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Considering the lower power output of microbial fuel cell (MFC), this study explored a new MFC coupled with a microbial electrolysis cell (MEC) system (MFC−MEC) to effectively promote extracellular electron transfer (EET) and consequently enhance MFC power output. Based on the principle of electric field superposition, we constructed a system of shared anode (SA-MFC-MEC) and cathode (SC-MFC-MEC) configurations between the MFC and MEC. In this system, the MEC provides an exogenous electric field (EEF) that strengthens its internal electric field, thereby enhancing the electrochemical properties of the anode biofilm. Our results demonstrated that the EEF effectively increased the electrochemical activity of the MFC anode biofilm, leading to the formation of more electrochemically active sites and thicker biofilms at the anode, resulting in increased power output. The SA−MFC−MEC system exhibited optimal performance, achieving a maximum power density of 120.9 mW/m2 and an EET rate constant of 1.9 × 10−5 cm/s, which were 2.17 and 2.4 times higher than the control R−MFC, respectively. These findings confirm that an EEF significantly contributes to the EET and is key to enhancing MFC power output. Additionally, the SA−MFC−MEC facilitated methane recovery through the independent operation of the MFC and MEC cathodes, with a gas production performance comparable to the control R−MEC. This study not only provides a deeper understanding of biofilm electrochemical behavior in an EEF environment but also demonstrates the potential of the MFC−MEC system in energy recovery applications. [Display omitted] • EET rate is 2.4 times higher than that of the R−MFC when sharing the anode. • The power output of MFC is improved by MEC coupling MFC. • EET characteristics are evaluated by SECM. • Suitable EEF can significantly improve anode electrochemical performance. [ABSTRACT FROM AUTHOR]

• Concept and definition of load carrying capability are proposed. • Characteristics of load carrying capability interval are analyzed. • A solution method of the optimal load carrying capability is proposed. • A linearization method is proposed to improve the computation efficiency. Evaluating the load carrying capability of regional electricity-heat energy systems is of great significance to its planning and construction. Existing methods evaluate energy supply capability without considering load characteristics between various users. Besides, the impact of integrated demand response is not fully considered. To address these problems, this paper builds a load carrying capability interval model, which uses reliability as a security constraint and considers integrated demand response. An evaluation method for the optimal load carrying capability considering uncertainties of load growth is proposed. First, this paper defines energy supply capability, available capacity, and load carrying capability. Interval models are built to achieve the visualization display of these indices. Their characteristics are studied and the impact factors of interval boundary are analyzed. Secondly, a two-layer optimization model for the evaluation of optimal load carrying capability is constructed, considering the uncertainties of load growth. The upper-layer model aims at optimizing the sum of load carrying capability benefit, integrated demand response cost, and load curtailment penalty. The lower-layer model maximizes energy supply capability. Thereafter, the lower-layer model is linearized based on piecewise linearization and the least square method. The computation efficiency is greatly enhanced. In the case study, a real regional electricity-heat energy system is used to validate the proposed model and method. [ABSTRACT FROM AUTHOR]

Due to the proliferation of distributed renewable energy, bidirectional electricity and carbon interaction appears in the coordinated transmission and distribution network and increases complexity in entities' decision-making. To address the issues, this paper develops a hierarchical market framework integrated with electricity and carbon trading. Specifically, an electricity market which combines locational marginal price (LMP) and peer-to-peer (P2P) trading is established considering transmission and distribution coordination. Meanwhile, based on carbon emission flow and P2P trading, a hierarchical coordinated carbon market is also designed to precisely measure and price carbon responsibility and contribution from prosumers. In addition, a hierarchical distributed algorithm is developed to clear the proposed market with privacy protection. Numerical results authenticate the necessity for transmission and distribution coordination, benefits of distribution electricity P2P trading as well as superiority in carbon measurement and pricing method. • An LMP-based electricity P2P market is designed for transmission and distribution network. • A systematic method is proposed to precisely price prosumers' carbon responsibility and contribution. • A hierarchical distributed algorithm is proposed to clear the P2P market under TSO-DSO-prosumer coordination. [ABSTRACT FROM AUTHOR]

