Imperial College London

Professor Goran Strbac

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Chair in Electrical Energy Systems
 
 
 
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Contact

 

+44 (0)20 7594 6169g.strbac

 
 
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Assistant

 

Miss Guler Eroglu +44 (0)20 7594 6170

 
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Location

 

1101Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

539 results found

Huang C, Strbac G, Zong Y, You S, Træholt C, Brandon N, Wang J, Ameli Het al., 2024, Modeling and optimal operation of reversible solid oxide cells considering heat recovery and mode switching dynamics in microgrids, Applied Energy, Vol: 357, ISSN: 0306-2619

The reversible solid oxide cell (rSOC) is a promising technology for advancing energy decarbonization by enabling bidirectional conversion between electricity and hydrogen in a single device. However, previous studies have not fully explored the operational flexibility of rSOCs due to inadequate consideration of heat recovery potentials and dynamics of operating mode transitions. To address this research gap, this paper presents a model-based optimal operation method for managing multi-energy transactions in rSOC-based microgrids, aiming to minimize operation costs. The method incorporates detailed operational models of the rSOC, including a lumped thermal model to account for heat recovery capability and modeling of various operating modes and their transitions. Additionally, a linearization process is introduced to address nonlinear and implicit operational constraints, resulting in a computationally efficient mixed-integer linear programming (MILP) formulation for the operation model. Comparative case studies are conducted using modified energy portfolios of a Danish energy island. The results demonstrate that the proposed method effectively captures operating mode transitions within the rSOC and enhances its profitability via waste heat recovery. Notably, the rSOC model contributes to enhanced operational flexibility through heat recovery behaviors and a wider temperature range, resulting in substantial economic savings for the microgrid.

Journal article

Cui H, Ye Y, Hu J, Tang Y, Lin Z, Strbac Get al., 2024, Online Preventive Control for Transmission Overload Relief Using Safe Reinforcement Learning With Enhanced Spatial-Temporal Awareness, IEEE Transactions on Power Systems, Vol: 39, Pages: 517-532, ISSN: 0885-8950

The risk of transmission overload (TO) in power grids is increasing with the large-scale integration of intermittent renewable energy sources. An effective online preventive control schemes proves to be vital in safeguarding the security of power systems. In this paper, we formulate the online preventive control problem for TO alleviation as a constrained Markov decision process (CMDP), targeted to reduce the load rate of overloaded lines by implementing generation re-dispatch, transmission and busbar switching actions. The CMDP is solved with a state-of-the-art safe deep reinforcement learning method, based on the computationally efficient interior-point policy optimization (IPO), which facilitates desirable learning behavior towards constraint satisfaction and policy improvement simultaneously. The performance of IPO method is further improved with an enhanced perception of spatial-temporal correlations in power gird nodal and edge features, combining the strength of edge conditioned convolutional network and long short-term memory network, fostering more effective and robust preventive control policies to be devised. Case studies on a real-world system and a large-scale system validate the superior performance of the proposed method in TO alleviation, constraint handling, uncertainty adaptability and stability preservation, as well as its favorable computational performance, through benchmarking against both model-based and reinforcement learning-based baseline methods.

Journal article

Sun Y, Li Y, Borozan S, Wang G, Qiu J, Strbac Get al., 2024, Battery Swapping Dispatch for Self-Sustained Highway Energy System Based on Spatiotemporal Deep-Learning Traffic Flow Prediction, IEEE Transactions on Industry Applications, Vol: 60, Pages: 1058-1070, ISSN: 0093-9994

