Design and Application of Scalable Evolutionary Algorithms in Electricity Distribution Network Expansion Planning

Abstract

In our modern daily life, many activities require electricity, for example, the usage of domestic appliances, manufacturing, communication, and transportation. It is therefore essential to maintain a reliable supply of electricity to ensure the operation of such activities. The electricity supply, in a large part, depends on the underlying electrical networks that transfer electricity from power plants to meet the demand of end users. In the past, electricity consumption has grown over time and, at some point, the electricity demand will exceed the current capacity of certain network assets, causing overloads on parts of the networks. Functioning under overload conditions reduces the reliability of the networks and also damages network assets. Network reinforcement is thus required. This incurs substantial investment costs and time-consuming activities, such as acquisitions of new assets, constructions of substations, and installations of suitable cables and other electrical devices. Network operator companies, therefore, need to properly predict the growth of electricity demand and make suitable expansion plans to enhance the capacity of their networks. In addition, the recent emergence of renewable energy sources and smart grid technologies changes electricity consumption behaviors of users, the growth of electricity demand in general, and also the directions of network flows (due to local generation). This poses additional challenges that need to be addressed by the network operators. In this dissertation, we are interested in medium-voltage distribution networks, which are electrical networks that deliver electricity from high-voltage transmission networks to low-voltage distribution networks. Medium-voltage distribution networks typically have more complicated structures than low-voltage networks and require more frequent reinforcement activities than high-voltage transmission networks. We aim to develop robust computational methods to assist distribution network operators (DNOs) in tackling network expansion planning problems.