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The recent development of distributed generation technologies is changing the focus of the production of electricity from large centralised power plants to local energy systems scattered over the territory. Under the distributed generation paradigm, the present research scenario emphasises more and more the role of solutions aimed at improving the energy generation efficiency and thus the sustainability of the overall energy sector. In particular, coupling local cogeneration systems to various typologies of chillers and heat pumps allows setting up distributed multi-generation systems for combined production of different energy vectors such as electricity, heat (at different enthalpy levels), cooling power, and so forth. The generation of the final demand energy outputs close to the users enables reducing the losses occurring in the energy chain conversion and distribution, as well as enhancing the overall generation efficiency. This book presents a comprehensive introduction to energy planning and performance assessment of energy systems within the so-called Distributed Multi-Generation (DMG) framework. Typical plant schemes and components are illustrated and modelled, with special focus on applications for trigeneration of electricity, heat and cooling power. A general approach to characterisation and planning of multi-generation systems is formulated in terms of the so-called lambda analysis, which extends the classical models related to the heat-to-power cogeneration ratio analysis in cogeneration plants. A unified theoretical framework leading to synthesise different performance assessment techniques is described in details. In particular, different indicators are presented for evaluating the potential energy benefits of distributed multi-generation systems with respect to classical case of separate production and centralised energy systems. Several case study applications are illustrated to exemplify the models presented and to point out some numerical aspects relevant to equipment available on the market. In particular, schemes with different cogeneration prime mover typologies, as well as electric, absorption and engine-driven chillers and heat pumps, are discussed and evaluated. A number of openings towards modelling and evaluation of environmental and economic issues are also provided. The aspects analysed highlight the prominent role of DMG systems towards the development of more sustainable energy scenarios.

The world is interested in applying grid codes to increase the reliability of power systems through a micro-grid (MG). In a common practice, the MG comprises a wind farm, and/or photovoltaic (PV) arrays that are integrated with diesel generators and energy storage devices. Fault ride-through (FRT) capability is an important requirement of grid codes. FRT means that the MG is still connected to the grid during numerous disturbances such as faults. This is required to ensure that there is no loss of power generated due to grid faults. Reactive currents must be injected into the grid to increase the power system stability and restore voltage. To enhance FRT for doubly fed induction generator (DFIG) based WT installation, internal control modifications of rotor-side converters and grid-side converters are applied. The solutions that depend on these modifications are traditional and advanced control techniques. Advanced control techniques are needed due to the non-linear nature and less robustness of traditional ones. External hardware devices are also added to improve the FRT of DFIG which are classified into protection devices, reactive power injection devices, and energy storage devices. A comprehensive review of FRT enhancements of DFIG-based WTs, PV systems, and MGs using hardware and software methods is presented in this effort. A classification of FRT of PV systems is characterized plus various inverter control techniques are indicated. Several FRT methods for hybrid PV-WT are presented, with full comparisons. The overall operation and the schematic diagrams of the DFIG-WT with FRT methods are discussed and highlighted. Many Robust control methods for controlling grid connected AC, DC and hybrid AC/DC MGs in power systems are addressed. A total of 210 reported articles were review, including the most up-to-date papers published in the literature. This review may be used as the basis to improve system reliability for those interested in FRT methods. Various traditional and advanced control techniques to improve the FRT abilities are summarized and discussed, including protection devices, reactive power injection devices, and energy storage. In addition, the classifications of FRT hardware methods for DFIG are presented, including grid code requirements.

Wind energy is a clean and renewable energy source with great potential for development, but the intermittent and stochastic characteristics of wind speed have brought great challenges to the effective development and utilisation of wind energy resources, resulting in high development costs. Therefore, how to accurately assess the wind energy resources and effectively predict the wind speed has become a key issue to be solved in the current wind energy field. In view of this, the study proposes the Weibull model to model the wind speed data, and then introduces the wolf pack intelligent optimisation algorithm and improves it through the pollination mechanism to improve the accuracy of wind energy resource assessment. Secondly, considering the complexity and diversity of wind speed data characteristics, data decomposition technique, autoregressive moving average (ARIMA) model and cuckoo search algorithm are used to achieve data preprocessing, serial data modelling and hybrid prediction. The experimental results show that the Weibull model has good fitting accuracy for wind speed data, with residual sum of squares, RMSE, and average coefficient of determination of 0.05, 0.014, and 0.96, respectively, accurately reflecting the statistical characteristics of wind speed data. The wind speed prediction performance of the hybrid prediction model is good, with a maximum deviation of no more than 3% from the true value, which is significantly better than the compared VMD-ISOA-KELM model and CNN-BLSTM model, and its prediction error is relatively small. The hybrid prediction model has a smaller relative error value compared to a single algorithm, with a maximum value of less than 0.2. It has better prediction performance than the combination model, with a coefficient of determination approaching 1.0, a fitting accuracy of 0.994, a mean square error of 0.1947, a root mean square error of 0.3847, and an average absolute percentage error of 15.23%. And the research method can effectively evaluate the status of wind energy resources, with low time complexity at different data scales, taking no more than 5 seconds, and improving operational efficiency. This research method can provide strong technical support and reference basis for the development and utilisation of wind energy resources, and help to promote the sustainable development of wind energy industry.

