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Showing papers on "Electric power system published in 2020"


Journal ArticleDOI
TL;DR: An important guiding source for researchers and engineers studying the smart grid, which helps transmission and distribution system operators to follow the right path as they are transforming their classical grids to smart grids.

472 citations


Journal ArticleDOI
TL;DR: In this paper, the authors introduce fundamental ways to integrate high levels of renewable energy (RE) and distributed energy resources (DERs) in the power system while creating a more flexible power system.
Abstract: Increasing inverter-based sources reduces the system’s inertia resulting in possible frequency stability issues. Understanding low-inertia systems and their stability properties is of crucial importance. This article introduces fundamental ways to integrate high levels of renewable energy (RE) and distributed energy resources (DERs) in the power system while creating a more flexible power system. Using RE and DER in the distribution system has many advantages such as reducing the physical and electrical distance between generation and loads, bringing sources closer to loads contributes to the enhancement of the voltage profile, reduction in distribution and transmission bottlenecks, improved reliability, lower losses, and enhances the potential use of waste heat. A basic issue for high penetration of DER is the technical complexity of controlling hundreds of thousands to millions of inverters. This is addressed through autonomous techniques using local measurements eliminating the need for fast control systems. The key issues addressed in this article include using inverter damping to stabilize frequency in systems with low or no inertia, autonomous operation, methods for relieving inverter overload, energy reserves, and their implementation in photovoltaics (PV) systems. This article provides important insight into the interactions between inverter bases sources and the high-power system. The distinction between grid-forming (GFM) inverter and grid-following (GFL) inverter is profound. GFM inverters provide damping to frequency swings in a mixed system, while GFL inverter can aggravate frequency problems with increased penetration. Rather than acting as a source of inertia, the GFM inverter acts as a source of damping to the system. On the other hand, the application of inverters in the power system has two major issues. One is the complexity of controlling hundreds of thousands to millions of inverters. This is addressed through autonomous techniques using local measurements. The other is the potential of high overcurrent in GFM inverters and techniques for explicitly protecting against overloading. To exploit the innate damping of GFM inverters, energy reserves are critical.

291 citations


Journal ArticleDOI
TL;DR: This article proposes a memory-based event-triggering load frequency control (LFC) method for power systems through a bandwidth-constrained open network, which couples the effects of METS and random deception attacks in a unified framework.
Abstract: This article proposes a memory-based event-triggering $H_{\infty }$ load frequency control (LFC) method for power systems through a bandwidth-constrained open network. To overcome the bandwidth constraint, a memory-based event-triggered scheme (METS) is first proposed to reduce the number of transmitted packets. Compared with the existing memoryless event-triggered schemes, the proposed METS has the advantage to utilize series of the latest released signals. To deal with the random deception attacks induced by open networks, a networked power system model is well established, which couples the effects of METS and random deception attacks in a unified framework. Then, a sufficient stabilization criterion is derived to obtain the memory $H_{\infty }$ LFC controller gains and event-triggered parameters simultaneously. Compared with existing memoryless LFC, the control performance is greatly improved since the latest released dynamic information is well utilized. Finally, an illustrative example is used to show the effectiveness of the proposed method.

270 citations


Journal ArticleDOI
TL;DR: The impact of variable renewable energy sources penetration on power system transient stability, small-signal stability, and frequency stability are discussed and flexibility measurement studies are investigated, and methods of providing flexibility are evaluated.

269 citations


Journal ArticleDOI
TL;DR: In this paper, the authors present an update on recent developments and generate a relevant database for costs and emissions for decision-making purposes for integrating an energy storage system with the power network to manage unpredictable loads.

241 citations


Journal ArticleDOI
TL;DR: This study combined support vector machine and improved dragonfly algorithm to forecast short-term wind power for a hybrid prediction model and has shown better prediction performance compared with the other models such as back propagation neural network and Gaussian process regression.

231 citations


Journal ArticleDOI
TL;DR: Although the recent integration requirements can improve the grid operation, stability, security, and reliability, further improvements are still required with respect to protective regulations, global harmonization, and control optimization.

