Energy Systems Research Research Articles

Infrastructures of power: Governance by energy systems and infrastructure

This paper suggests a synthetization of three classic approaches to analyze infrastructures as interconnected compositions that subtly distribute power and governance. We draw upon conceptualizations within large technological systems (LTS), Internet governance, and information infrastructures (II), and connect them to our own extensive research in energy infrastructure and systems spanning over a decade. This combination serves as the bedrock for our methodological work, which we establish here as pivotal signposts for the study of governance by infrastructures. Rooted empirically in energy research, we advocate for scholars to expand these methodologies to other infrastructures, such as the Internet and II, to illustrate how governance operates across diverse infrastructural networks and their intersections. Our empirical examples draw from studies in smart grids and smart energy technologies, electricity supply management and markets, and energy computer modeling. These diverse cases are utilized to inform the proposed methodological approach.

Configuration optimization of PV array for stratospheric airship under nonuniform radiation condition

The nonuniform solar irradiation on the stratospheric airship surface leads to overlooked mismatch losses in photovoltaic (PV) array. Proper configuration for PV array is crucial but frequently neglected in airship energy system research. In this paper, a mathematical model coupled with the layout of PV array on airship surface, radiation model, electrical model and energy balance model is established. The deployment of PV pack based on the distribution characteristics of solar radiation on airships surface is analyzed. Three maximum power point tracking configurations for the PV array are investigated in detail. The results indicate that mounting PV pack in axial series on stratospheric airships leads to higher power output compared to parallel-connected deployment, PV packs on stratospheric airships exhibit significant output power differences along the circumferential direction and minor differences along the axial direction, the peak of daily maximum output difference ratio is merely 0.4 % between the PV array under the distributed maximum power point tracking (DMPPT) and the partial distributed global maximum point tracking (PDMPPT) configuration, and PDMPPT configuration needs much fewer module, it emerges as the optimal choice for stratospheric airship PV array. This investigation can provide references for the further PV array configuration optimization under nonuniform radiation condition.

Energy innovation funding and social equity: Mediating role of just energy transition

Public investments in research, development, demonstration, and deployment in energy systems are crucial for accelerating the adoption of new energy technologies that can support a low-carbon just energy transition and promote the fair and equitable distribution of benefits and costs, improving social equity. However, the impact of energy innovation funding on social equity through progressing the just energy transition has not been explored in prior research for advanced economies. Thus, this research quantitively examines how energy innovation funding affects social equity in 23 developed economies for the period of 1995–2020. Employing a fixed effect, augmented mean group, and a system-generalized method of moments technique along with a mediating model, this study finds that increasing energy innovation funding significantly improves social equity directly as well as indirectly via accelerating energy justice in advanced economies. Therefore, proper allocation and utilization of public energy innovation budgets are necessary to promote clean energy technologies, advance the just energy transition, and improve social equity, inclusion, and community engagement. This study emphasizes the importance of policy interventions that promote a just energy transition and energy innovation funding to build resilient and inclusive energy systems that improve social equity while achieving Sustainable Development Goal 7, affordable and clean energy.

Comparative Study-Based Data-Driven Models for Lithium-Ion Battery State-of-Charge Estimation

Batteries have been considered a key element in several applications, ranging from grid-scale storage systems through electric vehicles to daily-use small-scale electronic devices. However, excessive charging and discharging will impair their capabilities and could cause their applications to fail catastrophically. Among several diagnostic indices, state-of-charge estimation is essential for evaluating a battery’s capabilities. Various approaches have been introduced to reach this target, including white, gray, and black box or data-driven battery models. The main objective of this work is to provide an extensive comparison of currently highly utilized machine learning-based estimation techniques. The paper thoroughly investigates these models’ architectures, computational burdens, advantages, drawbacks, and robustness validation. The evaluation’s main criteria were based on measurements recorded under various operating conditions at the Energy Systems Research Laboratory (ESRL) at FIU for the eFlex 52.8 V/5.4 kWh lithium iron phosphate battery pack. The primary outcome of this research is that, while the random forest regression (RFR) model emerges as the most effective tool for SoC estimation in lithium-ion batteries, there is potential to enhance the performance of simpler models through strategic adjustments and optimizations. Additionally, the choice of model ultimately depends on the specific requirements of the task at hand, balancing the need for accuracy with the complexity and computational resources available and how it can be merged with other SoC estimation approaches to achieve high precision.

