Energy Engineering

Effective thermal management is paramount for successfully deploying lithium-ion batteries in residential set-tings as storage systems for the exploitation of renewable sources. Uncontrolled temperature increases within battery packs can lead to critical issues such as cell overheating, potentially culminating in thermal runaway events and, in extreme cases, leading to fire or explosions. This work presents a comprehensive numerical thermal model of a battery pack made of prototype pouch cells based on lithium ferrophosphate (LFP) chemis-try. The multi-physical model is specifically developed to investigate real-world operating scenarios and to as-sess safety considerations. The considered energy storage system is a battery designed for residential applica-tions, in its integration with a photovoltaic (PV) installation. The actual electrochemical behavior of the proto-type cell during the charging and discharging processes is modeled and validated on the ground of experi-mental data. The essential steps leading to the numerical schematization of the battery pack are then presented to apply the model to two different use scenarios, differing for the user loads. The first scenario corresponds to a typical residential load, with standby lights being active during the night, solar generation with its peak at noon, and appliance use shifting in the afternoon and the evening. In the second scenario, a double demand for energy is present that makes the battery never reach 100% of the State of Charge (SoC) and discharge more rapidly with respect to what occurs under the first scenario. Comparing the simulated temperature with the assumed C-rate, namely the charge or discharge current divided by the battery nominal capacity, it is found that peaks coincide with the charging phase; subsequently, the current tends to a zero value, and consequently, the temperature suddenly reaches the value of the environment. Finally, the model is also utilized to simulate a condition of thermal runaway by introducing critical conditions within a specific pouch cell. In this simulation, the thermal exchange between the cell in thermal runaway and the rest of the system remains within accepta-ble limits. This occurs due to the short duration of the process and to the module casing coated with an insu-lating material. The work provides an essential foundation for conducting numerical simulations of battery packs operating also at higher power levels.

The expansion of renewable energy sources (RESs) in European Union countries has given rise to the development of Renewable Energy Communities (RECs), which are made up of locally generated energy by these RESs controlled by individuals, businesses, enterprises, and public administrations. There are several advantages for creating these RECs and participating in them, which include social, environmental, and financial. Nonetheless, according to the Renewable Energy Directive (RED II), the idea of RECs has given opportunities for researchers to investigate the behavior from all aspects. These RECs are characterized by energy fluxes corresponding to self-consumption, energy sales, and energy sharing. Our work focuses on a mathematical time-dependent model on an hourly basis that considers the optimization of photovoltaic-based RECs to maximize profit based on the number of prosumers and consumers, as well as the impact of load profiles on the community's technical and financial aspects using MATLAB software. In this work, REC's users can install their plant and become prosumers or vice versa, and users could change their consumption habits until the optimum configuration of REC is obtained. Moreover, this work also focuses on the financial analysis of the plant by comparing the Net Present Value (NPV) as a function of plant size, highlighting the advantage of creating a REC. Numerical results have been obtained investigating the case studies of RECs as per the Italian framework, which shows an optimal distribution of prosumers and consumers and an optimal load profile in which the maximum profitability is obtained. Optimization has been performed by considering different load profiles. Moreover, starting from the optimized configurations, an analysis based on the plant size is also made to maximize the NPV. This work has shown positive outcomes and would be helpful for the researchers and stakeholders while designing the RECs.