206 citations


Journal ArticleDOI
TL;DR: The values of three performance evaluation indicators, MBE, MAPE, and RMSE, show that the proposed hybrid deep learning model exhibits superior performance in both forecasting accuracy and stability.

200 citations


Journal ArticleDOI
TL;DR: In this article, the effects of temperature and dust accumulation on the solar PV panels on the design and performance of the hybrid power system in a desert region is investigated. But the main objective of the proposed off-grid hybrid renewable energy system is to increase the penetration of renewable energy in the energy mix, reduce the greenhouse gas emissions from fossil fuel combustion, and lower the cost of energy from the power systems.

193 citations


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

190 citations


Journal ArticleDOI
TL;DR: This study reviews the various control techniques and technologies that offset a decrease in inertia and discusses the inertia emulation control techniques available for inverters, wind turbines, photovoltaic systems, and microgrid.
Abstract: The utilization of power electronic inverters in power grids has increased tremendously, along with advancements in renewable energy sources. The usage of power electronic inverters results in the decoupling of sources from loads, leading to a decrease in the inertia of power systems. This decrease results in a high rate of change of frequency and frequency deviations under power imbalance that substantially affect the frequency stability of the system. This study focuses on the requirements of inertia and the corresponding issues that challenge the various country grid operators during the large-scale integration of renewable energy sources. This study reviews the various control techniques and technologies that offset a decrease in inertia and discusses the inertia emulation control techniques available for inverters, wind turbines, photovoltaic systems, and microgrid. This study attempts to explore future research directions and may assist researchers in choosing an appropriate topology, depending on requirements.

Journal ArticleDOI
TL;DR: A fault-mode controller is proposed which keeps the voltage-mode characteristics of the grid-forming structure while simultaneously limiting the converter currents to an admissible value and is evaluated in a detailed simulation model and verified through an experimental test setup.
Abstract: With an increasing capacity in the converter-based generation to the modern power system, a growing demand for such systems to be more grid-friendly has emerged. Consequently, grid-forming converters have been proposed as a promising solution as they are compatible with the conventional synchronous-machine-based power system. However, most research focuses on the grid-forming control during normal operating conditions without considering the fundamental distinction between a grid-forming converter and a synchronous machine when considering its short-circuit capability. The current limitation of grid-forming converters during fault conditions is not well described in the available literature and present solutions often aim to switch the control structure to a grid-following structure during the fault. Yet, for a future converter-based power system with no or little integration of synchronous machines, the converters need to preserve their voltage-mode characteristics and be robust toward weak-grid conditions. To address this issue, this article discusses the fundamental issue of grid-forming converter control during grid fault conditions and proposes a fault-mode controller which keeps the voltage-mode characteristics of the grid-forming structure while simultaneously limiting the converter currents to an admissible value. The proposed method is evaluated in a detailed simulation model and verified through an experimental test setup.

Journal ArticleDOI
TL;DR: A novel day-ahead PV power forecasting approach based on deep learning based on two novel deep convolutional neural networks, which involves historical PV power series, meteorological elements and numerical weather prediction is proposed and validated.

Journal ArticleDOI
TL;DR: The most widely used approaches, methods, and techniques in each of these categories, as well as the future trends for improving the power system resilience are reviewed in this article.
Abstract: Rare and extreme climate events may result in wide power outages or blackouts. The concept of power system resilience has been introduced for focusing on high-impact and low-probability (HILP) events such as a hurricane, heavy snow, and floods. Power system resilience is the ability of a system to reduce the likelihood of blackout or wide power outages due to HILP events. Indeed, in a resilient power system, as the severity of HILP events increases, the rate (but not the amount) of unserved loads diminishes. Suitable measures for managing power system resilience can be classified into three categories in terms of time, known as “resilience-based planning,” “resilience-based response,” and “resilience-based restoration.” The most widely used approaches, methods, and techniques in each of these categories, as well as the future trends for improving the power system resilience are reviewed in this article. The challenges of resilience in power systems with high penetration of renewable energy sources are also discussed in each of these categories.

