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With recent technology advances and price drop, battery energy storage systems (BESSs) are considered as a promising storage technology in power systems. In this paper, a stochastic BESS planning model is introduced, which determines optimal capacity and durations of BESSs to co-locate utility-scale solar photovoltaic (PV) systems in a high-voltage power system under the uncertainties of renewable resources and electric load. The optimization model minimizing total costs aims to obtain at least 20% electric energy from renewable sources, while satisfying all the physical constraints. Furthermore, two-stage stochastic programming is applied to formulate mathematical optimization problem to find out optimal durations and capacity of BESSs. In scheduling BESSs, chronology needs to be considered to represent temporal changes of BESS states; therefore, a scenario generation method to generate random sample paths with 1-h time step is adopted to explicitly represent uncertainty and temporal changes. The proposed mathematical model is applied to a modified IEEE 300-bus system that comprises 300 electric buses and 411 transmission lines. Optimal BESS durations and capacity are compared when different numbers of scenarios are employed to see the sensitivity to the number of scenarios in the model, and “value of stochastic solution” (VSS) is calculated to verify the impacts of inclusion of stochastic parameters. The results show that the building costs and capacity of BESSs increase when the number of scenarios increases from 10 to 30. By inspecting VSSs, it is observed that an explicit representation of stochastic parameters affects the optimal value, and the impacts become larger when the larger number of scenarios are applied.

In this work, we present results from the largest study of measured, whole-building energy performance for commercial LEED-certified buildings, using 2016 energy use data that were obtained for 4417 commercial office buildings (114 million m2) from municipal energy benchmarking disclosures for 10 major U.S. cities. The properties included 551 buildings (31 million m2) that we identified as LEED-certified. Annual energy use and greenhouse gas (GHG) emission were compared between LEED and non-LEED offices on a city-by-city basis and in aggregate. In aggregate, LEED offices demonstrated 11% site energy savings but only 7% savings in source energy and GHG emission. LEED offices saved 26% in non-electric energy but demonstrated no significant savings in electric energy. LEED savings in GHG and source energy increased to 10% when compared with newer, non-LEED offices. We also compared the measured energy savings for individual buildings with their projected savings, as determined by LEED points awarded for energy optimization. This analysis uncovered minimal correlation, i.e., an R2 < 1% for New Construction (NC) and Core and Shell (CS), and 8% for Existing Euildings (EB). The total measured site energy savings for LEED-NC and LEED-CS was 11% lower than projected while the total measured source energy savings for LEED-EB was 81% lower than projected. Only LEED offices certified at the gold level demonstrated statistically significant savings in source energy and greenhouse gas emissions as compared with non-LEED offices.

The application of renewable energy has become increasingly widespread worldwide because of its advantages of resource abundance and environmental friendliness. However, the deployment of hybrid renewable energy systems (HRESs) varies greatly from city to city due to large differences in economic endurance, social acceptance and renewable energy endowment. Urban policymakers thus face great challenges in promoting local clean renewable energy utilization. To address these issues, this paper proposes a combined multi-objective optimization method, and the specific process of this method is described as follows. The Hybrid Optimization Model for electric energy was first used to examine five different scenarios of renewable energy systems. Then, the Technique for Order Preference by Similarity to an Ideal Solution was applied using eleven comprehensive indicators to determine the best option for the target area using three different weights. To verify the feasibility of this method, Xiongan New District (XND) was selected as an example to illustrate the process of selecting the optimal HRES. The empirical results of simulation tools and multi-objective decision-making show that the Photovoltaic-Diesel-Battery off-grid energy system (option III) and PV-Diesel-Hydrogen-Battery off-grid energy system (option V) are two highly feasible schemes for an HRES in XND. The cost of energy for these two options is 0.203 and 0.209 $/kWh, respectively, and the carbon dioxide emissions are 14,473 t/yr and 345 t/yr, respectively. Our results provide a reference for policymakers in deploying an HRES in the XND area.

