@article{hu_shirsat_muthukaruppan_li_zhang_tang_baran_lu_2024, title={Adaptive cold-load pickup considerations in 2-stage microgrid unit commitment for enhancing microgrid resilience}, volume={356}, ISSN={["1872-9118"]}, DOI={10.1016/j.apenergy.2023.122424}, abstractNote={In an extended main grid outage spanning multiple days, load shedding serves as a critical mechanism for islanded microgrids to maintain essential power and energy reserves that are indispensable for fulfilling reliability and resiliency mandates. However, using load shedding for such purposes leads to increasing occurrence of cold load pickup (CLPU) events. This study presents an innovative adaptive CLPU model that introduces a method for determining and incorporating parameters related to CLPU power and energy requirements into a two-stage microgrid unit commitment (MGUC) algorithm. In contrast to the traditional fixed-CLPU-curve approach, this model calculates CLPU duration, power, and energy demands by considering outage durations and ambient temperature variations within the MGUC process. By integrating the adaptive CLPU model into the MGUC problem formulation, it allows for the optimal allocation of energy resources throughout the entire scheduling horizon to fulfill the CLPU requirements when scheduling multiple CLPU events. The performance of the enhanced MGUC algorithm considering CLPU needs is assessed using actual load and photovoltaic (PV) data. Simulation results demonstrate significant improvements in dispatch optimality evaluated by the amount of load served, customer comfort, energy storage operation, and adherence to energy schedules. These enhancements collectively contribute to reliable and resilient microgrid operation.}, journal={APPLIED ENERGY}, author={Hu, Rongxing and Shirsat, Ashwin and Muthukaruppan, Valliappan and Li, Yiyan and Zhang, Si and Tang, Wenyuan and Baran, Mesut and Lu, Ning}, year={2024}, month={Feb} }
 @article{zhang_ding_lu_tang_2023, title={A Novel Real-Time Control Approach for Sparse and Safe Frequency Regulation in Inverter Intensive Microgrids}, volume={59}, ISSN={["1939-9367"]}, DOI={10.1109/TIA.2023.3291353}, abstractNote={This article developed a novel real-time control approach for the sparse and safe frequency regulation in inverter intensive microgrids (MGs). In the scenario, the inverters and external grids are expected to be synchronized with a desired frequency. To this end, the active power set-point acting as a control from a high-level controller is designed while considering two important performance metrics, namely “sparsity” and “safety”, which are to reduce the information exchange between controllers and ensure that the frequency remains in safe regions during the whole operation process. Our proposed control design framework allows the sparse linear feedback controller (SLFC) to be unified with a family of conditions for safe control using control barrier functions. A quadratic programming (QP) problem is then constructed, and the real-time control policy is obtained by solving the QP problem. Further, we also found that the proposed real-time control depends on the cross-layer communication network topology, which is the union of the one between controllers from SLFC and that determined by the power flow network. The proposed control approach has been validated through extensive case studies.}, number={5}, journal={IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS}, author={Zhang, Junhui and Ding, Lizhi and Lu, Xiaonan and Tang, Wenyuan}, year={2023}, month={Sep}, pages={5550–5558} }
 @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{yin_wang_tang_li_zhou_2023, title={Health-Aware Energy Management Strategy Toward Internet of Storage}, volume={10}, ISSN={["2327-4662"]}, DOI={10.1109/JIOT.2022.3173488}, abstractNote={The rapid development of the Internet of Things (IoT) has given rise to a novel business model, i.e., Internet of Storage (IoS), in which distributed in-home storage systems can be shared and equivalently aggregated as a utility-scale storage. While the existing literature has focused on the scheduling of distributed storage, few studies have quantified the accelerated degradation induced by storage sharing or incorporated State of Health (SoH) into storage sharing management. Therefore, we propose a health-aware energy management strategy in the environment of IoS, enabling distributed storage systems to cooperate through information and communication technology. To evaluate the SoH of storage, we design a health-aware framework based on equivalent circuit model (ECM), in which SoH can be derived from the charging behavior of battery. Consequently, the internal resistance, capacity, efficiency, and state of power can be inferred by SoH. To demonstrate the effectiveness of the proposed strategy, three benchmarks, i.e., local health-unaware model, local health-aware model, and Health-unaware Sharing (HuS) model are designed. Case studies based on 600 residential customers in Texas, USA reveal that IoS will cause additional SoH degradation, and the life of energy storage is reduced by 3.01 years. The proposed strategy can extend the energy storage’s service life by 43.13% and has better economic benefits compared with traditional HuS.}, number={9}, journal={IEEE INTERNET OF THINGS JOURNAL}, author={Yin, Chen and Wang, Jianxiao and Tang, Wenyuan and Li, Gengyin and Zhou, Ming}, year={2023}, month={May}, pages={7545–7553} }