Advanced Integration of Forecasting Models for Sustainable Load Prediction in Large-Scale Power Systems

In the burgeoning field of sustainable energy, this research introduces a novel approach to accurate medium- and long-term load forecasting in large-scale power systems, a critical component for optimizing energy distribution and reducing environmental impacts. This study breaks new ground by integrating Causal Convolutional Neural Networks (Causal CNN) and Variational Autoencoders (VAE), among other advanced forecasting models, surpassing conventional methodologies in this domain. Methodologically, the power of these cutting-edge models is harnessed to assimilate and analyze a wide array of influential factors, including economic trends, demographic shifts, and natural phenomena. This approach enables a more nuanced and comprehensive understanding of power load dynamics, essential for accurate forecasting. The results demonstrate a remarkable improvement in forecasting accuracy, with a 15% increase in precision over traditional models. Additionally, the robustness of the forecasting under varying conditions showcases a significant advancement in predicting power loads more reliably. In conclusion, the findings not only contribute substantially to the field of load forecasting but also highlight the pivotal role of innovative methodologies in promoting sustainable energy practices. This work establishes a foundational framework for future research in sustainable energy systems, addressing the immediate challenges and exploring potential future avenues in large-scale power system management.

Model-based Parametric Study for Comparison of System Configurations and Control of a Hydrogen Hybrid Cargo Vessel

The current state of research in marine energy systems has concentrated on conventional diesel systems, while limited literature is available on the configuration and control of alternative energy sources such as hydrogen hybrid systems, which have attracted increasing interest recently owing to the energy transition. This paper presents a modelling and control study for conceptual retrofitting of a general cargo vessel to a hydrogen-hybrid version. Generic fuel cell, battery, and converter models are used, enabling easy adaptation to various powerplant sizes and ship types. A robustly coordinated Energy Management Strategy (EMS), which can be implemented for different vessel’s power profiles, was developed for power sharing, DC bus voltage control, and battery State of Charge (SoC) regulation. The total installed fuel cell power and battery capacity were heuristically selected from a range of power profiles of the ship. A database of fuel cells with stacks from different manufacturers was created to test different combinations in terms of fuel consumption, cost, and weight, based on the framework of the problem. Uncertainties in terms of fuel prices are presented using normal distribution graphs. The system configurations and control results are presented for one power profile of the vessel and the average fuel costs. It is demonstrated that with the proposed control method, the power losses are less than 1%, the DC bus voltage fluctuations are less than 0.5%, and the battery SoC remains between 35-65% for the entire duration of the analysed power profile. The configuration with eight stacks of 150 kW has the lowest total fuel cost (730 $) with an average difference of 7.1% from the other solutions, and the lowest total weight (10.54 tons) with an average difference of 15.4% from the other configurations. Overall, this study demonstrates the efficient configuration and control of hybrid energy systems using parameterized components.

Performance evaluation of a parabolic cylinder collector applying the Monte Carlo ray tracing method

The purpose of this article is to evaluate the performance of a prototype parabolic trough collector for three proposed scenarios, where geometric designs with optical characteristics are tested according to the appropriate technological paradigm; low cost of maintenance and implementation, intensive local labor, materials and resources of the implementation area. The device was developed by the Energy, Automation and Control Systems Research Group GISEAC of the Unidades Tecnológicas de Santander for low-cost water heating; using materials and labor for its fabrication easily available locally. Monte Carlo ray tracing methodology was applied using the open-access software SolTrace and Tonatiuh. To select the geometric scenario to be simulated, the edge angle and the width of the primary reflection system of the device were varied. The results obtained showed that the aperture of the parabola has a direct impact on the drop in the concentration ratio (CR) percentage value and the performance of the prototype.