To safeguard the ocean ecosystem, fuel cells are excellent candidates as the primary energy supply for marine vessels due to their high efficiency, low noise, and cleanliness. However, fuel cells in hybrid power systems are highly susceptible to load transients, which can severely damage fuel cells and shorten their lifespan. Therefore, the formulation of energy management strategies accounting for power degradation is crucial and urgent. In this study, an improved strategy for equivalent consumption minimization strategy (ECMS) considering power degradation is proposed. The improved energy control strategy effectively controls the energy distribution of hydrogen fuel cells, lithium batteries, and supercapacitors in hybrid power ships. The proposed control strategy combines the ECMS with an adaptive filtering method. The main objective of the ECMS is to allocate power to the fuel cell systems and energy storage systems (ESS) to stabilize the power output of fuel cells, prolong their service life, and reduce the hydrogen consumption in fuel cells. The adaptive filtering method, by low-pass filtering of the energy in the energy storage system, is used to allocate power between lithium batteries and supercapacitors to minimize the effect of transient and peak energy output on the lifespan of lithium batteries. To verify the superiority of the control strategy, a mathematical model for the hybrid power system is developed. In comparison to traditional ECMS and traditional ECMS considering degradation, the improved ECMS considering power degradation shows better performance in the overall economy, the durability of fuel cells and batteries, and system dynamic performance.

The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis. This paper presents an innovative framework, the Intelligent Power Stability and Scheduling (IPSS) System, which is designed to enhance the safety, stability, and economic efficiency of power systems, particularly those integrated with green energy sources. The IPSS System is distinguished by its integration of a CNN-Transformer predictive model, which leverages the strengths of Convolutional Neural Networks (CNN) for local feature extraction and Transformer architecture for global dependency modeling, offering significant potential in power safety diagnostics. The IPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm, which aims to minimize operational costs and grid instability. The performance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²). Experimental results demonstrate the superiority of the CNN-Transformer model, with the lowest RMSE and MAE values of 0.0063 and 0.00421, respectively, on the training set, and an R² value approaching 1, at 0.99635, indicating minimal prediction error and strong data interpretability. On the test set, the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673, respectively, and an R² value of 0.97233. The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms. The system’s efficacy is further supported by experimental results, highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability, making it a valuable contribution to the field of green energy systems.

One of the most important of these emissions is fine particulate matter, which is a harmful emission of diesel engines, leading to the imposition of strict regulations. Biodiesel, with its high oxygen content, is an effective alternative to significantly reduce these emissions. In this study, rapeseed methyl ester (RME) was used as a diesel engine fuel and the emitted particulate matter was compared with ultra-low sulfur diesel (ULSD). In most experimental studies, the emission of soot was measured. In this work, the effects of injection timing, injection pressure (IP), and engine load on fine particulate matter in both nucleation and accumulation modes were studied. The results show that IP increases the number of particles in the accumulation mode while the number of particles in the crystallization mode is higher for rapeseed methyl ester (RME) than for ultra-low sulfur diesel (ULSD). Conversely, the formation rates of particles in the accumulation mode are higher for ULSD. Cumulative concentration numbers (CCN) are generally higher for RME in crystallization mode but higher for ULSD in accumulation mode. Increasing the IP reduces the CCN values. The particle size in crystallization mode reaches a maximum of 22 nm at IPs of 800 and 1000 bar but decreases to 15 nm at 1200 bar. Most fine particles fall in the 5–100 nm diameter range. High engine loads reduce the particle size distribution in nucleation mode for both fuels, with a slight increase in particle size in nucleation mode. The study concluded that the use of rapeseed methyl ester as an engine fuel benefits the environment and improves air quality due to the significant reduction in the size, number, and concentration of nano-soot particles and total particles emitted from the engine.

With the severe challenges brought by global climate change, exploring and developing clean and renewable energy systems to upgrade the energy structure has become an inevitable trend in related research. The comprehensive park systems integrated with photovoltaic, energy storage, direct current, and flexible loads (PEDF) is able to play an important role in promoting energy transformation and achieving sustainable development. In order to fully understand the advantages of PEDF parks in energy conservation and carbon reduction, this paper summarizes existing studies and prospects future research directions on the low-carbon operation of the PEDF park. This paper first introduces carbon emission monitoring and evaluation methods, and then analyzes bi-level optimal dispatch strategies for flexible loads. Meanwhile, the paper provides a prospective analysis of the innovations that can be brought by advanced technologies to the PEDF park. Finally, this paper puts forward the challenges faced by current research and provides prospects for future research directions. This paper emphasizes that related research should focus on collaborating key technologies of PEDF systems and integrating advanced innovations to address challenges and fully leverage the advantages of PEDF technology in energy saving and carbon reduction. This paper aims to provide systematic theoretical guidance and supplements for the research and practice of the PEDF technology.

