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Optimal sizing of Solar Home Systems: Charge controller technology and its influence on system design

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Abstract

Solar Home Systems are a promising solution to enable access to affordable, reliable, sustainable, and modern energy for non-electrified areas, especially in the Global South. In this study, the influence of the charge controller technologies Maximum Point Tracker (MPPT) and Pulse Width Modulation (PWM) on the optimal system design and system performance is analyzed. Therefore, a multi-objective optimization that minimizes the number of days with Power-Cut-Offs and the Levelized Cost of Electricity for three Solar Home System sizes and three sub-saharan locations is conducted. Cost savings of the MPPT controller are in the range of 4.0% to 8.6% compared to the PWM controller. This is achieved by a reduction of the optimal installed photovoltaic peak power by 31.2% to 38.6% and the battery capacity by 2.8% to 8.8%. A sensitivity analysis shows the high robustness of cost savings regarding a variation in solar irradiance, load profile, and charge controller investment costs. Therefore, MPPT charge controllers are a promising solution even for the smallest Solar Home System sizes to reduce system costs compared to PWM controllers. The applied numerical simulation tool is open-source to enhance openness and transparency in modeling studies.

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This document summarizes the equations and applications associated with the photovoltaic array performance model developed at Sandia National Laboratories over the last twelve years. Electrical, thermal, and optical characteristics for photovoltaic modules are included in the model, and the model is designed to use hourly solar resource and meteorological data. The versatility and accuracy of the model has been validated for flat-plate modules (all technologies) and for concentrator modules, as well as for large arrays of modules. Applications include system design and sizing, 'translation' of field performance measurements to standard reporting conditions, system performance optimization, and real-time comparison of measured versus expected system performance.
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Sensitivity analysis is often performed in connection with uncertainty analysis, the latter being the prime objective. In this context, the aim of sensitivity analysis is to identify the main contributors to model output uncertainty. Therefore the term “Uncertainty Importance Analysis” is sometimes used.In general, uncertainty analysis proceeds by Monte Carlo simulation. The affordable number of model runs is usually small for processor-time or computationally intensive models. This has consequences for the type of uncertainty statements needed.For efficiency reasons, sensitivity analysis cannot afford a separate specifically chosen set of model runs but has to use those that were performed for the purpose of uncertainty analysis. Since their number n is small and the number m of uncertainties is frequently large, correlation coefficients and standardized regression coefficients, the latter possibly obtained from stepwise regression, are a reasonable choice of sensitivity measures. Spurious correlation is inevitably present in the multivariate sample of size n and can often not be reduced or eliminated. The correlation ratio is an indispensable sensitivity measure, whenever model uncertainty is expressed by more than two model alternatives or when measures, quantifying degrees of linear or monotone relationship, are not adequate. As a consequence of the small sample size, the correlation ratio is affordable only in approximate form.Results from analyses of applications of computationally intensive models serve as examples. The last section presents a practical example intended to illustrate the outstanding role played by uncertainty and sensitivity analysis in the quality assurance of the computer model and of its application.
Article
Different approaches for lifetime prediction for electrochemical energy storage devices are discussed with respect to their general concepts. Examples for their implementation and advantages and disadvantages are given. The models are based on: (a) physical and chemical processes and their interaction as regards ageing effects; (b) weighting of the Ah throughput whenever the operating conditions deviate from the standard conditions used for determining the lifetime under laboratory conditions; (c) an event-oriented concept from mechanical engineering (Wöhler curves) which is based on a pattern recognition approach to identify severe operating conditions.Examples and details are explained for lead-acid batteries. The approaches can be applied to other electrochemical technologies including fuel cells. However, it is beyond the scope of this paper, to describe the models in all mathematical details. The models are used in system design and identification of appropriate operating strategies and therefore they must have high computational speed to allow for a comparison of a large number of system variations.
Article
Manufacturers of photovoltaic panels typically provide electrical parameters at only one operating condition. Photovoltaic panels operate over a large range of conditions so the manufacturer’s information is not sufficient to determine their overall performance. Designers need a reliable tool to predict energy production from a photovoltaic panel under all conditions in order to make a sound decision on whether or not to incorporate this technology. A model to predict energy production has been developed by Sandia National Laboratory, but it requires input data that are normally not available from the manufacturer. The five-parameter model described in this paper uses data provided by the manufacturer, absorbed solar radiation and cell temperature together with semi-empirical equations, to predict the current–voltage curve. This paper indicates how the parameters of the five-parameter model are determined and compares predicted current–voltage curves with experimental data from a building integrated photovoltaic facility at the National Institute of Standards and Technology (NIST) for four different cell technologies (single crystalline, poly crystalline, silicon thin film, and triple-junction amorphous). The results obtained with the Sandia model are also shown. The predictions from the five-parameter model are shown to agree well with both the Sandia model results and the NIST measurements for all four cell types over a range of operating conditions. The five-parameter model is of interest because it requires only a small amount of input data available from the manufacturer and therefore it provides a valuable tool for energy prediction. The predictive capability could be improved if manufacturer’s data included information at two radiation levels.
Article
Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN<sup>3</sup>) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism approach; and (3) the need to specify a sharing parameter. In this paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN<sup>2</sup>) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best N solutions (with respect to fitness and spread). Simulation results on difficult test problems show that NSGA-II is able, for most problems, to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the Pareto-archived evolution strategy and the strength-Pareto evolutionary algorithm - two other elitist MOEAs that pay special attention to creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint nonlinear problem, are compared with another constrained multi-objective optimizer, and the much better performance of NSGA-II is observed
Article
After adequately demonstrating the ability to solve different two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must now show their efficacy in handling problems having more than two objectives. In this paper, we suggest three different approaches for systematically designing test problems for this purpose. The simplicity of construction, scalability to any number of decision variables and objectives, knowledge of exact shape and location of the resulting Paretooptimal front, and ability to control difficulties in both converging to the true Pareto-optimal front and maintaining a widely distributed set of solutions are the main features of the suggested test problems. Because of these features, they should be found useful in various research activities on MOEAs, such as testing the performance of a new MOEA, comparing different MOEAs, and having a better understanding of the working principles of MOEAs.
Tracking SDG 7: The Energy Progress Report
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Sensitivity analysis in the context of uncertainty analysis for computationally intensive models
  • Physics