Imperial College London

Professor Yujian Ye

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Honorary Lecturer
 
 
 
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Contact

 

yujian.ye11

 
 
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Location

 

1105Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
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71 results found

Hu J, Wang Q, Ye Y, Wu Z, Tang Yet al., 2024, A High Temporal-Spatial Resolution Power System State Estimation Method for Online DSA, IEEE Transactions on Power Systems, Vol: 39, Pages: 877-889, ISSN: 0885-8950

The rapidly increasing integration of renewable energy sources aggravates the uncertainty and fluctuation in modern power system, which promotes the development of online dynamic security assessment (DSA). Real-time acquisition of high resolution temporal-spatial information on the system states lays the foundation for online DSA, while limited PMU installation and complex dynamic characteristics in real systems impose severe challenges to estimate system states with high quality in real time. This paper proposes a high temporal-spatial resolution state estimation (SE) method, leveraging graph convolutional network (GCN) and dense connectivity structure to estimate states of whole system at PMU reporting rate. Based on proposed SE method, an online DSA framework is developed for transient stability assessment (TSA), which only relies on the hybrid measurements accessible to the control centers in practice. Numerical experiment results in different scenarios demonstrate that the proposed SE method exhibits high SE accuracy and efficiency under different PMU observability. The performance improvement of SE-based TSA approach versus raw-measurements-based TSA approaches is also verified both theoretically and experimentally.

Journal article

Yang X, Cui T, Wang H, Ye Yet al., 2024, Multiagent Deep Reinforcement Learning for Electric Vehicle Fast Charging Station Pricing Game in Electricity-Transportation Nexus, IEEE Transactions on Industrial Informatics, ISSN: 1551-3203

Transportation electrification, involving large-scale integration of electric vehicles (EV) and fast charging stations (FCS), constitutes one of the key enablers toward decarbonization. Coordination of EV charging routes and demand through suitably designed price signals constitutes an imperative step in secure and economic operation of the coupled transportation network (TN) and power distribution network (PDN). In this work, we model the noncooperative pricing game of self-interested FCSs, taking into account the complex interactions between the EV users and the coupled operation of TN and PND. The uncertainties stemming from the EV users' cost elasticity and their travel energy requirements are encapsulated in the modeling of the TN, while the power flows in the PDN are coordinated considering the penetration of renewable energy sources. A modified multiagent proximal policy optimization method is developed to solve the pricing game. It employs an attention mechanism to selectively incorporate agents' representative information for estimating the Q-values. As such, it not only mitigates the nonstationary effect without exploding the input of the centralized critic but also safeguards the business confidentiality of FCSs. Moreover, a sequential updating scheme is used to ensure policy monotonic improvement and a Bayesian inference technique is adopted to enhance the robustness of the pricing strategy. Case studies on a large-scale test CTPN system reveal that the proposed method facilitates sufficient competition among FCSs, which is able to drive down the average charging prices for EV users. It also smooths out the spatial distribution of EV charging demands, which reduces the traffic congestions in the TN while enhancing the wind absorption and cost efficiency of the PDN.

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

Ye Y, Wang H, Cui T, Yang X, Yang S, Zhang M-Let al., 2023, Identifying generalizable equilibrium pricing strategies for charging service providers in coupled power and transportation networks, ADVANCES IN APPLIED ENERGY, Vol: 12, ISSN: 2666-7924

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

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

Hu J, Wang Q, Ye Y, Tang Yet al., 2023, Toward Online Power System Model Identification: A Deep Reinforcement Learning Approach, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 38, Pages: 2580-2593, ISSN: 0885-8950

Journal article

Yuan Q, Ye Y, Tang Y, Liu X, Tian Qet al., 2023, Low Carbon Electric Vehicle Charging Coordination in Coupled Transportation and Power Networks, 13th IEEE Energy Conversion Congress and Exposition (IEEE ECCE), Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, Pages: 2162-2172, ISSN: 0093-9994

Conference paper

Yuan Q, Ye Y, Tang Y, Liu X, Tian Qet al., 2023, Low Carbon Electric Vehicle Charging Coordination in Coupled Transportation and Power Networks, IEEE Transactions on Industry Applications, Vol: 59, Pages: 2162-2172, ISSN: 0093-9994

