Dynamic Energy Systems Research Articles

Nanotechnology-Enabled PCR with Tunable Energy Dynamics.

This Perspective elucidates the transformative impacts of advanced nanotechnology and dynamic energy systems on the polymer chain reaction (PCR), a cornerstone technique in biomedical research and diagnostic applications. Since its invention, the optimization of PCR-specifically its efficiency, specificity, cycling rate, and detection sensitivity-has been a focal point of scientific exploration. Our analysis spans the modulation of PCR from both material and energetic perspectives, emphasizing the intricate interplay between PCR components and externally added entities such as molecules, nanoparticles (NPs), and optical microcavities. We begin with a foundational overview of PCR, detailing the basic principles of PCR modulation through molecular additives to highlight material-level interactions. Then, we delve into how NPs, with their diverse material and surface properties, influence PCR through interface interactions and hydrothermal conduction, drawing parallels to molecular behaviors. Additionally, this Perspective ventures into the energetic regulation of PCR, examining the roles of electromagnetic radiation and optical resonators. We underscore the advanced capabilities of optical technologies in PCR regulation, characterized by their ultrafast, residue-free, and noninvasive nature, alongside label-free detection methods. Notably, optical resonators present a pioneering approach to control PCR processes even in the absence of light, targeting the often-overlooked water component in PCR. By integrating discussions on photocaging and vibrational strong coupling, this review presents innovative methods for the precise regulation of PCR processes, envisioning a new era of PCR technology that enhances both research and clinical diagnostics. The synergy between nanotechnological enhancements and energy dynamics not only enriches our understanding of PCR but also opens new avenues for developing rapid, accurate, and efficient PCR systems. We hope that this Perspective will inspire further innovations in PCR technology and guide the development of next-generation clinical detection instruments.

Optimization on Photovoltaics and Energy Storage Integrated Flexible Direct Current Distribution Systems of Buildings Considering Load Uncertainty Using Scenario Generation Method

This study explores the intricate challenge of energy demand uncertainty in the design of Photovoltaics and Energy Storage integrated Flexible Direct Current Distribution (PEDF) systems. Our objective is to examine the impact of different scenario generation methods on PEDF system optimization. We compare four approaches, including probabilistic techniques based on Monte Carlo simulation, Latin Hypercube Sampling for base scenario sampling, and a simulation-based method using building performance modeling. We evaluate these approaches using the Independent Scenario Optimization (ISO) and Two-Stage Stochastic Programming (TSSP) models, aiming to minimize the annual total cost within PEDF systems while addressing uncertainties. Our findings shed light on the optimal PEDF design under uncertainty, offering valuable insights for future decision-making in dynamic energy systems.

TransHyDE‐Sys: An Integrated Systemic Approach for Analyzing and Supporting the Transformation of Energy Systems and Hydrogen Infrastructure Development

In addition to the long‐term goal of mitigating climate change, the current geopolitical upheavals heighten the urgency to transform Europe's energy system. This involves expanding renewable energies while managing intermittent electricity generation. Hydrogen is a promising solution to balance generation and demand, simultaneously decarbonizing complex applications. To model the energy system's transformation, the project TransHyDE‐Sys, funded by the German Federal Ministry of Education and Research, takes an integrated approach beyond traditional energy system analysis, incorporating a diverse range of more detailed methods and tools. Herein, TransHyDE‐Sys is situated within the recent policy discussion. It addresses the requirements for energy system modeling to gain insights into transforming the European hydrogen and energy infrastructure. It identifies knowledge gaps in the existing literature on hydrogen infrastructure‐oriented energy system modeling and presents the research approach of TransHyDE‐Sys. TransHyDE‐Sys analyzes the development of hydrogen and energy infrastructures from “the system” and “the stakeholder” perspectives. The integrated modeling landscape captures temporal and spatial interactions among hydrogen, electricity, and natural gas infrastructure, providing comprehensive insights for systemic infrastructure planning. This allows a more accurate representation of the energy system's dynamics and aids in decision‐making for achieving sustainable and efficient hydrogen network development integration.

