Artificial neural network based maximum power point tracking controller for photovoltaic standalone system

This article presents a two-stage maximum power point tracking (MPPT) controller using artificial neural network (ANN) for photovoltaic (PV) standalone system, under varying weather conditions of solar irradiation and module temperature. At the first-stage, the ANN algorithm locates the maximum power point (MPP) associated to solar irradiation and module temperature. Then, a simple controller at the second-step, by changing the duty cycle of a DC–DC boost converter, tracks the MPP. In this method, in addition to experimental data collection for training the ANN, a circuit is designed in MATLAB-Simulink to acquire data for whole ranges of weather condition. The whole system is simulated in Simulink. Simulation results show small transient response time, and low power oscillation in steady-state. Furthermore, dynamic response verifies that this method is very fast and precise at tracking the MPP under rapidly changing irradiation, and has very low power oscillation under slowly changing irradiation. Experimental results are provided to verify the simulation results as well.     

Design and analysis of artificial bee-colony-based MPPT algorithm for DFIG-based wind energy conversion systems

In this article, the proposed maximum power point tracking (MPPT) method is designed by taking rotor speed as an optimization problem, which is solved by artificial bee colony (ABC) algorithm to generate the maximum power output. The main advantage of this algorithm is that its optimal solution is independent of the initial positions and requirement of lesser number of control parameters, which leads to simple and robust MPPT algorithm than other algorithm. Furthermore, the hill climb search and particle swarm optimization-based MPPT algorithm are also discussed and the results obtained by these are compared to verify the effectiveness of proposed algorithm. Simulations for MPPT control along with doubly fed induction-generator-based wind energy conversion system is carried out in MATLAB/Simulink environment. Three statistical methods are used to evaluate the accuracy of each MPPT algorithm. All results are analyzed and compared under randomly selected wind as well as real wind speed configuration. Comparison of both numerical and simulation results under two different varying wind speed conditions strongly suggest that the proposed ABC-based MPPT algorithm is superior than other two MPPT algorithms.     

Analysis of PV module connected in different configurations under uniform and non-uniform solar radiations

This paper focuses on investigating the current--voltage (I--V) and power--voltage (P--V) characteristics of a photovoltaic (PV) module connected in various configurations like series, parallel, and series-parallel. The performance analysis of PV module has been carried out under uniform and non-uniform conditions such as change in irradiation (passing clouds), change in temperature, accumulation of dust, and change in wind speed using MATLAB-Simulink environment. From the observed results, it has been indicated that for a given number of PV modules, the array configurations affect the maximum available output power and more local maxima are found under partially shaded conditions. Moreover, the comparative analysis of PV module has been performed for various configurations under the above disturbances. From the results, it is evident that even under non-uniform conditions, the parallel configuration of PV modules is more prominent and maximum output power is obtained. Further, parallel layout is particularly convenient for minimizing shadowing effects. The parameters of the PV module have been obtained from the manufacturer datasheet (KC200GT) for these investigations.     

PCM thermal conductivity effect on mechanism of PV/PCM thermal control characteristics

ABSTRACT

According to the structure of photovoltaic/phase change material (PV/PCM), the mechanism of internal heat transfer, transmission, storage, and temperature control is analyzed, and a two-dimensional finite element analysis model of PV/PCM structure is established. This study is carried out on the effect of PCM thermal conductivity on internal temperature distribution characteristics of PV/PCM and temperature control characteristics of solar cells. The results show that the increase in thermal conductivity of PCM can prolong the temperature control time of solar cell in PV/PCM system, for example, when the thermal conductivity is increased from 0.2 W/(m·K) to1.5 W/(m·K) under a thickness of 4 cm, the duration when PV/PCM solar cell temperature is controlled below 40°C and extended from 52 min to 184 min. In addition, PV/PCM experimental prototypes are designed with the LA-SA-EG composite PCM peak melting point of 46°C and thermal conductivity of 0.8 W/(m·K) and 1.1 W/(m·K), respectively. The results indicate that compared with PCM-free solar cells, the maximum temperature of PV/PCM prototype solar cells with thermal conductivity of 0.8 W/(m·K) and 1.1 W/(m·K) is reduced by 10.8°C and 4.6°C, respectively, with average output power increased by 4.1% and 2.2%, respectively, under simulated light sources. Under natural light conditions, the average output power is increased by 6.9% and 4.3%, respectively. The results provide theoretical and experimental basis for the optimization of PV/PCM design by changing the thermal conductivity of PCM.     

