By Juan José Pichardo. MG Research Assistant, PUCMM
Changing the way we use energy
One of the main challenges of the energy sector is not only to generate all the energy that is required by the consumers but also to supply the demanded power at any given time. Ideally, the amount of energy that we generate must be equal to the energy that we consume. This is not possible due to energy losses in the power lines and all the components of the power grid, but how close could we get to equalizing the demanded energy to the generated energy?
One simple way of reducing the difference between generation and demand is by simply using more energy when the generation is high and less energy when the generation is low. To accomplish it, we need to find a way of continuously controlling the energy usage of all the consumers to match the generation. There are two ways of approaching this solution, one of them is to massively raise awareness on the subject and expect that everyone will control their energy usage according to the generation levels. This approach is somewhat delusional, the most probable outcome is that each individual would wait for “the others” to take action, and consequently, almost nobody will change their energy habits. A more realistic and effective approach will be to base the control strategy on not only raising awareness but also on the one thing that every single person cares about, money.
This is where “Demand Response” (DR) appears as a solution to the generation-demand problem. DR can be defined as “… the changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time”[1]. In other words, we could design a compensation method where the price of electricity is dynamic and is inversely proportional to the amount of energy being generated at the time, intending to reduce the energy demand on low generation times and increase it on high generation times. Additionally, demand response could be used to “ensure lower electricity usage during times of high-cost periods and lowers risks of jeopardizing grid reliability, hence reducing opportunities for forced outages or wide-scale blackouts”[2].
If we get creative, DR could be a key for the implementation of renewable energy generation. These generation sources are known for their unpredictability and poor stability. Storing renewable energy at low costs and on a large scale will be the optimal solution. But the storage technologies are currently in development. In the meantime, DR could be used to maximize the usage of energy in times of peak renewable generation by encouraging the consumers with lower prices. At the same time, we could do the exact opposite for fossil-fuel-based generation. And in the long-term, it could also lower the average price of energy for the final consumer.
IoT implementation for enhanced DR
One of the essential components of a demand response plan is effective and fast communication between devices of the grid. To achieve the goals of an ideal demand response scheme we need to monitor the amount of energy being produced by each generation plant in real-time as well as the instant power demand. We need to process this information and determine the price of energy at every moment. Moreover (to make the process transparent) the data needs to be presented live to the customers by using a user-friendly and understandable platform. The number of instantaneous variables that affect the price of electrical energy requires the implementation of IoT.
Here is an example:
To explain the idea in an oversimplified way, we could think of the following. Imagine for example a grid with the configuration shown in the figure below. In the first situation (SITUATION 1: Green), the price of energy is low (2 $/kWh) due to the high generation of renewable energy in comparison with fossil fuel generation and relatively low demand. At (SITUATION 2: Red) however, the price of energy is way higher (9 $/kWh) because 100% of the generation is fossil fuel-based and the demanded energy is very high as well. Another thing that can be shown in this figure is that the measurement devices are using IoT to transfer the data instantaneously, enabling the processing unit to calculate the price of energy in real-time and upload it to the internet for the customers to watch.
Source: Made by the author
Another application of IoT in a Demand Response system is to enable the utility company to have access to controlling individual devices of each customer (primarily air conditioning and water heating). Although the customer may see this as an invasive decision (giving control of your devices to the utility grid), in reality, things are different. If you think of it, the utility grid has always had complete control of your devices. The difference is that instead of shutting down your entire home, they’d have the choice to remove only the non-essential loads of your home (e.g, in a generation deficit they can shut down all your TVs and air conditioners but keep delivering energy to your fridge). The utility grid needs to have access to all the relevant real-time information to successfully make decisions. For example, factors such as the instant air temperature can help the utility grid control the air conditioners of the customers. As well, they could control how much time a given customer can have their water heater on.
Interesting fact: A reduction of 1 degree in the temperature selector of an air conditioner can reduce the power consumption by about 10% to 15% [3]. Moreover, one of the most common mistakes people make is to set the AC temperature way below the comfort range of humans (consuming unnecessary energy).
The same idea of the utility grid controlling devices via the internet can be applied to the customers. With the help of IoT, the customers could have access (through their phones or computers) to remotely control each of their devices and also to look at their power consumption at any given time. This also applies to industries and commercial buildings (e.g., The power consumption of any given branch office can be controlled from the central office of the company).