The geostrategic and economic impact of Türkiye’s hydrogen energy vision on Europe

The envisioned role for Türkiye in the context of Europe's energy strategies should be crucial. By promoting countries like Türkiye can contribute significantly to global efforts in advancing sustainable energy transitions. Moreover, Incorporating Türkiye into Europe's energy strategies can reduce reliance on external energy suppliers and enhance energy security. The findings of this study offer fresh perspectives on the intricacies of the energy system dynamics and make valuable contributions to the ongoing discourse surrounding transition routes. The study takes into account several key aspects, including the current state of hydrogen in Türkiye, the country's potential for renewable energy, the existing policy and regulatory framework, and the necessary infrastructure and market conditions required for the establishment of a hydrogen hub. These insights shed light on the feasibility and viability of hydrogen as an indispensable component within the context of Türkiye ‘s energy landscape, offering valuable guidance for policymakers, industry stakeholders, and researchers involved in shaping the future of the energy sector.

Machine learning approaches for fault detection in renewable microgrids

This study focuses on investigating and using machine learning (ML) methods to identify faults in renewable microgrids. It highlights the difficulties and intricacies associated with these dynamic energy systems. The examination of real-world data obtained from solar and wind power production, battery storage status, fault signals, and machine learning model performance highlights the complex nature of fault detection techniques in renewable microgrids. An analysis of data on renewable energy production demonstrates oscillations in the outputs of solar and wind power, highlighting differences of about 5-10% across certain time periods, thereby illustrating the intermittent characteristics of renewable energy sources. Simultaneously, the energy stored in batteries inside the microgrid shows a progressive decrease of about 3-5% in stored energy levels across time intervals, indicating possible consequences for the stability of the system. The fault detection signals display erratic patterns, which emphasize the intricacies involved in finding and categorizing issues inside the system. The assessment of machine learning models, which includes both supervised and unsupervised learning methods, reveals many performance measures. Supervised models provide greater accuracy rates, often ranging from 85% to 90%. However, they are prone to occasional misclassifications. In contrast, unsupervised models provide a moderate level of accuracy, often ranging from 75% to 80%. They exhibit flexibility in detecting faults, but their precision is limited. The study highlights the need of using a combination of supervised and unsupervised machine learning models to improve the accuracy of fault detection in renewable microgrids. These results provide valuable understanding of the intricacies and difficulties of fault detection procedures, which may lead to further progress in improving the dependability and durability of renewable microgrid systems.

Interaction Between Social Acceptance and Humanistic Green Techno Educational Capability in Supporting Environmentally Friendly Solar Energy Investments in Jember District

The development of a dynamic energy system requires adjustments to energy policies that are in line with and receive strong support from human resources in government and society. One of the most effective and sustainable policies is encouraging investment in environmentally friendly infrastructure, such as renewable energy. The potential of solar energy, which is a renewable energy source, offers a solution to the energy crisis experienced by developing countries. In this research, a new concept is proposed which is known as "the interaction between social acceptance and investment readiness (SONVES) as a mediator of green_ Techno Humanistic Education Capability towards solar PV investment readiness". This aims to determine the effect of increasing social acceptance, technology acceptance, environmental awareness, and economic value which are expected to have a positive effect on investment readiness. This research model uses a quantitative descriptive approach to describe the situation objectively using data in the form of numbers. overall investment readiness.

A fast method for the calculation of refrigerant thermodynamic properties in a refrigeration cycle

Modeling and simulating dynamic and complex energy systems that are composed of a large number of components where every component model is described by mathematical equations can take high computational time. The calculation of the refrigerant thermodynamic properties has a big impact on the computational speed of the model. In an attempt to speed up simulations, two implementation methods for the calculation of refrigerants’ thermodynamic properties were investigated, the Coolprop thermodynamic equations of state and the Implicit fitting method. A complex dynamic cascade refrigeration cycle was developed and simulated using the two fluids implementation methods. The advantages and disadvantages of each method were discussed. The results show that using the implicit fitting method instead of Coolprop for the simulations of complex dynamic systems in Dymola will accelerate the simulation by about 30 times. In addition, the implicit method is accurate and offers more flexibility for modeling complex realistic energy systems.

