With the growing concerns over the energy depletion and environmental challenges around the world, increasing attention is being paid to the issues in energy conservation, energy efficiency improvement, and emission reduction.
The European Union presented its energy targets for 2030, which will attain at least a 40% reduction in greenhouse gas emissions as compared to 1990 level, and increase the utilization of renewable energy to 27% of gross energy consumption. As to the United States, the greenhouse gas emission reduction target will reach 26–28% below the 2005 level by 2025. The Chinese government has also committed to reducing its greenhouse gas emissions per unit of GDP (i.e. carbon intensity) by 40-45% at 2020.
In order to realize these targets, the integrated community energy system (ICES) is attracting more and more attention in recent years, where heat, gas, and electrical energy are becoming tightly coupled at the community level, as shown in Fig. 1. Compared with the electric system, the ICES is not only able to provide new solutions for a more secure, sustainable and economical energy utilization but also beneficial for the improvement of energy efficiency and reduction of greenhouse gas emission.
Nowadays, the concept of ICES has been applied to practice by a number of demonstration projects in many countries, e.g. the Chiloe Islands of Chile, the Ubiquitous Energy Network in Zhaoqing New District of China and the HyLink system at Totara Valley of New Zealand.
To realize the optimal operation of ICES, a two-stage multi-objective scheduling method (TMSM) for ICES is proposed, which consists of a multi-objective optimal power flow (MOPF) calculation stage and a multi-attribute decision making (MADM) stage. Various operational indices are considered to characterize the operation of ICES, among which the operation cost (OC) and total emission (TE) of ICES are selected as the objectives at the MOPF calculation stage. And all operational indices are considered during the MADM stage to determine the final day-ahead scheduling schemes from the alternative solutions obtained in MOPF.
A typical ICES is utilized to verify the effectiveness of the developed TMSM. The multi-objective day-ahead Pareto Optimal Curve are obtained by the proposed MOPF algorithm, as shown in Fig. 2. Obviously, for each time period, the OC and TE of ICES are two opposite objectives that decreasing one of them increase the other one. Meanwhile, two typical time periods, namely time period 1 and 18, are compared by Fig. 2(b) and Fig. 2(c) respectively to demonstrate the details of results. It is observed that the proposed MOPF algorithm can provide day-ahead optimal scheduling schemes as many as possible. Furthermore, solutions that lie in the edges of the Pareto Optimal Curve represent the optimal scheduling schemes for OC and TE minimization of the ICES, respectively.
According to the results, it is difficult to distinguish the best solution since these attributes are conflicting, and none of the solutions is superior in terms of the different attributes. Therefore, the MADM stage is utilized to determine the final day-ahead scheduling scheme. It is observed from Fig. 3 that by applying the MADM, the value of average utility function for solution No.71 is maximum. Consequently, No. 71 is chosen as the final day-ahead scheduling scheme for time period 1, which shows the maximum benefit for the ICES in aspects of all operational indices.
Numerical studies demonstrate that the TMSM can also provide flexibility for the operation of ICES. The determined optimum day-ahead scheduling schemes are capable of satisfying and balancing operational needs in aspects of security, economy and environmental friendliness. Furthermore, optimal scheduling scheme is provided with the maximum benefit of ICES.
These findings are described in the article entitled A Two-stage Multi-objective Scheduling Method for Integrated Community Energy System, recently published in the journal Applied Energy. This work was conducted by Wei Lin, Xiaolong Jin, Yunfei Mu, Hongjie Jia, and Xiaodan Yu from Tianjin University, Xiandong Xu from Cardiff University, and Bo Zhao from the State Grid Zhejiang Electric Power Research Institute, Hangzhou, China.