Multi-energy System Research Articles

Microstructure and Mechanical Properties of a Weld Seam from Magnetron High-Current CO2 Welding

External magnetic field (EMF)-assisted high-current CO2 welding is beneficial for improving the large spatter and poor performance of the welding heat-affected zone for mild steels under high-current welding specifications. In this paper, the droplet transfer behaviors were determined using a high-speed camera on a self-developed magnetically controlled CO2 welding system. Based on these welding specifications, a three-dimensional, transient, multi-energy field coupling welding system model to investigate the mechanism of the droplet and molten pool in EMF-assisted welding was developed. The microstructure and mechanical properties of the welded joint were systematically studied. The results show that the Lorentz force applied by the EMF to twist the droplet decreases the accumulated energy in the short-circuited liquid bridge and changes the liquid metal flow condition, both of which reduce the spatter by 7% but increase the welded joint hardness by 10% and tensile strength by 8%.

Operational optimization of a rural multi-energy system supported by a joint biomass-solid-waste-energy conversion system and supply chain

Biomass, as one of the most accessible renewable resources, could reduce dependency on fossil fuels and mitigate associated environmental impacts. With economic development and improved living standards, biomass in rural areas is converted into various forms of energy to meet high energy demands. However, existing studies on rural multi-energy system operations assume energy production from biomass is a given input parameter and consider it to be a fixed constant or subject to upper and lower bounds, independent of the biomass supply chain. Indeed, biomass energy production is significantly influenced by the collection, transportation, storage, and other processes within the supply chain. This work proposes a mixed integer linear program (MILP) for the joint optimization of an integrated rural multi-biomass-solid waste energy conversion system with a source-grid-demand-based biomass-solid waste supply chain (IRMBS-BSC system). The IRMBS-BSC system encompasses three sub-systems: the biomass-solid waste source sub-system, the biomass-solid waste grid sub-system, and the biomass-solid waste demand sub-system. Various biomass-solid wastes are classified, and a source model is established to evaluate their availability. Subsequently, a supply chain model is developed within the biomass-solid waste grid sub-system. Based on the available and transported quantity from the biomass-solid waste source sub-system and grid sub-system, a multi-energy system operation model, considering refined biomass conversion processes, is proposed. A case study involving a multi-energy system that integrates multiple rural community biomass-solid waste supplies is conducted. The results demonstrate that the proposed MILP, which considers the biomass-solid waste supply chain, can reduce energy imports during the load peak periods and can also decrease the overall operational costs of the multi-energy system.

Optimizing energy hub systems: A comprehensive analysis of integration, efficiency, and sustainability

This study introduces a novel application of modified particle swarm optimization (PSO) for optimizing multi-energy hub systems (EHSs) to enhance efficiency and sustainability. The proposed method leverages PSO to optimize the scheduling of various energy resources, including gas turbines, biomass units, and renewable sources such as solar and wind power. Unlike traditional optimization approaches that rely on genetic algorithm (GA) and complex encoding schemes, the PSO algorithm simplifies the process using real-valued vectors and direct communication within the swarm, which significantly reduces implementation complexity. Key contributions of this work include the development of a tailored PSO algorithm that integrates seamlessly with the multi-objective optimization of EHSs. The algorithm simultaneously targets a reduction in operational costs and carbon emissions, offering a comprehensive solution for energy hub design. The proposed PSO approach has demonstrated a 10.35 % reduction in operating costs and an 85.03 % decrease in CO2 emissions compared to traditional baseline setups. In comparative analysis, the integration of renewable sources using the PSO algorithm resulted in a 77.91 % reduction in total CO2 emissions and an 85.61 % decrease in operating costs, showcasing its effectiveness in advancing both economic and environmental objectives. Furthermore, the study provides a detailed evaluation of various scenarios, revealing that the PSO-optimized EHS configuration achieves a significant reduction in reliance on non-renewable energy sources (RES). For instance, the incorporation of photovoltaics and wind turbines in the EHS setup led to a 46.39 % increase in energy sold to the grid and a 26.82 % decrease in electricity purchased from external sources. These quantitative results underscore the robustness and practical benefits of the proposed PSO method in designing and optimizing energy systems for improved sustainability and cost-effectiveness.

