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Modern power systems integrated with renewable energies (REs) contain many uncertainties. The proposed method introduces a novel approach to address the challenges associated with wind power generation uncertainty in probabilistic load flow (PLF) studies. Unlike conventional methods that use wind speed as an input, the paper advocates for utilizing wind generator output power (WGOP) as an input to the point estimate method (PEM) in solving PLF. The uniqueness lies in recognizing the distinct behavior of wind power uncertainty, where not all random samples of wind speed contribute to actual wind power production. The paper suggests a Nataf-KernelDensity-Spline-based PEM, combining the Nataf transformation, Kernel density estimation (KDE), and cubic spline interpolation. This innovative integration effectively manages wind power correlation within the analytical framework. By incorporating spline interpolation and kernel density estimation into the traditional PEM, the proposed method significantly enhances accuracy. To validate the effectiveness of the proposed approach, the method is applied to IEEE-9 and IEEE-57 bus test systems, considering uncertainties related to load, wind power generation (WPG), solar power generation (SPG), and conventional generator (CoG) outages. Comparative analysis with Monte Carlo simulation (MCS) results demonstrates that the proposed method outperforms the conventional PEM in terms of accuracy. Overall, the paper contributes a pioneering solution that not only highlights the importance of using WGOP as an input in PLF but also introduces a sophisticated method that surpasses traditional approaches, improving accuracy in power system studies involving renewable energy integration. The accuracy of the proposed method is validated by comparing its results with those obtained through Monte Carlo simulation (MCS), where the proposed method yields more accurate results than the conventional PEM. [ABSTRACT FROM AUTHOR]

With global climate change posing a major threat to human society, a growing number of countries have taken "carbon-neutral" as a national strategy and proposed a vision of carbon-free future. As an important supplement to traditional fossil energy, renewable energy is the main force to reduce the use of high-carbon energy and carbon dioxide emissions, which will become the trend of social development in the future. Finding the optimal renewable energy source is of particular significance for achieving the net zero emissions. However, the existing evaluation methods of renewable energy sources have obvious shortcomings. In terms of weight calculation methods, such as the randomness of the subjective method is strong and the index weights do not reflect the small changes of the evaluation matrix, which affect the reliability and accuracy of the evaluation result. The existing ranking methods can only achieve the complete ranking of the different objects, but cannot classify the renewable energy technical alternatives into different grades. Given this background, this paper proposes a novel evaluation method for renewable energy plans based on improved sparrow search algorithm and projection pursuit model. Firstly, this paper improves the traditional sparrow search algorithm from three aspects: population initialization, population update and population variation. Then, the projection pursuit model is constructed, and the improved sparrow search algorithm is applied to optimize the projection target to find the optimal projection direction, so as to determine the weight values of each evaluation index. Finally, the weighted rank-sum ratio method is used to select the best renewable energy technical plan, which can not only realize the complete ranking of different plans, but also classify the technical plans into different levels. Based on the actual renewable energy development data from a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some existing evaluation methods of renewable energy technical plans. [ABSTRACT FROM AUTHOR]

