Sizing Of Stand-alone Photovoltaic System Research Articles

Optimal Sizing of Stand-Alone Photovoltaic Systems for Rural Electrification

Solar system became very important sector in these days and expected to play a very significant role in the future especially in developing countries. The paper is considering off-grid solar system to cover the required load for rural area during the day hours. This study is taken in one of the rural villages which located in Vhembe District Municipality, Limpopo, South Africa. Sizing of stand-alone photovoltaic system to electrify rural areas is a suitable alternative source to power the rural households. The study presents a sizing of PV system to cover the required load during the day for the selected village. The load and solar radiation data of the typical household in the target village has been used to size the stand-alone photovoltaic system. Due to the high cost of batteries, the batteries will not be considered in this study; the viability of a batterryless system is facilitated by the high solar radiation at the selected site and long daily average insulation duration. MATLAB software being used to size the PV system. This software can be implemented to any site with different weather conditions.

Novel Approach to Investigate the Influence of Optimum Tilt Angle on Minimum Cost of Energy-Based Maximum Power Generation and Sizing of PV Systems: A Case Study of Diverse Climatic Zones in India

Optimum Photovoltaic (PV) system integration in power grid depend upon the total of power accessible from the PV. To figure the PV systems highest power yield, PV panels must be positioned at an optimal tilt angle (OPTA) to absorb maximum solar radiation (SR). This OPTA is a function of the latitude, clearness index, diffuse SR, global SR, direct SR and optimum PV size. Therefore OPTA has an impact on maximum power generation and optimal PV system sizing. The PV is not installed at OPTA for most of the sites in India which is important for maximum power generation and optimum sizing of standalone PV systems. This results in variation of OPTA from site to site and its effect on PV sizing needs to be investigated. The innovative aspect of this work is the calculation of OPTA, which are employed as sensitive factors in Hybrid Optimization of Multiple Energy Resources (HOMER), to determine their impact on maximum optimum sizing and power generation for 26 cities in India's various climate zones. This methodology can be applied all over the world to determine the impact of OPTA on maximum power generation and size. It is found that OPTA varies from 63° to 0° throughout year in India and it is maximum for December in India. The results indicates that Net Present Cost varies from $\$ $ 1105 to $\$ $ 1280 and Cost of Energy (COE) variation is 0.041 to 0.048 $\$ $ /kWh throughout India cities and low temperature sites are good for photovoltaic (PV) power generation. Two axis tracking system produces more power in comparison to other tracking systems. This research is beneficial for researcher and industry to install PV system in different climatic zones of India to generate maximum power at minimum cost of energy.

Optimal Sizing of Standalone Photovoltaic System Using Improved Performance Model and Optimization Algorithm

A standalone photovoltaic system mainly consists of photovoltaic panels and battery bank. The use of such systems is restricted mainly due to their high initial costs. This problem is alleviated by optimal sizing as it results in reliable and cost-effective systems. However, optimal sizing is a complex task. Artificial intelligence (AI) has been shown to be effective in PV system sizing. This paper presents an AI-based standalone PV system sizing method. Differential evolution multi-objective optimization is used to find the optimal balance between system’s reliability and cost. Two objective functions are minimized, the loss of load probability and the life cycle cost. A numerical algorithm is used as a benchmark for the proposed method’s speed and accuracy. Results indicate that the AI algorithm can be successfully used in standalone PV systems sizing. The proposed method was roughly 27 times faster than the numerical method. Due to AI algorithm’s random nature, the proposed method resulted in the exact optimal solution in 6 out of 12 runs. Near-optimal solutions were found in the other six runs. Nevertheless, the nearly optimal solutions did not introduce major departure from optimal system performance, indicating that the results of the proposed method are practically optimal at worst.

