(501a) A Systems Engineering Approach to the Optimal Measurement Network Design in Coal-Fired Power Plants By Incorporating Market Elasticity | AIChE

(501a) A Systems Engineering Approach to the Optimal Measurement Network Design in Coal-Fired Power Plants By Incorporating Market Elasticity


Somayajula, C. S. - Presenter, West Virginia University
Bhattacharyya, D., West Virginia University
Liu, X., West Virginia University
Making optimal investment decision in the measurement network especially for retrofitting existing plant is a complicated problem in general. One such problem is in the investment of corrosion rate sensors in the waterwall (WW) of coal-fired boilers. Due to increasing cycling operation of the coal-fired power plants, the WW section is facing increasing failure where one of the leading causes is the sulfidic corrosion [1]. Recently a novel type of electrochemical sensors is being developed for in-situ monitoring of the corrosion rate [2]. However, the corrosion rate varies significantly with the location and with the dynamics of temperature and concentration of sulfur species. Since it is not feasible to install these electrochemical sensors at all possible locations inside the WW due to cost, technical feasibility and extremely harsh operating conditions, an optimal sensor network needs to be designed by taking into account the conflicting and complicated impacts of the corrosion monitoring framework. While large number of sensors can help to achieve high spatial resolution with higher accuracy, they cost money. However, a satisfactory estimate of the thinning of the tubes due to corrosion can help to reduce the forced outages of these plants due to WW failure thereby reducing the operations & maintenance (O&M) cost. Improved monitoring also increases the plant availability increasing the plant profit. On the other hand, the improved availability can reduce the price of electricity due to similar improvements in other plants. In addition, the demand of electricity from the coal-fired power plant is also expected to change over the years. Thus, making the optimal investment decision is a complicated optimization problem. A systems engineering approach is developed in this work for estimating the optimal investment in the monitoring framework.

In the open literature, optimal sensor networks have been designed for various objectives such as maximizing process information, maximizing reliability, minimizing error covariance and so on subject to some constraints such as the number and cost of sensors [3-5]. There is hardly any work on sensor network design where the economic impact of the sensor network by considering the market elasticity has been considered as the design objective.

For the proposed design objective, stochasticity in the electricity demand and price over pre-specified number of years should be taken into account by including the effect of the improved availability. Other than the rapid deployment of renewables into the grid, many factors like population growth, industrial growth, electricity usages in new systems like electric cars also affect the market dynamics and in turn cost of electricity. To capture these aspects, an energy market forecasting software “TIMES” is used for computing the design objective rather than the net present value analysis which determines economic feasibility at a single point of time only [6]. Expected improvement in the availability is estimated by using the Generating Availability Data System (GADS) from North American Electric Reliability Council (NERC). An unscented Kalman filter based approach is developed for synthesizing the optimal measurement network. Sensitivity to different scenarios is analyzed.


  1. North American Electric Reliability Council (NERC), “State of Reliability,” 2017.
  2. N. Aung and X. Liu, “High temperature electrochemical sensor for in situ monitoring of hot corrosion,” Corros. Sci., vol. 65, pp. 1–4, 2012.
  3. Sumana and C. Venkateswarlu, “Optimal selection of sensors for state estimation in a reactive distillation process,” J. Process Control, vol. 19, pp. 1024-1035, 2009.
  4. Y. Guo, L. L. Zhang and J. X. Zhou, “Optimal placement of sensors for structural health monitoring using improved genetic algorithm,” Smart Mater. Struct., vol. 13, pp. 528-534, 2004.
  5. K. Singh and J. Hahn, “Determining optimal sensor locations for state and parameter estimation for stable nonlinear systems,” Ind. Eng. Chem. Res., vol. 44, pp. 5645-5659, 2005.
  6. 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.