Optimal scheduling of Distributed Energy Resources connected to Electricity Distribution Networks using Robust Mixed-Integer Second Order Cone Programming

Abstract

Tackling climate change is a global emergency, driving the electricity sector to go through rapid changes, including the increasing reliance on local generating assets, called distributed energy resources (DER). DER range from onsite energy storage systems, to gas or diesel generators, and renewable generators, but could also include other forms of generation such as electric vehicles with vehicle-to-grid capabilities. This PhD proposes a model to optimally schedule DER connected to radial distribution networks, which can form an active distribution network or a microgrid, aiming at delivering improvements in operational cost, security of supply and environmental sustainability. This is mathematically formulated using robust mixed-integer second-order cone programming. The proposed model takes into account an accurate power flow model for radial networks and a robust approach to deal with uncertainty in the market price, the electricity demand, the renewable generation, and the time and duration of a scheduled interruption from the main grid when DER form part of a microgrid. Computational experiments support the suitability of the proposed model, in a number of case studies informed by real-world data and operational scenarios. This research concludes the following. Firstly, that it is important to account for detailed modelling of network losses in operational decisions of such systems, as they profoundly affect both the cost and the network’s operating state and conditions. Secondly, that the robust approach used in this thesis in order to deal with uncertainty allows distribution system and/or microgrid operators to manage trade-offs between the level of the aforementioned uncertainties they are willing to tolerate, and the operational cost of network assets. Benefits of using the proposed model include, reduction of the operational cost, and mitigation of technical constraint violations in actual conditions. The proposed model can be used by a range of stakeholders including, microgrid operators, distribution system operators, and DER owners.