a wide-area damping controller (WADC) has been shown to be effective in mitigating such oscillation modes but the possibility of loss of communication channels due to cyber-attacks or failures can compromise the proper operation of this controller. Besides, traditional control design methods present difficulties for the WADC control design. This article proposes a method based on hybrid particle swarm optimization in order to design a WADC that ensures robustness to power system-operating uncertainties, time delays variations on the WADC channels, and the permanent failure of the WADC communication channels. Modal analysis and nonlinear time-domain simulations were conducted in the IEEE 68-bus power system considering a set of scenarios.
56 citations
TL;DR: This paper provides a comprehensive summary of inertia definitions for both synchronous generators and CIGs as well as their corresponding estimation methods and offers for the first time a framework to quantify the virtual inertia of Cigs at the component and aggregation levels.
56 citations
TL;DR: A distributed robust economic dispatch strategy is presented to achieve the energy management of IESs in the presence of misbehaving units, which consists of a neighbor-observe-based detection process and a reputation-based isolation process that possesses strong robustness against various colluding and noncolluding attacks.
Abstract: Distributed algorithms are increasingly being used to solve the economic dispatch problem of integrated energy systems (IESs) because of their high flexibility and strong robustness, but those algorithms also bring more risk of cyber-attacks in IESs. To solve this problem, this article investigates the distributed robust economic dispatch problem of IESs under cyber-attacks. First, as the first line of defense against attacks, a privacy-preserving protocol is designed for covering up some vital information used for economic dispatch of IESs. On this basis, a distributed robust economic dispatch strategy is presented to achieve the energy management of IESs in the presence of misbehaving units, which consists of a neighbor-observe-based detection process and a reputation-based isolation process. The proposed strategy is implemented in a fully distributed fashion and possesses strong robustness against various colluding and noncolluding attacks. In addition, the strategy can not only ensure the reliability of information transmission among energy units, but also solve the problem of incorrect measurement of distributed local load data caused by cyber-attacks. Finally, the effectiveness of the proposed strategy is illustrated by simulation cases on a 39-bus 32-node power–heat IES.
TL;DR: In this paper , a review article mainly focuses on the layers of microgrid, different techniques involved in DSM, mathematical models of DSM, latest optimization techniques and application of storage devices such as battery energy storage system and EVs in DSM.
Abstract: In a deregulated power system, Demand Side Management (DSM) plays a vital role in handling the uncertain renewable power generation and load. The flat load-profile can be obtained using the Demand Response (DR) techniques with the storage elements and proper switching. The increasing penetration of Renewable Energy Sources (RES) and Electric Vehicles (EVs) supports the DR measures which facilitate both the utility and the consumer. The objective of DSM is to minimize the peak demand, electricity cost and emission rate by the effective utilization of storage with RES. This review article mainly focuses on the layers of microgrid, different techniques involved in DSM, mathematical models of DSM, latest optimization techniques and application of storage devices such as battery energy storage system and EVs in DSM. The state of the art of this article lies on the critical analysis related to datascience, advanced metering infrastructure and blockchain technologies which are the uniqueness of this article. The key issues and approaches are examined critically with the existing works to show how DSM implementation can be effectively done in the microgrids to reduce the electricity cost. This article helps the researchers to identify the research gap by gaining knowledge on the implementation of DSM in the microgrid and the factors affecting the DSM implementation. Few recommendations are discussed to provide future directions for researchers who started working in the DSM implementation.
TL;DR: In this article, a hybrid modified GSA-PSO scheme is proposed to optimize the load dispatch of the microgrid containing electric vehicles, where the global memory capacity of the PSO is introduced into the GSA to improve the global search performance.
TL;DR: In this article , an improved wild horse optimization algorithm (IWHO) is proposed as a novel metaheuristic method for solving optimization issues in electrical power systems, which is devised with inspirations from the social life behavior of wild horses.
TL;DR: An innovative differential privacy (DP) compliant algorithm is developed to ensure that the data from consumer's smart meters are protected and provides privacy preservation in line with the consumer's preferences and does not lead to significant cost or loss increases for the energy retailer.
Abstract: The use of data from residential smart meters can help in the management and control of distribution grids. This provides significant benefits to electricity retailers as well as distribution system operators but raises important questions related to the privacy of consumers' information. In this article, an innovative differential privacy (DP) compliant algorithm is developed to ensure that the data from consumer's smart meters are protected. The effects of this novel algorithm on the operation of the distribution grid are thoroughly investigated not only from a consumer's electricity bill point of view but also from a power systems point of view. This method allows for an empirical investigation into the losses, power quality issues, and extra costs that such a privacy-preserving mechanism may introduce to the system. In addition, severalcost allocation mechanisms based on the cooperative game theory are used to ensure that the extra costs are divided among the participants in a fair, efficient, and equitable manner. Overall, the comprehensive results show that the approach provides privacy preservation in line with the consumer's preferences and does not lead to significant cost or loss increases for the energy retailer. In addition, the novel algorithm is computationally efficient and performs very well with a large number of consumers, thus demonstrating its scalability.
