Economical Regulating Strategies Based on Enhanced EVM Model in Electric Substation Construction Projects

Abstract

With the increasing demand for electricity in modern society, the scale of substation construction projects has greatly expanded, and the ever-increasing technical requirements have led to rising project costs year by year. Effective cost management not only enhances a company’s market competitiveness but also ensures the construction quality of projects. This paper addressed the issues of cost management in substation projects by exploring the application of unbalanced bidding, target costing, and improved earned value management (EVM) in cost control. By introducing quality indicators to improve traditional EVM, this study proposed a comprehensive evaluation model that considers cost, schedule, and quality to ensure a good construction performance of substations. Using LT 220 kV substation of Company A project as a case study, the paper analyzed specific measures of cost management in the bidding decision, preparation, and construction phases, verifying the feasibility and effectiveness of the improved model. The results indicated that the enhanced EVM can effectively improve cost control in substation projects, achieving an optimal balance among quality, schedule, and cost with significant practical application value.

1. Introduction

With the rapid growth of global energy demand, the stability and efficiency of the power system as the backbone of modern society play a crucial role in economic and social development [1]. In the power system, substations perform vital functions of power conversion, transmission, and distribution, becoming the hub of the power system [2]. As electricity demand continues to rise and power technology advances swiftly, the scale and complexity of substation construction projects are also increasing. Substation projects require substantial capital investment and must be completed under strict quality standards and schedule requirements. Thus, achieving efficient cost management throughout the project life cycle poses a core challenge for power companies and project managers [3].

The difficulty of controlling costs in electricity sector construction projects should not be underestimated. Firstly, the complexity of projects leads to diversified challenges in cost control. Electricity projects typically involve multiple phases, including design, construction, equipment procurement, and project management, each of which can incur various costs influenced by market fluctuations, policy adjustments, and technological changes [4]. Therefore, achieving cost reduction while ensuring project quality and progress is a significant challenge faced by project managers. Secondly, the characteristics of the electricity sector further complicate cost control. With the emergence of smart grids and renewable energy, the electricity sector is undergoing a profound transformation [5]. This transformation brings about new technological demands and investment requirements, particularly in the development and utilization of renewable energy. Although initial investments may be substantial, ineffective cost control can result in long-term financial pressures [6]. Moreover, the increasingly stringent environmental regulations add to compliance costs, making it crucial to manage costs within strict environmental requirements, which has become a significant task in electricity construction projects [7]. Additionally, the uncertainty during project implementation poses challenges for cost control. In the construction process of the electricity sector, unforeseen circumstances often arise, such as natural disasters, policy changes, and fluctuations in the supply and demand of the electric market, all of which can lead to increased project costs [8]. In such situations, project managers must possess the ability to respond flexibly and adjust project strategies in a timely manner to effectively control costs. Therefore, how to achieve a comprehensive balance of project costs, schedules, and quality through scientific cost-management methods is a critical focus in the current research on substation engineering management.

To meet the cost-management needs of substation projects, various methods and technologies have been developed in the field of engineering management, including unbalanced bidding, target costing, and earned value management, aiming to achieve the reasonable control of project costs. Unbalanced bidding dynamically adjusts the costs of various components of a project to avoid excessive resource consumption and is applicable to competitive bidding projects [9]. Target costing uses target costs as benchmarks for project cost management, ensuring that projects are completed within budget [10]. Meanwhile, earned value management serves as a monitoring tool for project schedule and cost, enabling managers to grasp the actual progress of projects in real time and take timely corrective actions when problems arise [11]. The PMBOK guide establishes the project management triangle as the foundational paradigm of project management, defining three intrinsically interdependent core constraints—scope, time, and cost—which exhibit deterministic coupling relationships that govern project execution dynamics [12]. However, these traditional cost-management methods often struggle to simultaneously consider cost, schedule, and quality, especially in high-tech and lengthy construction projects like substations [13]. A single cost control method may not adequately meet actual needs. Specifically, the classical triangle model treats quality as an external constraint achieved through isolated quality management processes. This approach exhibits inherent limitations, including the lack of quantifiable quality metrics and the disconnection between quality data and cost/schedule performance indicators.

