Optimization 0f Hybrid Renewable Energy Systems for Standalone and Grid-Connected Scenario

Abstract

The modern lifestyle of humans powered by fossil fuel consumption has grim footprints on the ecosystem of planet earth. The increasing greenhouse gas (GHG) emissions have a lasting and consequential adverse impact on the environment and habitability. Renewable energy has the potential to shift away from the dependence of humanity on finite, polluting, and unevenly distributed fossil fuels. The cost of the energy from solar and wind energy has already become less than conventional energy in most parts of the world aided by technological innovation and manufacturing of scales. However, the issue in the wider assimilation of solar and wind energy is that they are non-dispatchable resources. The combination of solar and wind energy with other renewable energy sources like bio and hydro and or storage system has the potential to mitigate the issue of temporal mismatch between demand load and power production. This type of integrated power system is known as Hybrid Renewable Energy System (HRES). This improves the reliability of the system and at the same time decreases the cost of energy acquired. The sizing of HRES is a challenging task as the system needs to cater to ever-changing load demand. In addition to that, solar radiation and wind speed are continuously varying. This is further complicated by the fact that power production from renewable conversion devices is not necessarily linearly proportional to the input parameters. These aspects in addition to the need to look into multiple assessment criteria make HRES sizing problem task worthy to deal with. A comprehensive overview of the literature of optimization techniques used for HRES is carried out. It is observed that the classical techniques were being applied to the HRES sizing in the early days. Nowadays, researchers are increasingly looking for heuristic techniques using evolutionary algorithms. It is important to optimize the HRES components for different assessment parameters like economic, reliability, environmental and social criteria. Three different approaches for the search of optimal configuration of HRES are considered in this treatise. First, HOMER software is used for HRES modeling, sizing, and optimization. Second, the use of the Generalized Reduced Gradient (GRG) method to optimize the size of components with different combinations of HRES is presented for two different sites, where the Levelized Cost of Energy (LCOE) is the objective function. Third, the NSGA-II is used to achieve inherent parallelism in the simulation process for effective multi-objective optimization. VI The rural hilly area of Bhiloda, Gujarat, India is chosen for the optimization of the HRES with the solar and wind-based resource using HOMER software. Standalone and gridconnected systems are simulated to analyze the range of probable scenarios. The human development potential of the HRES for the given region is also studied to evaluate the societal impact of the proposed system. The case study is performed to demonstrate the HRES optimization for a remote rural region Jakhau, Gujarat, India using the GRG method. The standalone system with reliability in the range of 0 to 30 Loss of Power Supply Probability (LPSP) is explored, where LCOE is in the range of 0.106-0.274 $/kWh. A grid-connected system is studied with and without payment of power supplied to the grid to explore the feasibility of HRES for different price regimes. The environmental impact assessment shows that the Carbon Emission (CE) in the HRES system in comparison to the conventional system is significantly less. The calculation shows higher Job Creation (JC), in the standalone system in comparison to the grid-connected system. The optimal sizing of the HRES with solar, bio and hydro sources for the tribal area of Dang, Gujarat, India is also demonstrated using the GRG method. The LCOE of 0.106-0.053 $/kWh is achieved in standalone mode for 0 to 30 LPSP. The costly battery storage system is unavoidable for higher reliability cases for less than 10 LPSP, which increases LCOE significantly. The grid-connected scenario for two cases with different purchase prices is simulated. The LCOE results around 0.06 $/kWh for the likely grid-connected scenario cases. The sensitivity analysis is also performed to get an idea about the effect of simulation parameters on the optimal configuration of HRES. It can be concluded that the standalone system proves to be better in terms of job creation and carbon emission. In contrast, grid-connected comes out to be better in terms of reliability and economics. The results of the GRG method are validated with HOMER software. The multi-criteria decision-making process is achieved using NSGA-II with its inherent capacity to simultaneously search for different objectives in search space. The CE, JC, LPSP, and LCOE are taken as the objective functions. The HRES is optimized for different decision variables like the capacity of Photovoltaic panels (PV), Wind Turbine (WT), Battery storage (BS), and PV angle and WT hub height. The outcome of simulations shows that low LPSP values result in higher LCOE, whereas HRES is better for the environment and job creation for these high-reliability cases. The results show that CE and LCOE are VII in tandem with each other for the cases considered, whereas JC and LCOE are not necessarily complementary and the trade-off between them is needed at the design stage. This thesis demonstrates the potential of the HRES system in overcoming the obstacles faced by the developing regions in their socio-economic upliftment through ecologically sustainable ways. The optimally configured HRES also has huge potential to augment and replace the existing energy mix, especially in the regions with abundant renewable potential. The HRES based on the combinations of components presented can be placed at respective sites with the help of the public and private sectors.