Abstract

This paper introduces a new rural microgrid model, including residents and agricultural greenhouses. Based on the new model framework, the precise energy scheduling of a rural microgrid is realized by means of load classification and load forecasting. Moreover, we also adopt a new energy-storage mode, cloud energy storage (CES), as the shared energy-storage unit of rural microgrid, and analyze the service and operation mechanism of CES in detail. The shared storage characteristic and adjustable storage capacity of CES are helpful for the precise management of power dispatching. At the same time, in order to accurately implement energy scheduling, we fully consider the load characteristics of rural areas and divide the load into residential load and agricultural load. Then the extreme gradient boosting (XGBoost) algorithm is used to predict the short-term power consumption of the two types of load respectively, which can effectively alleviate the uncertainty of load power consumption and improve the accuracy of scheduling. Finally, an illustrative example of rural energy scheduling is given. The example studies the impact of energy-storage capacity on the cost of the scheduling scheme, and designs a power-dispatching scheme based on load forecasting, which accurately solves the energy charging and discharging planning and grid energy trading planning.

Highlights

  • A smart grid is a highly integrated power system with information technology and physical grid [1]

  • The dispatching scheme and optimal storage capacity of rural microgrid can be solved by using the solver Linear Interactive and General Optimizer (LINGO), which is a software specially used for solving optimization problems

  • It is assumed that all rural users do not purchase distributed energy-storage devices, and they can meet their energy-storage requirements by ordering energy-storage services from cloud energy storage (CES) system

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Summary

Introduction

A smart grid is a highly integrated power system with information technology and physical grid [1]. Large commercial and industrial customers have been known as the active participants in DSM and demand-response (DR) programs because of their potential to achieve large peak-load and energy consumption reduction [3]. This ignores the potential value of residential and agricultural users. There are more than one billion people worldwide living in rural areas, and some are facing severe energy shortages [5] This makes the need to solve the energy management problem in rural areas urgent. As an important part of the smart grid, a microgrid can effectively solve the problem of power supply in remote areas and has broad application prospects in rural areas

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