The First Step for Implementing a Stochastic based Energy Management System at Campus Pinkafeld

Abstract

ABSTRACT: At Campus Pinkafeld the average demand for electricity in the last five years was 199 MWh. The supplier Burgenländische Elektrizitätswirtschafts-Aktiengesellschaft (BEWAG) states that it supplies only green electricity in its service area and average CO2 emissions are 0.0 kg/kWh. The average demand for thermal heat for the district heating system operated by KELAG in recent years was 219 MWh. The heat for the district heating network comes
from a maize-fired biomass power plant. Biomass power plants are generally considered to be CO2-neutral. Analyzing the potential of reducing marginal CO2 emissions by Distributed Energy Resources (DER) is the major objective of this paper and the EU-funded project
“Energy Efficiency and Risk Management in Public Buildings” (EnRiMa). The aim of EnRiMa is to develop a decision support system (DSS) to enable operators to manage energy flows in public buildings, delivering a holistic solution for meeting the energy needs in a more
efficient, less costly, and less CO2 intensive manner subject to comfort tolerances and long-term risk preferences. To get first deterministic optimization results for different DER options the exiting web version of the Distributed Energy Resources Customer Adoption Model (DER-
CAM) is used. The optimization runs using DER-CAM show that a switch to a natural-gas-fired CHP plant can result in financial advantages. Compared to the marginal power plant used to meet the avoidable peak power demand, a switch to local energy generation with combined heat and power (CHP) is certainly of interest. It can be assumed that BEWAG must purchase some of its peak power from other energy suppliers. Such peak power can be supplied from gas-fired plants with marginal CO2 emissions of approx. 0.44 kg/kWh. These emissions could be reduced by local CHP systems. With the use of stochastic weather information or other stochastic parameters the investment / planning as well as operational activity will be calculated within EnRiMa and will create a stochastic optimization platform not
available in DER-CAM.