The major aim of DG optimal placement is obtaining the best DGs units sizes and locations so as to have optimum operation and planning of the distribution network system while considering DG capacity constraint. This paper addresses the issues related to the improvement of voltage profile and power loss reduction by integrating DG units. The technique was applied to optimally placed optimum DG unit size in distribution systems for the improvement of candidate bus voltage and reduction of power loss in the system. The technique proposed was simulated on IEEE-10 bus and IEEE-13 bus standard test system, and the obtained results show that the proposed method is strong and effective for optimal placement of DG units.
Over the years, and despite the energy efficiency measures and possibilities, there has been an increase in energy consumption worldwide. However, the resources and primary energies are limited and short stocked, and the energy production technologies have environmental and social impacts on production and exploration. One alternative is to reuse the energy waste in the processes. In this study, a trigeneration system in a large scale out of grid consumption is analyzed and a technical-economic feasibility is elaborated. The case study is based on an isolated tropical island. For the baseline scenario, two traditional energy production systems that does not contain energy recovery in the engines are assumed to be implemented. For the improved scenario, a trigeneration system absorption chiller is analyzed. An economic analysis of this project was given the indicators obtained, it was possible to conclude that the use of a trigeneration system on an isolated large scale out of grid energy consumption system, is feasible and preferable.
In this work, an efficient solution based on the reducing forms approach is presented to extract the five parameters of the single-diode model of PV generators from their I-V curves. Thus, by reducing the number of the five unknown parameters to two unknowns, the analytical expression of the current based on the LambertW function will then depend only on the ideality factor and the series resistance, as the two unknowns to predict numerically using the non-linear least square technique. The three other parameters are calculated as functions of the two predicted parameters using a linear system of three equations. Two sets of experiments are used for the validation of the proposed approach, which first showed its rapidity and high accuracy compared to the best approaches from the literature. Then, the method was applied for the real-time identification of four PV modules operating outdoors during one reference day at Cocoa (Florida).
Researchers are increasingly using algorithms that are influenced by nature because of its ease and versatility, the key components of nature-inspired metaheuristic algorithms are investigated, involving divergence and adoption, investigation and utilization, and dissemination techniques. Grey Wolf Optimizer (GWO), a relatively recent algorithm influenced by the dominance structure and poaching deportment of grey wolves, is a very popular technique for solving realistic mechanical and optical technical challenges. Half of the recurrence in the GWO are committed to the exploration and the other half to exploitation, ignoring the importance of maintaining the correct equilibrium to ensure a precise estimate of the global optimum. To address this flaw, a Multi-tiered GWO (MGWO) is formulated, that further accomplishes an appropriate equivalence among exploration and exploitation, resulting in optimal algorithm efficiency. In comparison to familiar optimization methods, simulations relying on benchmark functions exhibit the efficacy, performance, and stabilization of MGWO.
To ensure the safe and stable operation of solar photovoltaic system-based power systems, it is essential to predict the PV module output performance under varying operating conditions. In this paper, the interest is to develop an accurate model of a PV module in order to predict its electrical characteristics. For this purpose, an artificial neural network (ANN) based on the backpropagation algorithm is proposed for the performance prediction of a photovoltaic module. In this modeling approach, the temperature and illumination are taken as inputs and the current of the mathematical model as output for the learning of the ANN-PV-Panel. Simulation results showing the performance of the ANN model in obtaining the electrical properties of the chosen PV panel, including I–V curves and P–V curves, in comparison with the mathematical model performance are presented and discussed. The given results show that the error of the maximum power is very small while the current error is about 10-8, which means that the obtained model is able to predict accurately the outputs of the PV panel.
Preliminary preparation of waste for anaerobic digestion at thermophilic temperature conditions is the most energy-intensive stage of the process of anaerobic bioconversion of production and consumption waste organic matter, therefore, the search for ways to reduce energy consumption at this stage remains an urgent task. The article proposes a technological solution to maintain the temperature regime of the digester operation due to the utilization of existing waste low-grade energy sources using a compression heat pump. The flow diagram of the experimental biogas plant is shown and a description of its operation is given. The dependences of the absolute and specific rates of heating of the influent and cooling of the effluent on the initial temperature of the effluent are given. The principal possibility of maintaining the temperature regime in the digester is shown by using the heat recovery of the effluent using a compression heat pump.
In this paper, a novel cascade control technique is proposed in order to identify the parameters of cascade controllers in a grid-connected photovoltaic (PV) system. Here, tuning of the inner and outer loop controllers is done simultaneously by means of an optimized genetic algorithm-based fractional order PID (GA-FOPID) control. Simulations are conducted using Matlab/Simulink software under different operating conditions, namely under fast-changing weather conditions, sudden parametric variations, and voltage dip, for the purpose of verifying the effectiveness of the proposed control strategy. By comparing the results with recently published optimization techniques such as particle swarm optimization (PSO) and ant colony optimization (ACO), the superiority and effectiveness of the proposed GA-FOPID control have been proven.