Multi-criteria PSO-based optimal design of grid-connected hybrid renewable energy systems

ABSTRACT

Human-induced climate change through the over liberation of greenhouse gases, resulting in devastating consequences to the environment, is a concern of considerable global significance which has fuelled the diversification to alternative renewable energy sources. The unpredictable nature of renewable resources is an impediment to developing renewable projects. More reliable, effective, and economically feasible renewable energy systems can be established by consolidating various renewable energy sources such as wind and solar into a hybrid system using batteries or back-up units like conventional energy generators or grids. The precise design of these systems is a critical step toward their effective deployment. An optimal sizing strategy was developed based on a heuristic particle swarm optimization (PSO) technique to determine the optimum number and configuration of PV panels, wind turbines, and battery units by minimizing the total system life-cycle cost while maximizing the reliability of the hybrid renewable energy system (HRES) in matching the electricity supply and demand. In addition, by constraining the amount of conventional electricity purchased from the grid, environmental concerns were also considered in the presented method. Various systems with different reliabilities and potential of reducing consumer’s CO₂ emissions were designed and the behavior of the proposed method was comprehensively investigated. An HRES may reduce the annualized cost of energy and carbon footprint significantly.     

A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China

Wind energy, one of the most promising renewable and clean energy sources, is becoming increasingly significant for sustainable energy development and environmental protection. Given the relationship between wind power and wind speed, precise prediction of wind speed for wind energy estimation and wind power generation is important. For proper and efficient evaluation of wind speed, a smooth transition periodic autoregressive (STPAR) model is developed to predict the six-hourly wind speeds. In addition, the Elman artificial neural network (EANN)-based error correction technique has also been integrated into the new STPAR model to improve model performance. To verify the developed approach, the six-hourly wind speed series during the period of 2000–2009 in the Hebei region of China is used for model construction and model testing. The proposed EANN-STPAR hybrid model has demonstrated its powerful forecasting capacity for wind speed series with complicated characteristics of linearity, seasonality and nonlinearity, which indicates that the proposed hybrid model is notably efficient and practical for wind speed forecasting, especially for the Hebei wind farms of China.     

Energy consumption estimation model for dual-motor electric vehicles based on multiple linear regression

ABSTRACT

The drive range of electric vehicle (EV) is one of the major limitations that impedes its universalism. A great deal of research has been devoted to drive range improvement of EV, an accurate and efficiency energy consumption estimation plays a crucial role in these researches. However, the majority of EV’s energy consumption estimation models are based on single motor EV, these models are not suitable for dual-motor EVs, which are composed of more complex transmission mechanisms and multiple operating modes. Thus, an energy consumption estimation model for dual-motor EV is proposed to estimate battery power. This article focuses on studying the operating modes and system efficiency in each operating mode. The limitation of working area of each mode ensures the vehicle dynamic performance, then PSO algorithm is adopted to optimize the torque (speed) distribution between two motors to improve the system efficiency in the coupled driving mode. Finally, the energy consumption estimation model is established by multiple linear regression (MLR). The result shows that the proposed model has a high precision in energy consumption estimation of dual-motor EV.     

A novel wind speed forecasting model for wind farms of Northwest China

Wind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon’s Signed-Rank test, and Morgan--Granger--Newbold test tell us that the proposed model is different from the compared models.     

