Imperial College London

DrFeiTeng

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6178f.teng Website CV

 
 
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Location

 

1116Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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104 results found

García-Muñoz F, Teng F, Junyent-Ferré A, Díaz-González F, Corchero Cet 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 and Networks, Vol: 32

A two-stage stochastic programming energy trading model is presented in this article to measure the distributed energy resources’ capability to provide reactive power as ancillary services from an energy community to the distribution system operator. The formulation proposed models the day-ahead and the intraday markets as first and second-stage decisions, respectively, using the second-order cone relaxation of the optimal power flow to represent the network limitations. In addition, the model considers that the energy community trades energy operating under a collaborative scheme and minimizing the global cost. Likewise, the formulation (i) includes community batteries to study the effect on the total cost, (ii) identifies the extra energy charged and discharged by the agents’ batteries to face the uncertainty related to the intraday market, as an agents’ flexibility service for the community, and (iii) prevents the simultaneous buying and selling of energy in the local energy market by an agent. The model has been programmed in Python-Pyomo and tested in three radial distribution systems under three different scenarios showing that the DERs can (i) self-satisfy the reactive power demand, (ii) provide reactive power to the DSO, and (iii) decrease the community cost significantly in an eventual ancillary services market.

Journal article

Ge P, Caputo C, Teng F, Cardin M-A, Korre Aet 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)

Conference paper

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

Journal article

Castiglione L, Hau Z, Ge P, Co K, Munoz Gonzalez L, Teng F, Lupu Eet 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

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.

Conference paper

O'Malley C, Badesa L, Teng F, Strbac Get al., 2022, Frequency response from aggregated V2G chargers with uncertain EV connections, IEEE Transactions on Power Systems, Pages: 1-14, 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.

Journal article

Caputo C, Cardin M-A, Korre A, Del Rio Chanona A, Ge P, Teng Fet al., 2022, Energy System Evolution Strategies for Mobile Micro-grids using Deep Reinforcement Learning Flexibility Analysis, Espoo, Finland, 32nd European Conference on Operational Research (EURO 2022)

Conference paper

Teng F, Chhachhi SAURAB, Ge PUDONG, Graham J, Gunduz Det al., 2022, Balancing privacy and access to smart meter data: an Energy Futures Lab briefing paper

Digitalising the energy system is expected to be a vital component of achieving the UK’s climate change targets. Smart meter data, in particular, is seen a key enabler of the transition to more dynamic, cost-effective, cost-reflective, and decarbonised electricity. However, access to this data faces a challenge due to consumer privacy concerns. This Briefing Paper investigates four key elements of smart meter data privacy: existing data protection regulations; the personal information embedded within smart meter data; consumer privacy concerns; and privacy-preserving techniques that could be incorporated alongside existing mechanisms to minimise or eliminate potential privacy infringements.

Report

Hou X, Sun K, Zhang N, Teng F, Zhang X, Green TCet al., 2022, Priority-driven self-optimizing power control scheme for interlinking converters of hybrid AC/DC microgrid clusters in decentralized manner, IEEE Transactions on Power Electronics, Vol: 37, Pages: 5970-5983, ISSN: 0885-8993

Hybrid AC/DC microgrid clusters are key building blocks of smart grid to support sustainable and resilient urban power systems. In microgrid clusters, the subgrid load-priorities and power quality requirements for different areas vary significantly. To realize optimal power exchanges among microgrid clusters, this paper proposes a decentralized self-optimizing power control scheme for interlinking converters (ILC) of hybrid microgrid clusters. A priority-driven optimal power exchange model of ILCs is built considering the priorities and capacities in subgrids. The optimization objective is to minimize the total DC-voltage/AC-frequency state deviations of subgrids. To realize the decentralized power flow control, an optimal-oriented quasi-droop control strategy of ILCs is introduced to not only achieve a flexible self-optimizing power flow management, but also provide an ancillary function of voltage support. Consequently, as each of ILCs only monitors the local AC-side frequency and DC-side voltage signals, the whole optimal power control of the wide-area microgrid clusters is achieved in a decentralized manner without any communication link. Thus, the proposed control algorithm has the features of decreased cost, increased scalability, reduced geographic restrictions and high resilience in terms of communication faults. Finally, the proposed method is validated by case studies with four interconnected microgrids through hardware-in-loop tests.

