Modern Power Grid Research Articles

Balancing-oriented hydropower operation makes the clean energy transition more affordable and simultaneously boosts water security

Reservoir hydropower offers a compelling combination of stability and flexibility services for modern water and power grids. However, its operating flexibility is poorly characterized in energy system planning, missing opportunities to cost-effectively uptake variable renewable energy (VRE) for a clean energy transition. In this study, we have developed a fully coupled reservoir operation and energy expansion model to quantify the economic and environmental benefits attained from adaptive hydropower operation in a high VRE future. Our case study of the China Southern Power Grid reveals that, in a 2050 net-zero grid, simply adapting hydropower operations to balance VRE can reduce 2018–2050 total system costs by 7% (that is, US$28.2 billion) and simultaneously save 123.8 km3 of water each year (that is, more than three times the reservoir capacity of the Three Gorges Dam). These vast, yet overlooked, cost- and water-saving potentials highlight the importance of incorporating balancing-oriented hydropower operation into future pathways to jointly decarbonize and secure power and water grids.

Event Detection, Localization, and Classification Based on Semi-Supervised Learning in Power Grids

Real-time situational awareness and event analysis are crucial to the security of the modern power grid, which is a complicated nonlinear system and hard to be completely modeled. Massive data is collected but the information hasn't been sufficiently leveraged. To effectively extract the event features, this paper proposes a framework for event detection, localization, and classification in power grids based on semi-supervised learning. Specifically, event detection is realized by invertible neural network (INN), hence to learn complex distributions of real-world measurements in a flexible way. Abundant normal measurements are learned by INN and explicit log-likelihoods then serve as the indicator to distinguish events with adequate sensitivity. Moreover, risks induced by events are assessed and spatial locations are determined. Since the majority of power system events are recorded without labels in practice, a pseudo label (PL) technique is leveraged to classify events with limited labels. The PL-based approach has an enhanced separating capability for events and outperforms other approaches under a low labeling rate. Case studies with simulated data in the IEEE 39-bus system and real-world measurements verify the effectiveness of the proposed framework.

On data-driven modeling and control in modern power grids stability: Survey and perspective

Modern power grids are fast evolving with the increasing volatile renewable generation, distributed energy resources (DERs) and time-varying operating conditions. The DERs include rooftop photovoltaic (PV), small wind turbines, energy storages, flexible loads, electric vehicles (EVs), etc. The grid control is confronted with low inertia, uncertainty and nonlinearity that challenge the operation security, efficacy and efficiency. The ongoing digitization of power grids provides opportunities to address the challenges with data-driven and control. This paper provides a comprehensive review of emerging data-driven dynamical modeling and control methods and their various applications in power grid. Future trends are also discussed based on advances in data-driven control.

D-Optimal Data Fusion: Exact and Approximation Algorithms

We study the D-optimal Data Fusion (DDF) problem, which aims to select new data points, given an existing Fisher information matrix, so as to maximize the logarithm of the determinant of the overall Fisher information matrix. We show that the DDF problem is NP-hard and has no constant-factor polynomial-time approximation algorithm unless P = NP. Therefore, to solve the DDF problem effectively, we propose two convex integer-programming formulations and investigate their corresponding complementary and Lagrangian-dual problems. Leveraging the concavity of the objective functions in the two proposed convex integer-programming formulations, we design an exact algorithm, aimed at solving the DDF problem to optimality. We further derive a family of submodular valid inequalities and optimality cuts, which can significantly enhance the algorithm performance. We also develop scalable randomized-sampling and local-search algorithms with provable performance guarantees. Finally, we test our algorithms using real-world data on the new phasor-measurement-units placement problem for modern power grids, considering the existing conventional sensors. Our numerical study demonstrates the efficiency of our exact algorithm and the scalability and high-quality outputs of our approximation algorithms. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: Y. Li and W. Xie were supported in part by Division of Civil, Mechanical and Manufacturing Innovation [Grant 2046414] and Division of Computing and Communication Foundations [Grant 2246417]. J. Lee was supported in part by Air Force Office of Scientific Research [Grants FA9550-19-1-0175 and FA9550-22-1-0172]. M. Fampa was supported in part by Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grants 305444/2019-0 and 434683/2018-3]. F. Qiu and R. Yao were supported in part by the U.S. Department of Energy Advanced Grid Modeling Program under [Grant DE-OE0000875]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2022.0235 .

