Techno-Economic Evaluation of Interconnected Nuclear-Renewable Micro Hybrid Energy Systems with Combined Heat and Power

2021

Energies

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Ocean-going ships are one of the primary sources of Greenhouse Gas (GHG) emissions. Several actions are being taken to reduce the GHG emissions from maritime vessels, and integration of Renewable Energy Sources (RESs) is one of them. Ocean-going marine ships need a large amount of reliable energy to support the propulsive load. Intermittency is one of the drawbacks of RESs, and penetration of RESs in maritime vessels is limited by the cargo carrying capacity and usable area of that ship. Other types of reliable energy sources need to be incorporated in ships to overcome these shortcomings of RESs. Some researchers proposed to integrate fossil fuel-based generators like diesel generators and renewable energy in marine vessels to reduce GHG emissions. As the penetration of RESs in marine ships is limited, fossil fuel-based generators provide most of the energy. Therefore, renewable and fossil fuel-based hybrid energy systems in maritime vessels can not reduce GHG emissions to the desired level. Fossil fuel-based generators need to be replaced by emissions-free energy sources to make marine ships free from emissions. Nuclear energy is emissions-free energy, and small-scale nuclear reactors like Microreactors (MRs) are competent to replace fossil fuel-based generators. In this paper, the technical, environmental, and economic competitiveness of Nuclear-Renewable Hybrid Energy Systems (N-R HES) in marine ships are assessed. The lifecycle cost of MR, reliability of the proposed system, and limitations of integrating renewable energy in maritime vessels are considered in this study. The proposed N-R HES is compared with three different energy systems, namely ‘Standalone Fossil Fuel-based Energy Systems’, ‘Renewable and Fossil Fuel-based Hybrid Energy Systems’, and ‘Standalone Nuclear Energy System’. The cost modeling of each energy system is carried out in MATLAB simulator. Each energy system is optimized by using the Differential Evolution Algorithm (DEA), an artificial intelligence algorithm, to find out the optimal configuration of the system components in terms of Net Present Cost (NPC). The results determine that N-R HES has the lowest NPC compared to the other three energy systems. The performance of the DE algorithm is compared with another widely accepted artificial intelligence optimization technique called ‘Particle Swarm Optimization (PSO)’ to validate the findings of the DE algorithm. The impact of control parameters in the DE algorithm is assessed by employing the Adaptive Differential Evolution (ADE) algorithm. A sensitivity analysis is carried out to assess the impact of different system parameters on this study’s findings.

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Section: Solar Energymentioning

confidence: 99%

Optimal Planning of Integrated Nuclear-Renewable Energy System for Marine Ships Using Artificial Intelligence Algorithm

2021

Energies

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“…N-R MHES provided the best resiliency and reliability to the demand for long term energy policy. Besides, modular N-R HES has the potential to reduce the amount of GHG significantly [7].…”

Section: B Literature Reviewmentioning

confidence: 99%

“…Where H eff , H a , H loss , ρ w , g, Q t ( ), and η t represent effective water head ( ), available water head (25 m), head loss of pipe (15 %) [56], density of water (1000 kg/m 3 ), gravitational constant (9.8 m/s 2 ), water flow rate (m 3 /s), and turbine efficiency (80 %) [57], respectively.…”

Section: Hydro Turbinementioning

confidence: 99%

“…It should be mentioned that the first year of the MMR installation also includes refueling cost since the factory-built MMR fuel module is also brought to the licensed-site in the first year. Equation (56)(57)(58) determines the MMR refueling cost.…”

Section: A Objective Functionmentioning

confidence: 99%

“…A vast amount of energy will be wasted in this type of off-grid energy system even if the energy storage is included. The stand-alone off-grid MMR-based energy system also elongates the energy storage sizing unnecessary since the surplus energy cannot be sold to the electric grid [7]. Hence, energy-mix is an essential pathway for the global energy crisis solution.…”

Section: Introduction a Background And Motivationmentioning

confidence: 99%

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Optimal Planning of Nuclear-Renewable Micro-Hybrid Energy System by Particle Swarm Optimization

2020

IEEE Access

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To minimize the anticipated shocks to economic, environmental, and social systems for developing and least developed countries, the reduction of Greenhouse Gas (GHG) emissions is mandatory to a large extend. The nuclear-renewable integrated system is proficient in optimal energy distribution to multiple production schemes to reduce GHG emissions and maximize profit. This paper addresses the hybridization of the micronuclear reactor and Renewable Energy Sources (RESs) Energy Sources (RESs) to develop a flexible, cost-effective, sustainable, and resilient off-grid Hybrid Energy System (HES). The paper presents three types of hybridization methods, termed "Direct Coupling," "Single Resource and Multiple products-based Coupling," and "Multiple Resources and Multiple products-based Coupling." The hybridization techniques are used to plan and identify the most efficient Nuclear-Renewable Micro-Hybrid Energy System (N-R MHES). The sizing, performance, and characterization of N-R MHES solely depend on the RES and load characteristics' availability. Based on proposed hybridization techniques, mathematical modeling of N-R MHES's economy is carried out in the MATLAB environment. An artificial intelligence optimization algorithm, namely Particle Swarm Optimization (PSO), is used to minimize the Net Present Cost (NPC) and achieve the optimal system configurations of different N-R MHESs. The simulation results determine that "Multiple Resources and Multiple Products-based N-R MHES" provides around 1.8 times and 1.3 times lower NPC than "Single Resource and Multiple products-based Coupling" and "Multiple Resources and Multiple products-based Coupling," respectively, with an acceptable margin of reliability. A sensitivity analysis has also been conducted in this paper to strengthen the findings of the study.

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Financial and technical planning of decarbonized hybrid energy systems for modern cities

Smart Cities Policies and Financing

2022