Chenye WU

Accurate carbon price forecasting is crucial for market participants, as it facilitates decision-making based on comprehensive information, thereby ensuring effective management and stable operation of the carbon market. However, due to the influence of various factors on carbon price data, exhibiting high levels of randomness and volatility, traditional point prediction models often fail to provide decision-makers with sufficient and effective information. To this end, this paper proposes a novel carbon price forecasting paradigm: MLP-Carbon. Utilizing two learning frameworks, MLP-Carbon-Point Forecasting (MLP-Carbon-PF in short) and MLP-Carbon-Interval Forecasting (MLP-Carbon-IF in short), the proposed paradigm delivers accuracy point forecasting results and high-quality probabilistic forecasting intervals, thereby enriching the provided information. Specifically, we first denoise the original carbon price data adopting Variational Mode Decomposition (VMD), and then extract multi-frequency and multi-scale information from the data using the multi-scale sample entropy (MSSE) based secondary decomposition–recombination method and fuzzy information granulation (FIG) respectively. Additionally, interpretable feature engineering techniques are utilized to select beneficial features to assist training. After the above multi-stage data processing, we propose two deep learning frameworks based on fully multi-layer perceptrons (MLP), which can fit and learn relevant information through parallel multi-frequency learning block and multi-scale learning block, and finally summarize the learned information and output the forecasting result. MLP-Carbon-IF achieves the simultaneous output of the forecasting interval’s upper and lower bounds through the parameter-sharing technique and a customized loss function based on Quantile Regression (QR), which focuses on the deviation of the actual values relative to the center of the interval. Experiments using real data from two carbon markets in Guangdong and Hubei demonstrate that MLP-Carbon significantly outperforms the benchmarks. Through ablation experiments, the unique contribution of each block in the learning framework is also verified.

 
@article{TIAN2025125330,
title = {MLP-Carbon: A new paradigm integrating multi-frequency and multi-scale techniques for accurate carbon price forecasting},
journal = {Applied Energy},
volume = {383},
pages = {125330},
year = {2025},
issn = {0306-2619},
doi = {https://doi.org/10.1016/j.apenergy.2025.125330},
url = {https://www.sciencedirect.com/science/article/pii/S0306261925000601},
author = {Zhirui Tian and Wenpu Sun and Chenye Wu},
}
 

The highly penetrated renewable energy resources have significantly increased the uncertainty faced by the power system. Accurate day-ahead economic dispatch (ED) is crucial for managing this uncertainty and ensuring the efficient use of energy resources. To guarantee the planned dispatch schedules align closely with actual operations, most existing methods focus on improving the accuracy of time-series load forecasting. However, minimizing the commonly adopted forecasting accuracy metrics, e.g., the mean squared error (MSE), cannot directly correspond to the fidelity of day-ahead ED. This misalignment for load forecasting limits the practical effectiveness of the existing methods. To this end, based on the multi-task learning technique and end-to-end learning framework, we propose the EDformer family which contains two learning frameworks, EDformer and EEDformer. Specifically, the proposed frameworks parallel multiple load forecasting tasks based on customized encoder–decoder structures and address the inconsistency between the forecasting accuracy metrics and actual ED costs through a customized loss function. Furthermore, the proposed frameworks adopt the Learning to Optimize (L2O) technique, utilizing fully connected neural networks based on data transposition strategies to learn the mapping relationships, replacing conventional solvers and thereby accelerating the training process. EDformer, through independent parameter training, achieves high forecasting accuracy while requiring high computational resources. EEDformer, on the other hand, adopts parameter-sharing techniques, slightly sacrificing forecasting accuracy to significantly improve computational efficiency, making the proposed EDformer family adaptable to different practical requirements. Validation using real data from Australia on the IEEE 9-Bus and 39-Bus systems proves the superiority of the EDformer family in enhancing day-ahead dispatch effectiveness.

 
@article{TIAN2025125319,
title = {EDformer family: End-to-end multi-task load forecasting frameworks for day-ahead economic dispatch},
journal = {Applied Energy},
volume = {383},
pages = {125319},
year = {2025},
issn = {0306-2619},
doi = {https://doi.org/10.1016/j.apenergy.2025.125319},
url = {https://www.sciencedirect.com/science/article/pii/S0306261925000492},
author = {Zhirui Tian and Weican Liu and Jiahao Zhang and Wenpu Sun and Chenye Wu},
}
 

This paper presents an approach to achieving user-perceptional privacy protection in Non-Intrusive Load Monitoring (NILM) datasets within the Differential Privacy (DP) framework. Our method is tailored to align privacy protection with genuine user expectations while simultaneously ensuring data authenticity and utility for downstream tasks dependent on pattern recognition. Specifically, based on DP, our privacy-preserving mechanism protects sensitive and critical user behaviors of appliance usage—namely, appliance start times, usage durations, and power levels. Based on user preferences, our mechanism allows for the flexible allocation of privacy budgets to these behaviors, offering customizable privacy protection. As protecting privacy harms data authenticity and utility for pattern recognition tasks, to solve this problem, we first utilize probability mass functions (pmfs) of user behaviors to effectively characterize appliance usage patterns. Then, our post-processing optimization utilizes a network flow framework to align the pmf of privacy-preserving behaviors closely with that of real behaviors. This alignment guarantees the authenticity and utility of appliance usage patterns in privacy-preserving data, ensuring that our privacy-preserving measures do not compromise the data’s accuracy and usefulness for analysis. To manage the significant computational burden of the network flow, we utilize the inherent sparsity of pmfs to reduce computational complexity, thereby enhancing the efficiency of our approach. Our approach allows users to prioritize the privacy of specific behaviors, demonstrating the method’s flexibility, efficiency, and applicability in real-world scenarios. Numerical validations reveal our method’s superiority in privacy preservation, data utility, and computational efficiency. Ultimately, this work underscores the importance of a user-centric approach to privacy that aligns with authentic user concerns and needs.

 
@article{ZHANG2025125233,
title = {User-perceptional privacy protection in NILM: A differential privacy approach},
journal = {Applied Energy},
volume = {382},
pages = {125233},
year = {2025},
issn = {0306-2619},
doi = {https://doi.org/10.1016/j.apenergy.2024.125233},
url = {https://www.sciencedirect.com/science/article/pii/S0306261924026175},
author = {Jiahao Zhang and Chenbei Lu and Hongyu Yi and Chenye Wu},
}
 

This study addresses the challenges of privacy, utility, and efficiency in releasing privacy-preserving operational data for AC Optimal Power Flow (AC-OPF) research. Traditional methods, injecting noise into operational data (i.e., demand data and dispatch profiles) within the Differential Privacy (DP) framework, often violate physical constraints within the data, leading to unrealistic and infeasible outcomes that diminish data utility. While AC-OPF-solver-based bi-level post-processing optimizations can enforce physical feasibility, the objective divergence between post-processing and AC-OPF leads to discrepancies, compromising data utility. Additionally, their non-convex and adversarial nature makes computation prohibitively expensive, further preventing efficient data release. To overcome these challenges, our research introduces a DP approach that combines strategic noise injection for demand data with the computation of corresponding dispatch profiles, ensuring the privacy-preserving data satisfy AC-OPF’s physical constraints. To accelerate data release, we employ Physics-Informed Neural Networks (PINNs). This ensures solutions’ physical feasibility while enhancing computational efficiency. Furthermore, we incorporate active learning to target the most informative data samples, enhancing PINN training and optimizing efficiency while maintaining solution accuracy. Comprehensive experiments on IEEE test systems reveal our approach’s improved performance and accelerated computation speed over traditional methods, highlighting its efficiency in maintaining data privacy and utility and decreasing computational burden amidst diverse privacy considerations.