The ultimate objective of all the IoT applications described before is to put them all together on the same platform. A platform where consumers can look at the price of energy and their consumption in real-time, compare them, and control its loads based on that info. Furthermore, with a big enough database, the platform can have a price prediction feature. Where customers can look at the profile of energy prices to schedule their loads most economically and efficiently, thus creating a win-win situation. The utility grid would have less saturated lines and energy losses, the customers can minimize the price of their electrical bill and the reduction of fossil fuel energy would be great for the environment too
In the island of Bornholm (Denmark), there is a platform called “EcoGrid Bornholm”, where users can monitor 24/7 the current price of electricity, watch the generation curves, price trends and many more. The user interface is shown below in Figure 2. The electrical grid of Bornholm is considered the world’s best and most advanced grid (more info on EcoGrid EU)
Source: “Overall evaluation and conclusions, 2015”, link: EcoGrid EU
Effects of DR in Grid Stability
Grid stability can be defined as “The reliability and consistency in power or electricity production”[4]. The conditions required for a grid to reach stability are:
- Constant magnitude of voltages
- Constant waveform of voltages (harmonics)
- Constant frequency
- Sufficient energy generation
These conditions may sound a bit simple but achieving them is one of the (if not “the”) most difficult tasks that humans want to achieve. In a utopian electrical grid, 100% of the energy produced will be carried to the customers at perfectly sinusoidal voltages with constant frequency and magnitude. But the number of variables involved in the process of “simply” delivering energy to every one of us makes it impossible. Luckily, some of these variables that affect grid stability can be tackled by smart Demand Response solutions, for example, the overconsumption of energy, which directly affects the “Sufficient energy generation” condition.
Obviously, the more we demand the more we need to generate. So, with DR, the consumption of unnecessary energy can be priced very high to reduce it to its minimum. Also, with the classification of loads between critical (hospitals, military, etc.) and noncritical (bars, cinemas, etc.), the utility could have the ability (in the case of non-sufficient generation) to cut the energy of all the noncritical loads to deliver energy to critical loads instead, preventing massive outages and blackouts (increasing the stability).
On the same topic, if we aspire to have a (nearly 100%) renewable energy matrix, we must take into consideration that renewables such as solar and wind power have the worst stability characteristics. To successfully increase the stability of a grid with a mostly renewable matrix DR is indispensable. As explained before, DR can force the load profile curve to look just like the renewable generation curve. This would eliminate the need for (very stable but unluckily contaminating) thermoelectric generation plants (carbon, gas, diesel, etc.).
Energy efficiency: Many big organizations are starting to get concerned with the world’s energetic problems. For that reason, the “International Organization for Standardization” has developed the “ISO 50001” standard with the objective of providing “a practical way to improve energy use, through the development of an energy management system” [5]. This standard can serve as a guide for companies and organizations to make decisions in order to maximize and control their energy efficiency. The implementation of this standard by organizations is aligned with the objectives of Demand Response. If you want to know more about this, visit the following link: ISO 50001 – Energy management systems.
To keep in mind…
Our universe is composed almost entirely of energy, from mass to light, in their intrinsic form, everything translates to energy. And unfortunately, although this thing that goes by the name of energy is almost unlimited in the universe, only an “extremely” tiny portion of it can be successfully (mostly inefficiently) converted into usable energy. It’s important to always keep that in mind the next time we make use of such rare, valuable, and almost magical form of energy that we call electricity.
References
[1] M. Albadi and E.-S. Ehab, “Demand Response in Electricity Markets: An Overview,” IEEE, 2007.
[2] L. Raju, “IoT based Demand Response Management in Microgrids,” IEEE, 2021.
[3] P. Yazdkhasti and C. Diduch, “A Mathematical Model for the Aggregated Power Consumptions of Air Conditioners,” IEEE, 2018.
[4] Nel, “Grid stability,” February 2022. [Online]. Available: https://nelhydrogen.com/glossary/grid-stability/#:~:text=Grid%20stability,The%20reliability%20and%20consistency%20in%20power%20or%20electricity%20production.,and%20the%20potential%20for%20cloud.
[5] International Organization for Standardization, “ISO 50001,” ISO, 2022. [Online]. Available: https://www.iso.org/iso-50001-energy-management.html.
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This article is derived from the Subject Data funded in whole or part by NAS and USAID under the USAID Prime Award Number AID-OAA-A-11-00012. Any opinions, findings, conclusions, or recommendations expressed in this article are those of the authors alone and do not necessarily reflect the views of USAID or NAS.