Semi-analytical and CFD formulations of a spherical floater

Today, humanity is facing the great pressure of fossil fuels exhaustion and environmental pollution. This obliges governments and industries to make accelerated efforts on producing green energy. The focus is spotted on marine environment which is a vast source of renewable energy. Among several classes of designs proposed for wave energy conversion, spherical Wave EnergyConverters (WECs) have received considerable attention. The problems of water wave diffraction and radiation by a sphere has been examined by a substantial amount of literature, i.e., [1]–[4], whereas in [5]–[8] linear hydrodynamic effects on a spherical WEC have been examined. All these research works are based on potential flow methodologies. Nevertheless, overthe last decade there has been a significant interest on Computational Fluid Dynamics CFD modelling due to its detailed results, focusing also to spherical WECs [9]–[10].In the present work a semi-analytical model is applied to solve the wave radiation problem around a spherical WEC (Figure 1), in the context of linear potential theory. The outcomes of the theoretical analysis are supplemented and compared with high fidelity CFD simulations (Figure 2 for a semi-submerged sphere). Furthermore, the two methodologies are compared with a theoretical approach for the hydrodynamic analysis of floating bodies with vertical axis as being presented in [11]. The method is based on the discretization of the flow field around the body using coaxial ring elements, which are generated from the approximation of the sphere’s meridian line by a stepped curve.Numerical results are given from the comparison of the three formulations, and some interesting phenomena are discussed concerning the viscous effects on the floater.
 [1] Havelock, T. H. 1955. Wave due to a floating sphere making periodic heaving oscillations. R. Soc. London,A231, 1-7.[2] Hulme, A. 1982. The wave forces acting on a floating hemisphere undergoing force periodic oscillation. J. FluidMech., 121, 443-463.[3] Wang, S. 1986. Motions of a spherical submarine in waves. Ocean Engng., 13, 249-271.[4] Wu, G.X. 1995. The interaction of water waves with a group of submerged spheres. Appl. Ocean. Res., 17, 165-184.[5] Srokosz, M.A. 1979. The submerged sphere as an absorber of wave power. J. Fluid Mech., 95, 717-741.[6] Thomas, G.P., Evans, D.V. 1981. Arrays of three-dimensional wave energy absorbers. J. Fluid Mech., 108, 67-88.[7] Linton, C.M. 1991. Radiation and diffraction of water waves by a submerged sphere in finite depth. Ocean Engng.,18, 61-74.[8] Meng, F., et al. Modal analysis of a submerged spherical point absorber with asymmetric mass distribution.Renew. Energy 130, 223-237.[9] Shami, E.A., et al. 2021. Non-linear dynamic simulations of two-body wave energy converters via identificationof viscous drag coefficients of different shapes of the submerged body based on numerical wave tank CFD simulation.Renew. Energy, 179, 983-997.[10] Katsidoniotaki, E., et al. 2023. Validation of a CFD model for wave energy system dynamics in extreme waves.Ocean Engng., 268, 113320.[11] Kokkinowrachos, K., et al. 1986. Behaviour of vertical bodies of revolution in waves. Ocean Engng., 13, 505-538

Characterisation of pressure‐concentration‐temperature profiles for metal hydride hydrogen storage alloys with model development

