• International Journal of Technology (IJTech)
  • Vol 12, No 4 (2021)

Holistic Operation and Maintenance Excellence (HOME): Integrating Financial and Engineering Analysis to Determine Optimum O and M Strategies for a Power Plant during its Lifetime

Holistic Operation and Maintenance Excellence (HOME): Integrating Financial and Engineering Analysis to Determine Optimum O and M Strategies for a Power Plant during its Lifetime

Title: Holistic Operation and Maintenance Excellence (HOME): Integrating Financial and Engineering Analysis to Determine Optimum O and M Strategies for a Power Plant during its Lifetime
Agus Wibawa, Djatmiko Ichsani, Muhammad Nur Yuniarto

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Wibawa, A., Ichsani, D., Yuniarto, M.N., 2021. Holistic Operation & Maintenance Excellence (HOME): Integrating Financial & Engineering Analysis to Determine Optimum O&M Strategies for a Power Plant during its Lifetime. International Journal of Technology. Volume 12(4), pp. 813-828

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Agus Wibawa Department of Mechanical Engineering, Institut Teknologi Sepuluh Nopember, Jl.Teknik Kimia Keputih Sukolilo Surabaya 60111, Indonesia
Djatmiko Ichsani Department of Mechanical Engineering, Institut Teknologi Sepuluh Nopember, Jl.Teknik Kimia Keputih Sukolilo Surabaya 60111, Indonesia
Muhammad Nur Yuniarto Department of Mechanical Engineering, Institut Teknologi Sepuluh Nopember, Jl.Teknik Kimia Keputih Sukolilo Surabaya 60111, Indonesia
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Abstract
Holistic Operation and Maintenance Excellence (HOME): Integrating Financial and Engineering Analysis to Determine Optimum O and M Strategies for a Power Plant during its Lifetime

Today, there is an oversupply of 23.5 GW (47.7%) in the electricity system of Indonesia. PT.PLN, the state-owned electricity company, needs decision criteria to decide whether the power plant should be continue operated, rehabilitated or demolished. Base on the literature review, none of the frameworks in the world could be used to solve this problem. Therefore, this research proposed a new method or framework called HOME (Holistic Operation & Maintenance Excellence). The method has proposed and analysed in this research combines engineering analysis (efficiency and reliability) and economic analysis, which are total cost (acquisition cost, fuel cost, operation cost, and maintenance cost) and revenue. The objective is to define decision criteria to maximize the profit and minimize the cost has spent by a power plant. The final results are the decision criteria for a power plant, wheater to continue operated, rehabilitated, relocated, or demolished. A sub-critical coal power plant, 400 MW, has been selected as a case study. Two scenarios of coals (LRC and HRC) and CF (79.46% and 60.96%) have been analyzed. Coal variation is used to evaluate its impact on efficiency and reliability, while CF change would represent the external and uncontrollable factor that impacts its revenue. The results showed that the thermal efficiency when using LRC (4,220 kcal/kg) reduced from 36.99% to 35.18% compared to HRC (4,917 kcal/kg), while the plant availability decreased from 97.93% to 97.45%. Nonetheless, the annualized profit when using LRC at the CF of 79.46% was 18.31 million USD/year, and it was a preferable option compared to 7.80 million USD/year when using HRC. Furthermore, the CF has predicted a reduction to 60.96%. In this situation, the power plant was better rehabilitated or relocated when it used HRC because it needs a minimum CF of 63.83% to get a break-even point (CFBEP). Conversely, the plant could continue to operate when LRC is used because CFBEP was 50.82%. Based on the analysis results, HOME is a good approach to determine and aid decision-making on the strategies required to operate and maintain a power plant comprehensively through its whole life cycle.

