To address weak emission-reduction incentives under conventional carbon trading and unclear low-carbon benefits of different demand response modes, this paper proposes a low-carbon economic dispatch model for an electricity–heat–cooling–gas integrated energy system (IES) considering ladder-type carbon trading and demand response (DR). According to load regulation characteristics, IES loads are classified into fixed loads, transferable loads (TLs), and replaceable loads (RLs). The objective is to minimize the total cost, including energy purchase cost, operation and maintenance cost, and carbon trading cost. The effects of carbon trading are evaluated under three scenarios: no-carbon-cost, conventional carbon trading, and ladder-type carbon trading. Under the ladder-type carbon trading scenario, four DR strategies are further compared: no-DR, TL-only, RL-only, and combined TL-RL. The results show that the model achieves the lowest total cost while reducing carbon emissions by 20.88%. Sensitivity analyses indicate that DR does not necessarily reduce emissions; its low-carbon effect depends on carbon price, proportion of TLs and proportion of RLs. The combined TL-RL strategy yields the lowest cost at all carbon prices, but its synergistic emission-reduction effect occurs only within an appropriate carbon price range. Sensitivity analysis of load proportions further shows that TLs contribute to synergistic emission reduction with RLs only when the proportion of RLs reaches a certain level and the proportion of TLs remains moderate.

With the rapid development of wind power, photovoltaic systems and advanced energy storage technologies, integrating diverse renewable resources into existing power grids has become essential for improving operational efficiency and economic performance. Recognizing that wind, solar and storage units can be represented as configurable components within transmission network models, this paper formulates a transmission network expansion planning (TNEP) problem aimed at minimizing total investment costs under power demand constraints. To address this challenge, a novel approach combines reinforcement learning with Gaussian process regression (GPR) to approximate the Q-function in high-dimensional, discrete action spaces. The GPR surrogate flexibly models nonlinear dependencies between expansion configurations and long-term outcomes while quantifying uncertainty to guide focused exploration. This targeted learning strategy avoids exhaustive search and significantly improves efficiency, making it particularly suited to the combinatorial complexity of TNEP. Compared to linear regression-based RL, which performs well only on small, smooth networks, the GPR-based method achieves strong performance on both the Garver 6-bus and IEEE 24-bus systems. It consistently outperforms benchmark algorithms-including grey wolf optimizer, particle swarm optimization, genetic algorithm, and the gradient-based BFGS method-in terms of convergence speed and solution quality, making it a practical tool for transmission expansion planning under high renewable penetration.

The study develops an optimal dispatch model for an electricity-heat-gas integrated energy system considering renewable energy output level and energy storage configuration. With the objective of minimizing the total operating cost of the system, the model comprehensively accounts for energy procurement cost, operation and maintenance cost, and penalties for wind and photovoltaic curtailment, thereby establishing an economic optimal dispatch framework for the integrated energy system. Four scenarios are designed for comparative analysis, namely, no energy storage, only electric storage, only heat storage, and combined electric and heat storage. The results show that, as renewable energy output level increases, the role of energy storage in reducing system operating cost and curtailment rate becomes more pronounced. Electric storage performs better than heat storage in both cost reduction and the improvement of renewable energy accommodation. Further analysis indicates that the effectiveness of heat storage is strongly constrained by electricity-to-heat conversion capability, and its performance is closely related to the capacity configuration of the electric boiler. The findings of this study can provide a useful reference for energy storage configuration and optimal dispatch of integrated energy systems under high renewable energy output level.