Due to the complexity of traffic flow and the stochastic swapping behavior of electric vehicles (EVs), efficient battery dispatch is challenging. Therefore, the battery swapping dispatch framework based on traffic flow prediction is proposed to overcome this inconvenience. The framework is solved by minimizing the total transportation cost and satisfying the EV battery swapping requirement. Naturally, precise traffic flow prediction plays a vital role in efficient battery dispatch. Therefore, this article designs a deep learning prediction framework by leveraging the graph convolutional network (GCN) and the temporal convolutional network (TCN), named Spatiotemporal traffic flow network (STFNet). GCN is applied to learn the topology characteristic of the daily spatiotemporal traffic, which enables STFNet to capture the spatial feature. TCN is developed to acquire the daily traffic flow temporal dependence. Additionally, a pre-partition method based on K-means clustering is applied to improve the effectiveness of the battery dispatch framework. The experimental results indicate that the proposed battery dispatch framework is skillful. Due to the precise prediction of STFNet, the battery swapping dispatch based on STFNet prediction is the most economical, achieving a minimum of 25.82% reduction in the total transportation cost compared to benchmark models. Furthermore, the impact of the pre-partition method has been proven in the case of studies with a huge routing distance declining, dramatically reducing the total transportation cost and making the dispatch more reasonable.

Journal article

Wang Y, Qiu D, Teng F, Strbac Get al., 2024, Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach, IEEE Transactions on Power Systems, Vol: 39, Pages: 1329-1345, 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.

Journal article

Wang Y, Qiu D, Wang Y, Sun M, Strbac Get al., 2024, Graph Learning-Based Voltage Regulation in Distribution Networks With Multi-Microgrids, IEEE Transactions on Power Systems, Vol: 39, Pages: 1881-1895, ISSN: 0885-8950

Microgrids (MGs), as localized small power systems, can effectively provide voltage regulation services for distribution networks by integrating and managing various distributed energy resources. Existing literature employs model-based optimization approaches to formulate the voltage regulation problem of multi-MGs, which require complete system models. However, this assumption is normally impractical due to time-varying environment and privacy issues. To fill this research gap, this paper suggests a data-driven decentralized framework for the cost-effective voltage regulation of a distribution network with multi-MGs. A novel multi-agent reinforcement learning method featuring an augmented graph convolutional network and a proximal policy optimization algorithm is proposed to solve this problem. Furthermore, the techniques of critical bus and electrical distance enhance the capability of feature extractions from the distribution network, allowing for the decentralized training with privacy preserving. Simulation results based on modified IEEE 33-bus, 69-bus, and 123-bus networks are developed to validate the effectiveness of the proposed method in enabling multi-MGs to provide distribution network voltage regulation.

Journal article

Sharifpour M, Ameli MT, Ameli H, Strbac Get al., 2023, A resilience-oriented approach for microgrid energy management with hydrogen integration during extreme events, Energies, Vol: 16, ISSN: 1996-1073

This paper presents a resilience-oriented energy management approach (R-OEMA) designed to bolster the resilience of networked microgrids (NMGs) in the face of extreme events. The R-OEMA method strategically incorporates preventive scheduling techniques for hydrogen (H2) systems, renewable units, controllable distributed generators (DGs), and demand response programs (DRPs). It seeks to optimize the delicate balance between maximizing operating revenues and minimizing costs, catering to both normal and critical operational modes. The evaluation of the R-OEMA framework is conducted through numerical simulations on a test system comprising three microgrids (MGs). The simulations consider various disaster scenarios entailing the diverse durations of power outages. The results underscore the efficacy of the R-OEMA approach in augmenting NMG resilience and refining operational efficiency during extreme events. Specifically, the approach integrates hydrogen systems, demand response, and controllable DGs, orchestrating their collaborative operation with predictive insights. This ensures their preparedness for emergency operations in the event of disruptions, enabling the supply of critical loads to reach 82% in extreme disaster scenarios and 100% in milder scenarios. The proposed model is formulated as a mixed-integer linear programming (MILP) framework, seamlessly integrating predictive insights and pre-scheduling strategies. This novel approach contributes to advancing NMG resilience, as revealed by the outcomes of these simulations.

