Comparison of optimization frameworks for the design of a multi-energy microgrid

• المؤلفون الرئيسيون: Rigo-Mariani, Rémy, Ooi, Sean Chea Wae, Mazzoni, Stefano, Romagnoli, Alessandro
• مؤلفون آخرون: School of Mechanical and Aerospace Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2021
• الموضوعات: Engineering::Mechanical engineering; Evolutionary Algorithms; Mixed Integer Linear Programming
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/147642
• المؤسسة: Nanyang Technological University

The scope of the paper is to investigate different strategies for the design of a multi-energy system considered as a systemic optimization problem. The objective is to determine the best sizes of the energy assets such as electrochemical and thermal storages, cogeneration units, solar generators and chillers. In these cases, the techno-economic optimization is a tradeoff between the operating costs and the capital expenditures in the form of integrated management and design of the system. The paper addresses the challenges of these optimization problems in two steps. The former implements generic piecewise linearization techniques based on non-linear models. That approach allows a significant reduction of the computational time for the management loop of the assets (i.e. optimal power dispatch). The latter takes into consideration the integration of that management loop in different architectures for optimal system planning. The main contribution of the paper toward filling the gap in the literature is to investigate a wide range of optimization frameworks - with bi-level optimizations (using both deterministic and evolutionary methods), Monte-Carlo simulations as well as a performant ‘all-in-one’ approach in which both sizes and controls are variables of a single mathematical problem formulation. Finally, a thorough results analysis highlights that the best solution tends to be the same whether the objective to optimize is the traditional net present value at the end of the system lifespan or the total yearly cost of ownership.