Multiple scenarios multi-objective salp swarm optimization for sizing of standalone photovoltaic system

The paper presents a new multiple scenario multi-objective salp swarm optimization (MS-MOSS) algorithm to optimally size a standalone PV system. An accurate estimation of the number of PV modules and storage battery is crucial as it affects the system reliability and cost. Three scenarios have been presented focusing on Pareto optimal solutions by minimizing two conflicting objectives. Loss of load probability (LLP) and life-cycle cost (LLC) are considered to obtain the Pareto front. The iterative method is employed for validation of the superiority results of the proposed MS-MOSS algorithm. The results show that the scenarios are able to find Pareto optimal configuration at a high level of accuracy and at a very low cost. The proposed three scenarios are faster than iterative approach approximately by 158, 194.2, and 141.6 times, respectively. The third scenario outperforms other scenarios in terms of coverage and convergence of the distribution of solution to the Pareto front. As a conclusion, The MS-MOSS algorithm is found to be very effective in sizing of SAPV system.

Comparison of optimum design, sizing, and economic analysis of standalone photovoltaic/battery without and with hydrogen production systems

Recent research has focused on the design and sizing of standalone photovoltaic systems. Unfortunately, due to highly fluctuating levels of solar energy and the power supply generated not coinciding with energy demand, high capacity of generation and energy storage systems are required to provide a highly reliable supply. However, a highly reliable system tends to produce high excess energy which needs to be discarded as a dump load, which is proving to be a major concern. For this reason, this paper proposes to use the excess energy for hydrogen production, which will then be sold to local consumers. This paper presents a comparison of optimization design and economic analysis of a standalone photovoltaic with a battery system and a standalone photovoltaic with battery-hydrogen production system using an iterative technique. For the feasibility study of the standalone photovoltaic with a battery and with a hydrogen production system, the proposed minimum hydrogen-selling price is determined by breakeven analysis and is compared to the past references’ hydrogen-selling prices and gasoline prices.

A novel method for sizing of standalone photovoltaic system using multi-objective differential evolution algorithm and hybrid multi-criteria decision making methods

Abstract Standalone photovoltaic system is promising sustainable energy source. Accurate modeling and sizing of these systems strongly affect the system's feasibility. Thus, in this paper, optimal sizing of standalone photovoltaic system is conducted based on multi-objective differential evolution algorithm integrated with hybrid multi-criteria decision making methods. Multi-objective differential evolution algorithm is used to optimize set of configurations of a system by minimizing technical and cost objective functions simultaneously. After that, an analytical hierarchy process integrated with a technique for order preference by similarity to ideal solution are used to order preference of configurations based on the loss of load probability and life cycle cost of system. The results of the proposed sizing method are validated by a numerical method to explain the superiority of the proposed method. According to results, the proposed sizing method is faster than numerical method by around 27 times. This is because the multi-objective differential evolution algorithm requires roughly 0.23 of simulations that is required by numerical method. Furthermore, the performance of multi-objective differential evolution algorithm is evaluated by various metrics. As a result, for the adapted load demand, the optimal configuration is 63 PV modules and 66 battery unit with maximum capacity of 500 Ah.

Sizing and economic analysis of stand alone photovoltaic system with hydrogen storage

This paper proposes a design steps in sizing of standalone photovoltaic system with hydrogen storage using intuitive method. The main advantage of this method is it uses a direct mathematical approach to find system’s size based on daily load consumption and average irradiation data. The keys of system design are to satisfy a pre-determined load requirement and maintain hydrogen storage’s state of charge during low solar irradiation period. To test the effectiveness of the proposed method, a case study is conducted using Kuala Lumpur’s generated meteorological data and rural area’s typical daily load profile of 2.215 kWh. In addition, an economic analysis is performed to appraise the proposed system feasibility. The finding shows that the levelized cost of energy for proposed system is RM 1.98 kWh. However, based on sizing results obtained using a published method with AGM battery as back-up supply, the system cost is lower and more economically viable. The feasibility of PV system with hydrogen storage can be improved if the efficiency of hydrogen storage technologies significantly increases in the future. Hence, a sensitivity analysis is performed to verify the effect of electrolyzer and fuel cell efficiencies towards levelized cost of energy. Efficiencies of electrolyzer and fuel cell available in current market are validated using laboratory’s experimental data. This finding is needed to envisage the applicability of photovoltaic system with hydrogen storage as a future power supply source in Malaysia.

