{"id":5404,"date":"2026-09-29T10:03:00","date_gmt":"2026-09-29T08:03:00","guid":{"rendered":"https:\/\/ghetool.eu\/?post_type=course&#038;p=5404"},"modified":"2026-10-05T16:33:43","modified_gmt":"2026-10-05T14:33:43","slug":"gelijktijdigheid","status":"publish","type":"course","link":"https:\/\/ghetool.eu\/nl_nl\/course\/simultaneity\/","title":{"rendered":"Gelijktijdigheid"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Simultaneity is one of the central design challenges when multiple end users are connected to a single geothermal system. In this chapter, both a simplified approach to accounting for the simultaneity and a more state-of-the-art approach with load shifting, as implemented in GHEtool Cloud, are presented.<\/p>\n\n\n\n\n\n\n\n\n\n<iframe title=\"Hoofdstuk 8.3: Gelijktijdigheid\" width=\"800\" height=\"450\" src=\"https:\/\/www.youtube.com\/embed\/GcuiSGPff5s?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n\n\n\n\n\n\n\n\n\n<h2 class=\"wp-block-heading\">What is simultaneity?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Knowing a building&#8217;s thermal demand is essential for designing shallow geothermal borefields. For a single building, this is straightforward, since if the building has 10 kW of installed heat pump capacity and 15 MWh\/year of heating demand, these values can simply be used for the geothermal simulation as well.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Now imagine an apartment building with 25 units, with each unit having an individual heat pump with a capacity of 5 kW and a heating energy demand of 7.5 MWh\/year. This gives a total installed capacity of 125 kW and a yearly demand of 187.5 MWh. The question, however, arises: should we size the borefield based on 100% of the installed capacity, or can we design it for a somewhat lower load, since the chance that all the peak powers overlap is rather small?<\/p>\n\n\n\n<div class=\"note\">In this chapter, the main focus is on the heating demand of the different units, since most of the scientific literature about simultaneity is about this. However, the same reasoning could be extended to work with the cooling load as well.<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Simultaneity factor<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">When combining multiple users (for example apartments) on a central system, the peaks in demand do not typically coincide. This is because different dwellings have different occupants (elderly people, couples with children, people living alone &#8230;) as well as different orientations. As a result, the timing of their peak loads differs. This phenomenon is quantified in the concept of the simultaneity factor.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The simultaneity factor (also sometimes called the diversity factor) is defined as the ratio of the maximum demand of a group of loads to the total installed power. In the literature, the graph below, derived from the work of Winter et al. (2001), is the most commonly used correlation for the simultaneity factor.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"711\" src=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Simultaneity-factor-1024x711.png\" alt=\"Simultaneity factor according to the research by (Winter et al., 2001).\" class=\"wp-image-4085\" srcset=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Simultaneity-factor-1024x711.png 1024w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Simultaneity-factor-300x208.png 300w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Simultaneity-factor-768x533.png 768w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Simultaneity-factor-1536x1067.png 1536w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Simultaneity-factor-18x12.png 18w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Simultaneity-factor.png 1948w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Simultaneity factor according to the research by Winter et al.<\/figcaption><\/figure>\n<\/div>\n\n\n<div class=\"note\">The graph above is no absolute truth, as it was derived empirically from measurements from a district heating system. Nowadays, in highly insulated buildings, the simultaneity in heating and cooling does not go below 70% (Jansen et al., 2021).<\/div>\n\n\n\n<div class=\"advanced\">The graph above is the result of the following formula:$$f(n)=a+\\frac{b}{1+\\left(\\frac{n}{c}\\right)^d}$$where $n$ is the number of connected users to the system and $a$ to $d$ are parameters fitted to the measurement data with an $R^2$ of 0.95. These values are:\n<ul>\n<li>$a=0.449677646267461$<\/li>\n<li>$b=0.551234688$<\/li>\n<li>$c=53.84382392$<\/li>\n<li>$d=1.76743268$<\/li>\n\n<\/ul><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">The graph clearly illustrates that as the number of units connected to the central system increases, the collective system will have a lower resulting peak power when compared to the total installed capacity. In our case with 25 apartments, the simultaneity factor is 89%, meaning that of the installed 125 kW, only 111 kW is expected to occur simultaneously. This adjusted value could therefore be used for the geothermal design.