An optimization framework to response flexible energy demand based on target market in a smart grid: A case study of greenhouses

• Published in: Sustainable computing informatics and systems Vol. 47; p. 101163
• Main Author: Shahrabi, Mehran Salehi
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2025
• Subjects: Energy planning; Extended epsilon-constraint; Flexible energy demand; Greenhouses; Renewable energies; Extended epsilon-constraint; Renewable energies; Flexible energy demand; Energy planning; Greenhouses
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2025.101163

Unlike many energy-consuming sectors, greenhouses can operate with varying energy inputs while producing crops of different qualities. Supplying greenhouse energy from the main grid faces two main challenges: fluctuating energy prices throughout the day and the risk of planned or unplanned outages. Similarly, relying solely on renewable energy resources is constrained by their intermittent availability. Consequently, this study investigates energy supply planning for greenhouses with flexible demand by leveraging renewable resources within a smart grid. In this respect, a bi-objective energy planning model is developed for greenhouses, aiming to minimize energy consumption costs and maximize crop quality. This model accounts for variable main grid energy prices, the opportunity to sell renewable electricity back to the grid, and limitations on renewable energy supply during specific hours. The extended epsilon-constraint method solves the model, generating non-dominated points that define various production modes. From these results, 9 distinct production modes are presented, allowing decision-makers to select based on preferences such as desired crop quality levels and/or the quantity of electricity sold to the grid. Furthermore, sensitivity analysis is performed under two scenarios: cost reduction and crop quality improvement. Results for the first scenario show that increasing the electricity selling price reduces production costs and increases the amount sold to the main grid. In the second scenario, a significant 25 % reduction in required energy leads to a substantial decrease in production costs, a key finding of this study. •A bi-objective model for energy costs and maximizing of crops quality.•A conceptual model to exchange energy between greenhouses and energy sources.•Considering renewable energy resources and main energy in smart grid.•Calculation of Pareto points using the extended epsilon-constraint method.•Sensitivity analysis for costs reduction and crops quality increasing.