This paper presents a sensorless model predictive torque control strategy based on an adaptive Takagi–Sugeno (T–S) fuzzy model for the design of a six–phase permanent magnet synchronous generator (PMSG)–based hydrokinetic turbine systems (PMSG-HTs), which not only provides clean electric energy and stable energy-conversion efficiency, but also improves the reliability and robustness of the electricity supply. An adaptive T–S fuzzy model is first formed to characterize the nonlinear system of the PMSG before a model predictive torque controller based on the T–S fuzzy model for the PMSG system is employed to indirectly control the stator current and the stator flux magnitude, which improves the performance in terms of anti–disturbance, and achieves maximum hydropower tracking. Finally, we consider two types of tidal current, namely the mixed semidiurnal tidal current and the northwest European shelf tidal current. The simulation results demonstrate that the proposed control strategy can significantly improve the voltage–support capacity, while ensuring the stable operation of the PMSG in hydrokinetic turbine systems, especially under uneven tidal current speed conditions.

This paper proposes a mathematical model based on mixed integer linear programming (MILP). This model aids the decision-making process in local generation use and demand response application to power demand contract adequacy by Brazilian consumers/prosumers. Electric energy billing in Brazil has some specificities which make it difficult to consider the choice of the tariff modality, the determination of the optimal contracted demand value, and demand response actions. In order to bridge this gap, the model considers local generation connected to the grid (distributed generation) and establishes an optimized solution indicating power energy contract aspects and the potential reduction in expenses for the next billing period (12 months). Different alternative sources already available or of interest to the consumer can be considered. The proposed mathematical model configures an optimization tool for the feasibility analysis of local generation use and, concomitantly, (i) checking the tariff modality, (ii) revising the demand contract, and (iii) suggesting demand response actions. The presented result shows a significant reduction in the energy and power expenses, which confirms the usefulness of this proposal. In the end, the optimized answers promote benefits for both, the consumer/prosumer and the electric utility.

Fundamentally, two main methodologies are used to reduce the electric energy bill in residential, commercial, and even industrial applications. The first method is to act on the supply side by integrating alternative means of power generation, such as renewable energy generators, having a relatively low levelized cost of energy. Whereas, the second methodology focuses on the management of the load to minimize the overall paid cost for energy. Thus, this article highlights the importance of demand side management by comparing it to the supply side management having, as criteria, the total achieved savings on the overall annual energy bill of a residential microgrid supplied by two power sources and equipped with an electric vehicle. The optimization takes into consideration the cost of kWh that is paid by the prosumer based on an economical model having as inputs the outcomes of the energy model. The adopted energy model integrates, on the demand side, an intelligent energy management system acting on secondary loads, and on the supply side, a photovoltaic (PV) system with and without battery energy storage system (BESS). The outcome of this work shows that, under the right circumstances, demand side management can be as valuable as supply side control.

Although the latest energy-efficient buildings use a large number of sensors and measuring instruments to predict consumption more accurately, it is generally not possible to identify which data are the most valuable or key for analysis among the tens of thousands of data points. This study selected the electric energy as a subset of total building energy consumption because it accounts for more than 65% of the total building energy consumption, and identified the variables that contribute to electric energy use. However, this study aimed to confirm data from a building using clustering in machine learning, instead of a calculation method from engineering simulation, to examine the variables that were identified and determine whether these variables had a strong correlation with energy consumption. Three different methods confirmed that the major variables related to electric energy consumption were significant. This research has significance because it was able to identify the factors in electric energy, accounting for more than half of the total building energy consumption, that had a major effect on energy consumption and revealed that these key variables alone, not the default values of many different items in simulation analysis, can ensure the reliable prediction of energy consumption.

With the increasing attention of the clean and efficient use of energy, the regional integrated energy system (RIES), as an efficient measure to improve energy efficiency, is tending to play an important role in the field of energy supply. The configuration of multiple energy storage equipment in the RIES can greatly improve the economy of the system, which is an important research direction of RIES planning. However, at present the research on the configuration optimization of electric-gas-thermal multi-energy storage devices in RIES is insufficient. Under this background, a method for configuring the rated capacity and power of various energy storage devices in the RIES under both off-grid and grid-connected operating modes was proposed in this paper, and the configuration optimization model was also established. Firstly, the RIES was divided into four parts: Energy supply, energy conversion, energy storage and the load. Based on the energy hub concept, the four parts were modeled respectively. Secondly, considering the influence of electric energy substitution and operation strategy, the optimal configuration of multi-energy storage devices was modeled as a MILP formulation and solved with the Gurobi optimizer. Finally, a case study verified the effectiveness of the proposed model and the method. Furthermore, the sensitivity analysis was carried out to quantify the influence degree of each factor (such as price, etc.) on the energy storage configuration.