<\/p>\n\n\n\n<div class=\"caution\">It is important to note that the simultaneity factor is only used to reduce the peak power for a collective system. The total collective energy demand remains the simple sum of the individual users\u2019 demands.\n\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Historically, the concept of the simultaneity factor was developed for designing pipe diameters in collective heating systems and for sizing central boilers. In those cases, the only design parameter was the maximum peak power, regardless of how long the peak lasted. Today, however, when designing a collective borefield, the duration of the peak becomes significantly more relevant, which is why the effect of the simultaneity on the peak duration is discussed next.<\/p>\n\n\n\n<div class=\"note\">Please note that when an hourly profile is used for the simulation, the peak duration is no longer relevant as it is implicitly embedded in the profile.<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Peak duration<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">As discussed back in <a href=\"https:\/\/ghetool.eu\/course\/monthly-vs-hourly-load-profiles\/\">Part 1.4<\/a>, the peak duration is a mathematical trick to simulate geothermal borefields without having the detailed hourly load profile. Therefore, an estimate of the duration of the peak power has to be made when working with monthly load profiles.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When multiple dwellings are combined onto a single geothermal borefield, not only does the maximum peak power change, but also the peak duration, as illustrated in the figure below for the case of three different buildings.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" width=\"1024\" height=\"524\" src=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Peak-duration-1024x524.png\" alt=\"Three buildings (yellow, green and pink) and the collective (blue) power profile.\" class=\"wp-image-4087\" srcset=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Peak-duration-1024x524.png 1024w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Peak-duration-300x154.png 300w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Peak-duration-768x393.png 768w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Peak-duration-1536x786.png 1536w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Peak-duration-18x9.png 18w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Peak-duration.png 1970w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Three buildings (yellow, green and pink) and the collective (blue) power profile. (Based on: Arneri\u0107&nbsp;and \u010casni&nbsp;(2021))<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">In the graph above, the individual peak power profiles of three separate buildings are shown. As can be seen, their peak times do not align, leading to a resulting collective power profile in blue that has a lower peak power than the sum of the installed capacity. This aligns with our understanding of the simultaneity factor, which explains why the peak demand of the total system is lower than the sum of individual peak demands.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">However, when it comes to peak durations, something changes as well. While each building has the same individual peak duration (as shown by the horizontal bars at the bottom of the graph), the peak duration of the combined profile differs. It is neither the sum of the individual durations, which would significantly overestimate the actual value, nor is it equal to them.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Therefore, it is not sufficient to merely adjust the peak power using the simultaneity factor while keeping the original peak duration the same. Especially in larger collective systems, the true peak duration can change substantially and must be changed accordingly.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Below, two different approaches to address this issue are discussed, being using a heuristic or using load shifting.<\/p>\n\n\n\n<div class=\"note\"><p>The proper way to address the issue of the peak duration is to make a dynamic simulation of all the building units connected to the collective borefield. The result of such a simulation is an hourly load profile that encompasses both the simultaneity and the peak duration.<\/p><p>However, often this data is not available, so one has to rely on other methods to calculate the simultaneity and the scaling of the peak duration.