System Model Research Articles

Drift compensation system of an autocollimator based on reciprocal roll angles and a feedback combination target

To address the issues of limited field of view and beam drift in traditional autocollimation measurements, a novel, to our knowledge, autocollimation measurement method is proposed. This method is based on the principle of full-circle continuous measurement using reciprocal roll angles of a compound biaxial system, combined with a feedback and compensation mechanism for beam drift using both calibration and compensation targets. A self-collimating small-angle measurement system model, coupled with a compound biaxial turntable and combined target, is established using ray tracing and perspective projection transformation techniques. Additionally, a closed-loop beam drift compensation system is developed by applying a BP neural network and a PID control algorithm. Experimental tests on the compensation system demonstrate a significant improvement in measurement accuracy and stability. The results show that this method satisfies the requirements for small-angle autocollimation measurement, and the method also enhances the overall stability of the autocollimation system.

Innovation and Sustainability in the Amazon with the Use of Blockchain

Objective: The general objective of this study is to model the implementation of a blockchain system for solid wastemanagement in a government agency. The specific objectives of the results are to identify the main challenges in implementing a blockchain system for solid waste management in Public Management (1); characterize the blockchain models identified in this investigation (2); and develop a blockchain system model for solid waste management in a government agency (3). Theoretical Framework: The Theory of New Public Management, discussed in Madan and Ashok (2022), is the basis of this study, considering the notes of these authors, who address the dominant paradigm in public administration. Method: This research has a qualitative or mixed approach, as it includes parametric elements based on architecture such as blockchain, supported by bibliographical research and documentary analysis. The bibliographical research was based on elements of an integrated solid waste management system, through a framework, as conceptualized in the specific topic of this study. The Content Analysis method was used to research documents and other textual or audiovisual databases on solid waste management and blockchain technology. This research method allowed us to extract relevant information from the data collected, organize it into thematic categories, and interpret it in light of the theoretical framework obtained. By applying Content Analysis, patterns, meanings, and nuances were identified that could enrich the understanding of the phenomenon studied, ensuring an in-depth and contextualized analysis. Results and Discussion: The scenario of research on solid waste management in Brazil is marked by significantchallenges that reflect the complexity of the country's socioeconomic and environmental context. According to Agência Brasil (2023), the generation of solid waste has increased alarmingly, with data from 2021 indicating that more than 80 million tons were produced, of which only 32.5% were correctly disposed of. Thisreality highlights the urgent need for effective and innovative public policies that address integrated waste management. Research Implications: The relevance of implementing a blockchain system in public governance is highlighted, considering the growing demand for innovative and sustainable solutions. Originality/Value: This study is of interest to government managers, academics involved in applied research onblockchain in waste management, politicians and other leaders committed to excellence in management, and other professionals committed to promising results in the relationship between innovation and sustainability in organizations.

Unifying Model Execution and Deductive Verification with Interaction Trees in Isabelle/HOL

Model execution allows us to prototype and analyse software engineering models by stepping through their possible behaviours, using techniques like animation and simulation. On the other hand, deductive verification allows us to construct formal proofs demonstrating satisfaction of certain critical properties in support of high-assurance software engineering. To ensure coherent results between execution and proof, we need unifying semantics and automation. In this paper, we mechanise Interaction Trees (ITrees) in Isabelle/HOL to produce an execution and verification framework. ITrees are coinductive structures that allow us to encode infinite labelled transition systems, yet they are inherently executable. We use ITrees to create verification tools for stateful imperative programs, concurrent programs with message passing in the form of the CSP and Circus languages, and abstract system models in the style of the Z and B methods. We demonstrate how ITrees can account for diverse semantic presentations, such as structural operational semantics, a relational program model, and CSP's failures-divergences trace model. Finally, we demonstrate how ITrees can be executed using the Isabelle code generator to support the animation of models.