AC Network-Constrained Peer-to-Peer Electricity Market Model in Low-voltage Power Distribution Networks

The rapid growth of Distributed Energy Resources (DERs) and demand response in modern power grids calls for decentralized electricity trading mechanisms to effectively induce and coordinate responses from massive DERs and load entities. This paper proposes a fully decentralized peer-to-peer (P2P) distribution electricity trading mechanism backboned by a modified version of Alternating Direction Method of Multipliers (ADMM). Comparing with the state-of-the-art P2P electricity markets, the proposed electricity trading mechanism comprehensively incorporates power network constraints formulated by an alternating current power flow model. In the proposed method, nodal voltages are designed as the only public variables shared among participants. Privacy of each participant is thus sufficiently preserved without exposing the bid data to the others. Meanwhile, the proposed P2P method can also ensure the global optimality of social welfare in electricity transactions. Comprehensive case studies are carried out using the IEEE 33-node distribution system to demonstrate the feasibility and efficiency of proposed P2P electricity trading mechanism. The results indicate that the proposed model can efficiently integrates network constraints in the market clearing process while maintaining computational performance, ensuring stable convergence regardless of distribution system congestion. The proposed P2P electricity market can achieve global maximization of social welfare, with a relative difference of no more than 5.0% compared to the conventional centralized market. Even in worst-case scenarios, the relative difference remains within 7.32%. Meanwhile, the method accurately accounts for network losses, guaranteeing the feasibility of trading plans within the physical network.© 2017 Elsevier Inc. All rights reserved.

Phasor data correction and transmission system state estimation under spoofing attacks

Cyberinfrastructure (e.g., sensors, actuators and the associated communication network) has become an integral part of our modern power grid. While these cyber technologies enhance situational awareness and operational efficiency, they also expose the physical system to cyber-attacks. In this paper, we consider the problem of transmission system state estimation based on measurements from a number of PMUs. In this context, two PMU data integrity attacks namely, Time Synchronization Attack (TSA) and Man-in-the-Middle (MitM) attacks that can potentially cause a severe impact on the grid, are analyzed. Specifically, we propose a novel method based on an alternate expectation–maximization framework to mitigate the effects of these attacks on the state estimation process. Numerical tests are conducted on IEEE-14, 30 and 118 bus systems with different attack scenarios to validate the developed method. Unlike existing works, the proposed algorithm provides accurate state estimates without any prior knowledge of the location of the attack, the number of meters being attacked, or the magnitude of the attack parameter.

A steady-state initialization procedure for generic voltage-source converters in electromagnetic transient simulations

Electromagnetic transient (EMT) simulations are often performed to analyze disturbances which occur during a steady-state operation of the power grid. In modern transmission and distribution power grids, a number of voltage-source converters (VSCs) are used for renewable energy interconnections and system control. To perform EMT simulations with such VSCs, a time step of the order of microseconds is used to represent the switching operations of the VSCs. In order to avoid a prohibitively-long computation time, a steady-state initialization method is required to directly start from a steady state. This paper proposes a systematic and heuristic procedure for the steady-state initialization of generic VSCs. Using an AC steady-state solution, detailed portions in the circuit part and the control-system part of a VSC are systematically initialized. For validation, EMT simulations of a 6.6-kV distribution grid with two VSCs are performed with and without the proposed initialization procedure in this paper. Practically no transient is observed in the result with the proposed procedure, and therefore it is confirmed that directly starting from a steady state is made possible. On the other hand, the result without the proposed procedure does not reach the steady state even after continuing the EMT simulation for 300 ms.