Cost; Efficiency; LCM; Reliability; Revenue

Introduction

        A coal-fired power plant is one of the most common options to meet base-load demand in the electricity system due to mature technology and competitive cost (Barros et al., 2016). It is expected to project relatively 31% of the world power generation by 2040 (IAE, 2017). The disadvantages are its negative impact on the environment, “dirty” image and the fact that it is non-renewable (Gonzalez-Salazara et al., 2018)It triggers the rapid development of a coal-fired power plant’s technology, increasing efficiency and reducing environmental impact (Fu et al., 2015). In a competitive and uncertain market, the main factors considered for the survival of a power plant are energy, economic, social, and environmental issues (Petrillo et al., 2016; Luo et al., 2020). Certain problems need to be handled appropriately. First is the way and manner to manage the efficiency and reliability of the power plant while maintaining a safe environmental impact during the operating period, under certain government regulations. Normally, a life cycle management (LCM) plan addresses all of these issues. Formal definition of life cycle management is an integration of operation, maintenance, engineering, and business activities to manage asset condition, optimize asset life, and maximize asset return on investment. The two main elements of asset management are physical and financial asset management (Figure 1). Physical asset management is used to improve and maintain the asset condition through implementing efficiency and reliability management. Financial asset management is used to maximize asset value by reducing costs and increasing revenues. 


Figure 1 LCM Concept

 

The first LCM framework was initially developed and implemented in a nuclear power plant (EPRI, 1998). In addition, it is known as Nuclear Asset Management (NAM). The LCM framework focuses on reliability improvement (Sliter and George, 2003; Raghawan and Chowdhury, 2012). Several preliminary studies reported that reliability does not consider the power plant's efficiency (Singh and Jaswal, 2013; Pariaman et al., 2017; Melani et al., 2018). Similarly, most studies carried out in ways that increase efficiency do not take into account reliability. Furthermore, there are five factors that affect the coal-based power plant's efficiency. The first factor is design choices (Li et al., 2010; Stover et al., 2011), second is fuel strategies (Xia et al., 2014; Xu et al., 2016), third is operational practices (Xiong et al., 2012; Hübela et al., 2017), fourth is pollutant control (Munir et al., 2011), and fifth is ambient conditions (Zhang, 2015; Petrescu et al., 2017). None of the aforementioned studies analyzed both efficiency and reliability. Secondly, the power plant needs to simultaneously pay attention to sustaining its revenue. This depends on uncontrollable external factors, such as electricity demand and competitors or the market's behavior. This has become a significant challenge in the Volatility, Uncertainty, Complexity, Ambiguity (VUCA) era. The COVID-19 pandemic has significantly reduced electricity demand worldwide (Elavarasan et al., 2020). This led to a change in customers' behavior, because most people prefer to work from home. In addition, there was an increase in residential load. In contrast, the commercial and industrial ones decreased due to the slackening of business activities as an attempt to minimize the spread of the virus (Berawi et al., 2020). However, the decline in demand causes a decrease in the capacity factor (CF) of the power plant. In Indonesia, the projected CF was reported as 28.33% between 2020 and 2024 compared to 54.96% recorded in 2019, due to oversupply and COVID-19 impact (PLN, 2020). With a 47.7% reserve margin, as a consequence, several power plants have to temporarily standby or permanently shut down. This also affected the expected revenue from the initial project. Therefore, there was a need to ascertain whether the power plant was continuously operated, rehabilitated, or demolished. The objective was to either maintain the targeted financial performance or at least minimize the losses. This led to the final problems related to ways to optimize and detect the economic life of an asset. According to asset management standard (ISO 55010, 2019), the optimum time for investment intervention is the point when the overall life cycle cost of an asset is minimal (Figure 2a). In the power generation sector, this approach is established in a framework named integrated life cycle management (ILCM), as the development of LCM (Esselman et al., 2012). This focuses on the equipment or component level and ways to minimize its cost. Early replacement makes a higher total cost because the probability of failure is still relatively low compare to investment cost (zone 1). But replacement too late also makes it higher due to higher force outage cost (zone 2). Integrated life cycle management could not analyze the system or power plant level because it does not consider the revenue, while the plant has to consider both cost and revenue. The cost is dominant from internal factors and controllable by the power plant. On the contrary, revenue is more dominant from external factors and uncontrollable. Incentives on feed-in tariffs or tax credits could improve its overall cost competitiveness and make it more viable (Yang et al., 2021). In the grid system, the plant configuration has a significant impact, economically and environmentally (Destyanto et al., 2017; Xu et al., 2017; Njoku et al., 2020). Based on the references above, there is a significant gap in the studies that separately investigated efficiency, reliability, and optimum replacement analysis. None of the studies analyzed a combination of efficiency and reliability, its impact on cost and revenue, or the ways to optimize an asset life cycle at the power plant level (Wibawa et al., 2019). This led to the introduction of a novel approach called the Holistic Operation and Maintenance Excellent (HOME). This approach is based on the combination of efficiency, reliability, and replacement analysis to optimize the asset's life cycle. Furthermore, it also combines the cost and revenue of the power plant. This approach significantly analyses all the factors associated with the VUCA era. Subsequently, this research is organized as follows: first is the concepts and methodology, followed by its implementation in the power plant (industrial case study), and finally, analysis, discussions, and conclusions to determine whether or not it is suitable to address all these problems.