To address the possible limitations of conventional energy-efficiency indicators in terms of restricted evaluation boundaries and the insufficient characterization of energy coupling and reuse processes in complex energy-utilization systems, this paper proposes a modified energy-efficiency-evaluation method based on unutilized-energy decomposition. First, a unified representation of basic energy-utilization units is established based on the concept of minimum functional units. Typical structural mappings, including series, feedback, delay, and auxiliary structures, are then introduced to describe complex energy interaction paths in a unified manner. Second, unutilized energy is distinguished from actual loss and is further decomposed into inevitable unutilized energy and potentially recoverable unutilized energy, so as to reveal the internal differences and reuse potential of energy that does not form useful output. On this basis, modified energy-efficiency indicators for downstream-utilization, feedback-recirculation, and auxiliary structures are developed, together with supporting indicators such as recovery efficiency gain, auxiliary efficiency gain, and the actual loss rate, thereby forming a comprehensive evaluation framework for complex energy-utilization systems. Finally, a gas engine combined heat and power (CHP) system is used as a case study. The results show that conventional power-generation efficiency cannot distinguish system performance differences under different heat-utilization conditions, whereas the proposed modified energy efficiency and actual loss rate can effectively reveal the effects of waste-heat utilization, thermal-load matching, and thermal-storage shifting on overall system performance. This study can provide a reference for the comprehensive energy-efficiency evaluation of combined heat and power systems, as well as other chain-type and multi-energy coupled energy-utilization systems.

Due to the length of the body, multiple number of wheels and the complexity of controlling, it is difficult for a multi-axle wheeled robot to avoid obstacles autonomously in narrow space. To solve this problem, this article presents window-zone division and gap-seeking strategies for local obstacle avoidance of a multi-axle multi-steering-mode all-wheel-steering wheeled robot. Firstly, according to the influence degree of lidar points on the robot, combining with the human driving characteristics of avoiding obstacles, a window-zone division strategy is proposed. The lidar points are selected and divided according to the degree of emergency. By eliminating irrelevant points, the work of obstacle avoidance calculation is reduced. Thus, this increases the response speed of obstacle avoidance. Based on this, the robot uses a multi-steering-mode to avoid emergency obstacle. Secondly, the gap-seeking theory of normal obstacle avoidance is proposed. It can seek the passable gap among the surrounding lidar points according to the prediction of the robot's driving trajectory corresponding to different steering angles. Thirdly, the on-board control system and the upper computer program of the robot were designed. Thereafter a multi-steering-mode algorithm was designed based on the front and rear wheel steering angles and speed, as well as the travel trajectory forecast-drawing module. Finally, the proposed methods have been implemented on a five-axle all-wheel steering wheeled robot. Some obstacle avoidance experiments are carried out with S-shaped, Z-Shaped, U-Shaped, and Random obstacle distribution. The results show that the proposed strategy can finish all obstacle avoidance successfully.

Against the backdrop of large-scale wind power grid connection, the uncertainty and intermittency of wind power result in a deviation between the output power and the grid dispatching curve. This deviation, in turn, causes wind curtailment and affects the system stability. To boost the enthusiasm of wind farms to actively participate in grid dispatching, this paper proposes a multi-factor reward and punishment mechanism for source-grid coordination. First, a reward and punishment mechanism model is built based on the wind volatility rate and the wind power capacity credibility evaluation index. Second, the model is adjusted by taking into account factors such as seasonal changes, intraday wind speed fluctuations, and load peak-valley distribution to enhance the mechanism’s adaptability and practicality. Finally, a trend correction factor is introduced based on historical fluctuation trends to refine the mathematical model of the reward and punishment mechanism. Through specific case analysis, the effectiveness of this mechanism in reducing the volatility of wind power output, enhancing capacity credibility, and addressing seasonal power generation differences and intraday wind speed fluctuations caused by natural climate has been verified. This provides theoretical support and application references for the economic operation of wind farms and the coordination between the power source and the grid.

In order to improve the utilization rate of wind power, this paper constitutes the battery bank-flywheel energy storage-supercapacitor-pumped storage plant into a hybrid energy storage device, which is divided into a DC part and an AC part, and applies it to wind farms, and utilizes the energy management system to formulate a strategy for controlling the charging and discharging of the hybrid storage device, so as to mprove the utilization rate of wind power. The battery pack-flywheel energy storage-supercapacitor constitutes the DC part, and the pumped storage power station forms the AC part, which combines the above energy-type and power-type energy storage devices to improve the overall performance of the energy storage device. The fluctuation of wind power generation is nonlinear, and the wind power fluctuation is transformed according to the Hilbert frequency to calculate the low-frequency, medium-frequency and high-frequency fluctuations, which correspond to the power distribution between the DC energy storage battery pack-flywheel energy storage and supercapacitors, so as to realize the smoothing of the real-time output fluctuation of the wind farm and improve the power quality. When the fluctuation rate of wind power is very large, the energy storage of pumped storage power stations with large capacity is used to shave peak and fill valley to directly improve the utilization rate of wind power. Also based on theoretical analysis and typical configuration scenarios, hybrid energy storage devices can effectively stabilize wind power fluctuations, peak shaving and valley filling, and ultimately improve the utilization rate of wind power.

