Nuclear-Renewable Hybrid Energy Systems Research Articles

Enhancing frequency regulation in Nuclear-Renewable hybrid energy systems through optimally configured FOPID controllers and adaptive pelican optimization algorithm

Enhancing frequency regulation in Nuclear-Renewable hybrid energy systems through optimally configured FOPID controllers and adaptive pelican optimization algorithm

A licensing plan for coupling nuclear–renewable hybrid energy systems

South Africa committed to reducing greenhouse gas emissions by 34% below "Business As Usual" levels by 2020 and 42% by 2025 at the Copenhagen climate change conference. However, global energy demand was projected to rise due to population and industrial growth, with energy usage expected to increase by 13% through 2040. In South Africa, a steady increase in electricity demand prompted the government to implement a new growth path (NGP), which prioritized job creation in all economic policies, including energy. To address the growing energy demand, an alternative energy source for combined heat and power was necessary, particularly for synthetic fuel production. The coupling of a high temperature reactor (HTR) with a chemical process plant could improve carbon and thermal efficiency, as seen in international organizations like the ARCHER (Advanced High-Temperature Reactors for Cogeneration of Heat and Electricity R&D) project. HTRs are still in their conceptual stages andaresuitable candidate forcouplingto an energy-intensiveindustrythat require process heat with sufficiently hightemperatures,with potential uses for NRHES (Nuclear-Renewable Hybrid Energy Systems). The South African team aimed to model the SASOL process, assess its financial viability, and develop a licensing plan.

A Multi-Scale method for combined design and dispatch optimization of nuclear hybrid energy systems including storage

Reliable, carbon-free power generation is increasing in importance. Nuclear-renewable hybrid energy systems (NHES) are a potential solution for current generation challenges, but design and dispatch optimization for these systems remains challenging particularly when stochastic effects, long time horizons and nonlinear modeling are needed. This work presents a multi-scale method for combining the design and dispatch optimization problems for nonlinear NHES over long time horizons. Rather than treating the entire horizon as a single optimization problem, a large number of combined optimization problems are solved for shorter samples of the horizon resulting in distributions of optimal capacities for each of the capacities being optimized. The distributions are then aggregated, and the techno-economics of each aggregate is analyzed. The result is a contextual knowledge about the techno-economic tradeoffs for the system rather than a single set of optimal capacities. This method is applied to two NHES of varying complexity. Results indicate that unit capacities for a grid-connected district energy system can be reduced by at least 18.9% by allowing for 3 periods of power import over the course of a 2000 day dispatch. The algorithm is used to solve combined optimization problems with dispatch horizons 112.5 times longer than are solvable directly.

Nuclear-Renewable Hybrid Energy System with Load Following for Fast Charging Stations

The transportation sector is a significant source of greenhouse gas emissions. Electric vehicles (EVs) have gained popularity as a solution to reduce emissions, but the high load of charging stations poses a challenge to the power grid. Nuclear-Renewable Hybrid Energy Systems (N-RHES) present a promising alternative to support fast charging stations, reduce grid dependency, and decrease emissions. However, the intermittent problem of renewable energy sources (RESs) limits their application, and the synergies among different technologies have not been fully exploited. This paper proposes a predictive and adaptive control strategy to optimize the energy management of N-RHES for fast charging stations, considering the integration of nuclear, photovoltaics, and wind turbine energy with a hydrogen storage fuel cell system. The proposed dynamic model of a fast-charging station predicts electricity consumption behavior during charging processes, generating probabilistic forecasting of electricity consumption time-series profiling. Key performance indicators and sensitivity analyses illustrate the practicability of the suggested system, which offers a comprehensive solution to provide reliable, sustainable, and low-emission energy to fast-charging stations while reducing emissions and dependency on the power grid.

Market model shows optimized strategies for implementing nuclear-renewable hybrid energy systems

Most detailed analysis of the impact of hybrid energy systems to date demonstrates income-generating boosts to efficiency when considering grid reserves and thermal load.

