Analysis of heating load diversity in German residential districts and implications for the application in district heating systems

Abstract

In recent years, the application of district heating systems for the heat supply of residential districts has been increasing in Germany. Central supply systems can be very efficient due to diverse energy demand profiles which may lead to reduced installed equipment capacity. Load diversity in buildings has been investigated in former studies, especially for the electricity demand. However, little is known about the influence of single building characteristics (such as building envelope or hot water demand) on the overall heating peak load of a residential district. For measuring the diversity, the peak load ratio (PLR) index is used to represent the percentage reduction of peak load of a district system from a simple sum of individual peak loads of buildings. A total of 144 residential building load profiles have been created with the dynamic building simulation software IDA ICE for a theoretical analysis in which the PLR reaches 1 PLR = Peak load ratio SFH = Single-family house MFH = Multi-family house PPH = People per household AIS = Aggregated individual supply CS = Central supply DH = District Heating 15%. Within this study, certain district features are identified which lead to higher diversity. Furthermore, these results are used in a district heating simulation model which confronts the possible advantage of reduced installed capacity with the practical disadvantage of heat distribution losses. Likewise, the influence of load density and the district´s building structure can be analyzed. This study shows that especially in districts with high load density, which consist of newly constructed buildings with low supply temperature and high influence of the hot water demand, the advantages of load diversity can be exploited.

An Innovative Control Framework for District Heating Systems: Conceptualisation and Preliminary Results

“…To fit with the evolving demand trends of end-users, the energy sector must better understand and analyse the present and future demand levels [18] (population developments, densification of cities vs. low-energy buildings, impact of new demands, and own production) [19,20]. Collection and efficient use of data are essential to that aim, since this information helps the network operator manage the grid efficiently, while at the same time the consumers will be better informed about their consumption and about any possibilities to decrease their energy bills [8,21].…”

A novel stochastic modeling method to simulate cooling loads in residential districts

“…Considering the occupancy and air-conditioning control as an example, the FTFS method assumes that all apartments are always occupied and air-conditioning is always on. The Fixed 5 Schedules method assumes that all occupants in all apartments have the same schedules, e.g., occupants go to work during the daytime and come home at night on workdays. They turn on airconditioning when the room is occupied.…”

“…Previous studies have analyzed load diversity mainly based on the building type, orientation, and envelope performance [5,15,17]. However, occupant behavior could be another key influencing factor in the load diversity among buildings.…”

“…Weissmann et al [5] used load profiles of two buildings to show the load diversity and its impact on the central supply peak load. Fonseca and Schlueter [15] emphasized the importance of understanding the characteristics of spatial and temporal load diversity of district systems for equipment sizing and control strategy application.…”

“…District heating and cooling (DHC) systems have become increasingly popular in recent years in China and other countries [4][5][6]. A DHC system can simultaneously provide heating and/or cooling to many buildings usually in a campus or urban district.…”