AbstractMetal hydride (MH) alloys have been applied to hydrogen storage and various energy conversion systems such as refrigeration, heat pump and heat transformer. However, to facilitate and efficiently investigate efficiently a particular application, an MH alloy must firstly be characterised with a purposely built test facility to measure profiles of pressure, MH hydrogen concentration and temperature (PCT). Obtaining detailed PCT profiles or curves could be an arduous and expensive task as each isothermal hydrogen absorption or desorption line requires hundreds of measurement points. It is thus desirable to develop an accurate correlative model for the PCT profiles with limited measurements of thermophysical property data for the purpose of characterisation of each MH alloy. This correlative model or characterisation process has been developed and is described in detail in this article. The correlative PCT MH alloy profiles can cover all applicable hydrogen storage phase regions of α, α + β and β as well as the phase transition dome curve and critical point such that a PCT phase diagram for a particular MH alloy can be depicted and characterised. As an application example, the correlative model is applied to predict an MH alloy's hydrogen storage capacity and hysteresis at a specific MH temperature. It has been discovered that each of these two parameters shows comparative trends in variation with reduced temperature. Correspondingly, for each parameter, a correlative function with reduced temperature has been produced. The MH alloy characterisation process is an essential step towards a detailed dynamic MH energy system modelling, simulation and optimisation as well as experimental investigation.

A comprehensive assessment of cost-optimal heat supply to new low-energy building areas in Sweden

Buildings in Sweden use a large share of energy produced nationally but produce a low share of direct carbon emissions due to high market share of heat pumps and district heating (DH), which uses biomass. Sweden has set stringent energy and climate goals for 2045 prescribing, among other things, reduced energy consumption in buildings. Consequently, new building areas are built based on low-energy building (LEB) standards. These buildings require space heating on cold days and hot water generally. There are four main heat supply options for these areas: (1) individual heat devices (2) decentralized DH, (3) connection to a nearby centralized DH, and (4) prosumers. In this study we developed and applied a dynamic energy system optimization model and designed a systematic analysis to assess cost-optimal heat supply to LEBs in the long-term, while addressing the climate targets. The results showed that low temperature decentralized DHs (LTDDH)s have the lowest cost and individual heat devices have the highest cost of heat supply to LEB areas. The cost-efficiency of LTDDH increases with if prosumers exist in more densely built LEB areas with higher annual heat demand. Additionally, stringent climate policy scenarios can further decrease the cost of LTDDH in LEB areas.

Linking Life Cycle and Integrated Assessment Modeling to Evaluate Technologies in an Evolving System Context: A Power-to-Hydrogen Case Study for the United States.

Carbon-neutral hydrogen (H2) can reduce emissions from hard-to-electrify sectors and contribute to a net-zero greenhouse gas economy by 2050. Power-to-hydrogen (PtH2) technologies based on clean electricity can provide such H2, yet their carbon intensities alone do not provide sufficient basis to judge their potential contribution to a sustainable and just energy transition. Introducing a prospective life cycle assessment framework to decipher the non-linear relationships between future technology and energy system dynamics over time, we showcase its relevance to inform research, development, demonstration, and deployment by comparing two PtH2 technologies to steam methane reforming (SMR) across a series of environmental and resource-use metrics. We find that the system transitions in the power, cement, steel, and fuel sectors move impacts for both PtH2 technologies to equal or lower levels by 2100 compared to 2020 per kg of H2 except for metal depletion. The decarbonization of the United States power sector by 2035 allows PtH2 to reach parity with SMR at 10 kg of CO2e/kg H2 between 2030 and 2050. Updated H2 radiative forcing and leakage levels only marginally affect these results. Biomass carbon removal and storage power technologies enable carbon-negative H2 after 2040 at about -15 kg of CO2e/kg H2. Still, both PtH2 processes exhibit higher impacts across most other metrics, some of which are worsened by the decarbonization of the power sector. Observed increases in metal depletion and eco- and human toxicity levels can be reduced via PtH2 energy and material use efficiency improvements, but the power sector decarbonization routes also warrant further review and cradle-to-grave assessments to show tradeoffs from a systems perspective.

