Planning Virtual Power Plants for Smart Factories and Industry 4.0 with Renewable and Thermal Cogeneration
Wind- and solar-based microgrid generation is a promising solution to reverse the climate change and achieve manufacturing sustainability. Virtual power plants (VPP) formed by a group of coordinated microgrid systems have the potential to accelerate the integration of variable renewable resources. We propose a facility-microgrid, location-allocation model to realize renewable and thermal cogeneration in smart factories and Industry 4.0. Under demand and supply uncertainty, the model jointly locates the factories and warehouse, assigns the transportation routes, and sizes wind turbines, photovoltaics, and energy storage for cost minimization. The model also performs operational planning for production, inventory, energy transactions, vehicle charging, and electricity and thermal cogeneration using combined heat and power. The proposed VPP planning model is demonstrated on multi-echelon supply chain networks using 11-year period, 2.3 million data. Several managerial insights are obtained. First, the experimental results show that the site with abundant renewables is opt for being the factory location despite of higher transportation and production cost. Second, the sizing of renewable microgrid is significantly influenced by time-of-use tariff or peaking price. Third, combined heat and power is proven to be a viable approach to streamline variable generation. Fourth, different decision-making criteria, such as value-at-risk and Minimax, may have huge impacts on the risk-averse planning. Future work can be focused on incorporating edge computing and vehicle-to-grid operations into the integration of renewable energy in smart factories.
virtual power plant, facility location-allocation, logistics electrification, manufacturing-energy nexus, microgrid sizing, prosumer energy, industry 4.0, deep learning
Li, H. (2023). Planning virtual power plants for smart factories and industry 4.0 with renewable and thermal cogeneration (Unpublished thesis). Texas State University, San Marcos, Texas.