Efficiency Metrics: Assessing the Impacts of Grid Reliability on Energy Efficiency in Smart Systems

I. Introduction

Smart grid technologies revolutionize energy efficiency and sustainability. Demand-side management and renewable energy integration aim to enhance energy distribution, consumption, and reliability (Xie et al., 2023). Measurements are crucial, as nations enhance their energy landscapes. This study examines China’s energy economy and the sensitive relationship between energy efficiency and smart grid installation. China’s energy reform and innovation are global priorities. Advanced energy technology has developed due to carbon emission reduction, renewable energy integration, and reliable energy access. Energy transformation demands smart grid integration (Fina & Monsberger, 2023). Smart grid technology has raised energy efficiency and environmental concerns. Prior research has discussed smart grid energy efficiency concerns. According to Kataray et al. (2023), smart grids with improved monitoring, control, and communication might change energy systems; these measures can change energy use patterns, particularly during peak demand. Demand-side management reduces grid pressure by increasing off-peak power consumption (Saleem et al., 2023). Smart grid development requires renewable energy integration. These sources promote energy efficiency and sustainability. Smart grid implementation and renewable source integration may improve energy efficiency and sustainability (Cicceri et al., 2023). Thus, technology infrastructure investments (TII) are vital. Increased smart grid investments in sensors, communication networks, and metering infrastructure improve grid stability and energy efficiency (Abdullah et al., 2023).

This study investigated the complex connection between smart grid technology and energy efficiency, opening new research avenues. We present practical actions for policymakers and energy experts aimed at utilizing alternative energy sources. Quantifying energy efficiency correlations may enhance smart grid technology design and deployment. Smart grid sustainability is the subject of our investigation. Examining the environmental and economic consequences of technology will promote the use of sustainable energy systems. Sustainable environmental and economic strategies must align with the long-term implications of smart grid installation. Finally, we explored how TII boosts energy efficiency. Our findings may assist decision-makers in allocating resources for energy efficiency. Our research helps firms make informed technology investments for long-term energy efficiency improvements. Understanding the study’s complex linkages will influence Chinese policy, energy planning, and sustainable development.

This paper begins with an outline of the inquiry. The second section details the study’s materials and methods. The third section discusses the research results. Final research remarks are provided in the final section.

II. Data and Methodology

The data was procured from the World Bank (2022). The study’s variables were as follows:

Dependent Variable

A. Energy Efficiency (EE): It assesses the productivity of the smart grid and energy management systems. An economy’s energy efficiency is measured by primary energy intensity (MJ/$2017 PPP GDP). Energy efficiency rises with reduced energy intensity, because less energy produces the same economic output.

Independent variables

The Smart Grid Penetration Rate (SGPR): It measures the incorporation of smart grid technologies into energy infrastructure—the percentage of the grid using smart grid solutions. SGPR is measured by renewable energy output (kWh), excluding hydropower.

Demand-Side Management Implementation (DSMI): It assesses the success of demand-side management measures, such as time-of-use pricing, demand response programs, and load-shifting strategies. It is represented by the percentage of the population with electricity.

Renewable Energy Integration (REI): It assesses the influence of renewable energy sources on the energy mix in the grid. Renewable Energy Consumption illustrates how much a country or region relies on renewable energy as a proportion of Total Final Energy Consumption.

Grid Reliability Metrics (GRM): It evaluates power outage frequency, duration, voltage stability, and resilience. These measures show smart grid resilience. Transmission and distribution lose a percentage of electric power production. Higher losses may reduce grid reliability.

Technical Infrastructure Investment (TII): It evaluates smart grid projects. This variable measures a firm’s commitment to a high-technology energy management system. A greater research and development (R&D) expenditure percentage of GDP focuses on innovation and advanced technologies, including smart grid components.

Energy Consumption Patterns (ECP): It examines energy use patterns before and after smart grid deployment. This may impact peak demand, load distribution, and consumption. The percentage of oil, gas, and coal electricity production measures energy demand. Smart grids and renewable energy may transform energy use.

Energy efficiency increases with SGPR and innovation dissemination. Behavioral theories of energy consumption have introduced DSMI, which may boost energy efficiency. REI may improve EE by lowering dependence on non-renewable sources, linking it to sustainability ideas (Liu et al., 2023). Grid dependability indicators boost EE by stabilizing energy systems and reducing energy losses. TII supports resource-based theories because higher investments improve energy efficiency. Socio-economic and environmental theories affect energy use (Perez-Bezos et al., 2023). Understanding these tendencies may boost energy efficiency.

