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Accurately capturing data on the external loads that large structural systems endure is crucial for improving the performance of energy equipment. This paper introduces a novel hybrid model-data-driven framework for the dynamic load identification of interval structures, which seamlessly combines finite-element modeling with machine learning techniques. To address potential ill-posed issues in model-driven methods and the interpretability limitations of data-driven methods, we propose a physics-informed neural network. This neural network effectively inverts uncertain modal responses with low data requirements and high predictive performance high by integrating the underlying modal transformation equation into the loss function of a fully connected neural network. To identify the modal loads using predicted modal displacement/acceleration responses, we introduce a pioneering dynamics inversion method. This method modifies the traditional Kalman filter with an assumption of unknown inputs to reduce the sensitivity of load identification process to different noises. In addition, our approach incorporates a subinterval Chebyshev expansion method to adaptively determine the interval boundaries of external loads. The efficiency of the proposed method is assessed through two numerical examples and validated through comparative research against baseline methods. Our findings suggest that this approach enhances precision, robustness, and generalization in dynamic load identification, even when facing challenges such as limited training data, significant noise interference, and non-zero initial conditions. • A novel hybrid model-data-driven dynamic load identification method for interval structures is proposed. • A physics-informed neural network is established to inverse the uncertain modal responses effectively. • Two improved Kalman filter algorithms are developed to identify the modal loads at specified parameters. [ABSTRACT FROM AUTHOR]

This paper investigates the scheduling problem in smart distribution networks equipped with distributed photovoltaic energy storage systems (PV-ESS) to address excessive power losses, economic revenue, and over-voltage issues. Accurately modeling the grid structure and ensuring adequate sensor coverage pose significant challenges in network settings of this nature, and therefore, we put forth a novel approach known as Principal Component Analysis-based incomplete data equivalence (PIDE) for constructing a data-driven power flow model under incomplete data. Moreover, the presence of distributed PV-ESS, coupled with the lack of data sharing, introduces a hybrid cooperation-competition dynamic, resulting in suboptimal solutions and local optima. To address this challenge, we approach the scheduling problem by formulating it as a multi-agent reinforcement learning task. Meanwhile, we present Counterfactual Multi-agent Soft Actor–Critic (COSAC), which incorporates stochastic policy learning to enhance exploration and facilitates credit assignment in the continuous action space, so as to accurately determine the individual contributions of agents involved in the task. Simulation results conducted on the IEEE 33 and 123 bus systems demonstrate the effectiveness of the proposed method. Specifically, we find that PIDE achieves a substantial reduction in the necessary data sampling coverage, and COSAC outperforms state-of-the-art multi-agent reinforcement learning methods by at least 4.14%. [Display omitted] • A non-convex multi-objective distributed PV-ESS scheduling problem is addressed. • Data-driven power flow model reduces training time and improves accuracy. • A novel machine learning method is proposed to reduce sampling coverage. • MARL realizes exploration-enhanced credit assignment in continuous action space. • Decentralized control policy is executed without communication among PV-ESSs. [ABSTRACT FROM AUTHOR]

Bipolar configuration electrochemical stacks with a common or circulating electrolyte supply usually embody a high amount of shunt currents that escape from the main current path to the manifold nozzles. This paper suggests a novel and simplified semiempirical model to reasonably predict the total shunt currents in industrial alkaline water electrolyzers (AWE). The aim of the study is to perform a sensitivity analysis of the model parameters and the process conditions that affect the shunt currents and the plant's specific energy consumption (SEC), and to determine the most important ones by analyzing the thermodynamic and fluidic properties of the stack. An in-house MATLAB dynamic energy and mass balance model of an industrial 3 M W , 16 b a r AWE plant process was developed. The semiempirical dynamic process model is updated with the essential shunt currents and electrochemical, fluidic, and circulation impurity models. The study revealed the influence of the supplied current, the electrolyte temperature, the process pressure, the electrolyte flow rate, and the potassium hydroxide concentration on the shunt currents and the plant's SEC. • Simulation of a validated 3 MW industrial-scale AWE semi-empirical transient model. • Sensitivity analysis of the process conditions affecting the shunt currents in bipolar-configuration stacks. • Effect of the current supply, stack temperature, and electrolyte flow rate on the plant SEC. • Plant model parametrization and uncertainty analysis for the electrochemical model. • At partial loads, the shunt currents are the major source of energy loss in the system. [ABSTRACT FROM AUTHOR]

The liberalization reform of the natural gas market has decoupled gas trading from storage and transportation services and introduced the new role, namely shippers. The shippers book network capacity from pipeline network operators (PNOs) by signing capacity contracts and nominate the gas of injection and withdraw in the day-ahead market. The PNOs then validate the feasibility of nominations and optimize the operation plan of gas network. How to efficient allocate the network capacity has become a new challenging subject due to the need to coordinate the interests of multiple market participants, including producers, merchants, local distribution companies and others. To address this issue, this paper established a bilevel optimization model for the optimal allocation of network capacity. Two approaches are proposed: (i) a single-level reformulation-based method (KKT reformulation), used as a benchmark, where a tailored outer approximation (OA) method is applied to handle the nonlinear constraints; (ii) a distributed method where a variant of Inexact-ADMM is proposed by exploiting the separable structure of coupling constraints. Case studies are performed on a real-life 352-node gas network. Initially, the proposed method is tested by a simplified 139-node gas network, showing that the solution speed of Inexact ADMM is 27.51 and 28.23 times faster than that of KKT-based method in two typical cases, while the objective value of the whole system only increased by 0.41% and 0.38%. Then, the Inexact-ADMM is successfully implemented in the whole size of the 352-node gas network by further decoupling the upper-level PNOs model into several sub-models. The maximal relative error of hydraulic was <0.4% in Case 1 and 0.7% in Case 2. This study demonstrates that the proposed ADMM-based distributed algorithm can guarantee the pipeline operation, safely and efficiently, by determining the suitable bookable capacities of the shippers. • A bilevel model for the optimal capacity allocation of natural gas network is established. • An ADMM based method is proposed to solve the bilevel model in a distributed way. • The solution speed is >20 times faster than that of KKT method in a 139-node gas network. • The maximal relative error of hydraulic for the 352-node gas network is <1%. [ABSTRACT FROM AUTHOR]

The accurate and comprehensive equivalent circuit model (ECM) of Proton Exchange Membrane electrolyzer (PEME) is needed urgently in the design process of power supply for electrolytic hydrogen production such as rectifier or dc/dc converter. However, the existing ECM can only simulate the steady-state and dynamic electrical characteristics of electrolyzer in the electrolysis process under current controlled mode. In this paper, in-situ electrochemical impedance spectroscopy (EIS) is employed to measure the electrode impedance (R) and double-layer capacitance (C) of PEME. It's worth noting that for gaining the value of R and C, only the electrochemical circle fit method is used without further proceeding. Based on this method to identify parameters, a novel and generic ECM of PEME is proposed to simulate stable electrical characteristics of three different PEMEs under current controlled mode and voltage controlled mode, respectively, in the start-up, electrolysis, and turn-off stages. Specially in this model, diodes' switching characteristics are innovatively employed to simulate the start-up stage; on the other hand, an RC circuit connects with the diode parallelly to model the turn-off stage. Moreover, it can precisely characterize the dynamic and transient electrical behaviors of PEME. Finally, the validity of proposed ECM of PEME is verified based on small scale experiments. • The model simulates PEM electrolyzer's electrical characteristics under voltage control. • The model applies to simulating PEM electrolyzer' electrical feature at various power levels and aging stages. • The model can simulate the multi-time scale electrical characteristics of PEM electrolyzers. • The model is based on circuit principles for its modeling approach and no longer employs empirical formulas. [ABSTRACT FROM AUTHOR]

Accurate cluster photovoltaic power prediction (CPPP) is crucial for the operation and control of renewable energy grid-connected power systems. The traditional modeling strategies for CPPP such as direct aggregation (DA) and statistical upscaling (SU) have limitations such as error accumulation and upscaling factor uncertainty. To address these issues, this paper proposed a novel hybrid approach for CPPP by combining machine learning models with an improved SU technique. Firstly, a robust broad learning system (BLS) model, in which the Generalized Maximum Correntropy Criterion (GMCC) is used to replace the original mean square error (MSE) loss in BLS, is proposed to solve the problem of multiple outliers affecting the prediction accuracy of regional cluster stations, and it is called GBLS. Then, the Relevance Vector Machine (RVM) as an effective nonlinear regression model is further utilized to compensate for the prediction errors obtained by the GBLS to form the hybrid prediction model, namely GBLS-RVM. Moreover, to mitigate the uncertainty associated with scaling factors in traditional SU strategy, a new SU strategy is developed to refine the relationship between the reference station and the cluster sub-region, enabling direct modeling for regional power prediction. Finally, data from two PV clusters in different regions of China are used to validate the effectiveness of the proposed model, and the results show that under the improved SU strategy, the GBLS-RVM model, reduced RMSE by approximately 33.7% compared to the traditional BLS model, and the RMSE decreased by 12.89% and 30.2% when compared to traditional DA and traditional SU strategies. • A novel hybrid method for CPPP is developed by combining ML and improved SU technique. • A BLS with GMCC (GMCC-BLS) as a prediction model is developed to handle the outliers in measurement data. • An innovative error correction strategy based on RVM is proposed to improve prediction reliability. • A new SU technique is used to establish direct relationships between reference stations and subregions. [ABSTRACT FROM AUTHOR]