Journal article

Bellizio F, Xu W, Qiu D, Ye Y, Papadaskalopoulos D, Cremer JL, Teng F, Strbac Get al., 2022, Transition to Digitalized Paradigms for Security Control and Decentralized Electricity Market, PROCEEDINGS OF THE IEEE, ISSN: 0018-9219

Journal article

Hua W, Jiang J, Sun H, Teng F, Strbac Get al., 2022, Consumer-centric decarbonization framework using Stackelberg game and Blockchain, APPLIED ENERGY, Vol: 309, ISSN: 0306-2619

Journal article

Ge P, Teng F, Konstantinou C, Hu Set al., 2022, A resilience-oriented centralised-to-decentralised framework for networked microgrids management, Applied Energy, Vol: 308, ISSN: 0306-2619

This paper proposes a cyber–physical cooperative mitigation framework to enhance power systems resilience against power outages caused by extreme events, e.g., earthquakes and hurricanes. Extreme events can simultaneously damage the physical-layer electric power infrastructure and the cyber-layer communication facilities. Microgrid (MG) has been widely recognised as an effective physical-layer response to such events, however, the mitigation strategy in the cyber lay is yet to be fully investigated. Therefore, this paper proposes a resilience-oriented centralised-to-decentralised framework to maintain the power supply of critical loads such as hospitals, data centres, etc., under extreme events. For the resilient control, controller-to-controller (C2C) wireless network is utilised to form the emergency regional communication when centralised base station being compromised. Owing to the limited reliable bandwidth that reserved as a backup, the inevitable delays are dynamically minimised and used to guide the design of a discrete-time distributed control algorithm to maintain post-event power supply. The effectiveness of the cooperative cyber–physical mitigation framework is demonstrated through extensive simulations in MATLAB/Simulink.

Journal article

Chu Z, Teng F, 2022, Voltage Stability Constrained Unit Commitment in Power Systems with High Inverter-Based Generator Penetration, IEEE Transactions on Power Systems, ISSN: 0885-8950

With the increasing penetration of renewable energy sources, power system operation has to be adapted to ensure the system stability and security while considering the distinguished feature of the Inverter-Based Generator (IBG) interfaced generators. The static voltage stability which is mainly compromised by heavy loading conditions in conventional power systems, faces new challenges due to the large scale integration of IBG units. This paper investigates the static voltage stability problem in high IBG-penetrated system. The analytic criterion that ensures the voltage stability at the IBG buses are derived with the interaction of different IBGs being considered. Based on this, an optimal system scheduling model is proposed to minimize the overall system operation cost while maintaining the voltage stability during normal operation through dynamically optimizing the active and reactive power output from IBGs. The highly nonlinear voltage stability constraints are effectively converted into Second-Order Cone (SOC) form, leading to an overall Mixed-Integer SOC Programming (MISOCP), together with the SOC reformulation of AC power flow and frequency constraints. The effectiveness of the proposed model and the impact of various factors on voltage stability are demonstrated in thorough case studies.

Journal article

Toubeau JF, Teng F, Morstyn T, Krannichfeldt LV, Wang Yet al., 2022, Privacy-Preserving Probabilistic Voltage Forecasting in Local Energy Communities, IEEE Transactions on Smart Grid, ISSN: 1949-3053

This paper presents a new privacy-preserving framework for the short-term (multi-horizon) probabilistic forecasting of nodal voltages in local energy communities. This task is indeed becoming increasingly important for cost-effectively managing network constraints in the context of the massive integration of distributed energy resources. However, traditional forecasting tasks are carried out centrally, by gathering raw data of end-users in a single database that exposes their private information. To avoid such privacy issues, this work relies on a distributed learning scheme, known as federated learning wherein individuals’ data are kept decentralized. The learning procedure is then augmented with differential privacy, which offers formal guarantees that the trained model cannot be reversed-engineered to infer sensitive local information. Moreover, the problem is framed using cross-series learning, which allows to smoothly integrate any new client joining the community (i.e., cold-start forecasting) without being plagued by data scarcity. Outcomes show that the proposed approach achieves improved performance compared to non-collaborative (locally trained) models, and is able to reach a trade-off between privacy and performance for different architectures of deep learning networks.

