Assessing temporal complementarity between three variable energy sources through correlation and compromise programming

Canales, Fausto A.; Jurasz, Jakub; Beluco, Alexandre; Kies, Alexander

doi:10.1016/j.energy.2019.116637

Cited by 75 publications

(41 citation statements)

References 58 publications

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“…The work objective is to focus on resources, and not on the technical solutions used to obtain energy. Since many works have reported ways to increase the efficient utilization of renewables, like suboptimal PV module orientation, which decreases energy yield but improves supply/demand fit [38], or optimizing PV orientation with regard to small-scale hydro in order to increase complementarity [39][40][41]. This approach saves us from limiting the results to arbitrarily selected PV modules and wind turbines (with their intrinsic energy conversion efficiencies), but also opens promising future research directions, as discussed later in the article conclusions.…”

Section: Input Datamentioning

confidence: 99%

Complementarity and ‘Resource Droughts’ of Solar and Wind Energy in Poland: An ERA5-Based Analysis

et al. 2021

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In recent years, Poland has experienced a significant increase in the installed capacity of solar and wind power plants. Renewables are gaining increasing interest not only because of Poland’s obligations to European Union policies, but also because they are becoming cheaper. Wind and solar energy are fairly-well investigated technologies in Poland and new reports are quite frequently added to the existing research works documenting their potential and the issues related to their use. In this article, we analyze the spatial and temporal behavior of solar and wind resources based on reanalysis datasets from ERA5. This reanalysis has been selected because it has appropriate spatial and temporal resolution and fits the field measurements well. The presented analysis focuses only on the availability of energy potential/resources, so characteristics intrinsic to energy conversion (like wind turbine power curve) were not considered. The analysis considered the last 40 years (1980–2019) of available data. The Spearman coefficient of correlation was considered as a complementarity metric, and the Mann–Kendal test was used to assess the statistical significance of trends. The results revealed that: The temporal complementarity between solar and wind resources exists mostly on a seasonal scale and is almost negligible for daily and hourly observations. Moreover, solar and wind resources in joint operation exhibit a smoother availability pattern (assessed based on coefficient of variation). Further findings show that the probability of ‘resource droughts’ (periods when cumulative generation was less than arbitrary threshold) lasting one day is 11.5% for solar resources, 21.3% for wind resources and only 6.2% if both resources are considered in a joint resource evaluation. This situation strongly favors the growth of local hybrid systems, as their combined power output would exhibit lower variability and intermittency, thus decreasing storage demand and/or smoothing power system operation.

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Section: Input Datamentioning

confidence: 99%

Complementarity and ‘Resource Droughts’ of Solar and Wind Energy in Poland: An ERA5-Based Analysis

et al. 2021

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“…This mode of complementary energy operation will reduce output fluctuations and maintain the power generation system's stability. According to Canales et al [35], a possible interpretation of the energy complementarity system's correlation coefficient values is presented in Table .1. This paper will study the standard centralized correlation coefficients individually and choose the correlation coefficient which is most appropriate for this study.…”

Section: A Complementarity Studymentioning

confidence: 99%

Hybrid Time-Scale Optimal Scheduling Considering Multi-Energy Complementary Characteristic

et al. 2021

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Evaluating the potential utilization of hybrid energy systems and determining the multi-scale optimal operation strategy is critical to power system planning in the context of energy structure adjustment, especially for large-scale hybrid energy systems. Considering the long-term and short-term complementary characteristics, this paper puts forward a coordinated optimization framework for the integrated energy system in the world's largest multi-energy complementary base on Yellow River's upper reaches. The main procedures are as follows: 1) cross-correlation method is introduced for individually analyzing the long-and short-term complementary characteristics of wind power, photovoltaic, and hydropower in this multi-energy complementary base; 2) a double-layer model combining the long-term optimal operation model and short-term optimal operation model for determining the proportion of multiple energy and optimizing the maximum peak-shaving ability; 3) Large-Scale System Decomposition-Coordination Method is applied for solving the proposed double-layer operation model. The results show that wind power 23%, photovoltaic 35%, hydropower 42% can keep the most stable generation in the long-term complementary operation. This proportion results can improve the system peak regulation capacity with 50.8% (sunny day's morning peak) and 24.2% (rainy day's morning peak) in the optimal short-term operation.INDEX TERMS Hybrid time-scale; Large-scale energy; Multi-energy complementary characteristics; Optimal operation strategy;

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“…The general idea is interesting but depends on the system's particular characteristics 105 and does not provide a direct complementarity metric. Canales et al (2020a); Arias et al (2015); Canales et al (2020b) introduced a method that assesses temporal complementarity between three variable energy sources, using a combination 110 of correlation coefficients, Euclidean vectors, compromise programming, and normalization. The method's basis is correlation analysis, and therefore their proposal shares the criticism we made above.…”

Section: Linearity Of the Relation Is Also An Issue Evenmentioning

confidence: 99%

Total variation as a metric for complementarity in energy resources time series

Cantor¹,

Ochoa²,

Sánchez³

2021

Preprint

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Complementarity has become an essential concept in energy supply systems. Although there are some other metrics, most studies use correlation coefficients to quantify complementarity. The standard interpretation is that a high negative correlation indicates a high degree of complementarity. However, we show that the correlation is not an entirely satisfactory measure of complementarity. As an alternative, we propose a new index based on the mathematical concept of the total variation. For two time series, the new index φ is one minus the ratio of the total variation of the sum to the sum of the two series' total variation. We apply the index first to an auto-regressive (AR) process and then to various Colombian electric system series. The AR case clearly illustrates the limitations of the correlation coefficient as a measure of complementarity. We then evaluate complementarity across various space-time scales in the Colombian power sectors, considering hydro and wind projects. The complementarity assessment on a broad temporal and geographical scale helps analyze large power systems with different energy sources. The case study of the Colombian hydropower systems suggests that φ is better than ρ because (i) it considers scale, whereas ρ, being non-dimensional, is insensitive to the scale and even to the physical dimensions of the variables; (ii) one can apply φ to more than two resources; and (iii) ρ tends to overestimate complementarity.

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Assessing temporal complementarity between three variable energy sources through correlation and compromise programming

Cited by 75 publications

References 58 publications

Complementarity and ‘Resource Droughts’ of Solar and Wind Energy in Poland: An ERA5-Based Analysis

Complementarity and ‘Resource Droughts’ of Solar and Wind Energy in Poland: An ERA5-Based Analysis

Hybrid Time-Scale Optimal Scheduling Considering Multi-Energy Complementary Characteristic

Total variation as a metric for complementarity in energy resources time series

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