Investing in energy efficiency measures is a major challenge for SMEs, both for environmental and economic reasons. However, certain barriers often make it difficult to invest in such measures. Although public financial support helps to overcome economic barriers, public bodies face the challenge of identifying which SMEs display the greatest potential to invest in energy efficiency measures. By applying a random forest technique and by using sampling balancing techniques, this paper identifies the profile of industrial SMEs that might be potential beneficiaries of public aid, thereby helping public institutions to target their calls and direct their efforts towards this group of SMEs. Specifically, liquidity and indebtedness are found to be the most useful predictors for SMEs in the industrial sector. The results are robust and reveal that applying a random forest approach for unbalanced samples offers greater predictive capacity and statistical power than applying traditional estimation techniques. By identifying potentially benefiting firms, this work helps to boost the effectiveness of public subsidies and to improve the channeling of public funds, which ultimately favors investment in energy efficiency. • Public subsidies favor energy efficiency measures by reducing the up-front cost and making the investment more profitable. • Subsidy effectiveness depends on the capacity to identify which SMEs are potential beneficiaries of energy subsidies. • We evidence that applying a random forest approach for unbalanced samples offers greater predictive capacity and statistical power than traditional techniques. • The most useful predictors for SMEs in the industrial sector are related to liquidity and indebtedness. [ABSTRACT FROM AUTHOR]

Low-carbon buildings (LCBs) are normally equipped with distributed energy resources (DERs) such as photovoltaic (PV) panels and batteries, thereby creating LCB prosumers that both produce and consume energy. Peer-to-peer (P2P) energy sharing among LCB prosumers could bring higher economic benefits for themselves, and facilitate better local power balance for the power grid. To fully harness the benefits of P2P energy sharing for both LCB prosumers and the power grid, a bi-level optimization method for LCB prosumers and the power grid operator is proposed in this paper. The grid operator at the upper level imposes the optimal time-varying network charge to LCB prosumers at the lower level to maximize its profit. And LCB prosumers with the objective of minimizing their costs adjust the schedules including P2P energy sharing and their heating loads to respond to the grid operator's optimal network charge prices. Additionally, to further exploit the energy resources including heating loads in LCBs, the model predictive control (MPC) approach is integrated with the bi-level optimization in the presence of uncertainties. The bi-level optimization belongs to the NP-hard category, making it challenging to solve. To reduce the computational complexity, the bi-level optimization problem is converted to the single-level programming using Karush-Kuhn-Tucker (KKT) conditions. Numerical results illustrate that the bi-level optimization method can obtain a balanced scheduling scheme between the grid operator and LCB prosumers. In addition, incorporating the thermal inertia of building heating loads can provide more operational flexibility for both the grid operator and LCB prosumers. Furthermore, the costs of LCB prosumers based on MPC approach can be reduced with the decrease of forecast error level. • A bi-level optimization for grid operator and LCB prosumers with P2P is developed. • The time-varying pricing strategy for network charge of grid operator is proposed. • The bi-level solution method integrated with MPC approach is investigated. • The utilities of bi-level optimization through comparative scenarios are analyzed. [ABSTRACT FROM AUTHOR]

Multi-energy systems with carbon capture system (CCS) and power to gas (P2G) can reduce carbon emissions while increasing the consumption of renewable energy. This paper proposed a long-term equilibrium including electricity, gas and carbon markets to study the price effect of the system and its impact on the carbon-gas- electricity sector. The simulation results indicate that the main impact of the system on the electricity sector is to reduce energy curtailment and provide flexible resource for the electric system. P2G used an excess of 22.21 TWh of renewable energy, while CCS used 2.65 TWh of electricity to operate the capture system. The multi-energy system will reduce the market clearing price in the gas market (the annual average gas price decreased from 276.90 to 276.30 yuan/MWh). However, the partial transfer of flexibility to the electricity sector may increase cost in the gas industry, which in turn will be transferred back to the electricity sector again. In addition, through the operation of multi-energy system, carbon elements will circulate in the three sectors. CCS will become a carbon price-maker when electricity scarcity and carbon quota tightening, and increase carbon prices, which will increase the utilization hours of CCS and P2G and their profitability. • A carbon-gas-electricity market coupling model is proposed. • The multi-energy system will reduce the market clearing price in the gas market. • The partial transfer of flexibility may increase cost in the gas industry • CCS will become a carbon price-maker when electricity scarcity. [ABSTRACT FROM AUTHOR]

Increasing renewable energies bring more fluctuating power outputs, and make operating reserve become more important for maintaining the system balance. Regulating flexible loads to provide operating reserve has been widely accepted as a promising alternative, while the communication network will face enormous challenges due to the frequent data transmission and explosive data volume of large-scale distributed loads. To address this issue, this paper proposes a coordinated optimization framework of power-communication coupling networks for dispatching large-scale flexible loads to provide operating reserve. A power-communication equivalent model is established to couple the regulation power and transmitted data from flexible loads. The data nodes and branches in the communication network are formulated equivalently with power nodes and branches in the power network. On this basis, considering spatially and temporally dynamic power-communication coupling networks, the power flow and communication flow are coordinately optimized to minimize the regulation costs and communication costs. The proposed scheme is validated based on the 5-bus and 118-bus power-communication coupling networks. Numerical results illustrate that the coordinated optimization framework reallocates operating reserve capacities and decreases the regulation cost of power-communication coupling networks. • A dispatching scheme of large-scale flexible loads to provide operating reserve. • A coordinated optimization framework of power-communication coupling networks. • An equivalent model of communication networks to power networks. • Reduce 6.49% of cost by the proposed scheme compared with traditional scheme. • Dynamic analysis of communication networks' impacts on dispatching reserve resources. [ABSTRACT FROM AUTHOR]

This paper presented the fabrication of a multifunctional microcapsule, integrating visible-light-driven photocatalysis and latent heat storage capabilities. The core-shell structure incorporated a phase change material in the core, facilitating latent heat storage. Meanwhile the shell was composed of fluorine-doped TiO 2 , enabling photocatalysis under visible light. Different from conventional fabrication methods necessitating stringent conditions, this study employed a synthesis approach viable at low temperatures (50–90 °C) and ambient pressure. The resulting microcapsule exhibited high photocatalytic capability under visible light, which was able to fully degrade organic dye within 7 h exposure to visible light. Parametric studies indicate photocatalytic efficiency was enhanced with reduced capsule size and elevated fabrication temperature. Optimal conditions were observed at a capsule size of 100 μm and a fabrication temperature of 90 °C. Furthermore, the microcapsule possessed a high thermal storage capacity of 99.4%, indicating the effectiveness of the shell in safeguarding the core material during the phase change process without compromising its energy storage capacity. Even after 100 h of exposure to visible light, the microcapsule demonstrated outstanding thermal stability and durability. • Fabrication of a multifunctional microcapsule under low temperature and pressure. • PCM in the core allowing the capsule to exhibit excellent thermal storage capacity. • F-doped TiO 2 shell enabling high photocatalytic capability under visible light. • A multifunctional microcapsule with high stability and durability. [ABSTRACT FROM AUTHOR]

The use of machine learning in building technology has become increasingly important in recent years. One of the applications is heating load prediction, which enables demand-side flexibility. Most studies consider the heating load prediction without sufficient context with the existing building characteristics. For an accurate load prediction, suitable features have to be selected according to their importance, the feature importance (FI). The scope of this paper is to investigate whether there is a relationship between the building characteristics and the FI and if so, how strong this relationship is. Additionally, an analysis has been conducted to determine which building characteristic have the most significant impact on FI. For this purpose, a full factorial design of a room with six different building characteristics is carried out. In total, the heating load is calculated for 15 552 room variants. The thermal balance, correlation, random forest FI, permutation FI and SHapley Additive exPlanations (SHAP) values are calculated for these different rooms. The local SHAP values were used to explain the model. These values also provide insight into the interaction of individual features with the heating load. For most variants, the outdoor temperature had the highest FI. It is investigated which building characteristics have the greatest influence on the thermal balance, correlation, FI and SHAP values. A relationship was found between the proportion of thermal balance, the correlation between the features and the label as well as the FI. The greatest association with the thermal balance characteristics was found for the SHAP values. The study shows the systematic relationship between building characteristics and FI. Therefore, FI should always be considered in the context of building characteristics. [Display omitted] • SHAP values provide an excellent way of showing how features affect heating load. • Comparison of various feature selection methods for 15 552 different rooms. • Characteristics of the building are reflected in the feature importance. • Air exchange rate has the greatest influence on the feature importance. [ABSTRACT FROM AUTHOR]