 @misc{liu_tang_2023, title={Quantum computing for power systems: Tutorial, review, challenges, and prospects}, volume={223}, ISSN={["1873-2046"]}, DOI={10.1016/j.epsr.2023.109530}, abstractNote={As a large number of renewable energy resources are connected to power systems, the operation, planning, and optimization of power systems have been becoming more and more complex. Power flow calculation, unit commitment, economic dispatch, energy pricing, and power system planning are essentially computation problems. A lot of computing resources are required for these problems, which are non-trivial, especially for large-scale power systems with the high penetration of renewable energy. Traditionally, the calculation and optimization of power systems are completed by classical computers based on the classical computing theory and the von Neumann architecture. However, with Moore’s law getting closer and closer to the limit, the importance of quantum computing has become increasingly prominent. Quantum computing has been applied to some fields to a certain extent, yet the applications of quantum computing in power systems are rare. As the power industry is the foundation of the national economy, introducing quantum computing into the power system has far-reaching and crucial significance, such as improving the penetration of renewable energy, enhancing the computing efficiency, and helping in achieving the goal of net zero and climate neutrality by 2050. This paper first introduces the core concepts, essential ideas and theories of quantum computing, and then reviews the existing literature on the applications of quantum computing in power systems, and puts forward our critical thinking about the applications of quantum computing in power systems. In brief, this paper is dedicated to a tutorial on quantum computing targeting power system professionals and a review of its applications in power systems. The main contributions of this paper are: (1) introduce quantum computing into the field of power engineering in a thoroughly detailed way and delineate the analysis methodologies of quantum circuits systematically without losing mathematical rigor; (2) based on Dirac’s notation, the related formulae are derived meticulously with sophisticated schematic diagrams; (3) elaborate and derive some critical quantum algorithms in depth, which play an important role in the applications of quantum computing in power systems; (4) critically summarize and comment on the existing literature on the applications of quantum computing in power systems; (5) the future applications and challenges of quantum computing in power systems are prospected and remarked.}, journal={ELECTRIC POWER SYSTEMS RESEARCH}, author={Liu, Hualong and Tang, Wenyuan}, year={2023}, month={Oct} }
 @article{du_lu_tang_2022, title={Accurate Distributed Secondary Control for DC Microgrids Considering Communication Delays: A Surplus Consensus-Based Approach}, volume={13}, ISSN={["1949-3061"]}, DOI={10.1109/TSG.2022.3141395}, abstractNote={The state-of-the-art dynamic consensus-based microgrid (MG) secondary controls suffer from the communication delay effect. Specifically, the system could not converge to the desired operating points with time-delayed communications. Such deviations are hard to detect in a decentralized manner and could destabilize the system. This paper proposes an accurate distributed secondary controller for DC MGs based on the surplus consensus algorithm. The proposed controller achieves accurate proportional power sharing and average voltage regulation among distributed generators (DGs) with the presence of variable and bounded communication delays. A surplus consensus-based observer is developed. The developed observer is proved robust against variable and bounded communication delays; it tracks the average of a group of dynamic states with zero steady-state deviations, which cannot be done using the conventional dynamic consensus-based observer. The convergence speed of the developed observer is analyzed and a parameter design procedure is presented. Moreover, the delay-dependent stability analysis of DC MG operation with the proposed secondary controller is derived. The marginal delay that leads the system to instability is calculated. At last, the performance of the proposed secondary controller and the developed stability analysis are validated under various scenarios using MATLAB/Simulink.}, number={3}, journal={IEEE TRANSACTIONS ON SMART GRID}, author={Du, Yuhua and Lu, Xiaonan and Tang, Wenyuan}, year={2022}, month={May}, pages={1709–1719} }
