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The increasing adoption of renewable energy sources (RESs) in public power grids has led to a demand for more intelligent energy management systems (EMSs) in large-scale buildings. A common approach for controlling EMSs for buildings is Model Predictive Control (MPC). For large-scale buildings, hierarchical MPC schemes have been proposed, offering the advantage of scalability through problem decomposition into multiple layers. However, hierarchical schemes often suffer from information mismatch due to information asymmetry between layers, leading to suboptimal control performance. This issue is worsened by model errors inherent to the models underlying the MPC controllers. To address these challenges, we propose a hierarchical MPC approach, which includes data-driven error compensation. Additionally, to mitigate information mismatch, a one-iteration communication step is introduced between the hierarchical layers. The proposed approach comprises two layers: an aggregator layer that controls overall energy flows of the building, and a distributor layer that allocates thermal energy to individual temperature zones. The distributor may request additional thermal budget by providing the aggregator with an otherwise expected performance loss, which it can trade off accordingly. The approach is evaluated in a software-in-the-loop (SiL) simulation using a physics-based digital twin model of a multi-zone commercial building, showing notable improvements in overall control performance in comparison to a naive hierarchical baseline and similar performance to a monolithic baseline. • New hierarchical MPC for energy management of a multi-zone commercial building. • One-iteration communication step between layers to mitigate information asymmetry. • Incorporation of data-driven error compensation of heat influences. • Evaluation using real-world data and physics-based digital twin simulation. [ABSTRACT FROM AUTHOR]

The integration of large-scale heat pumps and thermal energy storage can facilitate sector coupling, potentially lowering heating and cooling costs in industries and buildings. This cost reduction can be extended by optimizing the utilization of the available thermal energy storage capacity in accordance to fluctuating electricity prices. Although the literature offers methods for optimizing the operation of these integrated systems, they often overlook the impact of heat pump performance degradation over time, such as from fouling. This oversight can lead to suboptimal system performance and inaccurate operational cost estimates. The present study addresses this gap by introducing a novel operational scheduling framework that aimed to reduce the operational costs of a commercial large-scale heat pump system. The system comprised an open cooling tower, a thermal storage tank and two heat pumps affected by fouling. The framework incorporated a mixed-integer linear programming (MILP) model, thermal demand forecasting, and heat pump performance maps that account for varying fouling levels. These maps were obtained from online calibrated simulation models used as digital twins of the heat pumps. The results demonstrated that the proposed framework enhanced the thermal energy storage utilization in response to variable electricity prices and adjusted the heat pump operation based on the influence of fouling. This resulted in a reduction of operational costs of up to 5% compared to the conventional operation of the system. These savings were observed to vary depending on the forecasting accuracy and the prevailing fouling levels. Overall, this study demonstrates the potential of using the proposed framework for cost reduction in large-scale heat pump systems. • Operation scheduling framework using MILP optimization and demand forecasting. • Heat pump performance degradation due to fouling addressed through digital twins. • Framework tested on a commercial system with heat pumps, TES and cooling towers. • Up to 5% cost savings depending on forecasting accuracy and fouling levels. • Savings from enhanced TES use and fouling-driven adjustment of heat pump operation. [ABSTRACT FROM AUTHOR]

The carbon emissions of the construction industry have raised a lot of concern as it contributes about one third of global carbon emissions. Among them, the operation of residential areas is an important source of carbon emissions. However, due to the complexity of carbon emission sources, it is difficult to account carbon emissions in the operation phase of residential areas. The assessment of the carbon reduction potential for the transition to low carbon residential areas is also inadequate. In this study, carbon emission sources and carbon reduction methods in residential areas have been sorted out, while the carbon emission accounting methods and the estimation methods for carbon reduction potential have been proposed. Moreover, this article first proposes a theoretical framework based on digital twin (DT) for carbon emissions accounting and estimation of carbon reduction potential in the operation phase of residential areas. In this framework, building information modelling (BIM) and remote sensing (RS) image processing technology are integrated, and specific application processes are proposed to improve the intelligence level of carbon emission accounting and carbon reduction potential assessment. Additionally, the feasibility of this method is verified using a campus residential area as a case study. The case study indicates that the method proposed in this article has great potential for application. It also shows that the carbon sinks and new energy sources are the effective method to achieve low or even zero carbon residential areas. In this case, >60% of carbon emissions can be offset through the comprehensive use of various carbon reduction measures. • The carbon emissions and carbon reduction parts in the operation phase of residential areas are identified. • Carbon emissions accounting and estimation of carbon reduction potential is combined with digital twin. • The feasibility of the above method has been verified using a campus residential area located in China as a case study. [ABSTRACT FROM AUTHOR]