This paper explores the viability of utilizing agricultural biomass-based energy potential, employing mathematical, engineering, and economic modeling techniques. Moreover, the potential of a biogas-based co-digestion (CD) system, integrating its techno-economic performance and environmental sustainability in terms of electricity generation, has also been studied. In this investigation, the categorization of 25 different plant species into two groups: arable field crops (AFCs) and horticultural plants (HPs), was performed. Data was collected during the 2021‐–2022 cropping season in Bangladesh from various sources, including literature reviews, governmental, and non-governmental organizations. The findings revealed that the available agricultural biomass residues, totaling 1,02,585.75 KT, have the capacity to generate 1,33,815 million m3/year of biogas. This energy potential corresponds to 291,125.85 TJ/year or 9231.60 MW of electricity, which can fulfill 88% of the national total energy demand. In terms of levelized cost, the proposed approach is more competitive and shows a greater promise compared to other technologies. Furthermore, it demonstrates environmental friendliness by reducing CO 2 emissions by 156 tons at a cost of $7/ton while earning $1092 annually from the potential carbon-credit market. This approach presents a potential solution to address Bangladesh's energy crisis. The payback period of the system ranged from 2.93 to 3.75 years, with and without the inclusion of a slurry, respectively. The recommended methods hold significant promise for meeting national energy demands. A case study was provided as a proof-of-concept (PoC) to validate the approach. This study is the first of its kind, providing valuable insights into the renewable energy potential in Bangladesh. The results will assist policymakers in formulating sustainable energy policies. • Mathematics, engineering, economics and environmental modeling explored renewable energy. • Techno-economics and environmental sustainability validated the proposed methodology. • Bioenergy-based electricity could supply 88% of the national energy demand of Bangladesh. • A case study was provided as a proof-of-concept (PoC) to validate the approach. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is effective in identifying the dominant instability mode (DIM) of power systems. However, regular supervised learning for training DL models requires a large number of training samples with accurate labels provided by power system experts, which is prohibitively costly in practice. To address this issue, this paper proposes a weakly supervised learning framework to train DL models for DIM identification based on cheap but potentially inaccurate (noisy) labels from non-expert engineers. The framework comprises two stages to mitigate the detrimental effects of label noise. At Stage I, an auxiliary model is proposed to intelligently detect detrimental noisy samples while preserving truly-labeled informative hard samples based on the entire training loss dynamics of base DL models. Then Stage II incorporates virtual adversarial training to utilize all samples, including noisy ones with labels removed, to train a smooth DIM identification model in a semi-supervised learning way. It can help further mitigate the effects of undetected label noise. Case studies are conducted on CEPRI 36-bus system and Northeast China Power System (2131 buses). The results verify that the proposed framework can tolerate high-intensity feature-(in)dependent label noise and build reliable DL models for DIM identification with significantly less reliance on experts. • Robust deep learning for power system dominant instability mode identification. • Two-stage weakly supervised learning to mitigate label noise from non-experts. • Noisy samples are detected while hard ones are preserved. • Virtual adversarial training is proposed to use noisy samples as unlabeled ones. • Intelligent dominant instability mode identification becomes more affordable. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, a theoretical expression for the efficiency of a fuel cell system is derived. The studied system consists of a proton-exchange membrane fuel cell stack coupled at its output to a DC/DC boost converter. A fuel cell stack Larminie and Dicks model along with an efficiency curve for our boost converter are fitted using experimental data. One maximum efficiency point for the fuel cell stack coupled to the DC/DC power converter is shown and lies in the safe zone of the fuel cell stack. Experimental results on the fuel cell efficiency, DC/DC efficiency and their association on a test bench indicate a good agreement between the theoretical estimations and the experimentally obtained results at different values of the DC/DC converter output voltage. The results provide strong theoretical and experimental evidence for a unique maximum efficiency point for the fuel cell system and form a basis for developing maximum efficiency point tracking algorithms. • Experimental verification of theoretical expression on a fuel cell test bench. • Study of maximum efficiency point tracking of fuel cell + DC/DC converter system. • Derivation of a theoretical expression for the total efficiency of a fuel cell system. • Numerical methods for computing theoretical total efficiency. • International collaboration between Australian and French research teams. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage framework to facilitate the integration of distributed energy resources (DERs) within emerging distribution-level markets. Market prices significantly influence the decision-making process for prosumers and electric vehicle (EV) owners participating in the market, necessitating a robust optimization approach to formulate resilient offering and bidding strategies under uncertain market conditions. The proposed model is tailored for a distribution network equipped with a neighborhood battery, enabling prosumers to charge or discharge the battery profitably. Robust half-hourly-based offer and bid curves are submitted by prosumers and EV owners, reflecting their market price predictions and accommodating different levels of uncertainty, treated as scenarios. The social welfare maximization problem is formulated using a scenario-based approach, considering the received offer/bid curves to clear the market. Results show that considering the discharge capability of EVs leads to an increase in the overall social welfare of distribution networks. Additionally, when both the capabilities of discharge and export power are considered, it can be observed that the presence of a neighborhood battery can lead to an improvement of more than 35.83% in overall social welfare. • Employing robust optimization to drive prosumers and EV owners' strategies in markets • Ensuring robust solutions by incorporating offer/bid curves and AC OPF constraints • Assessing neighborhood battery impact on prosumers' bill and network constraints [ABSTRACT FROM AUTHOR]

The gradual degradation of the electrical properties of solid oxide fuel cells (SOFC) when operating on C-containing fuels poses a significant obstacle to their widespread implementation in industry. This paper investigates the distribution of carbon deposition and its structural evolution in the traditional SOFC electrode (Ni/YSZ). The carbon deposition weight in direct methane SOFC increases linearly with exposure times without electrochemical reactions. The carbon initially deposits at the triple-phase boundary (TPB) of the anode, subsequently covering the surface of Ni particles and eventually accumulating to form a carbon deposit layer. Three methods for alleviating carbon deposition are compared. The polarization current could inhibit the carbon deposition but the high stress within the cell had resulted in the brittleness of the electrode, as well as significant coking in the central region of the anode after 26 h. Alternating changes in methane and hydrogen can significantly reduce the degradation rate of the cell. The reasonable gas switching frequency is from 60 min to 120 min. After high electrolytic current, the gap between the anode and the electrolyte can be achieved at 68.2 μm. • Modulation and degradation evaluation for direct methane SOFCs. • The disordered amorphous carbon would be transferred into the graphite structure. • The polarization current could inhibit the carbon deposition. • Switching between hydrogen and methane at 0.5 A/cm2 current density could improve cell lifetime. • The high electrolytic current could dislodge the electrodes. [ABSTRACT FROM AUTHOR]

Equilibrium capacity expansion is an effective tool to help regulators predict the generation evolution of power systems by analyzing the decision-making of generation companies (Gencos). With the development of carbon markets (CMs) and electricity markets (EMs), Gencos' decisions in these two markets (encompassing investment and bidding decisions related to carbon emission allowances and electricity) are deeply intertwined through market clearing results. However, existing studies mainly focus on equilibrium capacity expansion with fixed carbon prices and usually ignore the realistic clearing process of CMs, which cannot simulate Gencos' coupled decision-making and accurately predict generation evolution under the CMs and EMs. This paper proposes a two-level equilibrium capacity expansion model for power systems which considers the coupled decisions of Gencos between CMs and EMs. At the upper level, multiple Gencos' coupled decision models are proposed based on their interaction with market clearing results. At the lower levels, CMs and EMs are respectively cleared to determine the varying carbon and electricity prices. Furthermore, the developed equilibrium capacity expansion model is reformulated as an equilibrium problem with equilibrium constraints (EPEC), which is solved by a diagonalization-based solution procedure. Finally, a provincial power system of southeast China is used to validate the performance of the proposed framework. The results illustrate that under CMs and EMs, non-fossil energy would account for 81.3% of total capacity and 73.88% of total generation in 2050. Compared to the existing model using fixed carbon prices, the consideration of realistic carbon market clearing will promote the installation of renewable energies and improve social welfare. The proposed model can provide beneficial references for regulators to perceive the evolution of power systems in CMs and EMs. • A two-level equilibrium capacity expansion model for power systems is proposed. • A novel coupled decision model for Gencos in CMs and EMs is developed. • The proposed model is solved by a diagonalization based solution procedure. • The results can guide the transition of power systems under the CMs and EMs. [ABSTRACT FROM AUTHOR]

Defossilization of the current energy system is a major requirement to decelerate anthropogenic climate change. However, a defossilized energy system is vastly more complex than current fossil-based energy systems: The integration of distributed energy resources and sector-coupling increases connectivity, demands interdisciplinary workflows, and creates a need for more sophisticated design processes. Inspired by the semiconductor and automotive industries, digitalization of the design process using platform-based design (PBD), coupled with the energy hub concept, can improve cost-effective energy systems design and accelerate the industry's contributions to achieving net-zero emissions. PBD is an efficient and effective methodology to manage and de-risk the complexity of integrated energy system design, leading to affordable and reliable solutions due to the inherent techno-economic analysis underlying the decision-making process. Combining the PBD framework with the energy hub concepts establishes a powerful design workflow for developing holistic energy systems from a single building up to the district and city scales. The fundamental tenets of this workflow, as discussed in this paper, are (1) the separation of functions from architectures, (2) the identification of abstraction levels at which systems can be analyzed and optimized, and (3) the ability to repurpose components at all levels of abstraction to aid design reuse and allow performance feedback at every stage of the process. We argue that PBD can become the next frontier in energy system design. PBD, as presented in this paper, is not limited to the energy sector, and it can also be a sub-process of an even more holistic infrastructure design. Spatial planning, architecture, and civil engineering can all be further integrated with the PBD concept, allowing societies to reach ambitious sustainability goals faster, at lower cost, and with greater resilience. • Innovative design methodologies to effectively navigate energy system transformation. • Novel framework combining platform-based design (PBD) and the Energy Hub concept. • Inspiration provided by the digitalization process of the semiconductor and automotive industries. • Proposal of a new "design-build-operate" workflow for the energy sector. • Augmentation of scalability, risk mitigation, and time-to-market reduction for energy systems. [Display omitted] [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]