Operational flexibility is to depict the ability of a power system in guaranteeing a secure and high-performance system operation, which is essential for distribution systems (DSs) with high renewable energy generation (REG) penetration. However, due to the dispersed integration of energy storage systems and uncertain REGs, the available regulation capability exhibits spatio-temporal coupling. It poses a challenge to quantify flexibility considering multiple locations and time periods. This paper proposes a novel analytical region-based method to characterize the flexibility considering spatio-temporal coupling, where the total dispatch budget and carbon emission constraints are considered to guarantee economic and environmental benefits. Leveraging the basic feasible solution theory, we derive an analytical formulation of the flexibility region, expressed as a set of linear inequalities. Note that no inner or outer approximation techniques are employed in the deduction procedure. The effectiveness and scalability of the proposed method are validated on test systems. Case studies demonstrate that the proposed method enables precise quantification and visualization of operational flexibility for DSs. The derived flexibility region is a closed polyhedron in a high-dimensional space, signifying the maximum ranges of uncertainties that can be accommodated. • A region-based method is developed for quantification and visualization of operational flexibility in distribution systems. • An analytical expression of the flexibility region under spatio-temporal coupling is derived from the dispatch model. • Total dispatch budget and carbon emission constraints are considered in flexibility quantification. [ABSTRACT FROM AUTHOR]

In the realm of economic scheduling and optimal operation within integrated energy systems, integrated energy load aggregation plays a pivotal role. Accurate prediction of integrated energy load aggregation enables the refined management of energy resources, thereby enhancing energy utilization efficiency. This paper merges the advantages of clustering algorithms, prediction algorithms and ensemble learning to propose a short-term prediction approach for integrated energy load aggregation. Firstly, the intrinsic distribution of energy consumption data is analyzed through Affinity Propagation (AP) clustering with multi-dimensional similarity. This analysis yields the division of all samples into clusters based on data characteristics. Subsequently, the Transfer entropy is harnessed to conduct correlation analysis within each cluster. For constructing predictive models for each individual cluster, a prediction algorithm based on the Feature-Temporal Attention-enhanced Bi-directional Simple Recurrent Unit (FTA-Bi-SRU) is employed. The Dynamic Weight Average (DWA)-based multi-task loss equilibrium and Multi-Term Adam (MTAdam) loss function are combined to enhance the efficiency of the forecasting model. Finally, a Light Gradient Boosting Machine (LightGBM) model with Bayesian optimization is employed to integrate the predictive outcomes of each individual cluster to generate the overall predictive outcomes for integrated energy load aggregation. The proposed methodology is experimented on real-world energy consumption data to ascertain the efficacy of the prediction method. The experimental results substantiate the superiority of the suggested forecasting approach. • This marks the inaugural discussion of load forecasting for integrated energy load aggregation is discussed. • A multi-dimensional similarity Affinity Propagation (AP) clustering algorithm is introduced to group multi-energy users. • A method based on transfer entropy for correlation analysis is presented. • An Feature-Temporal Attention based Bi-directional Simple Recurrent Unit (FTA-Bi-SRU) prediction model is introduced. • A Bayes-optimized Light Gradient Boosting Machine (BO-LightGBM) ensemble framework is proposed to enhance overall prediction. [ABSTRACT FROM AUTHOR]