Feasibility evaluation of a wind/P2G/SOFC/GT multi-energy microgrid system with synthetic fuel based on C-H-O elemental ternary analysis

Power to gas (P2G) uses electrical energy from access renewable power and captured carbon dioxide (CO2) to generate methane (CH4). The technology provides opportunity for replacing fossil fuels with green-powered hydrocarbon, benefiting the reducing of carbon emission. However, the methanation process in P2G requires high H2/CO2 ratio with available amount of hydrogen (H2) restricted by fluctuation of renewable power, bringing limits to the reusing of captured CO2. This paper presents a feasibility analysis of a novel wind/P2G/SOFC/GT multi-energy system (MES) for microgrid. Green-powered CH4 generated from P2G is mixed with captured CO2, bringing additional flexibility to balancing the overall H2/CO2 ratio for utilization. To comprehensively analyze the feasibility of synthesis CH4/CO2 fuel, evaluation of MES is carried out from both design and off-design conditions. For the design condition, a methodology of C-H-O elemental ternary analysis is applied to reflect the process of fuel utilization and reveal its connection with the trade-off feature of multiple components. For the off-design condition, fluctuations of user's load and renewable source during winter and summer scenarios are considered in a case study. Results show that under C-H-O distribution of 5.8 %, 61.2 % and 33.0 %, the SOFC/GT could operate safety with electrical efficiency of 62 %, capable of participating as a secondary power source for MES. Meanwhile, the overall H2/CO2 utilization ratio of the system is reduced from 4:1 to 2.4:1, where extremes conditions during winter and summer scenarios are evaluated with renewable penetration level of 94 % and wind curtailment rate below 5 % reached.

Research on short-term optimization and scheduling of multi-energy complementary systems based on forecast scenario dynamic correction

The inherent unpredictability and instability of renewable energy sources, such as wind and solar power, hinder the precise execution of power generation plans in complementary systems, posing significant challenges to their integration into power grids. Therefore, this study proposes a dynamic correction method for wind and solar output forecast scenarios in the short-term scheduling of wind-solar-hydro complementary systems. The method utilizes statistical analysis of forecast errors in wind and solar power outputs to characterize uncertainty patterns across different forecast levels and constructs a typical forecast scenario set based on single-day forecasts. This approach probabilistically models each scenario according to the temporal migration patterns of wind and solar power outputs and develops a neural network-based dynamic correction fusion model to refine the forecasts. Application of this method in a case study of the Yalong River Basin demonstrated that, after applying dynamic correction to the forecast scenarios, the mean absolute error in total wind and solar output predictions during the wet and dry seasons was reduced by 50.73 % and 47.95 %, respectively. Additionally, the dynamic correction reduced the maximum residual load on typical wet and dry days by 82.70 % and 62.37 %, respectively, and decreased the total intraday residual electricity by 91.17 % and 73.24 %, compared to single-day forecasts. The study concludes that the proposed dynamic correction method enhances power system stability and improves power generation efficiency and reliability.

Sustainable mega-seaports with integrated multi-energy systems: Life-cycle environmental and economic evaluation

Ports play a critical role in modern society by acting as crucial links between water and land transportation, and integrating transportation with energy systems. This integration results in a high demand for various types of energy uses, with polluting emissions produced by the diverse energy sources. Integrated renewable energy systems represent promising solutions to achieving high levels of energy supply while lowering carbon footprints. In this research, a framework is proposed for a port multi-energy system that encompasses solar energy, wind energy, a hydrogen system and a number of energy storage systems. The proposed framework is tailored for implementation at Ningbo Zhoushan Port, the largest port globally. Different system design schemes are compared based on the number and rated power of wind turbines. Then, a comprehensive life-cycle economic and environmental assessment of the system is conducted through simulation. The economic and environmental metrics such as LCOE, REF and CO2 emissions are considered. The outcomes demonstrate that the design scheme incorporating two wind turbines with high rated power outperforms others in both environmental and economic metrics. Over the life-cycle, the renewable energy fraction exceeds 72%, and the levelized cost of energy plummets to 0.46 yuan/kWh. The implementation of the proposed port integrated multi-energy system yields substantial environmental and economic benefits. Specifically, it allows for a reduction of 66.68% in CO2 emissions and a cost reduction of 70.94%. These outcomes highlight the potential that the proposed system holds for enhancing environmental sustainability and economic efficiency within the port context.