 
@article{ZHANG2025124714,
title = {Computationally efficient data synthesis for AC-OPF: Integrating Physics-Informed Neural Network solvers and active learning},
journal = {Applied Energy},
volume = {378},
pages = {124714},
year = {2025},
issn = {0306-2619},
doi = {https://doi.org/10.1016/j.apenergy.2024.124714},
url = {https://www.sciencedirect.com/science/article/pii/S030626192402097X},
author = {Jiahao Zhang and Ruo Peng and Chenbei Lu and Chenye Wu},
}
 

Independent system operators (ISOs) are pursuing day-ahead probabilistic load forecasting as it offers comprehensive load trend and pattern information. Compared with commonly adopted point forecasting, it enables ISOs to better understand the uncertainty of future demand through interval forecasting with varying confidence levels. In practice, this advantage could enable precise day-ahead forecasting for critical days with irregular load patterns (e.g., weekends or holidays), particularly when the data availability is limited. To this end, we customize a day-ahead probabilistic load forecasting framework with an emphasis on weekends based on data processing and probabilistic deep learning. Specifically, data processing combines data denoising and data augmentation techniques, incorporating peak and trend information into the denoised one-dimensional time series data to aid training. This procedure helps extract more information from the restricted training samples. The probabilistic deep learning, CNNs-Transformer, combines multi-layer Convolutional Neural Networks and Transformer, adopting QRLoss (quantile regression loss function) to achieve probabilistic forecasting. The loss penalty technique enhances the model鈥檚 attention to weekend data. Numerical studies based on field data suggest that the proposed framework can obtain accurate day-ahead probabilistic forecasting results (48-time points of the whole day) by using only two-week historical data, and the accuracy improvement over its rivals is remarkable on weekends.

 
@article{TIAN2024130666,
title = {CNNs-Transformer based day-ahead probabilistic load forecasting for weekends with limited data availability},
journal = {Energy},
volume = {293},
pages = {130666},
year = {2024},
issn = {0360-5442},
doi = {https://doi.org/10.1016/j.energy.2024.130666},
url = {https://www.sciencedirect.com/science/article/pii/S0360544224004389},
author = {Zhirui Tian and Weican Liu and Wenqian Jiang and Chenye Wu},
}
 

The smart grid benefits and suffers from smart meter data. Proper use of massive data can improve energy services but may raise privacy concerns. For example, user energy consumption profiling, a classic method, can identify energy consumption patterns based on the collected load profiles from users. Thus, the privacy of these individual load profiles needs to be protected. However, most of the existing works focus on data transmission and calculation privacy, and often require additional computation, communication, or platform construction costs. In contrast, noise-injection-based data source privacy-protecting works can avoid such additional costs and provide theoretical differential privacy (DP) guarantee. This paper theoretically analyzes noise-injection-based user profiling mechanisms in terms of both privacy protection and accuracy. Specifically, we establish the privacy-accuracy trade-off. We then propose an optimal user energy consumption pattern estimation method for heterogeneous noise-injection-based data. Finally, we design a valid information ratio-based pricing scheme for noisy data that is independent of downstream tasks and easy to implement. Numerical studies based on field data confirm the effectiveness of our theoretical results.

 
@ARTICLE{10251453,
  author={Lu, Chenbei and Cui, Jingshi and Wang, Haoxiang and Yi, Hongyu and Wu, Chenye},
  journal={IEEE Transactions on Smart Grid}, 
  title={Privacy Preserving User Energy Consumption Profiling: From Theory to Application}, 
  year={2023},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TSG.2023.3315690}}
 

Transitioning to a low-/zero-carbon energy ecosystem requires a thorough and accurate understanding of how energy is consumed on the demand side. To achieve this goal, user profiling has become a crucial data analytical tool to understand consumers energy activities and characterize the patterns/trends of different consumers. Though useful, user profiling remains a challenge due to the high irregularity and volatility inherited in consumer energy activities. Moreover, traditional user profiling requires time-series load consumption data to be collected and processed in a centralized server, which increases processing budgets and privacy leakage risks. To address these challenges, we propose a federated shift-invariant dictionary learning clustering approach to enable distributed and computationally efficient user profiling. The proposed approach elucidates the characteristics of each user by decomposing its time-series load consumption data into a linear combination of electricity consumption patterns. Furthermore, with the aid of a federated learning framework, the proposed approach allows most user profiling steps to be completed locally. Simulation studies based on real-world dataset show that compared with conventional federated clustering methods, the proposed approach is capable of better revealing the patterns behind the load series data, which improves the accuracy and effectiveness of user profiling.

 
@ARTICLE{10187702,
  author={Huang, Qiushi and Jiang, Wenqian and Shi, Jian and Wu, Chenye and Wang, Dan and Han, Zhu},
  journal={IEEE Transactions on Power Systems}, 
  title={Federated Shift-Invariant Dictionary Learning Enabled Distributed User Profiling}, 
  year={2024},
  volume={39},
  number={2},
  pages={4164-4178},
  keywords={Load modeling;Dictionaries;Time series analysis;Hardware;Energy consumption;Data models;Clustering methods;User profiling;dictionary learning;federated learning},
  doi={10.1109/TPWRS.2023.3296976}}
 

The big data era has raised public concern regarding private information leakage. Therefore, in the electricity sector, many classical privacy preserving mechanisms based on noise injection have been designed and implemented for meter data. However, injected noise of large magnitudes can affect the statistical structure of these data. Therefore, in this study, we identify the inherent randomness embedded in time series data to mitigate this issue. To this end, we study the potential of using this inherent randomness to protect the privacy for both high and low resolution time series data. We propose a privacy preserving mechanism using stochastic differential equation modeling. We theoretically prove the effectiveness of our proposed framework and design several methods to implement our mechanism to aid various data-driven consumer behavior analysis tasks in the electricity sector. The numerical results indicate that our framework can simultaneously maintain the desired level of privacy preservation and value of data in practice.