Hydraulic fracturing is a crucial technique for efficient development of coal reservoirs. Coal rocks typically contain a high density of natural fractures, which serve as conduits for fracturing fluid. Upon injection, the fluid infiltrates these natural fractures and leaks out, resulting in complex fracture morphology. The prediction of hydraulic fracture network propagation for coal reservoirs has important practical significance for evaluating hydraulic fracturing. This study proposes a novel inversion method for predicting fracture networks in coal reservoirs, explicitly considering the distribution of natural fractures. The method incorporates three distinct natural fracture opening modes and employs a fractal probability function to constrain fracture propagation morphology. Based on this method, the study compares hydraulic fracture network morphologies in coal reservoirs with and without the presence of natural fractures. The results show that while both reservoir types exhibit multi-branch fracture networks, reservoirs containing natural fractures demonstrate greater branching and a larger stimulated reservoir volume (SRV). Additionally, the study employs a fractal dimension calculation method to quantitatively describe the geometric distribution characteristics of fractures. The analysis reveals that the geometry and distribution of natural fractures, as well as reservoir geological parameters, significantly influence the fracture network morphology and fractal dimension. The contact angle between natural and hydraulic fractures affects propagation direction; specifically, when the contact angle is π/2, the fractal dimension of the hydraulic fracture network is maximized. Moreover, smaller lengths and spacings of natural fracture led to higher fractal dimensions, which can significantly increase the SRV. The proposed method offers an effective tool for evaluating the hydraulic fracturing of coal reservoirs.

Deep mining, characterized by high stress, elevated geothermal gradients, and significant moisture content, significantly increases the risk of Coal Spontaneous Combustion (CSC), posing a major threat to mine safety. This study delves into the impact of these factors on the self-ignition properties of coal, leveraging data from four distinct mines in Heilongjiang Province, China: Shuangyashan Dongrong No. 2 Mine, Hegang Junde Coal Mine, Qitaihe Longhu Coal Mine, and Jixi Ronghua No. 1 Mine. We have honed the theoretical framework to account for variations in gas content during CSC. Our investigation, conducted through programmed temperature rise experiments, scrutinized the generation and temperature-dependent evolution of gases, emphasizing individual indicators such as CO, O2, and CxHy, in addition to composite indicators like the ratio of change in CO to change in O2 concentration (∂CCO∂t:−∂CO2∂t) and the ratio of C2H4 to C2H6. These insights have catalyzed the development of a CSC state energy level transition model and a precise method for phase-based quantification of combustion progression. Our findings furnish a scientific foundation for the formulation of early warning and prevention strategies in deep mining settings.

In the background of the low-carbon transformation of the energy structure, the problem of operational uncertainty caused by the high proportion of renewable energy sources and diverse loads in the integrated energy systems (IES) is becoming increasingly obvious. In this case, to promote the low-carbon operation of IES and renewable energy consumption, and to improve the IES anti-interference ability, this paper proposes an IES scheduling strategy that considers CCS-P2G and concentrating solar power (CSP) station. Firstly, CSP station, gas hydrogen doping mode and variable hydrogen doping ratio mode are applied to IES, and combined with CCS-P2G coupling model, the IES low-carbon economic dispatch model is established. Secondly, the stepped carbon trading mechanism is applied, and the sensitivity analysis of IES carbon trading is carried out. Finally, an IES optimal scheduling strategy based on fuzzy opportunity constraints and an IES risk assessment strategy based on CVaR theory are established. The simulation shows that the gas-hydrogen doping model proposed in this paper reduces the operating cost and carbon emission of IES by 1.32% and 7.17%, and improves the carbon benefit by 5.73%; variable hydrogen doping ratio model reduces the operating cost and carbon emission of IES by 3.75% and 1.70%, respectively; CSP stations reduce 19.64% and 38.52% of the operating costs of IES and 1.03% and 1.80% of the carbon emissions of IES respectively compared to equal-capacity photovoltaic and wind turbines; the baseline price of carbon trading of IES and its rate of change jointly affect the carbon emissions of IES; evaluating the anti-interference capability of IES through trapezoidal fuzzy number and weighting coefficients, enabling IES to guarantee operation at the lowest cost.