Reducing greenhouse gas emissions is urgent for the global community with rising climates. Considering the importance of renewable energy in mitigating climate warming, forecasting renewable energy generation is vital for the Chinese government's future low-carbon and green development plan. This paper proposes a novel multivariable grey model based on historical data on China's renewable energy generation and three industries. A novel information accumulation mechanism with two adaptive factors is designed to improve the traditional multivariable grey modeling defect. Based on the proposed mechanism, this paper optimizes the initial and background values and nonlinear model structure with the whale optimization algorithm. The forecasting results show that the fitting MAPE is 1.13%, comprehensive MAPE is 2.60%, MSE is 50.86, and RMSE is 7.13, which significantly improve the forecasting accuracy of traditional GM(1,N) and are better than other compared models. The forecasting results show that China's renewable energy generation will gradually increase to 5834.02 TWh. The Chinese government should keep the previous Five-Year Plans rising trend of the three industries in the future Five-Year Plans to support renewable energy industries. In China's future energy system, it is necessary to promote incentive policies and capital investment for actively accelerated development to make renewable energy the leading force. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to designing and optimizing a Solar-Wind Hybrid Energy System (SWHS) for an Electric Vehicle Charging Station (EVCS) and a university shopping complex in India. Objectives include addressing sustainable energy challenges, ensuring reliability, and minimizing the Levelized Power Supply Price (LPSP). Key contributions encompass a two-stage framework for optimal SWHS configuration, consideration of uncertainties in renewable energy generation, and the use of the Ebola Optimization Search Algorithm (ESOA). The first stage models SWHS components, formulates an objective function, and addresses reliability to minimize LPSP. The second stage employs ESOA, inspired by Ebola spread, exploring both local and global search spaces. Simulation results and a load profile analysis validate the proposed framework and ESOA's performance, observing a significant improvement of 15% in LPSP and a 10% increase in system reliability. The study provides insights into optimal Distributed Generation (DG) sizing in SWHS, ensuring reliable and cost-effective power supply for EVCS and commercial applications, contributing to the renewable energy system design and optimization field. The analysis indicates an enhanced system reliability by 10 % and a notable reduction in LPSP by 15 %. [ABSTRACT FROM AUTHOR]

The ongoing transition in the electricity market, coupled with the rising penetration of renewable energy sources, has spurred demand for enhanced load forecasting accuracy. Traditional single predictors exhibit limited robustness and diversity, rendering them unsuitable for most forecasting situations. Moreover, weather conditions, holidays, and social events significantly influence load demand, making forecasting techniques that account for these factors increasingly relevant. To bridge the gaps and further enhance predictive performance, in this paper, a novel adaptive multivariate combined forecasting system is proposed, incorporating redundant feature elimination, adaptive model selection, multi-objective intelligent optimization with non-negative constraints, and reliable interval prediction theory. The proposed system employs non-linear feature selection to identify effective load features and develops an advanced Multi-Objective Optimization Strategy (MOS) for combining multiple optimal predictors under various scenarios. The weight coefficients of sub-predictors are theoretically demonstrated to be Pareto optimal, and the MOS exhibits superior distribution and convergence. Simulation results reveal that the developed system substantially outperforms benchmark models, achieving reliable uncertainty quantification, which facilitates the rational allocation of spinning reserves and enhances dispatching efficiency. [ABSTRACT FROM AUTHOR]

The demand for renewable energy sources is growing rapidly as a result of increasing energy consumption, fossil fuel depletion, climate change, and environmental degradation. The tide is one of the main sources of renewable energy in the ocean because of its huge potential to be harnessed. Wave-energy converters (WECs) are a potential alternative to present energy sources, with an emphasis on improving efficiency and increasing energy. With the goal of improving the uncertainty inherent in data representation, the literature gradually uses various fuzzy extensions that handle fuzzy information in subjective evaluations and decision-making processes. single-valued neutrosophic probabilistic hesitant fuzzy sets (SVNPHFS), a recent extension of neutrosophic fuzzy sets, address limitations associated with membership functions and the representation of hesitancy in multi-criteria decision-making (MCDM) approaches. In the decision-making process, uncertainty is successfully handled by SVNPHFS. The objective of this study is to investigate and select a potential converter for a WEC power plant using SVNPHFS. Seven criteria for WECs are selected as potential sites for establishing a wave-energy power plant. The paper proposes a novel hybrid MCDM methodology. Fuzzy SWARA (Stepwise Weighted Assessment Ratio Analysis) is utilized for assigning weights to the criteria for WEC selection, and fuzzy ELECTRE (Elimination and Choice Expressing Reality) is used to determine the most suitable alternative using these criteria weights. The proposed technique identifies the point absorber in the first rank for the best wave-to-energy converter power plant as it generates a high amount of electricity compared to other alternatives. The result shows that the proposed model is more robust compared to other approaches. [ABSTRACT FROM AUTHOR]

