Modeling Carbon Emission Flow in Multiple Energy Systems Multiple energy systems (MES) bring different types of energy together and are able to improve the energy utilization efficiency and promote the integration of renewable energy. The embedded carbon emission in MES is coupled among the energy flow across different energy systems. The understanding of how the embedded carbon emission should be allocated along with the energy delivery and conversion is significant in the policy making and market design to facilitate the carbon emission reduction. A novel analytical model for carbon emission flow (CEF) in MES is proposed in this paper to quantify the carbon emission associated with the energy delivery and conversion process. First, key indicators and basic principles of CEF are defined. Then, the explicit CEF models for different energy networks, including the power network, gas network and heating network, are established. On the basis of the CEF models of energy converters, the CEF model for an energy hub (EH) is further formulated to analyze the flow of carbon emission across different energy systems during the conversion process. Finally, the procedure of CEF calculation in MES is provided. The case study results based on two systems are presented to verify the effectiveness of the proposed method.

Optimal Configuration Planning of MultiEnergy Systems Considering Distributed Renewable Energy Multienergy systems (MESs) contribute to increasing energy utilization efficiency and renewable energy accommodation by coupling multiple energy sectors. The preferable characteristic of MESs raises the need for optimizing the configuration of MESs across multiple energy sectors at the planning stage. Based on the energy hub (EH) model, this research presents a twostage mixedinteger linear programming approach for district level MES planning considering distributed renewable energy integration. The approach models an MES as a directed acyclic graph with multiple layers. The proposed EH configuration planning procedure includes two stages: 1) optimizing what equipment (e.g., energy converters, distributed renewable energy sources and storages) should be invested in for each layer and 2) optimizing the connection relationships between the invested equipment in each two adjacent layers. The proposed approach is able to optimize both the equipment selection and the MES configuration. It can be applied to both expansion planning and initial planning of MESs from scratch. An illustrative example of planning a typical MES is given. A sensitivity analysis is performed to show the impacts of load profiles, energy prices and equipment parameters on the optimal MES configuration. A case study based on the MES in Beijingu0027s new subsidiary administrative center is conducted using the proposed approach.