Multi-timescale operations of nuclear-renewable hybrid energy systems for reserve and thermal product provision

In this paper, an optimal operation strategy of a nuclear-renewable hybrid energy system (N-R HES), in conjunction with a district heating network, is developed within a comprehensive multi-timescale electricity market framework. The grid-connected N-R HES is simulated to explore the capabilities and benefits of N-R HES of providing energy products, different reserve products, and thermal products. An N-R HES optimization and control strategy is formulated to exploit the benefits from the hybrid energy system in terms of both energy and ancillary services. A case study is performed on the customized NREL-118 bus test system with high renewable penetrations, based on a multi-timescale (i.e., three-cycle) production cost model. Both day-ahead and real-time market clearing prices are determined from the market model simulation. The results show that the N-R HES can contribute to the reserve requirements and also meet the thermal load, thereby increasing the economic efficiency of N-R HES (with increased revenue ranging from 1.55% to 35.25% at certain cases) compared to the baseline case where reserve and thermal power exports are not optimized.

Frequency control of nuclear-renewable hybrid energy systems using optimal PID and FOPID controllers

This paper investigates the applications of Proportional-Integrator-Derivative (PID) and Fractional Order PID (FOPID) controllers in Nuclear-Renewable Hybrid Energy Systems (N-R HESs). The N-R HES is a recent technology in the area of decarbonized energy systems. N-R HESs are expected to contribute immensely to providing carbon-free and sustainable energy infrastructure in the upcoming days. It is also anticipated that system resiliency will be the primary concern when nuclear reactors are incorporated with intermittent renewable energy resources. Therefore, in this research, the authors intend to evaluate the compatibility of two classical controllers, PID and FOPID, to ensure the stability of N-R HESs. The N-R HES of this paper consists of different energy sources, such as solar, wind, nuclear, fuel cell systems, Battery Energy Storage Systems (BESS), and Flywheel Energy Storage Systems (FESS). To encounter system performance requirements, the PID and FOPID controller parameters are adjusted using a metaheuristic algorithm, namely Artificial-Bee-Colony (ABC) optimization algorithm. Metaheuristic optimization algorithms always do not guarantee global maxima/minima. Hence, another metaheuristic optimization algorithm, Teaching-Learning-based Optimization (TLBO), is used to validate the results. The results clearly show that the optimal PID and FOPID controllers can handle the system frequency and maintain the stability of the studied N-R HES.

Real-Time Simulation of a Small Modular Reactor in-the-Loop within Nuclear-Renewable Hybrid Energy Systems

Advanced small modular reactors (SMRs) have recently been developed in many designs; therefore, nuclear energy stands out as a promising alternative to sustainability and reliability in replacing fossil fuel energies in microgrids. SMRs have been shown as the best option due to the fact of their lower initial capital, greater scalability, and siting flexibility compared to large nuclear plants. Nowadays, there are several simulators able to reproduce all the safety and control mechanics of different nuclear reactors; however, there exists a lack of emulators able to put these functionalities into a real scenario to ensure the feasibility of the use of nuclear energy within energy systems, especially in nonconventional systems. This paper aims to mimic the central control system of SMRs by modeling the nuclear processes aiming to contribute to real-time simulations using SMRs integrated with renewable energy in microgrids that could be applied for different scenarios, such as cogeneration systems or fast-charging stations for electric vehicles, by considering the impact on dispatch and reliability. The simulation process of the proposed model was validated experimentally using the hardware-in-the-loop technique, which consisted of the modeling being integrated into the hardware and tested using real-time simulators. The proposed system, also denominated as SMR-in-the-Loop, was designed and adapted to be easily integrated with existing microgrid systems to represent the behavior of an SMR in nuclear-renewable hybrid energy systems, avoiding high investments and complexity in testing and implementing actual nuclear reactors.

