131 results found
Li K, Guo H, Fang X, et al., 2023, Market Mechanism Design of Inertia and Primary Frequency Response With Consideration of Energy Market, IEEE Transactions on Power Systems, Vol: 38, Pages: 5701-5713, ISSN: 0885-8950
The shortage of inertia and primary frequency response (IPFR) will be more severe in future power systems since conventional fossil-based synchronous generators are gradually being replaced by variable renewable energy (VRE) generators. To relieve the shortage of IPFR, corresponding market mechanisms should be designed and incorporated to motivate appropriate provision from various sources. The mechanism of IPFR provision from different types of generators and its tight relation with energy production should receive particular attention. This paper proposes a novel IPFR market mechanism in which the energy market is taken into joint consideration. The virtual inertia and droop factor provided by VRE generators are defined and introduced, considering its dominant share in future power systems. A differentiated pricing scheme is designed towards incentive compatibility, considering provision from different types of generators with different quality levels and opportunity costs. Then, the proposed IPFR market mechanism is formulated, and a modified piecewise linearization method is utilized to simplify the non-linear nadir constraints. Finally, the model is tested on a modified IEEE 30-bus system according to historical data in CAISO. The results indicate the proposed mechanism could increase the utilization of VREs, decrease system operation costs, and guarantee reasonable payback for various types of generators.
Feng X, Badesa L, Wang X, et al., 2023, Editorial for the Special Issue on Emerging Technology and Advanced Application of Energy Storage in Low-Carbon Power Systems, Chinese Journal of Electrical Engineering, Vol: 9, Pages: 1-2, ISSN: 2096-1529
Zhang Z, Zuo K, Deng R, et al., 2023, Cybersecurity Analysis of Data-Driven Power System Stability Assessment, IEEE Internet of Things Journal, Vol: 10, Pages: 15723-15735
Machine learning-based intelligent systems enhanced with Internet of Things (IoT) technologies have been widely developed and exploited to enable the real-time stability assessment of a large-scale electricity grid. However, it has been extensively recognized that the IoT-enabled communication network of power systems is vulnerable to cyberattacks. In particular, system operating states, critical attributes that act as input to the data-driven stability assessment, can be manipulated by malicious actors to mislead the system operator into making disastrous decisions and thus cause major blackouts and cascading events. In this article, we explore the vulnerability of the data-driven power system stability assessment, with a special emphasis on decision tree-based stability assessment (DTSA) approaches, and investigate the feasibility of constructing a physics-constrained adversarial attack (PCAA) to undermine the DTSA. The PCAA is formulated as a nonlinear programming problem considering the misclassification constraint, power limits, and bad data detection, computing potential adversarial perturbations that reverse the 'stable/unstable' prediction of the real-time input while remaining invisible/stealthy. Extensive experiments based on the IEEE 68-bus system are conducted to evaluate the impact of PCAAs on predictions of DTSA and their transferability.
Wang C, Yan M, Pang K, et al., 2023, Cyber-Physical Interdependent Restoration Scheduling for Active Distribution Network via Ad Hoc Wireless Communication, IEEE Transactions on Smart Grid, Vol: 14, Pages: 3413-3426, ISSN: 1949-3053
This paper proposes a post-disaster cyber-physical interdependent restoration scheduling (CPIRS) framework for active distribution networks (ADN) where the simultaneous damages on cyber and physical networks are considered. The ad hoc wireless device-to-device (D2D) communication is leveraged, for the first time, to establish cyber networks instantly after the disaster to support ADN restoration. The repair and operation crew dispatching, the remote-controlled network reconfiguration and the system operation with DERs can be effectively coordinated under the cyber-physical interactions. The uncertain outputs of renewable energy resources (RESs) are represented by budget-constrained polyhedral uncertainty sets. Through implementing linearization techniques on disjunctive expressions, a monolithic mixed-integer linear programming (MILP) based two-stage robust optimization model is formulated and subsequently solved by a customized column-and-constraint generation (C&CG) algorithm. Numerical results on the IEEE 123-node distribution system demonstrate the effectiveness and superiorities of the proposed CPIRS method for ADN.