In the context of today's energy shortage, wind energy plays a crucial role as one of the most widely used renewable energy sources. However, in order to fully utilize the potential of wind energy, it is necessary to build a precise and reliable wind-speed prediction system. Conventional wind-speed prediction models frequently rely on a solitary model for forecasting, and sometimes these models cannot accurately fit the nonlinear characteristics of the data, resulting in poor prediction performance. Therefore, to address this deficiency, a new wind-speed point-interval prediction system is proposed in this study. The fuzzy information granulation technology is adopted in this system to reduce the dimension of data and solve the problem of redundancy in wind-speed data. In addition, this paper uses the multi-objective dragonfly algorithm and combines sub-models using the multi-nonlinear weight strategy to address the issue of inadequate precision exhibited by an individual prediction model. In order to verify the effectiveness of this wind-speed prediction system, the Penglai wind farm in Shandong Province is used as the dataset to construct the model, and several reference models are selected to conduct point prediction and interval prediction comparison experiments respectively. The experimental results show that the combined prediction system has the best prediction performance, with MAPE values of 14.16%, 15.02% and 15.74%, respectively. Compared with other benchmark models, the average improvement rates for the multi-step prediction were 14.97%, 16.48% and 19.11%, respectively. [ABSTRACT FROM AUTHOR]

Artificial rabbits optimization (ARO) is a swarm intelligence-based algorithm inspired by the survival strategies of rabbits. Although ARO has a good convergence rate, it is prone to get stuck in the local optima and converge prematurely. To overcome this, the present paper redesigns the exploration operator of the ARO algorithm with the roulette fitness-distance balance (RFDB) and dynamic fitness-distance balance (d FDB) strategies. In this context, three different versions of the fitness-distance balance-based artificial rabbits optimization (FDBARO) algorithm are developed. The performance of the original ARO and FDBARO versions (FDBARO-1, FDBARO-2, and FDBARO-3) are evaluated on CEC 2017 and CEC 2020 benchmark functions. The obtained results are analyzed with the Wilcoxon and Friedman statistical tests. Statistical and convergence analysis results showed that the FDBARO-3 algorithm designed with the d FDB selection method can explore the search space more successfully compared to other algorithms. This version was named the dynamic FDBARO (d FDBARO) algorithm. Moreover, the practicability of the proposed d FDBARO is highlighted by the solution of the optimal power flow (OPF) problem formulated with renewable energy sources (RESs) and flexible alternating current transmission system (FACTS) devices considering fixed and uncertain load demands. Experimental results showed that the proposed d FDBARO is a competitive algorithm for solving global optimization and constrained OPF problems. The source code of the d FDBARO algorithm is available at https://www.mathworks.com/matlabcentral/fileexchange/154845-dfdbaro-an-enhanced-metaheuristic-algorithm. [ABSTRACT FROM AUTHOR]

• Employs non-identical parallel machines in manufacturing with multiple energy sources. • Designs a bi-objective model for a high-mix, low-volume production scheduling problem. • Provides Pareto-optimal solutions that minimize both total cost and carbon emissions. • Validates a model with real-world application to a manufacturer in Ulsan, South Korea. • Analyzes the effects of price fluctuations, different combinations of energy sources. This paper addresses the production scheduling problem with non-identical parallel machines in a high-mix, low-volume production environment with due dates, considering carbon emissions savings and diverse types of energy sources for machine operation. To this end, we present a bi-objective mixed-integer programming model that minimizes both the total production-related cost and the total carbon emissions in the production process to determine the optimal production strategy. For each machine, we consider various types of energy sources with different electricity generation costs and different amounts of carbon emissions. The proposed model is validated with an application to a manufacturer in Ulsan, the industrial capital of the Republic of Korea. Our results showcase the potential use of non-identical parallel machines to minimize the total cost and carbon emissions in green manufacturing. Furthermore, the results identify the trade-off between different energy sources and the total cost under different carbon emissions limits. Generally, as the carbon emissions limit increases, our proposed model tends to replace natural gas with coal to minimize the total cost. Also, we perform sensitivity analyses with respect to the energy price of different types of energy sources combined with nuclear and renewable energy sources. We find that coal provides more stability than natural gas in terms of the total cost, particularly when there are fluctuations in the price for coal and natural gas. Additionally, we determine the optimal combination of energy sources for various carbon emissions limits, aiming to minimize the total cost while simultaneously satisfying all production-related and environmental constraints. [ABSTRACT FROM AUTHOR]