Amidst the global push for decarbonization, solar-powered Organic Rankine Cycle (SORC) systems are gaining significant attention. The small-scale Organic Rankine Cycle (ORC) systems have enhanced environmental adaptability, improved system flexibility, and achieved diversification of application scenarios. However, the power consumption ratio of the working fluid pump becomes significantly larger relative to the total power output of the system, adversely impacting overall system efficiency. This study introduces an innovative approach by incorporating a vapor-liquid ejector into the ORC system to reduce the pump work consumption within the ORC. The thermo-economic models for both the traditional ORC and an ORC integrated with a vapor-liquid ejector driven by solar parabolic trough collectors (PTCs) were developed. Key evaluation indicators, such as thermal efficiency, exergy efficiency, specific investment cost, and levelized cost of energy, were employed to compare the SORC with the solar ejector organic Rankine cycle (SEORC). Additionally, the study explores the effects of solar beam radiation intensity, PTC temperature variation, evaporator pinch point temperature difference, and condenser pinch point temperature difference on the thermo-economic performance of both systems. Results demonstrate that SEORC consistently outperforms SORC. Higher solar radiation intensity and increased PTC inlet temperature lead to better system efficiency. Moreover, there is an optimal PTC temperature drop where both thermal and exergy efficiencies are maximized. The influence of evaporator and condenser temperature pinches on system performance is found to be inconsistent.

To address the excessive complexity of monthly scheduling and the impact of uncertain net load on the chargeable energy of storage, a reduced time-period monthly scheduling model for thermal generators and energy storage, incorporating daily minimum chargeable energy constraints, was developed. Firstly, considering the variations in the frequency of unit start-ups and shutdowns under different levels of net load fluctuation, a method was proposed to reduce decision time periods for unit start-up and shut-down operations. This approach, based on the characteristics of net load fluctuations, minimizes the decision variables of units, thereby simplifying the monthly scheduling model. Secondly, the relationship between energy storage charging and discharging power, net load, and the total maximum/minimum output of units was analyzed. Based on this, daily minimum chargeable energy constraints were established to ensure the energy storage system meets charging requirements under extreme net load scenarios. Finally, taking into account the operational costs of thermal generators and energy storage, load loss costs, and operational constraints, the reduced time-period monthly scheduling model was constructed. Case studies demonstrate that the proposed method effectively generates economical monthly operation plans for thermal generators and energy storage, significantly reduces model solution time, and satisfies the charging requirements of energy storage under extreme net load conditions.

The development of efficient and clean heating technologies is crucial for reducing carbon emissions in regions with severe cold regions. This research designs a novel two-stage phase change heat storage coupled solar-air source heat pump heating system structure that is specifically designed for such regions. The two-stage heat storage device in this heating system expands the storage temperature range of solar heat. The utilization of the two-stage heat storage device not only makes up for the instability of the solar heating system, but can also directly meet the building heating temperature, and can reduce the influence of low-temperature outdoor environments in severe cold regions on the heating performance of the air source heat pump by using solar energy. Therefore, the two-stage phase change heat storage coupled to the solar energy-air source heat pump heating system effectively improves the utilization rate of solar energy. A numerical model of the system components and their integration was developed using TRNSYS software in this study, and various performance aspects of the system were simulated and analyzed. The simulation results demonstrated that the two-stage heat storage device can effectively store solar energy, enabling its hierarchical utilization. The low-temperature solar energy stored by the two-stage phase change heat storage device enhances the coefficient of performance of the air source heat pump by 11.1% in severe cold conditions. Using the Hooke-Jeeves optimization method, the annual cost and carbon emissions are taken as optimization objectives, with the optimized solar heat supply accounting for 52.5%. This study offers valuable insights into operational strategies and site selection for engineering applications, providing a solid theoretical foundation for the widespread implementation of this system in severe cold regions.