The integration of large-scale heat pumps and thermal energy storage can facilitate sector coupling, potentially lowering heating and cooling costs in industries and buildings. This cost reduction can be extended by optimizing the utilization of the available thermal energy storage capacity in accordance to fluctuating electricity prices. Although the literature offers methods for optimizing the operation of these integrated systems, they often overlook the impact of heat pump performance degradation over time, such as from fouling. This oversight can lead to suboptimal system performance and inaccurate operational cost estimates. The present study addresses this gap by introducing a novel operational scheduling framework that aimed to reduce the operational costs of a commercial large-scale heat pump system. The system comprised an open cooling tower, a thermal storage tank and two heat pumps affected by fouling. The framework incorporated a mixed-integer linear programming (MILP) model, thermal demand forecasting, and heat pump performance maps that account for varying fouling levels. These maps were obtained from online calibrated simulation models used as digital twins of the heat pumps. The results demonstrated that the proposed framework enhanced the thermal energy storage utilization in response to variable electricity prices and adjusted the heat pump operation based on the influence of fouling. This resulted in a reduction of operational costs of up to 5% compared to the conventional operation of the system. These savings were observed to vary depending on the forecasting accuracy and the prevailing fouling levels. Overall, this study demonstrates the potential of using the proposed framework for cost reduction in large-scale heat pump systems. • Operation scheduling framework using MILP optimization and demand forecasting. • Heat pump performance degradation due to fouling addressed through digital twins. • Framework tested on a commercial system with heat pumps, TES and cooling towers. • Up to 5% cost savings depending on forecasting accuracy and fouling levels. • Savings from enhanced TES use and fouling-driven adjustment of heat pump operation. [ABSTRACT FROM AUTHOR]

The carbon emissions of the construction industry have raised a lot of concern as it contributes about one third of global carbon emissions. Among them, the operation of residential areas is an important source of carbon emissions. However, due to the complexity of carbon emission sources, it is difficult to account carbon emissions in the operation phase of residential areas. The assessment of the carbon reduction potential for the transition to low carbon residential areas is also inadequate. In this study, carbon emission sources and carbon reduction methods in residential areas have been sorted out, while the carbon emission accounting methods and the estimation methods for carbon reduction potential have been proposed. Moreover, this article first proposes a theoretical framework based on digital twin (DT) for carbon emissions accounting and estimation of carbon reduction potential in the operation phase of residential areas. In this framework, building information modelling (BIM) and remote sensing (RS) image processing technology are integrated, and specific application processes are proposed to improve the intelligence level of carbon emission accounting and carbon reduction potential assessment. Additionally, the feasibility of this method is verified using a campus residential area as a case study. The case study indicates that the method proposed in this article has great potential for application. It also shows that the carbon sinks and new energy sources are the effective method to achieve low or even zero carbon residential areas. In this case, >60% of carbon emissions can be offset through the comprehensive use of various carbon reduction measures. • The carbon emissions and carbon reduction parts in the operation phase of residential areas are identified. • Carbon emissions accounting and estimation of carbon reduction potential is combined with digital twin. • The feasibility of the above method has been verified using a campus residential area located in China as a case study. [ABSTRACT FROM AUTHOR]