Journal article

Bugaje A-AB, Cremer JL, Strbac G, 2023, Generating quality datasets for real-time security assessment: Balancing historically relevant and rare feasible operating conditions, INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, Vol: 154, ISSN: 0142-0615

Journal article

Zhang T, Sun M, Qiu D, Zhang X, Strbac G, Kang Cet al., 2023, A Bayesian Deep Reinforcement Learning-Based Resilient Control for Multi-Energy Micro-Gird, IEEE Transactions on Power Systems, Vol: 38, Pages: 5057-5072, ISSN: 0885-8950

Aiming at a cleaner future power system, many regimes in the world have proposed their ambitious decarbonizing plan, with increasing penetration of renewable energy sources (RES) playing an alternative role to conventional energy. As a result, power system tends to have less backup capacity and operate near their designed limit, thus exacerbating system vulnerability against extreme events. Under this reality, resilient control for the multi-energy micro-grid is facing the following challenges, which are: 1) the effect from the stochastic uncertainties of RES; 2) the need for a model-free and fast-reacting control scheme under extreme events; and 3) efficient exploration and robust performance with limited extreme events data. To deal with the aforementioned challenges, this paper proposes a novel Bayesian Deep Reinforcement Learning-based resilient control approach for multi-energy micro-grid. In particular, the proposed approach replaces the deterministic network in traditional Reinforcement Learning with a Bayesian probabilistic network in order to obtain an approximation of the value function distribution, which effectively solves the Q-value overestimation issue. Compared with the naive Deep Deterministic Policy Gradient (DDPG) method and optimization method, the effectiveness and importance of employing the Bayesian Reinforcement Learning approach are investigated and illustrated across different operating scenarios. Case studies have shown that by using the Monte Carlo posterior mean of the Bayesian value function distribution instead of a deterministic estimation, the proposed Bayesian Deep Deterministic Policy Gradient (BDDPG) method achieves a near-optimum policy in a more stable process, which verifies the robustness and the practicability of the proposed approach.

Journal article

O'Malley C, de Mars P, Badesa L, Strbac Get al., 2023, Reinforcement learning and mixed-integer programming for power plant scheduling in low carbon systems: Comparison and hybridisation, APPLIED ENERGY, Vol: 349, ISSN: 0306-2619

Journal article

Aunedi M, Olympios AV, Pantaleo AM, Markides CN, Strbac Get al., 2023, System-driven design and integration of low-carbon domestic heating technologies, Renewable and Sustainable Energy Reviews, Vol: 187, ISSN: 1364-0321

This research explores various combinations of electric heat pumps (EHPs), hydrogen boilers (HBs), electric boilers (EBs), hydrogen absorption heat pumps (AHPs) and thermal energy storage (TES) to assess their potential for delivering cost-efficient low-carbon heat supply. The proposed technology-to-systems approach is based on comprehensive thermodynamic and component-costing models of various heating technologies, which are integrated into a whole-energy system optimisation model to determine cost-effective configurations of heating systems that minimise the overall cost for both the system and the end-user. Case studies presented in the study focus on two archetypal systems: (i) the North system, which is characterised by colder climate conditions and abundant wind resource; and (ii) the South system, which is characterised by a milder climate and higher solar energy potential. The results indicate a preference for a portfolio of low-carbon heating technologies including EHPs, EBs and HBs, coupled with a sizable amount of TES, while AHPs are not chosen, since, for the investigated conditions, their efficiency does not outweigh the high investment cost. Capacities of heat technologies are found to vary significantly depending on system properties such as the volume and diversity of heat demand and the availability profiles of renewable generation. The bulk of heat (83–97%) is delivered through EHPs, while the remainder is supplied by a mix of EBs and HBs. The results also suggest a strong impact of heat demand diversity on the cost-efficient mix of heating technologies, with higher diversity penalizing EHP relatively more than other, less capital-intensive heating options.

