Despite the increasing level of renewable power generation in power grids, fossil fuel power plants still have a significant role in producing carbon emissions. The integration of carbon capturing and storing systems to the conventional power plants can significantly reduce the spread of carbon emissions. In this paper, the economic-emission dispatch of combined renewable and coal power plants equipped with carbon capture systems is addressed in a multi-objective optimization framework. The power systems flexibility is enhanced by hydropower plants, pumped hydro storage, and demand response program. The wind generation and load consumption uncertainties are modeled using stochastic programming. The DC power flow model is implemented on a modified IEEE 24-bus test system. Solving the problem resulted in an optimal Pareto frontier, while the fuzzy decision-making method found the best solution. The sensitivity of the objective functions concerning the generation-side is also investigated.
Stochastic Multi-objective Low-Carbon Generation Dispatch Considering Carbon Capture Plants
This paper presents a multi-objective (MO) optimization for economic/emission dispatch (EED) problem incorporating hydrothermal plants, wind power generation, energy storage systems (ESSs) and responsive loads. The uncertain behavior of wind turbines and electric loads is modeled by scenarios. Stochastic programming is proposed to achieve the expected cost and emission production. Moreover, the carbon capture systems are considered to lower the level of carbon emission produced by conventional thermal units. The proposed optimization problem is tested on the IEEE 24-bus case study using DC power flow calculation. The optimal Pareto frontier is obtained, and a fuzzy decision-making tool determined the best solution among obtained Pareto points. The problem is modeled as mixed-integer non-linear programming in the General Algebraic Modelling System (GAMS) and solved using DICOPT solver.
- Award ID(s):
- 1757207
- Publication Date:
- NSF-PAR ID:
- 10229717
- Journal Name:
- IEEE Industry Applications Society Annual Meeting
- Page Range or eLocation-ID:
- 1 to 6
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1109/IAS44978.2020.9334846
