Publications
512 results found
Bugaje AAB, 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 and Energy Systems, Vol: 154, ISSN: 0142-0615
This paper presents a novel, unified approach for generating high-quality datasets for training machine-learned models for real-time security assessment in power systems. Synthetic data generation methods that extrapolate beyond historical data can be inefficient in generating feasible and rare operating conditions (OCs). The proposed approach balances the trade-off between historically relevant OCs and rare but feasible OCs. Unlike conventional methods that rely on historical records or generic sampling, our approach results in datasets that generalise well beyond similar distributions. The proposed approach is validated through experiments on the IEEE 118-bus system, where a decision tree model trained on data generated using our approach achieved 97% accuracy in predicting the security label of rare OCs, outperforming baseline approaches by 41% and 20%. This work is crucial for deploying reliable machine-learned models for real-time security assessment in power systems undergoing decarbonisation and integrating renewable energy sources.
Aunedi M, Olympios AV, Pantaleo AM, et 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.
Ye Y, Wang H, Chen P, et al., 2023, Safe Deep Reinforcement Learning for Microgrid Energy Management in Distribution Networks with Leveraged Spatial-Temporal Perception, IEEE Transactions on Smart Grid, Vol: 14, Pages: 3759-3775, ISSN: 1949-3053
Microgrids (MG) have recently attracted great interest as an effective solution to the challenging problem of distributed energy resources' management in distribution networks. In this context, despite deep reinforcement learning (DRL) constitutes a well-suited model-free and data-driven methodological framework, its application to MG energy management is still challenging, driven by their limitations on environment status perception and constraint satisfaction. In this paper, the MG energy management problem is formalized as a Constrained Markov Decision Process, and is solved with the state-of-the-art interior-point policy optimization (IPO) method. In contrast to conventional DRL approaches, IPO facilitates efficient learning in multi-dimensional, continuous state and action spaces, while promising satisfaction of complex network constraints of the distribution network. The generalization capability of IPO is further enhanced through the extraction of spatial-temporal correlation features from original MG operating status, combining the strength of edge conditioned convolutional network and long short-term memory network. Case studies based on an IEEE 15-bus and 123-bus test feeders with real-world data demonstrate the superior performance of the proposed method in improving MG cost effectiveness, safeguarding the secure operation of the network and uncertainty adaptability, through performance benchmarking against model-based and DRL-based baseline methods. Finally, case studies also analyze the computational and scalability performance of proposed and baseline methods.
Aunedi M, Al Kindi AA, Pantaleo AM, et 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.
Nordström H, Söder L, Flynn D, et 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.
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.
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
This paper proposes a method to compute corrective control actions for dynamic security in real-time and quantifies the economic value of corrective control. Lowered inertia requires fast control methods in real-time to correct system operation and maintain system security when equipment fails. However, using corrective control beyond such emergency failure measures does not make fully use of them. The key contribution of this work is the optimal use of corrective control applications in combination with preventive strategies to enhance the network utilisation, reduce the normal operating costs while maintaining adequate security levels. The proposed approach learns a neural network for safety certificates and models the predicted safe dynamic post-fault state as algebraic constraints in an AC optimal power flow (OPF) deciding close to real-time on the optimal corrective control. Considering these safety constraints within the ACOPF can balance simultaneously the system transient stability with the costs for preventive and corrective control. This proposed approach outperforms sub-optimal approaches aiming at sequentially finding the balance. Case studies were based on the IEEE 9-bus system with integrated electrical vehicles and shares of wind power up-to 40% and on the IEEE 39-bus and 118-bus systems. The proposed approach outperforms baseline control approaches in stability, economics, and carbon emissions. One baseline approach was preventive wind curtailment, against which the proposed approach reduced operating costs by up-to 60%, decreased unstable operations by 50% and reduced carbon emissions by 60% in the IEEE 9-bus. In the IEEE 39-bus and 118-bus systems, the approach was promising for larger systems.