The 2024 Africa Cup of Nations (AFCON) in Côte d’Ivoire highlighted the substantial energy demands of major sporting events, traditionally met by conventional, and nonrenewable sources. This study investigates the technoeconomic feasibility and environmental benefits of integrating solar and wind energy systems into the six stadiums used for the 2024 AFCON to enhance sustainability, long‐term economic benefits, and reduced carbon emissions. Utilizing the hybrid optimization of multiple electric renewables (HOMER) software for simulation and optimization, and the engineering equation solver for mathematical modeling, this study assesses the energy demand and potential renewable energy contributions for stadiums in Abidjan, Yamoussoukro, Bouaké, Korhogo, and San Pedro. The findings reveal that grid purchases dominate the energy mix across all cities, with varying contributions from solar and wind energy. Abidjan achieves 20.1% renewable energy penetration, while San Pedro reaches 69.9%. The proposed hybrid renewable energy systems offer substantial economic benefits, including payback periods of less than 2 years and high internal rates of return (IRR), with surplus energy generated in some locations potentially sold back to the grid. Additionally, the environmental impact assessment indicates an estimated annual CO 2 reduction of 6518.25 metric tonnes cumulatively, equivalent to planting ~2.17 million trees over 20 years. This study provides a detailed technoeconomic analysis, demonstrating the viability of hybrid wind–solar systems in large sports venues and contributing valuable insights for future implementations of renewable energy technologies in the sports infrastructure sector.

Continuously growing tariff rates for energy carriers required to generate electrical and thermal energy bring about the need to search for alternatives. Such alternatives are intended for the reduction in the electricity and heat net costs as well as the expenses for the operation and maintenance of system elements and damage from power outages or deteriorated power quality. A way to reduce electricity and heat costs is the introduction of distributed energy resources capable of operating on both conventional (natural gas) and alternative (solar and wind energy, biomass, etc.) fuels. The problem of reducing electricity and, in some cases, heat costs are solved by applying mathematical optimization techniques adapted to a specific element or system of the industry in question. When it comes to power industry facilities, optimization, as a rule, includes reducing active power losses by controlling the system mode or specific power unit parameters; planning generating equipment operating modes; defining the optimal equipment composition; improving the regime and structural reliability of grids; scheduling preventive maintenance of equipment; searching for effective power unit operating modes. Many of the problems listed are solved using direct enumeration techniques; modern technical tools allow quickly solving such local problems with a large number of source data. However, in the case of integrated control over the power system or its individual elements, optimization techniques are used that allow considering a lot of operating limitations and the target function multicriteriality. This paper provides an analytical review of optimization techniques adapted to solving problems of improving the efficiency of the power facility operating modes. The article is made on the basis of the research conducted by the authors in the area of optimization of operating modes for electric energy systems and grids. The authors drew conclusions on the applicability of mathematical optimization methods in the power energy area. While conducting the research, the authors relied on their expertise in the development and introduction of the method to optimize the operation modes of energy supply systems with heterogeneous energy sources.

The Lagrangian particle dispersion model FLEXPART in its original version in the mid-1990s was designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as those released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modeling and analysis and has attracted a global user community. Its application fields have been extended to a large range of atmospheric gases and aerosols, e.g., greenhouse gases, short-lived climate forcers like black carbon and volcanic ash, and it has also been used to study the atmospheric branch of the water cycle. Given suitable meteorological input data, it can be used for scales from dozens of meters to global. In particular, inverse modeling based on source–receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.4, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and data from the United States National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEXPART description (version 6.2), the model has been improved in different aspects such as performance, physicochemical parameterizations, input/output formats, and available preprocessing and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes in runs with very large numbers of particles. The deviation from 100 % efficiency is almost entirely due to the remaining nonparallelized parts of the code, suggesting large potential for further speedup. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g., to quantify dispersion in the vicinity of a point source. The wet deposition scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option of running backward in time from atmospheric concentrations at receptor locations for many years, but this has now been extended to also work for deposition values and may become useful, for instance, for the interpretation of ice core measurements. To our knowledge, to date FLEXPART is the only model with that capability. Furthermore, the temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing for more accurate simulations for species with intermediate lifetimes against the reaction with OH, such as ethane. Finally, user settings can now be specified in a more flexible namelist format, and output files can be produced in NetCDF format instead of FLEXPART's customary binary format. In this paper, we describe these new developments. Moreover, we present some tools for the preparation of the meteorological input data and for processing FLEXPART output data, and we briefly report on alternative FLEXPART versions.

