Operations and investment modelling of an energy microgrid with mixed-integer linear programming

Abstract

The increase in electricity demand worldwide is driven by economic growth and a rising population. The traditional power system, which is based on a centralised network, is facing many challenges and is not capable of meeting the demand. As a result, the advancement towards a smarter grid has occurred, and the concept of Microgrids has gained a lot of attention. Microgrids are seen as the building blocks of Smart Grid and can be de ned as local small-scale grids which enable the integration of Distributed Energy Resources (DER), storage systems and various loads. The microgrid system can operate collaboratively or independently from the main power grid. The adoption of a microgrid with alternative energy options is being considered by numerous individuals and investors. The investment in microgrids is especially considered by those who lack access to the traditional grid or when the main power grid cannot provide su cient and reliable power to end-users. However, the planning and implementation of a microgrid are not simple, and the investment in alternative energy sources and storage can be costly. The individual needs to know whether the investment is feasible and what to invest in, considering multiple options. A Mixed-Integer Linear Programming (MILP) model is proposed to minimise the total cost of an energy microgrid while addressing the operational and investment aspects. The model can minimise the total cost of a microgrid while determining the optimal operation, con guration and investment time for the technologies. The model examines a grid-connected system which supports the integration of DERs and battery storage options. The proposed model considers the daily operation and technical aspects of each technology, and based on that, determines the optimal design and investment plan over a long-term period. The veri cation process proves that the formulation and implementation of the proposed model are correct. The model complies with all constraints, and the model results are as expected during each evaluation. Furthermore, the model is validated by showing that it provides a feasible solution to several real-life scenarios. During the model validation, changing economic factors are incorporated to show the e ect thereof on the model results. The proposed optimisation model aims to provide individuals that are considering an investment in a microgrid with alternative energy sources and storage with a planning

and decision-making framework. The operational and investment components are closely connected and are therefore addressed in a single model. An important contribution of this study is to provide a way for investors to determine the optimal time to invest when considering a changing economic environment.