Abstract
The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.
Original language | American English |
---|---|
Number of pages | 8 |
State | Published - 2016 |
Event | 55th IEEE Conference on Decision and Control - Las Vegas, Nevada Duration: 12 Dec 2016 → 14 Dec 2016 |
Conference
Conference | 55th IEEE Conference on Decision and Control |
---|---|
City | Las Vegas, Nevada |
Period | 12/12/16 → 14/12/16 |
NREL Publication Number
- NREL/CP-5D00-66856
Keywords
- convex programming
- optimal power flow
- renewable sources of energy
- uncertainty