• PV hosting capability for multiple locations is modeled as a feasible region. • The uncertainty of PV output is addressed by data-driven WDRCC. • Active management with limited budget is used to improve the hosting capability. • A systematic solution procedure is proposed to observe the feasible region. • Each PV integration request in the feasible region is a feasible request. The increasing integration scale of photovoltaic (PV) systems brings enormous challenges on distribution networks (DNs). To provide an explicit boundary of feasible PV integration capacity (PVIC) associated with each integration location, this paper proposes a novel depiction of PV hosting capability, which is the feasible region for PVIC in high-dimensional space. Next, a multi-objective optimization model based on information gap decision theory (IGDT) is proposed to observe the feasible region, where active distribution network management (ADNM) schemes with a limited budget are deployed to improve PV hosting capability. The impact of PV output uncertainty on security constraints is addressed by data-driven Wasserstein-distance-based distributionally robust chance constraints (WDRCCs). Finally, a systematic procedure is developed to solve the proposed model. Note that the exact power flow model associated with a new equivalent WDRCC reformulation method is deployed so as to guarantee the accuracy of the assessment. The effectiveness, solution accuracy, computational efficiency, and scalability of the proposed model and method are verified with the 4-bus system, the IEEE 33-bus system, and the IEEE 123-bus system. The output of the common method based on the linearized power flow deviates by more than 10% from the output of the proposed method based on the exact power flow in the 33-bus system. Each multi-location PV integration request is feasible as its locations and capacities belong to the proposed region. It implies that this region can provide guidance for PV allocation. [ABSTRACT FROM AUTHOR]

• Wasserstein ambiguity sets are utilized to depict the uncertain locational marginal price and nodal carbon intensity from the main grid and variable renewables in the distribution network. • A carbon-oriented Wasserstein distributionally robust scheduling model is proposed for the flexible interconnected distribution system with different price and carbon signals from the main grid. • Calculation and decomposition methods of DLMPs are presented for the flexible interconnected distribution system to measure the marginal cost of energy consumption in different nodes. • A novel equivalent DLMP (EDLMP) mechanism is also developed for the flexible interconnected distribution system to measure the actual cost of energy consuming in different nodes. Nowadays, distribution locational pricing mechanism has been widely used in the radial distribution network to guide the energy consumption and promote the renewables utilization. Aiming to explore the reasonable pricing policy which is applicable to the merging flexible interconnected distribution system (FIDS), this paper develops two different distribution locational pricing mechanisms for distribution lines interconnected by soft open points (SOPs). First, based on the Wasserstein ambiguity sets for uncertainties from the main grid and distribution network, a carbon-oriented distributionally robust economic dispatch model is constructed for FIDS considering different price and carbon signals from the main grid. Then, by leveraging the optimal scheme from economic scheduling model, distribution locational marginal prices (DLMPs) and equivalent DLMPs (EDLMPs) are presented to measure the marginal cost and actual cost for energy consumption in the FIDS respectively. The calculation and decomposition methods of these price signals are also developed. In case studies, a practical 63-node FIDS is chosen to verify the effectiveness of the proposed methods. Numerical results illustrate the characteristics of DLMPs and EDLMPs in FIDS under multiple uncertainties. Comparing to DLMPs, EDLMPs are more appropriate to FIDS because this pricing mechanism can reflect the lower generation cost of renewables for each consumer. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) energy trading provides potential economic benefits to prosumers. The prosumers are responsible for managing their own resources/reserves within the energy community, especially for photovoltaic (PV). However, the intermittency of PV leaves a major issue for the optimal operation of P2P energy trading. This paper proposes a fully data-driven distributionally robust optimization (DRO) for P2P energy trading. Specifically, both the optimization approach and the ambiguity set of DRO are formed in a data-driven fashion. The proposed formulation minimizes the expected operation cost of each prosumer, which is modeled as a DRO problem considering the operational constraints. A decentralized energy negotiation mechanism and market clearing algorithm are proposed for P2P energy trading based on the alternating direction multiplier method. Furthermore, the ambiguity set is formed by deep Gaussian process under the framework of bootstrap aggregating. Finally, the equivalent linear programming reformulations of the proposed DRO model are carried out and solved in a distributed manner. Numerical results demonstrate that the proposed DRO-based approach has superior performance for handling the randomness of PV generation compared with robust optimization, stochastic programming, and other DRO variants. [Display omitted] • The uncertainties of PV generation are quantified by implicit posterior variational inference deep Gaussian process. • A novel data-driven ambiguity set is proposed for modeling P2P energy trading. • The tailor-made equivalent model is derived based on the proposed ambiguity set. • This is the first attempt to integrate the DRO model and machine learning approach to P2P energy trading. [ABSTRACT FROM AUTHOR]