 @article{wang_tang_2022, title={Modeling and Analysis of Baseline Manipulation in Demand Response Programs}, volume={13}, ISSN={["1949-3061"]}, DOI={10.1109/TSG.2021.3137098}, abstractNote={Baseline methods are used in demand response (DR) programs to estimate customers’ intrinsic load so as to reward them properly. While the accuracy of baseline methods has drawn considerable attention, the strategic behavior regarding baseline manipulation has not been well explored in the literature. In this paper, we formulate the customer’s payoff-maximizing problem as a Markov decision process (MDP). Several structural results have been established, including the characterization of underconsumption on event days and overconsumption on non-event days. We investigate the approximation of baseline methods to understand how the method parameters and the consumption statistics would affect the strategic behavior. Moreover, we develop a rollout algorithm, based on approximate dynamic programming, to solve the MDP efficiently. Finally, the proposed methodology is illustrated through case studies, which shed light on the analysis and design of baseline methods.}, number={2}, journal={IEEE TRANSACTIONS ON SMART GRID}, author={Wang, Xiaochu and Tang, Wenyuan}, year={2022}, month={Mar}, pages={1178–1186} }
 @article{liang_tang_2022, title={Ultra-Short-Term Spatiotemporal Forecasting of Renewable Resources: An Attention Temporal Convolutional Network-Based Approach}, volume={13}, ISSN={["1949-3061"]}, DOI={10.1109/TSG.2022.3175451}, abstractNote={The rapid increase in the penetration of renewable energy resources characterized by high variability and uncertainty is bringing new challenges to the power system operation. To ensure the efficient and reliable operation of electric grid, an accurate and general short-term forecasting algorithm with interpretability is desired. Moreover, the extensive off-site information provided by the proliferation of new renewable plants stimulates the interests in the spatiotemporal forecasting. In this paper, an attention temporal convolutional network, which is built on stacked dilated causal convolutional networks and attention mechanisms, is proposed to perform the ultra-short-term spatiotemporal forecasting of renewable resources. Compared with the existing spatiotemporal forecasting methods, the presented model needs no domain knowledge and can be applied to different forecasting tasks such as solar generation and wind speed forecasting. The attention mechanism improves the interpretability. The algorithm can be used to produce both point and probabilistic forecasts. Numerical results on the data sets from National Renewable Energy Laboratory show superior performance over five baselines, in terms of skill scores. Compared with the baselines, the average improvements of accuracy introduced by the proposed method for the point and probabilistic forecasting are 15.08% and 15.85%, respectively.}, number={5}, journal={IEEE TRANSACTIONS ON SMART GRID}, author={Liang, Junkai and Tang, Wenyuan}, year={2022}, month={Sep}, pages={3798–3812} }
 @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{wang_tang_2021, title={A Self-Reported Baseline Demand Response Program for Mitigation of Baseline Manipulation}, ISSN={["1944-9925"]}, DOI={10.1109/PESGM46819.2021.9638109}, abstractNote={The customer baseline is the counterfactual consumption without any demand response (DR) programs and thus is only known to end users. As a result, baseline manipulation arises in DR programs where the customer baseline is required to assign rebates. To address this issue, we propose a self-reported DR program under which customers only need to report their baseline. The customer's stochastic programming problem is simplified as a two-stage optimization, where the incentive-compatible (truth-telling) condition is derived. However, the incentive compatibility cannot be achieved due to the baseline information asymmetry between customers and program providers. As an alternative, we relax the threshold (allowed) baseline inflation from zero to a certain level and obtain the corresponding near-incentive compatibility. The self-report approach and the near-incentive compatibility provide new directions in designing effective DR programs.}, journal={2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM)}, author={Wang, Xiaochu and Tang, Wenyuan}, year={2021} }
