@MASTERSTHESIS\{IMM2014-06801,
    author       = "M. G. Nielsen",
    title        = "Probabilistic forecasting and optimization in {CHP} systems",
    year         = "2014",
    school       = "Technical University of Denmark, Department of Applied Mathematics and Computer Science",
    address      = "Richard Petersens Plads, Building 324, {DK-}2800 Kgs. Lyngby, Denmark, compute@compute.dtu.dk",
    type         = "",
    note         = "Supervised by Juan Miguel Morales Gonzalez, jmmgo@dtu.dk, {DTU} Compute, Henrik Madsen, hmad@dtu.dk, {DTU} Compute, Marco Zugno, {DTU} Compute, J{\o}rgen Boldt, {HOFOR,} Thomas Engberg Pedersen, {COWI,} Henrik Aalborg Nielsen, {ENFOR}",
    url          = "http://www.compute.dtu.dk/English.aspx",
    abstract     = "Denmark has committed towards increasing the wind power production to cover 50\% of the power consumption by 2020. As the amount of wind by nature is uncertain, an integration of wind power into the current highly efficient combined heat and power (CHP) system, requires new flexible measures to reduce forced heat production in periods of high wind. Heat pumps (HP) and electric immersion boilers (EB) show excellent potential to increase flexibility and utilize excess power. The {HP} is more efficient but requires higher investments while not being as flexible as the {EB}.

As a consequence of decreasing taxes for electricity based heat production, HPs and EBs start to appear in district heating systems around Denmark. However, the operational strategy for these units is still unexplored, which has instigated the search for a structured operational strategy. As heat dispatch occurs before electricity prices are known, uncertainty is present. This impacts the operational costs for the {HP} and {EB} which both depend highly on the electricity prices.

This master thesis analyzes a {CHP} system in the Copenhagen district heating system in order to define an appropriate framework for integrating a {HP} and {EB}. An operational strategy for a {HP} and {EB} operating in a {CHP} system comprising a {HP,} {EB} {CHP} and storage is developed. This strategy is based on illustrative probabilistic forecasts of the heat demand and electricity price, used in a stochastic two-stage optimization model with recourse. Both the heat demand and electricity price are included as stochastic variables. Furthermore, it is assumed that a fixed amount of power is sold in the first stage decision. Thus, the second stage decision is used to adjust the production to meet the realized heat demand and power price in the most optimal manner. This constitutes a novel approach for the integration of HPs and EBs in a {CHP} system. Illustrative examples of the stochastic model and the deterministic equivalent conrm the working principles and appropriability of this approach to be used as an operational strategy.

Results from model simulations of four representative weeks during 2013 show a potential for economical benefits when a stochastic instead of a deterministic equivalent approach is used, especially during summer. This is due to the high degree of flexibility resulting from the {HP,} {EB} and storage. Decreasing the capacity of the {HP} and {EB,} the benefits of a stochastic approach increase.
Cases, analyzing the sensitivity to system changes and investment decisions, indicate a potential for substantial monetary benefits of HPs and EBs. In the event of decreasing electricity prices the impact of a {HP} and {EB} is found most significant. Moreover, increasing the efficiency of the {HP} leads to reduced heat costs while a reduction in {HP} and {EB} capacity yields significant additional costs. 

This project thus successfully develops an operational strategy for a {HP} and {EB} in a {CHP} system, and results indicate substantial cost reduction resulting from the flexibility the {HP} and {EB} provide."
}