Journal article

Graham J, Heylen E, Bian Y, Teng Fet al., 2022, Benchmarking Explanatory Models for Inertia Forecasting using Public Data of the Nordic Area, 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Publisher: IEEE, ISSN: 2642-6730

Conference paper

Xu W, Jaimoukh IM, Teng F, 2022, Robust Moving Target Defence Against False Data Injection Attacks in Power Grids, IEEE Transactions on Information Forensics and Security, ISSN: 1556-6013

Recently, moving target defence (MTD) has been proposed to thwart false data injection (FDI) attacks in power system state estimation by proactively triggering the distributed flexible AC transmission system (D-FACTS) devices. One of the key challenges for MTD in power grid is to design its real-time implementation with performance guarantees against unknown attacks. Converting from the noiseless assumptions in the literature, this paper investigates the MTD design problem in a noisy environment and proposes, for the first time, the concept of robust MTD to guarantee the worst-case detection rate against all unknown attacks. We theoretically prove that, for any given MTD strategy, the minimal principal angle between the Jacobian subspaces corresponds to the worst-case performance against all potential attacks. Based on this finding, robust MTD algorithms are formulated for the systems with both complete and incomplete configurations. Extensive simulations using standard IEEE benchmark systems demonstrate the improved average and worst-case performances of the proposed robust MTD against state-of-the-art algorithms. All codes are available at https://github.com/xuwkk/Robust_MTD.

Journal article

Chen Y, Sun M, Chu Z, Camal S, Kariniotakis G, Teng Fet al., 2022, Vulnerability and Impact of Machine Learning-based Inertia Forecasting Under Cost-Oriented Data Integrity Attack, IEEE Transactions on Smart Grid, ISSN: 1949-3053

With the increasing penetration of renewables, the power system is facing unprecedented challenges of low-inertia levels. The inherent ability of the system to defense disturbance and power imbalance through inertia response is degraded, and thus, system operators need to make faster and more efficient scheduling operations. As one of the most promising solutions, machine learning (ML) methods have been investigated and employed to realize effective inertia forecasting with considerable accuracy. Nevertheless, it is yet to understand its vulnerability with the growing threat of cyberattacks. To this end, this paper proposes a methodological framework to explore the vulnerability of ML-based inertia forecasting models, with a special focus on data integrity attacks. In particular, a cost-oriented false data injection attack is proposed, for the first time, with the primary objective to significantly increase the system operation cost while retaining the stealthiness of the attack via minimizing the differences between the pre-perturbed and after-perturbed inertia forecasts. Moreover, we propose four vulnerability assessment metrics for the ML-based inertia forecasting models. Case studies on the GB power system demonstrate the vulnerability and impact of the ML-based inertia forecasting models, as well as the stealthiness and transferability of the proposed cost-oriented data integrity attacks.

Journal article

O'Malley C, Badesa L, Teng F, Strbac Get al., 2021, Probabilistic scheduling of UFLS to secure credible contingencies in low inertia systems, IEEE Transactions on Power Systems, ISSN: 0885-8950

The reduced inertia levels in low-carbon power grids necessitate explicit constraints to limit frequency's nadir and rate of change during scheduling. This can result in significant curtailment of renewable energy due to the minimum generation of thermal plants that are needed to provide frequency response (FR) and inertia. Additional consideration of fast FR, a dynamically reduced largest loss and under frequency load shedding (UFLS) allows frequency security to be achieved more cost effectively. This paper derives a novel constraint from the swing equation to contain the frequency nadir using all of these services. The expected cost of UFLS is found probabilistically to facilitate its comparison to the other frequency services. We demonstrate that this constraint can be accurately and conservatively approximated for moderate UFLS levels with a second order cone (SOC), resulting in highly tractable convex problems. Case studies performed on a Great Britain 2030 system demonstrate that UFLS as an option to contain single plant outages can reduce annual operational costs by up to 559m, 52% of frequency security costs. The sensitivity of this value to wind penetration, abundance of alternative frequency services, UFLS amount and cost is explored.