Conclusion

      The proposed HOME concept has been proved to fulfil the gap of the previous LCM framework.  It comprehensively combines all of the technical and financial analyses needed to support the decisions of the power plant owner, whether it needs to be kept, rejuvenated, or demolished for good.  A combined analysis of efficiency and reliability is realized through any change in fuel, operation, or maintenance strategies. The impact on cost and revenue tends to be simultaneously analyzed. The case of fuel changing strategies (HRC and LRC), studied and reported in this research, shows that the HOME frameworks are proven to aid in deciding what to do with the power plant under investigation. It is also capable of predicting the future impact of the external factors on the revenue. The optimum decision concerning whether the power plant needs to be continuously operated, rejuvenated, or demolished, has to be analyzed. The HOME project aids the power plants in simulating and predicting the possibility of all strategic options during its operational period. In addition, the power plant also needs to avoid unnecessary maintenance or rejuvenation, or rehabilitation activities by taking the appropriate decision towards the end of its life cycle. The implementation of the advanced and future power plant technology is easily evaluated and justified. In the case study analyzed in this paper, if it only takes into consideration reliability and efficiency, the power plant under investigation will have to use HRC. The higher the calorific value, the higher its reliability and efficiency. Unfortunately, as it has been simulated and analyzed, those two factors are not enough to justify the viability of the coal calorific values to be used. The other factor that has to consider is the total cost. The total cost will impact the minimum CF to reach the break-even point (CFBEP). Combining those three factors (reliability, efficiency, and CFBEP) into the analysis as suggested by the HOME framework, provides the best decision for all aspects of the power plant, such as operation maintenance, cost, and revenue. Based on Table 5, HRC and LRC could be used if the power plant has a CFPRED of 79.46%. The efficiency and reliability would decrease and generate more carbon emission when using LRC. It needs more expensive maintenance, but produces more profit than HRC. If the CFPRED reduces to 60.96%, then only the LRC is viable. Rehabilitation or rejuvenation must occur when using LRC. Based on the case study, the HOME framework was extremely effective and used to make the best decision concerning the power plant under investigation. This is necessary in order to remain competitive in an uncertain electricity market and business condition. It effectively guides the power plant operation and maintenance by providing the best decision at every stage (age). However, integrating and directly linking it to the power plant database, such as the DCS and the CMMS for operational and maintenance data, provides a dynamic and simultaneous analysis of the current position of the performance and prediction. This saves a lot of time and money and ensures the power plant is always a competitive edge in terms of the cost of electricity generated and, even more important, in the current VUCA condition. In this case study, the acquisition cost is constant. On the contrary, the disposal cost is negligible. In certain circumstances, such as asset reevaluation or divestment, the acquisition and disposal costs were very important to consider. It has a significant impact on the total cost and parameters that to consider for future research.

Acknowledgement

      Data support for this research was provided by PT PJB, as a subsidiary of PT PLN (Indonesia’s state electric company) and the authors gratefully acknowledged them.