Assessment of production potential and prediction of sweet spots in unexploited shale gas wells are crucial technologies for achieving a high success rate in drilling. Most existing methods are only applicable for yield forecasting of shale gas wells with known geological and drilling/completion factors after production. In addition, the shale gas reservoir in the Longmaxi Formation in the southern Sichuan Basin of China is characterized by low porosity, low permeability, and diverse gas reservoir states. Thus, forecasting production from unexploited wells and predicting the exact location of the sweet spot under complex geological conditions have become challenging topic for both academia and industry. According to the first law of geography, shale gas wells that are closer in space have more similar geologic features. Based on this foundation, a machine-learning prediction model based on spatial correlation (SC-MLPM) is proposed for evaluating potential production of shale gas wells in the Changning play located in Longmaxi Formation in the southern Sichuan basin, China. Specifically, an improved K -nearest neighbor algorithm (SC-KNN) based on the spatial correlation characteristics of the exploited wells is designed to estimate the relevant geological factors of the unexploited wells in the same area. In addition, an extreme gradient lifting model based on Huber loss function (Hu-XGB) is developed to predict the potential production of unexploited shale gas wells, and then the shale gas sweet spot area in the research area is predicted quickly and accurately. The experimental results on 88 shale gas wells from Changning play show that the mean relative error of the prediction results of the proposed method is 9.8%. Furthermore, the results also determine that the sweet spots of shale gas wells in the research area are mainly developed in the northwest and northeast directions, and gradually become worse in the southeast direction. Eventually, experimental results for the Fox Creek play in Canada show that the proposed model obtains better predictive performance as the number of gas wells increases, and also verifies the generalizability. This work provides a basis for the efficient development of the research area. • A machine-learning prediction model based on spatial correlation is proposed. • An improved KNN algorithm is designed on the first law of geography. • An extreme gradient lifting model is developed to predict potential production. [ABSTRACT FROM AUTHOR]

Understanding the transport mechanism in catalyst layer (CL) agglomerates is crucial to enhance Pt utilization and overall fuel cell performance. In this study, high-resolution porous agglomerates are stochastically reconstructed, and capillary condensation of water in agglomerates is directly simulated using a lattice Boltzmann pseudopotential model. On this basis, a pore-scale electrochemical model is developed to investigate the effects of agglomerate structure and operating humidity on local transport resistance and agglomerate effectiveness at different Pt loadings. The results reveal that liquid water formed above 70%RH improves the electrochemical surface area (ECSA) of agglomerates with low ionomer content. An optimum I/C of 0.4 in agglomerates is determined by balancing the activated ECSA and oxygen transport resistance. Through the comparison of local transport resistance with limiting current data, an additional oxygen dissolution resistance of 60–350 s m−1 at the pore/ionomer or ionomer/Pt interface is quantified. The lowest local transport resistance of the agglomerate is achieved at about 70%RH, while condensed water at higher humidities hampers oxygen diffusion within the agglomerate, leading to reduced Pt utilization. Agglomerate size significantly affects the local transport resistance only when excessive ionomer or liquid water severely blocks primary pores. Finally, a novel multiscale coupling strategy integrating the porous agglomerate sub-model with a continuous-scale CL model is proposed, offering innovative insights into comprehending the relationship between CL structure and the performance of proton exchange membrane fuel cells. • High-resolution porous structures of the agglomerate are reconstructed. • Quantify the interfacial dissolute resistance and permeability reduction of ionomer. • Condensed water formed above 70%RH degrades the oxygen diffusion process. • Agglomerate size affects the transport resistance when primary pores are blocked. • A multiscale coupling strategy for the pore-scale agglomerate model is proposed. [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]

