(339i) Economic Analysis of a Power Plant Under Health Monitoring in an Elastic Energy Market

Renewables are projected to be the primary source of electricity generation in the future. But fossil energy sources like coal, natural gas, etc., will still be prevalent in the foreseeable future and will be in use to ensure the stability an resiliency of the electricity grid by assisting in meeting the load. Due to this, coal-fired power plants (CFPP) are being subjected to load-following because of the increasing penetration of intermitted renewables. The existing supercritical pulverized coal-fired power plants are generally designed to operate at base-loaded conditions.[1] From 2012 to 2017, on average, about 6.4% of annual potential production lost due to forced outages was caused by waterwall failure, making it the top cause for loss of revenue. Several mitigation steps have been recommended for CFPP to minimize damage and operation and maintenance costs. One approach is to monitor the health of the critical components by using a network of sensors. Hot corrosion of critical boiler components is one of the key reasons for the tube failures and therefore this work particularly focuses on monitoring of hot corrosion in high temperature locations. Measuring hot corrosion in the challenging boiler environment, but recently a novel electrochemical sensor has been developed in-house for in-situ corrosion monitoring.[2] However, these sensors are novel and costly and therefore a return on investment be justified. The improved revenue due to the anticipated reduction in forced outages due to corrosion monitoring must justify the purchase, installation, and maintenance costs for sensors. However, the improvement in the revenue depends on several factors. First of all, the forced outage adds to the maintenance cost and reduces the capacity factor, which can increase the production cost for unit energy. The increase in availability due to a reduction in forced outage needs to be computed with due consideration of future energy market instead of historical data, i.e., elasticity in the energy market. Due to the anticipated ongoing increase of renewable power in the electric grid, they are expected to pick up the lost power production from the plant that went offline due to a forced outage. Therefore, improvement in availability may not proportionately lead to an increase in the number of operating hours or increased generation. Furthermore, measurement of variables such as temperature, and composition of the flue gas can provide indirect estimate of the rate of corrosion and therefore may be used in conjunction with the corrosion sensors thus enabling use of fewer corrosion sensors in the boiler. A scenario-based economic analysis and payback period analysis are conducted in this work to estimate the optimal investment in the sensor network.

In the existing literature, there is work on economic analysis of traditional CFPP using measures like net present value (NPV).[3] Economic analysis of CFPP combined with other technologies like solar energy, biomass gasification, CO₂ capture etc., have been conducted.[4]–[6] To the best of our knowledge, there is no work in the literature where the impact of the increased availability of CFPP on its net power generation has been analyzed by accounting for market elasticity.

For analyzing future of market, an energy market forecasting software “TIMES” is used.[7] TIMES provides the capability of analyzing various scenarios such as the impact of reduction in O&M cost availability etc., of coal-fired power plants on the increase in their power production. Changes in energy trends are incorporated into the model through scenario files. Technologies and corresponding factors significantly affecting electricity production of coal-fired power plants are identified.[8] Impacts of each of these technologies on the electricity production from the coal-fired power plant are analyzed. Using Latin Hypercube Sampling (LHS), the set of near-random scenarios that include all electric energy generation technologies are created. The electricity produced by coal-fired power plants with and without improved availability under these random scenarios is calculated and analyzed. Potential revenue gained by plants under increased availability is estimated. In-house data and information available in the open literature are used to calculate the investment cost for the sensor network. Finally, a comparison is made between the sensor network cost and potential revenue gain to determine the economic feasibility.

References

[1] G. Grol, Eric; Tarka, Thomas J.; Myles, Paul; Bartone, Jr, Leonard M.; Simpson, James; Rossi, “Impact of Load Following on the Economics of Existing Coal-Fired Power Plant Operations,” 2015.

[2] N. N. Aung and X. Liu, “High temperature electrochemical sensor for in situ monitoring of hot corrosion,” Corros. Sci., vol. 65, pp. 1–4, 2012, doi: 10.1016/j.corsci.2012.08.010.

[3] R. Kumar, A. K. Sharma, and P. C. Tewari, “Thermal performance and economic analysis of 210 MWe coal-fired power plant,” J. Thermodyn., vol. 1, no. 1, 2014, doi: 10.1155/2014/520183.

[4] C. Li, R. Zhai, B. Zhang, and W. Chen, “Thermodynamic performance of a novel solar tower aided coal-fired power system,” Appl. Therm. Eng., vol. 171, no. November 2019, p. 115127, 2020, doi: 10.1016/j.applthermaleng.2020.115127.

[5] B. Huang et al., “Industrial test and techno-economic analysis of CO2 capture in Huaneng Beijing coal-fired power station,” Appl. Energy, vol. 87, no. 11, pp. 3347–3354, 2010, doi: 10.1016/j.apenergy.2010.03.007.

[6] H. Chen et al., “Thermo-Economic analysis of a novel biomass Gasification-Based power system integrated with a supercritical CO2 cycle and a Coal-Fired power plant,” Energy Convers. Manag., vol. 266, no. March, pp. 1–18, 2022, doi: 10.1016/j.enconman.2022.115860.

[7] A. Mirakyan and R. De Guio, “Integrated energy planning in cities and territories: A review of methods and tools,” Renew. Sustain. Energy Rev., vol. 22, pp. 289–297, 2013, doi: 10.1016/j.rser.2013.01.033.

[8] J. E. T. Bistline and D. T. Young, “Economic drivers of wind and solar penetration in the US,” Environ. Res. Lett., vol. 14, no. 12, p. 124001, 2019, doi: 10.1088/1748-9326/ab4e2d.