Bridging Chance-Constrained and Robust Optimization in an Emission-Aware Economic Dispatch With Energy Storage

• Published in: IEEE transactions on power systems 2022-03, Vol.37 (2), p.1078-1090
• Main Authors: Gu, Nan, Wang, Haoxiang, Zhang, Jiasheng, Wu, Chenye
• Format: Article
• Language: English
• Subjects: Carbon; Carbon dioxide; Carbon tax; chance-constrained (CC) optimization; Clean energy; Economic dispatch (ED); Economics; Electric power grids; Electricity distribution; Emission; Energy storage; Environmental tax; Generators; Optimization; Power dispatch; Regional development; robust optimization (RO); Robustness (mathematics); storage; Uncertainty; Wind power
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2021.3102412

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.