Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actions

In a smart(er) grid context, the existence of dynamic tariffs and bidirectional communications will simultaneously allow and require an active role from the end-user concerning electricity management. However, the residential end-user will not be always available to manage energy resources and decide, based on price signals and preferences/needs, the best response actions to implement or the best usage of the electricity produced locally. Therefore, energy management systems are required to monitor consumption/generation/storage and to make the best decisions according to input signals and the user's needs and preferences. The design of adequate algorithms to be implemented in those systems require the prior characterization of domestic electricity demand and categorization of loads, according to availability, typical usage patterns, working cycles and technical constraints. Automated demand response actions must be tailored and chosen according to this previous analysis of load characteristics. In this paper, a characterization of household electricity consumption is presented and an operational categorization of end-use loads is proposed. The existing potential for demand response to a diversified set of management actions is described and a tool to assess the impact of implementing several actions with different rates of penetration of energy management systems is presented. The results obtained show the potential savings for the end-user and expected changes in the load diagram with a decrease of the aggregated peak electricity demand and a smoothed valley.