Novel heuristic and SVM based optimization algorithm for improving distribution feeder performance

Abstract: Secondary distribution networks generally perform as well as its LV feeders are performing. The main problem that a feeder is experiencing would be the load unbalancing due to the stochastic nature of its individual single-phase loads: larger losses in certain phases accompanied by bad voltage regulation and voltage unbalance. In order to address this problem, it may be economical to install apparatus to automatically balance or partially balance the loads progressing from the end of the feeder towards the front using smart devices based on a three-ways switch selector and an artificial intelligence algorithm to minimize the neutral current. The main idea behind this paper is therefore to keep the three phases progressively balanced along the whole length of the line. A Support Vector Machines (SVM) implementation and a heuristic method are presented as the numerical algorithm

A Multi-Objective Optimization Approach for Multi-Head Beam-Type Placement Machines

This paper addresses a highly challenging scheduling problem in the field of printed circuit board (PCB) assembly systems using Surface Mounting Devices (SMD). After describing some challenging optimization sub-problems relating to the heads of multi-head surface mounting placement machines, we formulate an integrated multi-objective mathematical model considering of two main sub-problems simultaneously. The proposed model is a mixed integer nonlinear programming one which is very complex to be solved optimally. Therefore, it is first converted into a linearized model and then solved using an efficient multi-objective approach, i.e., the augmented epsilon constraint method. An illustrative example is also provided to show the usefulness and applicability of the proposed model and solution method.PCB assembly. Multi-head beam-type placement machine. Multi-objective mathematical programming. Augmented epsilon-constraint method

Comprehensive STATCOM Control For Distribution And Transmission System Applications

This thesis presents the development of a comprehensive STATCOM controller for load compensation, voltage regulation and voltage balancing in electric power distribution and transmission networks. The behavior of this controller is first validated with published results. Subsequently, the performance of this STATCOM controller is examined in a realistic Hydro One distribution feeder for accomplishing the compensation of both mildly and grossly unbalanced loads, and balancing of network voltages using PSCAD/EMTDC software. The STATCOM voltage control function is utilized for increasing the connectivity of wind plants in the same distribution feeder. The thesis further presents a frequency scanning technique for simple and rapid identification of the potential of subsynchronous resonance in induction generator based wind farms connected to series compensated lines, utilizing MATLAB software. This technique is validated by published eigenvalue analysis results. The voltage control performance of the developed comprehensive STATCOM controller is then demonstrated for different scenarios in the modified IEEE First SSR Benchmark transmission system for mitigating subsynchronous resonance in series compensated wind farms using industry grade PSCAD/EMTDC software

Diagnostic Applications for Micro-Synchrophasor Measurements

This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems

Achieving the Dispatchability of Distribution Feeders through Prosumers Data Driven Forecasting and Model Predictive Control of Electrochemical Storage

We propose and experimentally validate a control strategy to dispatch the operation of a distribution feeder interfacing heterogeneous prosumers by using a grid-connected battery energy storage system (BESS) as a controllable element coupled with a minimally invasive monitoring infrastructure. It consists in a two-stage procedure: day-ahead dispatch planning, where the feeder 5-minute average power consumption trajectory for the next day of operation (called \emph{dispatch plan}) is determined, and intra-day/real-time operation, where the mismatch with respect to the \emph{dispatch plan} is corrected by applying receding horizon model predictive control (MPC) to decide the BESS charging/discharging profile while accounting for operational constraints. The consumption forecast necessary to compute the \emph{dispatch plan} and the battery model for the MPC algorithm are built by applying adaptive data driven methodologies. The discussed control framework currently operates on a daily basis to dispatch the operation of a 20~kV feeder of the EPFL university campus using a 750~kW/500~kWh lithium titanate BESS.Comment: Submitted for publication, 201

Advanced Optimization and Data-Driven Control in Smart Grid

The power grids are continuously evolving over the past decades, where new challenges and opportunities are embraced at the same time. On one hand, the penetration of renewable generations and other distributed energy resources (DER) is growing rapidly, whose different generation and control patterns could significantly impact the daily operation. On the other hand, the new communication, monitoring and regulating devices are gradually installed, which enable more control abilities of the generations, demands, and grids, and the feasibility to deploy more sophisticated control schemes.To leverage the new technique and overcome the new challenges in the smart girds, different optimization and control problems need to be solved for different roles including the system operator, demand, and financial traders. For the system operators, it is critical to maximizing the total social welfare while satisfying the operational constraints. To better coordinate the DER and improve the efficiency of distribution systems, the three-phase optimal power flow (OPF) problem algorithms are developed including the DCOPF algorithm for robustness and the ACOPF algorithm for optimality. Moreover, the deep reinforcement learning-based Volt-VAR control schemes are proposed to better maintain the voltage stability and electricity service quality.For demands resources, minimizing their energy bills will satisfy the energy needs is always their goal. Providing ancillary services by proactively adjusting their total demand is one of the potential choices. Through the provision of the services, the demands can not only receiving incentives from the system operators but also help to improve the reliability and stability of power grids. We develop control schemes specifically for the data centers to provide the phase balancing service in the distribution system and the frequency regulation service in the transmission system. The financial traders, it is desired to maximize their total profits. A better trading strategy with a more accurate forecast model can help increase the traders' gain and further improve the price convergence of the electricity market. Our machine learning based trading framework outperforms the existing approach and lays the foundation for market efficiency evaluation across the markets

Optimal operation of soft open points in medium voltage electrical distribution networks with distributed generation

A soft open point (SOP) is a power electronic device, usually using back-to-back voltage source converters (VSCs), installed at a previously normally open point of a distribution network. Due to its flexible and accurate control of power flows, an SOP is versatile, and increasingly being considered to mitigate voltage and thermal constraints in medium voltage (MV) networks with high penetrations of distributed generation (DG). A Jacobian matrix - based sensitivity method was used to define the operating region of an SOP when the grids/feeders at the two terminals of the SOP have various load and generation conditions, and the SOP operating region was visualized in a graphical manner. The exact operating set-points were determined by adopting a non-linear optimization considering separately different objectives. The methodology was demonstrated on an 11 kV network, considering three optimization objectives with different DG penetrations and different network observabilities. Results showed that the use of an SOP significantly increases the network’s DG hosting capacity. The objective for voltage profile improvement increased the headroom of the voltage limits by the largest margin, at the expense of increased energy losses. In contrast the objectives to achieve line utilization balancing and energy loss minimization showed the most improvement in circuit utilization and in limiting energy losses. The work helps electricity network operators to visualize an SOP’s operation status, and provides high level decision support, e.g. selecting control schemes and restraining SOP operational boundaries