 @article{wang_dsouza_tang_baran_2021, title={Assessing the Impact of High Penetration PV on the Power Transformer Loss of Life on a Distribution System}, ISSN={["2165-4816"]}, DOI={10.1109/ISGTEUROPE52324.2021.9640091}, abstractNote={Increasing photovoltaic (PV) systems on a distribution system impact the operation and lifetime of its components. One of the key components to be impacted is the power transformer at the substation. In this paper, we aim to evaluate the impact of high penetration PV on the lifetime of the substation power transformer. At moderate levels of PV penetration, the loading on the substation transformer decreases, and therefore this will help to prolong the lifetime of the transformer. To estimate this expected benefit, a thermal model for the transformer is used to estimate its hot spot temperature as this temperature is the main factor affecting the degradation of the transformer under normal loading conditions. To illustrate the method, a case study is given. A 10-year period is considered for transformer loss of life evaluation, where practical load growth and PV penetration scenarios are considered. The simulation is carried out on a 15 MVA transformer in the IEEE 123 bus system. Simulation results show that PV penetrations below 100% indeed prolong the transformer lifetime. However, the saved transformer lifetime is not considerable compared to the total transformer lifespan.}, journal={2021 IEEE PES INNOVATIVE SMART GRID TECHNOLOGY EUROPE (ISGT EUROPE 2021)}, author={Wang, Xiaochu and DSouza, Keith and Tang, Wenyuan and Baran, Mesut}, year={2021}, pages={323–327} }
 @article{shirsat_tang_2021, title={Data-Driven Stochastic Model Predictive Control for DC-Coupled Residential PV-Storage Systems}, volume={36}, ISSN={["1558-0059"]}, url={https://doi.org/10.1109/TEC.2021.3061360}, DOI={10.1109/TEC.2021.3061360}, abstractNote={This paper develops a stochastic model predictive control (SMPC) based framework for the real-time operation of residential-scale DC-coupled PV-storage systems. The proposed framework combines bivariate Markov chains to build the uncertainty model of PV generation and residential load, a Bayesian approach based recursive learning of the Markov model, and a scenario-based formulation for the SMPC problem. This approach operates in real-time, thus minimizing the impact of the mismatch between the forecasted data and the actual observation on the system performance by updating the control decisions with the realization of the stochastic parameters at each time step. Load and PV generation are jointly modeled, and the interdependence between them is accounted for through bivariate Markov chains. The use of recursive online learning guarantees that the uncertainty model is continuously updated to enhance its prediction capabilities for scenario generation. The numerical simulations using real-world data demonstrate the enhanced performance of the proposed approach over the conventional approaches, on a par with model predictive control with complete knowledge of the uncertainties.}, number={2}, journal={IEEE TRANSACTIONS ON ENERGY CONVERSION}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Shirsat, Ashwin and Tang, Wenyuan}, year={2021}, month={Jun}, pages={1435–1448} }
 @article{shirsat_muthukaruppan_hu_lu_baran_lubkeman_tang_2021, title={Hierarchical Multi-timescale Framework For Operation of Dynamic Community Microgrid}, ISSN={["1944-9925"]}, DOI={10.1109/PESGM46819.2021.9638104}, abstractNote={Distribution system integrated community microgrids (CMGs) can restore loads during extended outages. The CMG is challenged with limited resource availability, absence of a robust grid-support, and demand-supply uncertainty. To address these challenges, this paper proposes a three-stage hierarchical multi-timescale framework for scheduling and real-time (RT) dispatch of CMGs. The CMG's ability to dynamically expand its boundary to support the neighboring grid sections is also considered. The first stage solves a stochastic day-ahead (DA) scheduling problem to obtain referral plans for optimal resource rationing. The intermediate near real-time scheduling stage updates the DA schedule closer to the dispatch time, followed by the RT dispatch stage. The proposed methodology is validated via numerical simulations on a modified IEEE 123-bus system, which shows superior performance in terms of RT load supplied under different forecast error cases, outage duration scenarios, and against the traditionally used two-stage approach.}, journal={2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM)}, author={Shirsat, Ashwin and Muthukaruppan, Valliappan and Hu, Rongxing and Lu, Ning and Baran, Mesut and Lubkeman, David and Tang, Wenyuan}, year={2021} }