Abstract: Wind loads greatly affect the mechanical performance and tracking accuracy of heliostats. Therefore, the wind load must be accurately known to assure the operational reliability of heliostats. A simple wind load model was developed based on experiments on an actual heliostat rather than a wind tunnel heliostat model. The tested factors include the strain in the pedestal and the wind velocity at different heights. The test results were used to develop a mathematical wind load model for use in a finite element analysis of the heliostat. The finite element results agree with the experimental data; therefore, this experimental method provides a reference for wind-resistance designs of heliostats. [Copyright &y& Elsevier]

• A peer-to-peer trading mechanism is proposed for electricity-carbon integrated market on basis of Stackelberg game, which allows prosumers to change the roles between buyers and sellers flexibly. • The existence and uniqueness of Stackelberg equilibrium is demonstrated for the established electricity-carbon integrated market. • Both centralized analytical algorithm and decentralized iterative algorithm are expounded to solve the game model. • A two-stage peer-to-peer trading framework is extended to the electricity-carbon integrated market to support the transactive results of electricity and carbon allowance. The emergence of prosumers with considerable renewables promotes the lower-carbon operation of distribution network. However, prosumers' flexible roles also bring new challenges to establish a fairness distribution-level transactive market and ensure the security in distribution network. To address this issue, this paper presents a network-secure peer-to-peer (P2P) market with two-stage framework. In the first stage, a multi leaders multi followers (MLMF) Stackelberg game is utilized to establish the P2P electricity-carbon integrated market, which considers stakeholders' changeable roles between producer and consumer in the energy and carbon allowance transaction. The existence and uniqueness of the market equilibrium are also verified. In the second stage, network reconfiguration is taken into consideration to support the transactive results and avoid the restriction on node injected power to the largest extent. Numerical results show that the established P2P electricity-carbon integrated market can reduce carbon emissions by over 6% without harming prosumers' total revenues. [ABSTRACT FROM AUTHOR]

• The mathematical model of different uncertainties is presented. • The multi-objective stochastic-information gap decision model is developed considering multiple uncertainties in the SOP planning. • The general and systematic solving method of the multi-objective stochastic-information gap decision model is proposed. • The interaction between the power fluctuation and the growth uncertainty can be analyzed by means of the multi-objective stochastic-information gap decision model. Recently, soft open points (SOPs) attract much attention due to providing power flow control and voltage regulation to the distribution system (DS). However, multiple uncertainties and expensive capital cost are referred to as thorny issues in the SOPs planning. In this paper, a multi-objective stochastic-information gap decision (MS-IGD) model is presented to alleviate multiple uncertainties with the limited budget. First, a deterministic SOP planning model is built to minimize the comprehensive cost of distributed system operator (DSO). Then, a MS-IGD model is formulated considering power fluctuation and growth uncertainty in distributed generators (DGs) and loads. To solve this model effectively, it is transformed to a mixed integer linear programming (MILP) by the polyhedron linearization technology, linear programming theory and ε constraint algorithm. In case studies, based on a prospective DS from northern China, the SOPs planning scheme with corresponding accommodation range of multiple uncertainties is presented. Numerical results not only authenticate that the worst-case envelope bound should not be decided in advance because of the complicated relationship between multiple uncertainties and investments, but also demonstrate the negative correlation between the deviation of power fluctuation and growth uncertainty under the same budget. [ABSTRACT FROM AUTHOR]