Human-Cyber-Physical Systems for Energy Internet—A Review

As the research of HCPS in EI is ongoing, an increasing number of problems should be addressed, such as the leading role or aid function definition of humans, the coordination of humans with cyber and physical equipment, the state awareness of information handling, etc. Therefore, an increasing number of advanced techniques should be considered and adopted, such as IOT, AI and big data, etc., the main target of which is to improve the timely, digitalized, informationized and intelligent characters of the related tasks. This paper first introduces the applications of CPS in every industry, especially in the power grid and EI (Energy Internet), the introduction of which can be classified as a framework design, support technologies and algorithms, system function, power ancillary service, Energy Internet realization, etc. Then, this paper summarizes the related technologies of Human-Cyber-Physical Systems, which mainly include the areas of system design, theoretical analysis, technique development and application forecasting, which emphasizes its applications in EI. For the development of HCPS, it uses the human-in-the-loop concept and digital transformation as the future research and development directions, the performance of which can be largely promoted through these means. With the aid of HCPS, the optimal system performance of EI can be achieved more robustly, which finally evolves into an integrated and comprehensive modern power grid.

Phasor and EMT models of grid-following and grid-forming converters for short-circuit simulations

Phasor models have been widely used in short-circuit simulations, in which system operators verify the behaviour of the grid over thousands of different contingencies. However, it is still unclear if phasor models can still be used in fault studies in modern or future power grids dominated by renewable generation with power electronics and converters. Therefore, this paper analyses the suitability of phasor models to simulate short-circuit transients in grids with grid-following (GFL) and grid-forming (GFM) voltage-source converters (VSC). Phasor models of GFL and GFM were developed and tested in two test systems, one with 50% of converters in the generation mix and another powered 100% by converters. Asymmetrical and symmetrical faults were applied at different points of the systems and key variables were used to compare the phasor models against EMT models during the transients. The results showed that, despite neglecting transients in the grid, phasor models could be used in a preliminary stage of short-circuit studies as it was capable of tracking the steady-state value of almost all variables analysed. In this case, detailed EMT simulations, although necessary, would be used only at more advanced stages of the studies.

Load-Frequency and Power Sharing Control in a Multi-Area Power System Using Intelligent Sliding Surface Approach

With the penetration of DGs in power systems, some restrictions such as economic dispatch in non-dispatchable sources, their control technology and voltage/frequency stability may be challenging. In conventional power systems, the transient stability has been obtained by high-inertia of upstream network in all conditions; however, in modern power grids, the micro grids (MGs) which could operate in both islanded mode and grid-connected mode are taken into account to overcome this issue. AC or DC types of DGs usually connect to the loads through power electronic converters, in which for researchers, this issue has been interesting and applicable to continue. When loads have been changed, the frequency has influenced correspondingly and must not exceed the permissible range. Therefore in this paper, this intention has been studied using conventional droop controller and intelligent sliding surface approach (ISSA) to evaluate the DGs effects on load-frequency control. While two or more areas (as some individual MGs) connect to each other via a transmission line, the voltage/frequency stability and power balancing constrained may be changed, which the ISSA is hired to track the frequency changes. To examine this problem, in this paper there are three MGs operate separately to show if the load may not supply, they should connect to adjacent block. If this important concern has not considered, it may cause global black-out. The simulation results obtained by ISSA compared with droop controller method represent the superiority of proposed technique to achieve best power sharing.

Flexible Smart Energy-Management Systems Using an Online Tendering Process Framework for Microgrids

Currently, modern power grids are evolving into complex cyber-physical systems integrated with distributed energy resources that can be controlled and monitored by computer-based algorithms. Given the increasing prevalence of artificial intelligence algorithms, it is essential to explore the possibility of energy management in microgrids by implementing control methodologies with advanced processing centers. This study proposes a novel smart multi-agent-based framework under a tendering process framework with a bottom-up approach to control and manage the flow of energy into a grid-connected microgrid (MG). The tendering organization in this structure as an upstream agent allocates demand among generators, creates a balance between supply and demand, and provides optimal energy cost for the MG. To optimize the electricity cost and decrease the use of grid power, the first-price sealed-bid (FPSB) algorithm is implemented over the tendering process. The proposed approach from one side optimally allocates energy among generators, and, from the other side, guarantees the system from blackouts. Theoretical analysis and results demonstrate that the proposed technique is easy to implement and provides a robust and stable control for MGs, which can guarantee energy management as well as flexible and online control. Furthermore, results show the proposed framework besides the real-time allocation of power among providers to optimize the injected power from the grid so that the total injected power by the grid is 146.92 kWh and the injected power to the grid is 214.34 kWh.