Smart building digital twins represent a significant paradigm shift to optimize building operations, thereby reducing their substantial energy consumption and emissions through digitalization. The objective is to virtually replicate existing buildings' static and dynamic aspects, leveraging data, information, and models spanning the entire life cycle. The virtual replica can then be employed for intelligent functions, including real-time monitoring, autonomous control, and proactive decision-making to optimize building operations. To enable proactive decisions, models within the digital twin must continually evolve with changes in the physical building, aligning their outputs with real-time measurements through calibration. This continuous updating requires real-time physical measurements of model inputs. However, challenges arise in the uncertain conditions of buildings marked by sensor absence, malfunctions, and inherent limitations in measuring certain variables. This study introduces a novel calibration framework for physics-based models, addressing the challenges of continuous model calibration in smart-building digital twins while considering the uncertain environment of physical buildings. Within this framework, a novel generative model-based architecture is proposed. This architecture enables a fast and scalable solution while quantifying uncertainty for reliable calibration. Furthermore, a continuous model calibration procedure is presented based on a pre-trained generative calibrator model. A comprehensive evaluation was conducted via a case study employing a building energy model and multiple experiments. The experimental results demonstrated that the proposed framework effectively addresses the challenges of continuous model calibration in smart-building digital twins. The calibrator model accurately quantified uncertainties in its predictions and solved a single calibration problem in an average time of 0.043 second. For facility-level electricity consumption, Coefficient of Variation Root Mean Squared Error (CVRMSE) values of 6.33%, 10.18%, and 10.97% were achieved under conditions of observations without noise or missing data, with noise, and with noise and missing data, respectively. Similarly, for facility-level gas consumption, the corresponding values were 18.75%, 20.53%, and 20.7%. The CVRMSE scores in both cases met the standard hourly thresholds for building energy model calibration. • Proposed probabilistic calibration framework based on generative inverse models. • Proposed a novel generative calibrator model architecture for physics-based models. • The calibrator model is designed to handle noisy and missing sensor observations. • Presented a continuous model calibration procedure for smart-building digital twins. • The proposed approach enabled fast model calibration without iterative search. [ABSTRACT FROM AUTHOR]

With the rapid development of proton exchange membrane fuel cell (PEMFC) commercialization, a comprehensive knowledge of multi-physics fields in large-scale PEMFC stacks has become ever more critical. Although conventional three-dimensional computational fluid dynamic (CFD) models have achieved great success, the application in the commercial-size stack-scale simulation remains inapplicable due to enormous computational resource requirements. Herein, based on the latest 3D CFD model, multi-physics digital twin (DT) technology and 3D stack flow distribution prediction model, a novel multi-scale upscaling prediction model is proposed. The voltage, water and thermal management characteristics of a 164-cell PEMFC stack with an active electrode area of 292.5 cm2 are studied and analyzed in details. For the analysis of commercial-size PEMFC stacks, the most comprehensive multi-physics fields are covered in this paper to date. And the results suggest that by introducing the DT technology, the time requirement of the multi-physics field prediction for unit scale prediction can be reduced by hundreds of thousands of times with a maximum global relative deviation of 1% under 10 groups of random test conditions, giving a solution from the cell scale to stack scale performance prediction, design, heat and thermal management in the PEMFC research and application. • A novel 3D + Digital twin+3D upscaling strategy for PEMFC stack is proposed • The multi-physics fields in a commercial-size PEMFC stack are analyzed • The upscaling strategy can save plenty of computational resource [ABSTRACT FROM AUTHOR]

The energy consumption management (ECM) for the integrated heat pipe cooling (IHPC) systems has become a significant cost-cutting strategy, given the growing demand for the decreased cooling and maintenance costs in data centers. However, the traditional ECM strategies lack an integration with the real-time information and the automatic feedback control, causing the risks of system operation difficult to diagnose and the potential for energy saving hard to exploit. In this respect, a digital twin approach was proposed to efficiently and automatically implement the ECM strategy for an IHPC system. First, a digital twin architecture was established to enable seamless integration and real-time interaction between the physical system and the digital twin. Secondly, the digital twin models of monitoring, simulation, energy evaluation and optimization were developed to drive the corresponding services. Finally, the approach was verified on an IHPC system operating in a real-life data center. It is found that the approach can automatically detect and justify the abnormal states of the IHPC system. Moreover, the approach can reduce the power consumption by 23.63% while meeting the production requirements. The mean relative errors of the supply air temperature and the cooling capacity between the digital twin simulated and the on-site records are 1.43% and 1.46%, respectively. In summary, the proposed approach provides a digital twin workflow that can significantly improve the efficiency of the ECM strategy deployed on an IHPC system. • A digital twin method on energy consumption management of cooling system is proposed. • The digital twin models are developed for real-time monitoring and optimization. • The approach has reduced the energy consumption of the cooling system by 23.63%. [ABSTRACT FROM AUTHOR]

