engleski

Dynamic Reconfiguration of Electrical Power Distribution Systems with Distributed Generation and Storage

In this paper we present a nonlinear model predictive control strategy for dynamic reconfiguration of electrical power distribution systems with distributed generation and storage. Even though power distribution systems are physically built as interconnected meshed networks, as a rule, they operate in a radial topology. The network topology can be modified by changing status of the line switches (opened/closed). The goal of the proposed control strategy is to find the optimal radial network topology and the optimal power references for the controllable generators and energy storage units that will minimize cumulative active power losses while satisfying operating constraints. By utilizing recent results on convex relaxation of the power flow constraints, the proposed dynamic reconfiguration algorithm can be formulated as a mixed-integer second order cone program. Furthermore, if polyhedral approximations of second order cones are used then the underlying optimization problem can be solved as a mixed- integer linear program. Performance of the algorithm is illustrated on a small simulation case study based on actual meteorological and consumption data.

model predictive control ; dynamic reconfiguration ; power distribution system ; optimal power flow ; convex relaxation ; mixed-integer programming ; conic programming

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