Outline
Optimization and distributed optimization play a key role in modern energy systems, enabling efficient management of resources, coordination of distributed assets, and scalability of control strategies. This tutorial provides an introduction to convex optimization, duality-based decomposition techniques, and distributed optimization methods with a focus on energy applications. The tutorial will cover theoretical foundations and practical implementations, including parametric optimization, proximal methods, and iterative distributed algorithms. Through motivating examples and practical demonstrations, participants will gain an understanding of the challenges and solutions in energy optimization problems.
Organizer(s)
- Mariagrazia Dotoli, Politecnico di Bari, ti.ab1763097353ilop@1763097353iloto1763097353d.aiz1763097353argai1763097353ram1763097353
- Raffaele Carli, Politecnico di Bari, ti.ab1763097353ilop@1763097353ilrac1763097353.elea1763097353ffar1763097353
- Paolo Scarabaggio, Politecnico di Bari, ti.ab1763097353ilop@1763097353oigga1763097353barac1763097353s.olo1763097353ap1763097353
- Nicola Mignoni, Politecnico di Bari, ti.ab1763097353ilop@1763097353inong1763097353im.al1763097353ocin1763097353
Structure
This tutorial will be structured as follows:
- Introduction to Optimization of Complex Systems
– Motivating examples in energy systems
– System models, schemes, and architecture - Preliminaries on Optimization
– Basics of unconstrained and constrained optimization
– Parametric optimization and proximal operator
– Numerical solvers and hands-on Python - Duality and Decomposition Methods
– Lagrange multipliers and Karush-Kuhn-Tucker (KKT) conditions
– Duality-based algorithms: Waterfilling, Dual Ascent (DA), Augmented Lagrangian Method (ALM), Alternating
Direction Method of Multipliers (ADMM)
– Decomposition methods (primal/dual decomposition) - Non-centralized Optimization
– Problem setup and formulation
– Duality-based methods (decentralized and distributed DA, ALM, ADMM, Waterfilling)
– Iterative and consensus-based methods - Practical Applications
– Optimization of energy consumption and demand response strategies
– Optimal solar panel layout design in photovoltaic power plants
– Optimal photovoltaic panels tilt orientation in agrivoltaics
– Decentralized optimization of distributed resources in smart grids
– Distributed optimization for peer-to-peer energy trading and storage management in energy communities