Applying analytic hierarchy process to industrial process design in a Nuclear Renewable Hybrid Energy System

Nuclear Renewable Hybrid Energy Systems (NRHESs) co-locate a nuclear power plant with a renewable power generation source, such as solar or wind, with an industrial process(es), and can include a battery for energy storage and a fossil fuel power plant. By co-locating these various components, the nuclear-generated power can be directed to the industrial process when demand is low or renewable generation is high, as well as toward meeting grid demand when needed. This paper will apply the risk assessment and management technique of the fuzzy Analytical Hierarchy Process (AHP) to an NRHES. The goal is to develop a method of evaluating industrial components in preliminary NRHES design. Criteria selected in this study within which each process is assessed are profitability, flexible operation, and safety. Based on this, the fuzzy AHP approach determined desalination to be the strongest candidate based on system definition and related assumptions. Additional studies can be undertaken using this approach for more in depth analysis of NRHES design. With no NRHESs currently deployed, fuzzy AHP can offer a useful decision-making tool.

Hybrid energy system optimization model: Electrification of Ontario's residential space and water heating case study

Energy systems are becoming more complex as new energy sources are introduced in support of clean energy goals. These hybrid energy systems can be configured for cogeneration to account for multiple energy uses, including not only electricity but also space heating, water heating, and industrial process heat. Variable renewable energy systems are increasingly being added to hybrid systems to mitigate climate change and reduce greenhouse gas (GHG) emissions. This often creates additional challenges to meet energy demands due to variability associated with renewable generation. In support of energy planning for the new clean economy, the Hybrid Energy System Optimization (HESO) model has been developed to study the feasibility and benefits of nuclear-renewable hybrid energy systems. The model is formulated, as a mixed-integer linear programming (MILP) algorithm, to determine the best energy mix by minimizing annual cost. Because electrification will play a significant role in realizing a clean economy, this study explores the potential economic viability of electrification of residential water and space heating in Ontario. Different energy scenarios have been analyzed to understand the challenges associated with electrification and determine which energy sources will significantly reduce greenhouse gas emissions, while also maintaining competitive energy costs. The results show that electrification of residential water heating can be a viable alternative to natural gas heaters; reducing GHG emissions and energy cost. However, electrification of residential space heating is more challenging due to the large seasonal temperature variations that create significant energy demand fluctuations. Additional nuclear and wind generating capacity, as well as storage systems, are all important elements to support Ontario's transition to a low carbon economy through electrification.

A Comprehensive Review of Nuclear-Renewable Hybrid Energy Systems: Status, Operation, Configuration, Benefit, and Feasibility

One of the biggest concerns of the present century is energy security and climate change. Further, studies suggest that there would be a huge lack of fossil fuels in near future. Moreover, in terms of cleaner production, the most popular and practiced way of power generation is renewable energy sources which are intermittent in nature, require large land area, and also dependent on geographic positions and climatic conditions. Besides, nuclear energy is also having some limitations including government policies and public apprehensions. To overcome these hurdles, these two carbon-free technologies can be integrated and form a nuclear-renewable hybrid energy system (N-R-HES). Literature related to the proposed systems is extremely rare and the systems are not yet well-developed. Keeping that into concern, this paper discusses the operation, status, prospects, and benefits of N-R-HES. Various possible integration techniques along with their operation are discussed in detail. Moreover, six aspects of interconnections are identified: electrical, thermal, chemical, mechanical, hydrogen, and information. The paper also discusses the reactor licensing, permitting procedures along the different benefits of N-R-HES. Additionally, research limitations and needs are identified for further exploration of the topic throughout the paper.

Estimating energy storage size for Nuclear-Renewable hybrid energy systems using data-driven stochastic emulators