In this context, how to effectively combine various cost-management methods while integrating quality control factors to achieve comprehensive and all-encompassing cost management for substation projects becomes the central issue of this study. Therefore, this paper improved traditional earned value management by adding quality control indicators, allowing the model to meet quality management needs while achieving cost and schedule control, thus forming a more comprehensive engineering management model. Using LT220 kV substation of Company A project as a case study, this paper investigated the specific applications of unbalanced bidding, target costing, and improved earned value management in cost management for substation projects. Throughout the study, the paper analyzed the applicability and management effectiveness of each method from different phases, such as bidding decisions, construction preparation, and construction, and optimized the earned value management by introducing quality control factors to enable more effective comprehensive management of cost, schedule, and quality.

This study offered new cost-management approaches for power projects, aiding electric enterprises in substation construction cost control and efficiency, providing insights for engineering management.

2. Principles and Methods

2.1. Unbalanced Bidding

Unbalanced bidding refers to a strategy in the engineering bidding process where, based on the characteristics and specifications of the bid quantities listed, the bidder maintains a constant total bid price while making appropriate adjustments to certain unit prices on the bid list, thereby obtaining higher profits during the settlement process [14].

Unbalanced bidding manifests mainly in two forms in the bidding activities for transmission and transformation projects [15]:

Early Payments: For projects completed in the early stages of construction, the bidder can appropriately raise the bid price; conversely, they can lower the bid price to recover project payments quickly, improving fund utilization efficiency. The quick recovery of upfront project payments also aids in capital turnover, which can place the contractor in a favorable position in case of breach or unforeseen circumstances, reducing operational risks.

Increased Payments: The bill of quantities in the bidding documents may not be entirely accurate. In recent years, many special high-voltage transmission demonstration projects have faced challenges in accurately estimating quantities due to unique circumstances. During bidding, the quantities are estimated; actual settlement is determined by the joint confirmation of the design unit, supervision unit, and construction unit. Therefore, discrepancies often exist between individual project quantities in the bidding list and the final construction quantities.

During the actual bidding process, construction units can leverage past experiences to assess whether the bid quantities in the bill align with actual site conditions or whether there are opportunities for change. By anticipating discrepancies between the actual quantities and those listed, they can reasonably adjust their bid prices accordingly.

2.2. Target Costing

Target costing establishes predetermined cost benchmarks during the project planning phase, which are then rigorously monitored throughout execution. The methodology employs earned value analysis to compare actual expenditures against planned targets, enabling timely identification of cost variances [16]. This systematic approach facilitates proactive cost control measures, ensuring project expenses remain aligned with established financial objectives while maintaining required quality standards.

2.3. Earned Value Management (EVM)

The earned value theory is a precise method for cost evaluation that allows for the comprehensive management of project progress and costs [17]. Due to the numerous uncertainties present during project implementation, these factors can impact both project schedule and cost. EVM can effectively address these issues [18]. Traditional earned value management primarily integrates analyses of cost and duration but lacks consideration of quality indicators, which are essential in modern project contexts. Thus, it is imperative to include quality factors in the application of earned value management. A comprehensive EVM that considers cost, schedule, and quality integrates a new intermediate variable, the “Earned Quality Indicator (BCWPH)”, into the analysis to improve the application of EVM for actual project construction cost control.

2.3.1. Calculation of Traditional Earned Value Parameters and Evaluation Metrics

(1)

Calculation of Traditional Earned Value Parameters

The parameters and calculations of traditional earned value were shown in

Table 1. Calculation of traditional earned value parameters.

Traditional Earned Value Parameters	Calculation Formula		Note
Cost Variance (CV)	BCWP − ACWP	(1)	BCWP = Budgeted Cost of Work Performed BCWS = Budgeted Cost of Work Scheduled ACWP = Actual Cost of Work Performed
Schedule Variance (SV)	BCWP − BCWS	(2)
Cost Performance Index (CPI)	BCWP/ACWP	(3)
Schedule Performance Index (SPI)	BCWP/BCWS	(4)

.

(2)

Earned Value Analysis Curve (S-Curve)

The earned value method has proven effective in practice. By calculating various project cost parameters, an earned value analysis curve can be plotted with time as the horizontal axis and cost as the vertical axis, allowing for effective project cost analysis and management.

