Evaluation Of The Potential Of Electric Storage Using Decentralized Demand Side Management Algorithms

Abstract

In this paper, we report the results of simulations of a demand side management strategy, developed within the context of the GridSense project. GridSense is a multi-objective energy management system that aims at decreasing both the end user energy costs and the congestions on the local feeder. The algorithm controls single appliances, among which boilers, heat pumps, EV chargers and stationary batteries. The control strategy is completely decentralized and does not require communication outside the building. A multiphysics simulation framework, which allows simulating the electrical grid, buildings and appliances, has been implemented using the Modelica language, while the control algorithms are coded in C. The performance of the proposed solution against the two main optimization objectives (cost and grid friendliness) has been tested in simulation under different scenarios. We systematically varied the capacity and E-rate of the energy storage. Our analysis shows that savings are mostly dependent on the battery capacity, while being only slightly affected by the battery inverter-charger size. The break even system price of the different solutions was calculated for an example house in a typical Swiss tariff scheme.