Modelling and Co-simulation of Energy Utilization of an Industrial Park

Abstract

The concept of microgrid has gained a significant interest of many scholars and engineers worldwide. Microgrid offers substantial benefit such as high reliability, adaptive to disturbance, improved load and generation control and high utilization of renewable energy sources (RES). However, the utilization of renewable energy resources leads to a problem due to its intermittency of the generated power. The conventional solution to measure the intermittency problem, such as network expansion and electrical energy storage, require huge cost and complicated planning. Therefore, the utilization of thermostatically controlled become a more viable solution. Several forms of energies are involved in the industrial microgrid. The utilization of the various form of energies requires a platform that accommodates the co-simulation and data exchange of various models. The aim of this project is to observe the impact of a thermostatically controllable load to the energy saving and to offer a methodology that accommodates the co-simulation and data exchange of various components, such as microgrid network, boilers, and optimization algorithm. Several steps are required in order to achieve the project's goal. Firstly, a comprehensive literature study is conducted to determine the characteristic of the network that has to be modelled. Optimal power flow is used as the optimization algorithm in order to achieve the optimum operation of the system. The second step is performing a series of components modelling. The IEEE14 bus system is selected as a foundation of the network model. A separate boiler model is also developed since in the electrical domain, and it is modelled as a constant power load. A more detailed thermodynamic model is modelled for greater insights. Functional Mock-up Interface platform is used as a standard to accommodates the co-simulation between network model, boiler model, and optimization algorithm. The master code is developed to manage the simulation and data exchange of each component. Finally, a simulation of a different seasonal cycle is implemented to observe the performance of the system. Winter and summer season is chosen as the case scenario due to its different profile on the wind turbines power.