PowNet: A Network-Constrained Unit Commitment/Economic Dispatch Model for Large-Scale Power Systems Analysis

PowNet is a free modelling tool for simulating the Unit Commitment/Economic Dispatch of large-scale power systems. PowNet is specifically conceived for systems characterized by the presence of variable renewable resources (e.g., hydropower, solar, and wind), whose penetration on the grid is strongly...

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Bibliographic Details
Main Authors: A. F. M. Kamal Chowdhury, Jordan Kern, Thanh Duc Dang, Stefano Galelli
Format: Article
Language:English
Published: Ubiquity Press 2020-03-01
Series:Journal of Open Research Software
Subjects:
Online Access:https://openresearchsoftware.metajnl.com/articles/302
Description
Summary:PowNet is a free modelling tool for simulating the Unit Commitment/Economic Dispatch of large-scale power systems. PowNet is specifically conceived for systems characterized by the presence of variable renewable resources (e.g., hydropower, solar, and wind), whose penetration on the grid is strongly influenced by climatic variability and constrained by the availability of transmission capacity. To help users effectively capture the nuances of power system dynamics, PowNet is equipped with features that enable accuracy, transferability, and computational efficiency over large spatial and temporal domains. Specifically, the model (i) accounts for the techno-economic constraints of both generating units and transmission networks, (ii) can be easily coupled with models that estimate the status of generating units as a function of the climatic conditions, and (iii) explicitly includes import/export nodes, which are useful in representing cross-border systems. PowNet is implemented in Python and is compatible with any standard optimization solver (e.g., Gurobi, CPLEX). Its functionality is demonstrated on the Cambodian power system.   Funding statement: This research is supported by Singapore’s Ministry of Education (MoE) through the Tier 2 project ‘Linking water availability to hydropower supply—an engineering systems approach’ (Award No. MOE2017-T2-1-143).
ISSN:2049-9647