In deep drilling applications, such as those for geothermal energy, there are many challenges, such as those related to efficient operation of the drilling fluid (mud) pumping system. Legacy drilling rigs often use paired, parallel-connected independent-excitation direct-current (DC) motors for mud pumps, that are supplied by a single power converter. This configuration results in electrical power imbalance, thus reducing its efficiency. This paper investigates this power imbalance issue in such legacy DC mud pump drive systems and offers an innovative solution in the form of a closed-loop control system for electrical load balancing. The paper first analyzes the drilling fluid circulation and electrical drive layout to develop an analytical model that can be used for electrical load balancing and related energy efficiency improvements. Based on this analysis, a feedback control system (so-called “current mirror” control system) is designed to balance the electrical load (i.e., armature currents) of parallel-connected DC machines by adjusting the excitation current of one of the DC machines, thus mitigating the power imbalance of the electrical drive. The proposed control system effectiveness has been validated, first through simulations, followed by experimental testing on a deep drilling rig during commissioning and field tests. The results demonstrate the practical viability of the proposed “current mirror” control system that can effectively and rather quickly equalize the armature currents of both DC machines in a parallel-connected electrical drive, and thus balance both the electrical and mechanical load of individual DC machines under realistic operating conditions of the mud pump electrical drive.

This research presents a new method to boost the efficiency of evaporative coolers by integrating magnetized water and a heat exchanger. Magnetized water, known for its high evaporation rate and reduced surface tension, offers a promising way to enhance air cooler performance. Additionally, the advanced heat exchanger both improves air cooling capacity and controls humidity levels. Aloni 100 L, a locally manufactured evaporative cooling system, and tap water were used in experiments. Tap water was magnetized using recycled magnets extracted from computer hard drives. Twenty-six magnets meticulously arranged within rectangular grooves, each with a minimum strength of 0.5 to 1 T, were used to magnetize tap water. Our experiments show a significant rise in cooling efficiency, with magnetized water increasing from 70.62% to 91.43%. In a similar vein, adding the heat exchanger leads to a significant improvement, raising the cooling efficiency from 69.44% to 93.96%. Furthermore, the combined use of magnetized water and a heat exchanger results in exceptional performance, increasing cooling efficiencies by 29.5% and 35.3% compared to using only magnetized water or only a heat exchanger, respectively. This study also explores the largely untapped potential of magnetized water, providing valuable insights into its effects on water properties and its broader applications in various fields. These findings represent a significant advancement in air cooling technology and pave the way for more energy-efficient and sustainable solutions.

Following global catastrophic infrastructure loss (GCIL), traditional electricity networks would be damaged and unavailable for energy supply, necessitating alternative solutions to sustain critical services. These alternative solutions would need to run without damaged infrastructure and would likely need to be located at the point of use, such as decentralized electricity generation from wood gas. This study explores the feasibility of using modified light duty vehicles to self-sustain electricity generation by producing wood chips for wood gasification. A 2004 Ford Falcon Fairmont was modified to power a woodchipper and an electrical generator. The vehicle successfully produced wood chips suitable for gasification with an energy return on investment (EROI) of 3.7 and sustained a stable output of 20 kW electrical power. Scalability analyses suggest such solutions could provide electricity to the critical water sanitation sector, equivalent to 4% of global electricity demand, if production of woodchippers was increased post-catastrophe. Future research could investigate the long-term durability of modified vehicles and alternative electricity generation, and quantify the scalability of wood gasification in GCIL scenarios. This work provides a foundation for developing resilient, decentralized energy systems to ensure the continuity of critical services during catastrophic events, leveraging existing vehicle infrastructure to enhance disaster preparedness.

