Publications
541 results found
Wang Y, Qiu D, Sun M, et al., 2023, Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach, APPLIED ENERGY, Vol: 335, ISSN: 0306-2619
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- Citations: 2
Qiu D, Chen T, Strbac G, et al., 2023, Coordination for Multienergy Microgrids Using Multiagent Reinforcement Learning, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, Vol: 19, Pages: 5689-5700, ISSN: 1551-3203
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- Citations: 4
Wang J, Pinson P, Chatzivasileiadis S, et 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.
Giannelos S, Moreira A, Papadaskalopoulos D, et al., 2023, A machine learning approach for generating and evaluating forecasts on the environmental impact of the buildings sector, Energies, Vol: 16, Pages: 1-37, ISSN: 1996-1073
The building sector has traditionally accounted for about 40% of global energy-related carbon dioxide (CO2) emissions, as compared to other end-use sectors. Due to this fact, as part of the global effort towards decarbonization, significant resources have been placed on the development of technologies, such as active buildings, in an attempt to achieve reductions in the respective CO2 emissions. Given the uncertainty around the future level of the corresponding CO2 emissions, this work presents an approach based on machine learning to generate forecasts until the year 2050. Several algorithms, such as linear regression, ARIMA, and shallow and deep neural networks, can be used with this approach. In this context, forecasts are produced for different regions across the world, including Brazil, India, China, South Africa, the United States, Great Britain, the world average, and the European Union. Finally, an extensive sensitivity analysis on hyperparameter values as well as the application of a wide variety of metrics are used for evaluating the algorithmic performance.
Ye Y, Papadaskalopoulos D, Yuan Q, et al., 2023, Multi-Agent Deep Reinforcement Learning for Coordinated Energy Trading and Flexibility Services Provision in Local Electricity Markets, IEEE TRANSACTIONS ON SMART GRID, Vol: 14, Pages: 1541-1554, ISSN: 1949-3053
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- Citations: 6
Qiu D, Wang Y, Hua W, et al., 2023, Reinforcement learning for electric vehicle applications in power systems: A critical review, RENEWABLE & SUSTAINABLE ENERGY REVIEWS, Vol: 173, ISSN: 1364-0321
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- Citations: 8
Mauricette L, Dong Z, Zhang L, et 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
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- Citations: 1
Bugaje A-AB, Cremer JL, Strbac G, 2023, Split-based sequential sampling for realtime security assessment, INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, Vol: 146, ISSN: 0142-0615
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- Citations: 2
Dong Z, Zhang X, Zhang N, et al., 2023, A distributed robust control strategy for electric vehicles to enhance resilience in urban energy systems, Advances in Applied Energy, Vol: 9, ISSN: 2666-7924
Resilient operation of multi-energy microgrid is a critical concept for decarbonization in modern power system. Its goal is to mitigate the low probability and high damaging impacts of electricity interruptions. Electrical vehicles, as a key flexibility provider, can react to unserved demand and autonomously schedule their operation in order to provide resilience. This paper presents a distributed control strategy for a population of electrical vehicles to enhance resilience of an urban energy system experiencing extreme contingency. Specifically, an iterative algorithm is developed to coordinate the charging/discharging schedules of heterogeneous electrical vehicles aiming at reducing the essential load shedding while considering the local constraints and multi-energy microgrid interconnection capacities. Additionally, the gap between electrical vehicle energy and the required energy level at the departure time is also minimised. The effectiveness of the introduced distributed coordinated approach on energy arbitrage and congestion management is tested and demonstrated by a series of case studies.
Wang Y, Qiu D, Strbac G, et al., 2023, Coordinated Electric Vehicle Active and Reactive Power Control for Active Distribution Networks, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, Vol: 19, Pages: 1611-1622, ISSN: 1551-3203
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- Citations: 9
Ye Y, Wan C, Gu C, et al., 2023, Transition towards deep decarbonisation of modern energy systems, IET SMART GRID, Vol: 6, Pages: 1-4
Dong Z, Zhang X, Li Y, et al., 2023, Values of coordinated residential space heating in demand response provision, APPLIED ENERGY, Vol: 330, ISSN: 0306-2619
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- Citations: 3
Angeli D, Dong Z, Strbac G, 2023, Exact Aggregate Models for Optimal Management of Heterogeneous Fleets of Storage Devices, IEEE Transactions on Control of Network Systems
Future power grids will entail large fleets of storage devices capable of scheduling their charging/discharging profiles so as to achieve lower peak demand and reduce energy bills, by shifting absorption times in synchronous with the availability of renewable energy sources. Optimal management of such fleets entails large-scale optimisation problems which can be dealt with in a hierarchical manner, by clustering together individual devices into fleets. Based on recent results characterising the set of aggregate demand profiles of a heterogeneous fleet of charging devices, an exact aggregate model is proposed to achieve optimality, in a unit commitment problem, by adopting a simplified formulation with a number of constraints for the fleet that scales linearly in the number of time-slots and is independent of the size of the fleet. This is remarkable, as it shows that, under suitable conditions, a heterogeneous fleet of any size can effectively be treated as a single storage unit.
