Abstract
In order to improve the work efficiency of load characteristic analysis and realize lean management, scientific prediction, and reasonable planning of the distribution networks, this paper develops a multidimensional intelligent distribution network load analysis and prediction management system based on the fusion of multidimensional data for the application of multidimensional big data in the smart distribution network. First, the framework of the software system is designed, and the functional modules for multidimensional load characteristic analysis are designed. Then, the method of multidimensional user load characterization is introduced; furthermore, the application functions and the design process of some important function modules of the software system are introduced. Finally, an application example of the multidimensional user load characterization system is presented. Overall, the developed system has the features of interoperability of data links between functional modules, information support between different functions, and modular design concept, which can meet the daily application requirements of power grid enterprises and can respond quickly to the issued calculation requirements.
Highlights
With the continuous socioeconomic development, the maximum load of the power grid in Chinas Guangdong region continues to grow, the peak-to-valley differential gradually increases, the contradiction between supply and demand of the power grid at different periods is very prominent, and the difficulty of peak adjustment continues to increase, which poses a potential hazard to the stability of the power system [1], and brings obstacles to the planning and construction of the power grid, electricity market transactions, power load forecasting, and power market management and strategy analysis [2,3,4,5,6,7]
In order to realize the load characteristic analysis based on the industry division, extract the load characteristic indexes of each user, summarize the change law, and put forward the specific suggestions for the subsequent load prediction and power grid planning, this paper develops a set of multidimensional load characteristic analysis system based on the Java language, using microservice and microapplication architecture. e system should meet the following technical requirements: flexible expansion of all functions; data sharing among various functional modules; data collection interface that supports various collection protocols; storage requirements for various data types and good security protection; rapid response to issued demands; and the underlying algorithm that supports the development of multiple languages at the same time
AN aN−1 · · · ae, where as is the nearest nonmissing value to the first place in the daily load curve, ae is the nearest nonmissing value to the last place, and N is the data dimension of the single load data. As for the latter, if it is a single missing value, we take the average of the complete values before and after as its repair value; if it is missing at multiple consecutive points, we find a linear expression for the nonmissing data points before and after and find out all the missing data points according to the proportion, and the formula is as follows: ax am m
Summary
With the continuous socioeconomic development, the maximum load of the power grid in Chinas Guangdong region continues to grow, the peak-to-valley differential gradually increases, the contradiction between supply and demand of the power grid at different periods is very prominent, and the difficulty of peak adjustment continues to increase, which poses a potential hazard to the stability of the power system [1], and brings obstacles to the planning and construction of the power grid, electricity market transactions, power load forecasting, and power market management and strategy analysis [2,3,4,5,6,7]. To address the abovementioned problems, the research work of this paper is implemented based on the actual operational data in the Guangdong power grid metering system, using linear interpolation to fill in and recover the missing data in the massive load characteristic data On this basis, a flexible and extensible multidimensional load characteristic analysis system is developed using Java development language. E system takes microservices and microapplications as the core architecture, realizes the analysis and research of load characteristics of various industries in different time scales, and provides reference for power grid load prediction and planning and construction through comparison, statistics, and summary. In the specific implementation of the underlying algorithm API, the system is developed in Python, taking into account the data structure specification, code simplicity, and intuition
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