@article{shirsat_muthukaruppan_hu_paduani_xu_song_li_lu_baran_lubkeman_et al._2023, title={A Secure and Adaptive Hierarchical Multi-Timescale Framework for Resilient Load Restoration Using a Community Microgrid}, volume={14}, ISSN={["1949-3037"]}, url={https://doi.org/10.1109/TSTE.2023.3251099}, DOI={10.1109/TSTE.2023.3251099}, abstractNote={Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages. At such times, the CMGs are challenged with limited resource availability, absence of robust grid support, and heightened demand-supply uncertainty. This paper proposes a secure and adaptive three-stage hierarchical multi-timescale framework for scheduling and real-time (RT) dispatch of CMGs with hybrid PV systems to address these challenges. The framework enables the CMG to dynamically expand its boundary to support the neighboring grid sections and is adaptive to the changing forecast error impacts. The first stage solves a stochastic extended duration scheduling (EDS) problem to obtain referral plans for optimal resource rationing. The intermediate near-real-time (NRT) scheduling stage updates the EDS schedule closer to the dispatch time using new obtained forecasts, followed by the RT dispatch stage. To make the decisions more secure and robust against forecast errors, a novel concept called delayed recourse is designed. The approach is evaluated via numerical simulations on a modified IEEE 123-bus system and validated using OpenDSS and hardware-in-loop simulations. The results show superior performance in maximizing load supply and continuous secure distribution network operation under different operating scenarios.}, number={2}, journal={IEEE TRANSACTIONS ON SUSTAINABLE ENERGY}, author={Shirsat, Ashwin and Muthukaruppan, Valliappan and Hu, Rongxing and Paduani, Victor Daldegan and Xu, Bei and Song, Lidong and Li, Yiyan and Lu, Ning and Baran, Mesut and Lubkeman, David and et al.}, year={2023}, month={Apr}, pages={1057–1075} }
 @article{jia_li_yan_xu_chen_2022, title={A Reinforcement-learning-based Bidding Strategy for Power Suppliers with Limited Information}, volume={10}, ISSN={["2196-5420"]}, DOI={10.35833/MPCE.2020.000495}, abstractNote={The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors. Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy. However, this assumption may not be true in reality, particularly when a power market is newly launched. To help power suppliers bid with the limited information, a modified continuous action reinforcement learning automata algorithm is proposed. This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game. Simulation results verify the effectiveness of the proposed learning algorithm.}, number={4}, journal={JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY}, author={Jia, Qiangang and Li, Yiyan and Yan, Zheng and Xu, Chengke and Chen, Sijie}, year={2022}, month={Jul}, pages={1032–1039} }
 @article{huang_han_yan_li_sun_jia_2022, title={Bidding strategy of energy storage in imperfectly competitive flexible ramping market via system dynamics method}, volume={136}, ISSN={["1879-3517"]}, DOI={10.1016/j.ijepes.2021.107722}, abstractNote={Comparing to the energy market, the market scale of the newly built flexible ramping market is limited. As a result, it is easy for participants to utilize market power to manipulate the market clearing results. As one of the price maker participants, the bidding strategy of energy storage in such imperfectly competitive market is discussed at first. Punishments imposed by the independent system operator (ISO) on capacity withholding and bidding up the price are taken into consideration. The causal feedback and quantitative relationship among various factors in the bidding and market clearing process are analyzed. Bidding modules for different participants and the two-stage market clearing module for ISO are then established via the system dynamics (SD) method. The complete bidding and market clearing model is formed and simulated. Based on the simulation results, the adjustment process of the energy storage’s bidding strategy is investigated. Through a sensitivity test, the impacts of energy storage’s bidding strategy on the market clearing results are illustrated. The benefits of introducing energy storage into the flexible ramping market with imperfect competition are also evaluated.}, journal={INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS}, author={Huang, Chenyang and Han, Dong and Yan, Zheng and Li, Yiyan and Sun, Chenhao and Jia, Qiangang}, year={2022}, month={Mar} }
