Optimal Configuration Planning of Multi-Energy Systems Considering Distributed Renewable Energy

TLDR

MESs enhance energy utilization and renewable integration by coupling multiple energy sectors, necessitating optimized configuration at the planning stage. The study proposes a two‑stage mixed‑integer linear programming approach based on the energy hub model for district‑level MES planning with distributed renewable energy. The method represents the MES as a directed acyclic graph with multiple layers, first selecting equipment for each layer and then optimizing inter‑layer connections, as illustrated by an example and sensitivity analysis. The approach successfully optimizes equipment selection and MES configuration, is applicable to both expansion and initial planning, and was validated in a case study of Beijing’s new subsidiary administrative center.

Abstract

Multi-energy 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 two-stage mixed-integer 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 Beijing's new subsidiary administrative center is conducted using the proposed approach.