Carbon-embedded energy coordination strategy in park-level integrated energy system considering time-varying carbon emission measurement

Energy efficiency and carbon mitigation are important issues in modern energy systems research. Considering the couplings between energy and carbon, this paper investigates a carbon-embedded energy coordination problem in a park-level integrated energy system (PIES). Firstly, based on energy hub, a multilateral interactive transaction framework is introduced, consisting of energy hub operator (EHO), building users with photovoltaics, and an electric vehicle (EV) charging agent. Secondly, a time-varying carbon emission measurement model for the outsourced electricity is designed, taking into account the dynamic composition of the supply-side generators. Based on the time-varying carbon factor and virtual carbon emission flow, an energy-carbon pricing strategy is proposed to balance the carbon responsibility among different participants. Then, a multi-agent interactive trading model in the PIES is constructed, where the energy consumption plans of building users and EVs are guided by the dynamic energy-carbon integrated prices. Through the iterative interaction among different agents, the optimal trading results are finally obtained. Simulation results illustrate the effectiveness of the proposed energy-carbon integrated pricing method in reducing carbon emissions and promoting energy sharing.

From individuals to collectives in energy systems — A social practice, identity and rhythm inspired lens

The need for socially smart energy systems that incorporate both individual and collective social, spatial, and technical dimensions is increasing. However, the social, spatial, and technical interplay between individual and collective scales has been poorly studied and lacks a conceptual basis. Most research to date has focused on social, technical, socio-technical, or spatial approaches at the individual level, or at the collective level independently of the individual. Therefore, overlooking the complex underlying dynamics between the individual and the collective and the multidimensional and multimodal nature of energy use and demand across scales. In response, we propose a novel integration of social practice, social identity theory, and rhythm analysis, with a focus on the collective capabilities of energy users, observable across neighbourhood social contexts and spatial scales. Our conceptual framework offers analytical benefits to energy research and policy by viewing energy users within their individual and collective socio-technical contexts across rhythms of everyday routines. This approach can inform future research in energy systems, as well as policy and practice in the design of new governance mechanisms and engineering requirements for socially smart grids, devices, and collective energy systems.

Current Insights From Task Area 1 in NFDI4Energy: Building and Serving the Energy Research Community

Energy system research has become increasingly reliant on modeling and simulation approaches. These endeavors are enabled by continuously improving tools and methods for developing, maintaining and sharing models and data. Knowledge of how to better conduct, share and archive one’s research has become increasingly complex and hard to manage for individual researchers or single research groups. Identifying and including relevant scientists from energy research, social sciences, and further disciplines is sometimes difficult. Furthermore, a plethora of best practices and guidelines exist on how to prepare data, models and results in ways that make them easier to discover, verify and build upon. To present a sustainable, problem-solving, technical solution for the energy research community, NFDI4Energy develops in Task Area 1 (TA1) two services of the NFDI4Energy platform. Consequentially, the intention of this abstract within the disciplinary track “Engineering” is to provide an overview of the development process with a special focus on Competence and Best Practices. In addition, it discusses interconnections with other Task Areas as well as the chances and challenges that are associated with those connections. The academic audience, e.g., from the Information Systems and (Software) Engineering domain at CoRDI 2023, can observe and discuss our proposed procedure with other community members. Furthermore, we expect interested individuals to compare the proposed procedures with their own, which can lead to meaningful discussions and knowledge-sharing situations within the Engineering domain.

FAIR Data for Energy System Research

The NFDI4Energy consortium aims to establish new services filling a variety of needs for the energy system research community, from making FAIR research data easily accessible to promoting collaboration among community entities. Seven Task Areas (TAs) have been defined to achieve the consortium’s objectives, each with a specific focus. Task Area 4 (TA4): FAIR Data for Energy System Research shall develop ontologies, metadata standards, and services to promote semantic consistency and improve interoperability of energy research projects, thereby supporting the harmonization of data management among various institutions and research fields.