Ge P, Li P, Chen B, et al., 2023, Fixed-Time Convergent Distributed Observer Design of Linear Systems: A Kernel-Based Approach, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 68, Pages: 4932-4939, ISSN: 0018-9286
O'Malley C, Badesa L, Teng F, et al., 2023, Frequency response from aggregated V2G chargers with uncertain EV connections, IEEE Transactions on Power Systems, Vol: 38, Pages: 3543-3556, ISSN: 0885-8950
Fast frequency response (FR) is highly effective at securing frequency dynamics after a generator outage in low inertia systems. Electric vehicles (EVs) equipped with vehicle to grid (V2G) chargers could offer an abundant source of FR in future. However, the uncertainty associated with V2G aggregation, driven by the uncertain number of connected EVs at the time of an outage, has not been fully understood and prevents its participation in the existing service provision framework. To tackle this limitation, this paper, for the first time, incorporates such uncertainty into system frequency dynamics, from which probabilistic nadir and steady state frequency requirements are enforced via a derived moment-based distributionally-robust chance constraint. Field data from over 25,000 chargers is analysed to provide realistic parameters and connection forecasts to examine the value of FR from V2G chargers in annual operation of the GB 2030 system. The case study demonstrates that uncertainty of EV connections can be effectively managed through the proposed scheduling framework, which results in annual savings of Misplaced &6,300 or 37.4 tCO2 per charger. The sensitivity of this value to renewable capacity and FR delays is explored, with V2G capacity shown to be a third as valuable as the same grid battery capacity.
Ge P, Chen B, Teng F, 2023, Cyber-Resilient Self-Triggered Distributed Control of Networked Microgrids Against Multi-Layer DoS Attacks, IEEE TRANSACTIONS ON SMART GRID, Vol: 14, Pages: 3114-3124, ISSN: 1949-3053
Chu Z, Lakshminarayana S, Chaudhuri B, et al., 2023, Mitigating Load-Altering Attacks Against Power Grids Using Cyber-Resilient Economic Dispatch, IEEE TRANSACTIONS ON SMART GRID, Vol: 14, Pages: 3164-3175, ISSN: 1949-3053
Xu W, Higgins M, Wang J, et al., 2023, Blending Data and Physics Against False Data Injection Attack: An Event-Triggered Moving Target Defence Approach, IEEE TRANSACTIONS ON SMART GRID, Vol: 14, Pages: 3176-3188, ISSN: 1949-3053
Wan X, Sun M, Chen B, et al., 2023, AdapSafe: Adaptive and Safe-Certified Deep Reinforcement Learning-Based Frequency Control for Carbon-Neutral Power Systems, Pages: 5294-5302
With the increasing penetration of inverter-based renewable energy resources, deep reinforcement learning (DRL) has been proposed as one of the most promising solutions to realize real-time and autonomous control for future carbon-neutral power systems. In particular, DRL-based frequency control approaches have been extensively investigated to overcome the limitations of model-based approaches, such as the computational cost and scalability for large-scale systems. Nevertheless, the real-world implementation of DRL-based frequency control methods is facing the following fundamental challenges: 1) safety guarantee during the learning and decision-making processes; 2) adaptability against the dynamic system operating conditions. To this end, this is the first work that proposes an Adaptive and Safe-Certified DRL (AdapSafe) algorithm for frequency control to simultaneously address the aforementioned challenges. In particular, a novel self-tuning control barrier function is designed to actively compensate the unsafe frequency control strategies under variational safety constraints and thus achieve guaranteed safety. Furthermore, the concept of meta-reinforcement learning is integrated to significantly enhance its adaptiveness in non-stationary power system environments without sacrificing the safety cost. Experiments are conducted based on GB 2030 power system, and the results demonstrate that the proposed AdapSafe exhibits superior performance in terms of its guaranteed safety in both training and test phases, as well as its considerable adaptability against the dynamics changes of system parameters.
