The use of DH dates back to Roman times: Hot water was used and circulated in open trenches to heat the traditional communal baths and buildings (Kolanowski, 2011). District electrification started in 1882 with Thomas Edison in New York when he constructed an electric generating plant in two buildings in lower Manhattan (Tagliaferro, 2003). From that time until the beginning of the twentieth century, electrical power generation was in its early stage and most industries had to generate it themselves because the grid was undeveloped. As the dominant technology then was the reciprocating steam engine, waste heat was simultaneously also generated and was sometimes also used to heat the premises (Kutz, 2007). Benefitting from an increasing demand and decreasing fuel costs, the electrical power industry grew rapidly due to reducing costs and industrial companies abandoned their own heat and power generation (Kutz, 2007). Following the 1973 oil crisis (Venn, 2002), industrial companies and the governments like Denmark, which were 99% dependent on foreign oil in 1973, re-considered the benefits of cogeneration by developing an alternative-energy policy (DEA, 2012). To become less dependent on fossil fuel, Denmark had to generate heat and electricity more efficiently. DH systems were installed throughout the country to enable this greater efficiency and now over 63% of all homes in Denmark are heated by DH (IEA, 2011b).
The challenge of operating a DH system is to determine what kind of heat supply unit should be used and what should be the optimum water temperature levels (Benonysson et al., 1995).
The latter task is very challenging because of the simultaneous dynamics occurring in DH systems which are:
The heating load
The time delay to supply the heat to the consumers
The supply unit thermal efficiency variability with the generated heat temperature output
DH network simulation methods were developed using the Node Method to simulate the operation of DH networks.
The Node Method is used to record in time steps when a mass of water arrives at a node and at a consumer of the DH network. Because of the slower nature of computers in the 1990, DH network simplification by aggregation of its components was necessary to obtain quick simulation results for daily scheduling. A simplified model reduced the number of components in the model by aggregating branches and nodes which corresponds in real life to the pipes and the consumers of a DH network.
This aggregation development of a DH network was developed and used for approximately 15 years from the 1990s and was based on two main aggregation methods that were independently developed in Denmark and Germany (Risoe, 2004). Both methods aggregate the branches of a DH network in a similar way, see Figure 2- 1. As shown, the shorter branch with its node is aggregated to the longer branch by becoming an additional node.
Figure 2- 1 An illustration of how a tree structure is converted into an equivalent line structure
The main difference between the German and the Danish methods is to remove branches of the DH network model and is illustrated in Figure 2- 2. The German method removes a node by replacing two branches by one whereas the Danish method removes a branch replacing three branches by two.
Figure 2- 2 An illustration of how branches can be removed
As a DH system is composed of four components with the energy plant generating the electricity and/or heat, other software such as EnergyPro were developed to simulate the operation and management of a cogeneration plant (energyPro, 2012). EnergyPro is a software package for combined techno-economic analysis and optimisation of both cogeneration and trigeneration projects. It can simulate the operation of an energy plant with or without the use of an accumulator and the user is able to input a wide range of data, such as the annual heating load data time series with a 15-minute time interval.
With increasing enthusiasm by a range of governments about DH, researchers have investigated the viability of DH in low heat density areas. The International Energy Agency published a report entitled “District heating for energy efficient buildings” in 2012 (IEA, 2011a). Although some governments are in favour of DH, the implementation of DH systems are disadvantaged by the following two associated risk factors (Woods and Davies, 2009):
Fuel supply: As a DH system usually transforms a primary fuel to heat, and
subsequently pumps this heat to consumers, a DH system’s viability is dependent on the cost of fuel and the net cost of generated heat. For this reason the generated heat cost can only be minimised by DH operators with the constraint that fuel costs are recovered.
Take-off: Connection of 100% of possible consumers connected to a DH system will
never be achieved without mandating the population to connect to it. However, a DH network is said to have taken-off when the number of consumers connected to the DH network rises at a quick rate. Take-off is an essential parameter for a DH network,
delay revenue recovery in the early years. Moreover, a delay in take-off could result in a DH system being oversized leading to the system underperforming. Indeed, one of the complexities in a DH project is to balance the size of a DH network between the initial base load and the potential future load.
To address these risk factors and to improve DH systems implementation, Woods (2013) wrote: “Whilst an incentive for gas-fired CHP would be welcomed it may be better to incentivise DH through DH Incentive that would make payments retrospectively on an annual basis according to the amount of CO2 saved. This would encourage DH operators to develop lower CO2 forms of heat production and design efficient networks. In return for the DH Incentive they would have an obligation to provide data on their scheme on energy and CO2 emissions which would be made publicly available to aid designers and policy makers” (Heat 2014 Blog, 2014).
Although DH system is today considered a mature technology, every DH system is currently still uniquely constructed and none of the literature reviewed as part of this thesis discusses in detail the operational performance assessment of a DH system. This is a research gap worth investigation because DH systems frequently operate inefficiently.