The increased electricity production from renewable energy sources, such as wind, in recent years has contributed to an increase in grid variability. This creates the need for clean, reliable, and flexible back-up electricity generation to complement the renewables and match the grid demand. Nuclear power is a clean, reliable energy source for back-up energy generation but cannot be operated in a manner flexible enough to compensate for the high uncertainties in renewable energy production. To maintain the grid stability with clean energy production and limited operational flexibility of nuclear power, appropriately sized energy storage solutions are needed. Typically, such integrated energy systems are modeled using deterministic methods which are not suitable to accommodate fluctuating energy production and safety constraints on nuclear power plants. This work developed a continuous-time stochastic model for integrating wind and nuclear power with energy storage. The model was then demonstrated for an isolated microgrid using the yearly demand and wind generation data, with nuclear and wind energy as the only power generation sources. Multiple scenarios were analyzed with varying wind penetrations and nuclear flexibility limitations, and the energy storage sizes and their utilization fractions were calculated for three different energy storage types. Without the implementation of energy storage, the required nuclear power capacity should be above the average grid load, and most of the wind power is wasted. By implementing energy storage and using nuclear as a constant base-load, the wind power can account for a larger fraction of the grid, however the storage size requirements are large and utilization fractions are small. If the nuclear power is allowed to operate with greater flexibility than current ramp rate limitations, the storage requirement decreases and storage utilization increases. Of the three storage types analyzed, thermal storage is found to be lowest in size requirement with the highest utilization factors for all scenarios.

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

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.

Flexible control of nuclear cogeneration plants for balancing intermittent renewables

Both intermittent renewable energy (IRE) and nuclear fission energy can provide carbon-free electric power to the grid. The electric power generation from the intermittent renewable energy (IRE) sources such as the wind and solar are strongly dependent on the climatic conditions, geographical location and large land footprint. While, nuclear plants can provide consistent and clean power supply with small land footprint. Due to the strong complementarity between the IRE and nuclear, it is attractive to interconnect them to build nuclear-renewable hybrid energy systems (NRHESs) which are defined as the integrated facilities comprised of nuclear reactors, renewable energy generation and industrial processes. The flexibility of a NRHES for grid balance can be provided by actively adjusting the electric power output of nuclear cogeneration plants (NCPs), which leads to the frequent redistribution of main steam flow to the turbine and cogeneration process. For the main steam redistribution, it is necessary to guarantee the stability of grid frequency and main steam pressure, which heavily relies on the flexible control strategy of NCPs. In this paper, a new flexible control of NCPs with saturated main steam is proposed based on the idea of actively suppressing the total disturbance to the dynamics of grid frequency and main steam pressure. The stabilization problems of pressure and frequency are transferred to the disturbance attenuation Problem of a second-order disturbed dynamical system, and an active disturbance rejection control (ADRC) is then originally proposed for system stabilization. This ADRC is applied to the flexible control of a NCP composed of a nuclear heating reactor (NHR), a turbine-generator set and a multi-effect-desalination and thermal-vapor-compression (MED-TVC) process for seawater desalination. Numerical simulation results in the cases of load stepping and maneuvering show that the flexibility of this NCP is satisfactory in balancing the grid.

Economic analysis of a nuclear hybrid energy system in a stochastic environment including wind turbines in an electricity grid

The electricity market in the U.S. is moving toward an energy mix including more variable renewable energy (VRE) sources like wind and solar. This increasing VRE penetration is altering the profile of the net electricity demand. Traditionally, flexible generators (e.g. gas turbines) are used to absorb fluctuations in the electricity demand. Therefore, an increase in VRE also leads to an increase in installed capacity for such flexible generators which, in turn, may increase the price of electricity. Nuclear-Renewable Hybrid Energy Systems (N-R HES) are seen as a solution to this problem. The N-R HES contains a nuclear power plant electrically and/or thermally linked to an industrial process (IP). This paper demonstrates a new methodology and presents the corresponding RAVEN/Modelica-based software framework to evaluate the financial performance of N-R HES. The novelty of the method is that it explicitly incorporates the stochastic nature of N-R HES inputs such as electricity demand and wind speeds. The method is applied to a generic (rather than region-specific) N-R HES. The analysis shows that under the right market conditions, economic benefits can be achieved by adding a suitable IP to the base-load generator. In particular, there exists an optimal relationship between the IP capacity and the demand. Global trends also indicate VRE penetration raises the cost of electricity due to the added volatility, an issue only mitigated when operating with high profit margins for the IP.

