@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} }