As a part of the global decarbonization agenda, the electrification of the transport sector involving the large-scale integration of electric vehicles (EV) constitutes one of the key initiatives. However, the upstream generation carbon emissions associated with EV charging demand are still accountable which should not be overlooked. In this context, efficient coordination of EV flows and their charging demand in the coupled transportation and power networks promise significant decarbonization potential. Nevertheless, such potential can only be realized under adequate incentive mechanisms. In this article, a novel low carbon EV charging coordination approach in coupled transportation and power network is proposed and formulated as a bi-level optimization. In the upper level, an AC optimal power flow problem is formulated and solved to determine the optimal operation for power system. Then the carbon emission flow tracing is performed to compute rational locational-differentiated price signals. Given price-based incentives, the lower level employs traffic user equilibrium to describe the distribution of EV path flow and charging demands taking into account the uncertain traffic condition, and the aggregate charging demands are fed back to the upper level. The bi-level problem is solved iteratively through a modified particle swarm algorithm with enhanced convergence properties. Case studies demonstrate the effectiveness of the proposed coordination method in effectively mitigating the global carbon emission of the coupled networks.

Journal article

Ye Y, Papadaskalopoulos D, Yuan Q, Tang Y, Strbac Get 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

Journal article

Ye Y, Wan C, Gu C, Wu D, Strbac G, Sun H, Zhang P, Bo R, Tang Y, Tian Zet al., 2023, Transition towards deep decarbonisation of modern energy systems, IET SMART GRID, Vol: 6, Pages: 1-4

Journal article

Hu J, Ye Y, Tang Y, Strbac Get al., 2023, Towards Risk-Aware Real-Time Security Constrained Economic Dispatch: A Tailored Deep Reinforcement Learning Approach, IEEE Transactions on Power Systems, ISSN: 0885-8950

In the presence of increasing uncertainties brought by intermittent renewable energy sources, security and risk management continue to be the most critical concern in modern power system operation. Risk-aware, real-time security constrained economic dispatch (RT-SCED) provides an efficient solution towards promptly, economically and robustly responding to the changes in the power system operating state. Despite different model-based methods have been developed to handle uncertainties, significant computation burden arise to incorporate <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula>-1 contingency constraints with a higher temporal resolution in RT-SCED. Driven by similar computational challenges, risk evaluation is often overlooked in the current application of deep reinforcement learning (DRL) based data-driven methods in RT-SCED. This paper proposes a DRL-based risk-aware RT-SCED methodological framework by incorporating a novel data-driven risk evaluation model to foster efficient agent-environment interactions. The real-time dispatch policies are constructed with an improved twin delayed deep deterministic policy gradient method. The policy network features a residual network architecture and incorporates an active power allocation mechanism to integrate empirical dispatch knowledge, preventing early termination and fostering more efficient learning behavior. Case studies validate the superior performance of the proposed method in risk-aware RT-SCED on cost efficiency, uncertainty adaptability and computational efficiency, through benchmarking against model-based and data-driven baseline methods.

Journal article

Liu P, Wu Z, Gu W, Lu Y, Ye Y, Strbac Get 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

Journal article

Wang J, Qiu D, Strbac G, Ye Yet al., 2023, Market-Based Generation Planning with Carbon Target, 19th International Conference on the European Energy Market (EEM), Publisher: IEEE, ISSN: 2165-4077

Conference paper

Wang H, Wang Q, Tang Y, Ye Yet al., 2022, Spatial load migration in a power system: Concept, potential and prospects, INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, Vol: 140, ISSN: 0142-0615

Journal article

Ye Y, Wang H, Tang Y, 2022, Market-based hosting capacity maximization of renewable generation in power grids with energy storage integration, FRONTIERS IN ENERGY RESEARCH, Vol: 10, ISSN: 2296-598X

Journal article

Cui H, Ye Y, Tian Q, Tang Yet al., 2022, Security Constrained Dispatch for Renewable Proliferated Distribution Network Based on Safe Reinforcement Learning, FRONTIERS IN ENERGY RESEARCH, Vol: 10, ISSN: 2296-598X

Journal article

Liu Z, Wang Q, Ye Y, Tang Yet al., 2022, A GAN-Based Data Injection Attack Method on Data-Driven Strategies in Power Systems, IEEE TRANSACTIONS ON SMART GRID, Vol: 13, Pages: 3203-3213, ISSN: 1949-3053

