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Climate-neutral energy systems
Published in Kornelis Blok, Evert Nieuwlaar, Introduction to Energy Analysis, 2020
Kornelis Blok, Evert Nieuwlaar
This specific example shows us that if wind energy makes up more than 25 per cent of total electricity generation, there will already be moments that wind energy production is higher than the total electricity needed, i.e. the residual load becomes slightly negative. If the electricity production by intermittent sources increases further, negative residual loads occur more often, until ultimately about half of the time renewable generation is higher than what is needed, and about half of the time it is the other way around. This is an extreme case as in most cases the intermittent renewable energy sources, will, to some extent, be complemented with other energy sources. See for example Figure 16.5 that shows that a combination of wind and solar energy has a more favourable residual load duration curve than only wind or only solar. So, a combination of intermittent sources decreases the problem of matching supply and demand.
Smart Energy Resources: Supply and Demand
Published in Stuart Borlase, Smart Grids, 2018
Stuart Borlase, Sahand Behboodi, Thomas H. Bradley, Miguel Brandao, David Chassin, Johan Enslin, Christopher McCarthy, Stuart Borlase, Thomas Bradley, David P. Chassin, Johan Enslin, Gale Horst, Régis Hourdouillie, Salman Mohagheghi, Casey Quinn, Julio Romero Aguero, Aleksandar Vukojevic, Bartosz Wojszczyk, Eric Woychik, Alex Zheng, Daniel Zimmerle
The figure immediately makes evident that the slope of the line does not remain constant. Rather, the slope is steeper at the left end and at the right end of the graph and less steep in the middle. This signifies rapid change in power demand as you consider the top 1000 load hours and lowest 1000 load hours of the year. Figure 3.16 labels a few notable features of the load duration curve. In addition to the peak shown in this curve, the response of consumer loads can also add value when utilized to compensate for variations in output from renewable energy sources. The most common use of a load duration curve is for planning studies, when planners estimate the number of hours per year for which a system resource must be allocated. The load duration curve can also be used to estimate the maximum amount of load that should be curtailed during a certain period. Although from a generation mix perspective, this planning activity will essentially look at aggregated demand throughout the network from a network assets perspective. Planning focuses on the smallest transformers and other distribution assets since their suitability to deliver electricity reliably will depend on the localized load curves.
Power generation costs
Published in Jin Zhong, Power System Economic and Market Operations, 2018
Figure 3.9 is the load profile of a day for 24 hours. The power (MW) of each hour is plotted in a time series, 1 a.m.,…, 11 a.m., 12 p.m., 1 p.m.,…, 11 p.m., 12 a.m. The highest load is during the time period of 3 p.m., the second highest is 2 p.m., and the third is during 4 p.m. The lowest loads are during time periods 2 a.m. and 3 a.m. By rearranging the loads from the highest to the lowest, the load at 3 p.m. is ranked as number 1 in Figure 3.10, load at 2 p.m. is ranked as number 2, and so on. In the end, loads at 2 a.m. and 3 a.m. are ranked as the last two, which are number 23 and 24. The rearranged load curve is the load duration curve of the day. If the ranking of the load profile is implemented for the whole year of 8,760 hours, the load duration curve for the whole year is obtained as shown in Figure 3.11. Here, it is assumed that the peak load of the system is 8,000 MW and the lowest load of the year is 2,800 MW. This load duration curve can be used for generation planning to decide the capacities of different types of generators based on their optimal annual operation hours obtained from screening curves. The process is described in Figure 3.11.
A novel stochastic model for hourly electricity load profile analysis of rural districts in Fujian, China
Published in Science and Technology for the Built Environment, 2022
Bing Zhou, Xiao Wang, Da Yan, Jieyan Xu, Xuyuan Kang, Zheng Chen, Tianyi Hao
This study aimed to establish a stochastic simulation model to regenerate the whole-year hourly electricity consumption in rural districts. The model combined clustering, regression, and distribution fitting methods to gather advantages in electricity load simulation. It consisted of two submodels: a temperature-based typical daily electricity consumption nonlinear regression model (TDM) and clustering-based typical hourly load profile probability distribution band model (THM). The load duration curve is used for model validation. The load duration curve of electricity demand is a curve of the electricity demand sorted in the descending order for a time-series profile, illustrating the relationship between the demand magnitude and the lasting time (Richardson et al. 2010). As an application, the design of district energy storage systems in the Fujian Province, China, was conducted based on the simulation results of the proposed model.
Aggregating set-point temperature profiles for archetype-based: simulations of the space heat demand within residential districts
Published in Journal of Building Performance Simulation, 2020
Ina De Jaeger, Annelies Vandermeulen, Bram van der Heijde, Lieve Helsen, Dirk Saelens
In short, for every possible combination of occupant model, building model and evaluation method, the performance of the archetype model with the aggregated set-point temperature profile is compared to the full model for the three aggregation methods. The differences in both the annual energy demand for space heating (ESH) and the load duration curves (LDC) are assessed for the two neighbourhoods, visualizing amongst others the peak load. The load duration curve is the function that describes for which the number of hours a certain minimum heating power level is exceeded during the year. The LDC is constructed by ordering the values of the yearly heating demand time series in descending order. As a result, the LDC gives a first indication of the temporal behaviour, but does not fully represent the thermal behaviour, as time-dependent profiles do.