返回
A design and dispatch optimization algorithm based on mixed integer linear programming for rural electrification
DOI:10.1016/j.apenergy.2018.09.194.png)
摘要
En 中文
Off-grid microgrids constitute an increasingly viable alternative to grid extension for rural electrification. Thanks to significant reductions in hardware cost, energy cost for off-grid users in areas sparsely covered by the national grid has become competitive with grid-connection once infrastructural costs are accounted for. Microgrids offer advantages also from an environmental and social point of view, featuring generation systems with high renewable penetration and facilitating the involvement of local communities in development projects. To further enhance microgrids economic competitiveness, it is necessary to devise innovative control strategies and design algorithms, that can ensure an optimal operating performance at the lowest overall cost. In this paper we present a new predictive design and dispatch optimization algorithm based on Mixed Integer Linear Programming (MILP). The new method is compared to a previously developed heuristic methodology, applying both to the design and yearly performance estimation of local microgrids, characterized on the basis of real data relative to a sub-Saharan African region. The potential advantages of including the proposed microgrid design method in a regional electrification planning model are illustrated in a realistic case example.
Keyword:
Microgrid
Electrification
Design optimization
MILP
Economic dispatch
Off-grid systems
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11
论文数:
2.6W
被引数:
17.8W
机构
引用论文
A Reference Network Model for Large-Scale Distribution Planning With Automatic Street Map Generation自动生成街道地图的大规模配送规划参考网络模型
Optimal design and financial feasibility of a university campus microgrid considering renewable energy incentives考虑可再生能源激励的大学校园微电网优化设计及财务可行性
APPLIED ENERGY
IF11
The role of decentralized systems in providing universal electricity access in Sub-Saharan Africa - A model-based approach
ENERGY
IF9.4

