Maximum allowable loading

As described in the previous section, a part of the energy will be lost during the transport of electric power through an electric component. This energy loss will be converted to heat which will increase the temperature of the associated electric com- ponent. High temperature can result in premature ageing of insulation and more excessive temperatures can even result in melting of conductors or insulation [82]. Therefore, the temperature of the components should be kept below a maximum allowable temperature to prevent faults and dangerous situations, and not to accel- erate ageing.

The temperature in cables depends on the condition of the surrounding environ- ment (the thermal resistance and humidity of the soil, the type of soil, the tem- perature of the soil, etc.). These issues are treated in the international standard IEC 60287 that describes how the allowable current rating of continuously loaded electric cables can be calculated [63]. In case of cyclic loading of cables, the current can temporarily be allowed to exceed the continuous rating without the cable tem- perature exceeding its maximum allowable value. This is treated in the standard IEC 60853 [62] and in [126] that describes the thermal dynamics in cables based on a fully dynamic thermal model. In this thesis, the maximum allowable loading for cables is defined by Equation 4.4 (see Section 4.2.3). If loading patterns change in future, and the cables will for example be more continuously loaded, as is the case in Figure 4.5b, the cyclic current ratings will have to be adjusted downwards compared to the present situation. This means that the value of D in Equation 4.4 needs to be adjusted.

Just as for cables, the loading of transformers is limited by heating. When subject to cyclic loading, the loading of transformers may also be allowed to tem- porarily exceed the rated capacity1of the transformers. And thus also in the case of transformers, a more continuous or a more dynamic loading pattern will affect the maximum allowable loading. In accordance with Equation 4.4, this can be expressed as:

Pmax= D · Pnom (4.8)

where Pmax is the maximum allowable loading, Pnom the rated capacity and D a

factor to incorporate the thermal dynamics of the transformer.

1_{The rated capacity of the transformers is the capacity given by the manufacturer under stan-} dard conditions assuming continuous loading.

4.5

Summary and conclusions

At the MV grids tens to hundreds of houses are connected behind MV/LV transform- ers. To assess the capacity need of these networks with respect to future demand, including decentralised generation of electricity in residential areas, the aggregated (peak) load of the households at these transformers needs to be estimated. Subse- quently, these forecasted (peak) loads are used as input for load-flow calculations. Present methods for modelling residential loads that are most applied determine peak demands (or daily profiles) based on empirical data and the assumption that certain consumer groups have identical profiles. This in combination with extrapo- lating historical demand growth has been sufficient for demand estimates to be used for planning.

Although the existing approaches to model residential loads have performed very well up till now, they will become increasingly unsuitable in the future. The variation between households cannot be modelled by present models because these need user measurement data which are not available for future loads. In addition, the time element is mostly not taken into account, which is essential when new technologies and flexible, not time-critical loads are introduced. This asks for a method for modelling residential loads in such way that the aggregated (peak) load for tens to hundreds of households can be estimated, taking into account the variation between consumers and the variation over time to assess the capacity need of the distribution networks for future demand, including decentralised generation in residential areas. A method to model aggregated residential loads that takes into account these issues is introduced in the next chapter.

Subsequently, the effect of various developments in future demand on the re- quired capacity of future distribution networks can be assessed and the benefits of DSM in relation to network capacity can be investigated. Besides the effect on the maximum loadings, changing load profiles may affect other issues in distribution net- works. Energy losses are partly dependent on the shape of the load profiles. Energy losses can be separated into fixed and variable losses, dependent on the load and on the material characteristics of the network assets. When assessing the impacts of changing load profiles on the energy losses, both types of losses should be quantified, taking into account the fact that replacing assets influences the energy losses as well. Changing load profiles may also change the maximum allowable loading of an asset, because a more continuously loaded asset has a lower maximum allowable loading than a similar asset with a more dynamic loading. These issues should be taken into account when assessing the impacts of future demand on distribution networks.

5

Modelling residential demand profiles

5.1

Introduction

In the previous chapter, it has been shown that the available methods for modelling residential electricity demands are not satisfactory for modelling future demands for use in load-flow calculations to support the planning process of electricity distribu- tion networks. Therefore, to adequately model the aggregated load (and generation) of tens to hundreds of households to analyse the impacts on the distribution net- works a new modelling approach is developed. This approach aims at estimating the aggregated (peak) load while taking into account the variation between consumers and the variation over time, in order to assess the capacity needs of the distribution networks to accommodate future demand and decentralised generation in residen- tial areas. The approach takes into account the load (and generation) profiles of the different future residential technologies, like µ-CHPs, heat pumps, PV panels, and EVs. In this approach the profiles of the different future residential technologies which consume or produce electricity are first considered separately. After care- ful modelling of the individual load and generation profiles, they are subsequently combined to construct the aggregated profile of a group of households.

Constructing demand profiles in this way makes it possible to differentiate be- tween residential areas with various types and penetration degrees of emerging tech- nologies. Also, to analyse the influence of controlling and storing electrical energy it becomes important to assess the electricity consumption and production over time. With the proposed modelling method it is possible to analyse management of flexi- ble loads, for example smart charging of EVs or DSM. Besides the aggregated peak, the shape of the demand profile will change in these situations. The resulting aggre- gated (peak) demands can be used for load-flow calculations to assess the required capacity of electricity distribution networks. In addition, the benefits of smart grids can be assessed by comparing the impact of aggregated demands with and without management of flexible loads.

First, modelling the profiles of the individual load and generation technologies in future residential areas will be treated in Section 5.2. In Section 5.3, it is described how profiles can be modelled of flexible demands that can be shifted in time. The construction of load profiles of flexible demands that follow two different smart grid

strategies are further elaborated. In Section 5.4, examples are given of aggregated net load profiles of residential areas that are constructed by combining the individual load and generation profiles and the effect of the two different smart grid strategies on the net load profiles are presented. In the last section, the chapter is summarised and some conclusions are given.