Development of a geothermal-driven multi-output scheme for electricity, cooling, and hydrogen production: Techno-economic assessment and genetic algorithm-based optimization

This study aims to develop an environmentally friendly multi-energy system for sustainable production of electricity, cooling and hydrogen. The study introduces a pioneering geothermal-based system, integrating an ejector refrigeration cycle, a dual-loop organic Rankine cycle, and a hydrogen production unit with proton exchange membrane electrolyzers. The study provides a thorough analysis of the system's energy and exergy performance, as well as its economic feasibility. Through sensitivity and parametric analyses, the research identifies key parameters that significantly influence system performance. The system's innovative design promises minimal environmental impact while delivering multifaceted performance: generating 1.38 MW of electricity, supplying 436 kW of cooling load, and producing 5.39 kg/h of hydrogen. In the exergy analysis, Evaporator1 is identified as the primary contributor to exergy loss, representing 34 % of the total exergy destruction. This is followed by the electrolysis unit, the condenser, and the ejector refrigeration cycle, which contribute 18 %, 14 %, and 12 %, respectively. The system achieves optimal efficiency at an organic Rankine cycle turbine1 inlet temperature of 387 K, yielding a power generation of 885.4 kW and an exergy efficiency of 26.7 %. Beyond this temperature, any further increase leads to a decline in power output due to operational disturbances. A multi-criteria optimization using genetic algorithm is applied, resulting in an optimized system with a cost rate of 18.13 $/h and an exergy efficiency of 38.96 %.

Driving industrial symbiosis: Evaluating policy intervention effects on waste heat utilization in local district heating networks

This study aims to quantitatively assess the required policy support level to ensure economic viability for all stakeholders in the context of using industrial waste heat as source of local district heating (DH). Additionally, the research explores the potential of industrial symbiosis to support district-level energy transition in the context of Positive Energy Districts and Smart Cities. The proposed approach combines energy modelling tools, namely the Multi Energy System Simulator (MESS) model and financial concepts (i.e. Cash Flow Analysis, Net Present Value Evaluation) typical of industry’s project evaluation. It is then tested in the area of Bolzano, Italy. The study examines various policy scenarios, including financial incentives and the involvement of district heating operators (DHOs). Initial findings indicate that district heating offers significant cost advantages (up to –23%) over electrified systems. However, industries face challenges meeting standard payback periods without policy intervention in the context of projects using waste heat as a source of DH. A combined scenario involving DHOs handling connection costs with a 60% incentive and industries receiving a 15% Feed-In Premium shows promising results in achieving economic feasibility. The results underscore the critical role of policy support in overcoming barriers. Consequently, it is possible to strengthen cooperation between industries and districts fostering local energy transition. Nonetheless, the achievement of positive energy balance requires a stronger presence of RES particularly on the electricity side. The study highlights the broader implications for similar projects in other regions and the essential role of policy interventions in facilitating such industrial symbiosis.

Energy contribution and loss of greenhouse-type drying chamber in multi-energy drying system: Heat distribution and exergy efficiency

In this paper, the relationship between the structural design of the greenhouse drying chamber and energy contribution and loss is discussed from the perspective of energy saving and system performance, and the rationality of their design is analysed based on the exergy efficiency. Taking the multi-energy drying system of kelp as an example, the airflow and temperature distributions of different greenhouse-type drying chambers were simulated and optimized by using the computational fluid dynamics (CFD) method, and the heat transfer and loss were analyzed. The results show that the optimized drying chamber has an increase of about 40% in The results show that the optimized drying chamber has an increase of about 40% in exergy efficiency and a reduction of about 37% in exergy damage. The average energy efficiency of the greenhouse-type drying chamber was more than 80% with a size of 5000×2500×2300 mm, a tilt angle of 22.5°, and fans of 7. In addition, the energy efficiency was negatively correlated with the temperature difference between the inlet and outlet and positively correlated with the ambient temperature. Despite the heat loss in the drying chamber, its contribution to the system's overall energy efficiency is significant.