 
@ARTICLE{9993771,
  author={Wang, Haoxiang and Wu, Chenye},
  journal={IEEE Transactions on Smart Grid}, 
  title={Privacy Preservation for Time Series Data in the Electricity Sector}, 
  year={2023},
  volume={14},
  number={4},
  pages={3136-3149},
  doi={10.1109/TSG.2022.3230685}}
 

Smart meters have collected massive amounts of fine-grained load data from users, enabling various load profile analyses that can help improve the efficiency of smart grids. However, the smart meter data may leak private information, raising public concerns. To address this issue, current approaches typically employ data perturbation mechanisms or data generation mechanisms to ensure privacy when analyzing load profiles, but these approaches are either inflexible or not guaranteed to mitigate the leakage issue. To this end, we propose a differentially private Wasserstein Generative Adversarial Networks (DPWGAN) approach in this study. This approach can privately convert a real-world dataset into a high-quality synthetic load dataset so that studies and analyses conducted on the synthetic dataset can automatically satisfy user-level differential privacy guarantees. The extensive numerical studies highlight that our approach acts as an excellent substitute for the original dataset in real-world load profiling tasks.

 
@ARTICLE{9993791,
  author={Huang, Jiaqi and Huang, Qiushi and Mou, Gaoyang and Wu, Chenye},
  journal={IEEE Transactions on Smart Grid}, 
  title={DPWGAN: High-Quality Load Profiles Synthesis With Differential Privacy Guarantees}, 
  year={2023},
  volume={14},
  number={4},
  pages={3283-3295},
  doi={10.1109/TSG.2022.3230671}}
 

Price competition among electric vehicle (EV) charging stations is as fierce as the competition among gas stations. Nash equilibrium (NE) is a solution concept that can characterize a competition’s efficient and stable state. However, calculation of the equilibrium is often time-consuming and requires complete information on the charging stations. Rapidly changing charging stations often hinder reaching equilibrium. In this study, we analyze price competition with service capacity constraints and use an ordinal potential game framework to investigate the structure of the competition. By constructing the ordinal potential function, the equilibrium characterization is converted to identifying the solution through a single-objective optimization. We further propose a decentralized algorithm to enable effective price coordination to achieve equilibrium with maximized social welfare. To preserve the privacy of charging stations from internal collusion and external attacks, an advanced secure multi-party computation technology known as the Paillier Cryptosystem is customized for our proposed decentralized algorithm. Numerical studies based on field data suggest the significance of our framework.

 
@article{LU2022108355,
title = {Privacy-preserving decentralized price coordination for EV charging stations},
journal = {Electric Power Systems Research},
volume = {212},
pages = {108355},
year = {2022},
issn = {0378-7796},
doi = {https://doi.org/10.1016/j.epsr.2022.108355},
author = {Chenbei Lu and Jiaman Wu and Chenye Wu}
}
 

Smart meter devices enable a better understanding of the demand at the potential risk of private information leakage. One promising solution to mitigating such risk is to inject noises into the meter data to achieve a certain level of differential privacy. In this article, we cast one-shot non-intrusive load monitoring (NILM) in the compressive sensing framework, and bridge the gap between the NILM inference accuracy and differential privacy's parameters. We then derive the valid theoretical bounds to offer insights on how the differential privacy parameters affect the NILM performance. Moreover, we generalize our conclusions by proposing the hierarchical framework to solve the multi-shot NILM problem. Numerical experiments verify our analytical results and offer better physical insights of differential privacy in various practical scenarios. This also demonstrates the significance of our work for the general privacy preserving mechanism design.

 
@ARTICLE{9261407,
  author={Wang, Haoxiang and Zhang, Jiasheng and Lu, Chenbei and Wu, Chenye},
  journal={IEEE Transactions on Smart Grid}, 
  title={Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective}, 
  year={2021},
  volume={12},
  number={3},
  pages={2529-2543},
  doi={10.1109/TSG.2020.3038757}
  }
 

The ever-intensifying threat of climate change renders the electric power system undergoing a profound transition toward net-zero emissions. Energy efficiency measures, such as demand response, facilitate the transformation to jointly relieve consumers’ financial burden and improve the operability of the electric power grid, in a carbon-free way. In this paper, we design a cluster-based appliance-level-of-use demand response program, based on the massive volume of appliance consumption data, to expand the role demand response can play in the power grid’s low-carbon transition. We systematically model the appliance-level utility function to distinguish consumers’ distinct consumption patterns. We then develop a bi-level optimization model to capture the interactions between individual consumers and a distribution system operator (DSO) and enable appliance-level-of-use demand response functions. To further improve the efficiency and scalability of the proposed mechanism, we propose a cluster-based approach to capture the heterogeneity of users based on their energy consumption behaviors. Simulation results show that by capturing the detailed appliance-level response patterns, the proposed approach can systematically improve overall social welfare compared with conventional demand response mechanisms.

 
@ARTICLE{10543097,
  author={Gu, Nan and Yuan, Enming and Wu, Chenye},
  journal={IEEE Control Systems Letters}, 
  title={Uncertainty Set Learning for Adaptive Robust Economic Dispatch}, 
  year={2024},
  volume={8},
  number={},
  pages={1156-1161},
  keywords={Uncertainty;Optimization;Costs;Real-time systems;Generators;Adaptation models;Vectors;Machine learning;optimization;power systems},
  doi={10.1109/LCSYS.2024.3408039}}

 

The ever-intensifying threat of climate change renders the electric power system undergoing a profound transition toward net-zero emissions. Energy efficiency measures, such as demand response, facilitate the transformation to jointly relieve consumers’ financial burden and improve the operability of the electric power grid, in a carbon-free way. In this paper, we design a cluster-based appliance-level-of-use demand response program, based on the massive volume of appliance consumption data, to expand the role demand response can play in the power grid’s low-carbon transition. We systematically model the appliance-level utility function to distinguish consumers’ distinct consumption patterns. We then develop a bi-level optimization model to capture the interactions between individual consumers and a distribution system operator (DSO) and enable appliance-level-of-use demand response functions. To further improve the efficiency and scalability of the proposed mechanism, we propose a cluster-based approach to capture the heterogeneity of users based on their energy consumption behaviors. Simulation results show that by capturing the detailed appliance-level response patterns, the proposed approach can systematically improve overall social welfare compared with conventional demand response mechanisms.

