More Like this

1. Economic-Emission Dispatch Problem in Power Systems with Carbon Capture Power Plants

   https://doi.org/10.1109/tia.2021.3079329  

   Akbari-Dibavar, Alireza; Mohammadi-ivatloo, Behnam; Zare, Kazem; Khalili, Tohid; Bidram, Ali (May 2021, IEEE Transactions on Industry Applications)

   null (Ed.)

   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. 

   more » « less
2. Multi-Objective Logarithmic Extremum Seeking for Wind Turbine Power Capture with Load Reduction

   https://doi.org/10.23919/ACC50511.2021.9483187  

   Kumar, Devesh; Gans, Nicholas; Rotea, Mario A. (May 2021, Multi-Objective Logarithmic Extremum Seeking for Wind Turbine Power Capture with Load Reduction)

   null (Ed.)

   This paper describes a multi-objective ESC strategy that determines Pareto-optimal control parameters to jointly optimize wind turbine loads and power capture. The method uses two optimization objectives calculated in real time: (a) the logarithm of the average power and (b) the logarithm of the standard deviation of a measurable blade load, tower load or the combination of these loads. These two objectives are weighted in real-time to obtain a solution that is Pareto optimal with respect to the power average and the standard deviation of chosen load metric. The method is evaluated using NREL FAST simulations of the 5-MW reference turbine. The results are then evaluated using energy capture over the duration of the simulation and damage equivalent loads (DEL) calculated with MLife. 

   more » « less
3. MIP Formulation for Type-III Wind Turbines Considering Control Limits

   https://doi.org/10.1109/NAPS56150.2022.10012164  

   Alqahtani, Mohammed; Miao, Zhixin; Fan, Lingling (October 2022, 2022 North American Power Symposium (NAPS))

   Hitting control limits changes the behavior of the control systems and invalidates commonly adopted assumptions made when calculating the operating point of doubly fed induction generator (DFIG)-based wind turbines (WTs). The current computing methods rely on trials and errors and iterations. This paper proposes an optimization-based algorithm with control limits integrated into the problem formulation. Hitting or not hitting a control limit is modeled as a binary variable. Both rotor-side converter (RSC) current order limits and grid-side converter (GSC) current order limits are modeled in the proposed mixed-integer programming (MIP) formulation. For a WT with given terminal voltage and wind speed, the electrical and mechanical variables of the system can be computed directly from the optimization problem. The proposed formulation has included losses in the back-to-back converters and nonzero reactive power support through GSC. The computing results have been validated with the electromagnetic transient (EMT) simulation results. This formulation can help accurately detect whether control limits are hit for low voltage conditions and facilitate Type-III wind turbine fault ride through analysis. 

   more » « less
4. The quest for carbon-neutral industrial operations: renewable power purchase versus distributed generation

   https://doi.org/10.1080/00207543.2017.1394593  

   Jin, T.; Shi, K.; Park, T. (January 2018, International journal of production research)

   Integrating renewable energy into the manufacturing facility is the ultimate key to realising carbon-neutral operations. Although many firms have taken various initiatives to reduce the carbon footprint of their facilities, there are few quantitative studies focused on cost analysis and supply reliability of integrating intermittent wind and solar power. This paper aims to fill this gap by addressing the following question: shall we adopt power purchase agreement (PPA) or onsite renewable generation to realise the eco-economic benefits? We tackle this complex decision-making problem by considering two regulatory options: government carbon incentives and utility pricing policy. A stochastic programming model is formulated to search for the optimal mix of onsite and offsite renewable power supply. The model is tested extensively in different regions under various climatic conditions. Three findings are obtained. First, in a long term onsite generation and PPA can avoid the price volatility in the spot or wholesale electricity market. Second, at locations where the wind speed is below 6 m/s, PPA at $70/MWh is preferred over onsite wind generation. Third, compared to PPA and wind generation, solar generation is not economically competitive unless the capacity cost is down below USD1.5 M per MW. 

   more » « less
5. Net-Zero Scheduling of Multi-Energy Building Energy Systems: A Learning-Based Robust Optimization Approach With Statistical Guarantees

   https://doi.org/10.1109/TSTE.2024.3437210  

   Yang, Yijie; Shi, Jian; Wang, Dan; Wu, Chenye; Han, Zhu (October 2024, IEEE Transactions on Sustainable Energy)

   Buildings produce a significant share of greenhouse gas (GHG) emissions, making homes and businesses a major factor in climate change. To address this critical challenge, this paper explores achieving net-zero emission through the carbon-aware optimal scheduling of the multi-energy building integrated energy systems (BIES). We integrate advanced technologies and strategies, such as the carbon capture system (CCS), power-to-gas (P2G), carbon tracking, and emission allowance trading, into the traditional BIES scheduling problem. The proposed model enables accurate accounting of carbon emissions associated with building energy systems and facilitates the implementation of low-carbon operations. Furthermore, to address the challenge of accurately assessing uncertainty sets related to forecasting errors of loads, generation, and carbon intensity, we develop a learning-based robust optimization approach for BIES that is robust in the presence of uncertainty and guarantees statistical feasibility. The proposed approach comprises a shape learning stage and a shape calibration stage to generate an optimal uncertainty set that ensures favorable results from a statistical perspective. Numerical studies conducted based on both synthetic and real-world datasets have demonstrated that the approach yields up to 8.2% cost reduction, compared with conventional methods, in assisting buildings to robustly reach net-zero emissions. 

   more » « less