Journal ArticleDOI
01 Apr 2020-Energy
TL;DR: This paper proposes a novel sequence-to-sequence model using the Attention-based Gated Recurrent Unit (AGRU) that improves accuracy of forecasting processes and embeds the task of correlating different forecasting steps by hidden activations of GRU blocks.

Journal ArticleDOI
TL;DR: An open-source platform named Reinforcement Learning for Grid Control (RLGC) has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control.
Abstract: Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived “worst” case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, this paper developed novel adaptive emergency control schemes using deep reinforcement learning (DRL) by leveraging the high-dimensional feature extraction and non-linear generalization capabilities of DRL for complex power systems. Furthermore, an open-source platform named Reinforcement Learning for Grid Control (RLGC) has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control. Details of the platform and DRL-based emergency control schemes for generator dynamic braking and under-voltage load shedding are presented. Robustness of the developed DRL method to different simulation scenarios, model parameter uncertainty and noise in the observations is investigated. Extensive case studies performed in both the two-area, four-machine system and the IEEE 39-bus system have demonstrated excellent performance and robustness of the proposed schemes.

Journal ArticleDOI
TL;DR: A peer-to-peer (P2P) local electricity market model incorporating both energy trading and uncertainty trading simultaneously and enables more PV uncertainty to be balanced locally rather than propagating to the upper layer system is proposed.
Abstract: In the future power system, an increasing number of distributed energy resources will be integrated including intermittent generation like photovoltaic (PV) and flexible demand like electric vehicles (EVs). It has been long thought to utilize the flexible demand to absorb the PV output locally with the technical solution proposed while an effective commercial arrangement is yet to be developed due to the significant uncertainty associated with local generation. This paper proposes a peer-to-peer (P2P) local electricity market model incorporating both energy trading and uncertainty trading simultaneously. The novelty is to match the forecast power with demand having time flexibility and the uncertain power with demand having power flexibility. This market enables more PV uncertainty to be balanced locally rather than propagating to the upper layer system. In the test case, 55.3% of PV forecast error can be balanced locally in the proposed joint market. In comparison, 43.6% of PV forecast error is balanced locally when the forecast power and uncertain power are traded separately in a day-ahead market and a real-time market. The proposed P2P market can also motivate PV owners to improve forecast accuracy.

Journal ArticleDOI
TL;DR: A comprehensive review on the most recent studies whereby MLTs were developed for power system security and stability especially in cyberattack detections, PQ disturbance studies and dynamic security assessment studies is presented.
Abstract: Increasing use of renewable energy sources, liberalized energy markets and most importantly, the integrations of various monitoring, measuring and communication infrastructures into modern power system network offer the opportunity to build a resilient and efficient grid However, it also brings about various threats of instabilities and security concerns in form of cyberattack, voltage instability, power quality (PQ) disturbance among others to the complex network The need for efficient methodologies for quicker identification and detection of these problems have always been a priority to energy stakeholders over the years In recent times, machine learning techniques (MLTs) have proven to be effective in numerous applications including power system studies In the literature, various MLTs such as artificial neural networks (ANN), Decision Tree (DT), support vector machines (SVM) have been proposed, resulting in effective decision making and control actions in the secured and stable operations of the power system Given this growing trend, this paper presents a comprehensive review on the most recent studies whereby MLTs were developed for power system security and stability especially in cyberattack detections, PQ disturbance studies and dynamic security assessment studies The aim is to highlight the methodologies, achievements and more importantly the limitations in the classifier(s) design, dataset and test systems employed in the reviewed publications A brief review of reinforcement learning (RL) and deep reinforcement learning (DRL) approaches to transient stability assessment is also presented Finally, we highlighted some challenges and directions for future studies