Chen Y, Sun M, Chu Z, et al., 2023, Vulnerability and Impact of Machine Learning-Based Inertia Forecasting Under Cost-Oriented Data Integrity Attack, IEEE TRANSACTIONS ON SMART GRID, Vol: 14, Pages: 2275-2287, ISSN: 1949-3053
Yan M, Teng F, Gan W, et al., 2023, Blockchain for secure decentralized energy management of multi-energy system using state machine replication, Applied Energy, Vol: 337, Pages: 1-11, ISSN: 0306-2619
Decentralized energy management can preserve the privacy of individual energy systems while mitigating computational and communication burdens. However, most decentralized energy management methods are partially decentralized and cannot ensure information exchange security. Therefore, this paper provides a secure fully decentralized energy management by using blockchain. First, a fully decentralized energy management framework using the optimality condition decomposition (OCD) is provided, in which individual energy system operators only exchange the boundary information with their peers rather than submitting proprietary information to a centralized system operator. Then, an asynchronous mechanism is proposed for updating the information exchange in OCD, enabling the proposed decentralized management to work under potential communication latency or interruption. Furthermore, the blockchain-based framework with state machine replication (SMR) based consensus algorithm is provided to safeguard the information exchange among individual energy systems in a secure and tamper-proof manner. The proposed decentralized energy management is tested on a multi-energy system with seven subsystems and a real-world multi-energy system in North China. The numerical results demonstrate the effectiveness of the proposed method in privacy protection and data security enhancement. The proposed method can prevent the cost increase caused by cheating activities, which in some subsystems can reach 17.6%. Additionally, the proposed fully decentralized method outperforms the partially decentralized method by 37.7% in reducing computation time. Also demonstrated are the computational precision, scalability and adaptability of the proposed method.1
Caputo C, Cardin M-A, Ge P, et al., 2023, Design and planning of flexible mobile Micro-Grids using Deep Reinforcement Learning, Applied Energy, Vol: 335, ISSN: 0306-2619
Ongoing risks from climate change have significantly impacted the livelihood of global nomadic communities and are likely to lead to increased migratory movements in coming years. As a result, mobility considerations are becoming increasingly important in energy systems planning, particularly to achieve energy access in developing countries. Advanced “Plug and Play” control strategies have been recently developed with such a decentralized framework in mind, allowing easier interconnection of nomadic communities, both to each other and to the main grid. Considering the above, the design and planning strategy of a mobile multi-energy supply system for a nomadic community is investigated in this work. Motivated by the scale and dimensionality of the associated uncertainties, impacting all major design and decision variables over the 30-year planning horizon, Deep Reinforcement Learning (DRL) Flexibility Analysis is implemented for the design and planning problem. DRL based solutions are benchmarked against several rigid baseline design options to compare expected performance under uncertainty. The results on a case study for ger communities in Mongolia suggest that mobile nomadic energy systems can be both technically and economically feasible, particularly when considering flexibility, although the degree of spatial dispersion among households is an important limiting factor. Additionally, the DRL based policies lead to the development of dynamic evolution and adaptability strategies, which can be used by the targeted communities under a very wide range of potential scenarios. Key economic, sustainability and resilience indicators such as Cost, Equivalent Emissions and Total Unmet Load are measured, suggesting potential improvements compared to available baselines of up to 25%, 67% and 76%, respectively. Finally, the decomposition of values of flexibility and plug and play operation is presented using a variation of real options theory, with important impl
Deng R, Ten CW, Li C, et al., 2023, Guest Editorial: Introduction to Special Issue on 'Cloud-Edge-End Orchestrated Computing for Smart Grid', IEEE Transactions on Cloud Computing, Vol: 11, Pages: 1107-1110
Chu Z, Teng F, 2023, Voltage Stability Constrained Unit Commitment in Power Systems With High Penetration of Inverter-Based Generators, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 38, Pages: 1572-1582, ISSN: 0885-8950
Wang D, Zheng K, Teng F, et al., 2023, Quantum Annealing With Integer Slack Variables for Grid Partitioning, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 38, Pages: 1747-1750, ISSN: 0885-8950
Zhang H, Li R, Chen Y, et al., 2023, Risk-Aware Objective-Based Forecasting in Inertia Management, IEEE Transactions on Power Systems, ISSN: 0885-8950
The objective-based forecasting considers the asymmetric and non-linear impacts of forecasting errors on decision objectives, thus improving the effectiveness of its downstream decision-making process. However, existing objective-based forecasting methods are risk-neutral and not suitable for tasks like power system inertia management and unit commitment, of which decision-makers are usually biased toward risk aversion in practice. To tackle this problem, this paper proposes a generic risk-aware objective-based forecasting method. It enables decision-makers to customize their forecasting with different risk preferences. The equivalence between the proposed method and optimization under uncertainty (stochastic/robust optimization) is established for the first time. Case studies are carried out on a Great Britain 2030 power system with system operational data from National Grid. The results show that the proposed model with deterministic optimization can approximate the performance of stochastic programming or robust optimization at only a fraction of their computational cost.
