Evaluating the robustness of an active network management function in an operational environment

Incentives to renewable sources of energy are causing an increase of the number
of generators connected to distribution networks.
The cost of the network reinforcement are very high and utilities are interested in
active network management solutions in order to manage the generator
connection to the net, reducing to the minimum the network reinforcement.
So, automatic control systems, based on software tools, are becoming more
desirable in distribution power systems.
Primarily, such schemes are expected to manage system voltage fluctuations,
network power flows and fault levels.
Functionalities include also power balancing, system frequency control and
management of demand side resources for the primary system constraints.
A critical concern is the robustness of online and automatic active network
management (ANM) algorithms/schemes.
The ANM scheme’s functionality depends on convergence to a solution when
faced with uncertainty and its reliability can be reduced by data skew and
errors.
The work presented evaluates power flow management (PFM) functionality based
on the Constraint Satisfaction Problem (CSP) in an operational environment.
The objective is to assess performances when subjected to different levels of data
uncertainty and verify the introduction of a state estimator (SE) in the ANM
architecture to mitigate the data uncertainty effects on the control action.