<\/p><\/div>\n\n\n\n<h3 class=\"wp-block-heading\">Heuristic<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Since there is no literature on how the peak duration changes with the simultaneity, one conservative solution is to rely on a heuristic to scale the peak duration for collective systems. One approach we suggest is inspired by the Central Limit Theorem from statistics. The peak duration $PD$ of the collective system can be defined as follows:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">$$PD_{c}\\propto PD_i \\cdot \\sqrt{n}$$<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">with $n$ the number of units connected to the central system. This results in the graph below.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" width=\"1024\" height=\"711\" src=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Scaling-peak-duration-1024x711.png\" alt=\"Scaling of the peak duration based on the central limit theorem.\" class=\"wp-image-4088\" srcset=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Scaling-peak-duration-1024x711.png 1024w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Scaling-peak-duration-300x208.png 300w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Scaling-peak-duration-768x533.png 768w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Scaling-peak-duration-1536x1067.png 1536w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Scaling-peak-duration-18x12.png 18w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/06\/Scaling-peak-duration.png 1948w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Scaling of the peak duration based on the central limit theorem.<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Let us revisit the example with 25 apartments discussed above to illustrate how this heuristic works:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Number of units: 25<\/li>\n\n\n\n<li>Installed capacity: 125 kW (5 kW\/apartment)<\/li>\n\n\n\n<li>Annual heating demand: 187.5 MWh<\/li>\n\n\n\n<li>Peak duration: 8 hours (per apartment)<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">By applying both the simultaneity factor and a scaling factor for the peak duration, the following values are obtained for the actual design of the system:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Resulting peak load: 111 kW (with a simultaneity factor of 89%)<\/li>\n\n\n\n<li>Annual heating demand: 187.5 MWh<\/li>\n\n\n\n<li>Peak duration: 40 hours (using a scaling factor of $\\sqrt{25}=5$)<\/li>\n<\/ul>\n\n\n\n<div class=\"caution\">The heuristic presented above is a first estimate of the scaling of the peak duration in the context of simultaneity, but it is as such not yet proven by literature. A more accurate approach is to use the method of <strong>load shifting<\/strong> explained below.<\/div>\n\n\n\n<h3 class=\"wp-block-heading\">Load shifting<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Another option is to work with the concept of load shifting (also sometimes called peak shaving). Here, you start with an hourly load profile and you limit the peak (using the simultaneity factor). The part of the load that is above this threshold, you move to the earlier or later hours. By doing so, in fact, you increase the peak duration without relying on just monthly data. The concept of load shifting is shown below.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"759\" height=\"1024\" src=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Peak-shaving-759x1024.png\" alt=\"Example of load shifting. (Source: Elite Power Group)\" class=\"wp-image-5441\" srcset=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Peak-shaving-759x1024.png 759w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Peak-shaving-222x300.png 222w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Peak-shaving-767x1035.png 767w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Peak-shaving-9x12.png 9w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Peak-shaving.png 820w\" sizes=\"(max-width: 759px) 100vw, 759px\" \/><figcaption class=\"wp-element-caption\">Example of load shifting. (Source: Elite Power Group)<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">It should be clear that this concept of load shifting requires an hourly load profile for your building. Whenever this is not available, it is possible to create it in GHEtool Cloud using the functionality &#8216;generate hourly load&#8217; (see also <a href=\"https:\/\/ghetool.eu\/course\/required-inputs-building-demand\">Part 1.4<\/a>). Here, you also have the option to limit your maximum peak power in heating and cooling using the simultaneity factor. The load will then automatically be shifted to earlier and later hours.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Simultaneity in the ground?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Before jumping into GHEtool to show how the simultaneity can be considered, let us discuss another common misconception about simultaneity and interference. As discussed in the <a href=\"https:\/\/ghetool.eu\/course\/interference\">previous chapter<\/a>, there is an interference between neighbouring geothermal systems, so is there then no simultaneity in the underground as well?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The short answer is no. For the long answer, we take a look at the graph below.