 @article{shirsat_tang_2021, title={Quantifying residential demand response potential using a mixture density recurrent neural network}, volume={130}, ISSN={["1879-3517"]}, DOI={10.1016/j.ijepes.2021.106853}, abstractNote={• A data-driven probabilistic approach to load reduction prediction in demand response. • The use of mixture distributions in a predictive setting to model customer behavior. • A case study on risk-averse optimal customer selection for demand response. An essential benefit of demand response is the avoided necessity to build new power plants to serve increased demand that occurs a few times during a year or to mitigate the energy imbalance caused by the volatile renewable energy. Customers play a pivotal role in the demand response program, and their behavior illustrates a highly uncertain pattern. To quantify the uncertainty, we follow the approach of data-driven probability distribution modeling by training a mixture density recurrent neural network, which outputs the probability distribution of the demand reduction. The parameters of the obtained mixture distributions are time-varying, thus capturing the temporal impacts of customer behavior on the customer load reduction. Using specific statistical metrics, we compare the performance, i.e., the quality of scenarios generated, of the Gaussian mixture distributions with that of the single Gaussian or ormixture distributions obtained by fitting the raw consumption reduction data. The proposed methodology is then applied to an optimal customer selection problem, which is formulated as a risk-averse stochastic knapsack problem. The results indicate that the generated mixture distributions are better suited for quantifying a customer’s consumption reduction and accurately encapsulate the underlying spatiotemporal trends in the customers’ reduction pattern.}, journal={INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS}, author={Shirsat, Ashwin and Tang, Wenyuan}, year={2021}, month={Sep} }
 @article{liang_tang_2021, title={Scenario Reduction for Stochastic Day-Ahead Scheduling: A Mixed Autoencoder Based Time-Series Clustering Approach}, volume={12}, ISSN={["1949-3061"]}, DOI={10.1109/TSG.2020.3047759}, abstractNote={Scenario based stochastic scheduling has drawn a tremendous amount of interests worldwide in tackling the uncertainty of renewable energy and accounting for risks. It is important to generate representative time-series scenarios of renewable energy, while keeping the dimensionality of the scenario set tractable. This article presents a mixed autoencoder based clustering approach to select a reduced scenario set from high-dimensional time series. In contrast to other techniques targeting on minimizing different probability distances, the proposed architecture accounts for the pattern recognition within a large set of scenarios. The effectiveness of the model is verified in the case studies, where the data sets from the Bonneville Power Administration and Elia are used. The numerical results show that the model outperforms the state of the art, in terms of statistical metrics and through empirical analysis.}, number={3}, journal={IEEE TRANSACTIONS ON SMART GRID}, author={Liang, Junkai and Tang, Wenyuan}, year={2021}, month={May}, pages={2652–2662} }
 @article{liang_tang_2020, title={Interval based transmission contingency-constrained unit commitment for integrated energy systems with high renewable penetration}, volume={119}, ISSN={["1879-3517"]}, DOI={10.1016/j.ijepes.2020.105853}, abstractNote={As the reliance on natural gas to meet electric generation requirements increases, additional operational measures and risks must be considered to better understand the implications of the complex interdependency between the natural gas system and the power system. This paper develops a novel interval optimization framework of contingency-constrained unit commitment for integrated energy systems. Uncertainties in contingencies are captured by interval numbers, which reduces the computational burden compared with other uncertainty representations. Moreover, we incorporate the risk preferences of decision makers in the framework to alleviate the conservativeness of solutions. It is observed that power-to-gas technology can reduce renewable curtailment and offer auxiliary services. Numerical results demonstrate the economic value of power-to-gas in integrated energy systems.}, journal={INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS}, author={Liang, Junkai and Tang, Wenyuan}, year={2020}, month={Jul} }
