Multi-period planning of multi-energy microgrid with multi-type uncertainties using chance constrained information gap decision method

• Published in: Applied energy 2020-02, Vol.260, p.114188, Article 114188
• Main Authors: Wei, Jingdong, Zhang, Yao, Wang, Jianxue, Cao, Xiaoyu, Khan, Muhammad Armoghan
• Format: Article
• Language: English
• Subjects: Battery storage; Chance constrained (CC); Expansion planning; Information gap decision (IGD); Multi-energy microgrid; Multi-type uncertainties
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2019.114188

•Joint planning of electricity/heat supply system in the multi-energy microgrid.•Long-term uncertainty of the declining trend of battery storage investment cost.•Short-term uncertainty of renewable energy generation and multi-energy load.•Chance constrained information gap decision model coordinating two uncertainties.•Utilizing the strengthened bilinear Benders decomposition algorithm to solve model. In this paper, we study the multi-period planning problem of multi-energy microgrids considering the long-term uncertainty (i.e., the declining trend of battery storage investment cost) and the short-term uncertainty (i.e., renewable energy generation and electrical/heat load). We first present the joint deterministic multi-period planning approach for multi-energy microgrid coupling electricity and heat carriers. Then, an information gap decision (IGD)-based multi-energy microgrid multi-period planning model dealing with the long-term uncertainty is proposed, and the proposed model is further converted into a mixed integer linear planning (MILP) IGD-based planning model. Next, to coordinate the long-term uncertainty and the short-term uncertainty in multi-energy microgrid planning problems, we develop a chance constrained (CC) IGD-based multi-period planning model and then convert such model into a MILP CC-IGD equivalence. Finally, the strengthened bilinear Benders decomposition (SBBD) algorithm is adopted to efficiently solve our proposed MILP CC-IGD model for large-scale multi-energy microgrid planning problems. Our numerical results demonstrate the advantage of the joint planning of electricity and heat supply systems in multi-energy microgrids. Case studies verify the effectiveness of considering multi-type uncertainties in multi-energy microgrid planning, especially the declining trend uncertainty of battery storage investment cost. Experimental results also show that the SBBD algorithm is more efficient on computing our proposed MILP CC-IGD model compared to commercial solvers, such as CPLEX.