IPEM topical report: the first UK survey of cone beam CT dose indices in radiotherapy verification imaging for adult patients

Cone beam CT is integral to most modern radiotherapy treatments. The application of daily and repeat CBCT imaging can lead to high imaging doses over a large volume of tissue that extends beyond the treatment site. Hence, it is important to ensure exposures are optimised to keep doses as low as reasonably achievable, whilst ensuring images are suitable for the clinical task. This IPEM topical report presents the results of the first UK survey of dose indices in radiotherapy CBCT. Dose measurements, as defined by the cone beam dose index (CBDIw), were collected along with protocol information for seven treatment sites. Where a range of optimised protocols were available in a centre, a sample of patient data demonstrating the variation in protocol use were requested. Protocol CBDIwvalues were determined from the average dosimetry data for each type of linear accelerator, and median CBDIwand scan length were calculated for each treatment site at each centre. Median CBDIwvalues were compared and summary statistics derived that enable the setting of national dose reference levels (DRLs). A total of 63 UK radiotherapy centres contributed data. The proposed CBDIwDRLs are; prostate 20.6 mGy, gynaecological 20.8 mGy, breast 5.0 mGy, 3D-lung 6.0 mGy, 4D-lung 11.8 mGy, brain 3.5 mGy and head/neck 4.2 mGy. However, large differences between models of imaging system were noted. Where centres had pro-active optimisation strategies in place, such as sized based protocols with selection criteria, dose reductions on the 'average' patient were possible compared with vendor defaults. Optimisation of scan length was noted in some clinical sites, with Elekta users tending to fit different collimators for prostate imaging (relatively short) compared with gynaecological treatments (longest). This contrasts with most Varian users who apply the default scan length in most cases.

Memprot.GPCR-ModSim: Modelling and simulation of membrane proteins in a nutshell.

Memprot.GPCR-ModSim leverages our previous web-based protocol, which was limited to class-A G protein-coupled receptors, to become the first one-stop web server for the modelling and simulation of any membrane protein system. Motivated by the exponential growth of experimental structures and the breakthrough of deep-learning-based structural modelling, the server accepts as input either a membrane-protein sequence, in which case it reports the associated AlphaFold model, or a 3D (experimental, modelled) structure, including quaternary complexes with associated proteins and/or ligands of any kind. In both cases, the molecular dynamics (MD) protocol produces a membrane-embedded, solvated, and equilibrated system, ready to be used as a starting point for further MD simulations, including ligand-binding free energy calculations. Memprot.GPCR-ModSim web server is publicly available at https://memprot.gpcr-modsim.org/. The standalone modules for 3D modelling (PyModSim) or membrane embedding and MD equilibration (PyMemDyn) are available at the GitHub repository https://github.com/GPCR-ModSim/. Supplementary data are available at Bioinformatics online.

Simulation and Optimization of a Hybrid Photovoltaic/Li-Ion Battery System

The coupling of solar cells and Li-ion batteries is an efficient method of energy storage, but solar power suffers from the disadvantages of randomness, intermittency and fluctuation, which cause the low conversion efficiency from solar energy into electric energy. In this paper, a circuit model for the coupling system with PV cells and a charge controller for a Li-ion battery is presented in the MATLAB/Simulink environment. A new three-stage charging strategy is proposed to explore the changing performance of the Li-ion battery, comprising constant-current charging, maximum power point tracker (MPPT) charging and constant-voltage charging stages, among which the MPPT charging stage can achieve the fastest maximum power point (MPP) capture and, therefore, improve battery charging efficiency. Furthermore, the charge controller can improve the lifetime of the battery through the constant-current and constant-voltage charging scheme. The simulation results indicate that the three-stage charging strategy can achieve an improvement in the maximum power tracking efficiency of 99.9%, and the average charge controller efficiency can reach 96.25%, which is higher than that of commercial chargers. This work efficiently matches PV cells and Li-ion batteries to enhance solar energy storages, and provides a new optimization idea for hybrid PV/Li-ion systems.

Multi-layer model of cell-waveguide system for highly uniform illumination inside wide bio-optical waveguide fluorescence microscopy

Multi-layer model of cell-waveguide system for highly uniform illumination inside wide bio-optical waveguide fluorescence microscopy

TDOA-AOA Localization Algorithm for 5G Intelligent Reflecting Surfaces

5G positioning technology has become deeply integrated into daily life. However, in wireless signal propagation environments, there may exist non-line-of-sight (NLOS) conditions, which lead to signal blockage and subsequently hinder the provision of positioning services. To address this issue, this paper proposes an intelligent reflecting surface (IRS) NLOS time difference of arrival–angle of arrival (TDOA-AOA) localization (INTAL) algorithm. First, the algorithm constructs a system model for 5G IRS localization, effectively overcoming the challenges of positioning in NLOS paths. Then, by applying the multiple signal classification algorithm to estimate the time delay and angle, and using the Chan algorithm to obtain the user’s estimated coordinates, an optimization problem is formulated to minimize the distance between the estimated and actual coordinates. The tent–snake optimization algorithm is employed to solve this optimization problem, thereby reducing localization errors. Finally, simulations demonstrate that the INTAL algorithm outperforms the snake optimization (SO) algorithm and the gray wolf optimization (GWO) algorithm under the same conditions, reducing the localization error by 56% and 60% on average, respectively. Additionally, when the signal-to-noise ratio is 30 dB, the localization error of the INTAL algorithm is only 0.2968 m, while the errors for the SO and GWO algorithms are 0.6733 m and 0.7398 m, respectively. This further proves the significant improvement of the algorithm in terms of localization accuracy.