The role of electricity-based hydrogen in the emerging power-to-X economy

As energy system research into high shares of renewables has developed, so have the perspectives of the fundamental nature of a highly renewable economy. Early energy system transition research suggested that current fossil fuel energy systems would transition to a ‘Hydrogen Economy’, whereas more recent insights suggest that a ‘Power-to-X Economy’ may be a more appropriate term, as renewable electricity will become both the most important primary and final energy carrier through various Power-to-X conversion routes across the energy system. This paper provides a detailed overview on research insights of recent years on the core elements of the Power-to-X Economy and the role of hydrogen based on latest research results. These results suggest that, by 2050, upwards of 61,737 TWhLHV of hydrogen will be required to fully defossilise the global energy-industry system. Hydrogen, therefore, emerges as a central intermediate energy carrier and its relevance is driven by significant cost reductions in renewable electricity, especially of solar photovoltaics and wind power. Efficiency and cost drivers position direct electrification as the primary solution for defossilisation of the global energy-industry system; however, electron-to-molecule routes are essential for the large subset of remaining energy-related demands including chemical production, marine and aviation fuels, and steelmaking.

Regarding the generation of time resolved industrial waste heat profiles

The timely transient energy supply or insufficient temperature levels of industrial waste heat (IWH) hinders the full exploitation of its energy and CO2 emission reduction potentials. This outlines the necessity for investigating IWH in a time resolved manner, which, however, is still widely disregarded, especially in generalized industrial energy system analyses and research. Within this study, we present our developed first-of-its-kind approach, which addresses this topic from a holistic system perspective and enables the generation of time resolved IWH profiles for all industrial subsectors. The backbone of our approach is the distinction of energy-intensive subsectors (e.g. Non-Metallic Minerals) and non-energy-intensive subsectors (e.g. Food & Beverage Production), as both vary in regard to their underlying process designs, production routines and data accessibility. Concerning the first group we found that time resolved IWH profiles can be generated by combining process specific data in a bottom-up manner with temperature gradients of the whole production route throughout the paradigm of discrete event simulation. We investigated further that IWH profiles of non-energy-intensive subsectors are depicted by examining existing subsector resolved waste heat fractions and combining this information with plant-specific load profiles in a top-down manner. We practically prove our findings for both groups within (1) a case study of generating IWH profiles of a real-life cement production mill as part of energy-intensive subsectors and (2) outline the analysis of waste heat fractions for the non-energy-intensive subsector of food production exemplarily. Both cases reflect the above findings successfully and with satisfactory results, which can therefore be regarded as basis for future research work concerning time resolved IWH.

Experimental Validation for Artificial Data-Driven Tracking Control for Enhanced Three-Phase Grid-Connected Boost Rectifier in DC Microgrids

This article introduces the control and operation of a grid-connected converter with an energy storage system. A complete mathematical model was presented for the developed converter and its control system. The system under study was a small microgrid comprising an AC grid that is feeding a DC load through a converter. The converter was connected to the AC grid through an R-L filter. The classical linear controllers have limitations due to their slow transient performance and low robustness against parameter variations and load disturbances. In this paper, machine-learned controllers were used to dealing with those drawbacks of the traditional controller. First, a study for conventional nested loop Proportional Integral (PI) was introduced for both outer and inner loops PI-PI controller. A Data-Driven Online Learning (DDOL) controller was then proposed. A comparison between the normal traditional PI-PI controller and the proposed DDOL ones was made under different operating scenarios. The converter control was tested under various operational conditions, and its dynamic and steady-state behavior was analyzed. The model was done through a MATLAB Simulink to check the normal operation of the network in a grid-connected mode under different load disturbances and AC input voltage. Then, the system was designed, fabricated, and implemented in a hardware environment in our Energy Systems Research Laboratory (ESRL) testbed, and the hardware test results were verified. The results showed that the proposed DDOL controller was more robust and had better transient and steady state performances.

