A distributed control and coordination strategy based on voltage sensitivity for network management

Abstract

Advances in technology and incentives from governments are enabling growing deployment of distributed energy resources (DER) and this raises challenges over how distribution network operators (DNOs) should host these resources other than through traditional grid reinforcement. The alternative is active network management (ANM) which can control non-firm connections of DERs to ensure the network is compliant with voltage and thermal operating limits while increasing the capacity of the network to host DER. Furthermore, with changes expected in the regulatory paradigm to create ancillary service markets to promote other benefits of DERs, new streams of revenue for DER owners are anticipated. ANM schemes should manage growing volume of DERs while minimising costs of ancillary services. In this context, it is proposed and evaluated a distributed form of ANM for radial networks that manages voltage and thermal constraints and does this cost-effectively by including the costs of ancillary services in its objective function. To distribute the ANM, a mathematical formulation for local calculation of voltage sensitivities is derived that enables each local controller (LC) to take decisions over power injections to solve constraints. These estimates are combined in a message passing process that results in a coordinated set of decisions that solve local and non-local network constraint. The proposed ANM, based on estimated sensitivities and local message passing, is compared with a centralised ANM based on optimal power flow (OPF). Case-studies examined through time-domain simulations showed that the proposed ANM successfully identified the most appropriate DER to act and produced near identical control actions to the OPF. The approach is evaluated across multiple randomly generated scenarios, providing good confidence that the method is widely applicable and accurate. Mismatches of the order of 10^{-2} p.u where observed when compared to centralised algorithm and considerable decrease in computational time and data transmission was achieved.