The global demand for clean energy has fuelled research into ocean energy, but single systems such as tidal power and offshore wind show difficulties to provide stable power because of their intermittency and volatility. Pumped storage can help solve these problems, but it is too expensive to build individually. In this paper, a combined tidal power system with pumped storage function is proposed, where double reservoir tidal power and pumped storage share the upper and lower reservoirs without the need for new dams, and only a small increase in the construction cost with pumped storage units and pipelines is needed to make the tidal power plant obtaining a pumped storage function, and to realise synergistic operation of tidal power, pumped storage and offshore wind power. It can provide services such as peak shaving and valley filling and power fluctuation smoothing for the power grid, and further improve the power generation benefits through flat-tide pumping. Calculation and simulation results show that the proposed synergistic operation strategy can achieve effective suppression of wind power fluctuation power, and enable grid connection in compliance with the dispatchrequired power curve, which can increase the overall power generation and the capacity factor of the tidal power units, and improve the economic benefits and energy efficiency of the energy system.

As a vital renewable energy source, wind power is characterized by significant volatility and uncertainty, posing challenges to power forecasting. To improve the accuracy of wind power forecasting and its adaptability to scheduling, this paper proposes an optimization method based on forecast error correction and historical credibility analysis. The method conducts statistical analysis of forecast errors and incorporates historical data credibility to dynamically correct the forecast output curve and optimize dispatching strategies. A hybrid Particle Swarm Optimization-Simulated Annealing (PSO-SA) algorithm is employed to minimize the forecasting error, while a rewardpenalty mechanism is introduced to enhance the credibility of the forecast. Simulation results show that the proposed method reduces the Root Mean Square Error (RMSE) from 174.16 kW to 94.50 kW and increases the Safety Factor from 0.46 to 0.81, demonstrating significant improvements in forecast accuracy, stability, and dispatch reliability.

An economic low-carbon dispatch method considering comprehensive demand response and tiered carbon trading mechanisms is proposed to address the rational allocation and optimal operation of units in integrated energy microgrids. By introducing comprehensive demand response, the electric, gas, and thermal loads are classified into shiftable and substitutable loads according to their horizontal and vertical demand response capabilities, enabling load transfer not only among different types of loads but also adjustments in energy consumption timing. This optimizes the load profile and enhances the flexibility of system dispatch. Furthermore, a tiered carbon trading mechanism is incorporated to constrain carbon emission output from equipment, thereby reducing carbon trading costs during system operation and improving economic efficiency. Case studies validate the effectiveness of the proposed low-carbon economic model: after considering demand response, the overall fluctuation of the load curve decreases, the peak-to-valley difference of the electric load curve is reduced, and the total cost is lowered; compared with traditional carbon trading scenarios, carbon emissions are also reduced after considering tiered carbon trading.

Both thermal energy storage systems and electric vehicles (EVs) can serve as effective energy storage resources for wind power. In particular, thermal storage systems can decouple thermal demand from power output, thereby breaking the conventional “heat-determined electricity” constraint. This paper proposes a source-load coordination method that incorporates thermal energy storage and EVs to improve wind power accommodation. First, wind power output in a given region is simulated. Next, operational parameters of regional thermal storage systems and EVs are collected. Then, a source-load coordination optimization model is developed with the objective of maximizing the power grid's economic benefit, incorporating the participation of both storage systems and EVs. Finally, four scenarios-with or without thermal storage and EVs-are simulated using the CPLEX solver to compare wind curtailment rates and grid profits. Results demonstrate that the proposed method can absorb excess wind power through thermal storage and EV charging during surplus periods and shift power back into the grid during shortages, thus balancing grid power and reducing wind curtailment.