Anti-disturbance Adaptive Cruise Control Method Based on H∞ Robust Controller with Optimized Safe Velocity and Following Time Headway

Abstract To alleviate driver workload, many vehicles are now equipped with adaptive cruise control (ACC) systems. However, ACC performance can significantly deteriorate in real-world conditions due to disturbances such as sensor measurement errors, system uncertainties, and modeling errors. Therefore, an innovative anti-disturbance ACC method is proposed based on H∞ robust controller with optimized safe velocity and following time headway to overcome the above problems. Initially, we analyze the longitudinal following characteristics that are undergoing disturbances and establish the ACC system model. Then, the optimization models for the safe velocity and the following time headway during ACC are designed. Subsequently, based on H∞ theory, the design criterion of the H∞ robust controller for ACC is established by utilizing the linear matrix inequality technique. Finally, two typical experiment scenarios are given for hardware-in-the-loop simulation to demonstrate the effectiveness of the proposed method.

A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2

Abstract. Recent studies have suggested that injection of solid particles such as alumina and calcite particles for stratospheric aerosol injection (SAI) instead of sulfur-based injections could reduce some of the adverse side effects of SAI such as ozone depletion and stratospheric heating. Here, we present a version of the global aerosol–chemistry–climate model SOCOL-AERv2 and the Earth system model (ESM) SOCOLv4 which incorporate a solid-particle microphysics scheme for assessment of SAI of solid particles. Microphysical interactions of the solid particle with the stratospheric sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code (RTC) for the first time within an ESM. Therefore, the model allows simulation of heterogeneous chemistry at the particle surface as well as feedbacks between microphysics, chemistry, radiation and climate. We show that sulfur-based SAI results in a doubling of the stratospheric aerosol burden compared to the same mass injection rate of calcite and alumina particles with a radius of 240 nm. Most of the sulfuric acid aerosol mass resulting from SO2 injection does not need to be lifted to the stratosphere but is formed after in situ oxidation and subsequent water uptake in the stratosphere. Therefore, to achieve the same radiative forcing, larger injection rates are needed for calcite and alumina particle injection than for sulfur-based SAI. The stratospheric sulfur cycle would be significantly perturbed, with a reduction in stratospheric sulfuric acid burden by 53 %, when injecting 5 Mt yr−1 (megatons per year) of alumina or calcite particles of 240 nm radius. We show that alumina particles will acquire a sulfuric acid coating equivalent to about 10 nm thickness if the sulfuric acid is equally distributed over the whole available particle surface area in the lower stratosphere. However, due to the steep contact angle of sulfuric acid on alumina particles, the sulfuric acid coating would likely not cover the entire alumina surface, which would result in available surface for heterogeneous reactions other than the ones on sulfuric acid. When applying realistic uptake coefficients of 1.0, 10−5 and 10−4 for H2SO4, HCl and HNO3, respectively, the same scenario with injections of calcite particles results in 94 % of the particle mass remaining in the form of CaCO3. This likely keeps the optical properties of the calcite particles intact but could significantly alter the heterogeneous reactions occurring on the particle surfaces. The major process uncertainties of solid-particle SAI are (1) the solid-particle microphysics in the injection plume and degree of agglomeration of solid particles on the sub-ESM grid scale, (2) the scattering properties of the resulting agglomerates, (3) heterogeneous chemistry on the particle surface, and (4) aerosol–cloud interactions. These uncertainties can only be addressed with extensive, coordinated experimental and modelling research efforts. The model presented in this work offers a useful tool for sensitivity studies and incorporating new experimental results on SAI of solid particles.