Supplementary Material
FilenameDescription
R2-ME-4827-20210613180942.docx Research DataFile
References

Barros, J.J.C., Coira, M.L., de la Cruz Lopes, M.P., del Cano Gochi, A., 2016. Probabilistic Life Cycle Cost Analysis for Renewable and Non-Renewable Power Plant. Energy, Volume 112, pp. 774787

Berawi, M.A., Suwartha, N., Kusrini, E., Yuwono, A.H., Harwahyu, R., Setiawan, E.A., Yatmo, Y.A., Atmodiwirjo, P., Zagloel, Y.T., Suryanegara, M., Putra, N., Budiyanto, M.A., Whulanza, Y., 2020. Tackling the COVID-19 Pandemic: Managing the Cause, Spread, and Impact. International Journal of Technology. Volume 11(2), pp. 209214

Destyanto, A.R., Hidayatno, A., Amalia, A., 2017. Analysis of The Effects of CO2 Emissions from Coal-Fired Power Plants on the Gross Domestic Regional Product in Jakarta. International Journal of Technology, Volume 8(7), pp. 13451355

Electric Power Research Institute (EPRI), 1998. Nuclear Plant Life Cycle Management Implementation Guide.  EPRI Report 106109, Palo Alto, CA

Esselman, T., Bruck, P., Menger, C., 2012. Integrated Life Cycle Management: A Strategy for Plants to Extend Operating Lifetimes Safely with High Operational Reliability. IAEA, CN-194-034, pp. 1–8

Fu, C., Anantharaman, R., Jordal, K., Gundersen, T., 2015. Thermal Efficiency of Coal-Fired Power Plant: from Theoretical to Practical Assessment. Energy Conversion and Management, Volume 105, pp. 530544

Gonzalez-Salazara, M.A., Kirstena, T., Prchlik, L., 2018. Review of the Operational Flexibility and Emissions of Gas and Coal-Fired Power Plants in a Future with Growing Renewables. Renewable and Sustainable Energy Reviews, Volume 82(1), pp. 1497–1513

Hübela, M., Meinked, S., Andrénb, M.T., Wedding, C., Nockea, J., Gierowa, C., Hassela, E., Funkquist, J., 2017. Modelling and Simulation of a Coal-Fired Power Plant for Start-Up Optimization. Applied Energy, Volume 208, pp. 319–331

International Energy Agency (IAE), 2017. World Energy Outlook

International Organisation for Standardization, 2010. Guidance on the Alignment of Financial and Non-Financial Function in Asset Management. ISO/TS 55010:2019(E)    

Li, M., Rao, A.D., Brouwer, J., Samuelsen, G.S., 2010. Design of Highly Efficient Coal-Based Integrated Gasification Fuel Cell Power Plants. Journal Power of Sources, Volume 195, pp. 5707–5718

Luo X.J., Oyedele, L.O., Owolabi, H.A., Bilal, M., Ajayi, A.O., Akinade, O.O., 2020. Life Cycle Assessment Approach for Renewable Multi-Energy System: A Comprehensive Analysis. Energy Conversion and Management, Volume 224, https://doi.org/10.1016/j.enconman.2020.113354

McNerney, J., Trancik, J.E., Farmer, J.D., 2011. Historical Cost of Coal Fired Electricity and Implications for the Future. Energy Policy, Volume 39(6), pp. 3042–3054

Melani, A.H.A., Murad, C.A., Netto, A.C., de Souza, G.F.M., Nabeta, S.I., 2018. Criticality-Based Maintenance of a Coal-Fired Power Plant. Energy, Volume 147, pp. 767–781

Munir, S., Nimmo, W., Gibbs, B.M., 2011. The Effect of Air Staged, Co-Combustion of Pulverized Coal and Biomass Blends on NOx Emissions and Combustion Efficiency. Fuel, Volume 90(1), pp. 126135

Njoku, I.H., Oko, C.O.C., Ofodu, J.C., Diemuodeke, O.E., 2020. Optimal Thermal Power Plant Selection for a Tropical Region using Multi-Criteria Decision Analysis. Applied Thermal Engineering, Volume 179, https://doi.org/10.1016/j.applthermaleng.2020.115706