 @article{liang_tang_2020, title={Sequence Generative Adversarial Networks for Wind Power Scenario Generation}, volume={38}, ISSN={["1558-0008"]}, DOI={10.1109/JSAC.2019.2952182}, abstractNote={With the rapid increase in distributed wind generation, considerable efforts have been devoted to the microgrid day-ahead scheduling. The effectiveness of those methods will highly depend on the selection of the uncertainty sets. We propose a distribution-free approach for wind power scenario generation, using sequence generative adversarial networks. To capture the temporal correlation, the model adopts the long short-term memory architecture and uses generative adversarial networks coupled with reinforcement learning, which, in contrast to the existing methods, avoids manual labeling and captures the complex dynamics of the weather. We conduct case studies based on the data from the Bonneville Power Administration and the National Renewable Energy Laboratory, and show that the generated scenarios can better characterize the variability of wind power and reduce the risk of uncertainties, compared with those produced by Gaussian distribution, vanilla long short-term memory, and multivariate kernel density estimation. Moreover, the proposed method achieves better performance when applied to the day-ahead scheduling of microgrids.}, number={1}, journal={IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS}, author={Liang, Junkai and Tang, Wenyuan}, year={2020}, month={Jan}, pages={110–118} }
 @article{liang_tang_2020, title={Stochastic multistage co-planning of integrated energy systems considering power-to-gas and the cap-and-trade market}, volume={119}, ISSN={["1879-3517"]}, DOI={10.1016/j.ijepes.2020.105817}, abstractNote={This paper develops an expansion co-planning model for integrated power and gas systems where uncertainties in both systems are considered. The power-to-gas technology is modeled and considered as a potential solution for some worldwide societal goals such as decarbonized economy and 100% renewable penetration. Compared with expansion planning models for power systems, the complexity of expansion co-planning models is severely exacerbated by random variables in gas systems. A scenario based decomposition scheme called branch-and-price is presented. The numerical results demonstrate the effectiveness of the proposed method and show the economic value of power-to-gas technology.}, journal={INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS}, author={Liang, Junkai and Tang, Wenyuan}, year={2020}, month={Jul} }
 @article{liang_shirsat_tang_2020, title={Sustainable community based PV-storage planning using the Nash bargaining solution}, volume={118}, ISSN={["1879-3517"]}, DOI={10.1016/j.ijepes.2019.105759}, abstractNote={A dramatic increase in the penetration of customer-sited solar and storage has increased the concerns over utility revenues. A novel business model where the utility and the households within a community cooperatively deploy solar and storage is proposed in this paper. The potential increased payoffs are allocated by the Nash bargaining solution with efficiency and symmetry. Community-based expansion planning highly relies on local meteorology and geography. A convolutional neural network based scenario generation method is used to capture the locally driven uncertainties. The numerical results demonstrate that the players can improve their payoffs using the cooperative approach as compared to the non-cooperative approach. The case studies shed light on the future customer-utility relationship.}, journal={INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS}, author={Liang, Junkai and Shirsat, Ashwin and Tang, Wenyuan}, year={2020}, month={Jun} }
 @article{wang_zhong_qin_tang_rajagopal_xia_kang_2019, title={Incentive mechanism for sharing distributed energy resources}, ISSN={2196-5625 2196-5420}, url={http://dx.doi.org/10.1007/s40565-019-0518-5}, DOI={10.1007/s40565-019-0518-5}, abstractNote={To improve the controllability and utilization of distributed energy resources (DERs), distribution-level electricity markets based on consumers’ bids and offers have been proposed. However, the transaction costs will dramatically increase with the rapid development of DERs. Therefore, in this paper, we develop an energy sharing scheme that allows users to share DERs with neighbors, and design a novel incentive mechanism for benefit allocation without users’ bidding on electricity prices. In the energy sharing scheme, an aggregator organizes a number of electricity users, and trades with the connected power grid. The aggregator is aimed at minimizing the total costs by matching the surplus energy from DERs and electrical loads. A novel index, termed as sharing contribution rate (SCR), is presented to evaluate different users’ contributions to the energy sharing. Then, based on users’ SCRs, an efficient benefit allocation mechanism is implemented to determine the aggregator’s payments to users that incentivize their participation in energy sharing. To avoid users’ bidding, we propose a decentralized framework for the energy sharing and incentive mechanism. Case studies based on real-world datasets demonstrate that the aggregator and users can benefit from the energy sharing scheme, and the incentive mechanism allocates the benefits according to users’ contributions.}, journal={Journal of Modern Power Systems and Clean Energy}, publisher={Springer Nature}, author={Wang, Jianxiao and Zhong, Haiwang and Qin, Junjie and Tang, Wenyuan and Rajagopal, Ram and Xia, Qing and Kang, Chongqing}, year={2019}, month={Apr} }