Electronic Capacitive Voltage Transformer (ECVT), connecting the transmission system and measuring device, is the key equipment of digital substation in the distribution network. Its frequency characteristics and the rapid transient response performance exert decisive effects on whether it is in keeping with the power quality requirements for wide frequency domain and high precision detection. Many detection methods that require special testing equipment have limitations of high-cost and complexity in project implementation. We establish the mathematical model of ECVT, which is utilized to study its integrative characteristics based on the definitions of frequency response and transient voltage error. Besides, the harmonic and transient disturbance detection performances are analyzed comprehensively with the model by simulation. Then, we propose a multi-objective parameter optimization design model with minimization of transient voltage decay duration, maximization of frequency bandwidth and minimization of transient impulse voltage, which is solved through the entropy weighting method and genetic algorithm. The analysis and optimization results indicate that selecting resistor divider through the proposed method can achieve data transmission results with acceptable accuracy. Further, its superior performance on transforming transient disturbances conforms to the distribution network&rsquo, s electric energy quality requirements.

In most smart grids, load management techniques are required to handle multiple loads of several types. This paper studies decentralized demand-side management (DSM) in a grid with different types of appliances in two service areas: one with many residential households, and one bus with commercial customers. Each building runs an individual optimal DSM to reschedule the usage time of its flexible appliances to reduce its electric energy cost at a manageable sacrifice of inconvenience according to the forecasted time-varying electricity price. Using the developed model, we examined the effectiveness of decentralized DSM by comparing its performance on the operation status of the grid in terms of electricity cost saving, rooftop photovoltaic (PV) utilization efficiency, voltage fluctuation, power loss, voltage rises, and reverse power flows, which can easily be seen at the commercial load bus.

With the pervasiveness of electric vehicles and an increased demand for fast charging, stationary high-power fast-charging is becoming more widespread, especially for the purpose of serving pure electric buses (PEBs) with large-capacity onboard batteries. This has resulted in a huge distribution capacity demand. However, the distribution capacity is limited, and in some urban areas the cost of expanding the electric network capacity is very high. In this paper, three battery energy storage system (BESS) integration methods&mdash, the AC bus, each charging pile, or DC bus&mdash, are considered for the suppression of the distribution capacity demand according to the proposed charging topologies of a PEB fast-charging station. On the basis of linear programming theory, an evaluation model was established that consider the influencing factors of the configuration: basic electricity fee, electricity cost, cost of the energy storage system, costs of transformer and converter equipment, and electric energy loss. Then, a case simulation is presented using realistic operation data, and an economic comparison of the three configurations is provided. An analysis of the impacts of each influence factor in the case study is discussed to verify the case results. The numerical results indicate that the appropriate BESS configuration can significantly reduce the distribution demand and stationary cost synchronously.

Miniature gas turbines (MGT) are an important part of the production of electric energy in distributed systems. Due to the growing requirements for lower emissions and the increasing prices of hydrocarbon fuels, it is becoming more and more important to enhance the efficiency and improve the quality of the combustion process in gas turbines. One way to reduce NOx emissions is to add water to the fuel in the form of a water-based emulsion (FWE). This article presents the research results and the analysis of the impact of the use of FWE on CO and NOx emissions as well as on fuel consumption in MGT GTM-120. Experimental tests and numerical calculations were carried out using standard fuel (DF) and FWE with water content from 3% to 12%. It was found that the use of FWE leads to a reduction in NOx and CO emissions and reduction in the consumption of basic fuel. The maximum reduction in emissions by 12.32% and 35.16% for CO and NOx, respectively, and a reduction in fuel consumption by 5.46% at the computational operating point of the gas turbine were recorded.