The renewable economic emission transmit is a significant and new assignment in the modern power system. This article develops oppositional grasshopper optimization algorithm (OGOA) which depends on the social dealings of the grasshopper in nature, to solve renewable energy based economic emission dispatch (EED) considering uncertainty in wind power availability and a carbon tax on emission from the thermal unit. To speed up the convergence speed and advance the simulation results, opposition based learning (OBL) is integrated with the fundamental GOA in OGOA algorithm. To show the nonlinearity of wind power availability the Weibull distribution is used. A standard system, containing of two wind farms and six thermal units is used for testing the dispatch model for three different loads. The statistical outcomes of the applied OGOA technique are compared with basic GOA and quantum-inspired particle swarm optimization (QPSO) optimization. It is observed OGOA is more skillful than basic GOA technique for significantly reducing the computation time and developing hopeful outcomes.
Wind energy is generated via the use of wind blades, turbines and generators that are deployed over a given area. To achieve a higher energy and system reliability, the wind blade and other units of the system must be designed with suitable materials. In this paper however, a computational intelligent model based on an artificial neutral network has been propose for the evaluation of the reliability of the wind turbine blade designed with the FRP material. The simulation results show that there was a reduction in the training mean square error, testing (re–training) mean square error and validation mean square error, when the number of training epochs is increased by 50% such that the minimum mean square error and maximum mean square error were 0.0011 and 0.0061, respectively. The low validation mean square error in the simulation results implies that the developed artificial neural network has a good accuracy when determining the reliability and the failure probability of the wind turbine blade.
Global pandemics cause crises influencing all branches of economies. Basing on the currently striking COVID-19 pandemic, the authors analyze in this paper to what extent they may impact selected European electrical energy markets. For this, this research performs an empirical survey of the evolution of three market condition indicators: power demand, day-ahead energy prices, and prices of shares of power companies active in the generation sector. These are analyzed on examples of four European countries that all reacted differently to the spreading epidemic at governmental level: Italy, the United Kingdom, Poland, and Sweden. The evolution of indicators is analyzed for the period of COVID-19 outbreak in Europe, from January 27th 2020 until May 27th 2020 and checked with their behaviors in previous periods of time statistically. The study showed that global pandemics may have high impact on power demand and on share prices of power companies. Yet, the impact on day ahead energy prices is less evident and seems not present.
This work proposes a review of a recently developed swarm intelligence-based metaheuristic algorithm called Antlion Optimizer (ALO), its variants, and applications. The suitable blending of a random walk with an adaptive shrinking of hypersphere radius makes this algorithm more effective and impressive over other recent optimization algorithms. This paper elaborates on the recent variants of ALO by reviewing the concerned publications. It also summarized the applications of ALO for solving real-world complex optimization problems of a wide variety of areas. So, this paper comprises of summarized review of various recently published ALO papers. Firstly, the natural phenomena of ALO and the working principle of its various operators are described. Then the recently developed variants of ALO are described in detail depicting in various categories. The real-world applications using ALO and its variants are also described under global optimization, power and system engineering, electronics and communication engineering, machine learning, environmental engineering, and networking.
In the current analysis, a novel hybrid energy system operating on the basis of wind and hydrogen energy is designed. The simulation-based optimization has indicated the stochastic nature of wind power technology in comparison with hydrogen power specially when being integrated with the transportation network. The multi-criteria decision-making approach in the current analysis has also suggested that, among the examined cases, the most appropriate configuration of the hybrid energy system is leading to optimum levels of wind energy production, fuel flow rate, oxygen, hydrogen utilization, and stack consumption (including air and fuel) with the equivalents of 1,700 kW, 84 lpm, 75%, 717.37 kg/m3, 140 lpm, and 48 lpm, respectively. The maximum net revenues of the entire hybrid system are estimated to be €4,470 per month. It has been concluded that a transportation network fueled by wind and hydrogen systems can lead to a reduced level of environmental footprints.
This article presents the results of the development of a mathematical model for predicting the levelized cost of energy (LCOE) for solar concentrating thermal power systems (CSP systems) based on a nonlinear autoregressive neural network with exogenous inputs (NARX). A two-layer NARX network with sigmoid hidden neurons and linear output neurons has been developed. The input layer is made up of the following variables: the volume of input power of CSP systems in the world, the total world energy consumption, domestic energy consumption, domestic gas consumption, domestic consumption of coal and lignite, domestic energy consumption, the share of renewable energy in electricity generation, the share of wind and solar energy in the production of electricity, carbon dioxide emissions from fuel combustion, the price of Brent oil against the US dollar, and the average price for natural gas auctions. The output layer specifies LCOE values for CSP systems.
