The improvement of energy efficiency in the logistics sector is central to the European Union’s sustainability goals. To this purpose, the electrification of delivery fleets and the adoption of smart warehouses equipped with Renewable Energy Sources (RESs) and Battery Energy Storage Systems (BESSs) represent the main solutions. Current research often treats the logistics and warehouse task scheduling aspects and the energy management of warehouses and charging infrastructres for Electric Vehicles (EVs) as separate challenges, leaving a gap in solutions that capture the interdependence of logistics and energy flows. To fill this gap, the present paper proposes a comprehensive Energy Management System (EMS) that couples logistic task planning with energy optimization through a Mixed Integer Linear Programming (MILP) model. The proposed EMS coordinates a warehouse equipped with a Photovoltaic (PV) power plant, a BESS, Vehicle-to-Grid (V2G)-enabled EVs and a fleet of Automated Forklifts (AFs) minimizing, on the energy side, the electricity costs of the warehouse and, on the logistics side, the penalties related to unexecuted tasks. Dedicated task scheduling constraints are included in the EMS. Three operational scenarios are analyzed: (I) both the BESS and V2G-enabled EVs are in operation, (II) BESS is out of service but EVs still provide V2G support, and (III) BESS is unavailable and EVs cannot operate in V2G mode. The results demonstrate a 75% reduction in operating costs in Scenario I compared to Scenario III, while a 42% reduction in operating costs is observed when compared to Scenario II. Also, self-consumption increases by 15% in Scenario I with respect to Scenario III, while it increases by 6% in Scenario I with respect to Scenario II. The impact of EV arrival time and transportation demand is assessed too, showing how costs are negatively affected when considering longer traveled distances or shifted arrivals.
Optimized energy and task management in sustainable warehouses with Automated Forklifts and V2G-enabled Electric Vehicles
Alphonse Francis;Matteo Fresia;Silvia Siri;Stefano Bracco
2025-01-01
Abstract
The improvement of energy efficiency in the logistics sector is central to the European Union’s sustainability goals. To this purpose, the electrification of delivery fleets and the adoption of smart warehouses equipped with Renewable Energy Sources (RESs) and Battery Energy Storage Systems (BESSs) represent the main solutions. Current research often treats the logistics and warehouse task scheduling aspects and the energy management of warehouses and charging infrastructres for Electric Vehicles (EVs) as separate challenges, leaving a gap in solutions that capture the interdependence of logistics and energy flows. To fill this gap, the present paper proposes a comprehensive Energy Management System (EMS) that couples logistic task planning with energy optimization through a Mixed Integer Linear Programming (MILP) model. The proposed EMS coordinates a warehouse equipped with a Photovoltaic (PV) power plant, a BESS, Vehicle-to-Grid (V2G)-enabled EVs and a fleet of Automated Forklifts (AFs) minimizing, on the energy side, the electricity costs of the warehouse and, on the logistics side, the penalties related to unexecuted tasks. Dedicated task scheduling constraints are included in the EMS. Three operational scenarios are analyzed: (I) both the BESS and V2G-enabled EVs are in operation, (II) BESS is out of service but EVs still provide V2G support, and (III) BESS is unavailable and EVs cannot operate in V2G mode. The results demonstrate a 75% reduction in operating costs in Scenario I compared to Scenario III, while a 42% reduction in operating costs is observed when compared to Scenario II. Also, self-consumption increases by 15% in Scenario I with respect to Scenario III, while it increases by 6% in Scenario I with respect to Scenario II. The impact of EV arrival time and transportation demand is assessed too, showing how costs are negatively affected when considering longer traveled distances or shifted arrivals.
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