Zero‑carbon electricity is a pre-requisite for decarbonisation of the wider economy and many scenarios envisage rapid expansion of renewables. IEA estimates a need to double the annual rate of investment to 2030. A rapid transition to low carbon electricity creates a risk to investors because projects being financed now will generate their revenue in a zero‑carbon electricity system for which there is no track record of price formation. Although the cost of renewable power has fallen greatly, future revenues from renewables are still affected by market price risks that are often outside the control of generators. If risks are difficult to quantify, they may be mis-priced, leading to inefficient premiums being added to the cost of capital, which could increase the overall cost of the transition. In this context many countries around the world provide support for low carbon energy, such as the UK Contracts for Difference. In this paper we investigate exposure to price risk caused by uncertainty over the mix of technologies used to achieve decarbonisation, which we term 'transition risk', and how this varies under different technology mixes, and for different policy regimes. We show that exposing investors to transition risk could increase the cost of delivering the renewables needed for a zero‑carbon electricity system by around 25% or £7bn/year compared to policy options that reduce exposure to wholesale market price risk. The paper concludes that as long as transition risks remain high, de-risking policy mechanisms can help to minimise the overall cost of achieving net-zero. • We characterise the current stage of the low-carbon transition as a 'build phase', requiring large volumes of investment • Uncertainty over the final physical state of the decarbonised system causes significant financial risk to investors • 'Contracts for difference' (CfDs) help to manage this 'transition risk' and reduce the cost of capital for projects • Exposing projects fully to transition risk could increase costs by a third, and raise the cost of transition by £7bn per year [ABSTRACT FROM AUTHOR]

The concept of "more- and all-electric aircraft" has become increasingly popular over the last two decades, leading to a boost in the extensive utilization of airborne electromagnetic actuators such as permanent magnet synchronous machines (PMSMs). Inevitably, considerable waste heat is generated during operation due to the incomplete energy conversion, which is a great challenge for conventional onboard thermal control systems. If the huge generated heat cannot be removed efficiently there will be a sharp temperature rise in thermal-sensitive components of the onboard devices. This overheating will cause operational failures which are intolerable in aerospace applications. Aiming to improve the current thermal protection system and gain greater compatibility with the current PMSM system, this paper proposes a phase change material-based (PCM-based) thermal protection strategy using commercial wax in two equivalent PMSMs. A comparative investigation shows that the machine temperature with PCM-based cooling strategy can be reduced under most operating conditions (the maximum temperature drop is 13.7 °C), demonstrating the advantages of deploying the proposed PCM-based cooling scheme. Further data processing towards the heat absorption-dissipation matching between the utilized PCM and the working mode of the cooling object is conducted. A dimensionless correlation of dimensionless temperature peak difference (DTPD) as a function of the pulsed factor, presented in Eq. (17), reveals an average relative error of ±4.5%. Based on the correlation, an optimal pulsed factor of ∼1.764 is calculated to reach a matching condition. Such a method can be promoted to wider applications using PCM as a cooling medium beyond the currently-utilized two types of machines in this paper. • Phase-change-material-based cooling for electromagnetic machines is proposed. • Optimal intermittent operating conditions were identified experimentally. • Two different machine types were considered for a broad application. • Based on experimental data, a correlation was developed for a universal guide. [ABSTRACT FROM AUTHOR]

Compressed air energy storage (CAES) systems offer significant potential as large-scale physical energy storage technologies. Given the increasing global emphasis on carbon reduction strategies and the rapid growth of renewable energy sources, CAES has garnered considerable attention. However, the optimal design of CAES systems presents challenges due to the intricate coupling of multiple parameters, complex physical processes, and highly variable operating conditions. This paper comprehensively reviews three key aspects related to CAES optimal design. Firstly, it examines system analysis and optimization methodologies. Secondly, it investigates the off-design characteristics of CAES systems. Lastly, it explores system design methodologies specifically addressing off-design operations. Notably, the corresponding-point methodology (CPM) is proposed as a means to provide clearer guidance for system improvements. Future research should aim to combine CPM with traditional methods to establish more efficient and intuitive system optimization approaches. Furthermore, the exploration of active off-design regulation strategies, including the integration of various adjustment methods and multistage combined regulation for CAES systems experiencing wide load fluctuations, requires further investigation. Novel CAES configurations that enable efficient off-design conditions should also be explored, despite the inherent complexities associated with regulation and control. Finally, a coupled design methodology based on off-design operation data and probabilistic-load factor analysis is presented. This paper provides valuable insights and guidance for the optimal design and study of CAES systems. • CPM combined with traditional methods is needed to improve CAES round-trip efficiency • Not high temperature/pressure but "corresponding" is key factor to high round-trip efficiency • Multiple regulations and new system structure are needed for wide-load operation • Design methodology addressing complex off-design operations needs further improvement [ABSTRACT FROM AUTHOR]

The pressure head generated by the upper reservoir of a pumped hydro energy storage system can be sufficient for creating the pressure gradient required for a reverse osmosis desalination plant. Combined with the fact that many drought-stricken coastal areas have nearby mountains at the necessary elevation for these upper (mountaintop) reservoirs, a symbiotic relationship can be ascertained through the co-location of a pumped storage hydropower (PSH) system with a reverse osmosis (RO) desalination system. Merging PSH and RO into one Integrated Pumped Hydro Reverse Osmosis System (IPHROS) instead of implementing each individually could result in a number of benefits, including reduced capital investment, lower maintenance costs, and a natural mechanism for diluting the highly saline brine discharge generated from the RO process. This paper extends the work of Slocum et al. in 2016, who first introduced the concept of IPHROS, by optimizing the amount of seawater sent to and diverted from the upper reservoir for maximal energy recapture and freshwater production, respectively, while also seeking to maximize the RO system recovery ratio. For this multiobjective optimization, a new reverse osmosis model is created that utilizes a blend of empirical and fundamental equations based on the solution–diffusion model of membrane transport and boundary layer effects that naturally occur along reverse osmosis membranes. Additionally, surrogate models are developed to predict the permeate flowrate and fractional salt rejection rate for a Seamaxx™-440 RO element. Optimizing the presented IPHROS model reveals a 16% decrease in the break even time for IPHROS compared to PSH and RO being implemented individually, and that at the best design with regards to the energy, freshwater, and RO system recovery objectives, 79.5 million kWh of energy and 5.79 million cubic meters of fresh water can be delivered to a population, significant amounts for a population seeking to transition to a renewable energy-based grid and alleviate dire freshwater conditions. Enhanced modeling and optimization, as was initiated in this paper, will eventually aid in IPHROS' large-scale adoption into energy and freshwater infrastructures. • MDO of an Integrated Pumped Hydro Reverse Osmosis System (IPHROS) is presented. • Fractional salt rejection rate and permeate flowrate surrogate models are developed. • Tradeoffs exist between optimal IPHROS designs with respect to different objectives. • IPHROS cost savings results in 16% faster break even time from simple NPV estimates. [ABSTRACT FROM AUTHOR]

Concept drift (CD) and regional data sharing were considered potential factors critical to affecting data-driven model accuracy. In this paper, an incremental photovoltaic (PV) power prediction method is proposed for addressing the CD of streaming data and the privacy protection of regionally shared data. In the proposed scheme, a novel CD detection method is developed using Energy distance as a virtual drift criterion to measure the data distribution change and prediction error change as a real drift criterion to determine the drift time. In order to incorporate richer data features, this paper jointly trains a PV prediction model based on a broad learning system (BLS) through regional data sharing. Further, an incremental BLS model under the distributed federated learning (FL) framework is designed to maintain the privacy of each PV station's data. Finally, an incremental online model update strategy is proposed to learn new features continuously and without forgetting under the FL framework after CD occurs. The test results on the public dataset from DKASC, Alice Springs PV system, present that the proposed method can effectively detect the CD of streaming data and realize the fast online model update. Compared with the state-of-the-art techniques, the proposed method can significantly improve prediction accuracy while preserving privacy. • A real and virtual drift detection method for PV streaming data is proposed. • An online PV prediction model based on incremental FL is designed to protect privacy. • A privacy-preserved incremental update strategy after concept drift is proposed. • Ablation and comparison experiments verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Long-distance power support through High-voltage Direct Current (HVDC) has provided feasible solutions for power dispatch and control problems in multi-area power systems under high share of renewable energy. In this paper, an advanced multi-area intra-day dispatch strategy for power systems with high penetration of renewable energy considering power support capacity is established to increase the accommodation of wind power and enhance the stability of the power system. At first, a novel assessment method of regional active power support capacity is carried out in this paper, which can sufficiently quantify the regional power margin (RPM), the DC dispatchable power (DCDP), and the regional power support distance (RPSD) of a power system. Subsequently, a multi-area intra-day dispatch strategy based on power support capacity analysis is proposed, where constraints on conventional units, wind farms, transmission lines, and RPSD are developed. Finally, to enable the development of the dispatch model, a model predictive control (MPC) scheme is used to update the power support capacity indicators dynamically and improve model reliability. The effectiveness and superiority of the proposed strategy are verified by case studies. Numerical results show a 7.02% cost reduction and overload risk reduction by using the proposed strategy compared to conventional global optimization methods. • A novel assessment method of regional active power support capacity is proposed. • A multi-area dispatch strategy considering power support capacity is established. • A model predictive control scheme updates the power support capacity indicators. • Proposed strategy enhances the power system stability and reduces dispatch costs. [ABSTRACT FROM AUTHOR]