To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms, an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed. First, considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage, an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system. Next, an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries. Finally, utilizing the online measurement of the energy states of hydrogen storage and batteries, an online optimization strategy based on measurement feedback is designed. Case study results show: before and after smoothing the fluctuations in wind power, the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1% and 1.45% of the total time, respectively, the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range. Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks, effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.

Extracted natural gas hydrate is a multi-phase and multi-component mixture, and its complex composition poses significant challenges for transmission and transportation, including phase changes following extraction and sediment deposition within the pipeline. This study examines the flow and heat transfer characteristics of hydrates in a riser, focusing on the multi-phase flow behavior of natural gas hydrate in the development riser. Additionally, the effects of hydrate flow and seawater temperature on heat exchange are analyzed by simulating the ambient temperature conditions of the South China Sea. The findings reveal that the increase in unit pressure drop is primarily attributed to higher flow velocities, which result in increased friction of the hydrate flow within the development riser. For example, at a hydrate volume fraction of 10%, the unit pressure drop rises by 166.65% and 270.81% when the average inlet velocity is increased from 1.0 to 3.0 m/s (a two-fold increase) and 5.0 m/s (a four-fold increase), respectively. Furthermore, the riser outlet temperature rises with increasing hydrate flow rates. Under specific heat loss conditions, the flow rate must exceed a minimum threshold to ensure safe transportation. The study also indicates that the riser outlet temperature increases with higher seawater temperatures. Within the seawater temperature range of 5°C to 15°C, the heat transfer efficiency is reduced compared to the range of 15°C to 20°C. This discrepancy is due to the fact that as the seawater temperature rises, the convective heat transfer coefficient between the hydrate and the inner wall of the riser also increases, leading to improved overall heat transfer between the hydrate and the pipeline.

Size reduction of the gas turbines (GT) by reducing the inlet S-shaped diffuser length increases the power-to-weight ratio. It improves the techno-economic features of the GT by lesser fuel consumption. However, this Length reduction of a bare S-shaped diffuser to an aggressive S-shaped diffuser would risk flow separation and performance reduction of the diffuser and the air intake of the GT. The objective of this research is to propose and assess fitted energy promoters (EPs) to enhance the S-shaped diffuser performance by controlling and modifying the flow in the high bending zone of the diffuser. After experimental assessment, the work has been extended to cover more cases by numerical investigations on bare, bare aggressive, and aggressive with energy promoters S-shaped diffusers. Three types of EPs, namely co-rotating low-profile, co-rotating streamline sheet, and trapezoidal submerged EPs were tested with various combinations over a range of Reynolds numbers from 40,000 to 75,000. The respective S-shaped diffusers were simulated by computational fluid dynamics (CFD) using ANSYS software adopting a steady, 3D, standard k-ε turbulence model to acquire the details of the flow structure, which cannot be visualized in the experiment. The diffuser performance has been evaluated by the performance indicators of static pressure recovery coefficient, total pressure loss coefficient, and distortion coefficient (DC(45°)). The enhancements in the static pressure recovery of the S-shaped aggressive diffuser with energy promoters are 19.5%, 22.2%, and 24.5% with EPs at planes 3, 4 and 5, respectively, compared to the aggressive bare diffuser. In addition, the installation of the EPs resulted in a DC(45°) reduction at the outlet plane of the diffuser of about 43% at Re = 40,000. The enhancements in the performance parameters confirm that aggravating the internal flow eliminates the flow separation and enhances the GT intake efficiency.