Forecasting for the weather driven energy system - a new task under IEA wind

The energy system needs a range of forecast types for its operation in addition to the narrow wind power forecast that has been the focus of considerable recent attention. Therefore, the group behind the former IEA Wind Task 36 Forecasting for Wind Energy has initiated a new IEA Wind Task with a much broader perspective, which includes prospective interaction with other IEA Technology Collaboration Programmes such as the ones for PV, hydropower, system integration, hydrogen etc. In the new IEA Wind Task 51 (entitled “Foreacsting for the Weather Drive Energy System”) the existing Work Packages (WPs) are complemented by work streams in a matrix structure. The Task is divided in three WPs according to the stakeholders: WP1 is mainly aimed at meteorologists, providing the weather forecast basis for the power forecasts. In WP2, the forecast service vendors are the main stakeholders, while the end users populate WP3. The new Task 51 started in January 2022. Planned activities include 4 workshops. The first will focus on the state of the art in forecasting for the energy system plus related research issues and be held during September 2022 in Dublin. The other three workshops will be held later during the 4-year Task period and address (1) seasonal forecasting with emphasis on Dunkelflaute, storage and hydro, (2) minute-scale forecasting, and (3) extreme power system events. The issues and conclusions of each of the workshops will be documented by a published paper. Additionally, the Recommended Practice on Forecast Solution Selection will be updated to reflect the broader perspective.

Co-benefits between energy efficiency and demand-response on renewable-based energy systems

Driven by the recent trends towards a smart power system configuration, there has been a greater focus within the literature on the integration between supply and demand-side resources. The contribution of this research is multifold since it provides a timely and required study, offering valuable insights into how the integration between Demand-Side Management (DSM) resources with clean energy supply options might affect the long-term power planning strategies in high-renewable electricity systems. The innovative aspects of this study are strongly related to a framework proposal for assessing the co-benefits between energy efficiency and demand-response on renewable-based energy systems from a long-term perspective using a co-optimization modelling approach. Overall, the results indicate that a clear benefit of implementing DSM strategies is the percentage reduction of the new installed capacity (−1.0% to −20.6%), CO2 emissions (−2.4% to −11.8%), and total system costs (−0.5% to −15.8%) for all scenarios compared to Business-As-Usual scenario. The evidence from this study suggests that investments in Energy Efficiency (EE) seem more economically valuable than investing only in Demand-Response (DR) strategies. However, integrating EE and DR would empower the overall power system benefits. Strong evidence for a higher potential to delay investments was found for all scenarios for the first ten years of the planning period. Our findings might provide valuable insights to both governments and policy-makers by delivering supportive information in scaling up EE investments. The methodology proposed offers essential contributions to the scientific community and would benefit energy systems research beyond the cases addressed in this paper.

Mathematical Modeling of Battery Energy Storage System Operating Modes using Volterra Integral Equations

The integration of powerful photovoltaic systems and wind farms into energy systems significantly affects the operating modes due to frequent power fluctuations, thereby justifying the use of energy storage devices. The improvement of mathematical models describing operating modes of power facilities is a consequence of structure and process complication occurring in energy systems. The article presents a method for modelling the operating parameters of battery energy storage systems, based on the Volterra integral equations. The sequence of the solution is described, as well as its applicability in energy systems research. To determine the operating modes of battery energy storage systems, a non-standard class of Volterra integral equations of the first kind is used in the research. This makes it possible to calculate the operating modes of storage batteries in terms of the inverse problem, when the generation and power imbalance functions are known. To calculate the operating modes of several types of storage devices operating simultaneously within the same power system, the form of such equations can be specified in the form of piecewise constant functions. This method has a number of advantages, since it allows one to take into account nonlinear processes in the kernels of integral equations. Thus, the processes of degradation of energy storage devices that affect the efficiency of their operation are taken into account. The use of regularizing algorithms makes it possible to reduce the error. As an example, an autonomous photovoltaic system with batteries was considered. The obtained results and their visualization show the viability of applying the approach.

Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis

High quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Unfortunately, actionable information about energy systems is often of limited availability, incomplete, or only accessible for a substantial fee or through a non-disclosure agreement. Recently, remotely sensed data (e.g., satellite imagery, aerial photography) have emerged as a potentially rich source of energy systems information. However, the use of these data is frequently challenged by its sheer volume and complexity, precluding manual analysis. Recent breakthroughs in machine learning have enabled automated and rapid extraction of useful information from remotely sensed data, facilitating large-scale acquisition of critical energy system variables. Here we present a systematic review of the literature on this emerging topic, providing an in-depth survey and review of papers published within the past two decades. We first taxonomize the existing literature into ten major areas, spanning the energy value chain. Within each research area, we distill and critically discuss major features that are relevant to energy researchers, including, for example, key challenges regarding the accessibility and reliability of the methods. We then synthesize our findings to identify limitations and trends in the literature as a whole, and discuss opportunities for innovation. These include the opportunity to extend the methods beyond electricity to broader energy systems and wider geographic areas; and the ability to expand the use of these methods in research and decision making as satellite data become cheaper and easier to access. We also find that there are persistent challenges: limited standardization and rigor of performance assessments; limited sharing of code, which would improve replicability; and a limited consideration of the ethics and privacy of data.

Current research outlook on solar-assisted new energy ships: representative applications and fuel & GHG emission benefits

Shipping industries, one of the efficient modes of freight transport for global trade, are now being forced to demonstrate environmental commitment to reduce greenhouse gas emissions progressively. Stakeholders in the maritime community have proposed various countermeasures and action plans, including applying new energy resources for the future development of ship powers. This paper introduces a comprehensive review to categorize the pathways and highlight the core technological concepts, technical issues, current applications, implementation barriers, and future outlooks regarding integrating new energy resources into ship power systems, including single solar-powered and hybrid new energy resources. Eco-friendly energy sources were promising energy savings and GHG emission benefits. Still, integrating new energy source generation systems would significantly depend on several critical factors. Because ships require a large surface area for installing PV panels, the utilization of solar-assisted power generation on large-scale boats is currently uncommon. The energy generated by the PV generating system is mostly utilized for lighting and electricity. Integrating new energy source generating systems with current ship power systems is a viable approach for enhancing energy efficiency will likely be the focus of future ship-based energy system research. Furthermore, due to the variety of applications, the ship type, power system structure, and navigation area should be considered while selecting a suitable new energy source.

A comprehensive scientometric analysis on hybrid renewable energy systems in developing regions of the world

Energy crises, increasing electricity prices, and having no access to the grid electricity are the leading issues in developing countries of Asia, the Middle East, and Sub-Saharan Africa. Developing hybrid renewable energy systems in off-grid or grid-connected modes is the best way to overcome developing countries' economic and energy crises. However, the development of hybrid renewable energy systems faces severe technical and related economic challenges. This article provides an updated and comprehensive resource and economic overview of developed hybrid renewable energy systems in different locations in these aforementioned regions. The resource assessment shows that for economical hybrid energy system the average annual wind speed and average annual solar radiation should be 5 m/s and 5 KWh/m^2 respectively. This paper also provides a big picture of renewable energy impacts, challenges in the architecture of hybrid systems, and key organizations working in this domain in the developing regions. According to our review, hybrid optimization model for electric renewable, particle swarm optimization and genetic algorithm are frequently used tools for the optimization and sizing of hybrid energy systems. The map of hybrid renewable energy system research in developing regions is not available. Our study gives energy scenario and clear map of hybrid energy in developing regions of the world. The scientometric review of 2000 bibliographic data obtained from the Scopus database to perform co-author and co-occurrence analysis in this study. The data is used to trace the research pattern and thus to identify the most impactful authors, institutions, and countries in the hybrid renewable energy systems domain to obtain recommendations and make policies for the future uninterrupted and carbon dioxide emission free energy systems. The results of scientometric analysis shows that the Wang X. is the most prolific author, while India and Tanta University are the most productive country and institution in this domain. The scientometric analysis result will be beneficial in determining the future research directions in the hybrid renewable energy systems field.