Luo J, Teng F, Bu S, et al., 2023, Converter-driven stability constrained unit commitment considering dynamic interactions of wind generation, International Journal of Electrical Power and Energy Systems, Vol: 144, ISSN: 0142-0615
With increasing penetration of renewable energy in power systems, conventional unit commitment (UC) focusing on static constraints may fail to meet dynamic constraints, such as converter-driven stability. This paper proposes a practical paradigm to perform UC considering the requirement of converter-driven stability in wind generation penetrated power systems. First, wind generation is regarded as a constant source, and a regular UC problem is solved without considering the dynamic impact of wind generation. Second, converter-driven stability analysis is employed to evaluate the stability margin of the UC solution. Different wind penetration levels and dynamic interaction conditions are investigated to account for the dynamic impact of wind generation. In several time horizons, especially heavy loading periods, the stability margin may be very limited or negative, and a modification to the UC solution should be carried out. Thus, a power sensitivity-based power dispatch method is elaborated to enhance the stability margin as well as update the UC solution. It is substantiated that in strong interaction cases, the system has more unstable operating regions (e.g., 1.33 times of those in weak interaction cases) and greatly affects the feasibility of the UC solution, but can be effectively tackled with the proposed power dispatch method.
Hou J, Teng F, Yin W, et al., 2023, Preventive-Corrective Cyber-Defense: Attack-Induced Region Minimization and Cybersecurity Margin Maximization, IEEE Transactions on Power Systems, ISSN: 0885-8950
False data injection (FDI) cyber-attacks on power systems can be prevented by strategically selecting and protecting a sufficiently large measurement subset, which, however, requires adequate cyber-defense resources for measurement protection. With any given cyber-defense resource, this paper proposes a preventive-corrective cyber-defense strategy, which minimizes the FDI attack-induced region in a preventive manner, followed by maximizing the cybersecurity margin in a corrective manner. First, this paper proposes a preventive cyber-defense strategy that minimizes the volume of the FDI attack-induced region via preventive allocation of any given measurement protection resource. Particularly, a sufficient condition for constructing the FDI unattackable lines is proposed, indicating that the FDI cyber-attack could be locally rather than globally prevented. Then, given a non-empty FDI attack-induced region, this paper proposes a corrective cyber-defense strategy that maximizes the cybersecurity margin, leading to a trade-off between the safest-but-expensive operation point (i.e., Euclidean Chebyshev center) and the cheapest-but-dangerous operation point. Simulation results on a modified IEEE 14 bus system verify the effectiveness and cost-effectiveness of the proposed preventive-corrective cyber-defense strategy.
The forecast of electrical loads is essential for the planning and operation of the power system. Recently, advances in deep learning have enabled more accurate forecasts. However, deep neural networks are prone to adversarial attacks. Although most of the literature focusses on integrity-based attacks, this paper proposes availability-based adversarial attacks, which can be more easily implemented by attackers. For each forecast instance, the availability attack target, i.e., a subset of input features, is optimally solved by a mixed-integer reformulation of the artificial neural network. To tackle this attack, an adversarial training algorithm is proposed. In simulation, a realistic load forecasting dataset is considered and the attack performance is comparable to the integrity-based counterpart. Meanwhile, the adversarial training algorithm is shown to significantly improve robustness against availability attacks. All codes are available at https://github.com/xuwkk/AAA-Load-Forecast.
Wang Y, Qiu D, Teng F, et al., 2023, Towards Microgrid Resilience Enhancement Via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach, IEEE Transactions on Power Systems, ISSN: 0885-8950
Mobile power sources (MPSs) have been gradually deployed in microgrids as critical resources to coordinate with repair crews (RCs) towards resilience enhancement owing to their flexibility and mobility in handling the complex coupled power-transport systems. However, previous work solves the coordinated dispatch problem of MPSs and RCs in a centralized manner with the assumption that the communication network is still fully functioning after the event. However, there is growing evidence that certain extreme events will damage or degrade communication infrastructure, which makes centralized decision making impractical. To fill this gap, this paper formulates the resilience-driven dispatch problem of MPSs and RCs in a decentralized framework. To solve this problem, a hierarchical multi-agent reinforcement learning method featuring a two-level framework is proposed, where the high-level action is used to switch decision-making between power and transport networks, and the low-level action constructed via a hybrid policy is used to compute continuous scheduling and discrete routing decisions in power and transport networks, respectively. The proposed method also uses an embedded function encapsulating system dynamics to enhance learning stability and scalability. Case studies based on IEEE 33-bus and 69-bus power networks are conducted to validate the effectiveness of the proposed method in load restoration.
Graham J, Teng F, 2023, Vehicle-to-grid plug-in forecasting for participation in ancillary services markets
Electric vehicle (EV) charge points (CPs) can be used by aggregators to provide frequency response (FR) ser-vices. Aggregators must have day-ahead half-hourly forecasts of minimum aggregate vehicle-to-grid (V2G) plug-in to produce meaningful bids for the day-ahead ancillary services market. However, there is a lack of understanding on what features should be considered and how complex the forecasting model should be. This paper explores the dependency of aggregate V2G plug-in on historic plug-in levels, calendar variables, and weather conditions. These investigations are used to develop three day-ahead forecasts of minimum aggregate V2G plug-in during 30-minute window. A neural network that considers previous V2G plug-in values the day before, three days before, and seven days before, in addition to day of the week, month, and hour, is found to be the most accurate.