Net-Zero Scheduling of Multi-Energy Building Energy Systems: A Learning-Based Robust Optimization Approach With Statistical Guarantees

Net-Zero Scheduling of Multi-Energy Building Energy Systems: A Learning-Based Robust Optimization Approach With Statistical Guarantees

Cooperative Planning of Multi-Energy System and Carbon Capture, Utilization and Storage

Cooperative Planning of Multi-Energy System and Carbon Capture, Utilization and Storage

Coupling geospatial suitability simulation and life cycle carbon emissions towards uncertain optimization planning for wind-photovoltaic-hydrogen multi-energy complementary system

Building clean and modern multi-energy complementary systems is a direct path to decarbonization in energy sector. The introduction of hydrogen energy with high operational flexibility enables the multi-energy complementary system to fully accommodate renewable energy. The key issues of the optimization planning of the wind-photovoltaic-hydrogen multi-energy complementary system that need to be solved are how to finely characterize the carbon reduction potential of the system, consider the uncertainty of supply and demand in the modeling process, and incorporate the energy supply business of electric vehicles into the planning. This study constructs optimization planning model with the following three progressive stages: geospatial suitability simulation based on geographic information system, uncertainty analysis based on probabilistic scene generation technology, and capacity configuration optimization. Unlike previous studies, this model measures and optimizes the carbon emissions level of the system from a lifecycle perspective. The empirical study results indicate that carbon emissions optimization from the perspective of the entire life cycle would directly affect the system configuration results. From a lifecycle perspective, the carbon emissions accounting result is about 9 times higher than that obtained only during the operation phase. Compared to the traditional distribution system, the carbon emissions of the system constructed in this study decreased by 82.75 %. In addition, considering that the system provides charging services for electric vehicles, which can reduce carbon emissions by about 93.6 % but only increase costs by about 29.4 %. The computational results justify the proposed optimization framework. This study provides optimization models for the system, and the implementation framework can support technical reference for practice.

A virtual anti-scatter grid for multi-energy photon counting detector systems

Photon-counting (PC) systems are the next technological generation of medical computed tomography (CT) imaging and is being worked on by all major system providers. CT devices that are based on PC detectors enable multi-energy data collection. The information-content of this data can be used to obtain more detailed patient data, which improves the quality of reconstructions, compared to conventional detector systems. However, PC CT systems are subject to radiation scatter as just as any other imaging systems is. Conventionally anti-scatter grids (ASG) are used to reduce the scatter effect. These are however an imperfect solution, especially for PC detectors. In this work, a software-based scatter correction method, thus a virtual ASG is proposed. The method is tailoring a new statistical model in the measurement space and combining it with the statistical inversion method called Markov chain Monte Carlo (MCMC). The method can recover the measurement data from dense projections. We present the method on simulated data of a single photon emission computed tomography (SPECT) problem for which only under-sampled data is available. However, our approach can in principle be generalised to CT, PC-CT, Positron emission tomography (PET), radiotherapy, or even digital radiography problems. The results show that the proposed model performs similarly as physical ASGs and for cases where ASGs are not possible. The model further offers a significant improvement in the quality of the reconstruction image compared to the image reconstruction from original under-sampled data.

