Risk-Aware Integration of Photovoltaics and Battery Storage into Human–Cobot Manufacturing: A Digital-Twin-Ready Co-Optimization Model for Sustainable Industrial Transition

Abstract

The increasing penetration of variable renewable energy requires industrial facilities to manage energy supply, production flexibility, and cost exposure simultaneously. This study develops a risk-aware model for the co-optimization of human–cobot production scheduling, on-site photovoltaic generation, and battery energy storage within a digital-twin-ready decision environment. The artifact is an offline prescriptive model; it is not connected to a physical production system and does not include automatic or real-time data exchange. A two-stage computational experiment was conducted using mixed-integer linear programming with conditional value-at-risk and an out-of-sample Monte Carlo simulation comprising 1000 independent daily scenarios of electricity prices, photovoltaic generation, and grid carbon intensity. Five strategies were compared: grid-only supply, photovoltaic generation, photovoltaics with battery storage, risk-neutral co-optimization, and risk-aware co-optimization. Relative to the modeled baseline, the risk-neutral variant reduced mean daily operating cost by 59.2%, operational emissions by 46.1%, and grid-energy use by 48.3%, without delaying production. The risk-aware variant reduced the CVaR95 of daily cost by a further 2.6% relative to the risk-neutral solution, at a 4.2% increase in mean cost. These percentages are scenario-specific operational effects rather than plant-level, annual, or life-cycle estimates. A screening-level investment assessment indicates that economic viability remains contingent on site-specific capital costs, operating days, tariffs, and implementation expenditure. The findings show that scheduling flexibility is a resource of energy transition, while risk aversion should be calibrated explicitly as a managerial decision.