Inevitable model uncertainty could render active yaw control (AYC) to make suboptimal or even irrational decisions. To this end, this paper quantitatively analyzes the influential mechanisms of uncertainty on optimal yaw policy in terms of the wake model and power model. In order to address power model uncertainty that has received little attention in the AYC field, this paper proposes an efficient adaptive economic predictive control. This framework begins with the construction of a neural network-based compensator to provide online adaptive estimation of parameter uncertainty. On this basis, an adaptive economic predictive control with the economic objective of maximizing the total power is designed to increase the confidence of yaw policy in the presence of generation uncertainty, and a parallel heuristic solver is developed to allow real-time control of large-scale wind farms. Finally, extensive results based on three wind farm cases demonstrate the effectiveness and advantages of the proposed control strategy. • Quantitative uncertainty analysis of wake model and power model on AYC is conducted. • Adaptive economic predictive control is proposed to hedge against generation uncertainty. • Enhanced parallel SSA is developed to enable real-time operation. [ABSTRACT FROM AUTHOR]

To further explore the multi-energy complementary potential on multi-time scales under variable operating conditions, a refined modeling and collaborative configuration method for Electric-Hydrogen-Thermal-Gas Integrated Energy Systems (EHTG-IES) with hybrid energy storage system (HESS) is proposed in this paper. To commence with, an advanced operation model for the EHTG energy conversion equipment is formulated by incorporating their nonlinear operating characteristics under variable operating conditions. Next, the distinct features of HESS with different energy medium are accurately depicted with various effective linear-reduction and relaxation strategies. Then, the coupling design day method and the intra-day and inter-day state superposition strategy are combined to efficiently model the time horizon. Lastly, a co-optimal configuration model of the EHTG-IES is devised, with the aim of minimizing total annual cost. The case study validates that the refined modeling of coupled components leads to a 3.18% reduction in cost and 5.05% reduction in carbon emissions significantly. This paper also assessments the synergy and substitution benefits of multiple low-carbon technologies and finds that diurnal and seasonal hydrogen storage have a large part of overlapping roles, and hydrogen-based system contributes a deeper utilization of seasonal thermal storage. The sensitivity analysis indicates that the current carbon tax policy has a negligible impact on the carbon emissions, equivalent to about 1% of the gas price, but appropriately increasing attention on low carbon allows for an emission decrease of 28.62% at a cost increase of 4.38%. [Display omitted] • Various low-carbon technologies are finely modeled considering non-linear features. • Linear-reduction and relaxation strategies are developed for hybrid energy storage. • The complementary potential of low-carbon technologies is utilized and explained. • Cost and CO 2 emissions are reduced thanks to the refined model for the system. [ABSTRACT FROM AUTHOR]

The development of the theoretical model for the spherical collision triboelectric nanogenerator (SC-TENG) has been slow, despite its ability to generate electric energy through low-frequency vibration while simultaneously reducing the motion response of the primary system. Previous theoretical models have solely focused on energy harvesting, disregarding the damping effect of spherical collisions. This paper presents an electromechanical coupling control equation, which accounts for the dynamic and electrical characteristics of spherical particles, to describe the relationship between motion displacement, velocity, acceleration, and particle number. The particle motion process is divided into two phases: non-collision and collision process. Using the constant velocity before and after collision, this paper calculates voltage and current changes corresponding to two collisions of particles in one cycle. Cadence software simulates and analyzes the equivalent circuit parameters of the TENG, where output voltage is proportional to the resistor/capacitor. Single, two, and three-particle acceleration, velocity, and displacement before and after collision are simulated using Abaqus software. Particle collision simulation shows the energy loss is related to the number of particles, the more the number of particles, the more the energy loss. The single-degree-of-freedom experimental model tests vibration reduction and energy collection effects of particles. As mass ratio increases, the vibration reduction and energy collection effects improve for particles. If the mass ratio remains constant, the vibration reduction effect does not vary much for different particle diameters (10/12/15 mm), but the energy collection effect of particle diameter of 10 mm is superior. Multi-layer particles, vibration control, voltage and current increase linearly with the number of layers. SC-TENG can be applied in the future to reduce structural vibration, while its additional energy collection capabilities can be as self-powered sensing to monitor movement of vibrating structures in real-time. • The electromechanical coupling control equation of SC-TENG is proposed. • The voltage and current change equations when SC-TENG collides twice in one motion cycle are considered. • The simulations of particle collisions and electrical characteristics based on SC-TENG are performed. • SC-TENG experiment shows that increasing particle mass enhanced both energy collection and vibration control effects. • SC-TENG can be applied to reduce structural vibration and can also be used as a self-powered device. [ABSTRACT FROM AUTHOR]

In the energy transition context, restructuring energy systems and making informed decisions on the optimal energy mix and technologies is crucial. Energy system optimisation models (ESOMs) are commonly used for this purpose. However, their focus on cost minimisation limits their usefulness in addressing other factors like environmental sustainability and social equity. Moreover, by searching for only one global optimum, they overlook diverse alternative solutions. This paper aims to overcome these limitations by exploring near-optimal spaces in multi-objective optimisation problems, providing valuable insights for decision-makers. The authors extend the concepts of epsilon-optimality and necessary conditions to multi-objective problems. They apply this methodology to a case study of the Belgian energy transition in 2035 while considering both cost and energy invested as objectives. The results reveal opportunities to reduce dependence on endogenous resources while requiring substantial reliance on exogenous resources. They demonstrate the versatility of potential exogenous resources and provide insights into objective trade-offs. This paper represents a pioneering application of the proposed methodology to a real-world problem, highlighting the added value of near-optimal solutions in multi-objective optimisation. Future work could address limitations, such as approximating the epsilon-optimal space, investigating parametric uncertainty, and extending the approach to other case studies and objectives, enhancing its applicability in energy system planning and decision-making. [Display omitted] • We present an approach to exploring near-optimal spaces in multi-objective problems. • Using necessary conditions, better insights can be provided to decision-makers. • The methodology is applied to a case study of the Belgian energy transition. • We reveal minimum resource needs to achieve a reasonable cost and EROI. [ABSTRACT FROM AUTHOR]

The trend towards reducing greenhouse gas emissions by dipping the use of traditional sources of energy in marine power grids, as well as the rapid expansion of renewable energy sources (RESs), were the driving forces behind the incorporation of RESs in maritime microgrid systems and enquiry of the ensuing prevalent control mechanism. The frequency stability of a Marine Microgrid System (MMGS) is a critical aspect that directly affects its reliable and efficient operation. This study describes a method for frequency stabilization in independent maritime microgrids consists of diverse renewable energy resources including wind turbine generators, sea wave energy/tidal power generation, solar generation, bio-diesel generator and energy storage systems. This paper aims to develop a new optimal cascaded order proportional integral based proportional derivative for marine load frequency control. Due to the fact that the performance of the controller is highly dependent on the parameters of the controller, optimizing these coefficients can have a significant impact on the output performance of the LFC control. In light of this, this paper presents the sewing training-based optimization (STBO), a new human-based metaheuristic algorithm to optimize the coefficient of the suggested controller. The essential stimulation of STBO is the teaching procedure of sewing to beginner tailors. By utilizing a population of candidate solutions, the algorithm iteratively refines the control parameters to find the optimal set that minimizes frequency deviations and enhances system stability. The responses of the cascaded PI-PD controller are linked to those of the PID and PI controllers in order to demonstrate its superiority. To evaluate the efficiency of STBO, it is compared to well-established recent techniques such as fitness dependent optimization, grey wolf optimization and Jellyfish search optimization. From the results, it is observed that our proposed approach improved the settling time by 25.35%, 45.89%, and 29.35%, reduced peak overshoot by 78.34%, 67.71%, and 78.23%, and similarly reduced undershoot by 81.56%, 56.22% and 76.56% as compared to FDO, GWO and JSO techniques respectively. Finally, the sensitivity analysis is performed under ±50% load variation and ± 40% power system parameters to prove the robustness of the proposed controller. • A diverse renewable energy resources including renewable energy resources and energy storage systems (ESS,s) incorporating with battery energy storage, capacitive energy storage, and flywheel energy storage system has been considered for marine microgrid. • A novel cascaded order PI-PD controller has been designed and implemented for the suggested distinct renewable and energy storage based maritime microgrid. • The coefficient of the proposed controller is optimized with a new human-based meta-heuristic algorithm known as sewing training-based optimization (STBO). • Multi objective functions including integral time absolute error (ITAE), integral time square error (ITSE) and integral square error (ISE) are formulated for the proposed power system. • Finally, sensitivity testing of the STBO adjusted PI-PD controller under uncertain parameters such as turbine gain variation (K CE), droop factor (R), and inertia constant (M) is performed to prove the robustness of the proposed frequency control technique. [ABSTRACT FROM AUTHOR]