Validation of a CFD model for wave energy system dynamics in extreme waves

The design of wave energy converters should rely on numerical models that are able to estimate accurately the dynamics and loads in extreme wave conditions. A high-fidelity CFD model of a 1:30 scale point-absorber is developed and validated on experimental data. This work constitutes beyond the state-of-the-art validation study as the system is subjected to 50-year return period waves. Additionally, a new methodology that addresses the well-known challenge in CFD codes of mesh deformation is successfully applied and validated. The CFD model is evaluated in different conditions: wave-only, free decay, and wave–structure interaction. The results show that the extreme waves and the experimental setup of the wave energy converter are simulated within an accuracy of 2%. The developed high-fidelity model is able to capture the motion of the system and the force in the mooring line under extreme waves with satisfactory accuracy. The deviation between the numerical and corresponding experimental RAOs is lower than 7% for waves with smaller steepness. In higher waves, the deviation increases up to 10% due to the inevitable wave reflections and complex dynamics. The pitch motion presents a larger deviation, however, the pitch is of secondary importance for a point-absorber wave energy converter.

Synergy of robust adaptive emulated- controller and enhanced mud layers optimization for microgrid dynamics improvement

The paper presents an enhanced scheme to control the dynamic response that can be delivered by distributed generation (DG) and renewable energy resource (RER) to be more reliable and stable than other electric systems. Robust Adaptive Emulated- Controller (RAEC) has been included to control and tune the adoption of controller parameters across a broad range of operating conditions. Some existing challenges related to high loading profiles that will be handled by new performance features. The objective of paper is to propose robust adaptive emulated-controller that is incorporated with high-speed fluctuations and dynamic energy systems. That can achieve an integrated system to reflect the enhanced structure and operation of electric systems, in addition to, Enhanced Mud Layers (EML) optimization will be applied to control and optimize the performance parameters.

Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

Online reliability assessment of energy systems based on a high-order extended-state-observer with application to nuclear reactors

Online reliability assessment is not only crucial for the safe and stable operation of energy systems but also meaningful for guaranteeing satisfactory economic competitiveness. Due to the system reliability can be determined by the failure-rate and operation time, the central in online reliability assessment of energy systems is the evaluation of failure-rate. Further, it can be seen that the deviations of actual responses of process variables from their expectations reflect the effect of total disturbance, and large deviations usually denote the existence of the degradation of system reliability. Hence, the failure-rate can be evaluated based on the estimation of total disturbance and its differentiation. In this paper, a high-order extended state observer (HO-ESO) is proposed for the nonlinear dissipative system representing typical energy system dynamics, which provides globally bounded observations for not only system state-variables but also the total disturbance and its differentiation. Then, the evaluations of both the failure-rate and the system reliability can be given online based on the estimation provided by the HO-ESO. Further, this HO-ESO-based online reliability assessment method is applied to the health monitoring of pressurized water reactor (PWR). After checking the dissipation condition of PWR, the HO-ESO of PWR is designed, and the simulation results show the feasibility and effectiveness.

Urban energy system impact analysis: integration of household solar panels and electric vehicles into smart cities via storage and smart charging

Smart charging and battery storage can improve the integration of electric vehicles (EV's) and photovoltaic solar panels (PV's) into the residential buildings of a smart city. The impact of those two solutions can vary across households with an EV, PV, both, or no technologies. Therefore, it is unclear how smart charging and storage impact the energy, economic, and environmental benefits of each technology adoption group. To address this problem, an urban energy system dynamics model compares two smart charging scenarios that optimize PV energy consumption and carbon emissions as well as one scenario that optimizes storage. The results show that in general storage reduces carbon emissions and increases solar energy use more effectively than smart charging. Specifically, it reduces emissions at a rate of 17% and smart charging at 7%; it also increases PV self-consumption at a rate of 45% and smart charging at 28%. The main reason for this difference is that storage is able to shift a larger electricity load than smart charging without compromising user convenience. However, expenditures decline at a faster rate in the smart charging scenario (–91%) than the storage scenario (–52%), due to the ratio of Value of Solar to residential tariffs. Therefore, this article recommends storage as a solution to all technology adoption groups; furthermore, cities are encouraged to invest in energy storage solutions in the short term as well as smart devices in the long term, so that eventually smart charging could shift a larger share of the loads as well. The contribution of this study is that it compares several experimental groups across the energy, emission, and economic benefits derived from their respective clean energy technologies; it also provides specific guidelines for parties interested in optimizing the benefits of their technologies.