Large-scale heat pumps are a promising technology for the decarbonisation of heat supplied in buildings and industries, provided they operate as expected. However, common faults like fouling and unplanned downtime periods can significantly affect their performance and availability. This could limit the widespread adoption of large-scale heat pumps over other heating technologies such as gas and electric boilers. Approaches described in the literature to optimize the operation of large-scale heat pumps often lack validation under real-world conditions and do not account for performance degradation due to faults. This work demonstrates a step towards utilizing digital twins to improve the energy performance of a commercial large-scale heat pump affected by fouling. A framework was proposed based on the real-time adaptation of digital twins, where a simulation model was calibrated online based on measurements from the heat pump in operation, which was then used for set point optimization. This enabled to determine optimal intermediate pressure set points in the heat pump operating under varying levels of fouling over time. The framework was tested on different periods of heat pump operation spread over ten calendar months. The results showed that the use of online calibration rather than a single calibration decreased performance estimation errors between 3 and 17 percentage points. Moreover, the set points determined by the online-calibrated model, along with a simpler polynomial model derived from it, showed improvements in the heat pump performance by up to 3%, depending on the level of fouling. The findings of this study demonstrated the potential to extend the proposed framework using digital twins to enhance the energy efficiency of large-scale heat pumps. • Use of a model calibrated online to simulate a two-stage ammonia heat pump. • Online calibration enabled to assess fouling growth and mitigation in real-time. • Intermediate pressure set points were optimized through the online calibrated model. • Online calibration improved performance estimations by 3 to 17 percentage points. • Model-based fouling monitoring was beneficial for the set point optimization. [ABSTRACT FROM AUTHOR]

An accurate estimation of dynamic loads and structural dynamic responses is deemed an indispensable prerequisite for developing trustworthy digital twin (DT) models of dynamically excited structures. Traditional joint load–response estimation (JLRE) algorithms necessitate a full-rank feedthrough matrix, implying that accelerometers are required at all degrees of freedom with input loads. If an unknown input is a bending moment or torque, the rotational acceleration must be measured, which is usually difficult, if not impractical, in real applications. Therefore, the applications of the traditional JLRE in wind turbines (WTs) are rather limited due to the complex excitation mechanism. However, the unified linear input and state estimator (ULISE) algorithm presented in this paper eliminates this constraint. This paper experimentally tested this algorithm on an operating 1:50 scaled WT model. The influences from blade rotation were considered. A comprehensive structural health monitoring (SHM) sensing system was deployed on the WT tower to measure the tower's dynamic responses. A camera system, which integrated digital image correlation (DIC) technology and binocular stereo vision technology, was also adopted. The unknown excitations and unmonitored dynamic responses of the WT tower were estimated simultaneously using the ULISE algorithm. The reconstructed strain and acceleration responses achieved high accuracy, with a normalized root mean square error less than 9%. The blade rotation had a notable impact on the WT tower, primarily manifested as a bending moment whose dominant frequency was corresponding to the blade's rotational speed. Such joint estimations could function as the virtual sensing of unknown inputs and responses in the DT model of the WT tower, which will enable DT-based online remote structural condition assessment and preventive maintenance in the future. • Feasibility of digital twin technology for wind turbine towers is experimentally verified. • Joint load-response estimation for an operating wind turbine. • Excitation mechanism of blades on the wind turbine tower is analyzed. • Feasibility of using computer vision technology for wind turbine monitoring is discussed. [ABSTRACT FROM AUTHOR]