For the scheduling problem of virtual power plants (VPPs), this paper aims to propose a dynamic trigger scheduling strategy that integrates objective grid information and subjective judgment factors affecting VPP scheduling. The strategy design involves setting initial incentive electricity prices and optimizing price adjustment efficiency. Firstly, this paper establishes a reference incentive electricity price model to derive the initial reference electricity price for the dispatching process. Then, a response willingness model is proposed to establish the relationship between electricity price and VPP response, improving the accuracy of the initial reference electricity price and reducing the frequency of subsequent price adjustments in the actual trigger. Finally, this paper utilizes the groundbreaking dynamic trigger method to adjust the price based on the actual VPP response, obtaining the optimal VPP incentive electricity price. We use Matlab to verify this strategy, and the experimental results show that the average response error using this strategy is reduced by 3.33 kW compared to the contrastive literature. Moreover, scheduling task non-completion does not occur in this method. Thus, the accuracy and reliability of the method are verified. • Propose dynamic trigger strategy to consider the subjective judgment factors. • Improve the scheduling economy of VPPs scheduling. • Reduce the time consumption of dynamic triggers by response willingness model. • The problem of individual error and overall error of the model is solved. [ABSTRACT FROM AUTHOR]

District heat load forecasting is a challenging task that involves predicting future heat demand based on historical data and various influencing factors. Accurate forecasting is essential for optimizing energy production and distribution in district heating systems. However, most existing forecasting models lack transparency and interpretability and fail to capture the spatial–temporal dependencies in the data. Moreover, they often require a large amount of annotated data for training, which can be costly and time-consuming to obtain. In this paper, we present a novel approach to district heat load forecasting, which involves predicting future heat demand based on historical data and various influencing factors. The proposed approach is based on an Active Graph Recurrent Network (Ac-GRN), which leverages the strengths of active deep learning and graph neural networks to capture the complex spatial–temporal dependencies in the data. The approach also provides explainability for its predictions by using correlation-based attribution methods. The active deep learning component can effectively select the most informative and representative samples from a large pool of data, reducing the frequency and cost of data collection and human effort. The graph neural network component can model both linear and nonlinear relationships among heat meters using bidirectional recurrent connections, enhancing the accuracy and robustness of the predictions. We conduct extensive experiments and compare our approach with eleven state-of-the-art models on a real-world dataset of district heating consumption data from Danish residential buildings. Our results show that our approach outperforms other models in terms of accuracy, robustness, reliability, and computational efficiency for multi-horizon multi-step district heat load forecasting. Our approach also provides meaningful explanations for its predictions by highlighting the most influential factors and heat meters for each prediction. This paper makes a novel contribution to district heat load forecasting with explainability. • Novel approach for district heat load forecasting with active deep learning and graph neural networks. • Explainable predictions using gradient-based attribution methods. • Outperforms state-of-the-art models in accuracy and robustness. • Evaluates on real-world district heating data and conducts ablation studies. [ABSTRACT FROM AUTHOR]

This paper investigates the viability of CO 2 storage in geological formations, including depleted hydrocarbon reservoirs applying 3-dimensional seismic and well data of the Niger Delta region as a case study for CO 2 sequestration, which represents an essential initiative for the reduction of greenhouse gas emissions. Different theoretical and experimental studies from literature on CO 2 sequestration in geological formations across the world, including the Niger Delta, are reviewed. The Niger Delta basin has a high potential for CO 2 sequestration, and this indication is shown through the review of research papers undertaken outside and within the basin, which reveal the presence of massive hydrocarbon fields, lateral continuity of reservoir, reservoir-seal pairs, faults, and traps, developed hydrocarbon fields, appropriate reservoir depth. The gaps identified from the review of various research studies are analyzed to form the basis of a future research investigation for capturing, removing, and storing CO 2 in depleted hydrocarbon reservoirs and other geological formations. The authors deduced that accurate injection pressure, rate, and depth estimation are critical factors for CO 2 sequestration and need to be thoroughly investigated. • Niger Delta depleted oil/gas reservoir CO 2 capture, utilization & storage reviewed • Potential issues relating to CO 2 sequestration and modeling in Niger Delta noted • Appropriate CFD solution should combine static and dynamic model. • Research gaps identified and future directions recommended. [ABSTRACT FROM AUTHOR]

Organic Rankine cycle (ORC) is considered to be a good solution to the high-efficient recovery of low-grade heat for saving energy and reducing carbon emissions. As the core component, the heat exchanger plays a significant role in the thermo-economic performance evaluation of an ORC plant and its heat transfer correlations greatly influence the cycle performance. In this paper, 8 two-phase heat transfer correlations (4 flow boiling ones and 4 flow condensation ones) are utilized to design the plate-type heat exchangers, and a comparative study among different correlations is carried out. The result shows the designed evaporators are finitely affected by heat transfer correlations whereas the situation with the designed condensers is the opposite. The net output power is slightly influenced with a relative difference within 1.1% but the system's economic performance is significantly affected with the maximum relative difference of electricity production cost up to 11.2%. The environmental performance is hardly affected by correlations with the maximum relative difference of total greenhouse gas emission within 0.96%. This paper analyzed the performance variation of the ORC under different correlations and provided a guideline for designing heat exchangers and evaluating the off-design performance of ORC systems. • 8 two-phase heat transfer correlations are compared for designing the heat exchangers. • Designed condensers are affected by the heat transfer correlations more significantly. • Net output power is slightly influenced with a relative difference within 1.1%. • Electricity production cost is affected with a relative difference 11.2%. • Total carbon emissions exhibit minimal variation with a relative difference of only 0.96%. [ABSTRACT FROM AUTHOR]

This paper introduces a two-stage two-level optimization method for optimal day-ahead and real-time scheduling of multicarrier energy distribution systems and microgrids. The model considers the incentive-based and price-based demand response programs to encourage microgrids to transact electrical, heating, and cooling energy carriers with the energy distribution system, which is named hereafter as the energy system. Further, the model formulates the resilient operation of the energy system considering the energy transactions with the electrical, heating, and cooling markets. The main contribution of this paper is the integration of demand response procedures of microgrids in energy transactions with the energy system considering the switching of electrical switches and heating and cooling control valves. The optimization process is another contribution of this paper that is decomposed into two stages that consist of day-ahead and real-time horizons. The first stage is also decomposed into two levels that determine the optimal scheduling of the energy system and microgrids in day-ahead markets. The second stage is comprised of two levels that commit the energy system and microgrids resources. A resiliency index is proposed to assess the resiliency of the energy system in shock conditions. The proposed method was simulated for the 123-bus test system. Different types of microgrids, incentive-based and price-based demand response processes were considered. Simulation results confirmed that the proposed method can reduce the costs of residential, industrial, and commercial microgrids by about 4.47%, 3.88%, and 5.47% concerning only the real-time pricing process. Further, the model can increase the aggregated benefits of the energy system in the day-ahead and real-time markets by about 0.608 Million Monetary Units (MMUs) and 1.10 MMUs, respectively. • Optimal scheduling of a combined cool, heat, and power-based energy system. • Considering commitment scenarios of microgrids in multi-carrier energy markets. • Integrating resilient operational scheduling of multicarrier energy system. • Considering different demand response programs including incentive-based and price-based programs. [ABSTRACT FROM AUTHOR]

District heating is an efficient method of distributing heat in densely populated areas at a low cost. The heat is usually produced at central production plants and then distributed to consumers through large networks of pipes. However, district heating is gradually becoming more decentralised with additional heat sources, e.g. heat pumps, solar thermal farms, and industrial waste heat connected to the network. Therefore, the system is changing from a system with centralised heat sources to a more decentralised system with several different heat sources within the network, including also still a large production area. Operationally this is more complex than the previous setup, especially in terms of temperature optimisation. Typically, the temperature must be adjusted for each area in order to work efficiently with the decentralised heat sources, so a forecast of the local heat load is required. It is relatively easy to make a forecast for each area, but they are usually made independently and are therefore not necessarily coherent. In this paper, we propose a methodology to spatially reconcile hierarchies of individual localised heat load forecasts with a coherency constraint. This results in coherent reconciled forecasts. Enhancing forecast accuracy and making them coherent are essential for future decentralised systems as temperature and production optimisation need accurate information to yield optimal operation. We will use two different case studies to illustrate the proposed method. One case study has a few areas, while the other case study will have more areas, and here it is proposed to add a new level of aggregation to the hierarchy to increase accuracy. The results in this paper show that the reconciled forecast, where information is shared between areas through the spatial hierarchy, improves forecast accuracy by 1% to 20%, depending on the prediction horizon. • Spatial hierarchies are used to improve state-of-the-art heat load forecasts. • Recursive and adaptive covariance matrix estimation is used for reconciled forecasts. • Using multi-step forecasting base errors show higher accuracy than using only one-step. • Simulation study to investigate where the accuracy improvements origin from. [ABSTRACT FROM AUTHOR]