Linear parameter varying μ synthesis robust control of CDC semi‐active suspension system for nonlinearity and uncertainty

AbstractBecause the continuous damping control (CDC) semi‐active suspension system has complex nonlinear and uncertain characteristics that can significantly affect the performance of the system. And in the controller design process, achieving a balance of multiple performance objectives is often difficult. Therefore, this paper designs a linear parameter varying (LPV) μ synthesis robust controller for the vibration suppression and multi‐objective control of a 7‐degree‐of‐freedom (7‐DOF) vehicle suspension system equipped with CDC dampers. Firstly, the nonlinear force model of the solenoid valve CDC damper is developed based on the actual damping variation. Considering the nonlinear forces of the coil springs, the nonlinear model of the 7‐DOF suspension system is expressed as LPV form. Further, the linear fractional transformation (LFT) method is used to analyze and reconstruct the system model for multiple parameter uncertainties of the suspension. The actuator time delays are also simulated through a frequency domain transfer function, which is considered to be an unmodeled dynamic at the input of the system. Finally, an LPV‐μ synthesis robust controller based on nonlinearity and mixed uncertainty is designed to improve car ride comfort and achieve the best relationship between comfort and handling stability. Simulation and experimental results under random disturbance conditions show that the LPV‐μ synthesis controller has better anti‐jamming performance and controllability compared to the H∞ controller and μ synthesis controller.

UOWC spatial diversity techniques over hostile maritime environments: an approach under imperfect CSI and per-source power constraints

Optical communication in submarine environments has emerged as a novel technology that enables high bandwidth and high data rate links. However, the unique characteristics of the underwater channel impose new challenges, such as mitigating the remarkable absorption and scattering of hostile maritime environments. For the first time, we consider a per-source optical power constraint based on eye-safety regulations, which has never before been taken into account in Multiple-Input/Single-Output (MISO) systems within underwater optical wireless communication (UOWC) scenarios. Hence, we introduce an innovative spatial repetition coding (SRC) system model, which enables the analysis of an SRC scheme operating under either a per-source or a per-transmitter power constraint. In addition, a tractable generalized transmit laser selection (GTLS) model is presented in order to consider the impact of erroneous selections of the best laser source due to imperfect channel state information (CSI) at the transmitter, prevalent in underwater scenarios with dynamic fluctuations from water currents. Novel bit error rate closed-form expressions and asymptotic results are derived. The presented results demonstrate that an SRC system, when appropriately designed under a per-source power constraint, outperforms the TLS system by effectively mitigating the adverse effects of underwater links. Conversely, in situations where compact transmitters necessitate constraints that significantly modify eye-safety, TLS schemes are superior.

Transient energy transfer of wind-photovoltaic-storage grid-connected system under virtual synchronous coupling

In the new power system, the efficient capture of transient energy by a virtual synchronous generator (VSG) will be the key to improve the grid-connected stability of wind-photovoltaic-storage. This paper first analyzes the virtual synchronous coupling relationship between the VSG and the synchronous generator (SG), and constructs the dynamic model of grid-connected system. Secondly, based on the Hamiltonian theory, the transient energy transfer characteristics between the VSG and the SG under the virtual synchronous coupling are analyzed, and the conditions for the VSG to completely capture the transient energy is obtained, and a virtual inertia control strategy based on the efficient transfer of transient energy is proposed. Finally, a 9-node simulation system is built to verify that the proposed control strategy can significantly improve the performance of VSG in suppressing power oscillations and frequency changes.

Energy Management System for Polygeneration Microgrids, Including Battery Degradation and Curtailment Costs

Recent advancements in sensor technologies have significantly improved the monitoring and control of various energy parameters, enabling more precise and adaptive management strategies for smart microgrids. This work presents a novel model of an energy management system (EMS) for grid-connected polygeneration microgrids that allows optimizing the management of electrical storage systems, electric vehicles, and other deferrable loads such as heat pumps. The main novelty of this model is that it incorporates both climate comfort variables and the consideration of the degradation of the energy storage capacity in the control strategy, as well as a penalty for the dumping of surpluses. The model has been applied to a smart, sustainable building as a case study. The results show that the proposed model is highly adaptable to diverse weather conditions, minimizing renewable energy losses while satisfying the energy demand and providing comfort to the building’s users. The study shows (i) that EVs’ dynamic charging schedules play a crucial role, (ii) that it is possible to minimize a battery’s degradation by optimizing its cycling, averaging one cycle per day, and (iii) the critical impact of seasonal weather patterns on microgrid energy management and the strategic role of EVs and storage systems in maintaining energy balance and efficiency.