Phase change materials (PCMs) are one of the promising technologies for the carbon neutral future, since they can be effectively used for thermal energy storage (TES), and therefore can mitigate the energy supply/demand mismatch characteristic for renewable energy resources. Mathematical models describing thermal behaviour of PCMs require various material properties, such as specific heat capacity, thermal conductivity or density, which are in many cases difficult to obtain experimentally. This paper introduces an inverse identification procedure which utilises heuristic optimisation methods to solve the inverse problem. The air-PCM heat exchanger was considered as a studied device. In order to avoid any uncertainties tied to the experimental measurement, the exact solution was generated by the numerical model directly (referred to as "pre-simulated" scenario). The cost function was constructed as a root mean square difference between the given calculated and pre-simulated outlet air temperatures. In total seven different case studies (five with a base model and two with an extended model) were investigated for the inverse identification of the effective heat capacity of a PCM and seven different heuristic optimisation methods were utilised for each case study. The results have shown that the LSHADE and DE methods were the best performing ones and that one method outperformed the other depending on the problem settings. • Description of difficult inverse identification problem. • Extension of existing model for effective heat capacity curve. • Evaluation of seven metaheuristics on seven case studies. • The best-performing methods were LSHADE and DE. [ABSTRACT FROM AUTHOR]

The problem of precise modeling of Synchronous Generator (SG) devices is demanding and crucial for monitoring, stability, and control of electric grids and microgrids. SGs are known to exhibit nonlinear behavior, and because they may work in a wide range of operating points due to different electric network phenomena like load changes, current intermittent renewable energy resources, temperature variations, topological changes, etc., the modeling task of SG can be a challenging problem. This paper proposes a new method to find a global model for a synchronous generator from the input and output measurement data using state-of-the-art methods in artificial intelligence (AI) like fuzzy clustering, subspace identification, and Takagi–Sugeno (T–S) fuzzy modeling. The fuzzy C-means method is utilized to cluster the measurement data, and then using a subspace identification method based on Markov parameters concepts, the dynamic model of SG for each cluster is obtained in the form of a state space model. A Takagi–Sugeno fuzzy model is applied to determine the corresponding outputs based on the measured inputs and the available state space models. The effectiveness of the proposed approach is illustrated by applying it to the data obtained from simulations on a known fourth-order SG nonlinear model and the data obtained from real-world experimental tests. [ABSTRACT FROM AUTHOR]

• A novel fractional grey Chebyshev polynomial Bernoulli model is proposed. • The structure of the NGBM(1,1,Tn) model is variable. • Comparative study shows that the new model is superior to other five grey models. Accurate mid-to-long term China's renewable energy forecasting is becoming more and more important for integrating renewable energy systems with smart grid and energy strategic planning. For this purpose, this paper proposes a novel fractional structural adaptive grey Chebyshev polynomial Bernoulli model. The new model is based on NGBM(1,1) model to describe nonlinear phenomena. The proposed adjacent accumulation operator can balance old and new information accumulation with less fluctuation. The introduction of the Chebyshev polynomial enables the model to adaptively adjust the structure of the model, which reduces the difficulty of the modeler's operation from the point of view of the expert system. Meanwhile, an improved Grey Wolf optimization algorithm (IGWO) was used to optimize the parameters. Four real cases of renewable energy in China, including production and consumption are used for verification. In addition, Monte Carlo simulation and probability density analysis illustrate the robustness and accuracy of the proposed model. Finally, the development of the four cases in the next 5 years is predicted. [ABSTRACT FROM AUTHOR]