In this paper, polystyrene(PS)/n-dotriacontane(Dot) composite nanoencapsulated phase change material (NEPCM) for thermal energy storage was synthesized by a facile and time-saving ultrasonically initiated miniemulsion polymerization method. The influences of ultrasonic factors including ultrasonic power output, time and temperature, the ratio of composite emulsifiers composed by sodium dodecyl sulfate (SDS) and poly-(ethylene glycol) monooctylphenyl ether (OP-10), as well as the Dot/styrene ratio on the z-average particle size, conversion, encapsulation efficiency and the phase change enthalpy of NEPCM were systematically investigated. The morphology, chemical structure and thermal performance of NEPCM were characterized by particle size analyzer, transmission electron microscope (TEM), Fourier transform infrared (FT-IR), X-ray diffractometer (XRD), differential scanning calorimetry (DSC) and thermogravimetric analysis (TG), respectively. The results showed that the ultrasonic technique can be successfully applied for the synthesis of PS/Dot NEPCM with high conversion (95.02%) and excellent encapsulation efficiency (61.23%) in a relatively short reaction time (35 min). The synthesized composite nanocapsules with uniform spherical core/shell structure and z-average particle size of 168.2 nm, had a high latent heat capacity ( T m 70.9 °C, 174.8 J/g) and good thermal stability, which indicates that it has great potential for the thermal energy storage. [ABSTRACT FROM AUTHOR]

• An optimal sizing method with selection of typical days is presented in this paper. • The degradation process of PEMFC, PEMEL and BAT is considered in this paper. • A life cycle cost scheme of IES is proposed. • The results show the effect of degradation processes on system sizing. • The benefits of seasonal hydrogen storage are discussed. In order to analyze the feasibility and economy of island integrated energy system combined power-hydrogen-heat-cooling cogeneration in detail, this paper proposes a two-level optimal sizing method. First, a cost scheme of the integrated energy system based on the life cycle is proposed, and the degradation, remaining life, and replacement of proton exchange membrane fuel cell, proton exchange membrane electrolyzer and battery are considered. Then, considering the randomness and correlation of various environmental data and loads during the sizing process, a clustering and scenario generation method based on eigenvalues is designed. Finally, an optimal sizing method based on the random-trigonometric grey wolf optimizer and mixed integer linear programming is proposed and tested. The performance of the two-level optimal sizing method is verified by benchmarks and a case in Ningxia, China. The scheduling results and economics based on the sizing results are elaborated and analyzed in detail. In particular, the degradation cost accounts for 13.1% of the total life cycle cost, and the seasonal hydrogen storage provides 1.4317 × 105 kWh of energy for the system at a lower cost compared with battery, which reveal the necessity of considering system degradation and the economic advantages of the integrated energy system with seasonal hydrogen storage. [ABSTRACT FROM AUTHOR]

Abstract: Laboratory and field experiments were carried out along with numerical simulations in this paper to study the effects of porous media on thermal and salt diffusion of the solar ponds. From our laboratory experiments simulating heat transfer inside a solar pond, it is shown that the addition of porous media to the bottom of a solar pond could help enhance its heat insulation effect. The experiment on salt diffusion indicates that the upward diffusion of the salt is slowed down when the porous media are added, which helps maintain the salt gradient. Our field experiments on two small-scaled solar ponds indicate that when porous media are added, the temperature in the lower convective zone (LCZ) of the solar pond is increased. It is also found that the increase in turbidity is repressed by porous media during the replenishment of the salt to the LCZ. Thermal diffusivities and conductivities of brine layers with porous media such as pebble and slag were also respectively measured in this paper based on the unsteady heat conducting principles of a semi-infinite body. These measured thermal properties were then used in our numerical simulations on the effect of porous media on thermal performance of a solar pond. Our simulation results show that brine layer with porous media plays more positive role in heat insulation effect when thermal conductivity of the ground is big. On the other hand, when the ground has a very small thermal conductivity, the performance of solar pond might be deteriorated and total heat storage quantity of solar pond might be reduced by brine layer with porous media. [Copyright &y& Elsevier]

