An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria

In this paper we investigate, the possibility of using an adaptive Artificial Neural Network (ANN), in order to find a suitable model for sizing Stand-Alone Photovoltaic (SAPV) systems, based on a minimum of input data. The model combines Radial Basis Function (RBF) network and Infinite Impulse Response (IIR) filter in order to accelerate the convergence of the network. For the sizing of a photovoltaic (PV) systems, we need to determine the optimal sizing coefficients (K_PV, K_B). These coefficients allow us to determine the number of solar panels and storage batteries necessary to satisfy a given consumption, especially in isolated sites where the global solar radiation data is not always available. These coefficients are considered the most important parameters for sizing a PV system. Results obtained by classical models (analytical, numerical, analytical-numerical, B-spline function) and new models like feed-forward (MLP), radial basis function (RBF), MLP-IIR and RBF-IIR are compared with experimental sizing coefficients in order to illustrate the accuracy of the new developed model. This model has been trained by using 200 known optimal sizing coefficients corresponding to 200 locations in Algeria. In this way, the adaptive model was trained to accept and handle a number of unusual cases. The unknown validation sizing coefficients set produced very accurate estimation with a correlation coefficient of 98%. This result indicates that the proposed method can be successfully used for the estimation of optimal sizing coefficients of SAPV systems for any locations in Algeria. The methodology proposed in this paper however, can be generalized using different locations of the world.     

An optimum load management strategy for stand-alone photovoltaic power systems

The subject of load management for stand-alone photovoltaic (SAPV) power systems is addressed. The objective is to minimize the total life-cycle cost of the system while, at the same time, the battery is protected and the load priorities are observed. The first step in this approach involves a general load classification. The idea is to manipulate the controllable loads in order to reduce battery size. For this reason, optimum curves are obtained for the controllable loads. Then an optimum load management strategy is mathematically formulated. Finally, a tracking algorithm has been devised in order to implement the optimum load management scheme. The previously described method yields cost optimum SAPV systems. An illustrative example using data similar to the first village PV power system of Schuchuli, Arizona shows the practical application of the proposed optimum load management strategy.     

Load profile impact on the gross energy requirement of stand-alone photovoltaic systems

The sizing optimization of a Stand-Alone Photovoltaic system (SAPV) is a very complex issue. Therefore, a compromise solution must be made between having an acceptable energy and economic cost for the consumer, and a relatively correct energy supply quality. The Gross Energy Requirement (GER) of an SAPV system corresponds to the primary energy total amount required for the production, the maintenance and the recycling of this system. Reducing the GER is thus, an effective way to promote the development of SAPV systems. Therefore, the load profile management, in order to get closer to the ideal “solar” consumer, allows the downsizing of the system. In this paper, a methodology for studying the impact of load profiles on GER is proposed. Two different modifications parameters have been considered theoretically on idealized load and production profiles: the load shifting which seems simpler to implement in the reality, and the amplitude modulation. Furthermore, the NSGA-II genetic algorithm has been used to confirm theoretical outcomes and to optimize SAPV system sizing for three realistic load profiles, with the aim of quantifying the GER reduction, by minimizing the storage capacity (taking into account the replacements due to cycling) which is one of the weak points of such a system, and by PV panels downsizing.     

Sizing of the array for stand-alone photovoltaic systems

A brief critical review of stand-alone photovoltaic (SAPV) array sizing methods is presented, leading to the suggestion that a new class of methods is required in order to make the sizing exercise both accurate and accessible to a wide range of designers and users. The energy balance method is identified as an example of such a method. It is then used to obtain the array areas for a notional SAPV system situated in six widely-dispersed stations world-wide, with a total 24-hour load of 0.25, 2.5, or 25 kW h. Through correlational analysis of all the results and input data, it is recommended that the expression used to obtain array area is 1334Q^−2.272_TYL^0.986 with the terms and associated units being defined in the main body of the paper.     

Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure

This paper presents an adaptive artificial neural network (ANN) for modeling and simulation of a Stand-Alone photovoltaic (SAPV) system operating under variable climatic conditions. The ANN combines the Levenberg–Marquardt algorithm (LM) with an infinite impulse response (IIR) filter in order to accelerate the convergence of the network. SAPV systems are widely used in renewable energy source (RES) applications and it is important to be able to evaluate the performance of installed systems. The modeling of the complete SAPV system is achieved by combining the models of the different components of the system (PV-generator, battery and regulator). A global model can identify the SAPV characteristics by knowing only the climatological conditions. In addition, a new procedure proposed for SAPV system sizing is presented in this work. Different measured signals of solar radiation sequences and electrical parameters (photovoltaic voltage and current) from a SAPV system installed at the south of Algeria have been recorded during a period of 5-years. These signals have been used for the training and testing the developed models, one for each component of the system and a global model of the complete system. The ANN model predictions allow the users of SAPV systems to predict the different signals for each model and identify the output current of the system for different climatological conditions. The comparison between simulated and experimental signals of the SAPV gave good results. The correlation coefficient obtained varies from 90% to 96% for each estimated signals, which is considered satisfactory. A comparison between multilayer perceptron (MLP), radial basis function (RBF) network and the proposed LM–IIR model is presented in order to confirm the advantage of this model.     

Approaches for developing a sizing method for stand-alone PV systems with variable demand

Accurate sizing is one of the most important aspects to take into consideration when designing a stand-alone photovoltaic system (SAPV). Various methods, which differ in terms of their simplicity or reliability, have been developed for this purpose. Analytical methods, which seek functional relationships between variables of interest to the sizing problem, are one of these approaches.A series of rational considerations are presented in this paper with the aim of shedding light upon the basic principles and results of various sizing methods proposed by different authors. These considerations set the basis for a new analytical method that has been designed for systems with variable monthly energy demands.Following previous approaches, the method proposed is based on the concept of loss of load probability (LLP)—a parameter that is used to characterize system design. The method includes information on the standard deviation of loss of load probability (σ_LLP) and on two new parameters: annual number of system failures (f) and standard deviation of annual number of failures (σ_f).The method proves useful for sizing a PV system in a reliable manner and serves to explain the discrepancies found in the research on systems with LLP<10⁻². We demonstrate that reliability depends not only on the sizing variables and on the distribution function of solar radiation, but on the minimum value as well, which in a given location and with a monthly average clearness index, achieves total solar radiation on the receiver surface.     

A case study of a typical 2.32 kWP stand-alone photovoltaic (SAPV) in composite climate of New Delhi (India)

This paper presents rigorous experimental outdoor performance of a 2.32 kW_P stand-alone photovoltaic (SAPV) system in New Delhi (India) for four weather types in each month such as clear, hazy, partially cloudy/foggy and fully cloudy/foggy weather conditions respectively. The daily power generated from the existing SAPV system was experimentally found in the range of 4–6 kW h/day depending on the prevailing sky conditions. The number of days and daily power generated corresponding to four weather types in each month were used to determine monthly and subsequently annual power generation from the existing SAPV system. There are three daily load profiles with and without earth to air heat exchanger suitable for three seasons like summer (3.75–6.15 kW h/day), winter (2.79–5.19 kW h/day) and rainy (3.75 kW h/day). The hourly efficiency of the SAPV system components are determined and presented in this paper. The life cycle cost (LCC) analysis for the existing typical SAPV system is carried out to determine unit cost of electricity. The effect of annual degradation rate of PV system efficiency is also presented in this paper. The energy production factor (EPF) and the energy payback time (EPBT) of the SAPV system was also determined and presented in this paper.     