Journal article

Badesa L, Teng F, Strbac G, 2021, Conditions for regional frequency stability in power system scheduling—Part I: theory, IEEE Transactions on Power Systems, Vol: 36, Pages: 5558-5566, ISSN: 0885-8950

This paper considers the phenomenon of distinct regional frequencies recently observed in some power systems. First, a reduced-order mathematical model describing this behaviour is developed. Then, techniques to solve the model are discussed, demonstrating that the post-fault frequency evolution in any given region is equal to the frequency evolution of the Centre Of Inertia plus certain inter-area oscillations. This finding leads to the deduction of conditions for guaranteeing frequency stability in all regions of a power system, a deduction performed using a mixed analytical-numerical approach that combines mathematical analysis with regression methods on simulation samples. The proposed stability conditions are linear inequalities that can be implemented in any optimisation routine allowing the co-optimisation of all existing ancillary services for frequency support: inertia, multi-speed frequency response, load damping and an optimised largest power infeed. This is the first reported mathematical framework with explicit conditions to maintain frequency stability in a power system exhibiting inter-area oscillations in frequency.

Journal article

Badesa L, Teng F, Strbac G, 2021, Conditions for regional frequency stability in power system scheduling—Part II: application to unit commitment, IEEE Transactions on Power Systems, Vol: 36, Pages: 5567-5577, ISSN: 0885-8950

In Part I of this paper we have introduced the closed-form conditions for guaranteeing regional frequency stability in a power system. Here we propose a methodology to represent these conditions in the form of linear constraints and demonstrate their applicability by implementing them in a generation-scheduling model. This model simultaneously optimises energy production and ancillary services for maintaining frequency stability in the event of a generation outage, by solving a frequency-secured Stochastic Unit Commitment (SUC). We consider the Great Britain system, characterised by two regions that create a non-uniform distribution of inertia: England in the South, where most of the load is located, and Scotland in the North, containing significant wind resources. Through several case studies, it is shown that inertia and frequency response cannot be considered as system-wide magnitudes in power systems that exhibit inter-area oscillations in frequency, as their location in a particular region is key to guarantee stability. In addition, securing against a medium-sized loss in the low-inertia region proves to cause significant wind curtailment, which could be alleviated through reinforced transmission corridors. In this context, the proposed constraints allow to find the optimal volume of ancillary services to be procured in each region.

Journal article

Chu Z, Zhang N, Teng F, 2021, Frequency-Constrained Resilient Scheduling of Microgrid: A Distributionally Robust Approach, IEEE TRANSACTIONS ON SMART GRID, Vol: 12, Pages: 4914-4925, ISSN: 1949-3053

Journal article

Xiang Y, Wang Y, Xia S, Teng Fet al., 2021, Charging load pattern extraction for residential electric vehicles: a training-free nonintrusive method, IEEE Transactions on Industrial Informatics, Vol: 17, Pages: 7028-7039, ISSN: 1551-3203

Extracting the charging load pattern of residential electric vehicle (REV) will help grid operators make informed decisions in terms of scheduling and demand-side response management. Due to the multistate and high-frequency characteristics of integrated residential appliances from the residential perspective, it is difficult to achieve accurate extraction of the charging load pattern. To deal with that, this article presents a novel charging load extraction method based on residential smart meter data to noninvasively extract REV charging load pattern. The proposed algorithm harnesses the low-frequency characteristics of the charging load pattern and applies a two-stage decomposition technique to extract the characteristics of the charging load. The two-stage decomposition technique mainly includes: the trend component of the charging load being decomposed by seasonal and trend decomposition using loess method, and the low-frequency approximate component being decomposed by discrete wavelet technology. Furthermore, based on the extracted characteristics, event monitoring, and dynamic time warping is applied to estimate the closest charging interval and amplitude. The key features of the proposed algorithm include 1) significant improvement in extraction accuracy; 2) strong noise immunity; 3) online implementation of extraction. Experiments based on ground truth data validate the superiority of the proposed method compared to the existing ones.