Journal article

Cui H, Feng S, Chen J, Ye Y, Tang Y, Lei Jet al., 2022, Wide-area Location Method of Wide-band Oscillations Based on Autoencoder and Long Short-term Memory Network, Dianli Xitong Zidonghua/Automation of Electric Power Systems, Vol: 46, Pages: 194-201, ISSN: 1000-1026

The problem of wide-band oscillations caused by the high proportion of renewable energy and power electronic equipment is becoming increasingly prominent. However, the existing oscillation monitoring methods based on synchrophasor data are limited by the current communication bandwidth, and it is difficult to monitor the wide-band oscillations from several hertz to hundreds of hertz globally. Therefore, a wide-area location method of wide-band oscillations based on the signal compression of the autoencoder and long short-term memory (LSTM) network is proposed, which uses the data compression and decoding capability of the autoencoder to realize the wide-area monitoring analysis of wide-band oscillation signals. Firstly, the power system measurement signals are encoded and compressed at sub-stations to realize the transmission of wide-band oscillation signals under the existing bandwidth, and effectively reduce the redundancy of oscillation data. Secondly, a feature matrix can be directly generated based on the compressed data, and the LSTM network can be adopted to locate the source of oscillations at the master station. In addition, the master station can decode the compressed data uploaded by the sub-stations, so the compressed data or decoded data can be used for the analysis and control of wide-band oscillations according to the requirements. Finally, the subsynchronous, supersynchronous, as well as medium and high-frequency oscillations are fully considered, and the load changes and random noise are taken into account for simulation. The results show that this method has a high reproduction, location accuracy, and good anti-noise performance.

Journal article

Yuan Q, Ye Y, Tang Y, Liu Y, Strbac Get al., 2022, A novel deep-learning based surrogate modeling of stochastic electric vehicle traffic user equilibrium in low-carbon electricity-transportation nexus, APPLIED ENERGY, Vol: 315, ISSN: 0306-2619

Journal article

Cui H, Wang Q, Ye Y, Tang Y, Lin Zet al., 2022, A combinational transfer learning framework for online transient stability prediction, SUSTAINABLE ENERGY GRIDS & NETWORKS, Vol: 30, ISSN: 2352-4677

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

Ye Y, Yuan Q, Tang Y, Strbac Get al., 2022, Decentralized Coordination Parameters Optimization in Microgrids Mitigating Demand Response Synchronization Effect of Flexible Loads, Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, Vol: 42, Pages: 1748-1759, ISSN: 0258-8013

Despite its scalability and privacy advantages over centralized coordination schemes, decentralized price-based coordination in microgrids suffers from the demand response concentration effect, transferring flexible loads to low-price periods and yielding new demand peak, which hampers the efficient and secure operation of the system. Previous works have introduced auxiliary coordination parameters beyond electricity price to mitigate this effect. However, uniform values of these coordination parameters have been applied to all flexible loads, despite the effects of network constraints. To this end, this paper proposed an auxiliary parameters optimization for decentralized coordination in microgrids, applying optimal value for flexible loads in different nodes to mitigate demand response synchronization effect and minimize the total operational cost of the microgrid. Firstly, the decentralized coordination optimization model of microgrid and the demand response model of flexible loads, i.e. electric vehicles and smart appliances were established. Then a DRL-based approach to select the optimal values of auxiliary parameters was proposed, posing the parameter optimization problem in multi- dimensional continuous state and action spaces. Finally, simulation results demonstrated the effectiveness of the proposed optimization method.

Journal article

Han X, Zhang C, Tang Y, Ye Yet al., 2022, Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building, JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, Vol: 10, Pages: 482-491, ISSN: 2196-5625

Journal article

Feng S, Chen J, Ye Y, Wu X, Cui H, Tang Y, Lei Jet al., 2022, A two-stage deep transfer learning for localisation of forced oscillations disturbance source, INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, Vol: 135, ISSN: 0142-0615

Journal article

Ye Y, Wang H, Tang Y, Strbac Get al., 2022, Real-time Autonomous Optimal Energy Management Strategy for Residents Based on Deep Reinforcement Learning, Dianli Xitong Zidonghua/Automation of Electric Power Systems, Vol: 46, Pages: 110-119, ISSN: 1000-1026

