Event type

Stratification of DR by Constraint Management (CM) and Supply Following (SF) event types were examined using the absolute measure of DR (measured in kW). Figure 9.8 shows the weighted correlation network graph for root nodes of CM high price response and both low and high price response for Supply Following (SF) events.

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Figure 9.8: Weighted correlation network graph with root nodes representing Constraint Management (CM) and Supply Following (SF) DR (kW) high price CM and SF events, and low price in SF

events only.

High price CM event response correlates more strongly with the accommodation and appliance ownership variable groups (grey circles and squares) than high price SF event response. These additional variables represent computer equipment, 39 and 40, the numbers of traditional and halogen bulbs, 25 and 22, and electric ovens, 36. The signs of the correlation and the variables

(high price DR is a reduction in demand and therefore negative) indicates that increases in their numbers corresponds with an increase in CM response magnitude. This makes sense in the context of discretionary loads being switched off during a high price event. For example, households with lower efficiency lighting and electric ovens have more demand to reduce.

High price SF response correlates more strongly with self reported responsiveness variables (red and blue circles) than CM type events. This may indicate a greater conscious effort to respond. It may also be due to perceptions: With many more SF event days than CM, it may be that, when estimating self response level, survey responders considered the SF element of the trial to be the most significant element in their own response and therefore answered with respect to this.

Low price SF response showed similar trends as low price DR over all event types.

9.3.9 Time

Stratifications of DR by a selection of times-of-day and days-of-week were investigated. Average DR was calculated according to Eq. (5.7) where the sets of measurement indices (M) were limited to SF event type at the defined times and days. The following time-of-day stratifications were tested for high and low prices:

• Night: 00:00 to 07:00

• Morning: 07:00 to 10:00

• Afternoon: 10:00 to 17:00

• Evening: 18:00 to 21:00

These were represented for high price in variables 100–103 and for low price in variables 107–110.

Time stratifications were only calculated for weekdays. Stratifications of average DR over all times of day were made for for weekdays; variables 99 and 106; and weekend days; variables 104 and 111.

In general there were fewer correlations with time stratified DR variables than with the over-all DR variables. This is probably due to statistical noise introduced by averaging over fewer events. It was therefore difficult to determine whether differences in correlations between the time stratifications were the result of statistical noise or a significant feature of the stratification itself.

There were almost no significant correlations outside the primary variable group for both high and low price DR at night. The only exception to this was washing machine use (variable 125) for low price events. The number of correlations increased in the morning period with 6 and 8 non-primary correlations for high and low price events respectively. The afternoon period showed the most correlations for both high and low price events, with counts of 15 and 19 respectively.

Correlation numbers then reduced again in the evening, with a count of 6 for both low and high price events.

The types of correlations observed generally followed the same trends already noted in Sec-tion 9.3.5 and SecSec-tion 9.3.6: High price DR tended to correlate with the physical variables of accommodation and appliance ownership, while low price DR correlated with the self reported response variables for both low and high price. A full list of significant correlations can be found Appendix B.1.

There were no major differences in the relationship trends between weekday and weekend DR at both low and high price. Though it is worth noting that the self reported response variables for the period in question (weekdays or weekends) were always significantly correlated for that period.

This may be taken as validation of the self-report survey method for assessing engagement.

9.4 Summary and conclusions

This chapter presented a correlation analysis of the metadata variables collected in the Low Carbon London (LCL) residential dynamic Time-of-Use (dToU) trial. Spearman’s rank correlation coef-ficient was used to analyse the relationships between metadata and primary variables that were derived from the smart meter (SM) collected consumption data, such as demand response (DR).

Some 200 variables were analysed in total. Statistically significant correlations were also used to produce weighted correlation network graphs in order to display and further analyse the relation-ships. Only correlation coefficients with a p-value of less than 10⁻⁵ were considered significant.

Key findings were:

Annual consumption. Annual consumption was seen to correlate strongly with variables repre-senting physical parameters of the accommodation and appliance ownership. In addition, working from home was found to increase annual consumption.

Engagement rank. Engagement rank was seen to correlate strongly with both high and low price DR metrics and the self reported responsiveness data that was collected via survey. This cor-relation alludes to the possibility of increasing consumer responsiveness through behaviour change and also acts as a validation of the power of the data driven engagement ranking technique intro-duced in Chapter 6.

Acorn group. Acorn group correlated strongly with physical accommodation and appliance ownership related variables. Furthermore, increasing wealth was seen to correspond to increased numbers of halogen light bulbs and decreased uptake of energy efficiency measures such as wall in-sulation. Correlations with DR related variables were weak, with only one correlating significantly;

Constraint Management (CM) event response at high price.

Absolute high price DR. High price DR correlated strongest with physical accommodation or appliance ownership related variables. It was suggested that these may be a proxy for the overall consumption level, and that this is the underlying driver of high price DR magnitude.

Strong correlations were seen between the absolute DR variable and self reported responsiveness with appliances; tumble drier, dishwasher, ironing and washing machine use, in order of decreasing strength of relationship. The use of a substitute fuel (assumed to be gas as smoke emitting fuels are banned in London) also correlated strongly with enhanced DR. This alludes to synergies between the use of gas for heating and cooking, and the electricity system.

Absolute low price DR. Most striking for low price DR was that, in contrast to high price DR, there were no appliance ownership and almost no physical accommodation related variable correlations. Only the number of rooms in the accommodation was significant, but the relation-ship here was weak. Instead, strong correlations were found for variables relating to self reported responsiveness. Interestingly, while both were strong, correlation with high price responsiveness variables were even stronger than for low price responsiveness. This suggests the hypothesis that good low price responders may be a subgroup of good high price responders—a point that war-rants further investigation. Response limiters also correlated with reduced DR, with sentiments of inflexible appliance use cycles and savings being too small both correlating with reduced DR levels.

Consumption relative DR. Consumption relative DR variables were created to test the premise that DR response levels were strongly dependent on normal consumption levels. In making the

switch from high price DR measured absolutely (kW), to an annual-consumption relative metric, all appliance and physical accommodation variable correlations were seen to drop away. This is evidence that DR is strongly related to normal consumption levels and that there may be little in the way of objective/external data that can predict annual-consumption relative responsiveness levels.

DR by event type. Average DR variables for both CM and Supply Following (SF) event types were created to examine differences between the drivers for good response in each type. It was observed that high price CM response correlated much more with physical accommodation and appliance variables than SF event response.

Time stratifications. Stratifications of DR over both times-of-day and weekend/weekday yielded no new findings.

Summary and conclusions

This section presents an overall summary of the work described in this thesis. To provide context, it begins with a summary of the Low Carbon London (LCL) dynamic Time-of-Use (dToU) trial design and analysis basis, after which key findings form each of the results chapters are presented.

The chapter ends with suggestions for the further development of this work.