 
@article{WU2024123003,
title = {A cluster-based appliance-level-of-use demand response program design},
journal = {Applied Energy},
volume = {362},
pages = {123003},
year = {2024},
issn = {0306-2619},
doi = {https://doi.org/10.1016/j.apenergy.2024.123003},
url = {https://www.sciencedirect.com/science/article/pii/S0306261924003866},
author = {Jiaman Wu and Chenbei Lu and Chenye Wu and Jian Shi and Marta C. Gonzalez and Dan Wang and Zhu Han}
}
 

As renewable energy becomes more prevalent in the power grid, energy storage systems (ESSs) are playing an ever-increasingly crucial role in mitigating short-term supply–demand imbalances. However, the operation and control of ESS are not straightforward, given the ever-changing electricity prices in the market environment and the stochastic and intermittent nature of renewable energy generations, which respond to real-time load variations. In this paper, we propose a deep reinforcement learning (DRL) approach to address the electricity arbitrage problem associated with optimal ESS management. First, we analyze the structure of the optimal offline ESS control problem using the mixed-integer linear programming (MILP) formulation. This formulation identifies optimal control actions to absorb excess renewable energy and perform price arbitrage strategies. To tackle the uncertainties inherent in the prediction data, we then recast the online ESS control problem into a Markov Decision Process (MDP) framework and develop the DRL approach, which involves integrating the optimal offline control solution obtained from the training data into the training process and introducing noise to the state transitions. Unlike typical offline DRL training over a long time interval, we employ the Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) algorithms with smaller neural networks training over a short time interval. Numerical studies demonstrate the promising potential of the proposed DRL-enabled approach for achieving better online control performance than the model predictive control (MPC) method under different price errors. This highlights the sample efficiency and robustness of our DRL approaches in managing ESS for electricity arbitrage.

 
@article{XU2024122915,
title = {An optimal solutions-guided deep reinforcement learning approach for online energy storage control},
journal = {Applied Energy},
volume = {361},
pages = {122915},
year = {2024},
issn = {0306-2619},
doi = {https://doi.org/10.1016/j.apenergy.2024.122915},
url = {https://www.sciencedirect.com/science/article/pii/S0306261924002988},
author = {Gaoyuan Xu and Jian Shi and Jiaman Wu and Chenbei Lu and Chenye Wu and Dan Wang and Zhu Han}
}
 

Renewable energy penetration increases the power grid's operational uncertainty, threatening the economic effectiveness and reliability of the grid. In this paper, we examine how uncertainty affects unit commitment (UC), a classical electricity market procedure. Stochastic programming has helped handle uncertainty for UC and performed well with distribution knowledge, but the lack of such information in practice deteriorates the effectiveness. Such a dilemma becomes more pronounced when dealing with joint chance constraints solely based on samples. To address this issue, we introduce statistical feasibility into UC and develop robust sample-based algorithms employing appropriate uncertainty sets to hedge uncertainty without distribution dependence. We also propose a learn-to-optimize acceleration method to convexify UC. Furthermore, we construct an optimization kernel to boost computational efficiency.

 
@ARTICLE{10384836,
  author={Liang, Jinhao and Jiang, Wenqian and Lu, Chenbei and Wu, Chenye},
  journal={IEEE Transactions on Power Systems}, 
  title={Joint Chance-constrained Unit Commitment: Statistically Feasible Robust Optimization with Learning-to-Optimize Acceleration}, 
  year={2024},
  volume={},
  number={},
  pages={1-13},
  doi={10.1109/TPWRS.2024.3351435}}
 

The integration of distributed energy resources, particularly wind energy, presents both opportunities and challenges for the modern electrical grid. On the supply side, wind farms frequently encounter penalties due to wind power’s intermittency and variability. The incorporation of energy storage systems can mitigate these penalties through real-time power adjustments. However, the uncertainties in future renewable generation significantly impede optimal storage control, and existing algorithms either lack theoretical guarantees, or fail to effectively leverage data to attain better performance. This paper effectively addresses this dichotomy by bridging the gap between data utilization and theoretical guarantees based on the Markov decision process. Specifically, we first introduce a one-shot online storage control algorithm that utilizes historical data to make near-optimal decisions with theoretical performance guarantees. To further enable continuous learning from new data, we develop an online learning-based self-improving storage control algorithm, underscoring its asymptotic optimality. The numerical study using field data demonstrates the efficacy of the proposed approach.

 
@ARTICLE{10382539,
  author={Lu, Chenbei and Yi, Hongyu and Zhang, Jiahao and Wu, Chenye},
  journal={IEEE Transactions on Smart Grid}, 
  title={Self-Improving Online Storage Control for Stable Wind Power Commitment}, 
  year={2024},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TSG.2024.3350895}}
 

The high penetration of renewable energy brings significant uncertainty to the power grids. Taking economic dispatch (ED) as an example, the inaccurate prediction of renewable energy generations dramatically increases the dispatch cost and risks the power grid's reliable operation. The accurate distribution knowledge of the renewable generations enables modeling the ED as stochastic programming with joint chance constraints, which various classical methods can tackle. However, in practice, such distribution knowledge is inaccessible, and we can only observe samples from some unknown distribution. This makes conducting effective ED solely based on the observed samples challenging. It is particularly true when we need to handle the joint chance constraints. To tackle these challenges, we introduce the notions of statistical feasibility and statistically feasible ED to guarantee the satisfaction of the joint chance constraints. Specifically, we first propose a sample-adaptive robust optimization (RO) to decouple the joint constraints. We then identify that the inaccurate uncertainty set leads to RO's conservativeness, and then reconstruct the constraint-specific uncertainty sets. We design the corresponding sample-adaptive reconstruction-based RO (ReconRO) based on the reconstructed uncertainty sets to further enhance the ED's effectiveness.

 
@ARTICLE{10102582,
  author={Lu, Chenbei and Gu, Nan and Jiang, Wenqian and Wu, Chenye},
  journal={IEEE Transactions on Power Systems}, 
  title={Sample-Adaptive Robust Economic Dispatch With Statistically Feasible Guarantees}, 
  year={2024},
  volume={39},
  number={1},
  pages={779-793},
  doi={10.1109/TPWRS.2023.3267097}}
 

To fully exploit the flexible potential of distributed energy resources (DERs) in providing balancing service to the power system, Virtual Power Plants (VPPs) act as control centers to conduct the optimal real-time dispatch of their managed DERs. This study investigates a VPP’s auto-tuned robust policy based on a multi-stage distributionally robust optimization model (DRO) in response to the uncertainties from both the setpoint of the top-level system operator (SO) and the outputs of renewable DERs. We propose a concise paradigm to reduce the complexity of the original large-scale optimization task. Specifically, we first cast the multi-stage DRO problem into a dynamic programming (DP) formulation and further simplify it to derive a single-stage convex optimization control policy (COCP) at each time stage. Further, an automatic update method based on implicit differentiation is employed to tune the parameters of COCP. Case studies show that this method ensures higher solution quality and faster convergence during training than conventional tuning methods. The proposed COCP outperforms other stochastic optimization techniques in terms of robustness, efficiency, and computational speed.