An Optimization Framework for Waste Treatment Center Site Selection Considering Nighttime Light Remote Sensing Data and Waste Production Fluctuations

As urbanization accelerates, the management of urban solid waste poses increasingly intricate challenges. Traditional urban metrics, such as GDP and per capita consumption rates, have become inadequate for accurately reflecting the realities of waste generation; moreover, the linear correlation between these metrics and waste production is progressively diminishing. Consequently, this study introduces a novel methodology leveraging nighttime light remote sensing data to enhance the precision of urban solid waste production forecasts. By processing remote sensing data to mitigate noise and integrating it with conventional urban datasets, an innovative index system and predictive model were developed. Using Beijing as a case study, the gradient boosting regression algorithm yielded a prediction accuracy of 92%. Furthermore, in light of the substantial costs associated with waste recovery route planning and site selection for treatment facilities, this research further devised a location and distribution framework for waste treatment centers based on high-precision predictions of waste production while employing multi-objective evolutionary algorithms (MOEAs) alongside the non-dominated sorting genetic algorithm II (NSGA-II) for optimization. Distinct from prior studies, this study suggests that service point waste quantities are not fixed values but rather adhere to a normal distribution within specified ranges and thus provides a more realistic simulation of fluctuations in waste production while enhancing both the robustness and predictive accuracy of the model. In conclusion, by incorporating nighttime light remote sensing data along with advanced machine learning techniques, this study markedly improves forecasting accuracy for waste production while offering effective optimization strategies for site selection and recovery route planning—thereby establishing a robust data foundation aimed at refining urban solid waste management systems.

Multi-scenario flexibility requirement analysis of high proportion of new energy access to power system

Abstract To address climate change, the proportion of renewable energy integration into the grid system is gradually increasing, leading to higher demands for flexibility. Current research typically employs methods such as dynamic system modeling, the construction of flexibility indicators, and scenario analysis to measure the flexibility requirements of the power system across different time scales. The use of frequency decomposition algorithms to explore flexibility requirements from the perspective of net load curves is relatively rare. This study utilizes net load data from the Western Inner Mongolia Power Grid to explore the distribution patterns of net load across different time scales under various seasonal and penetration scenarios using frequency decomposition algorithms. The results reveal that renewable energy output exhibits significant fluctuations and distinct diurnal variations, while net load also shows notable patterns and considerable volatility and seasonality. The analysis of flexibility requirements on typical seasonal days highlights the differences in demand distribution at various frequencies, with long-time-scale components primarily reflecting the overall trend of net load, while the medium-to-long time scale components characterize smaller, more frequent fluctuations. Additionally, the uncertainty associated with wind and solar output significantly affects the diurnal fluctuations of net load, with seasonal changes mainly represented in short-time-scale components. The study also emphasizes the impact of different penetration levels on flexibility requirements, indicating that as penetration decreases, the midnight requirement peak diminishes, suggesting differences in flexibility requirements based on renewable energy integration levels. Furthermore, the paper proposes corresponding solution technologies for “generation–grid–load–storage” across different time scales of flexibility requirements, ensuring the stable operation of the grid amidst climate change and rising electricity demand.

Asymmetric ENSO teleconnections in a symmetric CO2 concentration pathway

Abstract El Niño-Southern Oscillation (ENSO) is the strongest interannual phenomenon occurring in the tropical Pacific, significantly affecting the entire world. The ENSO response to increasing CO2 concentrations have been extensively studied, but the reverse scenario is still not well understood. Here, we investigate the hysteresis of ENSO teleconnections in a CO2 removal simulation of an earth system model. During the ramp-up period of CO2 concentrations, Pacific-North American and Pacific-South American patterns are intensified, with the eastward shift of their poles, which are even further intensified during the ramp-down period. This ENSO teleconnection hysteresis is closely linked to the tropical-origin hysteresis, in which, during the ramp-down periods, the prevalence of the eastern-Pacific type El Niño leads to the hysteresis in the eastern Hemispheric ENSO teleconnections and the enhanced ENSO skewness and the eastward shift of ENSO-induced tropical atmospheric convection do to that in the western Hemispheric ENSO teleconnection. The alterations by the tropical origin are predominantly associated with intensified southward migration of the Intertropical Convergence Zone, along with a stronger El Niño-like warming trend. We also demonstrate that the hysteretic change in the mid-latitude mean state over the North Pacific region could lead to hysteresis of the ENSO teleconnection without invoking a tropical origin.

Understanding the relationship between land use/land cover changes and air quality: A GIS-based fuzzy inference system approach.

