loading page

Evaluating solar PV effects on California's hydropower generation with a hybrid LP-NLP optimization model
  • Mustafa Dogan,
  • Jay Lund
Mustafa Dogan
University of California Davis

Corresponding Author:[email protected]

Author Profile
Jay Lund
Univ California Davis
Author Profile


A hybrid Linear Programming (LP) and Nonlinear Programming (NLP) optimization model is developed for California’s hydropower operations. Built on top of Pyomo library, a high optimization modeling language in Python, the model can connect to several freely available, state-of-the-art solvers. In this model, fast evaluation of LP and detailed model representation of NLP are fully utilized. The hybrid model solves the same problem with linear approximation (a simplified objective function representation) and with NLP solver, where no simplification is made to objective function. Outputs from LP model are used as initial values (warmstart) for NLP model’s decision variables, which reduce number of iterations for convergence and so runtime. The model is capable of representing large network of hydropower plants that are in serial or parallel, or fixed and variable head plants. The model is used to evaluate effects of increased solar photovoltaic (PV) generation in California. California has a goal of generating electricity from renewable resources at least 33% by 2020, and 50% by 2030, and solar PV generation supplies most of renewable generation portfolio during daytime. This expanded use of solar PV changes generation pattern from one daily peak system to two daily peak system. Due to excess generation of solar PV, negative prices can occur during daytime. Therefore, evaluating effects of solar PV on hydropower operations and adapting to new conditions are essential.