A reliable methodology based on mine blast optimization algorithm for optimal sizing of hybrid PV-wind-FC system for remote area in Egypt

In this paper, a reliable methodology incorporated mine blast algorithm (MBA) is applied to solve the optimal sizing of a hybrid system consisting of photovoltaic modules, wind turbines and fuel cells (PV/WT/FC) to meet a certain load of remote area in Egypt. The main objective of the optimal sizing process is to achieve the minimum annual cost of the system with load coverage. The sizing process is performed optimally based on real measured data for solar radiation, ambient temperature and wind velocity recorded by the solar radiation and meteorological station located at national research institute of astronomy and geophysics, Helwan city, Egypt. Three other meta-heuristic optimization techniques, particle swarm optimization, cuckoo search and artificial bee colony are applied to solve the problem and the results are compared with those obtained by the proposed methodology. A power management strategy that regulates the power flow between each system component is also presented. The obtained results show that; applying the proposed methodology will save about 24.8% in the annual total cost of the proposed system compared with PSO, 8.956% compared with CS and 11.5576% compared with ABC. The proposed algorithm based on MBA is candidate for solving the presented optimization problem of optimal sizing the hybrid PV/WT/FC system.     

A sizing method for stand-alone PV installations with variable demand

The practical applicability of the considerations made in a previous paper to characterize energy balances in stand-alone photovoltaic systems (SAPV) is presented. Given that energy balances were characterized based on monthly estimations, the method is appropriate for sizing installations with variable monthly demands and variable monthly panel tilt (for seasonal estimations).The method presented is original in that it is the only method proposed for this type of demand. The method is based on the rational utilization of daily solar radiation distribution functions. When exact mathematical expressions are not available, approximate empirical expressions can be used. The more precise the statistical characterization of the solar radiation on the receiver module, the more precise the sizing method given that the characterization will solely depend on the distribution function of the daily global irradiation on the tilted surface H_gβi.This method, like previous ones, uses the concept of loss of load probability (LLP) as a parameter to characterize system design and includes information on the standard deviation of this parameter (σ_LLP) as well as two new parameters: annual number of system failures (f) and the standard deviation of annual number of system failures (σ_f).This paper therefore provides an analytical method for evaluating and sizing stand-alone PV systems with variable monthly demand and panel inclination. The sizing method has also been applied in a practical manner.     

Optimal sizing study of hybrid wind/PV/diesel power generation unit

In this paper, a methodology of sizing optimization of a stand-alone hybrid wind/PV/diesel energy system is presented. This approach makes use of a deterministic algorithm to suggest, among a list of commercially available system devices, the optimal number and type of units ensuring that the total cost of the system is minimized while guaranteeing the availability of the energy. The collection of 6 months of data of wind speed, solar radiation and ambient temperature recorded for every hour of the day were used. The mathematical modeling of the main elements of the hybrid wind/PV/diesel system is exposed showing the more relevant sizing variables. A deterministic algorithm is used to minimize the total cost of the system while guaranteeing the satisfaction of the load demand. A comparison between the total cost of the hybrid wind/PV/diesel energy system with batteries and the hybrid wind/PV/diesel energy system without batteries is presented.The reached results demonstrate the practical utility of the used sizing methodology and show the influence of the battery storage on the total cost of the hybrid system.     

OPTIMIZATION OF COLLECTOR AREA IN SOLAR WATER HEATING SYSTEMS

Domestic household thermosyphons are economically feasible and are used by over than 70% of houses in Palestine. Although domestic solar water heating for commercial applications has a good potential, only a few systems have been installed in Palestine. A systematic sizing approach for the solar system is presented in this paper and applied to a certain case study. The solar system sizing is based on the life-cycle cost LCC analysis. For the chosen case study of domestic water heating for a hotel, with hot water consumption of 2600 liters per day, the optimum collector area was found to be 37 m², the solar fraction of heating 0.78, the LCC of system is SI 3778, with annual savings of 1338$/year and a pay back period of 3 years. With this optimized system, the cost of water heating is 1.8 $/m³comparing with 2.6 $/m³ for the conventional system.     