The electrochemical proton exchange membrane fuel cell (PEMFC) is an electrical generator that utilizes a chemical reaction mechanism to produce electricity, serving as a sustainable and environmentally friendly energy source. To thoroughly analyze and develop the features and performance of a PEMFC, it is essential to use a precise model that incorporates exact parameters to effectively suit the polarization curve. In addition, parameter extraction plays a crucial role in the simulation analysis, evaluation, optimum control, and fault detection of the proton exchange membrane fuel cell (PEMFC) system. Despite the development of many algorithms for parameter extraction in PEMFC, obtaining accurate and trustworthy results rapidly remains a challenge. This study presents a hybridized algorithm, namely differential evolution ameliorated (DEA) for reliably estimating PEMFC model parameters. To evaluate the proposed DEA-based parameter identification, a comparison analysis with previously published methods is conducted using MATLAB/SimulinkTM (R2016b, MathWorks, Natick, MA, USA) in terms of system correctness and convergence process. The proposed DEA algorithm is tested to extract the parameters of two PEMFC models: SR-12 500 W and 250 W. The sum of the squared errors (SSE) between the experimental and the obtained voltage data is defined as an objective function. The simulation results prove that the suggested DEA algorithm is capable of identifying the optimal PEMFC parameters rapidly and accurately in comparison with other optimization algorithms.

Curtailment of turbine operations during low wind conditions has become an operational minimization tactic to reduce bat mortality at terrestrial wind energy facilities. Site-specific studies have demonstrated that bat activity is higher during lower wind speeds and that operational curtailment can effectively reduce fatalities. However, the exact nature of the relationship between curtailment cut-in speed and bat fatality reduction remains unclear. To evaluate the efficacy of differing curtailment regimes in reducing bat fatalities, we examined data from turbine curtailment experiments in the United States and Canada in a meta-analysis framework. We used multiple statistical models to explore possible linear and non-linear relationships between turbine cut-in speed and bat fatality. Because the overall sample size for this meta-analysis was small (n = 36 control-treatment studies from 17 wind farms), we conducted a power analysis to assess the number of control-treatment curtailment studies needed to understand the relationship between fatality reduction and change in cut-in speed. We also identified the characteristics of individual curtailment field studies that may influence their power to detect fatality reductions, and in turn, contribute to future meta-analyses. We found strong evidence that implementing turbine curtailment reduces fatality rates of bats at wind farms; the estimated fatality ratio across all studies was 0.37 (p < 0.001), or a 63% decrease in fatalities. However, the nature of the relationship between the magnitude of treatment and reduction in fatalities was more difficult to assess. Models that represented the response ratio as a continuous variable (e.g., with a linear relationship between the change in cut-in speed and fatalities) and a categorical variable (to allow for possible non-linearity in this relationship) both had substantial support when compared using AIC c . The linear model represented the best fit, likely due to model simplicity, but the non-linear model was the most likely without accounting for parsimony and suggested fatality rates decreased when the difference in curtailment cut-in speeds was 2m/s or larger. The power analyses showed that the power to detect effects in the meta-analysis was low if fatality reductions were less than 50%, which suggests that smaller increases in cut-in speed (i.e., between different treatment categories) may not be easily detectable with the current dataset. While curtailment is an effective operational mitigation measure overall, additional well-designed curtailment studies are needed to determine precisely whether higher cut-in speeds can further reduce bat fatalities.

The growing global demand for natural gas highlights the crucial role of underground gas storage (UGS) in ensuring supply security. Among various storage methods, salt cavern gas storage stands out due to its superior geological properties. However, the cyclic fluctuations in pressure and temperature during gas injection and withdrawal introduce significant challenges to the mechanical stability of the surrounding rock, potentially compromising the facility’s operational safety. To address these challenges, this study develops an innovative mathematical model that integrates thermal effects into the dynamic simulation of gas injection and withdrawal processes within a single salt cavern storage facility. The proposed model consists of two key components: (1) a borehole model formulated based on flow dynamics and energy conservation principles and (2) a salt cavern model incorporating the continuity equation and thermal energy balance. A numerical simulation approach is employed to solve the model, with a detailed derivation process provided. Unlike conventional models, this approach enables accurate prediction of both pressure and temperature variations within the cavern under continuous injection and withdrawal conditions while allowing bidirectional coupling between wellhead and cavern parameters. Model validation against field data confirms its reliability and applicability in optimizing gas injection and withdrawal strategies. Furthermore, the model serves as a valuable tool for single‐cavern inventory verification, contributing to enhanced safety and operational efficiency in salt cavern gas storage systems.