Co-optimization of building energy systems with renewable generations combining active and passive energy-saving

Abstract

Establishing clean and efficient building energy systems (BES) is an efficient path to promote the low-carbon energy transition to achieve the goal of carbon peak and carbon neutrality. To fully tap the energy saving potential of buildings, a co-optimization method combined active and passive energy-saving technologies is proposed for BES. The structures and characteristics of active and passive energy-saving means are modeled and analyzed. On this basis, a double-layer co-optimization model is built to optimize the planning and operation of BES separately. Furthermore, a carbon tax is introduced to establish the connection between active and passive means, and actual uncertainties of source-load are considered through the probabilistic scenario generation and interval linear programming approach. Case studies on the BES in Xiong’an New Area, China show that the proposed co-optimization method saves about 2.2%–3.4% costs than considering only the active energy-saving. Analysis of forecast errors for different scenarios reveals planning options and detailed costs under different levels of uncertainty.