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"771\" src=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/11\/Simultaneity-in-the-ground-1024x771.png\" alt=\"Illustration of the g-function of different borehole spacings.\" class=\"wp-image-4362\" srcset=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/11\/Simultaneity-in-the-ground-1024x771.png 1024w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/11\/Simultaneity-in-the-ground-300x226.png 300w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/11\/Simultaneity-in-the-ground-768x578.png 768w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/11\/Simultaneity-in-the-ground-1536x1156.png 1536w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/11\/Simultaneity-in-the-ground-16x12.png 16w, https:\/\/ghetool.eu\/wp-content\/uploads\/2025\/11\/Simultaneity-in-the-ground.png 1778w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Illustration of the g-function of different borehole spacings.<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">As mentioned in the previous chapter, interference is a real phenomenon and can be very significant for neighbouring systems, but the time scale of interference and that of simultaneity are very different. Above, we revisit the long-term effects on borefield temperature (see <a href=\"https:\/\/ghetool.eu\/course\/long-term-effects-g-functions\/\">Part 2.3<\/a>) for three different borehole spacings. As can be seen, all lines initially coincide, but after one to two months the case with a 6 m borehole spacing begins to diverge. This marks the moment when the heat has travelled far enough through the ground to reach the neighbouring borehole.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This time scale of months to years (depending on the distance) is much longer than the time scale at which simultaneity occurs, which is immediate or at least within the same hour. Therefore, interference is relevant for the long-term and for the collective imbalance of all systems, but it cannot be used to achieve lower peak power values. Therefore, we can conclude that there is no such thing as simultaneity in the ground.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Example in GHEtool<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In order to illustrate the effect of simultaneity on the design of collective borefields, the case of an apartment building with 80 apartments is discussed. The peak heating and cooling demand of each unit is respectively 3.4 kW and 2 kW (both with a peak duration of 8 hours) with a corresponding yearly energy demand of 5.1 MWh and 1.4 MWh. Each apartment has a domestic hot water demand of 1.6 MWh\/year.<\/p>\n\n\n\n<div class=\"hint\">If you want to recreate this example yourself in GHEtool Cloud, the report is given below.<\/div>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"744\" height=\"400\" src=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Without-simultaneity.png\" alt=\"Temperature profile of 80 apartments on a collective system without simultaneity.\" class=\"wp-image-5442\" srcset=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Without-simultaneity.png 744w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Without-simultaneity-18x10.png 18w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Without-simultaneity-300x161.png 300w\" sizes=\"(max-width: 744px) 100vw, 744px\" \/><figcaption class=\"wp-element-caption\">Temperature profile of 80 apartments on a collective system without simultaneity.<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">In the temperature profile above, the borefield of 88 boreholes is simulated. The imbalance is clearly visible and the minimum average fluid temperature is 0.06\u00b0C in this case.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Using the formula for the simultaneity factor above, it is found that with 80 units, the simultaneity is 63%. This reduces the peak load to 172 kW in heating and 101 kW in cooling. The yearly energy demand in heating, cooling and domestic hot water stays exactly the same, since they are not influenced by the simultaneity. As a first calculation, the peak duration is assumed to stay at 8 hours.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"744\" height=\"400\" src=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/With-simultaneity.png\" alt=\"Temperature profile of 80 apartments on a collective system with simultaneity and a peak duration of 8 hours.\" class=\"wp-image-5443\" srcset=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/With-simultaneity.png 744w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/With-simultaneity-300x161.png 300w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/With-simultaneity-18x10.png 18w\" sizes=\"(max-width: 744px) 100vw, 744px\" \/><figcaption class=\"wp-element-caption\">Temperature profile of 80 apartments on a collective system with simultaneity and a peak duration of 8 hours.