Integration of low carbon technologies in smart grids: planning, optimization and control for renewables, demand response and energy storage

The entire electricity system is undergoing a dramatic transformation driven mainly by challenging environmental and economic targets set out by government policies worldwide. The general consensus about the necessary changes towards clean energies has fostered growth in renewable generation through different incentives programs. The restructuring process aims at “decarbonising” the electricity sector while increasing requirements in terms of quality of supply and making electricity more affordable to end customers. This all results in more work and stress for network and market operators. In this context, most relevant decisions to be made by network agents within a fast-moving energy environment involve copying with large quantities of data and a significant level uncertainty. For example, future renewable electricity production is unknown when producers have to submit their offers to the market. In a similar fashion, at the time of procuring energy to be supplied, retailers do not know their consumers’ demand. In this respect, it is fundamental to properly address the uncertainty involved and exploit all the available information. Additionally, large and complex optimization problems, which are generally prone to numerical issues or simply take too long to converge, are needed to be solved to make informed and economic decisions. On the above premises, the thesis provides different opportunities and ideas to help face some of these challenges. In particular the work is focused on the effective integration of distributed low carbon technologies in the grid of the future. Planning and operation problems for different clean solutions, such as market bidding strategies for intermittent energy producers, demand side management algorithms for smart buildings, and electrical storage options for network operators, have been studied for facilitating the integration of renewable energy sources in the power system chain. However, the interdisciplinary research presented in this work lies at the intersection of power systems, optimization techniques and control. In fact, a number of optimization tools employing stochastic models and different physical-based heuristics that are amenable to fast and robust computation, are also provided. Particular attention is paid to electric power systems at large extent, including spot electricity markets and smart buildings, with a large integration of non-dispatchable sources, such as wind and solar power plants, and a strong need for demand energy management.