Accounting for the Life Cycle Cost of Power Grid Projects by Employing a System Dynamics Technique: A Power Reform Perspective

Wang, Yongli; Song, Shanshan; Gao, Mingchen; Wang, Jingyan; Zhu, Jianqin; Tan, Zhongfu

doi:10.3390/su12083297

Cited by 5 publications

(5 citation statements)

References 24 publications

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“…Thirdly, the fuzzy reference comparisons for "Promoting employment benefit", C6, were conducted, and Table 3 lists the result. Then, the A W is: Finally, the optimal fuzzy weights of the eight benefit sub-criteria from the comprehensive benefit evaluation of a power grid investment can be calculated according to Equations ( 8)- (11), which are shown in Figure 4. Moreover, the CR is 0.0455, which is less than 0.1.…”

Section: Calculating the Weights Of The Benefit Criteriamentioning

confidence: 99%

“…Under the new transmission and distribution policy, power grid enterprises charge grid fees based on fixed transmission and distribution electricity prices instead of earning the difference between the transmission and sales prices [11]. This functional orientation and operation mode change makes the power grid transmission and distribution investment management particularly important.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive Benefit Evaluation of Power Grid Investment Considering Renewable Energy Development from the Perspective of Sustainability

Tian¹,

Zhao²,

Guo

2023

Sustainability

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To cope with the rapid development of renewable energy, the power grid system needs to invest in and construct transmission and distribution projects. Means of evaluating the economic, social, and environmental benefits generated by power grid investment are of great significance for enterprise cost recovery and government policy formulation. In this paper, an evaluation of the comprehensive benefit of a power grid investment considering renewable energy development from the perspective of sustainability is developed through the use of a hybrid multi-criteria decision-making (MCDM) method. The entropy weight method and fuzzy best worst method (BWM) are jointly employed to weight the comprehensive benefit criteria, which include economic, social, and environmental criteria and eight sub-criteria, and the measurement of alternatives and ranking according to compromise solution (MARCOS) method is utilized to evaluate the comprehensive benefit of a power grid investment. Five power grid investment projects for connecting renewable energy generation to a power grid in Ningxia, China, are selected as a case study, and the results indicate that the power grid investment project PGIP#1 has the largest comprehensive benefit (0.7099), followed by PGIP#3 (0.6800), PGIP#2 (0.6709), PGIP#5 (0.5959), and PGIP#4 (0.5861). The sensitivity analysis shows that the comprehensive benefit of PGIP#1 is always the best, indicating the robustness of the proposed method. By employing the proposed MCDM method to assess the comprehensive benefit of power grid investment projects, this research identifies outstanding projects which can provide guidance for the management of power grid investment and promote the sustainable development of the power industry.

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Section: Calculating the Weights Of The Benefit Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comprehensive Benefit Evaluation of Power Grid Investment Considering Renewable Energy Development from the Perspective of Sustainability

Tian¹,

Zhao²,

Guo

2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…In a previous study, [25] researched on life cycle cost analysis which was integrated with dynamic systems in power grid projects. Regarding the research's impact on the calculation of operation and maintenance costs, it will be approached from four perspectives: material costs, repair costs, employee compensation, and other costs, with the goal of making comparisons before and after changes, such as the impact of accounting for operating and maintenance costs after electricity reform, stock changes, and the resulting increase.…”

Section: System Dynamicsmentioning

confidence: 99%

Improved Performance of Toll Road Projects Based on System Dynamics Integrated Life Cycle Cost Analysis Green Retrofitting

Yuliatti¹,

Husin²,

Sutikno³

2022

cea

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The increasing number of infrastructure development efforts, one of which is toll roads, of course has a coordinated impact on natural damage because it causes emissions, pollution, and environmental damage. Green road rankings can be an instrument for the degree of implementation of green infrastructure and the level of reforestation in toll road development efforts. Guidelines for green road ranking SE Minister of PUPR. Number: 04/SE/M/2018 is one of the efforts to encourage the implementation of sustainable construction in infrastructure implementation. Furthermore, these green road points can be implemented on existing toll roads and meet the sustainability criteria. This study aims in the innovation of Dynamic Model Implementation and Life Cycle Cost Analysis on Green Infrastructure-based Toll Road works which will result in Cost Efficiency and add value to Toll Roads. For data processing, we used SPSS (Statistical Package for the Social Sciences), a statistical analysis obtained from a questionnaire filled out by experts. From this process, we got the 10 best factors which are: 1. Authority to administer toll roads; 2. Reduction of air/dust pollution; 3. Greening; 4. Provision of access and public transportation facilities; 5. Recycle construction waste; 6. Renewable energy and application of innovation; 7. Renewable energy street lighting; 8. Modeling of dynamic systems; 9. Initial Retrofitting Costs; 10. Period of analysis. From the green retrofitting implementation scenarios, the optimistic scenario shows the highest rating score and the fastest return on investment in 3.92 years, with the toll road retrofitting cost of IDR 378,700,000,000.

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“…Xiang Chen (2022) [7], using the traditional LCC-EL model, introduced the factor of equipment operation reliability and found a reasonable selection scheme for main transformers. Yongli Wang et al, (2020) [8] used sensitivity analysis to explore important factors identified by power policy reforms for the whole life cycle of equipment, redetermined the impact of equipment breakdown on LCC cost analysis, and suggested rules for managing the apportionment of costs incurred by equipment.…”

Section: Introductionmentioning

confidence: 99%

“…While there are some studies [4][5][6][7][8] that have investigated the application of LCC and EL in the energy sector, the depth of the research is still insufficient in the dimensions of data processing, LCC breakdown determination, calculation of LCC, and determination of economic life, as the analytical methods remain imprecise and unscientific. Due to the severe uncertainty of decision environments, decision-makers can benefit from models that incorporate fuzzy analysis.…”

Section: Introductionmentioning

confidence: 99%

A Strategy for Determining the Decommissioning Life of Energy Equipment Based on Economic Factors and Operational Stability

Wang²,

Li³

et al. 2022

Sustainability

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LCC and EL models have been widely used in recent years to determine the decommissioning life of equipment in energy companies, with LCC (life-cycle cost) being the total “lifetime” cost of the equipment from the time it is put into operation until the end of its decommissioning and disposal; the average annual cost of the equipment can be calculated based on the LCC. The overall LCC can be calculated as the average annual LCC, while the EL is the age of the equipment at which its average annual LCC is the lowest. It is believed that the decommissioning of the equipment in the EL year will result in the lowest annual average equipment turnover, thus maximizing the economic benefits of the equipment. Recently, LCC and EL research has been gradually introduced to the energy field, but there remains a lack of research depth. In current practice, energy equipment LCCs are mainly determined by selecting a portion of inventoried equipment to serve as a sample record for all costs incurred. The intent is to derive the economic life of the equipment-year by directly seeking its average annual cost, but this method tends to downplay maintenance, overhaul, and other cost events as “random small probability events”. This method is also incomplete for evaluating the decommissioning life of equipment whose average annual cost strictly decreases year-by-year. In this study, we analyzed the use of 75,220 KV transformers that were put into service by an energy company in 1986 as a case study (costs for this type of equipment were first recorded strictly in terms of LCC in 1986), used Isolated Forest (IF) to screen the outliers of various types of data costs, and then probability-corrected the corrected dataset with a Welbull distribution (Welbull). Then, we employed a stochastic simulation (MC) to calculate the LCC of the equipment and determined its economic lifetime (EL) and compared the results of the stochastic simulation method with those of the traditional method to provide a more reasonable explanation for the “small probability” of cost occurrences. Next, we predicted the average cost of the equipment given a use-period of 38-41-years using AHA, Bi-LSTM, and other comparative algorithms, compared the MAE, MAPE, and RMES indexes, selected the most suitable prediction model, and produced a predicted cost under the chosen method to obtain the economic life of the equipment. Finally, we compared our results with the design life of the equipment (design life being the technical life expectancy of a product based on the expectations of the manufacturer), and determined its best retirement age by comprehensively studying and judging the economic and technical benefits. The retirement age analysis was guided by by a comprehensive study of economic and technical benefits. We refer to our decommissioning life determination model as Monte Carlo -artificial hummingbird algorithm–BiLSTM–lifecycle cost model (MC-AHABi-LCC). We found that the decommissioning life obtained by MC-AHABi-LCC is closer to the actual equipment decommissioning life than that given by standard LCC and EL analysis and that our model is more accurate and scientific.

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Accounting for the Life Cycle Cost of Power Grid Projects by Employing a System Dynamics Technique: A Power Reform Perspective

Cited by 5 publications

References 24 publications

Comprehensive Benefit Evaluation of Power Grid Investment Considering Renewable Energy Development from the Perspective of Sustainability

Comprehensive Benefit Evaluation of Power Grid Investment Considering Renewable Energy Development from the Perspective of Sustainability

Improved Performance of Toll Road Projects Based on System Dynamics Integrated Life Cycle Cost Analysis Green Retrofitting

A Strategy for Determining the Decommissioning Life of Energy Equipment Based on Economic Factors and Operational Stability

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