TL;DR: Using a case study from Electric Reliability Council of Texas (ERCOT), it is shown that the proposed tailored Benders decomposition outperforms the nested Bender decomposition in solving GEP and TEP simultaneously.
TL;DR: A novel ultra-short-term PV power forecasting method based on the satellite image data is proposed in this paper, which combines the spatio-temporal correlation between multiple plants with power and cloud information and outperforms the benchmark methods.
TL;DR: In this paper , a comprehensive review of various RL techniques and how they can be applied to decision-making and control in power systems is presented, including frequency regulation, voltage control, and energy management.
Abstract: With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility. Meanwhile, more and more data are becoming available owing to the widespread deployment of smart meters, smart sensors, and upgraded communication networks. As a result, data-driven control techniques, especially reinforcement learning (RL), have attracted surging attention in recent years. This paper provides a comprehensive review of various RL techniques and how they can be applied to decision-making and control in power systems. In particular, we select three key applications, i.e., frequency regulation, voltage control, and energy management, as examples to illustrate RL-based models and solutions. We then present the critical issues in the application of RL, i.e., safety, robustness, scalability, and data. Several potential future directions are discussed as well.
TL;DR: In this paper , an improved virtual synchronous generator control algorithm based on a fuzzy inference system is proposed, which adjusts the values of virtual inertia and damping coefficient dynamically through fuzzy logic rules to realize the coordinated control of the two.
Abstract: In the microgrid, virtual synchronous generator technology can significantly enhance the anti-interference characteristics of the system frequency and bus voltage, as well as solve the problems of insufficient damping and low inertia. However, the system frequency and active power oscillation caused by power fluctuations and grid faults threaten the stable operation of the grid seriously. Therefore, for an alternating current (AC) microgrid multi-virtual synchronous generator (VSG) parallel system, an improved virtual synchronous generator control algorithm based on a fuzzy inference system is proposed, which adjusts the values of virtual inertia and damping coefficient dynamically through fuzzy logic rules to realize the coordinated control of the two. The enhanced VSG algorithm described in this research has a substantial influence on power-frequency oscillation suppression, decreases active power and frequency overshoot, shortens the adjustment time, and improves system frequency stability active power, according to simulation and experimental findings.
TL;DR: In this article , a stochastic p-robust optimization method (SPROM) is proposed to guarantee robust operation of the system under the worst-case scenario, which combines both stochastically programming and robust optimization approaches where minimizes the worstcase cost or regret level.
01 Feb 2022
TL;DR: In this paper , a modified structure of the tilted integral derivative (TID) controller is developed for the load frequency control issue of a multi-area interconnected multi-source power system and a new optimization algorithm known as Archimedes optimization algorithm (AOA) is used to fine-tune the proposed ID-T controller parameters.
Abstract: In this work, a modified structure of the tilted integral derivative (TID) controller, i.e. an integral derivative-tilted (ID-T) controller, is developed for the load frequency control issue of a multi-area interconnected multi-source power system. Moreover, a new optimization algorithm known as Archimedes optimization algorithm (AOA) is used to fine-tune the proposed ID-T controller parameters. The performance of the proposed ID-T controller based on AOA is evaluated through a two-area interconnected power system, each area containing various conventional generation units (i.e., thermal, gas, and hydraulic power plants) and renewables (wind and solar power). Furthermore, system nonlinearities (i.e., generation rate constraints, governor deadband, and communication time delays), system uncertainties, and load/renewables fluctuations are considered in designing the proposed controller. The effectiveness of the proposed ID-T controller based on AOA is verified by comparing its performance with other control techniques in the literature (i.e. integral controller, proportional integral derivative (PID) controller, fractional-order PID controller, TID controller, and I-TD controller). The AOA's optimization superiority has also been verified against a variety of other sophisticated optimization methods, including particle swarm optimization and whale optimization algorithm. The simulation results exhibit that the proposed ID-T controller based on the AOA presents a great improvement in the system frequency stability under several contingencies of different load perturbations, system uncertainties, physical constraints, communication time delays, high renewables penetration.
TL;DR: In this article, an ensemble learning model that combines bagging and stacking methods applied to time series forecasting with very short-term (10 and 30-minutes) and shortterm (60 and 120 minutes) evaluations of wind power generation is evaluated.