• A region-user bi-level joint market architecture is constructed. • A regional level electricity-gas-heat integrated energy market framework is constructed. • A user-level P2P transaction mechanism is established based on a non-cooperative game. • The influence factors of region-level energy coupling market are analysed. • The economic and technical benefits of the P2P transaction are quantified analyzed. Electric power system reform and innovative energy transaction policies have become increasingly common. In view of the fact that research on today's multi-energy coupled market have not yet incorporated multi-energy substitution characteristics on the supply and demand sides, nor the coordination between regional and user level markets. A joint energy market was developed in this study for the procurement of local integrated energy system services considering demand flexibility. The transaction mechanisms of the regional level energy coupled market and user level Peer-to-Peer (P2P) distributed transaction market were determined accordingly. The multi-energy and complementary regional energy market was coupled with the user-level distributed transaction market that considering flexible demand response characteristics to establish a regional integrated energy system joint market. The electricity, gas, and heat markets in the regional market were modeled separately, then a joint clearing mechanism of the energy coupled market was established. An autonomous scheduling model for distributed market participants at the user level and a P2P transaction mechanism considering multi-agent non-cooperative game was proposed. The bidding behaviors of different energy operators in the regional level market were simulated to assess the influencing factors. The economic and technical benefits of P2P transactions at the user level as remitted to market participants were quantitatively determined with specific indexes. The correlations and interactions between the region level and user level markets were also analyzed. [ABSTRACT FROM AUTHOR]

• An optimized energy management strategy for hybrid tram is presented. • Adaptive recursive least square algorithm is proposed in this paper. • Degradation of fuel cell operation performance is considered in this paper. • Experimental results verify the effectiveness of the presented method. In order to realize optimal power distribution between proton exchange membrane fuel cell and supercapacitor in hybrid electric tram, an online extremum seeking-based optimized energy management strategy is proposed in this work. Considering that the fuel cell is a complex nonlinear system, its performance will vary as the external parameters change, so it is necessary to consider the performance state of stack. An online extremum seeking algorithm is investigated in this work to seek the maximum power and maximum efficiency points by searching the variation in fuel cell performance. Besides, this work also updates its "safe operating zone" based on the results of the online extremum seeking. This process is achieved by the adaptive recursive least square algorithm. Furthermore, in order to limit the power dynamic of fuel cell, the degradation of the stack is considered in this study. To guarantee the stable and continued operation of the electric tram, the state of charge fluctuation range of supercapacitor is also limited. The effectiveness of the presented method is successfully verified under scaled-down operating condition of hybrid electric tram on the reduced-scale test platform. The proposed method is also compared with state machine control and equivalent consumption minimization strategy to further demonstrate that it has advantages in hydrogen consumption, state of charge fluctuation, efficiency, and fuel cell output power dynamics. [ABSTRACT FROM AUTHOR]

• A lab-scale TES unit with homogeneous copper foam is experimentally studied. • A lab-scale TES unit with gradient porosity copper foam is experimentally studied. • Gradient porosity copper foam can significantly enhances TES's performances. • The heat transfer characteristic of both TES units is discussed in detailed. Latent thermal energy storage is a promising option for the flexible and efficient use of solar energy. However, the low conductivity of phase-change materials limits its practical applications. This study proposes a type of gradient porosity metal foam as a heat transfer-enhancement system to overcome the above-mentioned drawback. Specifically, the thermal performances of a gradient copper foam and commonly used homogeneous copper foam are experimentally investigated and compared in a mid-temperature solar energy storage system. In this study, two lab-scale shell-and-tube units are built with A153 as the phase-change material in the annulus. The two types of copper foam are embedded in the units, gradient porosity copper foam in one unit and homogeneous copper foam in the other. Silicon oil is used as the heat transfer fluid flowing in the inner tube. The charging and discharging processes of the two thermal energy storage units are analyzed in detail. Compared to the embedded homogeneous metal foam in the phase-change material, which has been studied by numerous researchers previously, the experimental data indicate that the gradient porosity copper foam can significantly enhance the heat transfer capacity. Moreover, it improved temperature uniformity in the thermal energy storage unit and reduced the overall melting time by 37.6%. This study is the first to confirm that the application of gradient porosity metal foam can enhance the performance of an energy storage system. This conclusion is important for developing thermal energy storage systems, and indeed, can promote the utilization of solar energy at medium temperatures. [ABSTRACT FROM AUTHOR]