The ability to forecast electricity generation for a small wind turbine is important both on a larger scale where there are many such turbines (because it creates problems for networks managed by distribution system operators) and for prosumers to allow current energy consumption planning. It is also important for owners of small energy systems in order to optimize the use of various energy sources and facilitate energy storage. The research presented here addresses an original, rarely predicted 48 h forecasting horizon for small wind turbines. This topic has been rather underrepresented in research, especially in comparison with forecasts for large wind farms. Wind speed forecasts with a 48 h horizon are also rarely used as input data. We have analyzed the available data to identify potentially useful explanatory variables for forecasting models. Eight sets with increasing data amounts were created to analyze the influence of the types and amounts of data on forecast quality. Hybrid, ensemble and single methods are used for predictions, including machine learning (ML) solutions like long short-term memory (LSTM), multi-layer perceptron (MLP), support vector regression (SVR) and K-nearest neighbours regression (KNNR). Original hybrid methods, developed for research of specific implementations and ensemble methods based on hybrid methods’ decreased errors of energy generation forecasts for small wind turbines in comparison with single methods. The “artificial neural network (ANN) type MLP as an integrator of ensemble based on hybrid methods” ensemble forecasting method incorporates an original combination of predictors. Predictions by this method have the lowest mean absolute error (MAE). In addition, this paper presents an original ensemble forecasting method, called “averaging ensemble based on hybrid methods without extreme forecasts”. Predictions by this method have the lowest root mean square error (RMSE) error among all tested methods. LSTM, a deep neural network, is the best single method, MLP is the second best one, while SVR, KNNR and, especially, linear regression (LR) perform less well. We prove that lagged values of forecasted time series slightly increase the accuracy of predictions. The same applies to seasonal and daily variability markers. Our studies have also demonstrated that using the full set of available input data and the best proposed hybrid and ensemble methods yield the lowest error. The proposed hybrid and ensemble methods are also applicable to other short-time generation forecasting in renewable energy sources (RES), e.g., in photovoltaic (PV) systems or hydropower.

The balancing of the power of the Polish Power System (KSE) is a key element in ensuring the safety of electric energy supplies to end users. This article presents an analysis of the power demand in power systems (PS), with emphasis on the typical power variability both in subsequent hours of the day and on particular days and in particular months each year. The methodology for calculating the costs of electric energy undelivered to the end users and the amount of these costs for KSE is presented. Different possibilities have been analyzed for balancing power systems&rsquo, peak load and assumptions have been formulated for calculating the amount of the related costs. On this basis, a comparative analysis has been made of the possibility to balance peak load using operators&rsquo, system services, trans-border connections, and various energy storage solutions. On the basis of the obtained results, optimal tools have been proposed for market-based influence from transmission and distribution system operators on energy market participants&rsquo, behaviors in order to ensure the power systems&rsquo, operating safety and continuous energy deliveries to end users.

Ecological concerns on the climatic effects of the emissions from electricity production stipulate the remuneration of electricity grids to accept growing amounts of intermittent regenerative electricity feed-in from wind and solar power. Germany&rsquo, s eager political target to double regenerative electricity production by 2030 puts pressure on grid operators to adapt and restructure their transmission and distribution grids. The ability of local distribution grids to operate autonomous of transmission grid supply is essential to stabilize electricity supply at the level of German federal states. Although congestion management and collaboration at the distribution system operator (DSO) level are promising approaches, relatively few studies address this issue. This study presents a methodology to assess the electric energy balance for the low-voltage grids in the German federal state of Baden-W&uuml, rttemberg, assuming the typical load curves and the interchange potential among local distribution grids by means of linear programming of the supply function and for typical seasonal electricity demands. The model can make a statement about the performance and development requirements for grid architecture for scenarios in 2035 and 2050 when regenerative energies will&mdash, according to present legislation&mdash, account for more than half of Germany&rsquo, s electricity supply. The study details the amendment to Baden-W&uuml, rttemberg&rsquo, s electricity grid required to fit the system to the requirements of regenerative electricity production. The suggested model for grid analysis can be used in further German regions and internationally to systematically remunerate electricity grids for the acceptance of larger amounts of regenerative electricity inflows. This empirical study closes the research gap of assessing the interchange potential among DSO and considers usual power loads and simultaneously usual electricity inflows.

