RELIABILITY AND RISK ANALYSIS OF POST FAULT CAPACITY SERVICES IN SMART DISTRIBUTION NETWORKS

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Abstract

Recent technological developments are bringing about substantial changes that are converting traditional distribution networks into “smart” distribution networks. In particular, it is possible to observe seamless integration of Information and Communication Technologies (ICTs), including the widespread installation of automatic equipment, smart meters, etc. The increased automation facilitates active network management, interaction between market actors and demand side participation. If we also consider the increasing penetration of distributed generation, renewables and various emerging technologies such as storage and dynamic rating, it can be argued that the capacity of distribution networks should not only depend on conventional asset.In this context, taking into account uncertain load growth and ageing infrastructure, which trigger network investments, the above-mentioned advancements could alter and be used to improve the network design philosophy adopted so far. Hitherto, in fact, networks have been planned according to deterministic and conservative standards, being typically underutilised, in order for capacity to be available during emergencies. This practice could be replaced by a corrective philosophy, where existing infrastructure could be fully unlocked for normal conditions and distributed energy resources could be used for post fault capacity services. Nonetheless, to thoroughly evaluate the contribution of the resources and also to properly model emergency conditions, a probabilistic analysis should be carried out, which captures the stochasticity of some technologies, the randomness of faults and, thus, the risk profile of smart distribution networks.The research work in this thesis proposes a variety of post fault capacity services to increase distribution network utilisation but also to provide reliability support during emergency conditions. In particular, a demand response (DR) scheme is proposed where DR customers are optimally disconnected during contingencies from the operator depending on their cost of interruption. Additionally, time-limited thermal ratings have been used to increase network utilisation and support higher loading levels. Besides that, a collaborative operation of wind farms and electrical energy storage is proposed and evaluated, and their capacity contribution is calculated through the effective load carrying capability. Furthermore, the microgrid concept is examined, where multi-generation technologies collaborate to provide capacity services to internal customers but also to the remaining network. Finally, a distributed software infrastructure is examined which could be effectively used to support services in smart grids. The underlying framework for the reliability analysis is based on Sequential Monte Carlo Simulations, capturing inter-temporal constraints of the resources (payback effects, dynamic rating, DR profile, storage remaining available capacity) and the stochasticity of electrical and ICT equipment. The comprehensive distribution network reliability analysis includes network reconfiguration, restoration process, and ac power flow calculations, supporting a full risk analysis and building the risk profile for the arising smart distribution networks. Real case studies from ongoing project in England North West demonstrate the concepts and tools developed and provide noteworthy conclusions to network planners, including to inform design of DR contracts.

Keyword(s)

demand response, distribution network planning, differentiated reliability, distributed energy resources ; sequential monte carlo simulation

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