An ultra-high gain boost converter with low switching stress for integrated multi-energy storage systems

In this paper, a high-gain low-switching-stress coupled-inductor with high voltage step-up voltage multiplier cells quadratic boost converter (VMC-QBC) is proposed. The turn ratio of the coupled inductors and the switch duty cycle increase the dynamic gain, and the two degrees of freedom adjustment and modularity of the voltage multiplier cells (VMC) make the structure more flexible. The use of the same drive signal for both switches makes control easier. While achieving multi-stage boosting and multiplication boosting from low to medium duty cycle, the passive clamping circuit absorbs the energy leaked by the coupled inductor, thus reducing the stress on the switching tube and alleviating the diode reverse recovery problem. A non-ideal model with parasitic parameters is developed to analyse the real voltage gain and the converter losses to give design guidelines. A 300 W prototype is designed and tested. The state space model of the converter is established and the working principle is analysed. Compared to other high-gain quadratic boost converters, the proposed converter has continuous input current, common ground characteristics, and high voltage gain at low to medium duty cycles to accommodate integrated multi-energy storage systems.

Aggregate power flexibility of multi-energy systems supported by dynamic networks

The multi-energy system, encompassing electricity networks, district heating networks (DHNs), and hydrogen-enriched compressed natural gas (HCNG) networks, provides an alternative for promoting intermittent renewable energy accommodation and enhancing operational flexibility. This paper investigates the aggregate flexibility of the multi-energy system based on dynamic network models and admissible power fluctuation regions of decomposed subsystems. The dynamic processes within HCNG networks/DHNs are analyzed, and the inner-box method is employed to approximately quantify the individual flexibility of the dynamic networks. Furthermore, the optimal dispatchable regions of decomposed subsystems, accounting for internal gas/heating load uncertainties, are evaluated based on distributionally robust optimization. Through distribution-level power aggregation, the flexibility of the multi-energy system is quantified utilizing geometric methods. Numerical results on two test systems verify the effectiveness of the proposed methodology.

Machine learning-based detection of DDoS attacks on IoT devices in multi-energy systems

With the growing integration of IoT devices in critical infrastructure, cybersecurity threats such as Distributed Denial of Service (DDoS) attacks on Energy Hubs (EH) have become a significant concern. This study aims to address these challenges by evaluating the effectiveness of various supervised machine learning (ML) algorithms in predicting DDoS attacks targeting EH systems through IoT devices. Using the CICDDOS2019 and KDD-CUP datasets, a comprehensive analysis was conducted on several classifiers, including Decision Tree (DT), Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest. The results highlight Gradient Boosting as the most effective model, particularly for the CICDDOS2019 dataset, demonstrating superior accuracy and predictive capability. Additionally, hybrid models combining Gradient Boosting with SVM or DT showed strong performance, though with varying precision and recall. This study provides valuable insights into the selection and tailoring of ML models for specific security challenges, emphasizing the need for ongoing research to enhance the resilience of EH systems and IoT devices against evolving DDoS threats.

Multi-Objective Short-Term Operation of Hydro–Wind–Photovoltaic–Thermal Hybrid System Considering Power Peak Shaving, the Economy and the Environment

In recent years, renewable, clean energy options such as hydropower, wind energy and solar energy have been attracting more and more attention as high-quality alternatives to fossil fuels, due to the depletion of fossil fuels and environmental pollution. Multi-energy power systems have replaced traditional thermal power systems. However, the output of solar and wind power is highly variable, random and intermittent, making it difficult to integrate it directly into the grid. In this context, a multi-objective model for the short-term operation of wind–solar–hydro–thermal hybrid systems is developed in this paper. The model considers the stability of the system operation, the operating costs and the impact in terms of environmental pollution. To solve the model, an evolutionary cost value region search algorithm is also proposed. The algorithm is applied to a hydro–thermal hybrid system, a multi-energy hybrid system and a realistic model of the wind–solar–hydro experimental base of the Yalong River Basin in China. The experimental results demonstrate that the proposed algorithm exhibits superior performance in terms of both convergence and diversity when compared to the reference algorithm. The integration of wind and solar energy into the power system can enhance the economic efficiency and mitigate the environment impact from thermal power generation. Furthermore, the inherent unpredictability of wind and solar energy sources introduces operational inconsistencies into the system loads. Conversely, the adaptable operational capacity of hydroelectric power plants enables them to effectively mitigate peak loads, thereby enhancing the stability of the power system. The findings of this research can inform decision-making regarding the economic, ecological and stable operation of hybrid energy systems.

