The development of the simulation environment for the energy. management of the solar assisted district heating grid in Wels

G. Steinmaurer

ASiC — Austria Solar Innovation Center, A — 4600 Wels, Roseggerstrasse 12, Austria.

steinmaurer, gerald@asic. at

Abstract

In Wels, Austria, an existing district heating grid with a fossil main heat source and a thermal storage tank will be extended by a solar thermal feed-in. The combination of an existing heat source, a solar thermal plant and a thermal storage tank makes it necessary to develop a ‘load management’ as a fundamental component, which coordinates the energy distribution by using optimization methods and solar radiation forecast. To asses the possibilities and the capability of the load management it was necessary to design an appropriate simulation environment within the well known dynamical simulation software Matlab/Simulink and the Carnot-Blockset. The use of this simulation environment makes it necessary to built up new components, which has not been included so far in the Software-library. To evaluate the validity of new parts, system identification methods and parameter estimation procedures have been implemented.

Keywords: solar thermal power plant, energy management, energy coordination, optimal power flow, Matlab, Simulink, Carnot-Blockset.

1. Introduction

In Wels, Upper Austria, an existing district heating grid with a fossil main heat source and a thermal storage tank will be extended by a solar thermal feed-in. By considering the economic efficient operation of the overall system, the energy-efficient integration of the solar thermal plant (approx. 3.700m2) has to be investigated particularly [1]. The existence of different energy sources (the heat source and the solar thermal plant) and the possibility to store energy in a thermal storage tank produces the necessity of a so called “load management”. This essential component coordinates and distributes the thermal energy within the overall plant.

The load management system is developed using optimization methods and includes additionally solar radiation forecast to estimate the heat demand of the district heating grid as well as the expected solar thermal power.

2. Initial Situation