Day-Ahead Bidding for an Aggregator of Crop-Aware Greenhouses

Commercial greenhouses are becoming significant, controllable, and weather-dependent electricity loads in regions pursuing controlled environment agriculture (CEA). Their electrical demands—such as supplemental lighting, heating, ventilation/cooling, irrigation pumps, and CO2 enrichment—exhibit substantial intra-day flexibility. This flexibility stems from the fact that plant productivity depends on time-integrated agronomic variables (e.g., Daily Light Integral, accumulated thermal time, and mean vapor pressure deficit) rather than instantaneous environmental setpoints. However, existing studies have predominantly focused on greenhouse thermal modeling and energy conservation, while decision-making models that integrate crop physiological characteristics into day-ahead (DA) market bidding remain limited. To bridge this gap, this paper develops a scenario-based stochastic DA bidding framework for a load aggregator, representing multiple smart greenhouses in a wholesale electricity market. In the DA stage, the aggregator submits hourly demand–price bidding curves based on price-conditional schedules of heating and lighting demand while satisfying coupled thermal–photon balance constraints. Fifty representative scenarios generated from 2023 to 2024 historical price and weather data through cGAN-based scenario generation and K-means scenario reduction are used for stochastic bid construction and feasibility analysis. A separate 30-day historical dataset from January 2025 is used for benchmark comparison. The aggregator portfolio consists of 100 greenhouses divided into five LAI-based crop groups, with 20 greenhouses in each group. Relative to the 15–25 °C trapezoidal temperature baseline, which is adopted as the primary practical benchmark, the proposed strategy reduces the mean daily electricity procurement cost by 15.80%. A reduction of 18.89% is also observed relative to the rigid 20 °C thermostat case, which is retained as a capacity-intensive reference. These results represent simulation-based operating-cost comparisons under a common equipment configuration and do not include equipment capital costs.