Planning of renewable energy regional heating system based on demand side uncertainty

Abstract

Effective planning of renewable energy district heating systems is essential to facilitate the energy transition and achieve environmental goals. In the face of demand-side uncertainty, the complexity of system planning increases significantly. To address this challenge, a new approach has been adopted to optimize heating systems by precisely analyzing and managing demand-side variables. Using simulation experiments to quantitatively assess uncertainty factors, it is found that room temperature, unit ventilation, equipment power, and lighting power had significant effects on system energy consumption, ranging from −17% to 12%. According to these factors, a series of optimization measures are formulated, and then a set of optimization scheme based on demand-side uncertainty is implemented. The experimental results show that the scheme can effectively reduce the daily power output, reduce the total cost input by 7.1%, and achieve a total emission reduction of 4.0% in environmental protection. The optimization strategy proposed in this study significantly reduces energy consumption and costs, and reduces carbon emissions, providing a new solution for the environmental protection and intelligent heating system.

Keywords

• Demand side uncertainty
• Regional heating system
• Renewable energy
• Long short-term memory network
• Quantile regression