2.3.2. Improved Model of Earned Value Method with Quality Indicators

(1)

Addition of New Key Variables and Measurements

To enhance the basic earned value method, this study incorporates a quality-related variable, the “Earned Quality Indicator” (BCWPH):

$$BCWPH=BCWP\times He$$

(5)

$$He=\left({\displaystyle \frac{AH}{BH}}\right)\times 100\%={\displaystyle \frac{\sum \left(W_{i}A{H}_i\right)}{\sum \left(W_{i}B{H}_i\right)}}$$

(6)

where He represents the project quality indicator; Wi represents the weight of each completed subtask in the project; AHi represents the actual quality level of each completed subtask; and BHi represents the specified quality level for each subtask. He = (AH/BH) × 100% = Σ(WiAHi)/Σ(WiBHi).

(2)

Measurement of Earned Quality Indicator

The specified project quality level BH is determined by qualified project managers who analyze the project’s quality requirements in relation to cost and schedule across all project entities. BH is derived through the following three steps:

Since quality differs across subtasks, affecting the project cost and schedule in various ways, each subtask must be assigned a weight. This weight is determined by a random forest-based weight allocation model in machine learning. Compared to traditional approaches where weights are assigned solely by expert teams or experienced project managers, the machine-learning model offers superior objectivity, scalability, and consistency. It mitigates human bias, leverages historical data patterns, and adapts dynamically to varying project conditions—ensuring more reliable and data-driven weight allocation than subjective expert judgment alone. For the t-th tree, the predicted weight of subtask “i” is the mean value of the leaf node:

• Assigning weights to each subtask: Since quality differs across subtasks, affecting project cost and schedule in various ways, each subtask must be assigned a weight. This weight is determined by a random forest-based weight allocation model in machine learning. The proposed random forest-based machine-learning model for weight allocation effectively mitigates human bias by leveraging historical data patterns and dynamically adapting to diverse project conditions, thereby ensuring more reliable and data-driven weight determination compared to traditional subjective judgment approaches.

For the t-th tree, the predicted weight of subtask i is the mean value of the leaf node:

$${\widehat{W}}_{i,t}={\displaystyle \frac{1}{\left|S_t\right|}}{\sum }_{jϵS_t}{W}_j^{expert}$$

(7)

where $S_t$ denotes the set of samples falling into the leaf node; $W_j^{expert}$ represents the expert-assigned weights, which serve as labeled data for supervised learning.

The final predicted weight is the average across all T trees:

$${\widehat{W}}_i={\displaystyle \frac{1}{T}}{{\displaystyle \sum }}_{t=1}^T{\widehat{W}}_{i,t}$$

(8)

b.

Assessing quality level of specified subtasks: The quality of each specified subtask is assessed.

c.

Calculation of BH: By multiplying the above two factors, the specified project quality level BH is obtained. The actual project quality level (AH) is obtained by applying the same calculation approach based on the actual quality inspection results.

(3)

New Deviation Analysis Variables Resulting from Quality Indicators

In response to the introduction of quality indicators, this study introduces three new deviation analysis variables: the modified schedule variance ($S{V}^{\text{'}}$), the quality variance (HV), and the quality cost variance (HCV). The relevant calculations were presented in

Table 2. Calculation of deviation analysis variables.

Deviation Analysis Variables	Calculation Formula
$S{V}^{\text{'}}$	$BCWPH-BCWS$	(9)
$HV$	$BCWPH-BCWP$	(10)
$HCV$	$HV-CV$	(11)

.

(4)

Evaluation Process of the Improved Earned Value Analysis Method

Project Quality Evaluation: The project quality level is assessed based on the signs of HV and HCV. If HV < 0, the actual quality fails to meet specified quality standards, necessitating quality control. If HV = 0, the actual quality meets specified standards and should be maintained. If HV > 0, the actual quality exceeds specified standards, requiring analysis of HCV. If HCV > 0, excess quality has increased costs, necessitating quality control; if HCV ≤ 0, a moderate quality increase does not incur additional costs.

Cost and Schedule Evaluation: After achieving the required quality level (HV ≥ 0, HCV ≤ 0), the cost and schedule are analyzed: When HV = 0, the actual cost and schedule status are evaluated through the signs of CV and SV. When HV > 0: If HCV = 0 and SV’< 0, it indicates that the actual costs match the planned costs but the project is behind schedule. If SV’≥ 0, both the cost and the schedule meet the planned requirements. If HCV < 0 and SV’ ≥ 0, it indicates that both the cost and the schedule meet the planned requirements. If SV’ < 0, there are cost savings, but the project is behind schedule. In such cases, additional cost investment may be considered to accelerate construction progress.