Optimal sizing of projects in a hydro-based power system

The author considers the problem of obtaining the optimal design of hydroelectric power systems with respect to determining each project's optimal size or capacity. The problem of sizing projects is analyzed from a theoretical and economical standpoint, and a mathematical model is presented to examine project sizing in the general framework of expanding a purely hydroelectric system. The interaction between sizing and sequencing decisions is also discussed. The results give design rules for choosing the optimal size and marginal cost for individual projects, and show the interdependence of scaling and sequencing decisions. According to these rules, it is optimal to make the current marginal cost of each new project equal to the discounted weighted average of the long-term marginal unit cost for all future projects     

Hybrid fuel cell and battery propulsion system modelling and multi-objective optimisation for a coastal ferry

Efforts from all sectors of society including the shipping industry are needed to limit the overall global temperature rise to within 2°C of pre-industrial levels by 2050. The hybridisation of Proton Exchange Membrane Fuel Cells (PEMFC) and Lithium-ion batteries for coastal ship propulsion systems may potentially offer beneficial emission performance. However, such hybrid systems are constrained by power and energy density limitations, lifetime; and costs as well as life-cycle emissions of alternative fuel/energy. There is a lack of holistic design methodology dealing with these uncertainties in the literature. This paper proposes a holistic design methodology for coastal hybrid ships based upon a developed model. The power source sizing problem is solved using constrained mixed-integer multi-objective optimisation in the external layer. The global optimum energy management strategies for an averaged operating profile are obtained from deterministic dynamic programming in the inner layer, while considering power source degradations in the sizing algorithm. The proposed methodology was applied to a coastal ferry to investigate the feasibility and benefit potential of adopting the hybrid PEMFC and battery propulsion system in Matlab. The case studies indicate that the proposed propulsion system can achieve at least a 65% life-cycle greenhouse gas reduction for the considered two cases.     

Optimal siting and sizing of hydrogen refueling stations considering distributed hydrogen production and cost reduction for regional consumers

Hydrogen fuel cell vehicles are currently facing two difficulties in achieving their general use: the lack of hydrogen refueling stations and high hydrogen prices. Hydrogen refueling stations are the middle stage for delivering hydrogen from its sources to consumers, and their location could be affected by the distributed locations of hydrogen sources and consumers. The reasonable siting and sizing of hydrogen refueling stations could both improve the hydrogen infrastructure and reduce regional consumers' cost of using hydrogen. By considering the hydrogen life cycle cost and using a commercial volume forecasting model, this paper creates a relatively thorough and comprehensive model for hydrogen station siting and sizing with the objective of achieving the optimal costs for consumers using hydrogen. The cost‐based model includes the selection of the hydrogen sources, transportation methods, and storage methods, and thus, the hydrogen supply chain can also be optimized. A numerical example is established in Section 4 with the solution algorithm and results.     

Optimal sizing method for stand-alone hybrid solar–wind system with LPSP technology by using genetic algorithm

System power reliability under varying weather conditions and the corresponding system cost are the two main concerns for designing hybrid solar–wind power generation systems. This paper recommends an optimal sizing method to optimize the configurations of a hybrid solar–wind system employing battery banks. Based on a genetic algorithm (GA), which has the ability to attain the global optimum with relative computational simplicity, one optimal sizing method was developed to calculate the optimum system configuration that can achieve the customers required loss of power supply probability (LPSP) with a minimum annualized cost of system (ACS). The decision variables included in the optimization process are the PV module number, wind turbine number, battery number, PV module slope angle and wind turbine installation height. The proposed method has been applied to the analysis of a hybrid system which supplies power for a telecommunication relay station, and good optimization performance has been found. Furthermore, the relationships between system power reliability and system configurations were also given.     

The economics of photovoltaic stand-alone residential households: A case study for various European and Mediterranean locations

The cost-effective sizing and evaluation of residential stand-alone photovoltaic systems at various European and Mediterranean locations is the subject of this paper. The stand-alone photovoltaic system is serving the energy needs of a medium-sized household inhabited by a typical four member family. A typical energy consumption daily profile is assumed, and the solar array, battery and back-up generator – if necessary – are optimally sized to minimise the system life-cycle cost (LCC). The calculations have been done assuming economic parameters and PV technology costs applicable to years 1998 and 2005.     