Techno-economic and Environmental Comparison of Internal Combustion Engines and Solid Oxide Fuel Cells for Ship Applications

In order to quantify the economic and environmental impact of technology selection in ship power systems, four different battery-supported hybrid configurations including diesel and gas combustion engines, as well as natural gas fueled solid oxide fuel cells (SOFCs) are modeled and analyzed. The investigations include component investments, maintenance and operational costs, as well as the components’ and fuels’ carbon footprints, operational greenhouse gases and other relevant emissions. Dynamic energy system models are used to derive economically optimal system designs for an appropriate technology comparison in a cruise ship case study. The assessment is conducted for a cruise ship case study with technology parameters for the near future and 2050. Results indicate that the auxiliary power system based on diesel combustion is inferior both economically and environmentally compared to SOFCs or gas combustion engines. While latter are the most cost efficient, SOFC application provides an environmental improvement without the need for a new fuel such as hydrogen. In a final outlook for the year 2050, SOFCs economically overtake gas combustion engines on the condition that their investment costs decrease and synthetic fuels are introduced to the market as a low emission solution.

Structure and control co-optimization for an ejector expansion heat pump coupled with thermal storages

Optimal design and control are often coupled tasks for improving energy system performances. There is a lack of study on the structure and control co-optimization of heat pump coupled with thermal energy storages, which can enhance the penetration levels of intermittent renewables in integrated energy systems, thus we proposed a coupled dynamic optimization method for the structure and control co-optimization of this kind of dynamic energy system, and applied it successfully to a transcritical CO2 ejector expansion heat pump coupled with hot and cold water storages for thermal energy charging. Based on a complicated nonlinear dynamic system model considering thermal stratification in thermal energy storages, the structure and control co-optimization solutions were obtained for this coupled system during energy charging by using the co-optimization method with genetic algorithm. The detailed structures of gas cooler, evaporator, hot and cold energy storage tanks were optimized, then a dynamic optimal control strategy was developed for the whole charging process. The overall coefficient of performance can be increased by 23.2% and the total power consumption can be reduced by 17.6% using this newly developed co-optimization strategy, compared to the constant control parameter strategy. This study would be helpful for a structure and control co-optimization of other dynamic or stable energy systems.

Multi-input, Multi-output Hybrid Energy Systems

Summary Jurisdictions and industries are setting ambitious goals to decarbonize energy systems. Low-cost wind, solar, and natural gas and the resultant dynamic electric grid require energy technologies to adapt in order to meet key attributes for modern energy systems: resilience, reliability, security, affordability, flexibility, and sustainability. When considering energy sources independently and competitively, value-added synergies among energy technologies may be overlooked for meeting demanding, multidimensional requirements. This paper presents novel concepts for tightly coupled hybrid energy systems that leverage capabilities of diverse energy generators, including renewable, nuclear, and fossil with carbon capture, to provide power, heat, mobility, and other energy services. The paper also presents a framework for engineering-based modeling and analysis for complex optimization of energy generation, transmission, services, processes and products, and market interactions. New modeling capabilities are needed to adequately represent multi-input, multi-output tightly coupled hybrid energy systems that utilize multiple feedstocks to create multiple products and services in novel and synergistic ways through increased coordination of energy systems and tightly coupled hybrid system configurations.

Intelligent Object-Oriented Approach to Dynamic Energy Systems’ Modelling

Proposed object-oriented approach and general architecture of intellectual software for mathematical modelling of dynamic energy systems. Introduced and considered in detail an architecture of knowledge bases for modelling of systems described by linear integral equations. Considered a knowledge-based system as a composition of a specific functional network and an expert system