3. Case Analysis

3.1. Project Overview

We conducted surveys on substations across China and found that most share similar configurations. Ultimately, we selected the representative LT 220 kV substation as our research subject. The LT 220 kV substation is invested in by Company A and is located in LT County, South-eastern China, adjacent to an expressway. The total area of the project is 32 acres, with a total investment of USD 16.5735 million. Construction commenced in May 2021, and the main construction activities include the new 220 kV transmission and transformation project and the 110 kV outgoing line project. The primary work tasks consist of pre-construction preparation, the installation and commissioning of the main transformer, the installation of GIS equipment in the 220 kV equipment area, the installation of cable supports, the testing of primary equipment, the installation of GIS in the 110 kV equipment area, the installation of 5 kV switchgear and station transformers, the installation of secondary control cabinets, cable laying and secondary wiring, the installation of lightning arresters and voltage transformers, the installation of reactive power compensation systems, and the commissioning of secondary protection systems.

The substation project implemented quality standards in strict compliance with the “Technical code for the design of 220~750 kV substation (DL/T 5218-2012)” and “Code for design of 35~110 kV substation (GB 50059-2011)” [19,20]. The target was set to achieve 1–3% cost savings through integrated cost-management approaches while meeting quality standards.

3.2. Optimization at the Bidding Decision Stage

3.2.1. Adjusting Project Item Prices Based on Completion Time to Ensure Timely Payments

The itemized cost breakdown for this project was presented in

Table 3. Division project pricing table.

Serial Number	Divisional Project Name	Unbalanced Quotation	Normal Quotation
1	Total price contract part	2,781,476	1,926,190
1.1	Measure item	2,781,476	1,926,190
2	Unit contract portion	14,557,700	154,129,092
2.1	Primary and secondary production systems	9,307,558	9,841,365
2.1.1	Master communication building	386,541	528,139
2.1.2	1# Relay room	268,350	272,836
2.1.3	2# Relay room	364,960	368,534
2.1.4	Substation electricity and 220 kV switchgear room	167,292	156,548
2.1.5	Fire pump house	178,397	182,722
2.1.6	Main transformer valve room	116,805	113,180
2.1.7	General drawing	1,937,234	2,282,545
2.1.8	Structure, support and equipment foundation	4,959,086	5,065,582
2.1.9	Water and electricity installation	889,830	845,238
2.1.10	HVAC installation	39,063	26,041
2.2	Individual works related to the site	5,250,141	5,573,021
2.2.1	Field leveling and pile foundation works	5,250,141	5,573,021
2.2.1.1	Field leveling	1,377,503	1,033,051
2.2.1.2	Pile foundation engineering	3,872,639	4,538,569
Total tender price		17,339,176	17,339,176

.

According to the bidding documents for the LT220 kV substation project, the total price contract section allowed for monthly claims for progress payments, with measured project portions to be claimed within the first two months after commencement. The site leveling, pile foundation, and main and auxiliary production systems (civil works) had durations of 1, 2, and 12 months, respectively (15 months in total, with no overlap considered). The annual commercial loan interest rate was 6.56%, and no consideration was given to prepayment at this time. Assuming the work volume for the site leveling, pile foundation, and civil works of the main production system was completed evenly each month and that progress payments were received on time, the present value of progress payments before and after applying the unbalanced bidding method was calculated as follows:

(1)

Present value of payments under standard pricing:

$$\begin{array}{c}P{V}_1=A_1\left(P/A,0.55\%,2\right)+B_1\left(P/A,0.55\%,1\right)+C_1\left(P/A,0.55\%,2\right)\left(P/F,0.55\%,1\right)\\ +D_1\left(P/A,0.55\%,12\right)\left(P/F,0.55\%,3\right)=\mathrm{USD}16,758,096\end{array}$$

(2)

Present value of payments under unbalanced bidding:

$$\begin{array}{c}P{V}_2=A_2\left(P/A,0.55\%,2\right)+B_2\left(P/A,0.55\%,1\right)+C_2\left(P/A,0.55\%,2\right)\left(P/F,0.55\%,1\right)\\ +D_2\left(P/A,0.55\%,12\right)\left(P/F,0.55\%,3\right)=\mathrm{USD}16,785,273\end{array}$$

