An MIQP formulation using a linearized power flow and proxy imbalance metrics outperforms a genetic algorithm and an exact MINLP for static multi-period phase reconfiguration in low voltage networks.
Optimal Connection Phase Selection of Residential Distributed Energy Resources and its Impact on Aggregated Demand
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abstract
The recent major increase in decentralized energy resources (DERs) such as photovoltaic (PV) panels alters the loading profile of distribution systems (DS) and impacts higher voltage levels. Distribution system operators (DSOs) try to manage the deployment of new DERs to decrease the operational costs. However, DER location and size are factors beyond any DSO's reach. This paper presents a practical method to minimize the DS operational costs due to new DER deployments, through optimal selection of their connection phase. The impact of such distribution grid management efforts on aggregated demand for higher voltage levels is also evaluated and discussed in this paper. Simulation results on a real-life Belgian network show the effectiveness of optimal connection phase selection in decreasing DS operational costs, and the considerable impact of such simple DS management efforts on the aggregated demand.
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Low voltage user phase reconfiguration as a planning problem
An MIQP formulation using a linearized power flow and proxy imbalance metrics outperforms a genetic algorithm and an exact MINLP for static multi-period phase reconfiguration in low voltage networks.