This paper focuses on the optimal intraday scheduling of a distribution system that includes renewable energy (RE) generation, energy storage systems (ESSs), and thermostatically controlled loads (TCLs). This system also provides time-of-use pricing to customers. Unlike previous studies, this study attempts to examine how to optimize the allocation of electric energy and to improve the equilibrium of the load curve. Accordingly, we propose a concept of load equilibrium entropy to quantify the overall equilibrium of the load curve and reflect the allocation optimization of electric energy. Based on this entropy, we built a novel multi-objective optimal dispatching model to minimize the operational cost and maximize the load curve equilibrium. To aggregate TCLs into the optimization objective, we introduced the concept of a virtual power plant (VPP) and proposed a calculation method for VPP operating characteristics based on the equivalent thermal parameter model and the state-queue control method. The Particle Swarm Optimization algorithm was employed to solve the optimization problems. The simulation results illustrated that the proposed dispatching model can achieve cost reductions of system operations, peak load curtailment, and efficiency improvements, and also verified that the load equilibrium entropy can be used as a novel index of load characteristics.

We propose an optimal electric energy management of a cooperative multi-microgrid community with sequentially coordinated operations. The sequentially coordinated operations are suggested to distribute computational burden and yet to make the optimal 24 energy management of multi-microgrids possible. The sequential operations are mathematically modeled to find the optimal operation conditions and illustrated with physical interpretation of how to achieve optimal energy management in the cooperative multi-microgrid community. This global electric energy optimization of the cooperative community is realized by the ancillary internal trading between the microgrids in the cooperative community which reduces the extra cost from unnecessary external trading by adjusting the electric energy production amounts of combined heat and power (CHP) generators and amounts of both internal and external electric energy trading of the cooperative community. A simulation study is also conducted to validate the proposed mathematical energy management models.

The integrated energy system coupling multi-type energy production terminal to realize multi-energy complementarity and energy ladder utilization is of great significance to alleviate the existing energy production crisis and reduce environmental pollution. In this paper, the topology of the cold-thermal-electricity integrated energy system is built, and the decoupling method is adopted to analyze the feasible interval of load rate under the strong coupling condition, so as to ensure the “source-load” power balance of the system. Establishing a multi-objective optimization function with the lowest system economic operation and pollution gas emission, considering the attribute differences and energy scheduling characteristics of different energy sources of cold, heat and electricity, and adopting different time scales to optimize the operation of the three energy sources of cold, heat and electricity, wherein the operation periods of electric energy, heat energy and cold energy are respectively 15 min, 30 min and 1 h; The multi-objective problem is solved by standard linear weighting method. Finally, the mixed integer nonlinear programming model is calculated by LINGO solver. In the numerical simulation, the hotel summer front load parameters of Zhangjiakou, China are selected for simulation and compared with a single time scale system. The simulation results show that the multi-time scale system reduces the economic operation cost by 15.6% and the pollution gas emission by 22.3% compared with the single time scale system, it also has a wider feasible range of load rate, flexible time allocation, and complementary energy selection.

Utilization of digital connectivity tools is the driving force behind the transformation of the power distribution system into a smart grid. This paper places itself in the smart grid domain where consumers exploit digital connectivity to form partitions within the grid. Every partition, which is independent but connected to the grid, has a set of goals associated with the consumption of electric energy. In this work, we consider that each partition aims at morphing the initial anticipated partition consumption in order to concurrently minimize the cost of consumption and ensure the privacy of its consumers. These goals are formulated as two objectives functions, i.e., a single objective for each goal, and subsequently determining a multi-objective problem. The solution to the problem is sought via an evolutionary algorithm, and more specifically, the non-dominated sorting genetic algorithm-II (NSGA-II). NSGA-II is able to locate an optimal solution by utilizing the Pareto optimality theory. The proposed load morphing methodology is tested on a set of real-world smart meter data put together to comprise partitions of various numbers of consumers. Results demonstrate the efficiency of the proposed morphing methodology as a mechanism to attain low cost and privacy for the overall grid partition.