Journal article

Zhao P, Gu C, Cao Z, Shen Y, Teng F, Chen X, Wu C, Huo D, Xu X, Li Set al., 2021, Data-Driven Multi-Energy Investment and Management Under Earthquakes, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, Vol: 17, Pages: 6939-6950, ISSN: 1551-3203

Journal article

Li H, Qiao Y, Lu Z, Zhang B, Teng Fet al., 2021, Frequency-Constrained Stochastic Planning Towards a High Renewable Target Considering Frequency Response Support From Wind Power, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 36, Pages: 4632-4644, ISSN: 0885-8950

Journal article

Yong P, Zhang N, Hou Q, Liu Y, Teng F, Ci S, Kang Cet al., 2021, Evaluating the Dispatchable Capacity of Base Station Backup Batteries in Distribution Networks, IEEE TRANSACTIONS ON SMART GRID, Vol: 12, Pages: 3966-3979, ISSN: 1949-3053

Journal article

Zhao J, Wu Q, Hatziargyriou ND, Li F, Teng Fet al., 2021, Decentralized Data-Driven Load Restoration in Coupled Transmission and Distribution System With Wind Power, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 36, Pages: 4435-4444, ISSN: 0885-8950

Journal article

Heylen E, Teng F, Strbac G, 2021, Challenges and opportunities of inertia estimation and forecasting in low-inertia power systems, Renewable and Sustainable Energy Reviews, Vol: 147, Pages: 1-12, ISSN: 1364-0321

Accurate inertia estimates and forecasts are crucial to support the system operation in future low-inertia power systems. A large literature on inertia estimation methods is available. This paper aims to provide an overview and classification of inertia estimation methods. The classification considers the time horizon the methods are applicable to, i.e., offline post mortem, online real time and forecasting methods, and the scope of the inertia estimation, e.g., system-wide, regional, generation, demand, individual resource. The framework presented in this paper facilitates objective comparisons of the performance of newly developed or improved inertia estimation methods with the state-of-the-art methods in their respective time horizon and with their respective scope. Moreover, shortcomings of the existing inertia estimation methods have been identified and suggestions for future work have been made.

Journal article

Pan G, Gu W, Hu Q, Wang J, Teng F, Strbac Get al., 2021, Cost and low-carbon competitiveness of electrolytic hydrogen in China, ENERGY & ENVIRONMENTAL SCIENCE, Vol: 14, Pages: 4868-4881, ISSN: 1754-5692

Journal article

Chu Z, Teng F, 2021, Short circuit current constrained UC in the high IBG-penetrated power systems, IEEE Transactions on Power Systems, Vol: 36, Pages: 3776-3785, ISSN: 0885-8950

Inverter Based Generators (IBGs) have been increasing significantly in power systems. Due to the demanding thermal rating of Power Electronics (PE), their contribution to the system Short Circuit Current (SCC) is much less than that from the conventional Synchronous Generators (SGs) thus reducing the system strength and posing challenges to system protection and stability. This paper proposes a Unit Commitment (UC) model with SCC constraint in high IBG-penetrated systems to ensure minimum operation cost while maintaining the SCC level at each bus in the system. The SCC from synchronous generators as well as the IBGs are explicitly modeled in the formulation leading to an SCC constraint involving decision-dependent matrix inverse. This highly nonlinear constraint is further reformulated into linear form conservatively. The influence of the SCC constraint on the system operation and its interaction with the frequency regulation are demonstrated through simulations on IEEE 30- and 118-bus systems.

Journal article

Zhao P, Gu C, Cao Z, Xie D, Teng F, Li J, Chen X, Wu C, Yu D, Xu X, Li Set al., 2021, A Cyber-Secured Operation for Water-Energy Nexus, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 36, Pages: 3105-3117, ISSN: 0885-8950

Journal article

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