Analysis of Nuclear Renewable Hybrid Energy Systems Modeling and Nuclear Fuel Cycle Simulators

Growing concerns over the impact of fossil fuels on climate change have driven efforts to find sources of energy with low emissions. In response, fluctuating renewable energy sources, such as solar and wind power, are growing to meet more of the electricity demand. However, maintaining reliable energy accessibility to the grid requires a stable, nonfluctuating source of power. Nuclear power plants (NPPs) provide nearly emissions-free, reliable energy to the grid (refer to “IPCC Fifth Assessment Report,” Intergovernmental Panel on Climate Change; https://www.ipcc.ch/report/ar5/). To best reduce reliance on fossil fuels while ensuring reliable energy generation and profitability, nuclear renewable hybrid energy systems (NRHESs) focus on tightly coupling renewable generation with a NPP by colocating the generation sources in an industrial park. The industrial park consists of at least the NPP, the renewable energy source, and some form of industrial process that consumes the energy not used by the grid. In this paper, we analyze the computational modeling approaches currently being pursued for NRHESs. We further investigate similarities between nuclear fuel cycle simulators (NFCSs) and NRHESs to determine how NRHES development can benefit from the development of NFCSs. This paper begins by reviewing past research on NRHESs to determine the necessary functionality of modeling software. After determining the necessary software capabilities for an NRHES model, we discuss the characteristics of a NFCS. The characteristics found common to both systems include desirability of a flexible modular design; open source; ability to be coupled to external pieces of software, including economic modeling, optimization methods, and sensitivity analysis; and results that are usable to technical and nontechnical people alike.

Dynamic performance analysis of a high-temperature steam electrolysis plant integrated within nuclear-renewable hybrid energy systems

A high-temperature steam electrolysis (HTSE) plant is proposed as a flexible load resource to be integrated with a light water reactor (LWR) in nuclear-renewable hybrid energy systems (N-R HESs). This integrated energy system is capable of dynamically apportioning electrical and thermal energy on an industrial scale to meet both grid demand and energy needs in the HTSE plant without generating greenhouse gases. A dynamic performance analysis of such a system is carried out to evaluate its technical feasibility and benefits, and safety operating under highly variable conditions requiring flexible output. To support the dynamic analysis, special emphasis is given here to the modeling and control design of the HTSE process, which employs planar solid oxide electrolysis cells, coupled to an LWR. The proposed model includes practical constraints, such as operating limits of equipment, and allows the simulation of transient response of the LWR/HTSE integration case with high penetration of renewable resources. Simulation results involving several case studies show that the HTSE integrated N-R HESs could lead to efficient utilization of clean energy generation sources at levels that maximize profits, i.e., steady-state operation of a nuclear reactor and high penetration of solar or wind energy without curtailment, by providing dispatchable electricity to the grid and producing alternative value-added products (hydrogen and oxygen). The results also indicate that the system can provide various types of ancillary services (i.e., regulation, operating reserves, and load following) to support grid stability while satisfying its operating constraints and control objectives; thus, it is capable of effectively and economically mitigating the increasing levels of dynamic variability and uncertainty introduced by renewables.

Nuclear-renewable hybrid energy systems: Opportunities, interconnections, and needs

As the U.S. energy system evolves, the amount of electricity from variable-generation sources is likely to increase, which could result in additional times when electricity demand is lower than available production. Thus, purveyors of technologies that traditionally have provided base-load electricity—such as nuclear power plants—can explore new operating procedures to deal with the associated market signals. Concurrently, innovations in nuclear reactor design coupled with sophisticated control systems now allow for more complex apportionment of heat within an integrated system such as one linked to energy-intensive chemical processes.This paper explores one opportunity – nuclear-renewable hybrid energy systems. These are defined as integrated facilities comprised of nuclear reactors, renewable energy generation, and industrial processes that can simultaneously address the need for grid flexibility, greenhouse gas emission reductions, and optimal use of investment capital. Six aspects of interaction (interconnections) between elements of nuclear-renewable hybrid energy systems are identified: Thermal, electrical, chemical, hydrogen, mechanical, and information. Additionally, system-level aspects affect selection, design, and operation of this hybrid system type. Throughout the paper, gaps and research needs are identified to promote further exploration of the topic.