Power system event detection and localization—A new approach

Time-synchronized estimates acquired using the Wide Area Measurement System (WAMS) have substantially helped enhance the health of the modern power grid. WAMS data when used in conjunction with appropriate tools can aid in the timely detection of power system events. With the continuous expansion of the WAMS network, identification of events and narrowing down on their location is emerging as a serious challenge for power system operators. Therefore, in this paper an event detection and localization tool is developed which analyzes data obtained from multiple Phasor Measurement Units (PMUs) installed throughout the WAMS network in order to identify the occurrence of an event. The effectiveness of this tool is demonstrated using practical signals from the ISO-NE power system and simulation based signals obtained from a 4-machine 10-bus system. Occurrence of an event is flagged by comparing the wavelet energy and standard deviation values of a PMU signal against a threshold. An event localization algorithm based on the number of PMUs involved in the event detection stage is proposed. Based on the presented algorithm, disturbances are classified as local or wide-spread events. Finally, a method to identify a loss-of-synchronism condition using phase angle difference (PAD) signals obtained across transmission lines is also proposed.

Electricity Pricing and Its Role in Modern Smart Energy System Design: A Review

Energy is the foundation for human survival and socio-economic development, and electricity is a key form of energy. Electricity prices are a key factor affecting the interests of various stakeholders in the electricity market, playing a significant role in the sustainable development of energy and the environment. As the number of distributed energy resources (DERs) increases, today’s power systems no longer rely on a vertical market model and fixed electricity pricing scheme but instead depend on power dispatch and dynamic pricing to match supply and demand. This can help prevent significant fluctuations in supply–load imbalance and maintain system stability. Modern power grids have evolved by integrating information, communication, and intelligent control technologies with traditional power systems, giving rise to the concept of smart electric grids. Choosing an appropriate pricing scheme to manage large-scale DERs and controllable loads in today’s power grid become very important. However, the existing literature lacks a comprehensive review of electricity pricing in power systems and its transformative impact on shaping the energy landscape. To fill this void, this paper provides a survey on the developments, methods, and frameworks related to electricity pricing and energy trading. The review mainly considers the development of pricing in a centralized power grid, peer-to-peer (P2P) and microgrid-to-microgrid (M2M) energy trading and sharing, and various pricing methods. The review will cover the pricing schemes in modern power systems, particularly with respect to renewable energy sources (RESs) and batteries, as well as controllable load applications, and the impact of pricing schemes based on demand-side ancillary services (DSAS) for grid frequency support. Lastly, this review article describes the current frameworks and limitations of electricity pricing in the current energy market, as well as future research directions. This review should offer a great overview and deep insights into today’s electricity market and how pricing methods will drive and facilitate the future establishment of smart energy systems.

Attacking IEC 61850 Substations by Targeting the PTP Protocol

Digital substations, also referred to as modern power grid substations, utilize the IEC 61850 station and process bus in conjunction with IP-based communication. This includes communication with switch yard equipment within the substation as well as the dispatch center. IEC 61850 is a global standard developed to standardize power grid communications, covering multiple communication needs related to modern power grid substations or digital substations. Unlike the legacy communication standards, IEC 60870-5-104 and DNP3, IEC 61850 is specifically designed for IP-based communication. It comprises several communication models and supports real-time communication by introducing the process bus to replace traditional peer-to-peer communication with standard network communication between substation equipment and the switch yard. The process bus, especially Sampled Measured Values (SMV) communication, in modern power grid substations relies on extremely accurate and synchronized time to prevent equipment damage, maintain power grid system balance, and ensure safety. In IEC 61850, time synchronization is provided by the Precision Time Protocol (PTP). This paper discusses the significance and challenges of time synchronization in IEC 61850 substations, particularly those associated with PTP. It presents the results of a controlled experiment that subjects time synchronization and PTP to cyber-attacks and discusses the potential consequences of such attacks. The paper also provides recommendations for potential mitigation strategies. The contribution of this paper is to provide insights and recommendations for enhancing the security of IEC 61850-based substations against cyber-attacks targeting time synchronization. The paper also explores the potential consequences of cyber-attacks and provides recommendations for potential mitigation strategies.