Pariaman, H., Garniwa, I., Surjandari, I., Sugiarto, B., 2017. Availability Analysis of the Integrated Maintenance Technique based on Reliability, Risk, and Condition in Power Plants. International Journal of Technology, Volume 8(3), pp. 497–507

Petrescu, L., Bonalumi, D., Valenti, G., Cormos, A-M., Cormos, C-C., 2017. Life Cycle Assessment for Supercritical Pulverized Coal Power Plants with Post-Combustion Carbon Capture and Storage. Journal of Cleaner Production, Volume 157, pp. 1021

Petrillo, A., De Felice, F., Jannelli, E., Autorino, C., Minutillo, M., Lavadera, A.L., 2016. Life Cycle Assessment (LCA) and Life Cycle Cost (LCC) Analysis Model for a Stand-Alone Hybrid Renewable Energy System, Renewable Energy, Volume 95, pp. 337355

Elavarasan, R.M., Shafiullah, G., Raju, K., Mudgal, V., Arif, M.T., Jamal, T., Subramaniang, S., Balaguru, V.S.S., Reddy, K.S., Subramaniam, U., 2020. COVID-19: Impact Analysis and Recommendations for Power Sector Operation. Applied Energy, Volume 279, https://doi.org/10.1016/j.apenergy.2020.115739

Singh J., Jaswal, R.A., 2013. Evaluation of Reliability Parameter of the Thermal Power Plant by BFT. International Journal of Advanced Engineering Technology, pp. 79–81

Sliter, George, E., 2003. Life Cycle Management in the US Nuclear Power Industry. In: Transactions of the 17th International Conference on Structural Mechanics in Reactor Technology (SMiRT 17), Prague, Czech Republic, August 17 –22, pp. 1–8

Raghawan, S., Chowdhury, B., 2012. Developing Life Cycle Management Plant for Power Plant Components. In: Conference North American Power Symposium (NAPS)

State Electric Company (PT. PLN), 2020. Electric Power Supply Plan for Java-Bali System (RPTL) in 2021-2025

Stover, B., Bergins, C., Klebes, J., 2011. Optimized Post Combustion Carbon Capturing on Coal Fired Power Plants. Energy Procedia, Volume 4, pp. 1637–1643

Wibawa, A., Ichsani, D., NurYuniarto, M., 2019. Power Plant Life Cycle Cost Management Framework: A Literature Review. Journal of Physics Conference Series, Volume 1485, pp. 1–9

Xia, J., Chen, G., Tan, P., Zhang, C., 2014. An Online Case-Based Reasoning System for Coal Blends Combustion Optimization of the Thermal Power Plant. Electrical Power and Energy System, Volume 62, pp. 299331

Xiong, J., Zhao, H., Zhang, C., Zheng, C., Luh, P.B., 2012. Thermoeconomic Operation Optimization of a Coal-Fired Power Plant. Energy, Volume 42(1), pp. 486496

Xu, C., Xu, G., Zhao, S., Dong, W., Zhou, L., Yang, Y., 2016. A Theoretical Investigation of Energy Efficiency Improvement by Coal Pre-Drying in Coal-Fired Power Plant. Energy Conversion and Management, Volume 122, pp. 580588

Xu, J., Gu, J., Chen, D., Li, Q., 2017. Data Mining-Based Plant-Level Load Dispatching Strategy for the Coal-Fired Power Plant Coal-Saving: A Case Study. Applied Thermal Engineering, Volume 119, pp. 553–559

Yang, B., Wei, Y-M., Liu, L-C., Hou, Y-B., Zhang, K., Yang, L., Feng, Y., 2021. Life Cycle Cost Assessment of Biomass Co-Firing Power Plants with CO2 Capture and Storage Considering Multiple Incentives. Energy Economics, Volume 96, https://doi.org/10.1016/j.eneco.2021.105173

Zhang, C., 2015. A Software for Optimizing the Thermal Power Plant Operation Under Environmental Constraints. In: Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015, pp. 2815–2818