Descripción expandida de los Casos de Uso del sistema

thermal storage size 55C [kWht]

thermal storage size 45C [kWht]

heat pump size 55C [kWe]

heat pump size 45C [kWe]

182 Figure 6-7b: The comparison of GSHPs and storages sizes with hot water output Tout=45^oC and 55^oC,

outdoor temperature lowered down by 20^oC, at different maximum energy content (Emax) with 100% maximum discharge power (Qmax=100%)

Figures 6-7a and 6-7b show that the optimal size of storage doesn’t change noticeably from 45^oC to 55^oC for both ASHP and GSHP. GSHP can bring benefits in the absence of storage, while the benefits decrease when the storage size getting closer to the optimum.

When comparing figure 6-6a with figure 6-7a, the performance of ASHP systems drop quite consistently with the temperature. Storage tends to even out the performance gap. The performance drop is less significant for GSHP compared to ASHP. Storage reduces the heat pump peak substantially in any cases.

In conclusion, the heat pump with hot water storage model has been implemented in this chapter.

The model can be used for heat pump and hot water storage sizing. The results from the model show that hot water storage decrease the heat pump size installed.

0

Storage size available (% optimal storage size)

Qmax = thermal peak demand = 5.5 kWt

thermal storage size 55C [kWht]

thermal storage size 45C [kWht]

heat pump size 55C [kWe]

heat pump size 45C [kWe]

183

Chapter 7 – UK heat demand modelling

This chapter presents the modelling of UK domestic heating and hot water demand for the year 2007 using 2002 data from [5],[30] and 2050 by using the 2050 pathways report scenarios [6] as a frame work. EnergyPlus and DesignBuilder are used to model all of UK typical residential building types in detail based on UK building characteristics. Starting from 2002 and 2007, the modelling of the demand for the year 2050 takes into account the improvement in building insulation for existing and new buildings, new build and demolition rate of existing buildings, population increases in required comfort levels and outdoor temperature arising from climate change. The electrification of heating demand through resistive heating, air-source and ground-source heat pump are compared later in this chapter. In the last section of this chapter, the potential impact of DSM from different approaches to electrified space heating and hot water for residential buildings for the year 2050 has been assessed based on the assumption of 100% electrification by air-source heat pumps. Three different approaches for peak minimisation: temperature set point scheduling, turning heating system on throughout the day and controlling the installed hot water storage with heat pump are modelled and compared.

7.1 UK domestic heat demand modelling for the year 2002

The preliminary aim of heat demand modelling performed is for comparison with the 2050 pathway report. In this report the base year is 2007 and this is based on Department of Energy and Climate Change, Energy Consumption in the UK, July 2009, Table 1.14 [62]. However, EnergyPlus software only provides hourly and regional temperature for the year 2002; therefore this has been used throughout with adjustments to compensate for different temperature data. Heat demand for the domestic sector is modelled by statistical information [30] for the year 2002 on domestic building characteristics and number. Actual gas sales for 2002 [59] is used to verify the heat demand results from the model. The detail of building characteristic and weather data are given in Appendix A. The heat demand for the year 2007 has been obtained by calibrating the heat demand for the year 2002 using 2007 average daily temperature from National Grid [59].

Figure 7-1 shows, the comparison of regional annual heat demand obtained from gas sales 2002 [59]

and those obtained from the model. The regional demands in the figure are the average demands for space heating per customer. In 2002, an assumption of 10% for gas network losses and with 27%

deducted for DHW demand [30] to give gas demand for space heating only. An assumption of 70%

boiler efficiency is assumed [60].

184 Figure 7-1: Comparison of regional average thermal heating demand for space heating per year per

house obtained from gas consumption data and seasonal simulation

The regional demands obtaining from EnergyPlus simulation are similar to those obtained from the gas data, i.e. less than 10% except for the South West region where the difference is 18%. This might be due to the fact that London weather is used as South West region. The South West tends to milder than London and this may explain the difference.

Average heating demand per house from CHP report [60] is used to compare the heating demand result from the model. These comparisons are shown in table 7-1 and figure 7-2. However, the most up to date information in the report is for the year 1991 (information for the year 2002 is not available). It is assumed in the report for the year 1991 that annual DHW demand was approximately 20% of total heating demand and the boiler efficiency of all houses is 65%.

0 2,000 4,000 6,000 8,000 10,000 12,000

North East North West Yorkshire and the … East Midlands West Midlands East of England London South East South West England Wales England and Wales Scotland GB

Heat demand(kWthh)

Heat(th) from gas data Model Heat Seasonal(th)

185 Table 7-1: Comparison of average heating demand per house per year from the report [60] and from each model

Detached houses 25,875 20,700 13,455 15,181 14,522

Semi-detached houses 19,210 15,368 9,989 9,224 9,222

Terraces 16,929 13,543 8,803 7,873 7,912

Flats 9,086 to 10,140 8,112 5,273 5,916 5,885

All houses - - - 9,695 9,545

Figure 7-2: Comparison of average thermal heating demand for space heating per year per house obtained from report [60] and seasonal simulation

0