After applying unbalanced bidding, the present value difference is

$$P{V}_2-P{V}_1=16,785,273-16,758,096=\mathrm{USD}27,177$$

Through the application of the unbalanced bidding method, Company A optimized the cost management of its substation project during the bidding decision phase, effectively addressing the issue of an imbalanced bidding structure. Subject to client-approved contractual provisions for progress-based payment adjustments and projected quantity variations, the implemented pricing strategy yielded demonstrable cash flow improvements. Comparative analysis of the project’s cash flow before and after the improvement showed an increase in net present value (NPV) from USD 16,758,096 to USD 16,785,273, marking an NPV gain of USD 27,177, equivalent to a 0.16% enhancement. This outcome demonstrated that, while keeping the total bid unchanged, Company A achieved a certain increase in present value returns, thereby improving the overall economic efficiency of the project. The strategic application of unbalanced bidding not only contributed to securing more adequate cash flow in the initial stages, alleviating financial pressure, but also ensured the profitability of the project throughout the construction process.

3.2.2. Sensitivity Analysis

(1)

Single-Factor Sensitivity Analysis

To evaluate the robustness of this optimization strategy, a sensitivity analysis was conducted to examine how changes in key factors affect the 0.16% PV gain. The single-factor sensitivity analysis was presented in

Table 4. Impact of changes in commercial loan interest rate.

Single-Factor Changes	PV1 (Normal Bidding)	PV2 (Unbalanced Bidding)	PV Difference (PV2-PV1)	Difference Rate
Baseline (6.56%)	16,758,096	16,785,273	+27,177	+0.16%
Interest Rate Change (5.56%)	16,821,078	16,848,907	+27,829	+0.17%
Interest Rate Change (7.56%)	16,696,120	16,722,563	+26,443	+0.15%
Civil Works Delay (1 Month)	16,725,504	16,752,171	+26,667	+0.15%
Site Leveling −10%	16,747,927	16,773,123	+25,196	+0.15%

.

(2)

Multi-Factor Sensitivity Analysis (Worst-Case Scenario)

The analysis assumed the simultaneous occurrence of a 1% interest rate increase (to 7.56%), a 1-month delay in civil works, and a 10% reduction in site leveling quantities, with the PV difference shown in the following

Table 5. Multi-factor sensitivity analysis.

Scenario	PV1 (Normal Bidding)	PV2 (Unbalanced Bidding)	PV Difference (PV2-PV1)	Difference Rate
Worst-Case Scenario	16,680,742	16,705,924	+25,200	+0.15%

.

The impacts of interest rates, payment timing, and engineering quantity changes were all minimal. The present value optimization of 0.16% remained stable within a reasonable fluctuation range, and it could still maintain a 0.15% return under the most unfavorable circumstances, indicating the robustness of the unbalanced bidding strategy. Sensitivity analysis showed that the cost saving of 0.16% was reliable, and the optimization strategy can maintain the expected return in most cases.

3.3. Optimization at the Construction Preparation Stage

3.3.1. Defining and Decomposing Target Costs

For the LT220 kV substation project, cost expenditures were categorized into direct and indirect costs. In construction projects, direct costs were primarily divided into direct materials, direct labor, machinery expenses, and other direct costs, using a traditional cost calculation approach [21].

3.3.2. Cost Analysis Results

After allocating actual indirect costs, they were consolidated with direct labor, direct materials, machinery expenses, and other direct costs listed in the summary table. The actual costs were then compared with the planned costs estimated using target costing, providing either overspending or underspending amounts.

Labor expenses for this construction phase were calculated by trade category (general labor, second-class, and third-class). Material costs included the purchase price and inspection fees. Machinery expenses were calculated on a per-shift basis using the cost and unit price of each 8 h work shift, based on the normal operational rate of construction machinery. The formulas for cost calculation were as follows:

$$Direct labor cost=\sum \left(\sum number of workers\times workdays\right)\times daily wage$$

(12)

$$Direct materials cost=\sum \left(material consumption\times base price\right)+inspection fee$$

(13)

$$Machinery usage cost=\sum \left(machinery consumption per shift\times shift rate\right)$$

(14)

Table 6. Comparison of planned and actual project costs unit: USD 10,000.