Sizing of stand-alone photovoltaic power supply system based on genetic algorithm and neuro-fuzzy: application for isolated areas

This paper deals with the application of genetic algorithm (GA) and an adaptive neuro-fuzzy inference scheme (ANFIS), for the prediction of the optimal sizing coefficient of stand-alone photovoltaic supply (SAPVS) systems in remote areas. A database of total solar radiation data for 60 sites in Algeria has been used to determine the iso-reliability curves of a PVS system (C _A, C _S) for each site. Initially, the GA is used for determining the optimal coefficient (C _Aop, C _Sop) for each site by minimising the optimal cost (objective function). These coefficients allow the determination of the number of PV modules and the capacity of the battery. Subsequently, an ANFIS is used for the prediction of the optimal coefficient in remote areas based only on geographical coordinates. Therefore, 56 couples of C _Aop and C _Sop have been used for the training of the network and four couples have been used for testing and validation of the proposed technique. The simulation results have been analysed and compared with the alternative techniques. The technique has been applied and tested for Algeria locations, but it can be generalised for any location in the world.     

基于改进灰狼算法的独立微电网容量优化配置

为降低独立微电网的综合发电成本,提高供电可靠性,研究基于改进灰狼优化算法的独立微电网电源容量优化配置方法.针对基本灰狼算法在进化后期由于种群多样性的缺失而易出现局部收敛或算法早熟的问题,提出一种具有全局寻优性能的改进灰狼优化算法.改进算法首先利用Tent混沌序列产生初始种群,以增强种群的多样性;其次,通过对收敛因子设置...     

Techno-economical optimization based on swarm intelligence algorithm for a stand-alone wind-photovoltaic-hydrogen power system at south-east region of Mexico

Renewable generating systems are alternative to produce electric energy in a clean manner. However, the high costs of the constituents limit their broad use. Thus, sizing is an important issue in the renewable generating systems design, in order to reach an efficient relationship between cost and benefit. Likewise, the random nature of the sources makes the sizing a complex task with regard to a conventional system. This paper is focused on calculate the optimal size of a wind-photovoltaic-fuel cell system to meet the power demand of an isolated residential load located in the south-east region of Mexico (Chetumal city 18°31′21.4″N 88°16′11.3″W), with a solar radiation range from 0 to 0.75 kW/m² and wind speed range from 5 to 7.8 m/s. Swarm intelligence techniques have been successfully applied in solving many combinatorial optimization problems in which the objective space possesses many local optimal solutions. This work employs the Particle Swarm Optimizer (PSO) algorithm to search the optimal sizing for the power plant minimizing the total costs of the system; as a metaheuristic procedure, the PSO was able to find the best configuration regardless the lack of a deep knowledge of the problem. Compared against the Differential Evolution (DE) technique, the PSO performance is faster and able to provide a configuration that saves around 10% of the total cost of the hybrid system.     

Assessing sizing optimality of OFF-GRID AC-linked solar PV-PEM systems for hydrogen production

Herein, a novel methodology to perform optimal sizing of AC-linked solar PV-PEM systems is proposed. The novelty of this work is the proposition of the solar plant to electrolyzer capacity ratio (AC/AC ratio) as optimization variable. The impact of this AC/AC ratio on the Levelized Cost of Hydrogen (LCOH) and the deviation of the solar DC/AC ratio when optimized specifically for hydrogen production are quantified. Case studies covering a Global Horizontal Irradiation (GHI) range of 1400–2600 kWh/m²-year are assessed. The obtained LCOHs range between 5.9 and 11.3 USD/kgH₂ depending on sizing and location. The AC/AC ratio is found to strongly affect cost, production and LCOH optimality while the optimal solar DC/AC ratio varies up to 54% when optimized to minimize the cost of hydrogen instead of the cost of energy only. Larger oversizing is required for low GHI locations; however, H₂ production is more sensitive to sizing ratios for high GHI locations.