Enhancing the efficiency of Rankine cycles is crucial for improving the performance of thermal power plants, as it directly impacts operational costs and emissions in light of energy transition goals. This study sets itself apart from existing research by applying a novel optimization technique to a basic ideal Rankine cycle, focusing on a specific power plant that has not been previously analyzed. Currently, this cycle operates at 41% efficiency and a steam quality of 76%, constrained by fixed operational parameters. The primary objectives are to increase thermal efficiency beyond 46% and raise steam quality above 85%, while adhering to operational limits: a boiler pressure not exceeding 15 MPa, condenser pressure not dropping below 10 kPa, and turbine temperature not surpassing 500°C. This study utilizes numerical simulations to model the effects of varying boiler pressure (Pb) and condenser pressure (Pc) within the ranges of 12 MPa < Pb < 15 MPa and 5 kPa < Pc < 10 kPa. By systematically adjusting these parameters, the proposed aim to identify optimal conditions that maximize efficiency and performance within specified constraints. The findings will provide valuable insights for power plant operators seeking to optimize performance under real-world conditions, contributing to more efficient and sustainable power generation.

Rational distribution network planning optimizes power flow distribution, reduces grid stress, enhances voltage quality, promotes renewable energy utilization, and reduces costs. This study establishes a distribution network planning model incorporating distributed wind turbines (DWT), distributed photovoltaics (DPV), and energy storage systems (ESS). K-means++ is employed to partition the distribution network based on electrical distance. Considering the spatiotemporal correlation of distributed generation (DG) outputs in the same region, a joint output model of DWT and DPV is developed using the Frank-Copula. Due to the model’s high dimensionality, multiple constraints, and mixed-integer characteristics, bilevel programming theory is utilized to structure the model. The model is solved using a mixed-integer particle swarm optimization algorithm (MIPSO) to determine the optimal location and capacity of DG and ESS integrated into the distribution network to achieve the best economic benefits and operation quality. The proposed bilevel planning method for distribution networks is validated through simulations on the modified IEEE 33-bus system. The results demonstrate significant improvements, with the proposed method reducing the annual comprehensive cost by 41.65% and 13.98%, respectively, compared to scenarios without DG and ESS or with only DG integration. Furthermore, it reduces the daily average voltage deviation by 24.35% and 10.24% and daily network losses by 55.72% and 35.71%.

The photovoltaic (PV) output process is inherently complex, often disrupted by a multitude of meteorological factors, while conventional detection methods at PV power stations prove inadequate, compromising prediction accuracy. To address this challenge, this paper introduces a power prediction method that leverages modal switching (MS), weight factor adjustment (WFA), and parallel long short-term memory (PALSTM). Initially, historical PV power station data is categorized into distinct modes based on global horizontal irradiance and converted solar angles. Correlation analysis is then employed to evaluate the impact of various meteorological factors on PV power, selecting those with strong correlations for each specific mode. Subsequently, the weights of meteorological parameters are optimized and adjusted, and a PALSTM neural network is constructed, with its parallel modal parameters refined through training. Depending on the prediction time and input data mode characteristics, the appropriate mode channel is selected to forecast PV power station generation. Ultimately, the feasibility of this method is validated through an illustrative analysis of measured data from an Australian PV power station. Comparative test results underscore the method’s advantages, particularly in scenarios where existing detection methods are lacking and meteorological factors frequently fluctuate, demonstrating its superior prediction accuracy and stability.

This paper presents a new capacity planning method that utilizes the complementary characteristics of wind and solar power output. It addresses the limitations of relying on a single metric for a comprehensive assessment of complementarity. To enable more accurate predictions of the optimal wind-solar ratio, a comprehensive complementarity rate is proposed, which allows for the optimization of wind-solar capacity based on this measure. Initially, the Clayton Copula function is employed to create a joint probability distribution model for wind and solar power, enabling the calculation of the comprehensive complementarity rate. Following this, a joint planning model is developed to enhance the system’s economy and reliability. The goal is to minimize total costs, load deficit rates, and curtailment rates by applying an Improved Multi-Objective Particle Swarm Optimization algorithm (IMOPSO). Results show that when the proportion of wind power reaches 70%, the comprehensive complementarity rate is optimized. This optimization leads to a 14.83% reduction in total costs and a 9.27% decrease in curtailment rates. Compared to existing studies, this paper offers a multidimensional analysis of the relationship between the comprehensive complementarity rate and the optimal wind-solar ratio, thereby improving predictive accuracy and providing a valuable reference for research on the correlation between wind and solar power.

