Network Reconfiguration Based on an Edge-Cloud-Coordinate Framework and Load Forecasting

Li Ning,Zhong Fei,Liu Nian,Li Si

doi:10.3389/fenrg.2021.679275

Abstract

With the development of distributed energy resources (DERs), the power flow (PF) in the distribution network (DN) is changed from unidirectional to bidirectional, resulting in complex control and coordinate measures. Network reconfiguration (NR) is a feasible solution for the power grid side to deal with the complex PF. This paper proposed an edge-cloud-coordinated reconfiguration framework with edge servers (ESs) in the prosumer side and cloud servers (CSs) in the utility grid side, where the edge computing (EC) technology is implemented in ES to support load forecasting (LF), while cloud computing (CC) is used in CS to reconstruct the DN. LF is implemented by the long–short-term memory network to acquire the load information in advance, and the social preference of prosumers has been considered. The NR is formulated as a complex combinatorial optimization problem with the goal of minimizing power losses, while satisfying the power flow and voltage requirement. The NR problems are solved by the proposed advanced harmony search algorithm, which can find the optimal global solution, while satisfying the complex constraints of the NR problem. Numerical results are conducted based on an IEEE 33-bus network, which shows the high accuracy of LF and demonstrates the effectiveness of the proposed framework in terms of reducing more than 40% power losses and satisfying the voltage requirement.

Highlights

With the wide use of distributed energy resources (DERs), such as photovoltaic (PV) and wind energy, an entity with the competence of both production and consumption called prosumer was created (Liu et al, 2018)
The cloud servers (CSs) minimizes the network losses by changing the state of the switches when the load information of prosumers is obtained from edge servers (ESs), that is, conducting Network reconfiguration (NR) based on the forecasting power flow (PF)
The switching pattern under the new PF is calculated through advanced HSA (AHSA), as shown in Figure 8, and the open switches of the minimum power losses in each loop are in the branches {6–7, 11–21, 9–10, 16–17, 27–28}, which satisfy the radial structure of the distribution system

Summary

Introduction

With the wide use of distributed energy resources (DERs), such as photovoltaic (PV) and wind energy, an entity with the competence of both production and consumption called prosumer was created (Liu et al, 2018). An edge-cloud-coordinated reconfiguration framework (ECCRF) was proposed to manage the LF tasks in the prosumer side and NR tasks in the power grid side.

Results

Conclusion