Modeling and Optimization of Isolated Combined Heat and Power Microgrid for Managing Universiti Teknologi PETRONAS Energy

With the rapid growth of isolated microgrids, combined heating and power (CHP) can be integrated with photovoltaic (PV) system. The integration of CHP-PV systems has a tremendous potential to increase system reliability and efficiency as well as reduce energy consumption and CO2 emission. However, the operating models require considerable analysis due to the uncertainty load demand, so it is not easy to apply the models in order to simulate the current trend of baseline systems. In this paper, we developed models based on optimum clustering data to perform the behavior of CHP-PV using the integrating of accelerated particle swarm optimization (APSO) and fuzzy subtractive clustering (FSC). The objective of the APSO algorithm is to tune the parameters of data clustering-based FSC using proportional integral (PI) controller. The paper’s main goal is the minimal total energy and fuel consumption without compromising load demand of cooling. The proposed model interacts to the energy by gas turbine generators (GTGs) and PVs system. Also, it subdivides cooling load with the use of the partial load condition according to the outdoor weather. A case study of Universiti Teknologi Petronas (UTP), Malaysia was used to investigate its CHP plant. The model is validated using actual data obtained from University CHP plant. The results demonstrate that the proposed optimum system including CHP, PV, and storage systems is outperformed on the baseline system and basic CHP models. The proposed optimum models save 7 % and reduce 4.72 % of total daily electrical and steam production, respectively. Also, the optimal system is kept cooling demand satisfied.