Utilizing controlled plug-in electric vehicles to improve hybrid power grid frequency regulation considering high renewable energy penetration

This study proposes an incorporated strategy based on energy storage systems (ESSs) like plug-in electric vehicles (PEVs) with load frequency control (LFC) to enhance the frequency stability during high penetration of renewable energy sources (RESs). Furthermore, an innovative-cascaded controller is used in the LFC loop, and PEVs are considered in the two identical areas' multi-source power grids. The proposed control structure is based on using two terms, the first term is the fractional-order-proportional-derivative (FOPD), and the second cascaded term is a fractional-order-proportional-integral (FOPI) to form the cascaded (FO-(PD-PI)) controller. Additionally, the gains of the proposed controller are well designed, utilizing an improved version of the manta ray foraging optimization algorithm (MRFO) labeled as leader MRFO (LMRFO). The effectiveness of the proposed FO-(PD-PI) controller based on the LMFRO algorithm is validated by comparing its performance with that of other control approaches such as (i.e., the proportional-integral-derivative (PID) controller, the tilt-integral-derivative (TID) controller, and the fractional-order-PID (FOPID) controller) based on different optimizers. Different load perturbation patterns, a high penetration level of RESs, and the communication delay time are broadly implemented for ensuring the superiority of the proposed controller in penalizing the power grid perturbations. Furthermore, for validating the efficiency of the proposed controller, an IEEE 39 bus is used. As a result, the proposed integrated strategy is a viable and effective control scheme for modern power grids with high-RESs penetration.

Load frequency regulation of multi-area power systems with communication delay via cascaded improved ADRC

Load frequency regulation (LFR) is regarded as a significant method for the stable operation of modern power grids. However, the LFR is facing many challenges such as communication delays in the area control error, and external disturbances caused by renewable energy integrating into power grids. This paper presents a cascaded improved active disturbance rejection control (ADRC) to handle these control challenges, where the inner and outer controllers are the improved ADRC and regular ADRC, respectively. The advantages of the improved ADRC to reject external disturbances and system uncurtains are discussed by simulations. Then a practical parameter tuning method is provided to guide the selection of controller parameters. Finally, comparative simulations are implemented to illustrate the abilities of the proposed control scheme in different cases. The proposed control structure can handle the communication delay well and have a strong ability to reject the disturbances of uncertain consumed power and wind power. The simulation results imply that the cascaded improved ADRC may have broad application prospects for the LFR.

Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models

Ensuring a reliable and uninterrupted supply of electricity is crucial for sustaining modern and advanced societies. Traditionally, power systems analysis was mostly dependent on formal commercial software, mathematical models produced via a mix of data analysis, control theory, and statistical methods. As power grids continue to grow and the need for more efficient and sustainable energy systems arises, attention has shifted towards incorporating artificial intelligence (AI) into traditional power grid systems, making their upgrade imperative. AI-based prediction and forecasting techniques are now being utilized to improve power production, transmission, and distribution to industrial and residential consumers. This paradigm shift is driven by the development of new methods and technologies. These technologies enable faster and more accurate fault prediction and detection, leading to quicker and more effective fault removal. Therefore, incorporating AI in modern power grids is critical for ensuring their resilience, efficiency, and sustainability, ultimately contributing to a cleaner and greener energy future. This paper focuses on integrating artificial intelligence (AI) in modern power generation grids, particularly in the fourth industrial revolution (4IR) context. With the increasing complexity and demand for more efficient and reliable power systems, AI has emerged as a possible approach to solve these difficulties. For this purpose, real-time data are collected from the user side, and internal and external grid faults occurred during a time period of three years. Specifically, this research delves into using state-of-the-art machine learning hybrid models at end-user locations for fault prediction and detection in electricity grids. In this study, hybrid models with convolution neural networks (CNN) have been developed, such as CNN-RNN, CNN-GRU, and CNN-LSTM. These approaches are used to explore how these models can automatically identify and diagnose faults in real-time, leading to faster and more effective fault detection and removal with minimum losses. By leveraging AI technology, modern power grids can become more resilient, efficient, and sustainable, ultimately contributing to a cleaner and greener energy future.