Yan M, Wang L, Teng F, et al., 2023, Review and Prospect of Transactive Energy Market for Distributed Energy Resources, Dianli Xitong Zidonghua/Automation of Electric Power Systems, Vol: 47, Pages: 33-48, ISSN: 1000-1026
The transactive energy market can encourage prosumers to trade energy, and guide them to balance the random fluctuations of renewable energy output by adjusting the real-time electricity price, which can improve the accommodation level of renewable energy and achieve the goal of carbon emission peak and carbon neutrality in China. Firstly, the concept, characteristics and framework of the transactive energy market for distributed energy are introduced. Then, the trading mechanisms of the existing transactive energy markets and its advantages and disadvantages are comprehensively analyzed, and the platforms and pilot projects of the existing transactive energy markets are introduced. Finally, from the point of view of actual market operation, the present challenges and future development direction of the transactive energy markets are described.
Toubeau J-F, Teng F, Morstyn T, et al., 2023, Privacy-Preserving Probabilistic Voltage Forecasting in Local Energy Communities, IEEE TRANSACTIONS ON SMART GRID, Vol: 14, Pages: 798-809, ISSN: 1949-3053
Xu W, Jaimoukha IM, Teng F, 2023, Robust Moving Target Defence Against False Data Injection Attacks in Power Grids, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, Vol: 18, Pages: 29-40, ISSN: 1556-6013
Castiglione L, Hau Z, Ge P, et al., 2022, HA-grid: security aware hazard analysis for smart grids, IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, Publisher: IEEE, Pages: 446-452
Attacks targeting smart grid infrastructures can result in the disruptions of power supply as well as damages to costly equipment, with significant impact on safety as well as on end-consumers. It is therefore of essence to identify attack paths in the infrastructure that lead to safety violations and todetermine critical components that must be protected. In this paper, we introduce a methodology (HA-Grid) that incorporates both safety and security modelling of smart grid infrastructure to analyse the impact of cyber threats on the safety of smart grid infrastructures. HA-Grid is applied on a smart grid test-bed to identify attack paths that lead to safety hazards, and todetermine the common nodes in these attack paths as critical components that must be protected.
Garcia-Munoz F, Teng F, Junyent-Ferre A, et al., 2022, Stochastic energy community trading model for day-ahead and intraday coordination when offering DER?s reactive power as ancillary services, SUSTAINABLE ENERGY GRIDS & NETWORKS, Vol: 32, ISSN: 2352-4677
Higgins M, Xu W, Teng F, et al., 2022, Cyber-physical risk assessment for false data injection attacks considering moving target defences Best practice application of respective cyber and physical reinforcement assets to defend against FDI attacks, International Journal of Information Security, Vol: 22, Pages: 579-589, ISSN: 1615-5262
In this paper, we examine the factors that influence the success of false data injection (FDI) attacks in the context of both cyber and physical styles of reinforcement. Existing research considers the FDI attack in the context of the ability to change a measurement in a static system only. However, successful attacks will require first intrusion into a system followed by construction of an attack vector that can bypass bad data detection to cause a consequence (such as overloading). Furthermore, the recent development of moving target defences (MTD) introduces dynamically changing system topology, which is beyond the capability of existing research to assess. In this way, we develop a full service framework for FDI risk assessment. The framework considers both the costs of system intrusion via a weighted graph assessment in combination with a physical, line overload-based vulnerability assessment under the existence of MTD. We present our simulations on a IEEE 14-bus system with an overlain RTU network to model the true risk of intrusion. The cyber model considers multiple methods of entry for the FDI attack including meter intrusion, RTU intrusion and combined style attacks. Post-intrusion, our physical reinforcement model analyses the required level of topology divergence to protect against a branch overload from an optimised attack vector. The combined cyber and physical index is used to represent the system vulnerability against FDIA.
Ge P, Caputo C, Teng F, et al., 2022, A Wireless-Assisted Hierarchical Framework to Accommodate Mobile Energy Resources, Singapore, IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
Heylen E, Browell J, Teng F, 2022, Probabilistic Day-Ahead Inertia Forecasting, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 37, Pages: 3738-3746, ISSN: 0885-8950
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