Construction Costs	Direct Labor Cost	Direct Material Cost	Machinery Operating Cost	Other Direct Cost	Indirect Cost	Total
Planned expenditure	319.54	1162.54	105.58	34.85	56.02	1678.53
Planned expenditure	308.03	1165.57	105.81	33.57	44.37	1657.35
Deviation rate	−3.66%	0.26%	0.22%	−3.67%	−20.8%	−1.26%

shows that, compared to the budget, the actual direct labor expenses were USD 115,033 lower, while the direct materials overspent by USD 30,226, machinery expenses overspent by USD 2322, other direct costs saved USD 12,780, and indirect costs saved USD 116,517, totaling a project cost saving of USD 211,792, or 1.26% of the project’s total cost.

The variance rates between the actual and budgeted costs for direct labor, direct materials, machinery expenses, other direct costs, and indirect costs were −3.36%, 0.26%, 0.22%, −3.67%, and −20.8%, respectively. The slight increases in machinery and direct material costs, with a deviation rate of approximately 1%, were primarily due to fluctuations in raw material and energy prices. Direct labor costs decreased by 3.36%, demonstrating cost savings achieved through open bidding processes. A reduction of 3.67% in other direct costs reflects the positive impact of strategic information management, which lowered communication and negotiation costs. Furthermore, there were notable reductions in indirect costs for both the substation and transmission parts, achieving a savings rate exceeding 20%, making the cost-saving effect particularly significant. This provided strong evidence that the target costing method allowed project cost managers to promptly identify inefficient operations and accurately trace the sources of unreasonable costs.

During the construction preparation phase, by clarifying target costs and their breakdowns, and effectively applying the target costing method, Company A achieved significant cost savings in the LT 220 kV substation project. In this project, direct labor, other direct costs, and indirect costs all experienced savings, resulting in total savings of USD 211,792, which accounted for 1.26% of the total project cost. Overall, the cost-management optimization measures implemented during the construction preparation phase provided robust support for subsequent project cost control.

3.4. Optimization During the Construction Phase

3.4.1. Integration of Progress and Cost Management Using Earned Value Method

The LT220 kV substation project included land leveling, pile foundation work, and primary and auxiliary production systems (main civil works) with durations of 1 month, 2 months, and 12 months, respectively. The land leveling and pile foundation phases were categorized as Phases I and II, while the main civil works were divided into Phases III, IV, and V. Each project phase required periodic cost assessments to ensure costs remained manageable. For analysis, the cable, electrical, and communications sections of the LT220 kV substation project were examined.

Table 7. Accumulated actual costs and total investment.

Phase	Cumulative Actual Cost (USD)	Cumulative VAT (USD)	Total Actual Investment (USD)	Planned Progress	Actual Progress
Phase I	523,846.5	3740.1	527,586.6	10%	8%
Phase II	1,574,288.8	141,292.3	1,715,581.2	35%	25%
Phase III	6,509,248.3	1,026,792.9	7,536,041.2	70%	78%
Phase IV	8,389,995.0	1,026,807.9	9,416,802.9	90%	95%
Phase V	8,950,607.5	1,026,793.0	9,977,400.4	100%	100%

presented accumulated actual costs and total investment for each phase.

The estimated budget for the project was USD 10.206 million, with planned progress targets of 10% by the end of Phase I, 35% by the end of Phase II, and completion within 15 months. However, actual progress for Phase I reached only 8%, and 25% in Phase II, reflecting delays primarily due to environmental conditions. To mitigate this, the project team made adjustments in Phase III, achieving and even surpassing planned progress with an actual completion rate of 78%. By Phase IV, they had exceeded the planned targets by 5%, and the LT220 kV substation’s actual final investment totaled USD 9,977,400.4–USD 228,201.8 below the budgeted investment, indicating effective cost control for the project.

Using these figures and earned value analysis, the BCWP, BCWS, and ACWP were calculated. And based on the data from the five phases, an Earned Value Performance graph was created as shown in Figure 1. BCWP was less than BCWS in Phases I and II, resulting in a negative SV and indicating delays due to external factors like adverse weather conditions and regional political constraints. After Phase II, BCWP exceeded BCWS, resulting in a positive SV and demonstrating progress ahead of schedule, a result of increased efficiency among the construction team. This graph clearly illustrates that the ACWP curve consistently falls below the BCWP curve across all phases, illustrating that the actual costs did not exceed the budgeted costs and that cost control was effective. The project followed the planned schedule and operated efficiently throughout.