• A novel structure-adaptive grey model is initially designed. • The proposed model can grasp the nonlinear and seasonal patterns. • This method exhibits generalizability in predicting renewable energy generation. • The proposed technique strikingly outperforms many prevalent benchmarks. To provide accurate renewable energy forecasts that adapt to the country's sustainable development, a novel seasonal model combined with the data-restacking technique is proposed in this paper. Specifically, the data-restacking technique is initially utilized to eliminate the seasonal fluctuations of the collected observations, which can eliminate the fundamental flaws in conventional seasonal grey models. Subsequently, the time function term is originally designed to incorporate into the dynamic structure to reflect the cumulative time effects, which can smoothly describe the dynamic changes and significantly improve the robustness of the novel model. Further, the self-adaptive parameters optimized using particle swarm optimization can effectively enhance the adaptability and generalization of the proposed model. For elaboration and verification purposes, experiments on forecasting American monthly renewable energy consumption in the commercial sector and industrial solar energy have been implemented compared to a range of benchmark models, including other prevalent grey prediction models, statistical approaches, and machine learning methods. Experimental results demonstrate that this new model presents more successful outcomes than the other benchmarks in overall and restacking performance. [ABSTRACT FROM AUTHOR]

The conventional data envelopment analysis (DEA) method suffers from its inability to incorporate the decision-makers' preferences and cope with the uncertainty that exists in real-life decision problems. Exclusive-or (XOR for short) is an uncertain logic that describes a situation in which there is only one choice between two or more competitive actions and neither is strong enough to overcome the others. In this paper, a new research thread of the DEA paradigm, named XOR-DEA, is proposed to deal with decision-making problems under xorness (or XOR input/output data). To incorporate decision-makers' preferences in the optimization process, three types of preferences are proposed: positive, negative, and neutral. To cope deeply with uncertainty, a new concept of "the output mechanism of the XOR function" is developed to support the analyst in controlling this phenomenon based on two channels: controlled and uncontrolled. Moreover, to enrich the analysis of practical applications, a new visual analytic material is designed to detect the behavior of the XOR functions during the optimization process. To show the models' applicability, an illustrative example and an application of ranking renewable energy technologies are presented. • A new research thread of the DEA paradigm, named XOR-DEA, is proposed. • Decision-makers' preferences have been integrated. • A new concept of "the output mechanism of the XOR function" is developed. • A visual analytic material is designed to detect the behavior of the XOR functions. • An application of ranking renewable energy technologies is presented. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new decision making method for selection problems under uncertainty is proposed. • The method considers both the objective weights and the subjective preferences. • The method is applied to the selection of renewable energy technologies. • The best option varies depending on the coefficient factor of the subjective weight. • The method is applicable to other selection problems in different areas. The use of renewable energy technologies is a key factor for sustainable development but their selection from several alternatives is a difficult task that relies on the careful assessment of relevant criteria. While Multiple Criteria Decision Making (MCDM) methods have been used successfully in various renewable energy technology selection problems, the decision process becomes more challenging when preferential judgements are made on the basis of non-homogenous and imprecise input data, and when there is uncertainty due to disparities among decision makers. This paper presents a hybrid MCDM method capable of overcoming these problems by taking into account quantitative and qualitative data under a probabilistic environment in the context of group decision making. In this method, qualitative data is fuzzified and used along with quantitative data to develop a hybrid model. A coefficient factor allows decision makers to vary the weight of each quantitative model so that the resultant criteria weights and overall alternatives' scores consider both subjective considerations and objective information. An example is presented to showcase the usability of the method developed for ranking and evaluating renewable energy technologies in the mining industry. In addition, the impact of different coefficient factors on the final results was assessed by means of sensitivity analysis. The results indicate that the method developed is able to minimise the loss of valuable objective information, caused by the subjective bias of qualitative weights during the evaluations, by adjusting the coefficient factors of the hybrid model during the calculations. [ABSTRACT FROM AUTHOR]