<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">The minimum average fluid temperature with the simultaneity is 1.22\u00b0C, which is significantly less than the case without simultaneity. However, this was still assuming the peak duration stayed the same. When this is also increased with the square root of 80 (~9), the peak duration in both heating and cooling becomes 72 hours. This brings the minimum average fluid temperature down to 0.9\u00b0C, still almost 1\u00b0C better than the case without the simultaneity.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In order to check the validity of the heuristic method, the load shifting approach is also considered. Here, first the load is converted to an hourly load profile using the method in GHEtool (as discussed in <a href=\"https:\/\/ghetool.eu\/course\/required-inputs-building-demand\/\">Part 1.4<\/a>). The hourly load profile without simultaneity is shown below as well as the resulting hourly temperature profile.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"767\" height=\"400\" src=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Hourly-load-profile-without-simultaneity.png\" alt=\"\" class=\"wp-image-5444\" srcset=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Hourly-load-profile-without-simultaneity.png 767w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Hourly-load-profile-without-simultaneity-300x156.png 300w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Hourly-load-profile-without-simultaneity-18x9.png 18w\" sizes=\"(max-width: 767px) 100vw, 767px\" \/><figcaption class=\"wp-element-caption\">Hourly load profile without simultaneity.<\/figcaption><\/figure>\n<\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"744\" height=\"400\" src=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Hourly-without-regeneration.png\" alt=\"Hourly temperature profile without simultaneity.\" class=\"wp-image-5445\" srcset=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Hourly-without-regeneration.png 744w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Hourly-without-regeneration-300x161.png 300w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Hourly-without-regeneration-18x10.png 18w\" sizes=\"(max-width: 744px) 100vw, 744px\" \/><figcaption class=\"wp-element-caption\">Hourly temperature profile without simultaneity.<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">The minimum average fluid temperature in the profile below goes to -0.35\u00b0C, which is below the threshold. However, the temperature violation is just 1 Kh (meaning 1 hour 1 degree below the threshold), so it can be ignored and assumed to be accurately sized.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Now, by setting the simultaneity to 63.26% as shown in the print screen below, the peaks in the hourly load profile will be limited to 172 kW and 101 kW for heating and cooling respectively.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"810\" height=\"646\" src=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Printscreen-1.png\" alt=\"Print screen of the input screen to generate the hourly load in GHEtool Cloud.\" class=\"wp-image-5446\" srcset=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Printscreen-1.png 810w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Printscreen-1-15x12.png 15w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Printscreen-1-300x239.png 300w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Printscreen-1-767x612.png 767w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><figcaption class=\"wp-element-caption\">Print screen of the input screen to generate the hourly load in GHEtool Cloud.<\/figcaption><\/figure>\n<\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"767\" height=\"400\" src=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Hourly-load-profile-with-simultaneity.png\" alt=\"Hourly load profile with simultaneity.\" class=\"wp-image-5447\" srcset=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Hourly-load-profile-with-simultaneity.png 767w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Hourly-load-profile-with-simultaneity-300x156.png 300w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Hourly-load-profile-with-simultaneity-18x9.png 18w\" sizes=\"(max-width: 767px) 100vw, 767px\" \/><figcaption class=\"wp-element-caption\">Hourly load profile with simultaneity.<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">In the load profile above, it is clear that the peak load is shaved and that the resultant profile looks more flattened because of this. When this profile is simulated in GHEtool, a minimum average fluid temperature of 0.67\u00b0C is found, again 1\u00b0C better than the case without simultaneity being considered.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"744\" height=\"400\" src=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Close-up-of-hourly-profile.png\" alt=\"Close-up of hourly temperature profile.