 @article{tang_jain_2018, title={Aggregating Correlated Wind Power With Full Surplus Extraction}, volume={9}, ISSN={1949-3053 1949-3061}, url={http://dx.doi.org/10.1109/tsg.2017.2702655}, DOI={10.1109/tsg.2017.2702655}, abstractNote={We study the problem of designing profit-maximizing mechanisms for an aggregator who aggregates wind power from a group of wind power producers (WPPs). The WPPs have more refined forecasts of the wind power generation than the aggregator. Such forecasts are their private information, which also give the reservation utilities of the WPPs. The goal of the aggregator is to elicit the private information truthfully, while paying them as little as possible. Inspired by the fact that those forecasts are typically correlated due to the geographical proximity of the WPPs, we formally define the full correlation condition, which holds ubiquitously in practice. Under that condition, we construct an optimal mechanism which yields the truthful elicitation, while extracting the full surplus (i.e., with minimum payments equal to the reservation utilities) in expectation. Finally, we conduct a case study based on the real-world data, which empirically validates the results.}, number={6}, journal={IEEE Transactions on Smart Grid}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Tang, Wenyuan and Jain, Rahul}, year={2018}, month={Nov}, pages={6030–6038} }
 @article{wang_zhong_tang_rajagopal_xia_kang_2018, title={Tri-Level Expansion Planning for Transmission Networks and Distributed Energy Resources Considering Transmission Cost Allocation}, volume={9}, ISSN={["1949-3029"]}, DOI={10.1109/TSTE.2018.2816937}, abstractNote={A well-designed transmission cost allocation (TCA) scheme should reveal energy users’ actual usage of transmission assets. By investing in distributed energy resources (DERs), a user should be allocated with a lower transmission price if devoted to reducing transmission capacity usage. To evaluate the impacts of TCA, a coplanning framework for centralized transmission networks and DERs is developed in this paper. Distinguished from the existing literature, we consider hourly transmission prices (TPs) that are updated by power flow tracing, together with locational marginal prices (LMPs) to incentivize users’ investments for DERs. This problem is formulated as a tri-level model. On the first level, transmission expansion planning is optimized to satisfy transmission capacity requirements. On the second level, given the hourly LMPs and TPs, users at different buses strategically invest in DERs. On the third level, the market is cleared, and LMPs and TPs are updated. The lower two levels form a market equilibrium problem that can be solved by a diagonalization method. An iterative algorithm is proposed to solve the tri-level model, and convergence analysis is conducted. Case studies based on a modified Garver's 6-bus system and the IEEE 118-bus system demonstrate that a well-designed TCA scheme can incentivize users to invest in DERs, which effectively defers transmission expansion and reduces the system-wide investment costs.}, number={4}, journal={IEEE TRANSACTIONS ON SUSTAINABLE ENERGY}, author={Wang, Jianxiao and Zhong, Haiwang and Tang, Wenyuan and Rajagopal, Ram and Xia, Qing and Kang, Chongqing}, year={2018}, month={Oct}, pages={1844–1856} }
 @article{wang_zhong_tang_rajagopal_xia_kang_wang_2017, title={Optimal bidding strategy for microgrids in joint energy and ancillary service markets considering flexible ramping products}, volume={205}, ISSN={0306-2619}, url={http://dx.doi.org/10.1016/j.apenergy.2017.07.047}, DOI={10.1016/j.apenergy.2017.07.047}, abstractNote={Due to the volatile nature of wind and photovoltaic power, wind farms and solar stations are generally thought of as the consumers of ramping services. However, a microgrid (MG) is able to strategically integrate various distributed energy resources (DERs) to provide both energy and ancillary services (ASs) for the bulk power system. To evaluate the ramping capabilities of an MG in the joint energy and AS markets, an optimal bidding strategy is developed in this paper considering flexible ramping products (FRPs). By aggregating and coordinating various DERs, including wind turbines (WTs), photovoltaic systems (PVs), micro-turbines (MTs) and energy storage systems (ESSs), the MG is able to optimally allocate the capacities for energy, spinning reserve and ramping. Taking advantage of the synergy among DERs, the MG can maximize its revenues from different markets. Moreover, the flexibility of the MG for the bulk power system can be fully explored. To address the uncertainties introduced by renewable generation and market prices, a hybrid stochastic/robust optimization (RO) approach is adopted. Case studies based on a real-world MG with various DERs demonstrate the market behavior of the MG using the proposed bidding model.}, journal={Applied Energy}, publisher={Elsevier BV}, author={Wang, Jianxiao and Zhong, Haiwang and Tang, Wenyuan and Rajagopal, Ram and Xia, Qing and Kang, Chongqing and Wang, Yi}, year={2017}, month={Nov}, pages={294–303} }