Considering the abundant wind and solar energy resources on waters, this paper integrates wind assistant propulsion and photovoltaic technology with ship electric power system as sail-photovoltaic-hybrid power system (sail-PV-HPS). When properly designed, sail-PV-HPS can present environmental and economic benefits compared with conventional sail-diesel power system. However, the optimal design of sail-PV-HPS is a complex task due to environmental uncertainties. In order to fill such gap, this paper proposes a probabilistic optimization method to determine the size parameters of sail-PV-HPS, pursing minimum GHG emission and lifecycle cost. Two pairs of joint distributions of wind speed and wind direction as well as solar irradiance and ambient temperature are established based on coupla function considering the interconnection and seasonal characteristics of the meteorology variables. The performance of the proposed approach is assessed by the retrofit of the power system of an unmanned ocean surveillance trimaran sailing in the Yellow Sea area. A deterministic optimization and a quasi-probabilistic optimization are performed to highlight the effect and importance of taking the uncertainties and their correlation into consideration. Results show that the sail-PV-HPS designed by the proposed probabilistic method outperforms the sail-PV-HPS given by the deterministic optimization and a quasi-probabilistic optimization, as well as, the original power system of the trimaran in terms of GHG emission and lifecycle cost. • Probabilistic optimization is proposed perusing minimum GHG emissions and life cycle cost for hybrid power systems. • Uncertainties in wind direction, wind speed, solar irradiance and ambient temperature are considered simultaneously. • Joint distributions are established with copula function to describe interconnection between the meteorology variables. [ABSTRACT FROM AUTHOR]

Load shedding could be a solution to water accumulation alleviation during the operation of proton exchange membrane fuel cell (PEMFC) engines. In this paper, multiple-step loading reduction strategies based on two laboratory-scale (25 cm2) single PEMFCs (assembled with triple-serpentine and single-serpentine flow field plates on the both electrodes and thus named after cell TS and cell SS respectively) are experimentally discussed and evaluated. The real-time pressure evolution and HFR are recorded, compared and analyzed to characterize water transfer phenomenon among the components. The investigation results suggest that: 1. From the perspective of output voltage and power after load reduction, the number of reduction step has little effect on the output performance; 2. Cell TS exhibits better homogeneous trend of pressure drop although with less water removal quantity comparing with that of cell SS, which is resulted from multi-channel configuration; 3. The cathode pressure drop of cell SS decreases obviously throughout the experiment, indicating the convinced water removal capability of single-serpentine configuration; 4. Constant increase of HFR reflects membrane dehydration when the loading reduces. This paper demonstrates the possibility of removing accumulated liquid water by reducing the load of PEMFCs. • Triple-serpentine configuration ensures a controllable fluctuation range of pressure. • Triple-serpentine results in little effect on water removal when the loading sheds. • Single-serpentine configuration alleviates water accumulation effectively. • Single-serpentine configuration leads to back diffusion during the shedding. • Membrane dehydration should be noticeable during the shedding process. [ABSTRACT FROM AUTHOR]

Shared energy storage is widely recognized as an energy hub for the coordinated operation of regional integrated energy systems (RIESs). Multi-energy systems (MESs) share centralized energy storage to store excess renewable energy sources (RESs). This paper proposes a novel energy-sharing framework considering hydrogen trading, which is suitable for residential, industrial, and commercial RIES structures. Shared energy storage operator (SESO) promotes hydrogen energy transactions by formulating time-of-use (TOU) hydrogen prices. The proposed hydrogen energy trading method can be regarded as a master-client structure, and a hierarchical optimal scheduling method based on the Stackelberg game relationship is designed. Finally, a case study is performed based on an optimal scheduling algorithm combining particle swarm optimization (PSO) and mixed integer linear programming (MILP). The cases show the electricity‑hydrogen shared energy storage mechanism in RIESs can improve the RESs utilization rate and effectively reduce the operating costs of each system. Moreover, compared with RIESs with a single centralized electric energy storage, the TOU hydrogen price mechanism can further lower the energy prices and improve the economy of the RIES. The cases show that hydrogen energy sharing reduces the operating cost of RIES by 9.96% and increases the regional energy utilization rate by 2.97%. And the shared energy storage business model can satisfy the economy of both SESO and RIES. It confirms the rationality of the electric hydrogen shared energy storage project. • A sharing energy storage mechanism in multi-energy systems is presented in this paper. • The hydrogen trading methods based on time-of-use hydrogen prices are considered in this paper. • A hierarchical optimal scheduling method based on the Stackelberg game is designed. [ABSTRACT FROM AUTHOR]

In this paper, a scheme of pulse combustion mode with pulse input of gas is proposed to solve the problems of low output electrical performance and energy conversion efficiency of combustion-based thermoelectric systems under limited heat dissipation conditions, which is based on experiments. The characteristics of the thermoelectric system show that pulse combustion mode can increase the instantaneous temperature difference, so as to achieve the excitation effect on the output power. The relationships among output power, system efficiency, excitation intensity and pulse input parameters, and the geometry of thermoelectric modules are obtained. The influence of pulse input parameters on the excitation intensity is interactive, and top dead center of pulse inlet power is the parameter that has the greatest influence on the performance. Compared to the constant combustion mode, the pulse combustion mode can increase the output power by up to 28.3%. A maximum output power of 7.15 W with air-cooled heat dissipation and a maximum system efficiency of 3.26% were experimentally obtained, which is a 30% increase in efficiency over current air-cooled heat dissipation thermoelectric systems. Therefore, this paper can provide solutions for more practical power supply, provide effective and feasible guidance for the study of thermoelectric systems, and provide useful insights into the electrical properties of thermoelectric systems. [Display omitted] • An air-cooled combustion based thermoelectric system was built. • A scheme of pulse combustion mode with pulse input of gas is proposed. • The pulse combustion mode can increase the output power by up to 28.3%. • High output power and efficiency results were obtained by experiment. • The power shortage is alleviated under limited heat dissipation conditions. [ABSTRACT FROM AUTHOR]

The load forecasting methods based on similar days has been widely studied in decades, where similar days were mostly used as an auxiliary means to improve model performance. The process of transitioning from similar days to load forecasting results requires the use of complex models, which are often opaque and lack interpretability. Thus, this paper proposes a new method called NSDAR (Neural-based Similar Days Auto Regression) based on the similar days analyses. Different from the conventional methods based on similarity of meteorological factors or simple policy for similar day screening, the NSDAR uses neural networks for similar day screening, which can detect many unconventional similar days that are difficult for people to notice, and in order to obtain interpretable load forecasting results, NSDAR uses a linear model to generate load forecasting results based on the similar day screening result. In addition, the model results can also be used for regional load analysis, load clustering, and other purposes. This paper guides the structural design of the model through theoretical derivation and verifies the feasibility of this method. NSDAR performs well in similar day screening and load forecasting tasks, and its results have better interpretability compared to the other existing load forecasting models. • Analyzed and modeled the similarity of power load similarity. • An interpretable method for similar day screening and load forecasting is proposed. • The multi-time-scale input structure is introduced into the proposed method. • Results of the proposed method can be used for region load analysis and load clustering. [ABSTRACT FROM AUTHOR]

Water flooding is a crucial factor affecting the lifetime of a proton exchange membrane (PEM) fuel cell. In this paper, A laboratory-scale (25 cm2) single PEM fuel cell was tested to investigate the characteristics of fuel cell under a load shedding process with various air flow rates. The performance was assessed using output power and electrochemical impedance spectroscopy, while the cathode flooding was identified using voltage and cathode pressure drop. The results show that reducing the load within a specific range can lower the impedance and lessen floods. The results also demonstrate that there is an optimal inlet flow rate interval considering its effect on system efficiency, durability, and water flooding. In addition, a stepped load shedding pattern was proposed to solve the flooding problem of a fuel cell while taking the power demand into account. The effect of load shedding cycle and air inlet flow rate on flooding was evaluated by comparing the flooding severity coefficient and output power. The results prove that a longer current reduction cycle is more favorable in alleviating flooding problems. The optimal mode for the fuel cell in this paper was determined according to the flooding situation and power variation. Finally, the approach to obtaining the optimal load shedding pattern for other PEM fuel cells system was summarized. The flooding mitigation approach proposed in this paper can be utilized as a guide for developing strategies to extend the life of PEM fuel cells. [Display omitted] • Diagnosis of flooding using a combination of pressure drop and voltage is more accurate. • Load shedding mainly affects the concentration resistance and charge transfer resistance. • There is an optimal inlet flow rate interval considering its effect on system efficiency, water flooding and durability. • Long stepped load shedding cycle contributes to flooding mitigation under a heavy condition. • Purging with load shedding is a useful approach for water management under a heavy condition. [ABSTRACT FROM AUTHOR]