The Generalized Method of Moments (GMM) is a widely used statistical technique in time series modeling that allows researchers to estimate model parameters while accounting for potential endogeneity and other sources of bias. Regarding EE modeling, with the provided variables, GMM can provide valuable insights into the relationships between the dependent variable, i.e., EE, and the independent variables. The GMM identifies a time that captures the relationship between the variables and estimating the parameters that minimize the discrepancy between the sample and theoretical moments. Equation (1) shows the GMM specification for estimation, i.e.,

$$\begin{aligned} \ln (E E)_t & = \alpha_0+\alpha_1 \ln (S G P R)_t+\alpha_2 \ln (D S M I)_t\\ & \quad +\alpha_3 \ln (R E I)_t+\alpha_4 \ln (G R M)_t\\ & \quad + \alpha_5 \ln (T I I)_t +\alpha_6 \ln (E C P)_t+\varepsilon_t \end{aligned}\tag{1}$$

Where,

EE shows Energy Efficiency

SGPR shows Smart Grid Penetration Rate

DSMI shows Demand-Side Management Implementation

REI shows Renewable Energy Integration

GRM shows Grid Reliability Metrics

TII shows Technical Infrastructure Investment

ECP shows Energy Consumption Patterns

$\alpha_0$ shows constant, while $\alpha_1$ to $\alpha_6$ are the slope of the coefficients

‘ln’ shows natural logarithm, ‘t’ shows time period, and $\varepsilon$ shows error term.

The GMM estimate technique was employed in this study, because it addresses endogeneity and other statistical biases in panel data analysis. Although instrumental variable (IV) methodologies and fixed effects models can be used, the GMM is often preferred because it tackles endogeneity, especially where IV estimation instruments are scarce.

III. Analysis and Discussion

Table 1 shows the descriptive statistics of the variables. The average EE score was approximately 9.75, which indicates the mean of EE levels in the dataset. The negative skewness of -1.14 suggests that the distribution of EEF scores is skewed to the left with potentially higher scores. The kurtosis value of 2.67 indicates that the distribution’s tails are heavier than those of a normal distribution, signifying some peakedness.

Smart grid technology adoption and integration averaged 65,500,000,000. The positive skewness of 1.33 suggests a right-skewed distribution with lower values. The average DSMIwas 97.85. A somewhat right-skewed distribution with a positive skewness of 0.69 suggests a concentration of lower values. REI averaged 24.89, indicating the grid’s renewable energy sources. Average GRM was 6.78. The average TII was 1.13, indicating smart grid investments. Average ECP was 78.82; ECP compares energy usage before and after smart grid adoption. Left-skewed distributions with -0.75 skewness may concentrate greater values. Table 2 provides GMM estimations for reference.

The Lagged EE coefficient was 0.768. This positive correlation shows that previous EE levels significantly affect present EE. The SGPR coefficient was -3.69E-12, suggesting that SGPR decreases EE. This finding suggests that smart grid solutions may slowly improve EE (Waseem et al., 2023).

The DSMI coefficient was -0.047. Thus, more demand-side control reduces EEF. DSMI strategies may temporarily counterbalance their efficiency advantages due to initial expenses and modifications. The positive REI coefficient, 0.044, suggests that the grid integration of renewable energy sources may improve EE. This supports the economic argument that renewable sources are more efficient and sustainable (Bai & Song, 2023).

The GRM coefficient was 0.335. A positive coefficient indicates that grid dependability improves EE. When the grid is dependable, interruptions and energy losses are reduced, improving EE. TII was -0.530; investment in smart grid technology decreases EE. Interestingly, the high initial expenses of technology adoption may take time to generate considerable efficiency advantages. The ECP coefficient was 0.007, indicating that changes in ECP before and after smart grid adoption considerably affect EE (Ye et al., 2023). The robustness test for GMM estimations is displayed in Table 3.

This study suggests that several vital factors affect the Chinese economy’s EE. Smart grid adoption, DSMI, REI, and TII negatively correlate with EE. This indicates that more implementation and investment in these fields lower EE. Integration and investment in diverse technologies and infrastructures may provide inefficiencies, challenges, or constraints. However, the positive correlation between ECP and EE suggests that changing energy consumption behavior, perhaps towards more environmentally friendly and efficient practices, improves China’s energy efficiency.

IV. Conclusion

This study reveals that prior EE is a predictor of current EE. The data shows an association between SGPR and EE, suggesting that a delay in efficiency increases due to smart grid implementation. DSMI is vital for EE, because it reveals its challenges and costs. As renewable energy sources provide efficiency benefits, their integration positively correlate with EE. GRM improve EE, emphasizing the need for reliable infrastructure. Investing in TII highlights the difficulties of balancing upfront costs and future rewards.

These findings affect China’s energy revolution policymakers and practitioners. These methods hinge on prior efficiency advancements to increase grid reliability and optimize technological investments. This study contributes to scholarly discussions on sustainable energy transitions and sheds light on China’s initiatives regarding improving EE in smart systems.