Low voltage distribution networks deliver power to the last mile of the network, but are often legacy assets from a time when low carbon technologies, e.g., electrified heat, storage, and electric vehicles, were not envisaged. Furthermore, exploiting emerging data from distribution networks to provide decision support for adapting planning and operational strategies with system transitions presents a challenge. To overcome these challenges, this paper proposes a novel application of digital twins based reinforcement learning to improve decision making by a distribution system operator, with key metrics of predictability, responsiveness, interoperability, and automation. The power system states, i.e., network configurations, technological combinations, and load patterns, are captured via a convolutional neural network, chosen for its pattern recognition capability with high-dimensional inputs. The convolutional neural networks are iteratively trained through the fitted Q-iteration algorithm, as a batch mode reinforcement learning, to adapt the planning and operational decisions with the dynamic system transitions. Case studies demonstrate the effectiveness of the proposed model by reducing 50% of the investment cost when the system transitions towards the winter and maintaining the power loss and loss of load within 5% compared to the benchmark optimisation. Doubled power consumption was observed in winter under future energy scenarios due to the electrification of heat. The trained model can accurately adapt optimal decisions according to the system changes while reducing the computational time of solving optimisation problems, for a range of scales of distribution systems, demonstrating its potential for scalable deployment by a system operator. • Key features of network configurations, technology installations, and load patterns are digitally represented. • A novel digital twin-based distribution network model to adapt planning and operational decisions with dynamic state transitions. • Informed decisions to minimise the investment cost, power loss, loss of load, and renewable curtailment. • Synthesising scalable and computational efficient distribution networks. [ABSTRACT FROM AUTHOR]

Large-scale grid-connected lithium-ion batteries are increasingly being deployed to support renewable energy roll-out on the power grid. These battery systems consist of thousands of individual cells and various ancillary systems for monitoring and control. Although many studies have focused on the behaviour of single lithium-ion cells, the impact of system design choices and ancillary system controls on long-term degradation and efficiency of these systems, containing thousands of cells, has rarely been considered in detail. Here, we simulate a 1 MWh grid battery system consisting of 18,900 individual cells, each represented by a separate electrochemical model, as well as the thermal management system and power electronic converters. Simulations of the impact of cell-to-cell variability, thermal effects, and degradation effects were run for up to 10,000 cycles and 10 years. It is shown that electrical contact resistances and cell-to-cell variations in initial capacity and resistance have a smaller effect on performance than previously thought. Instead, the variation in degradation rate of individual cells dominates the system behaviour over the lifetime. The importance of careful thermal management system control is demonstrated, with proportional control improving overall efficiency by 5%-pts over on–off methods, also increasing the total usable energy of the battery by 5%-pts after 10 years. • Simulation study of 1 MWh grid battery system with 18900 cells cycled for 10 years. • All cells modelled with individual electrochemical models including degradation. • Cells thermally and electrical coupled and supported by ancillary systems. • Exploration of cell variability, interconnection resistance, and thermal management. • Results show key influence of thermal management design and control on lifetime. [ABSTRACT FROM AUTHOR]

We describe the integration of Bayesian decision theory in a digital twin framework to provide a process optimization tool for a biomass boiler. Our application is the Atikokan Generating Station, a 200 MW, biomass-fired tower boiler operated by Ontario Power Generation in Ontario, Canada. For this analysis, we use all available prior information as well as data from the biomass plant and from science-based models. Our objective is to determine a single-valued operational setpoint for the boiler that satisfies a set of objectives/constraints while accounting for uncertainty in boiler operations and in boiler measurements. This setpoint is then continuously updated at the frequency required by plant operations to provide dynamic control. This process of decision-making under uncertainty is a form of artificial intelligence and provides a formal methodology for making optimized decisions in complex systems. Our methodology consists of defining the decision space where all possible solutions reside, identifying the probability of outcomes given that a specific decision was made, creating a decision/cost model that relates the quantities of interest (QOIs) in the physical system (e.g. gross power output) to the decision QOIs (e.g. dollars), identifying the utility (the value to the user) of each outcome, and maximizing the expected utility (i.e. the decision). Once the decision (operational setpoint) is computed, we can predict all of the QOIs (boiler efficiency, O 2 concentration at the outlet, etc.) at the decision point from the science-based model using the parameter distributions computed as part of the Atikokan Digital Twin. • Digital twin of biomass boiler incorporates formal Bayesian decision theory to provide optimized operating-parameter set points which account for uncertainty. • Cost model translates the physics quantity to the decision quantity (often cost). • Utility maps the cost model to the value of the decision to the decision maker. • Optimal decision results from maximizing the expected utility. • Digital twin computes 790 outputs and their distributions at the optimal decision. [ABSTRACT FROM AUTHOR]