The district heating system (DHS) is one of the essential carriers for coordinating renewable energy with fossil energy and achieving flexible energy consumption. Considering the uncertainty of both the renewable energy units' power and the consumers' aggregate loads impact the transport process of the DHS, the thermal characteristics of the DHS transportation and the node temperature response under the source-load uncertainty need to be quantified and analyzed. This paper proposed a method to calculate the nodes' temperature dynamic response of the DHS under multiple source-load uncertainty scenarios. The prediction and combination methods of supply and demand probability intervals of source and load nodes under varying credibility levels were investigated. Additionally, the effects of source-load uncertainty boundary conditions on the DHS transport process were analyzed. The temperature response of the nodes was calculated based on the established dynamic DHS model under multiple source-load uncertainty scenarios. A DHS in Beijing was selected for case validation and analysis, and it has 90 nodes and 109 pipes. The simulation results showed that the relative average error of node flow rate in the dynamic DHS model was 3.02%, and the average difference in node temperature was 1 °C. The probability distribution semi-analytical method and the Bayesian credible interval calculation method can accurately describe the uncertainty on both source and load sides. The models and algorithms proposed in this paper could effectively quantify the nodes' dynamic temperature response under the source-load uncertainty. • Proposed uncertainty quantification models for the source and load nodes of DHS. • Developed a novel credible interval calculation method for nodes' thermal power. • Quantified nodes' temperature dynamic response considering source-load uncertainty. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for integrating renewable energy sources (RES) and power-to-gas (P2G) technologies to optimize energy utilization efficiency as RES capacity increases. The method introduces the potential RES penetration index (PRPI) to select the appropriate capacity of P2G facilities for improved RES acceptance rates. In order to establish input constraints for RES and P2G facilities, this study determines the saturation point of RES (R-SP) and the saturation point of P2G (PG-SP). Additionally, the paper introduces the marginal capacity rate of RES to P2G (MCRG) as a useful tool for evaluating the adequacy of planned facilities. This paper's index is limited to PV as a renewable energy source. By incorporating PRPI into the input plan of P2G facilities, the proposed method improves PV's acceptance rate, contributes to facilities' economic planning, and streamlines optimization complexity. Previous studies have demonstrated the potential for P2G technology to optimize the integration of PV into the grid. However, the proposed method considers the PV's acceptance rate and provides system planners with necessary information on RES and P2G facility capacity. The method proposed in this paper can be extended to include other RES. • Novel Index for PV-P2G integration (PRPI). • Enhancing Acceptance rate of PV applying PRPI • Reduced optimization complexity. • Adaptable to various renewables. [ABSTRACT FROM AUTHOR]

Accurate short-term forecasting of building cooling load (CLF) in an integrated energy system (IES) is essential for effective building energy management. However, the existing CLF models for IES often treat each building as an independent entity and neglect the spatiotemporal correlation among buildings. To address this research gap and achieve accurate CLF, this paper proposes a new hybrid deep learning model that considers spatiotemporal coupling. First, the coupled spatial–temporal features among different buildings were analyzed, and the meteorological factors were screened based on the Spearman's rank order correlation coefficient (SROCC). Second, synchrosqueezing wavelet denoising (SWT) was adopted to denoise the historical cooling load (CL) data, remove high-frequency noise, and improve data quality. Third, the TTGAT-GTC model was constructed for the CLF of an IES. A temporal trend-aware graph attention network (TTGAT) captured the spatial correlation of CL between buildings. A gated temporal convolution layer (GTC) was constructed to extract the trend in the dynamic temporal variation in historical load. Residual and skip connections were applied to avoid gradient disappearance and increase the computational efficiency of the model. To validate the effectiveness of the proposed SWT-TTGAT-GTC model, this paper compared the proposed model with four benchmark models using MAPE, RMSE, MAE, and R2. The experimental results showed that the performance of the proposed CL forecasting model is superior and that the proposed model appropriately introduces the spatio-temporal coupling information between buildings. • The spatiotemporal information between related buildings is appropriately considered in CL. • TTGAT captures the spatial correlation of cooling load between buildings. • GTC extracts the trend in dynamic temporal variation between buildings. • Residual and skip connections are used to avoid gradient disappearance. [ABSTRACT FROM AUTHOR]

This paper introduces and formalizes the concept of T-shape data, which arises in several engineering and natural contexts, where the initial data are richer and cover a wider range of operations than the data acquired in the following time. The specific context considered is the Li-ion battery experimental testing before the actual operation, where the T-shape data is cast as right-censored data in the application of battery remaining useful life (RUL) prediction. Existing RUL prediction techniques focused on RUL point estimation and provided rough calculations concerning the RUL distribution, thus are insufficient for battery degradation information. Based on the T-shape structure, this paper investigates a time-varying construction of the RUL probability density function. The proposed computationally efficient method, the multiple non-crossing quantiles Long Short-Term Memory (MNQ-LSTM), learns long-term dependencies among battery RUL and operation process quantities, including operating conditions. With several predicted quantile levels, the construction of the RUL conditional probability density function can capture the underlying survival distribution and statistical inferences of battery RUL with richer and more accurate information. Numerical results verify the performance of the proposed MNQ-LSTM with T-shape data. Compared to the conventional LSTM, Gaussian process regression, and a hybrid deep learning model of convolutional neural network and the bi-directional gated recurrent unit, the proposed model outperforms and can achieve the coefficient of determination up to 95.74% regarding point predictions. 100% testing results of battery RUL are not outside the range of 90% prediction intervals even in the worst case of 80% T-shape data. • Formalize a novel T-shape data concept that arises in several practical contexts. • Develop first time multiple quantile LSTM for probabilistic battery RUL prediction. • Introduce Kaplan–Meier weight for quantile LSTM model training with T-shape data. • Resolve crossing quantile problem without imposing sophisticated constraints. [ABSTRACT FROM AUTHOR]

Efficient capture and global optimization of organic Rankine cycle (ORC) operating features in road environment is the key to obtain actual waste heat recovery potential. The complexity of time-varying characteristics intensifies the nonlinear coupling relationship between ORC performance and parameters. This paper first designs and builds an ORC experimental system for recovering waste heat from internal combustion engine (ICE). The experimental system includes ICE and its measurement subsystem, ORC subsystem, expander lubrication subsystem, cooling subsystem, and ORC measurement subsystem. R245fa is used as working fluid; The expander adopts a specially designed single screw expander. The performance of ORC waste heat recovery is tested by adjusting the operating conditions of the ICE, expander, pump, and valve opening. The temperature range of waste heat is 627–686 K. Subsequently, a dynamic feature capture and global multi-objective optimization approach for ORC is proposed. The results show that the precision of dynamic feature capture can be improved at least 41.78% with the introduction of information theory in complex environment. Frequent fluctuation of vehicle speed is not conducive to the improvement of evaporator inlet temperature at working fluid side. As the speed of the vehicle is greatly reduced, the evaporator inlet temperature drops sharply. The frequent fluctuation of speed will intensify the nonlinear correlation between operating parameters and system performance and make the system performance more dependent on the coupling correlation between operating parameters. The approach proposed in this paper can provide a new idea for efficient capture of dynamic characteristics and global multi-objective optimization of ORC in road environment. • Proposed dynamic feature capture modeling for organic Rankine cycle (ORC). • A global multi-objective optimization approach for ORC considering road environment. • Information theory-based model show excellent generalization and robustness. • The road environment data confirmed the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This paper proposes a novel compressed CO 2 energy storage system based on 13X zeolite temperature swing adsorption (TSA). Based on 13X zeolite adsorption gas storage and exothermic and desorption heat storage characteristics, the system could accomplish high-density storage of low-pressure CO 2 and efficient heat circulation. Evaluate the system investment by economic analysis model, and apply the law of thermodynamics to perform energy and exergy analysis. Under the design conditions, the round-trip efficiency, the efficiency of the system, the energy storage density, and the investment cost per unit power were 88.88%, 69.30%, 6.23 kWh/m3, and 1.22 k$/kW, respectively, which demonstrates that the system is strongly feasible and has broad engineering application prospects. Sensitivity analysis results suggest that adsorption temperature and desorption temperature positively and negatively affect system performance. The storage pressure and temperature have a trade-off relationship and almost opposite effect on the system. The lower pinch point temperature difference assists in improving the thermodynamic performance of the system but leads to added cost. System thermal and economic performance have a gamble relationship in the equipment efficiency dimension. • A trans-critical compressed CO 2 energy storage system based on 13X zeolite temperature swing adsorption is proposed. • The contact heat exchange solution enables instant internal circulation of heat, eliminating the need for heat storage media and storage tanks. • In this paper, the system feasibility is evaluated in terms of both performance and cost. [ABSTRACT FROM AUTHOR]

With the continuous development of the global economy, the demand for natural gas is increasing year by year. At the same time, the widespread adoption of emission reduction measures around the world prompts many countries to switch to cleaner energy sources, which is driving the growth in demand for natural gas. In order to regulate the balance of natural gas supply and demand, maintain the stable operation of natural gas systems and reduce environmental pollution, this paper proposes an incentive-based demand respond (DR) model for natural gas, which takes carbon emissions and load volatility into account, and in the end, it concludes an effective response strategy. In order to get the multi-objective incentive-based DR, first of all, the reward function is constructed by adopting the TOPSIS method, and then it is solved by using the DQN algorithm, in the end, a multi-objective reinforcement learning method based on DQN is proposed. The DR model is evaluated by adopting a real load data from Xi'an city and DR strategies are proposed for different gas users in two periods: the heating and non-heating periods. The DR strategy proposed in this paper can effectively increase gas users motivation, reduce peak loads, maximize the revenue of gas suppliers and gas users, mitigate load volatility and reduce carbon emissions. • An incentive-based DR model for natural gas is proposed. • A multi-objective RL method based on the DQN algorithm is proposed. • Comfort loss cost parameters are improved to enhance DR model adaptability. • An evaluation of the DR strategy is carried out using data from the city of Xi'an. [ABSTRACT FROM AUTHOR]