###### Abstract  
The operating environment of power grid enterprises is complex with a range of monitoring indicators. To grasp the overall operational status in time and find the key affecting factors, Balanced Scorecard Card (BSC), Interpretive Structural Model (ISM), Principal component analysis (PCA) should be applied. This paper proposed several grid enterprise operators and monitoring indicator systems (which include achievement indicators and driver indicators), and applied PCA for verification and evaluation. The achievement indicators mainly reflected the effectiveness of business operations, which included corporate value, social value, customer value, and so on. Driver indicators mainly reflected the core resources and operations process management of business operations, which have a direct impact on the achievement indicators. The driver and achievement indicators were used as input and output indicators for the provinces to assess the efficiency of operations, and appropriate measures were proposed for improvement. The results showed that the dynamic data envelopment analysis (DEA) model could reflect the time lag of the grid enterprises operating investment and income much better than the other two methods, and the static changes compared to assess efficiency had an average around 4%. distributed energy; achievement indicator; drive indicator; efficiency assessment; big data +
Footnote †: journal: Applied Soft  
grid enterprises mainly focus on financial indicators, and ignore the entire process of operations management, which leads to some existing problems of evaluation as the evaluation method is limited to the feasibility study of the single power grid construction project, and the non-financial indicators are always despised. Furthermore, these types of evaluation mainly use the financial internal rate of return and financial net present value to reflect the performance of a single power grid construction project, so lack indicators that can accurately reflect supply reliability, voltage, and cannot completely reflect power grid development and efficiency. Therefore, this paper puts forward the analysis of operational performance indicators based on the whole process of grid operation perspective, extracts key indicators through scientific and reasonable methods, and uses them as input-output indicators to analyze the efficiency of the assessment objects. Second, a proposal has been made for the use of the Cobb Douglas production function as an intelligent technology evaluation method in the analysis of the technical progress of a grid. This method reflects the development level of an intelligent power grid through the contribution of technology for economic benefits [1]. To evaluate the technical and scale efficiency of a power grid, integrated data envelopment analysis was adopted [2]. The Stochastic Frontier Model (SFM) was proposed to evaluate the efficiency of a smart grid [3]; furthermore, some scholars have used data envelopment analysis (DEA) to evaluate the technical efficiency of hydraulic power generation enterprises and transmission-distribution systems [4; 5]. However, the results of these studies were based on static data, and power grid operation is unique as it exhibits a strong time lag in investment and income, which traditional DEA and SFM methods cannot solve. To fuse the time factor problem, big data theory may provide some favorable resources. To resolve the above-mentioned issues, this paper proposed the selection of monitoring indicators that reflected the operational efficiency of these enterprises. The Interpretive Structural Model (ISM) was used to filter the driving and achievement indexes, and the improved DEA method was used to evaluate the operational performance of the power grid enterprises to improve the efficiency of the evaluation.