Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid

A potential breakthrough of the electrification of the vehicle fleet will incur a steep rise in the load on the electrical power grid. To avoid huge grid investments, coordinated charging of those vehicles is a must. In this paper, we assess algorithms to schedule charging of plug-in (hybrid) electric vehicles as to minimize the additional peak load they might cause. We first introduce two approaches, one based on a classical optimization approach using quadratic programming, and a second one, market based coordination, which is a multi-agent system that uses bidding on a virtual market to reach an equilibrium, price that matches demand and supply. We benchmark these two methods against each other, as well as to a baseline scenario of uncontrolled charging. Our simulation results covering a residential area with 63 households show that controlled charging reduces peak load, load variability, and deviations from the nominal grid voltage

Combined Unbalanced Distribution System State and Line Impedance Matrix Estimation

To address the challenges that the decarbonization of the energy sector is bringing about, advanced distribution network management and operation strategies are being developed. Many of these strategies require accurate network models to work effectively. However, distribution network data are known to contain errors, and attention has been given to techniques that allow to derive improved network information. This paper presents a novel method to derive line impedance values from smart meter measurement time series, with realistic assumptions in terms of meter accuracy, resolution and penetration. The method is based on unbalanced state estimation and is cast as a non-convex quadratically constrained optimization problem. Both line lengths and impedance matrix models can be estimated based on an exact nonlinear formulation of the steady-state three-phase network physics. The method is evaluated on the IEEE European Low Voltage feeder (906 buses) and shows promising results

Frequency restoration reserves : provision and activation using a multi-agent demand control system

In this work a control system for restoration reserve providers is proposed in which optimal biddings of restoration reserve capacity are made based on the predicted flexibility of the reserve resources within the portfolio of the reserve provider. I t is assumed that the gate closure time for submitting reserve capacit y bids is 1 hour before activation time. The reserve capacity bids need to be formed so that activation of the capacity is always feasible, irrespective of the consumption of the portfolio before an activation request. The determination of the optimal reserve capacity bids is only based on aggregated flexibility constraint information received by the individual flexible resources within the portfolio of the reserve provider. No further resource-spe cific information is used to determine the optimal reserve capacity bid. The activation and dispatch of the required power consumption at real time is done through a market-based multi-agent control system. A simulation example, in which the reserve capacity of a portfolio of batteries is simulated, proves the feasibility of the proposed approach and shows that a high precision of the portfolio response can be obtained

Voltage Sensitivity Analysis of a Laboratory Distribution Grid With Incomplete Data

© 2010-2012 IEEE. New voltage control algorithms are necessary to cope with the increasing amount of distributed generation and electric vehicles in distribution networks. Many of the newly proposed voltage control algorithms are based on linearized dependencies between the voltage magnitude, and the active and reactive power consumption. These linearized dependencies are normally obtained by algorithms, which rely on accurate grid topology information. Due to the traditionally passive operation of low voltage (LV) distribution networks, this information is typically missing, incomplete, or inaccurate. Therefore, this paper introduces a method to extract these linear dependencies based on historical smart meter data only. No information about the grid topology is required. The model adapts to the changing load conditions in the network. The algorithm has a low complexity and is applied to an unbalanced LV distribution network. Data of a practical laboratory setup is used to validate the proposed method in real-life conditions. With the obtained voltage sensitivity factors a voltage management strategy was implemented for the laboratory grid.status: publishe

Locational Pricing to Mitigate Voltage Problems Caused by High PV Penetration

© 2015 by the authors. In this paper, a locational marginal pricing algorithm is proposed to control the voltage in unbalanced distribution grids. The increasing amount of photovoltaic (PV) generation installed in the grid may cause the voltage to rise to unacceptable levels during periods of low consumption. With locational prices, the distribution system operator can steer the reactive power consumption and active power curtailment of PV panels to guarantee a safe network operation. Flexible loads also respond to these prices. A distributed gradient algorithm automatically defines the locational prices that avoid voltage problems. Using these locational prices results in a minimum cost for the distribution operator to control the voltage. Locational prices can differ between the three phases in unbalanced grids. This is caused by a higher consumption or production in one of the phases compared to the other phases and provides the opportunity for arbitrage, where power is transferred from a phase with a low price to a phase with a high price. The effect of arbitrage is analyzed. The proposed algorithm is applied to an existing three-phase four-wire radial grid. Several simulations with realistic data are performed.status: publishe

Primary and Secondary Frequency Support by a Multi-Agent Demand Control System

© 1969-2012 IEEE. Decentralized demand control can help to ensure the balance between electricity demand and supply. In this paper, a multi-agent demand control system is proposed where residential demand is controlled to provide spinning reserves. With the proposed control framework, an aggregator of dynamic demand is able to control the consumption and the response on frequency changes of a cluster of loads. The primary frequency support by the cluster of loads can emulate the primary control of a conventional generator. The total customer welfare remains maximal during the frequency support by applying utility functions for each device.status: publishe

Distributed Residential Load Control of Binary behaving loads

In this paper, a distributed algorithm is described to schedule binary behaving loads, such as dishwashers, washing machines or tumble dryers in a near optimal way. We consider a smart grid equipped with a two-way communication system. Energy producers and end-users exchange information through the communication infrastructure in order to converge to near optimal power consumption schedules. The near optimal scheduling is obtained by a lagrangian relaxation of the utility maximization problem. Utility functions that are decomposable in time as well as utility functions that are not decomposable in time are considered. © 2013 IEEE.status: publishe

A Local Voltage Control Mechanism for LV Distribution Networks using Smart Appliances: Experimental Tests and Validation

Experimental lab test results of a local voltage control mechanism for LV distribution networks using smart appliances are presented in this work. These lab tests are part in the development of a full-scale voltage control system to be rolled out in the LINEAR residential demand response pilot for approximately 100 families, from December 2013 onwards. The main goal of the voltage control system is to mitigate local overand under voltages. The control mechanism uses the available flexibility of residential smart appliances. The main advantage of the control system is that it does not require a communication network between the different households within the LV network. The control system merely requires communication within one household: between the smart appliances and the home energy gateway in which the control algorithm runs. The voltage control only uses locally available measurements, such as the household supply voltage provided by a smart meter.status: publishe