Zhang L, Dong Z, Zhang X, et al., 2023, Resilience Oriented Planning for Multi-energy Microgrids in an Urban Area
In recent years, various extreme weather-induced high-impact, low-probability (HILP) events occur frequently, such as typhoons, avalanches, hurricanes and etc., causing serious losses to the energy system and the whole society. This inspires energy system planners to consider how to make the system more resilient to better respond to HILP events when formulating strategies. In this paper, a resilience-oriented planning framework is proposed that can help to design more resilient multi-energy microgrids (MEMGs) in urban areas against HILP events. The preventive control method is introduced within it. The value of preventive control is innovatively analysed and quantified through case studies. The results show that the preventive control technique is critical in facilitating resilience-oriented planning of urban energy systems. It can reduce load shedding and planning costs when dealing with disruptive events, which brings more economy and safety to the MEMGs' planning.
Qiu D, Dong Z, Wang Y, et al., 2023, Decarbonising the GB Power System Via Numerous Electric Vehicle Coordination, IEEE Transactions on Power Systems, ISSN: 0885-8950
The increasing penetration of renewable energy has promoted fast decarbonisation of the GB power system. However, low-carbon transitions should be considered from a whole energy system perspective, such as through localised vehicle-to-grid (V2G) techniques. Despite the potential benefits of utilising decentralised V2G flexibility for decarbonisation, the absence of an appropriate incentive mechanism has limited its effectiveness. This paper aims at studying the carbon-aware electric vehicle (EV) power scheduling problem by introducing a carbon emission flow model. The model enables EVs to be cognisant of carbon intensity (CI) signals, thereby enabling them to provide carbon services. To solve this problem, a novel decentralised control approach is proposed to coordinate numerous EVs in a computationally efficient manner with privacy perseverance. Case studies on a 14-node GB power network with a large population of 556,733 EVs are conducted to validate its efficacy in simultaneously maximising the EVs' carbon service provision and decarbonising the GB power system by intelligently linking local behaviours with global interest. Finally, the proposed control approach shows its generalisation performance for various day and season scenarios as well as evidence for realising the GB's decarbonisation ambitions by 2030 and 2050.
Pudjianto D, Strbac G, 2023, OPERATIONAL STRATEGIES FOR MAXIMISING THE VALUE OF CUSTOMER FLEXIBILITY, Pages: 2606-2610
This paper compares and analyses the energy system benefits and implications of distributed flexibility operated under three operational coordination strategies: (i) whole-system, (ii) distribution system operator (DSO) centric, and (iii) non-DSO centric approaches. The study used the future Great Britain net-zero energy systems as test cases and two heat decarbonisation pathways: hydrogen heating and electrification. Integrated Whole-Energy System (IWES) model was used to support the analyses. The results demonstrate that the whole system approach is the best option and can save £1bn - £5.1bn/year more than the DSO and non-DSO approaches.