TL;DR: In this article , a detailed spatial and temporal characterization of China's wind and solar energy resource potential is provided, which is necessary to identify pathways to achieve a deep decarbonization of its electric power system as this nation pursues carbon neutrality by 2060.
TL;DR: In this article , a distributed robust economic dispatch strategy is presented to achieve the energy management of IESs in the presence of misbehaving units, which consists of a neighbor-observe-based detection process and a reputation-based isolation process.
Abstract: Distributed algorithms are increasingly being used to solve the economic dispatch problem of integrated energy systems (IESs) because of their high flexibility and strong robustness, but those algorithms also bring more risk of cyber-attacks in IESs. To solve this problem, this article investigates the distributed robust economic dispatch problem of IESs under cyber-attacks. First, as the first line of defense against attacks, a privacy-preserving protocol is designed for covering up some vital information used for economic dispatch of IESs. On this basis, a distributed robust economic dispatch strategy is presented to achieve the energy management of IESs in the presence of misbehaving units, which consists of a neighbor-observe-based detection process and a reputation-based isolation process. The proposed strategy is implemented in a fully distributed fashion and possesses strong robustness against various colluding and noncolluding attacks. In addition, the strategy can not only ensure the reliability of information transmission among energy units, but also solve the problem of incorrect measurement of distributed local load data caused by cyber-attacks. Finally, the effectiveness of the proposed strategy is illustrated by simulation cases on a 39-bus 32-node power–heat IES.
TL;DR: The experimental results demonstrate that the proposed hybrid model has significant advantage over other state-of-the-art models involved in this study in terms of prediction accuracy and stability.
TL;DR: The comparison between the performance of the models in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values validates the fact that the hybrid forecasting models will provide a more optimal solution.
Abstract: The main and pivot part of electric companies is the load forecasting. Decision-makers and think tank of power sectors should forecast the future need of electricity with large accuracy and small error to give uninterrupted and free of load shedding power to consumers. The demand of electricity can be forecasted amicably by many Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) techniques among which hybrid methods are most popular. The present technologies of load forecasting and present work regarding combination of various ML, DL and AI algorithms are reviewed in this paper. The comprehensive review of single and hybrid forecasting models with functions; advantages and disadvantages are discussed in this paper. The comparison between the performance of the models in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are compared and discussed with literature of different models to support the researchers to select the best model for load prediction. This comparison validates the fact that the hybrid forecasting models will provide a more optimal solution.
TL;DR: In this paper , a hybrid model, using unique strengths of Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Deep-learning-based Long Short-Term Memory (LSTM), was proposed to handle different components in the power time series of an offshore wind turbine in Scotland, where neither the approximation nor the detail was considered as purely nonlinear or linear.
TL;DR: In this paper, a case study of three distinct approaches to probabilistic wind power forecasting is presented using an open dataset, and the case study provides an example of exemplary forecast evaluation, and open source code allows for its reproduction and use in future work.
Abstract: Installed capacities of wind and solar power have grown rapidly over recent years, and the pool of literature on very short-term (minutes- to hours-ahead) wind and solar forecasting has grown in line with this. This paper reviews established and emerging approaches to provide an up-to-date view of the field. Knowledge transfer between wind and solar forecasting has benefited the field and is discussed, and new opportunities are identified, particularly regarding use of remote sensing technology. Forecasting methodologies and study design are compared and recommendations for high quality, reproducible results are presented. In particular, the choice of suitable benchmarks and use of sufficiently long datasets is highlighted. A case study of three distinct approaches to probabilistic wind power forecasting is presented using an open dataset. The case study provides an example of exemplary forecast evaluation, and open source code allows for its reproduction and use in future work.
TL;DR: In this article , a blockchain-based distributed economic dispatch (SCED) algorithm is proposed, which allows the use of hierarchical SCED algorithms in the absence of a coordinator and can disable malicious participants.
Abstract: Distributed optimization algorithms for security-constrained economic dispatch (SCED) problems have been the subject of significant research interest in recent years. However, existing distributed SCED algorithms can be ineffective in the presence of malicious participants and inefficient in the absence of a coordinator. On the other hand, blockchain, an emerging technique known as the trust machine, has not shown its potential to address the above challenges in state-of-the-art literature. This paper proposes a blockchain-based distributed SCED algorithm. Using blockchain to form a coordination committee and enable balance among committee members, the proposed method allows the use of hierarchical SCED algorithms in the absence of a coordinator and can disable malicious participants. Numerical results show the robustness and necessity of the proposed blockchain-based SCED algorithm, by comparing the SCED results with and without blockchain.
TL;DR: In this paper , the authors present an overview of the latest research of EV charging stations and highlight some important issues and challenges in power architectures design, energy storage techniques, control strategies of micro-grid, and energy management optimization.