The literature on multi-attribute optimization for renewable energy source (RES) placement in deregulated power markets is extensive and diverse in methodology. This study focuses on the most relevant publications directly addressing the research problem at hand. Similarly, while the body of work on optimal location and sizing of renewable energy generators (REGs) in balanced distribution systems is substantial, only the most pertinent sources are cited, aligning closely with the study’s objective function. A comprehensive literature review reveals several key research areas: RES integration, RES-related optimization techniques, strategic placement of wind and solar generation, and RES promotion in deregulated power markets, particularly within transmission systems. Furthermore, the optimal location and sizing of REGs in both balanced and unbalanced distribution systems have been extensively studied. RESs demonstrate significant potential for standalone applications in remote areas lacking conventional transmission and distribution infrastructure. Also presents a thorough review of current modeling and optimization approaches for RES-based distribution system location and sizing. Additionally, it examines the optimal positioning, sizing, and performance of hybrid and standalone renewable energy systems. This paper provides a comprehensive review of current modeling and optimization approaches for the location and sizing of Renewable Energy Sources (RESs) in distribution systems, focusing on both balanced and unbalanced networks.

A centralized-distributed scheduling strategy for distribution networks based on multi-temporal and hierarchical cooperative game is proposed to address the issues of difficult operation control and energy optimization interaction in distribution network transformer areas, as well as the problem of significant photovoltaic curtailment due to the inability to consume photovoltaic power locally. A scheduling architecture combining multi-temporal scales with a three-level decision-making hierarchy is established: the overall approach adopts a centralized-distributed method, analyzing the operational characteristics and interaction relationships of the distribution network center layer, cluster layer, and transformer area layer, providing a “spatial foundation” for subsequent optimization. The optimization process is divided into two stages on the temporal scale: in the first stage, based on forecasted electricity load and demand response characteristics, time-of-use electricity prices are utilized to formulate day-ahead optimization strategies; in the second stage, based on the charging and discharging characteristics of energy storage vehicles and multi-agent cooperative game relationships, rolling electricity prices and optimal interactive energy solutions are determined among clusters and transformer areas using the Nash bargaining theory. Finally, a distributed optimization algorithm using the bisection method is employed to solve the constructed model. Simulation results demonstrate that the proposed optimization strategy can facilitate photovoltaic consumption in the distribution network and enhance grid economy.

Electrical energy can be harvested from the rotational kinetic energy of moving bodies, consisting of both mechanical and kinetic energy as a potential power source through electromagnetic induction, similar to wind energy applications. In industries, rotational bodies are commonly present in operations, yet this kinetic energy remains untapped. This research explores the energy generation characteristics of two rotational body types, disk-shaped and cylinder-shaped under specific experimental setups. The hardware setup included a direct current (DC) motor driver, power supply, DC generator, mechanical support, and load resistance, while the software setup involved automation testing tools and data logging. Electromagnetic induction was used to harvest energy, and experiments were conducted at room temperature (25°C) with controlled variables like speed and friction. Results showed the disk-shaped body exhibited higher energy efficiency than the cylinder-shaped body, largely due to lower mechanical losses. The disk required only two bearings, while the cylinder required four, resulting in lower bearing losses for the disk. Additionally, the disk experienced only air friction, whereas the cylinder encountered friction from a soft, uneven rubber material, increasing surface contact losses. Under a 40 W resistive load, the disk demonstrated a 17.1% energy loss due to mechanical friction, achieving up to 15.55 J of recycled energy. Conversely, the cylinder body experienced a 48.05% energy loss, delivering only 51.95% of energy to the load. These insights suggest significant potential for designing efficient energy recycling systems in industrial settings, particularly in manufacturing and processing industries where rotational machinery is prevalent. Despite its lower energy density, this system could be beneficially integrated with energy storage solutions, enhancing sustainability in industrial practices.