Multi-level task network scheduling and electricity supply collaborative optimization under time-of-use electricity pricing

Abstract

This paper examines intricate Multi-level Task Network Scheduling and Electricity Supply Collaborative Optimization (MTNS & ESCO) in the context of time-of-use electricity pricing. It investigates three distinct models aimed at minimizing completion time and energy costs, accommodating multi-level task networks, inter-level constraints, and task precedence. Model I addresses collaborative planning for production task scheduling and electricity supply under time-of-use pricing. Model II integrates Distributed Energy Resources (DERs) and Energy Storage Systems (ESS) to mitigate conflicts between normal production and high electricity costs during peak periods, building upon Model I. Model III extends this by incorporating feedback to the main grid, maximizing DERs’ power generation potential while reducing costs. To tackle these models, the paper proposes a hybrid algorithm merging Particle Swarm Optimization (PSO) with Tabu Search. This algorithm is customized for the problem’s complexities, employing tailored strategies for encoding, decoding, workstation selection, particle updating, and Tabu Search. The study offers theoretical insights beneficial for equipment manufacturing enterprises seeking to implement distributed energy systems and optimize production and energy management under time-of-use electricity pricing policies. Numerical experiments based on real cases show the performance of our method on reducing the energy consumptions and manufacturing cost.