Journal ArticleDOI
TL;DR: Unless the discussed challenges are satisfactorily addressed and solved, arriving at an AEA that can properly operate over commercial missions will not be possible.
Abstract: Narrow body and wide body aircraft are responsible for more than 75% of aviation greenhouse gas (GHG) emission and aviation, itself, was responsible for about 2.5% of all GHG emissions in the United States in 2018. This situation becomes worse when considering a 4-5% annual growth in air travel. Electrified aircraft is clearly a promising solution to combat the GHG challenge; thus, the trend is to eliminate all but electrical forms of energy in aircraft power distribution systems. However, electrification adds tremendously to the complexity of aircraft electric power systems (EPS), which is dramatically changing in our journey from conventional aircraft to more electric aircraft (MEA) and all electric aircraft (AEA). In this article, we provide an in-depth discussion on MEA/AEA EPS: electric propulsion, distributed propulsion systems (DPS), EPS voltage levels, power supplies, and EPS architectures are discussed. Publications on power flow (PF) analysis and management of EPS are reviewed, and an initial schematic of a potential aircraft EPS with electric propulsion is proposed. In this regard, we also briefly review the components required for MEA/AEA EPS, including power electronics (PE) converters, electric machines, electrochemical energy units, circuit breakers (CBs), and wiring harness. A comprehensive review of each of the components mentioned above or other topics except for those related to steady state power flow in MEA/AEA EPS is out of this article's scope and should be found somewhere else. At the close of the paper, some challenges in the path towards AEA are presented. Unless the discussed challenges are satisfactorily addressed and solved, arriving at an AEA that can properly operate over commercial missions will not be possible.

Journal ArticleDOI
TL;DR: Variational mode decomposition (VMD) algorithm can use VMD to decompose the wind speed signal to obtain different scale fluctuations or trends, so as to fully exploit the potential information of wind speed, and obtain more accurate prediction results.

Journal ArticleDOI
TL;DR: The obtained results of the simulation confirm the effectiveness of using blockchain to enhance the social welfare for power system users and show that ISO can modify its polices and use the potential and benefits of distributed generation units to increase social welfare and reduce line density by concluding contracts in accordance with the production values given.
Abstract: Using blockchain technology as one of the new methods to enhance the cyber and physical security of power systems has grown in importance over the past few years. Blockchain can also be used to improve social welfare and provide sustainable energy for consumers. In this article, the effect of distributed generation (DG) resources on the transmission power lines and consequently fixing its conjunction and reaching the optimal goals and policies of this issue to exploit these resources is investigated. In order to evaluate the system security level, a false data injection attack (FDIA) is launched on the information exchanged between independent system operation (ISO) and under-operating agents. The results are analyzed based on the cyber-attack, wherein the loss of network stability as well as economic losses to the operator would be the outcomes. It is demonstrated that cyber-attacks can cause the operation of distributed production resources to not be carried out correctly and the network conjunction will fall to a large extent; with the elimination of social welfare, the main goals and policies of an independent system operator as an upstream entity are not fulfilled. Besides, the contracts between independent system operators with distributed production resources are not properly closed. In order to stop malicious attacks, a secured policy architecture based on blockchain is developed to keep the security of the data exchanged between ISO and under-operating agents. The obtained results of the simulation confirm the effectiveness of using blockchain to enhance the social welfare for power system users. Besides, it is demonstrated that ISO can modify its polices and use the potential and benefits of distributed generation units to increase social welfare and reduce line density by concluding contracts in accordance with the production values given.

Journal ArticleDOI
TL;DR: The results show that it is increasingly important to focus on the short-term system operations in the planning models integrating variable renewables, specially the constraints of flexible generation, interregional transmission as well as energy storage and demand side response.

Journal ArticleDOI
TL;DR: This paper makes a review on the above mentioned aspects, including the emerging frequency regulation services, updated grid codes and grid-scale ESS projects, and some key technical issues are discussed and prospects are outlined.
Abstract: Electric power systems foresee challenges in stability due to the high penetration of power electronics interfaced renewable energy sources. The value of energy storage systems (ESS) to provide fast frequency response has been more and more recognized. Although the development of energy storage technologies has made ESSs technically feasible to be integrated in larger scale with required performance, the policies, grid codes and economic issues are still presenting barriers for wider application and investment. Recent years, a few regions and countries have designed new services to meet the upcoming grid challenges. A number of grid-scale ESS projects are also implemented aiming to trial performance, demonstrate values, and gain experience. This paper makes a review on the above mentioned aspects, including the emerging frequency regulation services, updated grid codes and grid-scale ESS projects. Some key technical issues are also discussed and prospects are outlined.