 @article{jia_chen_li_yan_2022, title={Deviation insurance for Risk-Averse wind power producers in the Nordic power market}, volume={134}, ISSN={["1879-3517"]}, DOI={10.1016/j.ijepes.2021.107431}, abstractNote={In the Nordic power market, a wind power producer is charged for the deviation between its actual production and its energy bid. The fluctuating wind power output and regulating market prices lead to uncertain deviation charges, reducing risk-averse wind power producers’ willingness to participate in the energy market. This letter proposes a risk management instrument, i.e., wind power deviation insurance. Wind power producers can reduce deviation risks through purchasing the insurance offered by insurance providers. Insurance provider can make profit by offering insurance products. A case study based on the Norway power market shows the effectiveness of the proposed instrument.}, journal={INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS}, author={Jia, Qiangang and Chen, Sijie and Li, Yiyan and Yan, Zheng}, year={2022}, month={Jan} }
 @article{han_huang_ren_zhao_li_2022, title={Machine learning analytics for virtual bidding in the electricity market}, volume={143}, ISSN={["1879-3517"]}, DOI={10.1016/j.ijepes.2022.108489}, abstractNote={• A framework of virtual bidding problem is established and the model is formulated as a MDP problem. • A machine learning method is proposed to solve the virtual bidding problem from the spatio-temporal dimensions. • The DQN algorithm improves the cumulative profits of virtual bidders and hedges the transaction risks effectively. • The simulations are performed to verify the proposed method. In order to solve the problem of the high risks and low efficiency caused by the inconsistency of the day-ahead and real-time prices in two-settlement electricity market, virtual bidding is used to arbitrage on the difference between such two market prices that are unknown to virtual bidders to promote the price convergence. The problem of optimal bidding for virtual bidders from the spatio-temporal dimensions is addressed in this paper. The model takes the budget constraints of virtual bidders into account, as well as considers decrement and increment bids of virtual bidding to maximize the cumulative payoff of virtual bidders, which is formulated as a Markov Decision Process problem. Meanwhile, the conditional value-at-risk is used to quantify and hedge the risks faced by virtual bidders. A deep reinforcement learning algorithm is used to achieve an effective solution to the optimal bidding strategy problem through continuous interaction with a simulated building environment to obtain feedback and update the parameters of the neural network without referring to any prior model knowledge. The PJM data from 2016 to 2018 is used to calculate the cumulative profits and Sharpe ratio of virtual bidders. Compared with greedy algorithm and dynamic programming, the deep reinforcement learning algorithm is verified the effectiveness and superiority in this paper.}, journal={INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS}, author={Han, Dong and Huang, Wei and Ren, Hengyu and Zhao, Wenkai and Li, Yiyan}, year={2022}, month={Dec} }
 @article{jia_chen_yan_li_2022, title={Optimal Incentive Strategy in Cloud-Edge Integrated Demand Response Framework for Residential Air Conditioning Loads}, volume={10}, ISSN={["2168-7161"]}, DOI={10.1109/TCC.2021.3118597}, abstractNote={In the residential demand response area, currently the incentive-based method (e.g., direct load control, DLC) may impair users’ comfort and autonomy, while the price-based method can hardly guarantee users’ engagements. This paper proposes an edge-cloud integrated demand response framework to achieve an effect-predictable residential demand response without harming users’ benefits. First, we combine the cloud-computing resource (cloud) and the home-installed smart thermostats (edges) to formulate an efficient, cost-effective, and data-secured infrastructure to implement the demand response program. Then, we model the demand response problem between the load aggregator and its served residential users as a bi-level optimization problem, and the key is for the load aggregator to find the optimal incentive strategy. To solve this problem, we introduce an RL algorithm, i.e., Continuous Action Reinforcement Learning Automata, to quickly obtain the optimal incentive strategy under an incomplete information scenario. Simulation results based on 136 real-world residential users in Austin area demonstrate that the proposed CEI-DR framework can increase the social welfare by about $8.6/h compared to the traditional DLC method during a normal DR event.}, number={1}, journal={IEEE TRANSACTIONS ON CLOUD COMPUTING}, author={Jia, Qiangang and Chen, Sijie and Yan, Zheng and Li, Yiyan}, year={2022}, month={Jan}, pages={31–42} }
