The security of electricity supply has always been important, but it has recently
become one of the critical issues for the planning and operation of modern electricity
networks. There are several reasons for that, including increased demands and
deregulation of electricity markets, resulting in much lower infrastructural investments,
which both pushed existing networks to operate closer to their security limits. The
increasing penetration levels of variable and inherently non-dispatchable renewable
energy resource, as well as the implementation of demand-responsive controls and
technologies on the demand side, together with the application of real-time thermal
ratings for system components, have introduced an unprecedented level of
uncertainties into the system operation. These uncertainties present genuinely new
challenges for the maintenance of high system security levels.
The first contribution of this thesis is the development of advanced computational tools
to strengthen the decision-making capabilities of system operators and ensure secure
and economic operation under high uncertainty levels. It initially evaluates the hosting
capacities for wind-based generation in a distribution network subject to operational
security limits. In order to analyse the impacts of variations and uncertainties in the
wind-based generation, loads and dynamic thermal ratings of network components,
both deterministic and probabilistic approaches are applied for hosting capacity
assessment at each bus, denoted as “locational hosting capacity”, which is of interest
to distributed generation (DG) developers. Afterwards, the locational hosting
capacities are used to determine the hosting capacity of the whole network, denoted as
“network hosting capacity”, which is of primary interest to system operators. As the
available hosting capacities change after the connection of any DG units, a sensitivity
analysis is implemented to calculate the variations of the remaining hosting capacity
for any number of DG units connected at arbitrary network buses.
The second contribution of this thesis is a novel optimisation model for the active
management of networks with a high amount of wind-based generation and utilisation
of dynamic thermal ratings, which employs both probabilistic analysis and interval/affine arithmetic for a comprehensive evaluation of related uncertainties.
Affine arithmetic is applied to deal with interval information, where the obtained
interval solutions cover the full range of possible optimal solutions, with all
realisations of uncertain variables. However, the interval solutions overlook the
probabilistic characteristics of uncertainties, e.g. a likely very low probabilities around
the edges of intervals. In order to consider realistic probability distribution information
and to reduce overestimation errors, the affine arithmetic approach is combined with
probabilistic (Monte Carlo) based analysis, to identify the suitable ranges of
uncertainties for optimal balancing of risks and costs.
Finally, this thesis proposes a general multi-stage framework for efficient management
of post-contingency congestions and constraint violations. This part of the work uses
developed thermal models of overhead lines and transformers to calculate the
maximum lead time for system operators to resolve constraint violations caused by
post-fault contingency events. The maximum lead time is integrated into the
framework as the additional constraint, to support the selection of the most effective
corrective actions. The framework has three stages, in which the optimal settings for
volt-var controls, generation re-dispatch and load shedding are determined
sequentially, considering their response times. The proposed framework is capable of
mitigating severe constraint violations while preventing overheating and overloading
conditions during the congestion management process. In addition, the proposed
framework also considers the costs of congestion management actions so that the
effective corrective actions can be selected and evaluated both technically and
economically