The aim of this work is to use multi-layered perceptron artificial neural networks and multiple linear regressions models to predict the efficiency of the magnetic refrigeration cycle device operating near room temperature. For this purpose, the experimental data collection was used in order to predict coefficient of performance and temperature span for active magnetic refrigeration device. In addition, the operating parameters of active magnetic refrigerator cycle are used for solid magnetocaloric material under application 1.5 T magnetic fields. The obtained results including temperature span and coefficient of performance are presented and discussed.
In the paper there is the developed methodology for selecting the installation places and number of sectionalizing units which allows solving the problem according to the criterion of providing the necessary sensitivity of the protective devices installed on the line input. The obtained expression allows calculating the maximum distance to the installation place of a sectionalizing unit. It also allows determining the need to install a sectionalizing unit in a power line. If a sectionalizing unit is installed to protect an outgoing line, the technique can also be used taking into account the adjustment of the original calculated data. The developed methodology for determining the installation place of a sectionalizing unit can be used for other purposes. For example, it allows selecting the cross section of power lines in terms of the protection sensitivity when designing power lines or when reconstructing them.
Under conditions of limited space for the placement of photovoltaic modules, exploitation of rooftop and facade photovoltaic power plants are highly relevant. One of their main advantages is the possibility to integrate them into both existing and new consumer infrastructures, regardless of their configuration, using the building envelope. On the example of the specific tilt angles relative to the horizon of the receiving surfaces of the modules when they are placed on the enclosing structures of the building for southern spatial orientation, analysis of the utilization level of solar energy potential of the region is carried out. The inclination angles of the enclosing structures, which are characterized by the closest electric energy generation to the generation by modules of optimal spatial orientation, are revealed. There were also determined spatial orientations of the enclosing structures, for which the installation of modules of photovoltaic power plants is not recommended.
Global pandemics cause crises influencing all branches of economies. Basing on the currently striking COVID-19 pandemic, the authors analyze in this paper to what extent they may impact selected European electrical energy markets. For this, this research performs an empirical survey of the evolution of three market condition indicators: power demand, day-ahead energy prices, and prices of shares of power companies active in the generation sector. These are analyzed on examples of four European countries that all reacted differently to the spreading epidemic at governmental level: Italy, the United Kingdom, Poland, and Sweden. The evolution of indicators is analyzed for the period of COVID-19 outbreak in Europe, from January 27th 2020 until May 27th 2020 and checked with their behaviors in previous periods of time statistically. The study showed that global pandemics may have high impact on power demand and on share prices of power companies. Yet, the impact on day ahead energy prices is less evident and seems not present.
This paper assesses the performance of electricity generation using wind/hydrogen/fuel-cell technology. The intermittency of renewables, especially wind, and the need for storage of excess energy make them unattractive for continuous generation of electricity. This paper focuses on the wind resource of Anloga (Ghana) and the potential of hydrogen production from water electrolysis. The assessment of this system covers three main areas including the potential energy generation, environmental impacts, and economic impacts. The paper adopted analytical models of energy generation of fuel cell and hydrogen technologies and further performs their assessment using HOMER software. It was revealed that the annual electricity production from the hydrogen fuel cell is 25,999kW/yr, with an annual capacity shortage of 392kW/yr representing a 10% capacity shortage. The levelized cost of electricity was 0.602$/kWh and the emissions have been completely minimized as compared to diesel generation plants.
The article discusses the features of heat losses by convection and radiation of flat solar collectors on the basis of a non-stationary thermal model and program. The connection of heat losses with the generalized heat loss coefficient of solar collectors is shown. The article also deals with the problem of determining the distribution of temperatures and heat losses by convection and radiation of a thermal insulation system – accumulating body (water) for a spherical heat accumulator under symmetric boundary conditions. The problem is solved numerically according to the program developed on the basis of the proposed “gap method.” The method and program for the numerical calculation of heat losses and temperatures in time in a spherical two-layer heat accumulator with symmetric boundary conditions, taking into account both incident and intrinsic radiation, have been developed. The program has been developed for a “long” two-layer cylindrical heat accumulator of solar collectors.
The increasing penetration of photovoltaic technology calls for the development of an effective method for optimization of grid-connected photovoltaic power plants. Although extensive studies on the definition, implementation, and optimization of these systems have been conducted, the design and management of a smart energy system remains a critical challenge. The purpose of the work is to increase efficiency of power supply system of agricultural facility by optimizing the placement and selection of parameters of photovoltaic sources of distributed generation. Application of photovoltaic sources of distributed generation as part of power supply system of agricultural enterprise allows to reduce the total consumption from the network in average basing on solar intensity data for 22 years by 40-53% from April to October, which is most expedient in view of the fact that this is a period of intensive work of the enterprise and the greatest load on its consumers; by 24-30% in February, March, and October; and by 14-17% the rest of the time.