Journal article

Qiu D, Wang Y, Wang J, Jiang C, Strbac Get al., 2023, Personalized retail pricing design for smart metering consumers in electricity market, APPLIED ENERGY, Vol: 348, ISSN: 0306-2619

Journal article

Shahbazbegian V, Shafie-khah M, Laaksonen H, Strbac G, Ameli Het al., 2023, Resilience-oriented operation of microgrids in the presence of power-to-hydrogen systems, Applied Energy, Vol: 348, ISSN: 0306-2619

This study presents a novel framework for improving the resilience of microgrids based on the power-to-hydrogen conceptand the ability of microgrids to operate independently (i.e., islanded mode). For this purpose, a model is being developedfor the resilient operation of microgrids in which the compressed hydrogen produced by power-to-hydrogen systems caneither be used to generate electricity through fuel cells or sold to other industries. The model is a bi-objective optimizationproblem, which minimizes the cost of operation and resilience by (i) reducing the active power exchange with the maingrid, (ii) reducing the ohmic power losses, and (iii) increasing the amount of hydrogen stored in the tanks. A solutionapproach is also developed to deal with the complexity of the bi-objective model, combining a goal programmingapproach and Generalized Benders Decomposition, due to the mixed-integer nonlinear nature of the optimizationproblem. The results indicate that the resilience approach, although increasing the operation cost, does not lead to loadshedding in the event of main grid failures. The study concludes that integrating distributed power-to-hydrogen systemsresults in significant benefits, including emission reductions of up to 20% and cost savings of up to 30%. Additionally,the integration of the decomposition method improves computational performance by 54% compared to using commercialsolvers within the GAMS software

Journal article

Angeli D, Dong Z, Strbac G, 2023, On optimal coordinated dispatch for heterogeneous storage fleets with partial availability, IEEE Transactions on Control of Network Systems, Vol: 10, Pages: 1137-1148, ISSN: 2325-5870

This paper addresses the problem of optimal scheduling of an aggregated power profile (during a coordinated discharging or charging operation) by means of a heterogeneous fleet of storage devices subject to availability constraints. Devices have heterogeneous initial levels of energy, power ratings and efficiency; moreover, the fleet operates without cross-charging of the units. An explicit feedback policy is proposed to compute a feasible schedule whenever one exists and scalable design procedures to achieve maximum time to failure or minimal unserved energy in the case of unfeasible aggregated demand profiles. Finally, a time-domain characterization of the set of feasible demand profiles using aggregate constraints is proposed, suitable for optimization problems where the aggregate population behaviour is of interest.

Journal article

Qiu D, Wang J, Dong Z, Wang Y, Strbac Get al., 2023, Mean-Field Multi-Agent Reinforcement Learning for Peer-to-Peer Multi-Energy Trading, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 38, Pages: 4853-4866, ISSN: 0885-8950

Journal article

Ye Y, Wang H, Chen P, Tang Y, Strbac Get al., 2023, Safe Deep Reinforcement Learning for Microgrid Energy Management in Distribution Networks With Leveraged SpatialTemporal Perception, IEEE TRANSACTIONS ON SMART GRID, Vol: 14, Pages: 3759-3775, ISSN: 1949-3053

Journal article

Aunedi M, Al Kindi AA, Pantaleo AM, Markides CN, Strbac Get al., 2023, System-driven design of flexible nuclear power plant configurations with thermal energy storage, Energy Conversion and Management, Vol: 291, Pages: 1-14, ISSN: 0196-8904

Nuclear power plants are expected to make an important contribution to the decarbonisation of electricity supply alongside variable renewable generation, especially if their operational flexibility is enhanced by coupling them with thermal energy storage. This paper presents a system modelling approach to identifying configurations of flexible nuclear plants that minimise the investment and operation costs in a decarbonised energy system, effectively proposing a system-driven design of flexible nuclear technology. Case studies presented in the paper explore the impact of system features on plant configuration choices. The results suggest that cost-efficient flexible nuclear configurations should adapt to the system they are located in. In the main low-carbon scenarios and assuming standard-size nuclear power plants (1,610 MWel), the lowest-cost system configuration included around 500 MWel of additional secondary generation capacity coupled to the nuclear power plants, with 4.5 GWhth of thermal storage capacity and a discharging duration of 2.2 h. Net system benefits per unit of flexible nuclear generation for the main scenarios were quantified at £29-33 m/yr for a wind-dominated system and £19-20 m/yr for a solar-dominated system.