 
@ARTICLE{10097549,
  author={Gu, Nan and Cui, Jingshi and Wu, Chenye},
  journal={IEEE Transactions on Smart Grid}, 
  title={An Auto-Tuned Robust Dispatch Strategy for Virtual Power Plants to Provide Multi-Stage Real-Time Balancing Service}, 
  year={2023},
  volume={14},
  number={6},
  pages={4494-4507},
  doi={10.1109/TSG.2023.3265398}}
 

Flexible resources are increasingly significant for the reliable operation of power grids due to the high penetration of renewable energy. Thermostatically controlled loads (TCLs) are one of the common flexible resources, whose control has been extensively studied. Yet, much can be improved. We investigate the scheduling of TCLs facing uncertain temperatures and dynamic prices. Classical approaches often employ the chance-constrained program or robust optimization to handle such uncertainties. However, these approaches either require specific distribution knowledge or yield too conservative solutions. The distribution knowledge can be rather challenging to obtain especially when the uncertainties from different sources are correlated. To this end, we adopt the notion of statistical feasibility and propose a robust sample-based scheduling scheme for TCLs. Such a sample-based scheme relieves the reliance on the distribution knowledge and is able to characterize the coupling effects of the two uncertainty sources. Besides, through integrating the real-time domain knowledge into uncertainty set reconstruction, we relax the solutions’ conservation by exploring the consumers’ tolerance to the room temperature. Numerical studies highlight the remarkable performance of our proposed scheme. Specifically, our approach is able to simultaneously effectively reduce the electricity bills of consumers and satisfy the consumers’ tolerance to the room temperature.

 
@ARTICLE{10024307,
  author={Jiang, Wenqian and Lu, Chenbei and Wu, Chenye},
  journal={IEEE Transactions on Smart Grid}, 
  title={Robust Scheduling of Thermostatically Controlled Loads With Statistically Feasible Guarantees}, 
  year={2023},
  volume={14},
  number={5},
  pages={3561-3572},
  doi={10.1109/TSG.2023.3238997}}
 

Conventional wisdom to improve economic dispatch effectiveness is to design the load forecasting method as accurately as possible. However, this approach can be problematic due to the temporal and spatial correlations between system cost and load prediction errors. This observation motivates us to jointly treat the two forms of correlations by adopting the notion of end-to-end machine learning. Thus, we first propose a task-specific learning criterion to conduct economic dispatch for maximal economic benefits. To reduce the task-specific approach's computational burden and over-fitting issues, we design an efficient optimization kernel to speed up the learning process. Additionally, we propose a more practical and robust model-free end-to-end learning framework and offer theoretical analysis and empirical insights to highlight the effectiveness and efficiency of our three proposed learning frameworks. Our numerical study highlights that the model-free framework decreases the additional cost from an inaccurate prediction by 5.49% in the IEEE 39-bus system compared with the conventional approach.

 
@ARTICLE{9762035,
  author={Lu, Chenbei and Jiang, Wenqian and Wu, Chenye},
  journal={IEEE Transactions on Network Science and Engineering}, 
  title={Effective End-to-End Learning Framework for Economic Dispatch}, 
  year={2022},
  volume={9},
  number={4},
  pages={2673-2683},
  doi={10.1109/TNSE.2022.3168845}}
 

The Internet of Things (IoT) enables reliable and fast data collection and transmission, providing key infrastructure for power generation, distribution, and control in the smart grid. This IoT-enabled smart grid tackles challenges brought by renewable penetration in new ways: Accurate and real-time information allows for the application of artificial-intelligence-powered computation. We employ the deep learning framework and consider the problem of storage control facing uncertainties in renewable generation. We propose both model-based and model-free storage control frameworks to identify the value of information. For the first framework, opposing to most deep-learning-oriented research in the electricity sector, we use the one-shot load decomposition technique to encode structural information into the learning framework. The structural information refers to the fact that the one-shot load decomposition maintains the control strategy space. Based on this structural information, we develop the storage control policy by utilizing a deep learning framework for price and renewable prediction, which is the basis of our deep-learning-enabled storage control. For the model-free framework, we regard historical price and demand data as input and directly output the control actions. For each model, we further establish theoretical analysis on how the uncertainties in price and renewables influence the cost. Numerical evaluations illustrate the remarkable performance of our proposed frameworks and reveal the value of information.

 
@ARTICLE{9733941,
  author={Wu, Jiaman and Lu, Chenbei and Wu, Chenye},
  journal={IEEE Systems Journal}, 
  title={Learning-Aided Framework for Storage Control Facing Renewable Energy}, 
  year={2023},
  volume={17},
  number={1},
  pages={652-663},
  doi={10.1109/JSYST.2022.3154389}}
 

In the electricity sector the carbon tax is a common environmental policy aiming to reduce CO 2 emissions, but is often regarded as economically unfriendly, especially for areas relying on coal-fire and other carbon-intensive generators. A power grid utilizing an energy storage system can be a promising solution to alleviate the regional economy pressure in a grid where the carbon tax is enforced. With the increasing exploitation of clean energy, e.g., solar and wind power, in this work, we characterize the stochastic emission-aware economic dispatch with a storage system utilizing two frameworks, namely a chance-constrained framework and a robust optimization framework. We highlight their differences and connections by studying the trade-offs between robustness and overall cost. Specifically, we bridge the two frameworks with a novel distributed robust optimization framework that considers practical bounds to estimate the optimal system performance under the reliability requirement. Numerical studies on the six-bus model and the IEEE-118 bus model further justify our findings.

 
@ARTICLE{9508851,
  author={Gu, Nan and Wang, Haoxiang and Zhang, Jiasheng and Wu, Chenye},
  journal={IEEE Transactions on Power Systems}, 
  title={Bridging Chance-Constrained and Robust Optimization in an Emission-Aware Economic Dispatch With Energy Storage}, 
  year={2022},
  volume={37},
  number={2},
  pages={1078-1090},
  doi={10.1109/TPWRS.2021.3102412}}
 

Dynamic pricing is both an opportunity and a challenge to the end-users. It is an opportunity as it better reflects the real-time market conditions and hence enables an active demand side. However, demand’s active participation does not necessarily lead to benefits. The challenge conventionally comes from the limited flexible resources and limited intelligent devices on the demand side. The decreasing cost of the storage system and the widely deployed smart meters inspire us to design a data-driven storage control framework for dynamic prices. Our work first establishes a stylized model by assuming the knowledge on the structure of dynamic price distributions and designs the optimal storage control policy. Based on Gaussian Mixture Model, we propose a practical data-driven control framework, which helps relax the assumptions in the stylized model. Numerical studies illustrate the remarkable performance of the proposed data-driven framework.

 
@ARTICLE{9149932,
  author={Wu, Jiaman and Wang, Zhiqi and Wu, Chenye and Wang, Kui and Yu, Yang},
  journal={IEEE Transactions on Smart Grid}, 
  title={A Data-Driven Storage Control Framework for Dynamic Pricing}, 
  year={2021},
  volume={12},
  number={1},
  pages={737-750},
  doi={10.1109/TSG.2020.3012124}}
 

The widespread adoption of electric vehicles (EVs) renders conventional EV charging modes unable to relieve the spatial and temporal charging inconvenience of drivers. To tackle this issue, we consider a promising business model for charging stations providing hybrid fixed and mobile EV charging modes in this paper. According to factors such as personal preferences and distance to charging stations, EV drivers are divided into two types: fixed charging drivers who only consider choosing the fixed charging mode, and mobile charging drivers who only choose mobile charging mode. We first characterize the utility functions of these two types of drivers, respectively. The characterization relies on the unit waiting costs and charging valuations of drivers, which are often estimated empirically. To facilitate more accurate estimation, we employ a data-driven computational model to quantify these two factors based on the qualitative choice theory. Next, under the rationality assumption of EV drivers, integrating the estimated results into the utility functions allows us to calculate the equilibrium fractions of fixed and mobile charging drivers who ultimately charge their EVs. Furthermore, to maximize the revenue of EV charging stations, we derive optimal prices for fixed and mobile charging modes. Finally, numerical analyses validate the effectiveness of our proposed future business model for EV charging stations.

