Publications
77 results found
Zhang X, Dong Z, Huangfu F, et al., 2024, Strategic dispatch of electric buses for resilience enhancement of urban energy systems, Applied Energy, Vol: 361, ISSN: 0306-2619
The increasing frequency of the occurrence of high impact low probability (HILP) disruptive events has posed huge threats to the power system. Therefore, power system resilience improvement has drawn world-wide attention. Electric buses (EB) are equipped with a large capacity of batteries which could provide sufficient energy and capacity values to run islanded micro-grids (MG) for hours, with a particular focus on V2G, which will deliver system resilience during extreme operational conditions. In this paper, an innovative reciprocal transport-power system coordination scheme is proposed to simultaneously mitigate the adverse impacts on both systems. By using this economic incentive-free approach, EBs are endogenously motivated to provide resilience-oriented V2G service to the power system, while increasing the transport service survivability during HILP disruptive events. Additionally, a novel EB dispatch rescheduling method considering the trade-off of both the requirement of energy supply security and transport service fulfilment is proposed. By using this method, the essential load curtailment of local MGs can be further alleviated, while the impacts of reduced charging service on transport service can also be further mitigated. Through a series of case studies, we illustrate that EBs have significant potential to provide resilience enhancement to the urban energy system based on appropriate dispatch strategies. Meanwhile, the interests of the power system and the transport system can be well reconciled by using the proposed approach during HILP disruptive events.
Amiri MM, Ameli MT, Aghamohammadi MR, et al., 2024, Day-Ahead Coordination for Flexibility Enhancement in Hydrogen-Based Energy Hubs in presence of EVs, Storage, and Integrated Demand Response, IEEE Access, ISSN: 2169-3536
Yang X, Cui T, Wang H, et al., 2024, Multiagent Deep Reinforcement Learning for Electric Vehicle Fast Charging Station Pricing Game in Electricity-Transportation Nexus, IEEE Transactions on Industrial Informatics, Vol: 20, Pages: 6345-6355, 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.
Hu J, Ye Y, Tang Y, et al., 2024, Towards Risk-Aware Real-Time Security Constrained Economic Dispatch: A Tailored Deep Reinforcement Learning Approach, IEEE Transactions on Power Systems, Vol: 39, Pages: 3972-3986, 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 N-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 article 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.
Qiu D, Wang Y, Wang J, et al., 2024, Resilience-oriented coordination of networked microgrids: a shapley Q-value learning approach, IEEE Transactions on Power Systems, Vol: 39, Pages: 3401-3416, ISSN: 0885-8950
High-impact and low-probability extreme events have occurred more frequently than before because of rapid climate change, which can seriously damage distribution systems. However, conventional distribution management can be dysfunctional after an event, destroying its centralized supervision towards resilience enhancement. In this context, networked microgrids (NMGs) with distributed energy resources provide a viable solution for the resilience enhancement of distribution systems. Existing literature tends to employ model-based optimization approaches for resilient operations of NMGs, which require complete system models and can be time-consuming. To address these challenges, this article suggests a decentralized framework for resilience-oriented coordination of NMGs and proposes a novel multi-agent reinforcement learning (MARL) method to solve it. Specifically, the proposed MARL method develops an efficient credit assignment scheme for NMGs to learn their contributions to the distribution system resilience via the Shapley Q-value technique with more efficient resilience enhancement. Case studies based on two modified IEEE 15- and 69-bus distribution networks are conducted to validate the effectiveness of the proposed MARL method in enabling effective coordination among NMGs and providing a high resilience level.
Sun Y, Li Y, Borozan S, et 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.
Hu J, Wang Q, Ye Y, et 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.
Cui H, Ye Y, Hu J, et 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.
Liu P, Wu Z, Gu W, et al., 2024, Fast Resilience Assessment of Integrated Electricity-Gas Systems Considering Non-Convex Gas Flow Dynamics, IEEE Transactions on Smart Grid, ISSN: 1949-3053
This paper proposes a novel spatial branch-and-cut approach for fast resilience analysis of integrated electricity-gas systems. The proposed method makes two improvements to the traditional branch-and-cut algorithms to accelerate the solution process of the scenario-based post-contingency load restoration. First, a new cone-specific spatial branching strategy is developed to handle the non-convex nature of the integrated electricity-gas flow. Compared with the existing branching strategies which can only handle bilinear equality constraints, the scale of branching variables is reduced by two-thirds without affecting the accuracy of solutions. Second, a combinatorial cut generation strategy is proposed to convert the traversal of all failure scenarios into a guided generation of single branch-and-cut search tree. The key idea is to equate the solution process of each failure scenario to a subset of the search tree, so that the load restoration problem only needs to be solved once. The performance of the proposed approach is tested on three integrated electricity-gas systems of different sizes. The comparison against seven alternative solution methods further verifies the superiority of the proposed method in terms of accuracy and efficiency.
Qiu D, Wang Y, Ding Z, et al., 2024, Graph Reinforcement Learning for Carbon-Aware Electric Vehicles in Power-Transport Networks, IEEE Transactions on Smart Grid, Pages: 1-1, ISSN: 1949-3053
Ye Y, Wang H, Cui T, et 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
Ye Y, Wang H, Chen P, et 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
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- Citations: 1
Hu J, Wang Q, Ye Y, et 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
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- Citations: 3
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
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- Citations: 3
Yuan Q, Ye Y, Tang Y, et 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
Yuan Q, Ye Y, Tang Y, et 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.
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
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
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
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
Wang H, Wang Q, Tang Y, et 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
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- Citations: 1
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
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- Citations: 3
Cui H, Ye Y, Tian Q, et al., 2022, Security Constrained Dispatch for Renewable Proliferated Distribution Network Based on Safe Reinforcement Learning, FRONTIERS IN ENERGY RESEARCH, Vol: 10, ISSN: 2296-598X
Liu Z, Wang Q, Ye Y, et 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
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- Citations: 2
Cui H, Feng S, Chen J, et 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.
Yuan Q, Ye Y, Tang Y, et 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
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- Citations: 4
Cui H, Wang Q, Ye Y, et al., 2022, A combinational transfer learning framework for online transient stability prediction, SUSTAINABLE ENERGY GRIDS & NETWORKS, Vol: 30, ISSN: 2352-4677
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- Citations: 3
Bellizio F, Xu W, Qiu D, et al., 2022, Transition to Digitalized Paradigms for Security Control and Decentralized Electricity Market, PROCEEDINGS OF THE IEEE, ISSN: 0018-9219
Ye Y, Yuan Q, Tang Y, et 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.
Han X, Zhang C, Tang Y, et 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
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- Citations: 2
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