Multi-objective-based optimal transmission switching and demand response for managing congestion in hybrid power systems

ABSTRACT

This paper proposes a novel congestion management (CM) approach by using the optimal transmission switching (OTS) and demand response (DR) for a system with conventional thermal generators and renewable energy sources (RESs). In this paper, wind and solar PV units are considered as the RESs. The stochastic behavior of wind and solar PV powers are modeled by using the appropriate probability density functions (PDFs). The proposed CM methodology simultaneously optimizes the generation dispatch, demand response, and also the network topology of the power system. The OTS identifies the branches that should be taken out of service by significantly reducing the operating cost of the system while respecting the system security. Here, the total operating cost minimization/social welfare maximization and system losses minimization are considered as the objectives to be optimized. The proposed CM problem is solved using the multi-objective Jaya algorithm and it is used to determine a set of Pareto-optimal solutions. The Jaya algorithm is simple and it does not have any algorithmic-specific parameters to be tuned. This aspect reduces the designer’s effort in tuning the parameters to arrive at the optimum objective function value. A fuzzy logic-based approach is used to identify the best compromise solution. The effectiveness of the proposed CM approach is examined on modified IEEE 30 and practical Indian 75 bus test systems. The obtained simulation results are analyzed and they show the effectiveness of the proposed approach.     

Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm

ABSTRACT

Wind speed forecasting plays an important role in power grid dispatching management. This article proposes a short-term wind speed forecasting method based on random forest model combining ensemble empirical modal decomposition and improved harmony search algorithm. First, the initial wind speed data set is decomposed into several ensemble empirical mode functions by EEMD, then feature extraction of each sub-modal IMF is performed using fast Fourier transform to solve the cycle of each sub-modal IMF. Next, combining the high-performance parameter optimization ability of the improved harmony search algorithm, two optimal parameters of random forest model, number of decision trees, and number of split features are determined. Finally, the random forest model is used to forecast the processing results of each submodal IMF. The proposed model is applied to the simulation analysis of historical wind data of Chaoyang District, Liaoning Province from April 27, 2015 to May 22, 2015. To illustrate the suitability and superiority of the EEMD-RF-IHS model, three types of models are used for comparison: single models including ANN, SVM, RF; EMD combination models including EMD-ANN, EMD-SVM, EMD-RF; EEMD combination models including EEMD-ANN, EEMD-SVM, EEMD-RF. The analysis results of evaluation indicators show that the proposed model can effectively forecast short-term wind data with high stability and precision, providing a reference for forecasting application in other industry fields.     

A novel framework-based cuckoo search algorithm for sizing and optimization of grid-independent hybrid renewable energy systems

Hybrid renewable energy systems (HRES) turned into an appealing choice for supplying loads in remote areas. The application of smart grid principals in HRES provides a communication between the load and generation from the HRES. Using smart grid in the HRES will optimally utilize the generating resources to reschedule the loads depending on its importance. This paper presents a new proposed design and optimization simulation program for techno-economic sizing of grid-independent hybrid PV/wind/diesel/battery energy system using Cuckoo search (CS) optimization algorithm. Using of CS will help to get the global minimum cost condition and prevent the simulation to be stuck around local minimum. A new proposed simulation program (NPSP) is acquainted using CS to determine the optimum size of each component of the HRES for the lowest cost of generated energy and the lowest value of dummy energy, at highest reliability. A detailed economic methodology to obtain the price of the generated energy has been introduced. Results showed that using CS reduced the time required to obtain the optimal size with higher accuracy than other techniques used iterative techniques, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Numerous significant outcomes can be extracted from the proposed program that could help scientists and decision makers.     

Thermal Modeling and Performance Evaluation of Photovoltaic Thermal (PV/T) Systems: A Parametric Study

Conventional solar photovoltaic (PV) module converts the light component of solar radiation into electrical power, and heat part is absorbed by module increasing its operating temperature. Combined PV module and heat exchanger generating both electrical and thermal powers is called as hybrid photovoltaic/thermal (PV/T) solar system. The paper presents the design of a PV/T collector, made with thin film PV technology and a spiral flow absorber, and a simulation model, developed through the system of several mathematical equations, to evaluate the performance of PV/T water collectors. The effect of various parameters on the thermal and electrical efficiency has been investigated to obtain optimum combination of parameters. Finally, a numerical simulation has been carried out for the daily and annual yield of the proposed PV/T collector, and comparison with a standard PV module is discussed.     