 
@article{CUI2025125033,
title = {Pricing mechanism design for future EV charging station with hybrid fixed and mobile charging modes},
journal = {Applied Energy},
volume = {380},
pages = {125033},
year = {2025},
issn = {0306-2619},
doi = {https://doi.org/10.1016/j.apenergy.2024.125033},
url = {https://www.sciencedirect.com/science/article/pii/S0306261924024176},
author = {Jingshi Cui and Wenqian Jiang and Chenye Wu},
}
 

Sharing economy is believed to be able to improve the efficiency of the electricity sector by sharing the underutilized electricity storage. However, this is a delicate task because the optimal decision making often requires the distributional information of demand, which is often hard to obtain and characterize. Hence, the decision making has to rely on the historical samples. While the literature has investigated the sample-based storage control, little is known about historical samples’ theoretical impact on the storage sharing performance. To this end, we theoretically conduct the performance analysis for the sample-based storage sharing decisions. To highlight the impact of the samples, we consider two kinds of historical samples: one is pure demand information, and the other is additionally associated with external feature information. We investigate how these two kinds of samples enable the decision making with a performance guarantee. We further compare the theoretical performance bounds with the empirical results, indicating the effectiveness of the proposed methods and the tightness of the derived bounds. Our theoretical analysis provides a new perspective for efficient demand sample collection and cost-effective sharing mechanism design.

 
@ARTICLE{10230294,
  author={Jiang, Wenqian and Huang, Jiaqi and Xu, Gaoyuan and Wu, Chenye},
  journal={IEEE Transactions on Smart Grid}, 
  title={Sample-Oriented Electricity Storage Sharing Mechanism Design With Performance Guarantees}, 
  year={2024},
  volume={15},
  number={2},
  pages={2030-2043},
  keywords={Costs;Complexity theory;Load modeling;Sharing economy;Power system stability;Kernel;Visualization;Electricity storage;sample complexity;sharing economy},
  doi={10.1109/TSG.2023.3308686}}
 

Prior sample-based mechanisms rely predominately on empirical validations for their efficiency, with little attention to how finite samples theoretically impact decision-making. Additionally, differentially private noise injection before data publication further complicates the understanding of the samples' impact. To this end, taking electricity procurement as an example, we seek to theoretically quantify the impact of authentic and privacy-preserving samples on decision-making. Specifically, based on the customized sample average approximation procurement solution, we derive the minimum number of samples to guarantee near-optimal decisions. Numerical studies validate the theoretical bounds by comparing them to empirical observations. Our analysis offers practical insights into effective demand forecast mechanism design and efficient sample collection.

 
@ARTICLE{10420467,
  author={Jiang, Wenqian and Wu, Chenye},
  journal={IEEE Transactions on Energy Markets, Policy and Regulation}, 
  title={Optimal Electricity Procurement Enabled by Privacy-Preserving Samples}, 
  year={2024},
  volume={},
  number={},
  pages={1-11},
  keywords={Procurement;Decision making;Games;Electricity supply industry;Complexity theory;Regulation;Power markets;Electricity procurement;privacy-preserving;sample-based decision-making},
  doi={10.1109/TEMPR.2024.3361873}}
 

The increasingly popular electric vehicles (EVs) are changing the control paradigm of the power grid due to their uncoordinated charging behaviors. However, if well coordinated, smart homes, workplaces, and other locations that support EV charging could provide the grid with the urgently required flexibility via virtual power plants (VPP). In this paper, we develop the EV charging schedule model by capturing the unwillingness of EV drivers to alter their initial charging behaviors, referred to as the discomfort function. Predictability and the value of charging time, which represent the electricity consumption stability and the time value of EV drivers, characterize the discomfort function. Rather than existing works capturing discomfort by a direct simple parameter, such a computable data-driven quantification of discomfort enables us to customize an efficient VPP dispatch mechanism for EVs. In addition, to deal with the unknown charging efficiencies of EVs, we apply chance constraints only with the knowledge about mean and standard deviation of charging efficiencies, rather than their specific distribution. Using the concept of conditional value-at-risk (CVaR), we build an effective algorithm to solve the practical non-convex VPP dispatch model considering charging efficiencies. The effectiveness of our proposed models and associated algorithms are validated by numerical studies.

 
@article{CUI2023121984,
title = {Electric vehicles embedded virtual power plants dispatch mechanism design considering charging efficiencies},
journal = {Applied Energy},
volume = {352},
pages = {121984},
year = {2023},
issn = {0306-2619},
doi = {https://doi.org/10.1016/j.apenergy.2023.121984},
author = {Jingshi Cui and Jiaman Wu and Chenye Wu and Scott Moura}
}
 

Owners of renewable energy resources (RES) often choose to invest in energy storage for joint operation with RES to maximize profitability. Standalone entities also invest in energy storage systems and use them for arbitrage. In this paper we examine how these two forms of ownership affect the value of energy storage. Our study reveals that in a perfectly competitive market, energy storage holds equal value for both types of owners if they are risk-neutral. However, when agents are able to exert market power or exhibit risk aversion, the value of energy storage can differ between the two ownership structures. Additionally, we discuss how differential pricing and market barriers influence the value of energy storage. In the numerical studies, we explore how factors such as seasonal price volatility, RES types, and the siting of energy storage influence investment decisions.

 
@ARTICLE{10379467,
  author={Gu, Nan and Wu, Chenye and Kirschen, Daniel S.},
  journal={IEEE Transactions on Energy Markets, Policy and Regulation}, 
  title={Economic Value of Energy Storage Systems: The Influence of Ownership Structures}, 
  year={2024},
  volume={},
  number={},
  pages={1-15},
  doi={10.1109/TEMPR.2023.3349134}}
 

The high penetration of renewable energy increases the price volatility between the day-ahead (DA) and real-time (RT) markets, with heightened power system operational risks. Virtual bidding, a rising financial instrument, allows financial entities without energy-generating capacity or demand to arbitrage between the DA and RT markets, which can in turn reduce the market spread between the two markets and thus contain system operation risks. However, in practice, incomplete information often affects the effectiveness of virtual bidding, which poses uncertainties to strategic bidding behaviors, and makes it more challenging to understand the market manipulation. To control such risks, in this paper, we first game theoretically characterize the Nash Equilibrium of virtual bidding with both complete and incomplete information, and evaluate the benefits of virtual bidding for both virtual bidders (VBs) and the system as a whole. Then, we design a joint tax-subsidy mechanism for VBs with truthfulness and individual rationality guarantees against the market manipulation. We also prove that the system average forecast is the key to influencing the virtual bidding equilibrium. Further, we design two information mechanisms to enable VB privacy protection and market risk control separately. Numerical studies based on ISO-NE electricity market data verify our theory.

