Load Forecasting Based Distribution System Network Reconfiguration-A Distributed Data-Driven Approach

In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, the proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.Comment: 5 pages, preprint for Asilomar Conference on Signals, Systems, and Computers 201

Power measurements and analysis for dynamic circuit specialization

Dynamic Circuit Specialization (DCS) is a technique for optimized FPGA implementation and is built on top of Partial Reconfiguration (PR). Dynamic Partial Reconfiguration (DPR) provides an opportunity to share the silicon area between different Partially Reconfigurable Modules (PRMs) and therefore results in smaller and faster designs that potentially also reduce the power consumption. In this paper, we show that energy consumption is an important factor that has to be considered while implementing a parameterized design using DCS. In order to make a good choice for implementing a parameterized design with the goal of power optimized implementation, it is important to have a good power consumption estimate of the Dynamic Circuit Specialization. In this context, our paper presents a detailed investigation of the power consumption of a parameterized design implemented using DCS on the Xilinx Zynq-SoC FPGA. We propose an energy analysis of DCS and investigate the benefits of the use of DCS in comparison with a classic static FPGA implementation. We see that the power needed for the reconfiguration is much higher than the gain in power using the reconfiguration over the static implementation. An important reason is because of the CPU involved during the reconfiguration and the interface between the AXI bus and the HWICAP. To reduce the reconfiguration power, we include a clock gating technique to the reconfiguration interface AXI-HWICAP that makes DCS more power efficient. We also relate the power gain to the size of the implementation and to the allowed time to reconfigure versus the useful run time. We conclude that for an implementation with 10 FIR filters, the reconfiguration time should not take more than 30.3% of the total time in order to remain energy efficient. Considering a specific use case with 10 FIR filters at a reconfiguration rate of 0.01, the energy consumption using DCS implementation is 20.5% lower than using the static FIR

A Genetic Algorithm Approach for Optimal Distribution System Network Reconfiguration

Electrical energy is an essential ingredient for the industrial and all-round development of any country. Power distribution systems are radial in configuration and this makes the networks hard to manage, thus, the need for optimization. This paper presents the optimization of network reconfiguration of distribution system using genetic algorithm to get the optimal switching scheme for network reconfiguration with objective function to reduce power loss and improve active power of the system. Load flow for the network reconfiguration problem was formulated as single objective optimization problem. The optimization model was simulated using MATLAB/SIMULINK and validated on standard IEEE 13-bus and 25-bus distribution test feeders. The result shows that active power increases by 91.1% (1.6469p.u.) while the power loss reduced by 99.4% (1.6372p.u.) for 13-bus system. For 25-bus system, active power increased by 27% (0.9154p.u.) and power loss reduced by 96.2% (4.3074p.u.) after optimization. The results provide solutions to the power distribution system for the optimal switching scheme for network reconfiguration with improvement in active power of the system. Total real power loss was minimized according to the corresponding fitness values of the genetic algorithm solutions. The paper provides technical information that could help in the future expansion and operation planning of the power distribution network. Keywords: Distribution System, Load Flow, Network Reconfiguration, Distribution Test Feeder, Active Power, Power Loss, Genetic Algorithm

Distribution Network Reconfiguration Using Binary Particle Swarm Optimization to Minimize Losses and Decrease Voltage Stability Index

Power losses and voltage drop are existing problems in radial distribution networks. This power losses and voltage drop affect the voltage stability level. Reconfiguring the network is a form of approach to improve the quality of electrical power. The network reconfiguration aims to minimize power losses and voltage drop as well as decreasing the Voltage Stability Index (VSI). In this research, network reconfiguration uses binary particle swarm optimization algorithm and Bus Injection to Branch Current-Branch Current to Bus Voltage (BIBC-BCBV) method to analyze the radial system power flow. This scheme was tested on the 33-bus IEEE radial distribution system 12.66 kV. The simulation results show that before reconfiguration, the active power loss is 202.7126 kW and the VSI is 0.20012. After reconfiguration, the active power loss and VSI decreased to 139.5697 kW and 0.14662, respectively. It has decreased the power loss for 31.3136% significantly while the VSI value is closer to zero