Mixed Integer Nonlinear Programming for Optimal Design of Energy Grids: Optimization Problems, Possible Solutions, and Model Formulation

TL;DR

This paper explores the use of mixed integer nonlinear programming (MINLP) to optimize the design and operation of microgrids, considering environmental and resource constraints, providing a comprehensive model formulation for energy grid planning.

Contribution

It introduces a novel MINLP model for microgrid optimization that incorporates multiple real-world constraints such as excess electricity, CO2 emissions, and water shortages.

Findings

Optimized microgrid designs under various resource constraints.

Demonstrated the effectiveness of MINLP in energy grid planning.

Provided detailed model formulations and empirical equations for equipment.

Abstract

The energy sector has become priority around the world with developing technology and increasing power and energy demand. That all sources for energy production are not renewable increases greenhouse gas emissions and causes global warming. Turkey's dependence on foreign sources in terms of vital energy resources and the suboptimality of the energy distribution has increased the need for optimum design of energy grids and long-term production planning of established grids. Microgrid is an energy grid consisting of distributed generators, storage units and loads. In this study, optimum design and generation planning of microgrids using mixed integer nonlinear programming (MINLP) is investigated. The optimal design under conditions such as excess electricity, CO2 emission, and water shortage are examined. Finally, the constraints of the optimization models and the empirical equations for each equipment are investigated. Keywords: Mixed integer nonlinear programming (MINLP), Energy grid optimization, Microgrid, Excess (surplus) electricity, Carbon dioxide emission, Water shortage