This paper describes a methodology based on the digital-twin concept applied to the condition monitoring of three-phase power electronics converters. The proposed solution does not need additional hardware circuits or sensor calibration. The digital replica acts as a permanent watchdog, working in parallel with the converter control system and providing in real-time the signals to be monitored. An application for a Three Leg Neutral Point Clamped (3L-NPC) converter is demonstrated including theoretical analysis for the digital model, practical considerations about the real-time execution of the virtual representation, and experimental verification. The combination of two optimization algorithms (Particle Swarm Optimization (PSO) and a genetic algorithm (GA)) working in series is employed to estimate the circuit parameters of interest, specifically the degradation of the output LC filter. The results shown in this paper serve as a first step for attaining a low-cost and robust parallel digital-twin model as a basis for its application to the concepts of monitoring and reliability. • Power electronics provide an excellent solution due to their energy efficiency conversion. • A real-time Digital Twin system is proposed as a condition monitoring solution. • The parameters estimation is performed through a combination of optimization algorithms. • The whole system has been validated in simulation and on a real converter. [ABSTRACT FROM AUTHOR]

With the growing percentage of the intermittent renewable power generation, the energy system is under increasing pressure in balancing the supply and the demand. As a major part of urban energy consumptions, buildings can provide considerable regulation flexibility for the energy system by actively managing their energy demands. For the building energy flexibility (BEF) provided by thermostatically controlled loads (TCL), its dispatch performance is vulnerable to the building thermal parameter errors, and in some cases, occupants need to provide the critical information related to the indoor temperature state and the occupancy state to the energy management system outside buildings, which decreases the availability of the BEF and raises privacy concerns. For these issues in the BEF utilization, this paper proposes a data-model fusion dispatch strategy based on the digital twin (DT). The proposed strategy is capable of parameter fault tolerance and privacy protection by combining the model-free advantage of the data-driven method with the analytical optimization advantage of the model-driven method. Firstly, a DT-based BEF dispatch framework is proposed. Secondly, the building DT is established by combining the building thermal dynamics (BTD) data-driven model and the TCL operation mechanism model. And the building response deduction is carried out based on the DT. Finally, under the rolling optimization framework, the data-model fusion dispatch strategy is devised by uniting the DT deduction and the optimization constructed by the BTD mechanism model, in which the multi-dimensional modeling of the BTD is carried out from the state dimension and the energy dimension. The simulation results show that the optimization result can reach 98.4% of the ideal result under the scenario with 15% parameter random error, and 98.3% of the ideal result under the scenario with 15% random state noise injection. • Data-model fusion is realized by uniting the digital twin deduction and the mechanism. • The building thermal dynamics are modeled from both the state & the energy dimensions. • The fusion dispatch is capable of parameter fault tolerance and privacy protection. • Optimized result can reach 98.4% of ideal situation under 15% random parameter errors. • Optimized result can reach 98.3% of ideal situation under 15% random state noises. [ABSTRACT FROM AUTHOR]