Liu P, Wu Z, Gu W, et al., 2023, Spatial Branching for Conic Non-Convexities in Optimal Electricity-Gas Flow, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 38, Pages: 972-975, ISSN: 0885-8950
Badesa L, Matamala C, Zhou Y, et al., 2023, Assigning Shadow Prices to Synthetic Inertia and Frequency Response Reserves From Renewable Energy Sources, IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, Vol: 14, Pages: 12-26, ISSN: 1949-3029
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- Citations: 1
Wang J, Qiu D, Strbac G, et al., 2023, Market-Based Generation Planning with Carbon Target, 19th International Conference on the European Energy Market (EEM), Publisher: IEEE, ISSN: 2165-4077
Qiu D, Chrysanthopoulos N, Strbac G, 2023, Tariff Design for Local Energy Communities Through Strategic Retail Pricing, 19th International Conference on the European Energy Market (EEM), Publisher: IEEE, ISSN: 2165-4077
Giannelos S, Borozan S, Moreira A, et al., 2023, Techno-economic analysis of smart EV charging for expansion planning under uncertainty, IEEE Belgrade PowerTech Conference, Publisher: IEEE
Bugaje A-AB, Cremer JL, Strbac G, 2022, Real-time transmission switching with neural networks, IET GENERATION TRANSMISSION & DISTRIBUTION, ISSN: 1751-8687
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- Citations: 1
Qiu D, Wang Y, Zhang T, et al., 2022, Hybrid Multiagent Reinforcement Learning for Electric Vehicle Resilience Control Towards a Low-Carbon Transition, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, Vol: 18, Pages: 8258-8269, ISSN: 1551-3203
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- Citations: 9
Qiu D, Dong Z, Ruan G, et al., 2022, Strategic retail pricing and demand bidding of retailers in electricity market: A data-driven chance-constrained programming, ADVANCES IN APPLIED ENERGY, Vol: 7, ISSN: 2666-7924
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- Citations: 2
Blatiak A, Bellizio F, Badesa L, et al., 2022, Value of optimal trip and charging scheduling of commercial electric vehicle fleets with Vehicle-to-Grid in future low inertia systems, SUSTAINABLE ENERGY GRIDS & NETWORKS, Vol: 31, ISSN: 2352-4677
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- Citations: 9
Pang Q, De Paola A, Trovato V, et al., 2022, Value of Interconnectors Operating in Simultaneous Energy-Frequency Response Markets, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 37, Pages: 3381-3393, ISSN: 0885-8950
Shahbazbegian V, Ameli H, Shafie-Khah M, et al., 2022, Optimal scheduling of gas and electricity distribution networks in microgrids: a decomposition approach, IEEE International Conference on Environment and Electrical Engineering (EEEIC) / IEEE Industrial and Commercial Power Systems Europe (I and CPS Europe) Conference, Publisher: IEEE, Pages: 1-6
The transition towards increasingly renewables-based energy system is ongoing. During this transition microgrids are seen as a key concept and sub-system which can enable the transition and improve the security of supply at distribution network level. From generation perspective, flexible and rapidly controllable gas-based generation units can be utilized to deal with the variable output of weather-dependent renewable energy resources. Due to these complementary characteristics, it is of interest to study the integrated operation of gas and electricity distribution networks also in future microgrids. In this paper, the optimized scheduling of resources in a microgrid with gas and electricity distribution networks is studied. For this purpose, a mathematical model is first determined. After that, due to the complexity of this model, a decomposition method is developed to solve the optimization problem. This method splits the original problem into two subproblems, which reduces the complexity of solving. In order to validate the efficacy of the proposed model, a case study is derived based on a 15-node gas distribution network and a 13-node electricity distribution network. Based on the results, integrated scheduling improves the costs compared to separated scheduling, and the decomposition method reduces the solving time considerably.
Zhang X, Dong Z, Huang W, et al., 2022, A Novel Preheating Coordination Approach in Electrified Heat Systems, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 37, Pages: 3092-3103, ISSN: 0885-8950
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- Citations: 2
Karasavvidis M, Papadaskalopoulos D, Strbac G, 2022, Optimal Offering of a Power Producer in Electricity Markets With Profile and Linked Block Orders, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 37, Pages: 2704-2719, ISSN: 0885-8950
Aunedi M, Yliruka M, Dehghan S, et al., 2022, Multi-model assessment of heat decarbonisation options in the UK using electricity and hydrogen, Renewable Energy, Vol: 194, Pages: 1261-1276, ISSN: 0960-1481
Delivering low-carbon heat will require the substitution of natural gas with low-carbon alternatives such as electricity and hydrogen. The objective of this paper is to develop a method to soft-link two advanced, investment-optimising energy system models, RTN (Resource-Technology Network) and WeSIM (Whole-electricity System Investment Model), in order to assess cost-efficient heat decarbonisation pathways for the UK while utilising the respective strengths of the two models. The linking procedure included passing on hourly electricity prices from WeSIM as input to RTN, and returning capacities and locations of hydrogen generation and shares of electricity and hydrogen in heat supply from RTN to WeSIM. The outputs demonstrate that soft-linking can improve the quality of the solution, while providing useful insights into the cost-efficient pathways for zero-carbon heating. Quantitative results point to the cost-effectiveness of using a mix of electricity and hydrogen technologies for delivering zero-carbon heat, also demonstrating a high level of interaction between electricity and hydrogen infrastructure in a zero-carbon system. Hydrogen from gas reforming with carbon capture and storage can play a significant role in the medium term, while remaining a cost-efficient option for supplying peak heat demand in the longer term, with the bulk of heat demand being supplied by electric heat pumps.
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