This thesis describes the trial design and analysis of the Low Carbon London (LCL) residential dynamic Time-of-Use (dToU) trial. This trial investigated the potential for dToU tariffs to deliver residential demand response to the Supplier, where it may contribute to system balancing through Supply Following (SF) actions, and to the distribution network operator (DNO), where it may be used for network Constraint Management (CM). 5,533 households from the London area participated in the trial and their consumption was measured at 30 minute resolution. 1,119 of these received the dToU tariff, which subjected them to CM and SF price events that were designed according to the specific requirements of these respective use cases. A novel, data driven, engagement ranking index was developed that allowed stratification of subsequent results into sets of the most engaged consumers, who may be indicative of a future populace that is more experienced/engaged in home energy management. Demand response (DR) was calculated relative to baseline model that used the dToU group mean demand as an input, with aggregate response levels calculated over a range of time, socio-economic and household occupancy related variables. Taking a network perspective, the reliability of CM event response was examined and two simple linear models presented as candidate predictors of response level, which was found to be consistent with an 8% reduction in demand. The network capacity contribution of residential DR was theorised to consist of two components: “mean response” and “variance response”, and the real impact of these was investigated using the LCL gathered data. Potential risks to the network from low price induced demand spikes were explored empirically using the SF event data and the times of highest risk were identified. The extensive metadata set gathered from trial participants was processed into some 200 numerical variables. A correlation analysis was performed which was visualised using weighted correlation network graphs. A number of parameters were found to predict response level, but responsivenessud(the level of deliberate engagement) could only be reliably measured by engagement rank.
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