 @article{song_li_lu_2022, title={ProfileSR-GAN: A GAN Based Super-Resolution Method for Generating High-Resolution Load Profiles}, volume={13}, ISSN={["1949-3061"]}, url={https://doi.org/10.1109/TSG.2022.3158235}, DOI={10.1109/TSG.2022.3158235}, abstractNote={This paper presents a novel two-stage load profile super-resolution (LPSR) framework, ProfileSR-GAN, to upsample the low-resolution load profiles (LRLPs) to high-resolution load profiles (HRLPs). The LPSR problem is formulated as a Maximum-a-Posteriori problem. In the first-stage, a GAN-based model is adopted to restore high-frequency components from the LRLPs. To reflect the load-weather dependency, aside from the LRLPs, the weather data is added as an input to the GAN-based model. In the second-stage, a polishing network guided by outline loss and switching loss is novelly introduced to remove the unrealistic power fluctuations in the generated HRLPs and improve the point-to-point matching accuracy. To evaluate the realisticness of the generated HRLPs, a new set of load shape evaluation metrics is developed. Simulation results show that: i) ProfileSR-GAN outperforms the state-of-the-art methods in all shape-based metrics and can achieve comparable performance with those methods in point-to-point matching accuracy, and ii) after applying ProfileSR-GAN to convert LRLPs to HRLPs, the performance of a downstream task, non-intrusive load monitoring, can be significantly improved. This demonstrates that ProfileSR-GAN is an effective new mechanism for restoring high-frequency components in downsampled time-series data sets and improves the performance of downstream tasks that require HR load profiles as inputs.}, number={4}, journal={IEEE TRANSACTIONS ON SMART GRID}, author={Song, Lidong and Li, Yiyan and Lu, Ning}, year={2022}, month={Jul}, pages={3278–3289} }
 @article{hu_li_zhang_shirsat_muthukaruppan_tang_baran_lubkeman_lu_2021, title={A Load Switching Group based Feeder-level Microgrid Energy Management Algorithm for Service Restoration in Power Distribution System}, ISSN={["1944-9925"]}, DOI={10.1109/PESGM46819.2021.9638231}, abstractNote={This paper presents a load switching group based energy management system (LSG-EMS) for operating microgrids on a distribution feeder powered by one or multiple grid-forming distributed energy resources. Loads on a distribution feeder are divided into load switching groups that can be remotely switched on and off. The LSG-EMS algorithm, formulated as a mixed-integer linear programming (MILP) problem, has an objective function of maximizing the served loads while minimizing the total number of switching actions. A new set of topology constraints are developed for allowing multiple microgrids to be formed on the feeder and selecting the optimal supply path. Customer comfort is accounted for by maximizing the supply duration in the customer preferred service period and enforcing a minimum service duration. The proposed method is demonstrated on a modified IEEE 33-bus system using actual customer data. Simulation results show that the LSG-EMS successfully coordinates multiple grid-forming sources by selecting an optimal supply topology that maximizes the supply period of both the critical and noncritical loads while minimizing customer service interruptions in the service restoration process.}, journal={2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM)}, author={Hu, Rongxing and Li, Yiyan and Zhang, Si and Shirsat, Ashwin and Muthukaruppan, Valliappan and Tang, Wenyuan and Baran, Mesut and Lubkeman, David and Lu, Ning}, year={2021} }
 @article{li_zhang_hu_lu_2021, title={A meta-learning based distribution system load forecasting model selection framework}, volume={294}, ISSN={["1872-9118"]}, DOI={10.1016/j.apenergy.2021.116991}, abstractNote={This paper presents a meta-learning based, automatic distribution system load forecasting model selection framework. The framework includes the following processes: feature extraction, candidate model preparation and labeling, offline training, and online model recommendation. Using load forecasting needs and data characteristics as input features, multiple metalearners are used to rank the candidate load forecast models based on their forecasting accuracy. Then, a scoring-voting mechanism is proposed to weights recommendations from each meta-leaner and make the final recommendations. Heterogeneous load forecasting tasks with different temporal and technical requirements at different load aggregation levels are set up to train, validate, and test the performance of the proposed framework. Simulation results demonstrate that the performance of the meta-learning based approach is satisfactory in both seen and unseen forecasting tasks.}, journal={APPLIED ENERGY}, author={Li, Yiyan and Zhang, Si and Hu, Rongxing and Lu, Ning}, year={2021}, month={Jul} }