Journal article

Nordström H, Söder L, Flynn D, Matevosyan J, Kiviluoma J, Holttinen H, Vrana TK, van der Welle A, Morales-España G, Pudjianto D, Strbac G, Dobschinski J, Estanqueiro A, Algarvio H, Martín Martínez S, Gómez Lázaro E, Hodge B-Met al., 2023, Strategies for continuous balancing in future power systems with high wind and solar shares, Energies, Vol: 16, Pages: 1-43, ISSN: 1996-1073

The use of wind power has grown strongly in recent years and is expected to continue to increase in the coming decades. Solar power is also expected to increase significantly. In a power system, a continuous balance is maintained between total production and demand. This balancing is currently mainly managed with conventional power plants, but with larger amounts of wind and solar power, other sources will also be needed. Interesting possibilities include continuous control of wind and solar power, battery storage, electric vehicles, hydrogen production, and other demand resources with flexibility potential. The aim of this article is to describe and compare the different challenges and future possibilities in six systems concerning how to keep a continuous balance in the future with significantly larger amounts of variable renewable power production. A realistic understanding of how these systems plan to handle continuous balancing is central to effectively develop a carbon-dioxide-free electricity system of the future. The systems included in the overview are the Nordic synchronous area, the island of Ireland, the Iberian Peninsula, Texas (ERCOT), the central European system, and Great Britain.

Journal article

O'Malley C, Badesa L, Teng F, Strbac Get 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.

Journal article

Bellizio F, Cremer JL, Strbac G, 2023, Transient Stable Corrective Control Using Neural Lyapunov Learning, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 38, Pages: 3245-3253, ISSN: 0885-8950

Journal article

Giannelos S, Borozan S, Aunedi M, Zhang X, Ameli H, Pudjianto D, Konstantelos I, Strbac Get al., 2023, Modelling smart grid technologies in optimisation problems for electricity grids, Energies, Vol: 16, Pages: 1-15, ISSN: 1996-1073

The decarbonisation of the electricity grid is expected to create new electricity flows. As a result, it may require that network planners make a significant amount of investments in the electricity grids over the coming decades so as to allow the accommodation of these new flows in a way that both the thermal and voltage network constraints are respected. These investments may include a portfolio of infrastructure assets consisting of traditional technologies and smart grid technologies. One associated key challenge is the presence of uncertainty around the location, the timing, and the amount of new demand or generation connections. This uncertainty unavoidably introduces risk into the investment decision-making process as it may lead to inefficient investments and inevitably give rise to excessive investment costs. Smart grid technologies have properties that enable them to be regarded as investment options, which can allow network planners to hedge against the aforementioned uncertainty. This paper focuses on key smart technologies by providing a critical literature review and presenting the latest mathematical modelling that describes their operation.

Journal article

Wang Y, Rousis AO, Qiu D, Strbac Get al., 2023, A stochastic distributed control approach for load restoration of networked microgrids with mobile energy storage systems, INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, Vol: 148, ISSN: 0142-0615

Journal article

Alvarado D, Moreno R, Street A, Panteli M, Mancarella P, Strbac Get al., 2023, Co-Optimizing Substation Hardening and Transmission Expansion Against Earthquakes: A Decision-Dependent Probability Approach, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 38, Pages: 2058-2070, ISSN: 0885-8950