Alongside the wide proliferation of distributed energy resources at the residential sector, how to meet the needs of real-time autonomous energy management while considering the heterogeneous operating characteristics of these resources so as to maximize the utility for residential end-users deserves significant research attention. In this area, conventional model-based optimization methods are generally burdened with inaccurate system modeling and inability to efficiently deal with uncertainties stemmed from multiple sources. In order to address these challenges, this paper proposes a model-free method based on deep reinforcement learning to achieve real-time autonomous energy management optimization. First, the user's resources are classified into different categories, their operating characteristics are then described using a unified 3-element tuple, and the associated energy management actions are also identified. Next, the long short-term memory neural network is employed to extract the future trends of multi-source sequential data from the environment states. Then, based on the proximal policy optimization algorithm,it enables efficient learning of the optimal energy management policies in the multi-dimensional continuous-discrete mixed action space, which can adaptively adjust to system uncertainties towards the user's electricity cost minimization objective. Finally, the effectiveness of the proposed method is verified by benchmarking its performance against several existing methods through case studies on an actual scenario.

Journal article

Sun W, Wang Q, Ye Y, Tang Yet al., 2022, Unified modelling of gas and thermal inertia for integrated energy system and its application to multitype reserve procurement, APPLIED ENERGY, Vol: 305, ISSN: 0306-2619

Journal article

Ye Y, Tang Y, Wang H, Zhang X-P, Strbac Get al., 2021, A Scalable Privacy-Preserving Multi-agent Deep Reinforcement Learning Approach for Large-Scale Peer-to-Peer Transactive Energy Trading, IEEE Transactions on Smart Grid, Pages: 1-1, ISSN: 1949-3053

Peer-to-peer (P2P) transactive energy trading has emerged as a promising paradigm towards maximizing the flexibility value of prosumers’ distributed energy resources (DERs). Despite reinforcement learning constitutes a well-suited model-free and data-driven methodological framework to optimize prosumers’ energy management decisions, its application to the large-scale coordinated management and P2P trading among multiple prosumers within an energy community is still challenging, due to the scalability, non-stationarity and privacy limitations of state-of-the-art multi-agent deep reinforcement learning (MADRL) approaches. This paper proposes a novel P2P transactive trading scheme based on the multi-actor-attention-critic (MAAC) algorithm, which addresses the above challenges individually. This method is complemented by a P2P trading platform that incentivizes prosumers to engage in local energy trading while also penalizes each prosumer’s addition to rebound peaks. %The proposed method is applied to the coordination of prosumers operating multiple and diverse DERs, including photovoltaic (PV) generators, energy storage (ES) units and two types of shiftable loads. Case studies involving a real-world, large-scale scenario with 300 residential prosumers demonstrate that the proposed method significantly outperforms the state-of-the-art MADRL methods in reducing the community’s cost and peak demand.

Journal article

Li J, Ye Y, Papadaskalopoulos D, Strbac Get al., 2021, Distributed consensus-based coordination of flexible demand and energy storage resources, IEEE Transactions on Power Systems, Vol: 36, Pages: 3053-3069, ISSN: 0885-8950

Distributed, consensus-based algorithms have emerged as a promising approach for the coordination of distributed energy resources (DER) due to their communication, computation, privacy and reliability advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the DER coordination problem in independent time periods and therefore are inherently unable to capture the time-coupling operating characteristics of flexible demand (FD) and energy storage (ES) resources. This paper demonstrates that state-of-the-art algorithms fail to converge when these time-coupling characteristics are considered. In order to address this fundamental limitation, a novel consensus-based algorithm is proposed which includes additional consensus variables. These variables express relative maximum power limits imposed on the FD and ES resources which effectively mitigate the concentration of the FD and ES responses at the same time periods and steer the consensual outcome to a feasible and optimal solution. The convergence and optimality of the proposed algorithm are theoretically proven while case studies numerically demonstrate its convergence, optimality, robustness to initialization and information loss, and plug-and-play adaptability.

Journal article

Qiu D, Ye Y, Papadaskalopoulos D, Strbac Get al., 2021, Scalable coordinated management of peer-to-peer energy trading: A multi-cluster deep reinforcement learning approach, APPLIED ENERGY, Vol: 292, ISSN: 0306-2619

Journal article

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