 
@ARTICLE{10269719,
  author={Lu, Chenbei and Liang, Jinhao and Gu, Nan and Wang, Haoxiang and Wu, Chenye},
  journal={IEEE Transactions on Energy Markets, Policy and Regulation}, 
  title={Manipulation-Proof Virtual Bidding Mechanism Design}, 
  year={2023},
  volume={},
  number={},
  pages={1-13},
  doi={10.1109/TEMPR.2023.3321649}}
 

Increasing public concern over climate change calls for high-level penetration of renewable energy sources into the future power grid, which makes the operation of the power grid fragile. One way to enhance the reliability of power grid operation is to equip each renewable generator with uncertainty management facilities such as conventional fast-responding generation units or storage systems. We identify a unified risk-limiting model for these diverse facilities. Specifically, in this paper, we consider two kinds of such facilities. The first one is the storage system, which has been traditionally utilized to enhance system reliability and reduce carbon emissions. We then propose the carbon allowance reserve (CAR), which, in a carbon emission aware economic dispatch, achieves the same goal as storage system does. The key to CAR is that it adopts conventional fast-responding generation units to conduct uncertainty management. We characterize the value of CAR by comparing the two kinds of facilities in the unified risk-limiting model. However, this is challenging because in a multi-period setting, solving the unified model alone is often intractable. Thus, we design an effective algorithm under mild assumptions on the renewable generation distributions. Next, we theoretically examine the robustness of the proposed algorithm, which highlights the practicability of the proposed algorithm. Numerical simulations further verify its effectiveness and provide comprehensive comparisons between the two kinds of uncertainty management facilities.

 
@inproceedings{10.1145/3575813.3576879,
author = {Sun, Jian and Zhang, Yaoyu and Wu, Chenye},
title = {Effective Risk-limiting Carbon Emission Aware Economic Dispatch: An Algorithmic Perspective},
year = {2023},
isbn = {9798400700323},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3575813.3576879},
doi = {10.1145/3575813.3576879},
booktitle = {Proceedings of the 14th ACM International Conference on Future Energy Systems},
pages = {84–98},
numpages = {15},
location = {Orlando, FL, USA},
series = {e-Energy '23}
}
 

The increasing penetration of distributed energy resources (DERs) brings the valuable flexibility to the system as well as challenges, which requires the coordination between transmission system operator (TSO) and distribution system operators (DSOs). This paper quantitatively models the goals and constraints of TSO and DSOs in procuring flexibility resources from DERs and proposes a flexibility market. We resolve the potential conflicts between TSO and DSOs in the flexibility market via mutual economic compensation, specified by Nash Bargaining (NB) theory. Two alternative TSO-DSOs interaction modes, i.e., the DSO-managed mode and the hybrid-managed mode, and two detailed payment schemes are discussed and compared. The hybrid-managed mode guarantees efficiency and fairness, while the DSO-managed mode is less complicated and can protect information privacy. Numerical studies further validate our findings.

 
@article{GU2022108329,
title = {A general Nash Bargaining solution to the TSO-DSOs coordinated flexibility trading market},
journal = {Electric Power Systems Research},
volume = {212},
pages = {108329},
year = {2022},
issn = {0378-7796},
doi = {https://doi.org/10.1016/j.epsr.2022.108329},
author = {Nan Gu and Jingshi Cui and Chenye Wu}
}
 

The increasing number of electric vehicles (EVs) on the road brings both opportunities and challenges to the power system. For the EV charging stations (EVCSs), it is often difficult to conduct effective operations due to the incomplete information in EVs’ departure times and the opacity of their preference information. To tackle this challenge, we seek to design the optimal deadline differentiated dynamic price menu that offers multiple choice-pairs of deadlines and charging prices. We prove that such price menus can incentivize EVs to truthfully reveal their departure time. We then analyze the properties of the optimal price menu with complete EV information, i.e., social optimality and first-degree price discrimination. For the incomplete information case, we first design a systematic method to estimate the utility and demand information for a large population of EVs based on EV behavior data. Then, we employ mixed-integer quadratic programming for the efficient optimal price menu design. The numerical study based on field data in California verifies the remarkable performance of our designed price menu.

 
@ARTICLE{9840998,
  author={Lu, Chenbei and Wu, Jiaman and Cui, Jingshi and Xu, Yanyan and Wu, Chenye and Gonzalez, Marta C.},
  journal={IEEE Transactions on Smart Grid}, 
  title={Deadline Differentiated Dynamic EV Charging Price Menu Design}, 
  year={2023},
  volume={14},
  number={1},
  pages={502-516},
  doi={10.1109/TSG.2022.3193898}}
 

The rapid development of carbon capture technology speeds up its industrialization and wide application with the help of massive investment. In addition to the capital market, such investment may also come from a well-designed carbon market. This paper proposes a green certificate auction to maximize the auction revenue for enabling the carbon capture technology. Besides political and regulatory requirements, the goodwill from contributing to carbon neutrality may also incentivize the generating companies to participate. The auction design is challenging as it associates with the economic dispatch procedure in the electricity market. Using the notion of virtual demand, we decouple the auction from economic dispatch, and we prove that our designed auction enjoys optimality, truthfulness, and individual rationality. We also show that our auction can be extended to the multi-period scenario, highlighting the impact of leftover certificates. We further provide an upper bound for sample complexity when the willingness of participants cannot be well-identified. Numerical studies verify the effectiveness of the proposed auction and the tightness of the derived sample complexity bound.

 
@inproceedings{10.1145/3538637.3538847,
author = {Wang, Haoxiang and Wu, Chenye},
title = {Optimal green certificate auction design embedding economic dispatch},
year = {2022},
isbn = {9781450393973},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
doi = {10.1145/3538637.3538847},
booktitle = {Proceedings of the Thirteenth ACM International Conference on Future Energy Systems},
pages = {157–171},
numpages = {15},
location = {Virtual Event},
series = {e-Energy '22}
}
 

The COVID-19 has slowed down global economic growth. Meanwhile, it also significantly cuts the global carbon emission, which provides a golden opportunity for the whole world to combat the climate change together. While the former policies (e.g., the CAFE standards, renewable portfolio standards, etc.) have reduced certain level of fossil fuel consumption, the most effective measures (such as carbon tax, cap-and-trade programs) are still far from ready for global implementation. This paper investigates an alternative way to achieve a more carbon efficient power grid using the uplift payment scheme. Specifically, we propose an effective algorithm to guarantee carbon efficiency with minimal uplift payments. We also submit that this scheme provides more flexibility to realize carbon reduction than carbon tax, which is exemplified by thorough numerical studies. Furthermore, we show that the stability of the power grid can be ensured under our uplift payment scheme, both from theoretical analysis and numerical studies. The results strengthen our belief that our uplift payment scheme is practicable for carbon reduction in the electricity market.

