Efficient planning of energy production and maintenance of large-scale combined heat and power plants

Abstract

In this study, an efficient optimization framework is presented for the simultaneous planning of energy production and maintenance in combined heat and power plants, and applied in the largest coal-fired cogeneration plant of Kazakhstan. In brief, the proposed optimization model considers: (i) unit commitment constraints for boilers and turbines; (ii) minimum and maximum runtimes as well as minimum idle times for boilers and turbines; (iii) bounds on the operating levels for boilers and turbines within desired operating regions; (iv) extreme operating regions for turbines; (v) energy balances for turbines; (vi) total electricity and heat balances for satisfying the corresponding demands for electricity and heat (for each heat network); and (vii) maintenance tasks for units that must occur within given flexible time-windows. The minimization of the annual total cost of the cogeneration plant constitutes the optimization goal here, and it consists of startup and shutdown costs, fixed operating and fuel costs, maintenance costs, and penalties for deviation from heat and electricity demands, and penalties for turbines for operating outside the desired operating regions. An extensive data analysis of historical data has been performed to extract the necessary input data. In comparison to the implemented industrial solution that follows a predefined maintenance policy, the solutions derived by the proposed approach achieve reductions in annual total cost more than 21% and completely avoid turbines operation outside their desired operating regions. Our solutions report substantial reductions in startup/shutdown, fuel and fixed operating costs (about 85%, 15%, and 13%, respectively). The comparative case study clearly demonstrates that the proposed approach is an effective means for generating optimal energy production and maintenance plans, enhancing significantly the resource and energy efficiency of the plant. Importantly, the proposed optimization framework could be readily applied to other cogeneration plants that have a similar plant structure.

Set of the data for modeling large-scale coal-fired combined heat and power plant in Kazakhstan

“…The wide deployment of CHP plants was dictated by Soviet central planning, and it was convenient utilization of low-grade heat for district heating due to the cold climate and abundance of coal reserves. The aim of the paper is to explain the input data (excel file), which was used for a detailed analysis of combined heat and power plants and their demand profiles in different cities of Kazakhstan [1] . The data file (Datasets for Case 1 and Case 2) associated with this article can be found in the online version at [2] .…”

Multi‐objective optimisation of generation maintenance scheduling in restructured power systems based on global criterion method

“…It should be noted that the objectives of GenCo and ISO are separately optimised as shown in (20). Maximising the MO gives the best borderline solution for OF SR and OF GU .…”

“…A reliability criterion modified GMS model including modified maintenance window constraints is presented in [19]. In [20], an efficient optimisation framework based on the mathematical programming for the simultaneous planning of generation and maintenance of large‐scale power system is proposed. (c) Techniques: Different tools and methods have been used in the literatures for the GMS problem. The authors of [21] have used the game‐theoretic approach to solve the GMS problem.…”

Optimal generation maintenance scheduling considering financial return and unexpected failure of distributed generation

“…The problem is solved by using simulated annealing and ant colony optimisation. A co‐generation power plant considering boilers and turbines to maximise the objective is proposed by [17]. Hybrid particle swarm optimisation, genetic algorithm and shuffled frog leaping method are developed by [18], while mathematical approaches of assisted differential evolution algorithm and clonal selection algorithm are presented in [19] and [20], respectively.…”