The Design of a New Energy-conservation Type Solar-powered Lighting System Using a High‐pressure Sodium Lamp

This study designs and applies a new energy-conservation type solar-powered lighting system using a high-pressure sodium lamp to areas not having any utility company's electricity. The proposed system uses a zero-voltage-switching (ZVS) DC/DC converter in the batteries’ charge circuit to reduce the switching loss for a higher charging efficiency. Said system also adopts the maximum power point tracking (MPPT) technique to maximize the solar panels’ photovoltaic conversion capability. When dark, the batteries in the proposed system will discharge, with a raised voltage, through a push-pull DC/DC converter; said voltage, as the input voltage of the series-parallel resonant inverter, will be regulated to dim the lamp. To enable the efficient usage of the batteries’ stored energy capacity, this control scheme of the proposed system may adjust the night-time discharge time lengths, according to season difference, and compute the usable capacity for the load, according to the batteries’ charged voltage, so as to select a suitable pre-scheduled light-dimming curve for the lamp to achieve energy conservation for the batteries and continuity in lighting when dark.     

Robust reliability and electrical performances by the bulk-contact modulation in 60-V p-channel LDMOS power components

To increase reliability and electrical performance, shallow-trench isolation (STI) (or called field-oxide (FOX)) structures were inserted in the bulk-contact region of 60-V high-voltage p-channel lateral-diffused MOSFET (pLDMOS) devices in this study. As the FOX ratio increased with the addition of FOX segments, the value of the secondary breakdown current (I_t2) was enhanced. Therefore, the anti-electrostatic discharge ability of a pLDMOS device can be efficiently improved using this novel method. In addition, when the weighting ratio of FOX structures increased, variation values in the trigger voltage (V_t1) and holding voltage (V_h) of the corresponding samples remained within the range of approximately 1–4 V. The R_on value decreased because of more uniform conduction. The experimental data for the FOX structures added to the bulk revealed that the I_t2 value was improved by approximately 13.98%, V_h values were greater than 60 V (which is favorable for latch-up immunity), and the R_on value was decreased by approximately 12.62% compared with a reference device under test (without FOX segments in the bulk-contact region).     

Risk-based optimal operation of hybrid power system using multiobjective optimization

ABSTRACT

This paper solves an optimal generation scheduling problem of hybrid power system considering the risk factor due to uncertain/intermittent nature of renewable energy resources (RERs) and electric vehicles (EVs). The hybrid power system considered in this work includes thermal generating units, RERs such as wind and solar photovoltaic (PV) units, battery energy storage systems (BESSs) and electric vehicles (EVs). Here, the two objective functions are formulated, i.e., minimization of operating cost and system risk, to develop an optimum scheduling strategy of hybrid power system. The objective of proposed approach is to minimize operating cost and system risk levels simultaneously. The operating cost minimization objective consists of costs due to thermal generators, wind farms, solar PV units, EVs, BESSs, and adjustment cost due to uncertainties in RERs and EVs. In this work, Conditional Value at Risk (CVaR) is considered as the risk index, and it is used to quantify the risk due to intermittent nature of RERs and EVs. The main contribution of this paper lies in its ability to determine the optimal generation schedules by optimizing operating cost and risk. These two objectives are solved by using a multiobjective-based nondominated sorting genetic algorithm-II (NSGA-II) algorithm, and it is used to develop a Pareto optimal front. A best-compromised solution is obtained by using fuzzy min-max approach. The proposed approach has been implemented on modified IEEE 30 bus and practical Indian 75 bus test systems. The obtained results show the best-compromised solution between operating cost and system risk level, and the suitability of CVaR for the management of risk associated with the uncertainties due to RERs and EVs.     

Improving the dynamic response of frequency and power in a wind integrated power system by optimal design of compensated superconducting magnetic energy storage

The frequency deviation and power fluctuation need to be controlled in a wind-integrated power system (WIPS) for keeping the balance between system power generation and demand, which support the quality and stability of overall power system. The present paper addresses this problem while concerning the integration of intermittent wind power and load disturbance into the WIPS. With this intent, it proposes the compensated superconducting magnetic energy storage (CSMES) system with proportional integral derivative (PID) controller for improving the frequency and power deviation profile. A novel swarm intelligence-based artificial bee colony (ABC) algorithm is used for optimal design of PID-CSMES system. Robustness of the proposed ABC-based PID-CSMES control strategy is tested in WIPS under various disturbance patterns of load and wind power. To demonstrate the improved dynamic response, their simulation results are compared with particle swarm optimization-based PID-CSMES, PID with SMES, and only PID controller technique. The performance indices and transient response characteristics of frequency and power deviation are used to evaluate and compare the accuracy and efficiency of each controller. Stability of various system configurations is analyzed using eigenvalue location. Comparing the results of different controller in WIPS indicates a substantial improvement in the dynamic response of system frequency and power deviations by utilizing the proposed control strategy.     

