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The increase in households with grid connected Photovoltaic (PV) battery system poses challenge for the grid due to high PV feed-in as a result of mismatch in energy production and load demand. The purpose of this paper is to show how a Model Predictive Control (MPC) strategy could be applied to an existing grid connected household with PV battery system such that the use of battery is maximized and at the same time peaks in PV energy and load demand are reduced. The benefits of this strategy are to allow increase in PV hosting capacity and load hosting capacity of the grid without the need for external signals from the grid operator. The paper includes the optimal control problem formulation to achieve the peak shaving goals along with the experiment set up and preliminary experiment results. The goals of the experiment were to verify the hardware and software interface to implement the MPC and as well to verify the ability of the MPC to deal with the weather forecast deviation. A prediction correction has also been introduced for a short time horizon of one hour within this MPC strategy to estimate the PV output power behavior.
Demand Side Management for Thermally Activated Building Systems based on Multiple Linear Regression
(2015)
Intelligent und nachhaltig
(2013)
Die starke Nachfrage für wirtschaftliche und technische Lösungen zur Umsetzung der Energiewende wirkt sich auch auf Projektinitiativen der Forschungsgruppen am Institut für Energiesystemtechnik (INES) der Hochschule Offenburg aus. Am Campus Nord entsteht seit 2012 ein intelligentes Energienetz als Reallabor, das als Teil eines Smart Grid betrieben werden kann.
The aim of the smart grid is to achieve more efficient, distributed and secure supply of energy over the traditional power grid by using a bidirectional information flow between the grid agents (e.g. generator node, customer). One of the key optimization problems in smart grid is to produce power among generator nodes with a minimum cost while meeting the customer demand, known as Economic Dispatch Problem (EDP). In recent years, many distributed approaches to solve EDP have been proposed. However, protecting the privacy-sensitive data of individual generator nodes has been largely overlooked in the existing solutions. In this work, we show an attack against an existing auction-based EDP protocol considering a non-colluding semi-honest adversary. We briefly introduce our approach to a practical privacy-preserving EDP solution as our work in progress.
This paper focuses on appropriately measuring the accuracy of forecasts of load behavior and renewable generation in micro-grid operation. Common accuracy measures like the root mean square of the error are often difficult to interpret for system design, as they describe the mean accuracy of the forecast. Micro-grid systems, however, have to be designed to handle also worst case situations. This paper therefore suggests two error measures that are based on the maximum function and that better allow understanding worst case requirements with respect to balancing power and balancing energy supply.
Home Automation, Smart Metering, reduction of energy consumption for climate preservation – those buzz words are flooding the daily press.The European Directive 2006/32/EC (Energy-Use Efficiency and Energy Services) commits its members to reduce their energy consumption about nine percent from 1996 to 2015, and supports smart metering and the need of new developments for encouraging energy efficiency and CO² reduction in households and commercial buildings. The European countries have started different projects to achieve this goal. Also outside Europe, smart metering gains interest, for energy savings and better distribution network control.