Wind turbine power generation is becoming one of the most critical renewable energy sources. As wind power grows, there is a need for better monitoring and diagnostic strategies to maximize energy production and increase its security. In this paper, a fault diagnosis approach based on a data-driven technique, which represents the system behavior employing a Takagi–Sugeno (TS) model, is developed. An adaptive neuro-fuzzy inference system (ANFIS) method is used to obtain a set of polytopic-based linear representations and a set of membership functions to interpolate the linear models of the convex TS model. Then, considering the TS model, a fault diagnosis strategy based on convex state observers generate residuals to detect and isolate sensor faults. Unlike other methods, this proposal only needs to be trained with fault-free data. The proposed methodology is tested under different fault scenarios on a well-known wind turbine benchmark built upon fatigue, aerodynamics, structures, and turbulence (FAST). The results demonstrate the method's effectiveness in detecting and isolating different sensor faults. • A wind turbine uncertain model is obtained based on sensor data and an ANFIS method. • Based on the ANFIS model, an uncertain Takagi–Sugeno (TS) model is obtained. • A set of interval TS observers is designed to detect and isolate sensor faults. • The method is based on healthy data and does not require a mathematical model. • Numerical experiments are performed in a well-accepted wind turbine benchmark. [ABSTRACT FROM AUTHOR]

Urban sustainability is a significant factor in combating climate change. Replacing polluting by renewable energies is fundamental to reduce the emission of greenhouse gases. Photovoltaic (PV) facilities harnessing solar energy, and particularly self-consumption PV facilities, can be widely used in cities throughout most countries. Therefore, locating spaces where photovoltaic installations can be integrated into urban areas is essential to reduce climate change and improve urban sustainability. An open-source software (URSUS-PV) to aid decision-making regarding possible optimal locations for photovoltaic panel installations in cities is presented in this paper. URSUS-PV is the result of a data mining process, and it can extract the characteristics of the roofs (orientation, inclination, latitude, longitude, area) in the urban areas of interest. By combining this information with meteorological data and characteristics of the photovoltaic systems, the system can predict both the next-day hourly photovoltaic energy production and the long-term photovoltaic daily average energy production. • Decision-making software for optimal photovoltaic location has been developed. • A data-mining process to assess the software development was used. • Evaluation of photovoltaic potential in large urban areas is automated. • Features of this software include open-source, online availability and ease-of use. • Roof characteristics in urban areas are automatically analysed. [ABSTRACT FROM AUTHOR]

This paper presents an Energy Management System (EMS) for hybrid systems (HS) composed by a combination of renewable sources with the support of different storage devices (battery and hydrogen system) that allow its operation without the necessity of grid connection (i.e. a stand-alone system). The importance of the proposed EMS lies in taking into account economic issues that affect to the decision of which device of the HS must operate in each moment. Linear programming was used to meet the objective of minimizing the net present value of the operation cost of the HS for its whole lifespan. The total operation costs depend largely on the reposition costs of its components. Instead of considering predefined reposition years for each component and calculate their net present cost from them (as is commonly considered in other works), in this work it was proposed to use lifetime degradation models - based on the well-known statement that the lifetime depends on the hours of operation and the power profiles that the components are subjected to- from which the repositions are made to check how they affect to the cost calculation and, consequently, to the EMS performance. The behavior of the proposed control is checked under a long term simulation, in MATLAB-Simulink environment, whose duration is the expected lifespan of the HS (25 years). A conventional state-machine EMS is used as a case study to validate and compare the results obtained. The results demonstrate that the proposed HS and EMS combination assures reliable electricity support for stand-alone applications subject to different techno-economic criteria (generation cost and sustenance of battery SOC and hydrogen levels), achieving to minimize the operation cost of the system and extend their lifespan. [ABSTRACT FROM AUTHOR]