 
@ARTICLE{9721117,
  author={Zhang, Jiasheng and Sun, Jian and Wu, Chenye},
  journal={IEEE Transactions on Sustainable Energy}, 
  title={Enable a Carbon Efficient Power Grid via Minimal Uplift Payments}, 
  year={2022},
  volume={13},
  number={3},
  pages={1329-1343},
  doi={10.1109/TSTE.2022.3152774}}
 

Power electronic devices are being widely deployed in the distribution network due to their voltage control and reactive compensation capabilities. These capabilities can significantly simplify the power flow formulation, relieving the computational burden of the distributed locational marginal price (distributed LMP), which is a promising pricing scheme for the distribution network. It not only enjoys the benefits of LMP for the transmission network, but also incentivizes the distributed renewable energy integration. Furthermore, it also relieves the pressure on the heavy-loaded power supply paths. Specifically, we propose a transactive energy market where the distributed generators act as independent suppliers. The market is practically formed by the remote-control switches-enabled reconfiguration of the distribution network. We model this market as a cooperative Stackelberg game and study the motivations and the dynamic interaction of various participants. Also, we design a budget-balanced and individual rational market-clearing rule taking into account the power loss. We also discuss in detail the effects and benefits of forming the transactive energy market. Finally, we provide numerical studies based on an IEEE 33-bus and a modified IEEE 123-bus distribution network to demonstrate the performance of this market design and further validate our conclusions.

 
@ARTICLE{9612584,
  author={Gu, Nan and Cui, Jingshi and Wu, Chenye},
  journal={IEEE Transactions on Smart Grid}, 
  title={Power-Electronics-Enabled Transactive Energy Market Design for Distribution Networks}, 
  year={2022},
  volume={13},
  number={5},
  pages={3968-3983},
  doi={10.1109/TSG.2021.3127544}}
 

Uncertainties in renewable generation make accurate load forecast essential for reliable power system operation. This paper considers energy imbalance market (EIM), where market players are allowed to procure energy ahead of time and trade the mismatch due to forecast error and strategic behaviors. The ISO sets the trading prices according to the market conditions, and pursues various system-level objectives. We first identify the power-law relationship between data volume and forecast accuracy, which enables the formulation of forecast cost model. Then, we cast the interactions in the EIM in the Stackelberg game framework with the ISO acting as the leader. We offer explicit subgame perfect equilibrium among the players in EIM, and derive the sufficient condition for the existence of unique equilibrium. Then, we show that this equilibrium, if exists, supports the maximal social welfare and under certain conditions, minimizes the total mismatch. We further examine the local and global impacts of the forecast errors under mild conditions, together with robustness analysis. Such analysis provides mechanism design guidelines for the ISO to enable the data sharing and forecast method sharing among market players in the EIM. Numerical studies further examine the effectiveness, robustness and sensitivity of the subgame.

 
@ARTICLE{9560085,
  author={Cui, Jingshi and Gu, Nan and Zhao, Tianyu and Wu, Chenye and Chen, Minghua},
  journal={IEEE Transactions on Power Systems}, 
  title={Forecast Competition in Energy Imbalance Market}, 
  year={2022},
  volume={37},
  number={3},
  pages={2397-2413},
  doi={10.1109/TPWRS.2021.3117967}}
 

Due to the increasing penetration of renewable energies, the energy imbalance market (EIM) is proposed to better facilitate the real time supply demand balance in the power system, by rewarding the market participants with better forecasts for the market conditions (i.e., the mismatch in the system). Together with many other financial instruments in the electricity sector, EIM calls for the market participants to strive for improving their forecast abilities. This increases the need for a data market in place. However, data market, compared with conventional commodity markets, has numerous unique impediments, such as distrust and data mutability issues. To tackle these challenges, we design blockchain enabled data transmission for centralized and decentralized EIM, respectively. We submit that although decentralized market is often a trade off between autonomy and market efficiency, there are conditions when decentralized market and centralized EIM achieve the same efficiency. Numerical studies further suggest, even when the conditions are violated, the efficiency loss in the decentralized EIM is still acceptable.

 
@ARTICLE{9525182,
  author={Cui, Jingshi and Gu, Nan and Wu, Chenye},
  journal={IEEE Transactions on Sustainable Energy}, 
  title={Blockchain Enabled Data Transmission for Energy Imbalance Market}, 
  year={2022},
  volume={13},
  number={2},
  pages={1254-1266},
  doi={10.1109/TSTE.2021.3108170}}
 

Convex hull pricing (CHP) was proposed to align the unit commitment and economic dispatch of the electricity market's sequential processes, by providing the minimal uplift payment. The implementation of CHP and its variants has generated much attention in both academia and industry. However, the vulnerability of CHP has been rarely assessed due to its complex structure. In this paper, to tackle this challenge, an equivalent form of CHP is identified, which provides valuable economic and structural insights. This equivalent form helps in revealing how generator bidding can influence the CHP. Based on this understanding, a vulnerability index is proposed to evaluate the risk that each generator brings to the CHP scheme. Numerical studies suggest the existence of vulnerability in the CHP and also highlight the complex nature of CHP scheme.

 
@inproceedings{10.1145/3447555.3464862,
author = {Sun, Jian and Wu, Chenye},
title = {Temporal Vulnerability Assessment for Convex Hull Pricing},
year = {2021},
isbn = {9781450383332},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
doi = {10.1145/3447555.3464862},
booktitle = {Proceedings of the Twelfth ACM International Conference on Future Energy Systems},
pages = {124–136},
numpages = {13},
keywords = {Convex Hull Pricing, Market Monitoring, Mixed Integer Programming},
location = {Virtual Event, Italy},
series = {e-Energy '21}
}
 

Sharing economy has disrupted many industries. We foresee that electricity storage systems will enable sharing economy in the electricity sector, though its optimal utilization is a delicate task, especially for general Time-of-Use (ToU) pricing, which consists of multiple tiers. The difficulty comes from the hedging against multiple tiers and the temporally coupled decisions. To design the efficient energy sharing mechanism for single peaked ToU scheme, we first identify that it suffices to understand the arbitrage policies for two forms of 3-tier ToU schemes. Based on such analysis, we propose our sharing mechanism design, with two coupled games, namely the capacity decision game and the aggregator user interaction game. We then submit that under mild conditions, the sharing mechanism yields a unique equilibrium, which supports the maximal social welfare.

 
@article{WANG2021107176,
title = {Optimal electricity storage sharing mechanism for single peaked time-of-use pricing scheme},
journal = {Electric Power Systems Research},
volume = {195},
pages = {107176},
year = {2021},
issn = {0378-7796},
doi = {https://doi.org/10.1016/j.epsr.2021.107176},
author = {Kui Wang and Yang Yu and Chenye Wu}
}