To characterize the energy use level of multi-stage, multi-energy-coupled high-energy-consumption systems, this paper develops an energy flow model. The model, formulated under the principle of energy conservation, decomposes input energy into output energy and dissipated energy. Dissipated energy is further divided into recovered energy and ineffective energy, and refined into unavoidable dissipated energy and avoidable dissipated energy. On this basis, three indicators are proposed to evaluate energy use at the process level: process energy consumption, energy efficiency, and energy inefficiency ratio. The steelmaking process is then employed as a representative case to analyze the energy-using processes in steel production. The results show that the blast furnace ironmaking process accounts for about 63% of total energy consumption, while its energy efficiency is only 42.75%, indicating that it is the stage with the greatest energy-saving potential along the whole process chain. From the perspective of unavoidable dissipated energy and avoidable dissipated energy, five energy-saving measures are designed and their impacts on energy consumption, energy efficiency and the energy inefficiency ratio are compared, thereby verifying the applicability of the proposed model to energy efficiency diagnosis and optimization of energy-saving pathways in high-energy-consumption systems.

This study introduces a novel wind-driven hydroelectric power generation system equipped with a water storage buffer, delineated as a sealed system. It principally encompasses a hydraulic wind energy conversion mechanism and a water storage buffer-based power generation module. The system harnesses wind energy to instigate blade rotation, thereby transforming kinetic energy into mechanical energy. This energy is subsequently conveyed through the rotor shaft and hydraulic transmission, culminating in the mechanical operation of the impeller within the sealed conduit. The impeller propels the circulation of water within the conduit, facilitating electricity generation via the hydroelectric generator. A distinctive feature of this system is its integration with a water storage buffer device, designed to modulate the water flow within the sealed conduit. In instances where hydroelectric power generation exceeds the demand, surplus water flow is directed to the storage tank through an inlet valve. Conversely, when the load has a demand for electricity or when the output power of the hydroelectric generator is intermittent or uneven, the outlet valve of the water storage tank is activated. This ensures a regulated discharge of stored water through the outlet conduit, passing through the hydraulic generator at a consistent flow rate, thereby guaranteeing a steady and continuous electrical output. This innovative model, focusing exclusively on power supply through the hydraulic generator, negates the necessity for inverter and other related equipment, thereby streamlining the system architecture. Moreover, by synergizing power generation with the water storage buffer, the system ensures the sustained and uniform release of energy. This effectively mitigates the inherent unpredictability and intermittency associated with conventional wind power generation, substantially enhancing the penetration of wind energy, and significantly diminishing the incidence of wind energy wastage.

The continuous enlargement of doubly-fed wind farms has led to an exponential increase in the number of doubly-fed wind turbines within each wind farm, with projections suggesting that the count may even surpass thousands. If all components of an individual doubly-fed wind turbine are simulated, the wind farm model will reach thousands of orders. Simulating such a large model will produce a lot of calculations, which makes the results difficult to converge. Thus, it is crucial to perform an equivalent simulation of the wind farm and establish a corresponding model. Firstly, this paper summarizes the static equivalence and time-varying equivalence methods for modeling the wind farm at the station level. Then, the single-machine modeling method of typical DFIG is analysed, and the development direction of single-machine generalized modeling is proposed. Finally, the accuracy evaluation method and error correction method of the equivalent model summarized above are summarized, and the improvement and development direction of the accuracy evaluation method are proposed.

In order to effectively solve the large-scale wind power transmission alone power absorption and instability, to counter China’s energy and load consumption present reverse distribution. The large-scale wind-fire power is delivered through UHVDC, but because of the long distance and the random intermittency of new energy, the possibility of transmission line failure is greatly increased. In this paper, a model of UHVDC delivery of wind-fire bundling is established, the effect of short circuit on commutation failure and the formation mechanism of transient voltage characteristics of the AC system at the receiving end are described, and the effects of different wind and fire ratio delivery scenarios are analyzed, the effect of different types of short circuit at the receiving end on the transient voltage. The simulation results show that the single-phase-to-ground fault will do more harm to the stable operation and fault recovery of power grid with the increase of the proportion of outside wind power, it is beneficial to new energy absorption and stable operation of the system.