• A novel invasive weed optimization algorithm based on levy flight (LFIWO) is proposed. • Nine different layout systems are proposed for optimizing wind turbine placement. • A new repair operator is developed in order to make the candidate solutions feasible. • Experimental studies are performed with ten binary variants for both of IWO and LFIWO. • LFIWO obtains the better results than those of the other algorithms in the literature. Wind energy has attracted great attention in recent years due to the increasing demand for alternative energy sources. Gathering the maximum amount of energy from wind energy is directly related to the layout of wind turbines in wind farm. This study focuses on grid-based wind turbine layout problem in an area of 2 km × 2 km. In order to solve this problem, 9 novel grids of 11 × 11, 12 × 12, ... and 19 × 19 are proposed in this paper in addition to 10 × 10 and 20 × 20 turbine grids which have already used in the literature. A new repair operator is recommended, taking into account the distance constraint between two adjacent cells in turbine layout problem, and thus the obtained solutions are made feasible. Furthermore, a levy flight-based IWO (LFIWO) algorithm is developed to optimize the layout of the turbines in wind farm. The basic IWO algorithm and the proposed LFIWO algorithm are compared on 11 different turbine layouts. Experimental studies are carried out for both algorithms under equal conditions with the help of 10 different binary versions. According to the comparisons performed using 11 different grids, LFIWO demonstrates a much superior success than the IWO algorithm. In addition, the proposed 19 × 19 grid reveals the best success among the other grids. When compared to the other studies in the literature, it is seen that LFIWO surpasses the other algorithms in terms of solution quality. As a result, it can be clearly stated that the proposed binary version of LFIWO is a competitive and effective binary algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Nowadays, modern power networks have to face a number of challenges such as growing electricity demand, aging utility infrastructure and not to forget the environmental impact of the greenhouse gases produced by conventional electric generation. In order to increase renewable energy penetration but without disregarding security and reliability matters during the process, distribution power networks need to evolve to a flexible power network, better known as smart grid, in which distributed intelligence, communication technologies and automated control systems work as the driving factors. Taking into consideration this new frame, intelligent optimization techniques emerge as the only suitable way to optimally design this smart grid. In this paper, a generalized optimization formulation is introduced to determine the optimal location of distributed generators to offer reactive power capability. In order to find a suitable solution to such Reactive Power Management problem, genetic algorithms are applied in those cases where different multiobjective functions are to be considered. A more detailed description of the genetic algorithm evolution process is shown in a microgrid example. [Copyright &y& Elsevier]

Abstract: Appraising the Renewable Energy Sources (RES) options’ contribution to Sustainable Development (SD) is a complex task, considering the different aspects of SD the imprecision and uncertainty of the related information as well as the qualitative aspects embodied, that cannot be represented by numerical values. The objective of the paper is to show how energy policy objectives towards SD and RES options are related and assessed using linguistic variables. The presented method extends the numerical multicriteria method TOPSIS for processing linguistic information, eliminating in parallel the loss of information caused by the approximation procedures and the ambiguity of the fuzzy ranking method’s selection. Moreover, the application of the method’s computerized software to the RES interventions proposed in the Hellenic National Action Plan for Greenhouse Gases is presented and discussed. [Copyright &y& Elsevier]

Abstract: The main purpose of this paper was to explore strategies of how to design the interface with brand-new techniques for collecting running parameters of small-scaled wind-power electricity generators as well as its monitoring and controlling Graphic Interface System software. Affected by shortage of oil energy, the whole world was positively developing renewable energy such as solar, wind-power, biologic, tide etc. For Taiwan, the renewable energy policy was actively pushed by government and civic organizations, and how to encourage industrial manufacturers to invest the related technologies of renewable energy; furthermore, to research and develop advanced techniques in this field so that those techniques could create another prosperous business for Taiwan after 3C business. In this study, WSN (Wireless Sensor Network) and its related modules such as ZigBee and Bluetooth would be combined together to construct transmitting interface of running parameters of small-scaled wind-power electricity generator. The study content included building ZigBee wireless network, communication interfaces of ZigBee and Bluetooth with microprocessor, coding and decoding techniques of ZigBee and Bluetooth, and software programming of graphic interface system (GIS). This study was sponsored by Taiwan Education Ministry under Teaching Superiority Project, and the system implemented in real wind-power electricity generator system and the research outcomes has been proved to be very successful and stable. [Copyright &y& Elsevier]