A landscape vulnerability analysis method proposal and its integration in the EIA

Landscape is the area primarily affected by proposed human projects. The prediction and evaluation of the potential anthropogenic impacts under Environmental Impact Assessment (EIA) is therefore one of the major environmental tools for prevention of any deteriorative or destructive actions. To conduct EIA properly requires inclusion of a determination of landscape vulnerability which expresses the possible landscape reaction to impacts of any exogenous factors. When it is designated correctly, the suitable human activities are determined more accurately. Even though many techniques for this have been suggested worldwide, lots of deficiencies have surfaced in their application. This paper presents a method for landscape vulnerability analysis which consists of qualitative evaluation of landscape receptors, their scoring, the vulnerability degree calculation, and overall reliability evaluation. The method proposed can improve the impact objectivity of prediction and evaluation and the suggestion of precise mitigation measures with the purpose of achieving sustainable landscape management.     

Assessment of the small modular reactor (SMR) marketing using modified network dynamics-based system dynamics (SD) method

Following the export success of the South Korean small modular reactor (SMR), it has been investigated for the marketing strategy of nuclear power plants (NPPs). The information feedback oriented method for the social-business matters, system dynamics (SD), is applied to the assessment of the marketing strategy in which the forecasting skills are performed. Each element has the Boolean value as 0 or 1 in which the values are selected by the random number generation. If the generated values are higher than the designed value decided by the operator, it is 1. Otherwise, it is 0. The networking based dynamical modeling is discussed. The modification of the linear networking is changed by the SD algorithm where the feedback and multiple connections are added to the original network dynamics theory. This new method has shown the complexity of the marketing strategy, especially for the NPPs.     

Urban water resources planning by using a modified particle swarm optimization algorithm

A new optimization algorithm by coupling the mutation process to the particle swarm optimization (PSO) is developed in this paper. This algorithm, entitled particle swarm optimization with mutation similarity (PSOMS), is successfully applied to an urban water resources management problem for the large city of Tabriz, Iran. The objective functions of the optimization problem are to minimize the cost, maximize water supply and minimize the environmental hazards. The constraints are physical limits such as pipelines capacity, ground water, the demand and the impact of conservation tools. Due to the parameters uncertainty, the water supply objective is modeled with fuzzy set theory and the objectives are then combined with compromise programming. The resulted single objective is solved using PSOMS, and its efficiency is then compared with the basic PSO and two kinds of genetic algorithms. Among them, PSOMS shows rapid convergence and suitable results compared to other methods. PSOMS is also improved to provide the Pareto frontier, which is needed to proper selecting of the optimal solutions in the uncertain conditions. Finally, the diversity of solutions is checked based on an indicator of the distances between different solutions, which show the efficiency of the PSOMS algorithm with respect to the genetic algorithm. Then by using the non-symmetric Kalai–Smorodinsky method a guideline is provided for comfort selection of the most preferred solution in the Pareto frontier. Based on these outcomes, the multi-objective PSOMS provides more appropriate results needed for urban systems management.     

Numerical estimation of photovoltaic–electrolyzer system performance on the basis of a weather database

We performed a numerical simulation to investigate the performance of a photovoltaic (PV)–electrolyzer on the basis of a simulated weather database during the summer solstice (SS), autumnal equinox (AE), and winter solstice (WS), and all year round. First, we selected a location in southern Taiwan (latitude: 22.65°N) to create a local weather simulation database that included daily solar radiation, wind speed, and ambient temperature. The I–V curves of a PV system and an electrolyzer were obtained numerically by using Simpson integration computation. Subsequently, the optimal configuration of a PV driving system comprising the electrolyzer and the PV panel was determined. The database of weather conditions was input into the numerical estimation model of the PV–electrolyzer system, and the hydrogen generation rates and hydrogen production volumes under both clear skies and changeable weather conditions were obtained.