Sistema cooperativo de planificación de demanda de electricidad agregadacomunidades sostenibles que optimizan el consumo de renovables

Abstract

Energy efficiency is emerging as a key element in the fight against climate change and to reduce our carbon footprint. A more sustainable energy system is being influenced by consumers’ lifestyle choices such as the one related to, for instance, the use of hybrid/electric vehicles, food from sustainable agriculture and energy-efficient households. Public institutions are also providing programme support, e.g., funding equipment retrofitting, and energy efficiency subsidies for public and private facilities, with the aim at encouraging citizens to play a more active role within the electricity system. Moreover, utility companies commit towards the same goal and a more intelligent network that better allows the management of flexibility of demand, the participation of consumers in balancing services, and the integration of energy storage services and self – consumption. Within the smart city paradigm, smart community initiatives embrace a citizen/consumer-centric approach, which enable energy consumers to pursue common goals through cooperation and coordinated behavior. In particular, aiming at efficient energy utilization, smart appliances and other connected devices through the Internet of Things have raised expectations about the real deployment and consumer engagement in demand response programmes. This thesis aims to provide sustainable evidence of the benefits and viability of an ICT – supported demand-side management system that contributes to balance supply and demand, maximise renewable energy use and promote a behavioural change within a community of collaborative electricity consumers. We design, implement and evaluate a novel scheduling algorithm that aggregates and optimises the community demand maximising the utilisation of the available supply of renewable energy. Validation of performance, feasibly, quality of service and security measures is performed over a laboratory testbed using lightweight cost – effective hardware platforms such as Raspberry Pi boards and living lab datasets. Emulated scenarios throw realistic and efficient results that allow us to identify a number of three main behavioural patterns of aggregated community, where the consumers’demand volume, curve and flexibility are shown as the key factors. The identification of these patterns assists in the establishment and prediction of a better strategy when deploying demand-side management programmes in real communities that pursue a sustainable and efficient energy usage and behavioural change.