• A novel 3D multi-physics field digital twin model for PEMFCs is proposed. • The computational fluid dynamic technique is integrated in the digital twin model. • The model is demonstrated within twenty randomly selected working conditions. • The proposed model can predict PEMFC physics field characteristics in 0.913 s. In times of the commercialization process of proton exchange membrane fuel cells (PEMFCs), a full knowledge of in-situ state in PEMFCs is of critical significance to the in-situ operational process and the evaluation of material stage and potential damage. The conventional experimental observation and in-situ prediction models can only obtain very limited information while the computational fluid dynamics approach takes too long time to get the detailed information. To reach a full knowledge of PEMFC real-time state, a novel 3D multi-physics digital twin model for PEMFCs is proposed based on the proper orthogonal decomposition (POD) method. In the model, firstly, for one kind of PEMFC, 139 ex-situ snapshots are designed and simulated based on the three-dimensional two-phase non-isothermal numerical model with the assumption of liquid pressure continuity in the whole membrane electrode assembly. Then the modes of each field in snapshots are extracted by singular value decomposition method using Jacobi algorithm. Finally, the coefficients in the POD prediction equation are obtained by using the multivariate adaptive regression splines. The digital twin results of voltage, temperature, membrane water content and liquid water saturation fields are exhibited and analyzed. Results suggest that for the studied PEMFC, the digital twin technique can capture the global values and the local distribution characteristics of each above physical fields well in 0.913 s. The mean global deviations of the above four fields of 20 groups of random conditions within wide current density and operational condition ranges are 5.7 %, 1.3 %, 8.9 % and 12.0 % respectively. Even though the practical results can only be applied for the studied PEMFC, the proposed methodology has its general application range. [ABSTRACT FROM AUTHOR]

• Multiple utility generation incorporating cold energy recovery system is proposed. • Individual component performance degradation is considered. • A digital twin approach is applied for real-time and life-cycle optimization. • Cold energy recovery unit yields 0.72 % improvement of average daily PESR. • Digital twin driven-optimization achieves 1.37 % average daily PESR advancement. Natural gas is expected to be the dominant fossil fuel in the coming decades. Improving the sustainability of natural gas usage is imperative to achieving a low-carbon society. This study proposes a combined cooling, heating, and power incorporating cold energy recovery (CCHP-CER) system to utilize both heat and cold energies of liquified natural gas (LNG) in a cascade way. The system is comprised of four subsystems, namely, gas turbine, water-lithium bromide absorption chiller, hot water heat exchanger, and cold energy recovery unit. A digital twin approach is applied to this system for real-time and life-cycle operational optimization. The cascade forward neural network (CFNN) is employed to construct the virtual representation while a parameter-free intelligent algorithm is adopted to seek the optimal operating parameters. Key results from this study revealed that incorporating the cold energy recovery (CER) unit produces additional electricity and cooling effect, bringing a 0.72 % improvement in the average daily primary energy saving rate (PESR). The digital twin-based optimization process updates the optimal operation parameters in time when the system suffers degradation. Consequently, the degradation performance is alleviated by the living parameters. Compared to static model-based optimization, the digital twin-based optimization improves the daily PESR by 2.23 %, 0.35 %, and 1.53 % during respective winter, summer, and transition days, particularly when the compressor and turbine of the gas turbine suffer degraded efficiency of −2 %. [ABSTRACT FROM AUTHOR]

• A model using graph neural network is proposed to predict state points and performance metrics. • A configuration representation method based on thermodynamic graph is developed. • GNN can extract structure features from different graphs of three SCO 2 power systems. • GNN achieves excellent accuracy and efficiency and outperforms classical data-driven models. Considering the increasing energy consumption and greenhouse gas emissions, the Supercritical CO 2 (S-CO 2) power system has attracted more and more attention. Due to the expensive computation resource and time cost, data-based solutions for performance prediction are urged. The surrogate model by machine learning is a promising alternative, but it only focuses on the objective functions and ignores the importance of topological structures and physical states of cycles. Aiming at providing a comprehensive model to predict physical states as well as thermodynamic characteristics, a deep learning method based on graph neural network (GNN) are devised in this paper. With the modeling calculation results as training dataset, a well-trained model can accurately reconstruct the physical states consisting of temperature, pressure, enthalpy, entropy (relative error of most samples <5 %) and exergy as well as thermal and exergy efficiency (relative error of most samples <5 %). Moreover, this model shows superior performance compared with traditional machine learning models including Regression Tree, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Gaussian Process Regression (GPR). Finally, the comparison between different training sizes demonstrate the model can help reduce sampling costs for complex systems. Overall, the presented deep learning model can provide a reliable and competitive choice for the digital twin of S-CO 2 power system and other power systems. [ABSTRACT FROM AUTHOR]