Distributed Electricity and Carbon Allowance Sharing Among Interconnected Discrete Manufacturing Systems with Feasibility Guarantees

Abstract

The operation of discrete manufacturing systems (MSs) results in significant energy consumption and carbon emissions. Previous studies have explored integrating renewable energy sources and electric vehicles into MSs to reduce their energy costs and carbon emissions. However, the potential benefits of sharing electricity and carbon allowance among interconnected MSs remain underexplored. In this paper, we formulate a local electricity and carbon allowance sharing problem with the aim of exploring distributed electricity and carbon allowance sharing among MSs, considering both algorithmic and physical feasibility. Firstly, the formulated problem inevitably involves numerous binary decision variables when considering the operation of discrete manufacturing facilities and energy storage, making it challenging to solve in a distributed manner. We propose an alternating optimization procedure (AOP)-based distributed method to solve the problem while ensuring algorithmic feasibility. Secondly, the commonly used second-order cone relaxation program (SOCP)-based power flow model cannot guarantee the exactness of the distribution system model when conducting local electricity sharing, endangering the safety of the distribution system. We address this challenge by employing a convex-concave procedure (CCP)-based feasibility recovery procedure (FRP) to recover the exactness of the SOCP relaxation and guarantee physical feasibility. Simulation results demonstrate that conducting local electricity and carbon allowance sharing can effectively reduce energy costs and carbon emissions for MSs. Compared with the alternating direction method of multipliers (ADMM), the proposed distributed method can guarantee algorithmic and physical feasibility when solving the problem.

Keywords: Manufacturing systems, Local resource sharing, Alternating optimization procedure, Convex-concave procedure, Feasibility guarantees