\" class=\"wp-image-5448\" srcset=\"https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Close-up-of-hourly-profile.png 744w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Close-up-of-hourly-profile-18x10.png 18w, https:\/\/ghetool.eu\/wp-content\/uploads\/2026\/09\/Close-up-of-hourly-profile-300x161.png 300w\" sizes=\"(max-width: 744px) 100vw, 744px\" \/><figcaption class=\"wp-element-caption\">Close-up of hourly temperature profile.<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">In the close-up above, for the latest winter period, there are clear regions where the fluid and borehole wall temperature go continuously down. This is exactly where the peak power has been saved, leading to a longer period of constant load.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In this example, the heuristic gave a slightly more optimistic (0.9\u00b0C) result than the method with the load shifting (0.67\u00b0C). This could be because of a number of reasons, for example the initial estimate of a peak duration of 8 hours was not accurate for the apartments simulated. Since the peak duration is always a subject of discussion, it is advised to use the load shifting method instead, due to its more nuanced results.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In this chapter, the concept of simultaneity for collective geothermal systems was discussed. Depending on the number of users connected to the central borefield, the simultaneity factor decreases, leading to a lower resulting peak power on the borefield.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For the extension of the peak duration, two methods were discussed: a heuristic and the concept of load shifting. In the example in GHEtool it was shown that the heuristic gave a slightly more optimistic temperature profile than the more accurate load-shifted one. Both methods, however, clearly showed the benefit of the simultaneity on the resulting temperature profile of the borefield.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">References<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Winter, W., Haslauer, T., Obernberger, I. (2001). Untersuchungen der Gleichzeitigkeit in kleinen und mittleren Nahw\u00e4rmenetzen. <em>Euroheat &amp; Power<\/em>, Bd. 09&amp;10\/2001: S. 1-17<\/li>\n\n\n\n<li>Jansen, S., Verhoeven, R., Elswijk, M. (2021). <em>Technisch handboek koele warmtenetten<\/em>.<\/li>\n\n\n\n<li>Arneri\u0107&nbsp;, J., \u010casni, A.\u010c. (2021). <em>Business statistics<\/em>. Published online at <a href=\"https:\/\/bookdown.org\/jarneric\/spring_school\/2-7-applications-of-normal-gaussian-distribution.html\" target=\"_blank\" rel=\"noopener\">https:\/\/bookdown.org\/jarneric\/spring_school\/<\/a> [last visited 20\/12\/2025]<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Questions<\/h2>\n\n\n\n<div class=\"question\" data-chapter=\"3\">How does the required borefield size change when considering this simultaneity with the heuristic?<\/div>\n\n\n\n<div class=\"question\" data-chapter=\"3\">How does the required borefield size change when considering this simultaneity with the load shift?<\/div>\n\n\n\n<div class=\"question\" data-chapter=\"3\">How would the temperature profile change if a maximum simultaneity of 70% is assumed? Would this change the required borefield size?<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Downloads<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Download GHEtool simulation from this chapter <a href=\"https:\/\/ghetool.eu\/wp-content\/uploads\/course\/resources\/Course%208.3\/Course%208.3.pdf\" data-type=\"link\" data-id=\"https:\/\/ghetool.eu\/wp-content\/uploads\/course\/resources\/Course%208.3\/Course%208.3.pdf\">here<\/a>.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Gelijktijdigheid is een van de belangrijkste ontwerpuitdagingen wanneer meerdere eindgebruikers zijn aangesloten op \u00e9\u00e9n geothermisch systeem. In dit hoofdstuk wordt het begrip gelijktijdigheid ge\u00efntroduceerd, samen met verschillende manieren om dit te berekenen.<\/p>","protected":false},"template":"","section":[124],"chapter":[146],"authors":[39],"class_list":["post-5404","course","type-course","status-publish","hentry","section-chapter-3","chapter-part-8","authors-wouter-peere"],"_links":{"self":[{"href":"https:\/\/ghetool.eu\/nl_nl\/wp-json\/wp\/v2\/course\/5404","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ghetool.eu\/nl_nl\/wp-json\/wp\/v2\/course"}],"about":[{"href":"https:\/\/ghetool.eu\/nl_nl\/wp-json\/wp\/v2\/types\/course"}],"wp:attachment":[{"href":"https:\/\/ghetool.eu\/nl_nl\/wp-json\/wp\/v2\/media?parent=5404"}],"wp:term":[{"taxonomy":"section","embeddable":true,"href":"https:\/\/ghetool.eu\/nl_nl\/wp-json\/wp\/v2\/section?post=5404"},{"taxonomy":"chapter","embeddable":true,"href":"https:\/\/ghetool.eu\/nl_nl\/wp-json\/wp\/v2\/chapter?post=5404"},{"taxonomy":"authors","embeddable":true,"href":"https:\/\/ghetool.eu\/nl_nl\/wp-json\/wp\/v2\/authors?post=5404"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}