Journal article

Badesa L, O'Malley C, Parajeles M, Strbac Get al., 2023, Chance-constrained allocation of UFLS candidate feeders under high penetration of distributed generation, INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, Vol: 147, ISSN: 0142-0615

Journal article

Wang H, Ye Y, Wang Q, Tang Y, Strbac Get al., 2023, An Efficient LP-Based Approach for Spatial-Temporal Coordination of Electric Vehicles in Electricity-Transportation Nexus, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 38, Pages: 2914-2925, ISSN: 0885-8950

Journal article

Pudjianto D, Frost C, Coles D, Angeloudis A, Smart G, Strbac Get al., 2023, UK studies on the wider energy system benefits of tidal stream, Energy Advances, Vol: 2

With high predictability and a consistent energy availability profile, Tidal Stream (TS) could play an important part in the optimal future low-carbon energy mix, improving the supply reliability and system resilience through diversification of renewable energy supplementing wind and solar power. This paper summarises key findings from UK studies on the benefits of TS by assessing its impact on the overall energy system. The studies use the Integrated Whole Energy System (IWES) model to minimise the overall cost of the 2050 GB energy system with and without TS under different scenarios while respecting the net-zero emission target and reliability requirement. The results show that TS could displace some capacity of mid-merit or peaking plants, indicating some capacity value of offshore wind and lowering the levelised cost of wind power because of lower system integration costs. Diversifying energy resources and improving flexibility are crucial to coping with low-carbon energy resource variation. The studies also demonstrate that the value of TS by 2050 should be around £50 per MW per h, and this cost target could be achieved if a sufficient learning rate (10-15%) with 10 GW of installed capacity could be obtained in the transition period. Other sensitivity studies provide insight into the impact of location, heat decarbonisation pathways, lower annual wind capacity factor, system flexibility, and interconnection capacity on TS's wider energy system benefits.

Journal article

Qiu D, Wang Y, Zhang T, Sun M, Strbac Get al., 2023, Hierarchical multi-agent reinforcement learning for repair crews dispatch control towards multi-energy microgrid resilience, APPLIED ENERGY, Vol: 336, ISSN: 0306-2619

Journal article

Wang Y, Qiu D, Sun M, Strbac G, Gao Zet al., 2023, Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach, APPLIED ENERGY, Vol: 335, ISSN: 0306-2619

Journal article

Wang J, Pinson P, Chatzivasileiadis S, Panteli M, Strbac G, Terzija Vet al., 2023, On machine learning-based techniques for future sustainable and resilient energy systems, IEEE Transactions on Sustainable Energy, Vol: 14, Pages: 1230-1243, ISSN: 1949-3029

Permanently increasing penetration of converter-interfaced generation and renewable energy sources (RESs) makes modern electrical power systems more vulnerable to low probability and high impact events, such as extreme weather, which could lead to severe contingencies, even blackouts. These contingencies can be further propagated to neighboring energy systems over coupling components/technologies and consequently negatively influence the entire multi-energy system (MES) (such as gas, heating and electricity) operation and its resilience. In recent years, machine learning-based techniques (MLBTs) have been intensively applied to solve various power system problems, including system planning, or security and reliability assessment. This paper aims to review MES resilience quantification methods and the application of MLBTs to assess the resilience level of future sustainable energy systems. The open research questions are identified and discussed, whereas the future research directions are identified.

Journal article

Qiu D, Chen T, Strbac G, Bu Set al., 2023, Coordination for Multienergy Microgrids Using Multiagent Reinforcement Learning, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, Vol: 19, Pages: 5689-5700, ISSN: 1551-3203

Journal article

Mauricette L, Dong Z, Zhang L, Zhang X, Zhang N, Strbac Get al., 2023, Resilience Enhancement of Urban Energy Systems via Coordinated Vehicle-to-grid Control Strategies, CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, Vol: 9, Pages: 433-443, ISSN: 2096-0042

Journal article

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