Towards sustainable energy-efficient communities based on a scheduling algorithm

The Internet of Things (IoT) and Demand Response (DR) combined have transformed the way Information and Communication Technologies (ICT) contribute to saving energy and reducing costs, while also giving consumers more control over their energy footprint. Unlike current price and incentive based DR strategies, we propose a DR model that promotes consumers reaching coordinated behaviour towards more sustainable (and green) communities. A cooperative DR system is designed not only to bolster energy efficiency management at both home and district levels, but also to integrate the renewable energy resource information into the community's energy management. Initially conceived in a centralised way, a data collector called the "aggregator" will handle the operation scheduling requirements given the consumers' time preferences and the available electricity supply from renewables. Evaluation on the algorithm implementation shows feasible computational cost (CC) in different scenarios of households, communities and consumer behaviour. Number of appliances and timeframe flexibility have the greatest impact on the reallocation cost. A discussion on the communication, security and hardware platforms is included prior to future pilot deployment.Comunidad de Madri

Behavioural patterns in aggregated demand response developments for communities targeting renewables

Encouraging consumers to embrace renewable energies and energy-efficient technologies is at stake, and so the energy players such as utilities and policy-makers are opening up a range of new value propositions towards more sustainable communities. For instance, developments of turn-key demand response aggregation and optimisation of distributed loads are rapidly emerging across the globe in a variety of business models focused on maximising the inherent flexibility and diversity of the behind-the-meter assets. However, even though these developments" added value is understood and of wide interest, measurement of the desired levels of consumer engagement is still on demonstration stages and assessment of technology readiness. In this paper, we analyse the characteristics of the loads, the behaviour of parameters, and in a final extent, the behaviour of each kind of consumer participating in aggregated demand scheduling. We apply both non-automatic and machine learning methods to extract the relevant factors and to recognise the potential consumer behaviour on a series of scenarios that are drawn using both synthetic data and living labs datasets. Our experimentation showcases a number of three patterns in which factors like the community"s demand volume and the consumer"s flexibility dominate and impact the performance of the tested development. The experimentation also makes current limitations arise within the existing electricity consumption datasets and their potential for inference and forecasting demand flexibility analytics.Comunidad de Madri

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools