Comparison of load profiles in a mini-grid: Assessment of performance metrics using measured and interview-based data

Hartvigsson, Elias; Ahlgren, Erik O.

doi:10.1016/j.esd.2018.01.009

Cited by 66 publications

(31 citation statements)

References 25 publications

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“…As high as 426 Wh/day per consumer of mean absolute error has been observed over a study of eight rural minigrids in Kenya (Blodgett et al 2017). Hartvigsson and Ahlgren (2018) compare the load profiles in a minigrid from interviews as well as measured data. Their study concludes that purely interview-based data falls short in accurately estimating energy needs.…”

Section: Literature Reviewmentioning

confidence: 99%

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Stochastic load profile construction for the multi-tier framework for household electricity access using off-grid DC appliances

et al. 2018

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To improve access to electricity, decentralized, solar-based off-grid solutions like Solar Home Systems (SHSs) and rural micro-grids have recently seen a prolific growth. However, electrical load profiles, usually the first step in determining the electrical sizing of off-grid energy systems, are often nonexistent or unreliable, especially when looking at the hitherto un(der)-electrified communities. This paper aims to construct load profiles at the household level for each tier of electricity access as set forth by the multi-tier framework (MTF) for measuring household electricity access. The loads comprise dedicated offgrid appliances, including the so-called super-efficient ones that are increasingly being used by SHSs, reflecting the off-grid appliance market's remarkable evolution in terms of efficiency and price. This study culminated in devising a stochastic, bottom-up load The original version of this article was revised: Figure 5 image top right legends, the solid black line was defined "CF" and the broken blue line was described "CF" too. The figure image was updated to correct the mistake.

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Section: Literature Reviewmentioning

confidence: 99%

“…Coincidence factor (CF) is defined as the ratio of the peak load power in the load profile to the total rated power of all the installed appliances in the energy system, as shown in Eq. 4 (Hartvigsson and Ahlgren 2018). It is a measure of the likelihood of all the loads constituting the load profile functioning simultaneously.…”

Section: Coincidence Factormentioning

confidence: 99%

Stochastic load profile construction for the multi-tier framework for household electricity access using off-grid DC appliances

et al. 2018

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“…The relevance of the accuracy of surveyed data is widely recognized in the literature. In [22] the authors compared load profiles and performance metrics based on interviews and on measurements relating to a rural mini-grid in Tanzania, finding distinct differences between estimations and measured data. The largest difference was in the calculated energy, which is also reflected in the load factor and capacity factor, which are underestimated by 34-117% using the interview-based method, whereas the estimate of the peak load shows a much smaller error (11%).…”

Section: The Relevance Of Accuracy In the Energy Need Assessmentmentioning

confidence: 99%

Methodology for the Energy Need Assessment to Effectively Design and Deploy Mini-Grids for Rural Electrification

et al. 2019

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In order to successfully deploy a large number of decentralized energy systems in developing countries, it is necessary to standardize effective methodologies and procedures to develop off-grid/mini-grid systems. Considering that the energy need assessment provides inputs and assumptions used in business modelling and mini-grid design, the accuracy of its results directly affects the technical and financial feasibility studies. Thus, the approach for applying a proven methodology for the energy need assessment of rural communities is aimed at obtaining reliable input data for the mini-grid development. This helps in reducing both the financial challenges by mitigating the uncertainties in electricity demand and the technical challenges by contributing to adequately size off-grid power generation systems, with a view to boost toward a common overall objective of mini-grid’s optimization methods and tools. Hence, taking into consideration that target communities differ in terms of needs and context conditions, the proposed paper describes an inclusive methodology that can be adapted case-by-case. It provides an effective applied solution the lack of proven guidelines from project developers or literature, giving priority to data collection methods able to achieve a large sample representative of the market, with high accuracy in estimating the energy consumptions from electricity substitutes.

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“…For developers with operating data, historical energy use from existing customers may inform these estimates. Developers and recent studies have shown that customers are not good at predicting their own energy use behavior; as a result, the inventory method tends to overestimate electricity demand (Blodgett et al 2017;Hartvigsson and Ahlgren 2018).…”

Section: Analysis Techniquesmentioning

confidence: 99%

“…It is not surprising that users who have never had a connection to electricity would have a difficult time predicting their own consumption. Further, Hartvigsson and Ahlgren (2018) found that consumers who already have a connection have difficulty estimating their consumption using the same method.…”

Section: Customer Surveys In Tanzaniamentioning

confidence: 99%

Survey Use in Micro-Grid Load Prediction, Project Development, and Operations: Review and Best Practices

Williams

Booth

Baring-Gould

2019

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spectrum, some developers are skeptical that forecasting load is feasible and rather deploy modular systems that can exert more control over consumption. We are unable to draw conclusions on the relative performance of these approaches due to limited data and a small sample. The objectives of using surveys to predict demand are twofold. Developers seek to identify the most financially viable micro-grid sites which is in large part a function of electricity demand. This information is also required to design systems that meet customer demand at minimum cost. The timing of these surveys introduces a trade-off. Surveys provide valuable information on the basis of which developers select micro-grid sites but are expensive to run. If surveys are conducted before site selection decisions, additional expense will be incurred for unselected sites. However, if surveys are completed only after site selection, good sites may be overlooked. A middle ground is to conduct surveys on a sample of potential customers during initial site visits prior to site selection. Drawing on experience from a range of eight sub-Saharan African micro-grid developers, the second main outcome of this work is a set of best practices for the use of surveys at different stages of micro-grid development and operations. These best practices include: 2 Findings .

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Comparison of load profiles in a mini-grid: Assessment of performance metrics using measured and interview-based data

Cited by 66 publications

References 25 publications

Stochastic load profile construction for the multi-tier framework for household electricity access using off-grid DC appliances

Stochastic load profile construction for the multi-tier framework for household electricity access using off-grid DC appliances

Methodology for the Energy Need Assessment to Effectively Design and Deploy Mini-Grids for Rural Electrification

Survey Use in Micro-Grid Load Prediction, Project Development, and Operations: Review and Best Practices

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