Abstract:
This chapter presents the current state of integration of big data, data mining and artificial intelligence techniques in advanced Energy Systems Optimization. Recent trends in Energy systems were mainly directed towards hybrid energy systems which are made of a combination of two or more different distributed energy resources (DER). Majority of these distributed energy supplies which derived from renewable energy are related to variables that vary randomly in time and that become hardly predictable. The high intermittency observed with solar irradiation, wind speed velocity and direction to mention few, are illustrations of the variability of the DER resources. Massive data collected over years and generated by multiple sensors are sometimes needed to accurately predict most of these stochastic variables and therefore the reality of big data and the intervention of data mining cannot be undermined. Moreover the integration of many DERs into a hybrid energy supply poses the problem of cost optimization and continuity of power supply which have become complex to solve due to the volume of DERs to integrate and the complexity in modelling each individual DER. Traditional optimization solvers for linear and non-linear optimization problems have become obsolete in resolving the current HES optimization problem. In this regard, the need for modern Artificial intelligence techniques best suited for this nature of problem becomes paramount. A comprehensive review of relevant artificial techniques applicable to the optimization of DER has been elaborated in this chapter in addition to a review of relevant software needed to model advanced DERs.