Enlightening Load Modeling by Means of Power Factor Decompositions

Considering the proliferation of power electronics applications and distributed energy resources, modern power grids are facing a significant increase in harmonic currents circulation and supply voltage deterioration, occasionally associated with small frequency variations. In such a context, the understanding of power phenomena in circuits with linear and non-linear loads under non-sinusoidal voltage conditions is nontrivial and still does not allow for an easy interpretation of harmonic sources, harmonic power flow or the identification of the parameters of a proper equivalent circuit. The main challenge is to develop modern theoretical approaches for load characterization, modeling, and parameter estimation so that new techniques can be formulated to provide adequate guiding for the analysis, compensation, revenue metering, accountability and other applications of power systems. Thus, based on the Conservative Power Theory (CPT) and further decomposition of its apparent power and power factor definitions, this paper proposes a novel methodology for estimating equivalent parameters and for proposing proper equivalent circuits capable of representing/modeling the main characteristics of single-phase generic loads (black boxes) and the related power phenomena in terms of passive dipoles (linear loads) or of harmonic voltage/current sources and their intrinsic transimpedances/transadmittances (non-linear loads). Simulation and experimental results were depicted to support and validate the proposed approach, showing that it might be a powerful modeling technique to represent generic loads in a modern power grid scenario, while being used for complex applications such as reactive power compensation or accountability in circuits with nonlinear loads and distorted voltages.

Massively Parallel Modeling of Battery Energy Storage Systems for AC/DC Grid High-Performance Transient Simulation

Extensive integration of power electronics apparatuses complicates the modern power grid and consequently necessitates time-domain transients study for its planning and operation. In this work, a heterogeneous computing architecture utilizing the CPU and graphics processing unit (GPU) is proposed for the efficient study of interactions between a power grid network and massive utility-scale battery energy storage systems (BESSs). The device-level electromagnetic transient (EMT) simulation aiming at enhanced fidelity of the BESS is conducted simultaneously with electro-mechanical transient stability (TS) simulation which suffices system-level dynamic security assessment. Since the reservation of a large amount of energy storage units is computationally intensive for the CPU, the concurrent multi-streaming, multi-threading capability of GPU is exploited to achieve asynchronous sequential-parallel processing, so that the proposed EMT-TS co-simulation can flexibly harness all available computing resources. The multi-rate scheme is adopted for further computational burden alleviation in addition to achieving timely information exchange. It shows that the heterogeneous computation of an IEEE 118-bus system integrated with a substantial number of distributed batteries becomes feasible following the achievement of a remarkable speedup of over 200, and the device- as well as system-level accuracy are validated by MATLAB/Simulink^® and DSATools^TM/TSAT simulation, respectively.

Online Power Transfer Regulation Between Transmission and Active Distribution Systems for High-Voltage Support

Regulation of power transfer between High-voltage Side (HVS) and Distribution Power Systems (DPS) is an urgent issue for modern power grids. During light loading, active DPS can export the extra power to the transmission system and may create voltage rise at HVS. In other words, active DPS are highly required to provide ancillary services to HVS during its unexpected failures. Therefore, this study suggests an online centralized control approach to satisfy the international Demand Connection Codes (DCC) at Transmission/Distribution interconnection point or fulfill the power requests from HVS. A linear power-flow taking into account the voltage-dependence characteristics of electric loads is used for problem formulation. The problem is formulated as a mixed integer quadratic optimization problem (MIQP). The MIQP problem can easily be solved to its optimality solution while reducing the complex computations. The proposed method can regulate both active and reactive power transfer at the interconnection point and thus it can be operated at different modes. The proposed control method uses the Distributed Generation (DGs), Capacitor Banks (CBs), and controllable loads as control variables. The use of controllable loads can increase the responsibility level of DPS in satisfying the DCC requirements. The suggested approach was checked on a 11 kV 77-bus active DPS.