Upper and Lower Bounds for Large Scale Multistage Stochastic Optimization Problems: Application to Microgrid Management

TL;DR

This paper develops decomposition algorithms for large-scale multistage stochastic optimization in microgrid management, providing bounds and policies efficiently, outperforming standard methods in accuracy and speed for complex systems.

Contribution

It introduces a novel decomposition approach that computes bounds and policies for large microgrid optimization problems by solving nodal sub-problems independently.

Findings

Decomposition algorithms outperform SDDP in bounds and policy quality.

Methods are significantly faster, enabling larger problem instances.

Applicable to microgrids with over 60 state variables.

Abstract

We consider a microgrid where different prosumers exchange energy altogether by the edges of a given network. Each prosumer is located to a node of the network and encompasses energy consumption, energy production and storage capacities (battery, electrical hot water tank). The problem is coupled both in time and in space, so that a direct resolution of the problem for large microgrids is out of reach (curse of dimensionality). By affecting price or resources to each node in the network and resolving each nodal sub-problem independently by Dynamic Programming, we provide decomposition algorithms that allow to compute a set of decomposed local value functions in a parallel manner. By summing the local value functions together, we are able, on the one hand, to obtain upper and lower bounds for the optimal value of the problem, and, on the other hand, to design global admissible policies for the original system. Numerical experiments are conducted on microgrids of different size, derived from data given by the research and development centre Efficacity, dedicated to urban energy transition. These experiments show that the decomposition algorithms give better results than the standard SDDP method, both in terms of bounds and policy values. Moreover, the decomposition methods are much faster than the SDDP method in terms of computation time, thus allowing to tackle problem instances incorporating more than 60 state variables in a Dynamic Programming framework.