Optimal sizing of stand-alone photovoltaic system by minimizing the loss of power supply probability

This paper presents Firefly Algorithm-based Sizing Algorithm (FASA) for sizing optimization of a Stand-Alone Photovoltaic (SAPV) system. Firefly Algorithm (FA) was used to optimally select the model of each system component such that a technical performance indicator is consequently optimized. Prior to implementation of FASA, an Iterative-based Sizing Algorithms known as ISA had been developed to determine the optimal solutions which were used as benchmark for FASA. Although ISA was capable in determining the optimal design solutions when there are numerous models for each system component being considered, the computation time of ISA can be very long as ISA tested every possible combination of PV module, battery, charge controller and inverter during sizing process. Therefore, FASA was introduced to accelerate the sizing optimization for SAPV system. FA was incorporated into sizing algorithm with the technical performance indicator was set to optimize the Loss of Power Supply Probability (LPSP). Besides that, two design cases of PV-battery system, i.e. system with standard charge controller denoted as Case 1 and system with MPPT-based charge controller denoted as Case 2 were investigated. The results showed that FASA had successfully found the optimal LPSP in all design cases. In addition, sizing algorithm with FA was also discovered to outperform sizing algorithm with selected computational intelligence in producing the lowest computation time in the sizing optimization.

Optimal sizing of stand-alone photovoltaic systems in residential buildings

Solar photovoltaic (PV) system is one of the matured solar-to-electricity conversion technologies with a great potential for residential applications. For wider adoption of PV systems, there is a need for an accurate sizing and economic assessment tool to inform decision makers. In this study, we propose a new optimization model based on integer programming for the adoption of stand-alone PV systems in the residential sector. The proposed model not only determines the optimal number of PV modules and batteries but also assesses the economic feasibility of the system through annualized cost. The model takes into account site-specific data in finding the optimal sizes. The effectiveness of the proposed model is assessed through a case study in Bursari, Nigeria. The result obtained from the model reveals that establishing a solar PV system is not only environmentally friendly but also about 30% cheaper than the diesel generators that are currently in use at Bursari. In particular, annually about 361 USD per residential building could be saved if the proposed system is used to replace the diesel generators. Finally, this study also provides valuable managerial insights about the effect of several parameters on the performance of the proposed PV system.

A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system

One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt.

A novel analytical model for optimal sizing of standalone photovoltaic systems

This paper presents a novel analytical model for the determination of optimal sizing of standalone photovoltaic (PV) systems with least cost and predetermined reliability to satisfy load. Algebraic equations for optimal PV array area (Aopt), optimal useful battery storage capacity (Cu,opt) and the constant of integration (kopt) has been formulated. The proposed model provides the system output directly without going through the calculation of PV array capacity (Ca) and battery storage capacity (Cb). It is different from previous models, which only optimize the system parameters Ca and Cb without involving load. The isoreliability curves of proposed model are compared with other analytical and numerical methods with the pair of Ca and Cb in different environmental conditions without load involvement. The formulated model is also applied for the optimal sizing of a standalone PV system for domestic purpose at Kuching, Sarawak, Malaysia. The required optimal PV array area (Aopt) and useful battery storage capacity (Cu,opt) is determined with various load demands and loss of load probability (LLP). Proposed analytical model is more constructive due to incorporation of many useful variables namely desired LLP value, latitude and clearness index of location, load demand and unit cost of PV array and battery capacity. It is also rational in terms of power reliability and cost, and simple to implement for the size optimization of standalone PV systems as compared to existing models.