Optimal scheduling of a multi-energy complementary system simultaneously considering the trading of carbon emission and green certificate

This paper uses a multi-energy complementary system composed of thermal, wind, photovoltaic power generation, and electric energy storage units to participate in four market mechanisms and construct the optimal scheduling model of these trading mechanisms. These four mechanisms are carbon emission trading, green certificate trading, these two trading that are not joint, and joint. Under the latter two mechanisms, the excess carbon emissions of the system can be reduced to 9258t, and the utilization rate of renewable energy can reach 100 %. However, the operating costs under the fourth mechanism are 9695.80 $ lower than the third one because 5947 pieces of green certificates are converted into carbon emission rights. When the carbon price rises, the cost of the fourth mechanism increases slower than that of the third mechanism. The conversion volume of green certificates increases from 5947 to 10947, and the cost difference between the two increases to 67558.33 $. When green certificate prices rise, the cost under the fourth trading mechanism decreases faster. As that cost steadily declines, the carbon emission rights are converted into 907 green certificates, and the cost difference between this and the third mechanism increases to 4695.52 $.

Exergy Flow as a Unifying Physical Quantity in Applying Dissipative Lagrangian Fluid Mechanics to Integrated Energy Systems.

Highly integrated energy systems are on the rise due to increasing global demand. To capture the underlying physics of such interdisciplinary systems, we need a modern framework that unifies all forms of energy. Here, we apply modified Lagrangian mechanics to the description of multi-energy systems. Based on the minimum entropy production principle, we revisit fluid mechanics in the presence of both mechanical and thermal dissipations and propose using exergy flow as the unifying Lagrangian across different forms of energy. We illustrate our theoretical framework by modeling a one-dimensional system with coupled electricity and heat. We map the exergy loss rate in real space and obtain the total exergy changes. Under steady-state conditions, our theory agrees with the traditional formula but incorporates more physical considerations such as viscous dissipation. The integral form of our theory also allows us to go beyond steady-state calculations and visualize the local, time-dependent exergy flow density everywhere in the system. Expandable to a wide range of applications, our theoretical framework provides the basis for developing versatile models in integrated energy systems.

Optimization and simulation of a novel multi-energy complementary heat pump system in cold regions

Solar-assisted ground source heat pump systems(SAGSHPSs) and solar- assisted air source heat pump systems (SAASHPSs) are recognized renewable energy technologies for clean and feasible heating and domestic hot water supply. However, in cold regions, the poor thermal comfort of SAASHPSs and the soil thermal imbalance of SAGSHPSs during heating periods must be resolved urgently. Therefore, to provide an efficient and stable heat source, a multi-energy complementary heat pump system(MECHPS) with seasonal heat storage was designed to meet the needs of building energy consumption. The system comprehensively utilizes solar energy and ambient air for seasonal heat storage during non-heating periods, and then directly provides heat coupled with the ground source heat pump during heating periods. The composition and control strategy of the system were introduced. The system was numerically modeled by the simulation software Transient System Simulation Program (TRNSYS), and the main MECHPS modules were experimentally verified. Additionally, to study the optimal capacity of MECHPS in terms of economy and energy savings, the Hooke-Jeeves algorithm was used for MECHPS capacity optimization with the objectives of minimising the annual cost and maximising the annual savings of standard coal. The results showed that compared with traditional SAASHPSs, the MECHPS exhibited better thermal comfort while maintaining the room temperature above 18 °C. Compared to SAGSHPSs, the MECHPS was able to achieve soil thermal balance, and its soil temperature remained largely constant after 15 years of operation. Finally, compared with the initial scheme, the annual cost reduction rate was 4.98 % under the annual cost optimization scheme and an additional 1310 kg of standard coal could be saved under the annual savings of standard coal optimization scheme. The proposed system provides a way to satisfy the building energy supply for widespread application in cold regions.