A parallel hybrid electric vehicle (PHEV) is used to investigate the fuel economy effect of the equivalent fuel consumption minimization strategy (ECMS) with the equivalent factor as the core, where the equivalent factor is the conversion coefficient between fuel thermal energy and electric energy. In the conventional ECMS strategy, the battery cannot continue to discharge when the state of charge (SOC) is lower than the target value. At this time, the motor mainly works in the battery charging mode, making it difficult to adjust the engine operating point to the high-efficiency zone during the acceleration process. To address this problem, a relationship model of the battery SOC, vehicle acceleration a, and equivalent factor S was established. When the battery SOC is lower than the target value and the vehicle demand torque is high, which makes the engine operating point deviate from the high-efficiency zone, the time that the motor spends in the power generation mode during the driving process is reduced. This enables the motor to drive the vehicle at the appropriate time to reduce the engine output torque, and helps the engine operate in the high-efficiency zone. The correction function under US06 condition was optimized by genetic algorithm (GA). The best equivalent factor MAP was obtained with acceleration a and battery SOC as independent variables, and the improved global optimal equivalent factor of ECMS was established and simulated offline. Simulation results show that compared with conventional ECMS, the battery still has positive power output even when the SOC is less than the target value. The SOC is close to the target value after the cycle condition, and fuel economy improved by 1.88%, compared with the rule-based energy management control strategies, fuel economy improved by 10.17%. These results indicate the effectiveness of the proposed energy management strategy.

This paper presents the development of a heuristic-based algorithm for a Home Electric Energy Management System (HEEMS). The novelty of the proposal resides in the fact that solutions of the Pareto front, minimizing both the energy consumption and cost, are obtained by a Genetic Algorithm (GA) considering the renewable energy availability as well as the user activity level (AL) inside the house. The extensive solutions search characteristic of the GAs is seized to avoid the calculation of the full set of Pareto front solutions, i.e., from a reduced set of non-dominated solutions in the Pareto sense, an optimal solution with the best fitness is obtained, reducing considerably the computational time. The HEEMS considers models of the air conditioner, clothes dryer, dishwasher, electric stove, pool pump, and washing machine. Models of the wind turbine and solar PV modules are also included. The wind turbine model is written in terms of the generated active power exclusively dependent on the incoming wind profiles. The solar PV modules model accounts for environmental factors such as ambient temperature changes and irradiance profiles. The effect of the energy storage unit on the energy consumption and costs is evaluated adapting a model of the device considering its charge and discharge ramp rates. The proposed algorithm is implemented in the Matlab® platform and its validation is performed by comparing its results to those obtained by a freeware tool developed for the energy management of smart residential loads. Also, the evaluation of the performance of the proposed HEEMS is carried out by comparing its results to those obtained when the multi-objective optimization problem is solved considering weights assigned to each objective function. Results showed that considerable savings are obtained at reduced computational times. Furthermore, with the calculation of only one solution, the end-user interaction is reduced making the HEEMS even more manageable than previously proposed approaches.

Broiler production in modern poultry farms commonly uses mechanical ventilation systems. This mechanical ventilation requires an amount of electric energy and a high level of investment in technology. Nevertheless, broiler production is affected by periodic problems of mortality because of thermal stress, thus being crucial to explore the ventilation efficiency. In this article, we analyze a cross-mechanical ventilation system focusing on air velocity distribution. In this way, two methodologies were used to explore indoor environment in livestock buildings: Computational Fluid Dynamics (CFD) simulations and direct measurements for verification and validation (V&V) of CFD. In this study, a validation model using a Generalized Linear Model (GLM) was conducted to compare these methodologies. The results showed that both methodologies were similar in results: the average of air velocities values were 0.60 ± 0.56 m s-1 for CFD and 0.64 ± 0.54 m s-1 for direct measurements. In conclusion, the air velocity was not affected by the methodology (CFD or direct measurements), and the CFD simulations were therefore validated to analyze indoor environment of poultry farms and its operations. A better knowledge of the indoor environment may contribute to reduce the demand of electric energy, increasing benefits and improving the thermal comfort of broilers. © 2013 by the authors., This work was funded by the project GV04B-511 (Generalitat Valenciana, Spain) and by the Vicerrectorado of Investigación of the Universitat Politècnica de València (Programa de Apoyo a la Investigación y Desarrollo Multidisciplinar Project PAID register 2614).

Industrial production in developing countries (DC) is frequently perturbed by electric energy supply difficulties. To overcome this problem, generators are used in self-generation of energy, but this leads to an increase of electricity-related expenses. This article assesses the impact of electricity self-generation on Cameroonian industrial companies. The model described in this article is based on data collected through a survey of a representative sample of industrial companies and from numerous previous thematic and statistical studies. The results of our analyses show that expenses related to electricity in industrial companies in Cameroon have increased five times due to electricity rationing and untimely power cuts. The article also suggests some solutions to improve the electricity self-generation capacity of industrial companies.