Further analysis of Phase II data indicated that the cumulative actual cost (ACWP) was USD 1,715,581.2, BCWS was USD 3,571,960.8 (35% of the total budget), and BCWP was USD 2,551,400.6 (25% of the total budget). The cost variance (CV = BCWP − ACWP) was USD 835,819.4, suggesting a positive cost variance and indicating reduced investment. However, the schedule variance (SV = BCWP − BCWS) was −USD 1,020,560.2, showing that actual progress lagged behind the planned schedule. This delay stemmed from uncontrollable factors such as weather and local political requirements. Despite delays, analysis revealed that investment reduction and schedule delays in Phase II were within a reasonable range. To prevent further delays, measures such as stricter material quota controls, tax optimization, improved labor productivity, and schedule acceleration were implemented. These measures effectively improved progress, and the final cumulative costs remained below the budget, demonstrating successful cost control with the final actual investment amounting to USD 228201.8 less than the budget, achieving favorable economic outcomes.

3.4.2. Enhanced Earned Value Analysis Incorporating Quality Indicators

In this example, the project was divided into eight subtasks, with the planned duration segmented into five phases. The currency unit was set as USD, and subsequent units were omitted for brevity. It was assumed that the expenditure plan for each project subtask was calculated based on the average cost rate, thereby allowing for the determination of its planned cost. This paper analyzed the data from Phase II of the case study, establishing the project’s actual costs, schedule assessment, quality levels, and weight distribution. The earned value (EV) of the project was obtained by multiplying the percentage of actual completion by the planned budget cost. The actual costs of the project had statistical results, allowing for the determination of the ACWP. Relevant data is presented in

Table 8. Basic data.

Project Code	Item	Weight	BH	AH
A	Distribution Cabinet Installation	0.3	90	92
B	Enclosed Bus Installation	0.2	85	90
C	Cable Tray Installation	0.05	90	72
D	Cable Laying	0.1	85	86
E	Conduit Installation	0.1	80	67
F	Cable Pulling in Conduit	0.05	85	82
G	Fireproof Seal	0.05	80	72
H	Lightning and Grounding System	0.15	85	83
Integral Project		1

, and using the parameters and formulas related to the improved earned value principles discussed above, the related parameters for each subtask can be calculated and analyzed, as shown in

Table 9. Calculation results.

Project Code	BCWS	ACWP	BCWP	CV	He	BCWPH	HV	SV’	HCV
A	1,071,588.2	514,674.4	765,420.2	250,745.8	1.022222	782,429.3	17,009.2	−289,158.9	−233,736.7
B	714,392.2	343,116.2	510,280.1	167,163.9	1.058824	540,296.8	30,016.7	−174,095.3	−137,147.2
C	178,598.0	85,779.1	127,570.0	41,791.0	0.8	102,056.0	−25,514.0	−76,542.0	−67,305.0
D	357,196.1	171,558.1	255,140.1	83,581.9	1.011765	258,141.8	3001.7	−99,054.3	−80,580.2
E	357,196.1	171,558.1	255,140.1	83,581.9	0.8375	213,679.8	−41,460.3	−143,516.3	−125,042.2
F	178,598.0	85,779.1	127,570.0	41,791.0	0.964706	123,067.6	−4502.5	−55,530.5	−46,293.4
G	178,598.0	85,779.1	127,570.0	41,791.0	0.9	114,813.0	−12,757.0	−63,785.0	−54,548.0
H	535,794.1	257,337.2	382,710.1	125,372.9	0.976471	373,705.3	−9004.8	−162,088.8	−134,377.7
Integral project	3,571,960.8	1,715,581.2	2,551,400.6	835,819.4	0.984302	2,511,348.7	−40,051.9	−1,060,612.1	−875,871.3

.