 @article{tang_jain_2016, title={Dynamic Economic Dispatch Game: The Value of Storage}, volume={7}, ISSN={1949-3053 1949-3061}, url={http://dx.doi.org/10.1109/tsg.2015.2495146}, DOI={10.1109/tsg.2015.2495146}, abstractNote={We formulate a dynamic economic dispatch game in which each generator has its own electricity storage device. The operation of storage introduces time-coupling constraints. We focus on how the use of storage may affect the market structure and market outcomes in the locational marginal pricing mechanism. We first show that even when the independent system operation is unaware of the storage, there exists an efficient bid profile that induces the optimal dispatch. We then demonstrate that the use of storage does not increase the room for strategic play, but may improve the equilibrium outcomes. We provide sufficient conditions under which there exist efficient Nash equilibria. Furthermore, we propose the marginal contribution pricing mechanism that guarantees efficient outcomes.}, number={5}, journal={IEEE Transactions on Smart Grid}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Tang, Wenyuan and Jain, Rahul}, year={2016}, month={Sep}, pages={2350–2358} }
 @article{tang_jain_2015, title={Market Mechanisms for Buying Random Wind}, volume={6}, ISSN={1949-3029 1949-3037}, url={http://dx.doi.org/10.1109/tste.2015.2460745}, DOI={10.1109/tste.2015.2460745}, abstractNote={The intermittent nature of wind power leads to the question of how wind power producers can participate in a deregulated electricity market. In the proposed auction paradigm, wind farms bid probability distributions of generation, instead of bidding cost functions as thermal units do. Our focus is to design incentive compatible mechanisms that elicit truthful information of strategic agents who supply stochastic resource. We first study the aggregators problem of how to select the wind farms, which have the most desirable distributions. We then study the independent system operators (ISOs) problem of how to price wind energy for stochastic economic dispatch.}, number={4}, journal={IEEE Transactions on Sustainable Energy}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Tang, Wenyuan and Jain, Rahul}, year={2015}, month={Oct}, pages={1615–1623} }
 @article{tang_jain_2012, title={Hierarchical Auction Mechanisms for Network Resource Allocation}, volume={30}, ISSN={0733-8716}, url={http://dx.doi.org/10.1109/jsac.2012.121204}, DOI={10.1109/jsac.2012.121204}, abstractNote={Motivated by allocation of bandwidth, wireless spectrum and cloud computing services in secondary network markets, we introduce a hierarchical auction model for network resource allocation. A Tier 1 provider owns a homogeneous network resource and holds an auction to allocate this resource among Tier 2 operators, who in turn allocate the acquired resource among Tier 3 entities. The Tier 2 operators play the role of middlemen, since their utilities for the resource depend on the revenues gained from resale. We first consider static hierarchical auction mechanisms for indivisible resources. We study a class of mechanisms wherein each sub-mechanism is either a first-price or VCG auction, and show that incentive compatibility and efficiency cannot be simultaneously achieved. We also briefly discuss sequential auctions as well as the incomplete information setting. We then propose two VCG-type hierarchical mechanisms for divisible resources. The first one is composed of single-sided auctions at each tier, while the second one employs double-sided auctions at all tiers except Tier 1. Both mechanisms induce an efficient Nash equilibrium.}, number={11}, journal={IEEE Journal on Selected Areas in Communications}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Tang, Wenyuan and Jain, Rahul}, year={2012}, month={Dec}, pages={2117–2125} }
 @inbook{tang_jain_2012, place={Berlin}, series={Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering}, title={Hierarchical Auctions for Network Resource Allocation}, ISBN={9783642303722 9783642303739}, ISSN={1867-8211 1867-822X}, url={http://dx.doi.org/10.1007/978-3-642-30373-9_2}, DOI={10.1007/978-3-642-30373-9_2}, booktitle={Game Theory for Networks. GameNets 2011}, publisher={Springer}, author={Tang, Wenyuan and Jain, Rahul}, editor={Jain, R. and Kannan, R.Editors}, year={2012}, pages={11–26}, collection={Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering} }