The analysis and calculation of dynamic energy flow for different energy forms are the basic work for the coordinated dispatching and operation of multi‐energy networks (MENs). A universal mathematical description of energy transfer is proposed based on the concept of intensity quantity and extensive quantity in thermodynamics. This paper derives the unified energy dynamic transfer equations of the electrical energy, incompressible viscous fluid, compressible gas, and heat transfer, that is, a generalised wave equation is established with the distributed parameters that characterise energy dissipation, storage and conductivity. Furthermore, the finite difference method is adopted to calculate the dynamic energy flow that varies with time and space. In the case analysis, the independent solution is firstly performed under four designed cases, and then the MEN composed of heat and gas is jointly solved. The calculation example verifies the feasibility and effectiveness of the proposed method.

With the significant expansion of new energy, the challenges arising from its uncertainty are increasingly pressing. Uncertainty poses difficulties for the energy supply and demand equilibrium within the IES, leading to substantial fluctuations in carbon emissions across system network nodes. In the hydrogen market, while new energy enables zero-carbon hydrogen production, its instability hinders full satisfaction of hydrogen user demands. Consequently, this paper undertakes two exploratory schemes based on characteristics of new energy hydrogen production: proposing a “source-load” coordinated response mechanism that considers the flexibility of new energy hydrogen production to mitigate uncertainty's impact on IES' energy supply and demand imbalance; and suggesting a comprehensive method for hydrogen production that accounts for the “negative carbon emission” traits of new energy hydrogen production to alleviate uncertainty's influence on unstable hydrogen supply.

Integrated energy system (IES) represents a crucial avenue of research in the pursuit of energy conservation and carbon reduction, as it facilitates collaborative management and complementarity among multiple energy sources. This study integrates disparate energy networks within the park into a integrated energy system and formulates coordinated scheduling strategies for multiple energy outputs. Configure rooftop photovoltaic and energy conversion equipment in the system. The operation planning objective of the IES is minimizing annual average cost of the system, while taking into account carbon trading expenses and the advantages of substituting purchased electricity with new energy generation. By considering constraints such as supply-demand balance, energy trading, installation requirements for energy conversion equipment, and energy conversion relationships, a mixed integer linear programming (MILP) algorithm is employed to address the problem. This algorithm enables the determination of installation quantity and configuration capacity of distributed energy, energy trading schemes, and daily operation strategies for the system. The calculation results indicate that the addition of photovoltaic power generation units has a positive effect on reducing system carbon emissions and lowering carbon trading costs. Furthermore, energy conversion devices enhance system resilience by efficiently absorbing new energy generation during winter and summer, selling excess electricity to the grid during spring and autumn, and ensuring a stable supply of thermal energy and natural gas.

Constructing a new power system centered around renewable energy sources represents the developmental trajectory of the power sector and a pivotal avenue towards achieving carbon neutrality. In comparison to conventional power systems, the unique attributes of the new power system pose distinct challenges, necessitating the deployment of energy storage technologies as a crucial solution. This paper delineates the characteristics of the new power system and scrutinizes the demand for energy storage technologies within this paradigm. Various energy storage technologies are evaluated based on metrics such as capacity scalability, response time, and duration of continuous charge and discharge. By addressing the specific requirements of the new power system, the suitability of different energy storage technologies is deliberated upon, culminating in the establishment of a quantitative evaluation framework to assess their applicability. This framework enables the quantification of energy storage performance tailored to diverse power system needs, facilitating informed decision-making for users in selecting the most suitable energy storage technology.

Building is the largest single subject of terminal energy consumption. In light of the “double carbon” objectives, the new PEDF (Photovoltaics, Energy Storage, Direct Current, and Flexibility) distribution system for buildings has rapidly evolved. This system is instrumental in transforming buildings from primary sources of carbon consumption to principal contributors to carbon neutrality, while optimizing capacity allocation is an important part of its design. This paper presents a capacity optimization allocation model aimed at maximizing system benefits. The model incorporates key components such as photovoltaic power generation, energy storage systems, AC/DC interfaces, and bidirectional charging stations. Particle swarm optimization algorithm is employed to solve the model, confirming its effectiveness in optimizing equipment configuration, enhancing power supply reliability, and improving overall system economics. Furthermore, the model significantly boosts the flexibility of electric vehicles and the utilization capacity of building-integrated photovoltaics, thereby smoothing peak demand fluctuations, reducing carbon emissions, and advancing the sustainable development of the PEDF system.