  This paper considers the day-ahead operational planning problem of radial
distribution networks hosting Distributed Energy Resources, such as Solar
Photovoltaic (PV) and Electric Vehicles (EVs). We present an enhanced AC
Optimal Power Flow (OPF) model that estimates dynamic Distribution nodal
Location Marginal Costs (DLMCs) encompassing transformer degradation as a
short-run network variable cost. We decompose real/reactive power DLMCs into
additive marginal cost components representing respectively the costs of
real/reactive power transactions at the T\&D interface, real/reactive power
marginal losses, voltage and ampacity congestion, and transformer degradation,
capturing intertemporal coupling. Decomposition is useful in identifying
sources of marginal costs and facilitating the employment of distributed AC OPF
algorithms. DLMCs convey sufficient information to represent the benefit of
shifting real/reactive power across time and achieve optimal Distribution
Network and DER operation. We present and analyze actual distribution feeder
based numerical results involving a wealth of future EV/PV adoption scenarios
and illustrate the superiority of our approach relative to reasonable
conventional scheduling alternatives. Overwhelming evidence from extensive
numerical results supports the significant benefits of internalizing short-run
marginal asset - primarily transformer - degradation.

Optimization and Control