Journal ArticleDOI
22 Jul 2020-Energies
TL;DR: This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts.
Abstract: The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature.

Journal ArticleDOI
TL;DR: The main MPPT techniques for PV systems are reviewed and summarized, and divided into three groups according to their control theoretic and optimization principles.

Journal ArticleDOI
TL;DR: The update presented here introduces a generation mix more representative of modern power systems, with the removal of several nuclear and oil-generating units and the addition of natural gas, wind, solar photovoltaics, concentrating solar power, and energy storage.
Abstract: The evolving nature of electricity production, transmission, and consumption necessitates an update to the IEEE's Reliability Test System (RTS), which was last modernized in 1996. The update presented here introduces a generation mix more representative of modern power systems, with the removal of several nuclear and oil-generating units and the addition of natural gas, wind, solar photovoltaics, concentrating solar power, and energy storage. The update includes assigning the test system a geographic location in the southwestern United States to enable the integration of spatio-temporally consistent wind, solar, and load data with forecasts. Additional updates include common RTS transmission modifications in published literature, definitions for reserve product requirements, and market simulation descriptions to enable benchmarking of multi-period power system scheduling problems. The final section presents example results from a production cost modeling simulation on the updated RTS system data.

Journal ArticleDOI
TL;DR: The concepts of narrow versus broad band signals are first recalled along with the limitation of the meaning of apparent power, power factor and reactive power, and the adequacy of the phasor representation of voltages and currents waveforms are discussed.

Journal ArticleDOI
TL;DR: This article presents a low-inertia case study that includes SMs and converters controlled under various grid-forming techniques and analyzes how and when the interaction between the fast GFC and the slow SM dynamics can contribute to the system instability.
Abstract: An inevitable consequence of the global power system transition toward nearly 100% renewable-based generation is the loss of conventional bulk generation by synchronous machines (SMs), their inertia, and accompanying frequency- and voltage-control mechanisms. This gradual transformation of the power system to a low-inertia system leads to critical challenges in maintaining system stability. Novel control techniques for converters, so-called grid-forming strategies, are expected to address these challenges and replicate functionalities that, so far, have been provided by SMs. This article presents a low-inertia case study that includes SMs and converters controlled under various grid-forming techniques. In this article, the positive impact of the grid-forming converters (GFCs) on the frequency stability of SMs is highlighted, a qualitative analysis that provides insights into the frequency stability of the system is presented, we explore the behavior of the grid-forming controls when imposing the converter dc and ac current limitations, the importance of the dc dynamics in grid-forming control design as well as the critical need for an effective ac current limitation scheme are reported, and finally, we analyze how and when the interaction between the fast GFC and the slow SM dynamics can contribute to the system instability.

Journal ArticleDOI
TL;DR: In this paper, a multi-objective two-stage stochastic unit commitment scheme for integrated gas and electricity networks taking into account novel flexible energy sources such as P2G technology and demand response (DR) programs as well as high penetration of wind turbines was proposed.

Journal ArticleDOI
TL;DR: In this article, the authors propose a peer-to-peer energy trading architecture, in two configurations, that couples peer topeer interactions and distribution network operations, assuming that these interactions are settled by the utility in a centralized manner, while the second one is peer-centric and does not involve the utility.
Abstract: Peer-to-peer interactions between small-scale energy resources exploit distribution network infrastructure as an electricity carrier, but remain financially unaccountable to electric power utilities. This status-quo raises multiple challenges. First, peer-to-peer energy trading reduces the portion of electricity supplied to end-customers by utilities and their revenue streams. Second, utilities must ensure that peer-to-peer transactions comply with distribution network limits. This article proposes a peer-to-peer energy trading architecture, in two configurations, that couples peer-to-peer interactions and distribution network operations. The first configuration assumes that these interactions are settled by the utility in a centralized manner, while the second one is peer-centric and does not involve the utility. Both configurations use distribution locational marginal prices to compute network usage charges that peers must pay to the utility for using the distribution network.