Air pollution is a global issue that demands urgent attention due to its detrimental effects on human health and the environment. Land Use and Land Cover (LULC) change is an essential factor that significantly impacts ambient air quality through alterations in emission sources, vegetation cover, natural processes, and urban design. This study investigates the spatio-temporal variation of key air pollutants resulting from urban LULC changes in the Delhi region. Findings reveal a notable increase in pollutant concentrations, particularly particulate matter, in 2019 (PM10: 318.65 ± 45.80 µg/m3) and 2023 (PM10: 383.70 ± 61.49 µg/m3), compared to 2008 (PM10: 246.76 ± 30.66). LULC change analysis demonstrates a rise in built-up areas 24.59%(2008 to 2019), 33.62% (2008 to 2023) and a decline in vegetation cover 27.49% (2008 to 2019),32.37% (2008 to 2023). Correlation analysis indicates a positive correlation between PM10 and urban indices (+ 0.63) and a negative correlation between PM10 and vegetation indices (- 0.61), highlighting the impact of LULC on air quality deterioration. Subsequently, a fuzzy inference system model integrates LULC information to develop an air quality index (AQI). Incorporating LULC changes in AQI assessment offers a realistic approach to address the complexity arising from combined air pollutant effects, surpassing conventional AQI calculation methods. The findings underscore the significance of understanding the impact of Land Use and Land Cover (LULC) change on ambient air quality in formulating effective air quality management programs and policies. Integrating this knowledge into policymaking is crucial for the successful abatement of air pollution in urbanized areas.

Uncertainty analysis of digital twin model of mine ventilation system

The digital twin model of mine ventilation system (DTMVS) plays an important role in intelligent safety management. However, the uncertainty of the ventilation resistance coefficient, which is the core parameter of the model, makes it challenging to accurately construct a DTMVS. In this study, Latin Hypercube Sampling (LHS) and ventilation resistance coefficient estimation models (VRCEMs) are used to analyze the uncertainty. First, the LHS method was used to explore the effect of uncertainty in the simulated airflow by continuously increasing the level of uncertainty in the ventilation resistance coefficients. Subsequently, the ventilation resistance coefficients were estimated using the VRCEMs, and the uncertainty of the ventilation resistance coefficient and the simulated airflow was analyzed. The results showed that the ventilation resistance coefficients with a 5% coefficient of variation can cause the DTMVS to lose 34% of the real airflow data points. The degree of uncertainty in the ventilation resistance coefficients estimated by the VRCEM-GA (VRCEM using genetic algorithm) and VRCEM-DE (VRCEM using differential evolution algorithm) methods was enhanced by 27.4% and 4.4%, respectively, compared with VRCEM-ES (VRCEM using evolutionary strategy algorithm). The VRCEM-ES model had the least influence on the uncertainty of the simulated airflow of DTMVS. The simulated airflow of the DTMVS constructed based on VRCEMs fluctuated normally within the confidence interval. VRCEMs had a higher sensitivity to the ventilation resistance coefficients of branches with low coefficients of variation.

The effects of fair allocation principles on energy system model designs

Abstract What constitutes socially just or unjust energy systems or transitions can be derived from the philosophy and theories of justice. Assessments of distributive justice and utilising them in modelling lead to great differences based on which justice principles are applied. From the limited research so far published in the intersection between energy systems modelling and justice, we find that comparisons between the two principles of utilitarianism and egalitarianism dominate in assessments of distributive justice, with the latter most often considered representing a 'just energy system'. The lack of recognition of alternative and equally valid principles of justice, resting on e.g. capabilities, responsibilities and/or opportunities, leads to a narrow understanding of justice that fails to align with the views of different individuals, stakeholders and societies. More importantly, it can lead to the unjust design of future energy systems and energy systems analysis. 
 
In this work, we contribute to the growing amount of research on distributive justice in energy systems modelling by assessing the implications of different philosophical views on justice on modelling results. Through a modelling exercise with a power system model for Europe (highRES), we explore different designs of a future (2050) net-zero European electricity system, and its distributional implications based on the application of different justice principles. In addition to the utilitarian and egalitarian approach, we include, among others, principles of 'polluters pay' and 'ability-to-pay', which take historical contributions of greenhouse gas emissions and the socio-economic conditions of a region into account. 
 
We find that fair distributions of electricity generating infrastructure look significantly different depending on the justice principles applied. The results may stimulate a greater discussion among researchers and policymakers on the implications of different constructions of justice in modelling, expansion of approaches, and demonstrate the importance of transparency and assumptions when communicating such results.