Fast Distributed Stochastic Scheduling for A Multi-Energy Industrial Park

TL;DR

This paper presents a fast distributed stochastic scheduling algorithm for multi-energy industrial parks, optimizing energy costs and efficiency under uncertainty without prior stochastic knowledge.

Contribution

It introduces a novel distributed stochastic gradient algorithm with a fast scheme for real-time energy management in industrial parks under uncertainty.

Findings

Achieves asymptotic social welfare maximization

Reduces energy costs effectively

Improves energy efficiency

Abstract

The multi-energy management framework of industrial parks advocates energy conversion and scheduling, which takes full advantage of the compensation and temporal availability of multiple energy. However, how to exploit elastic loads and compensate inelastic loads to match multiple generators and storage is still a key problem under the uncertainty of demand and supply. To solve the issue, the energy management problem is constructed as a stochastic optimization problem. The optimization aims are to minimize the time-averaged energy cost and improve the energy efficiency while respecting the energy constraints. To achieve the distributed implementation in real time without knowing any priori knowledge of underlying stochastic process, a distributed stochastic gradient algorithm based on dual decomposition and a fast scheme are proposed. The numerical results based on real data show that the industrial park, by adopting the proposed algorithm, can achieve social welfare maximization asymptotically.