Electric energy replacement is the umbrella term for the use of electric energy to replace oil (e.g., electric automobiles), coal (e.g., electric heating), and gas (e.g., electric cooking appliances), which increases the electrical load peak, causing greater valley/peak differences. On-load capacity regulating distribution transformers have been used to deal with loads with great valley/peak differences, so reasonably replacing conventional distribution transformers with on-load capacity regulating distribution transformers can effectively cope with load changes after electric energy replacement and reduce the no-load losses of distribution transformers. Before planning for on-load capacity regulating distribution transformers, the nodal effective load considering uncertainties within the life cycle after electric energy replacement was obtained by a Monte Carlo method. Then, according to the loss relation between on-load capacity regulating distribution transformers and conventional distribution transformers, three characteristic indexes of annual continuous apparent power curve and replacement criteria for on-load capacity regulating distribution transformers were put forward in this paper, and a set of distribution transformer replaceable points was obtained. Next, based on cost benefit analysis, a planning model of on-load capacity regulating distribution transformers which consists of investment profitability index within the life cycle, investment cost recouping index and capacity regulating cost index was put forward. The branch and bound method was used to solve the planning model within replaceable point set to obtain upgrading and reconstruction scheme of distribution transformers under a certain investment. Finally, planning analysis of on-load capacity regulating distribution transformers was carried out for electric energy replacement points in one urban distribution network under three scenes: certain load, uncertain load and nodal effective load considering uncertainties. Results showed that the planning method of on-load capacity regulating distribution transformers proposed in this paper was very feasible and is of great guiding significance to distribution transformer planning after electric energy replacement and the popularization of on-load capacity regulating distribution transformers.

With the increasing consumption of electric energy, how to improve the capacity of transmission lines within safe margins is an urgent problem to be solved. This paper presents a transient thermal rating method under real-time meteorological conditions. The result of thermal ratings under different conditions shows that this rating approach can significantly increase the capacity of the line. As some of the most critical variables are remaining time and initial temperature, their influence upon the rating is studied. The method of statistical analysis is used to determine the ampacity at different risk levels. The result indicates that with smaller remaining time, the ampacities are larger, and with larger ampacities, the risk of thermal overload is greater. The choice of risk level would heavily affect the values of ampacity.

In the search for alternatives to reduce the consumption of electric energy, the possibility of using natural light for lighting through TDD (tubular daylight devices) or TDGS (tubular daylight guidance systems) appears. These natural luminaires are used in rooms where you want to save electricity and enjoy the benefits of natural light. The present work proposes the construction of a tubular system for the conduction of natural light that replaces aluminum with silver (currently marketed by several companies) by polymer metallized with aluminum, offering a low cost. The polymer acrylonitrile butadiene styrene (ABS), coated with aluminum by physical vapor deposition (ionization), was evaluated for some tests to verify characteristics of the structure and the metallized surface. After the tests, the construction of the reflective tube was performed and validated in a real scale of application. The results proved the technical viability of the proposed tube construction for the realization of direct sunlight for illumination using polymeric material. Although it has produced 35% less than the reference tube, it can be marketed at an estimated cost of 50% less.

Generation of electric energy through wind turbines is one of the practically inexhaustible alternatives of generation. It is considered a source of clean energy, but still needs a lot of research for the development of science and technologies that ensures uniformity in generation, providing a greater participation of this source in the energy matrix, since the wind presents abrupt variations in speed, density and other important variables. In wind-based electrical systems, it is essential to predict at least one day in advance the future values of wind behavior, in order to evaluate the availability of energy for the next period, which is relevant information in the dispatch of the generating units and in the control of the electrical system. This paper develops ultra-short, short, medium and long-term prediction models of wind speed, based on computational intelligence techniques, using artificial neural network models, Autoregressive Integrated Moving Average (ARIMA) and hybrid models including forecasting using wavelets. For the application of the methodology, the meteorological variables of the database of the national organization system of environmental data (SONDA), Petrolina station, from 1 January 2004 to 31 March 2017, were used. A comparison among results by different used approaches is also done and it is also predicted the possibility of power and energy generation using a certain kind of wind generator.