TY - JOUR
T1 - Wavelet-based grid-adaptation for nonlinear scheduling subject to time-variable electricity prices
AU - Schäfer, Pascal
AU - Schweidtmann, Artur M.
AU - Lenz, Philipp H.A.
AU - Markgraf, Hannah M.C.
AU - Mitsos, Alexander
PY - 2020
Y1 - 2020
N2 - Using nonlinear process models in discrete-time scheduling typically prohibits long planning horizons with fine temporal discretizations. Therefore, we propose an adaptive grid algorithm tailored for scheduling subject to time-variable electricity prices. The scheduling problem is formulated in a reduced space. In the algorithm, the number of degrees of freedom is reduced by linearly mapping one degree of freedom to multiple intervals with similar electricity prices. The mapping is iteratively refined using a wavelet-based analysis of the previous solution. We apply the algorithm to the scheduling of a compressed air energy storage. We model the efficiency characteristics of the turbo machinery using artificial neural networks. Using our in-house global solver MAiNGO, the algorithm identifies a feasible near-optimal solution with < 1% deviation in the objective value within < 5% of the computational time compared to a solution considering the full dimensionality.
AB - Using nonlinear process models in discrete-time scheduling typically prohibits long planning horizons with fine temporal discretizations. Therefore, we propose an adaptive grid algorithm tailored for scheduling subject to time-variable electricity prices. The scheduling problem is formulated in a reduced space. In the algorithm, the number of degrees of freedom is reduced by linearly mapping one degree of freedom to multiple intervals with similar electricity prices. The mapping is iteratively refined using a wavelet-based analysis of the previous solution. We apply the algorithm to the scheduling of a compressed air energy storage. We model the efficiency characteristics of the turbo machinery using artificial neural networks. Using our in-house global solver MAiNGO, the algorithm identifies a feasible near-optimal solution with < 1% deviation in the objective value within < 5% of the computational time compared to a solution considering the full dimensionality.
KW - Adaptive refinement
KW - Artificial neural networks
KW - Discrete-time scheduling
KW - Global optimization
KW - Reduced-space
UR - http://www.scopus.com/inward/record.url?scp=85074748980&partnerID=8YFLogxK
U2 - 10.1016/j.compchemeng.2019.106598
DO - 10.1016/j.compchemeng.2019.106598
M3 - Article
AN - SCOPUS:85074748980
SN - 0098-1354
VL - 132
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
M1 - 106598
ER -