@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{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{a load switching group based feeder-level microgrid energy management algorithm for service restoration in power distribution system_2020, year={2020}, month={Nov} }
 @article{hierarchical multi-timescale framework for operation of dynamic community microgrid_2020, year={2020}, month={Nov} }
 @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} }