Loss-of-load probability model for stand-alone photovoltaic systems in Europe

Sizing of stand-alone photovoltaic systems (SAPVS) requires knowledge of their reliability. Because of primary influence of the solar irradiance and meteorological conditions, simulations are the best way to compute an accurate reliability for a given location and fixed sizing parameters. These studies have been developed for more than two decades, but have had a narrow geographical applicability.In this paper, we perform a complete (in time and space) simulation of a standard SAPVS in Europe using 23year radiation data corresponding to almost 2300 geographical points. At each point, the tilt angle that maximizes the energy reaching the PV array in December is estimated and the relation among sizing parameters and reliability is computed for wide ranges of values. Finally, multilayer perceptrons are trained for both computations, allowing (after their training) simple and fast estimations of the sizing parameters of this type of plants for any location in Europe.The procedure presented in this paper, although focused particularly in Europe, can be easily extended to almost any other region in the world.

A New Approach for Optimal Sizing of Standalone Photovoltaic Systems

This paper presents a new method for determining the optimal sizing of standalone photovoltaic (PV) system in terms of optimal sizing of PV array and battery storage. A standalone PV system energy flow is first analysed, and the MATLAB fitting tool is used to fit the resultant sizing curves in order to derive general formulas for optimal sizing of PV array and battery. In deriving the formulas for optimal sizing of PV array and battery, the data considered are based on five sites in Malaysia, which are Kuala Lumpur, Johor Bharu, Ipoh, Kuching, and Alor Setar. Based on the results of the designed example for a PV system installed in Kuala Lumpur, the proposed method gives satisfactory optimal sizing results.

A stochastic method for stand-alone photovoltaic system sizing

Photovoltaic systems utilize solar energy to generate electrical energy to meet load demands. Optimal sizing of these systems includes the characterization of solar radiation. Solar radiation at the Earth’s surface has random characteristics and has been the focus of various academic studies. The objective of this study was to stochastically analyze parameters involved in the sizing of photovoltaic generators and develop a methodology for sizing of stand-alone photovoltaic systems. Energy storage for isolated systems and solar radiation were analyzed stochastically due to their random behavior. For the development of the methodology proposed stochastic analysis were studied including the Markov chain and beta probability density function. The obtained results were compared with those for sizing of stand–alone using from the Sandia method (deterministic), in which the stochastic model presented more reliable values. Both models present advantages and disadvantages, however, the stochastic one is more complex and provides more reliable and realistic results.

Sizing of stand-alone photovoltaic systems using neural network adaptive model

In this paper, we investigate using an adaptive radial basis function (RBF) network with infinite impulse response (IIR) filter in order to find a suitable model for sizing coefficients of the stand-alone photovoltaic (PV) systems, based on minimum of input data. These sizing coefficients allow to the users of stand-alone PV systems to determine the number of solar panel and storage batteries necessary to satisfy a given consumption, especially in isolated sites where the global solar radiation data is not always available. Obtained results by feed-forward MLP, RBF and an adaptive RBF-IIR model have been compared with real sizing coefficients. The adaptive RBFIIR has been trained by using 200 known sizing coefficients values corresponding to 200 locations in Algeria. In this way, the adaptive model was trained to accept and even handle a number of unusual cases. The unknown validation sizing coefficients set produced very set accurate estimation with the correlation coefficient between the actual and the RBF-IIR model estimated data of 97% was obtained. This result indicates that the proposed method can be successfully used for estimating of optimal sizing coefficients of PV systems for any locations in Algeria, but the methodology can be generalized using different locations in the world.

Prediction of a photovoltaic system performance using cumulative frequency curves of radiation

Abstract The system performance of stand-alone photovoltaic systems is analysed. From the hourly radiation data for Bangkok (from 1984 to 1987) the cumulative frequency curves of radiation are generated and a typical meteorological day (TMD) is created each year. The system performance is determined using both the TMD radiation and the actual radiation values. The comparison results show that the TMD method can be applied for the sizing of stand-alone photovoltaic systems. The storage batteries of realistic sizes usually exhibit a daily cyclic variation in state-of-charge, with constant load consumption. Only very large and unrealistic sizes of batteries show a seasonal variation in state-of-charge. This is the fact that prompted the attempt to predict the system performance for a season by using a single representative day (TMD) of that season. Apart from giving reliable results, the TMD method significantly reduces the computation time and simplifies the process.