Alvarado D, Moreno R, Street A, et 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
In light of the rising frequency and impact of natural hazards on power systems, planning resilient network investments is becoming increasingly important. This task, however, needs, in addition to widely accepted investment options focused on installing new infrastructure, explicit recognition of investment propositions to harden existing infrastructure such as substations. Hardening networks is fundamentally challenging to incorporate in optimization problems since it affects outage probabilities. Therefore, we propose an optimization approach to determine optimal portfolios of resilient network investments, considering endogenous probabilities that change with hardening investment options. This decision-dependent-probability model finds the optimal network enhancements in a cost-benefit fashion, minimizing investment plus operational costs, including demand curtailments. The proposed model also considers distributed energy resources (DER), which can displace costly network investments. Additionally, the model takes into account the lack of fully accurate fragility curves; thus, outage probabilities are not only affected by hardening decisions but also by the inherent uncertainty associated with fragility modeling. This is a key concern in practical resilience assessment and is addressed in this work through a global-convergent exact algorithm. Case studies applied on earthquakes in Chile demonstrate the benefits of our proposed network planning approach.
Badesa L, O'Malley C, Parajeles M, et 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
Wang H, Ye Y, Wang Q, et 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
A growing integration of electric vehicles (EV) and fast charging stations (FCS) has paved the way for the emergence of transportation and power distribution networks. However, previous work on the coordination of EV in the transportation system mainly focuses on static traffic assignment (STA) models which are unable to characterize the temporal evolution of EV flows on traffic links. Meanwhile, limited prior effort in dynamic traffic assignment (DTA) employs a static FCS model which is unable to describe the temporal evolution of charging and discharging (C&D) behavior of EV flows, while respecting the state-of-charge (SoC) related operating constraints, hindering full exploitation of EV flexibility potentials. To bridge the knowledge gap, this paper proposes a novel DTA model to optimize the spatial-temporal distribution of EV flows on roads and at FCSs, taking into account their flexible C&D options and SoC-related constraints, through the solution of only a single linear program. Case studies validate the effectiveness of the proposed DTA model by benchmarking its performance against traditional STA models, and corroborate the core benefits from the proposed spatial-temporal coordination of EV C&D demand in the power distribution network in terms of peak demand reduction, improved RES absorption, reduced carbon emission as well as more efficient network congestion management.
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
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.
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
Local electricity markets (LEM) have recently attracted great interest as an effective solution to the challenging problem of distributed energy resources' (DER) management. However, LEM designs combining the market functions of local energy trading and flexibility services (FS) provision to wider system operators have not attracted sufficient attention. In the context of addressing this research gap, this paper firstly provides a new model-based system-centric formulation for the coordination of such a LEM, which provides a theoretical optimality benchmark. Compared to previous formulations, it considers the time-coupling operating characteristics of flexible DERs, and optimizes the two market functions simultaneously. Furthermore, this paper explores for the very first time a model-free prosumer-centric coordination approach for such a LEM, in order to address the practical limitations of model-based system-centric approaches. This is achieved through a new multi-agent deep reinforcement learning method which combines the beneficial properties of the multi-actor-attention-critic and the prioritized experience replay approaches. Case studies on a real-world, large-scale setting validate that the proposed LEM design successfully encapsulates the economic benefits of both local energy trading and FS provision functions, and demonstrate that the proposed learning method outperforms previous methods.
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
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
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, 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, 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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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
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
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.
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
Cui H, Ye Y, Hu J, et al., 2023, Online Preventive Control for Transmission Overload Relief Using Safe Reinforcement Learning with Enhanced Spatial-Temporal Awareness, IEEE Transactions on Power Systems, 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.
Bugaje A-AB, Cremer JL, Strbac G, 2022, Real-time transmission switching with neural networks, IET GENERATION TRANSMISSION & DISTRIBUTION, ISSN: 1751-8687
Angeli D, Dong Z, Strbac G, 2022, On optimal coordinated dispatch for heterogeneous storage fleets with partial availability, IEEE Transactions on Control of Network Systems, Pages: 1-12, 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.
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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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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