Taking Phase II as an example, we applied the improved earned value analysis method. First, an overall analysis of the project’s quality levels revealed that HV = −40,051.9 < 0, HCV = −875,871.3 < 0, and CV = 835,819.4 > 0. This indicated that while the quality of the project meets acceptable standards, and some subtasks exceeded the specified quality levels, a significant portion of the subtasks did not meet the required quality standards. Therefore, it was necessary to analyze the relevant subtasks, identify the causes of quality issues, and enhance the supervision and control of engineering quality. For projects A, B, and D, where HV > 0 and CV > 0, the increase in costs due to quality excess was greater than the cost savings arising from fluctuations in resource unit prices. This ultimately led to increased costs for these subtasks. In future project management, it is advisable to appropriately reduce the quality levels of these subtasks to meet the required standards. Next, an analysis of cost deviations showed that CV = 835819.4 > 0, indicating that the overall actual consumption of the project was below the budget, demonstrating effective cost control. Additionally, the CV values for each subtask were greater than 0, suggesting the effective implementation of cost control measures across all parts of the project, which should be maintained moving forward. Given the profit distribution mechanism in multi-party collaboration scenarios, future research will focus on using asymmetric Nash bargaining (NB) to quantify the contribution weights of each party in quality control and progress management, and leveraging TPCA-ADMM to achieve distributed cost data privacy protection, thereby establishing a comprehensive management system for “single-project cost optimization and multi-party interest coordination” [22].

In summary, through the application of the improved earned value method (EVM), the project achieved remarkable cost control outcomes. The actual total investment amounted to USD 9.9774 million, which was USD 228.6 thousand (a reduction of 2.24%) lower than the budget of USD 10.206 million. Meanwhile, the quality input standards of six subtasks in Phase II were optimized, thereby averting approximately USD 400 thousand in potential costs associated with quality over-provisioning. The expected targets have also been successfully achieved, as detailed in

Table 10. Objectives and completion status.

Category	Objective	Target Achievement	Remarks
Overall Quality	Comply with national standards	Achieved	Technical code for the design of 220~750 kV substation (DL/T 5218-2012) Code for design of 35~110 kV substation (GB 50059-2011)
Schedule Time	Take the time determined by the owner as the baseline	Achieved
Cost	Achieve 1–3% cost saving	1.7%
Safety	“Zero accident” construction	Achieved
Construction Waste Recycling Rate	≥80%	90%
Social Responsibility Objectives	Minimize disturbance to residents	Achieved	Noise complies with Environmental Noise Emission Standard for Construction Sites Temporary sheds and power supply comply with fire protection codes, etc.

. These data demonstrated that the improved EVM not only led to budget savings but also established a quantifiable cost control mechanism, providing a reliable technical means for cost management in engineering projects. In the future, we plan to explore the possibility of combining CVaR with improved EVM to enhance the robustness of cost prediction under complex conditions and strengthen the study’s risk management capabilities in engineering practice [23].

4. Conclusions

This paper analyzed the critical nodes and methods for cost management in substation engineering within power systems, proposing a comprehensive management framework. Notably, it innovatively incorporated quality factors into an improved earned value method (EVM) model, facilitating the integrated control of cost, schedule, and quality. By conducting a case study on Company A’s 220 kV LT substation project, this research validated the feasibility and practical application value of the improved EVM model. The findings indicated that methods such as the unbalanced bidding method, target cost method, and the enhanced EVM incorporating quality factors significantly enhance cost control levels in substation projects. These methods enabled the rational allocation of resources and improved capital utilization efficiency, thereby achieving reduced costs, ensured quality, and optimized schedules, resulting in an effective cost savings of approximately 1.7% for the project. This research not only expanded the theoretical application of engineering cost management within substation projects but also provided valuable insights for cost management in future project construction within the electricity industry.

When determining parameters, the model needs to specify a relatively clear range for the variation patterns of each parameter. For instance, when the model is applied to pipeline construction, due to the excessive complexity of groundwater levels and geological conditions, there are numerous uncertain factors, making parameter calibration difficult. This constitutes the limitation of the model in its application to other engineering projects. Given the proliferation of cutting-edge technologies, future research will adopt emerging technologies such as digital twins, blockchain, and BIM [24]. For instance, BIM models can be used to export equipment foundation coordinates and elevation data, which can then be combined with total stations for on-site layout, ensuring GIS equipment installation errors remain within ≤2 mm. BIM models can also automatically generate cable schedules and routing diagrams, reducing cross-installation and saving 10–15% on cable usage. Additionally, quantities such as concrete, steel structures, and cable lengths can be directly extracted from BIM models to assist in material procurement and cost control. The adoption of these advanced technologies will enhance both the applicability and sophistication of technical implementations.