For utilities, the situation is more complex than ever. Dispatchers have to try to find a balance between optimal use of assets on the one hand and the demand for more flexibility on the other. To keep up with the ongoing transformation, you need to bring your control room to the next level.
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The energy transition is no longer a pie in the sky, it has arrived. Over a decade ago the government liberalized the market. Back then, there were only a few energy suppliers. Now, there are many parties operating on the grid; consumers have become producers by locally generating power; and in the meantime the market authorities are pressing grid companies to lower their tariffs.
The situation is more complex than ever as dispatchers have to try to find a balance between optimal use of assets to lower costs and the demand for more flexibility because of the many market participants. Times are hard for dispatchers to keep up with the ongoing transformation.
How will the grid respond to power fluctuations from renewable energy? Or how can an outage be prevented? The true answer is that no one really knows. However, there is software out there that does know. State estimation or simulation solutions mimic the grid to help dispatchers understand the grid in these turbulent times. Simulation software predicts the behavior of the grid to identify potential problems.
In order to fully unleash the power of simulation solutions, you need a real time integrated architecture. If you'd like to know how, have a look at this article: 3 steps to create an integrated decision center in your grid company.
In this article you will be guided through the process of integrating simulation solutions in the grid company.
Why Grid Simulation Is Essential to Stay on Top
To start, let’s focus on the two major hurdles dispatchers need to take in the era of the energy transition:
- The Need for New Information
First of all, the liberalization of the market has made the energy landscape increasingly dynamic. As more and more parties start operating on the grid, the dispatcher’s responsibility to balance the grid is more complex than ever. To be able to make the right decisions and assure a steady energy supply at all times, the dispatcher needs to be well-informed.
In daily operations of Transmission System Operators (TSO) and Distribution System Operators (DSO), most decisions are based on the experience and knowledge of dispatchers that have been operating the grid for a long time. During the years of gaining these experiences and knowledge, most scenarios have become familiar and are hence acted upon by means of habit. But, habits are not changing with the same velocity as the market.
- The Need to Limit Costs
Another difficulty dispatchers are facing is the stricter regulation of the energy transport tariffs, forcing grid operators to lower their operational costs. This binds the freedom of movement of dispatchers. It’s a challenge to operate assets in the most cost-efficient way, without compromising on a secure energy supply.
The question arises: how to cope with this changing environment and become a high performer in the industry? Simulation software is the answer.
Nowadays, grid simulation solutions are available that can support operational decisions based on real data and accurate forecasts. Simulation solutions support dispatchers in uncertain situations or provide insight into the consequences of their actions. During an outage for example, simulation software brings you the latest insights on how to create alternatives routes for transporting energy, so that downtime is kept to a minimum.
Furthermore, grid simulation provides insight into the real time status of the grid using advanced analytics, even of locations where no measurement devices are installed. Thus, no need to invest tons of money in an abundance of field devices.
In short, grid simulation helps the dispatcher to operate its network more effectively and more economically. In order to remain relevant in the changing energy landscape, a good control room requires a good simulation solution.
How It Works and How It Is Used
So how does it work? The solution simulates the physical behavior of the grid by combining the static grid data with the dynamic field data, offtake, temperature and laws of nature. The static data contains information about the physical properties of network elements such as power lines, transformers, pipelines, and compressors. The dynamic data contains all real time measurements of voltage, current, frequency, pressure, flow, temperature, and other relevant energy transport parameters.
There are two types of simulation: current state simulation and future state simulation.
Current state simulation has one major advantage: it decreases dependency on measurements. Using the laws of physics, a handful of measurements like voltage, pressure, current and flow can be analyzed to estimate values in any place in the network, and correct for measurement inaccuracies. This allows dispatchers to follow how certain properties propagate through the network. The simulation fills in the blind spots in the grid where certain measurements are not available.
These Are the 3 Main Purposes of Current State Simulation:
- First, it provides insights that cannot be measured directly by meters. That way, grid operators are able to precisely track gas qualities or assess the total power consumption for example.
- Secondly, the information provided by current state estimation gives dispatchers better insights in whether they will meet contractual agreements with customers.
- Lastly, when consistent discrepancies are found between metering and simulation results, a check on the metering can reveal faulty equipment, improving the safety of the grid.
Future state simulation combines the current status of the network with the energy demand and supply forecasts to predict the future status of the network. Future state simulation brings you insights about voltage, frequency, gas flow, pressure and composition in the future. Formulas and algorithms are in place that mimic the behavior of individual network components. This allows the model to predict how these components will react to changing circumstances. The forecasted energy demand and (matched) supply shape the future boundary conditions. This information is combined in the future state simulation model to calculate what the network will look like over the next several minutes or hours.
These Are the 3 Main Purposes of Future State Simulation:
- First and most important, the predictive model is used to help the dispatcher make well-informed decisions and test the effects of actions before carrying them out. A dispatcher will know whether to turn on a gas compressor or whether to disconnect a power plant or not, for example. This way, the predictive model helps to optimize resources like compressors, leading in turn to reduced operational costs and environmental benefits.
- Secondly, under critical circumstances, the predictive model indicates where the weak spots are in the network, so dispatchers know where to focus their attention on. Think of operating the grid in extreme temperatures that test the limits of the assets.
- Thirdly, a simulation model that shows the effects of potential actions can serve as the perfect playground for new dispatchers that want to learn how the network will respond to their actions, or to rare and extreme conditions.
How to Implement Simulation Software in Your Organization
As you would have probably guessed by now, simulation software can bring you indispensable insights to become an outstanding performer in changing circumstances. So what do you need to be aware of when implementing simulation software into your organization?
Integration between ADMS and the simulation solution is key, either to provide data or to present simulation results. There are three possible levels of integration between simulation software and ADMS. The more ADMS and simulation software are integrated, the more value you will be able to obtain. These are the three levels of integration:
- To make sure the simulation software is not lagging behind, the minimum level of integration needed is the integration of real time data into the simulation solution. That way the solution can provide results based on the latest information for the decision making process.
- The general recommended level is to fully integrate the results of the simulation solution back into the main ADMS system. This way dispatchers can base their decisions on information presented in a single application or even a single screen, making their work more efficient, especially in time critical situations.
- Finally, you can consider integrating the static data as well. At this level of integration a (simplified) network will be send to the simulation solution. That way, the network can be maintained with just one application. This level of integration is most interesting for companies that have a high frequency of changes in their network. The major advantages are lower maintenance costs and less risk of discrepancies between the different applications.
Based on multiple implementations done in Europe, we’ve distilled the following 7 best practices to implement simulation software in your organization:
- Make your grid more manageable by dividing it into sub-grids. This allows you to grow your simulation solution in a controlled, agile and incremental way.
- To perform the actual configuration of the simulation model, make sure you put together the right team. An effective and efficient team consists of a product simulation expert, a limited number of business experts and a team of smart consultants with a more general scientific background. This will save cost while improving efficiency.
- Create a reference document. It contains agreements about the simplifications, algorithms and required formulas. The reference document dictates for example whether each power unit is modeled individually or as a group. This will help to create a uniform well maintainable solution.
- Set up acceptance criteria upfront for a number of parameters (flow, pressure, load, reaction time etc.) to be recorded in the reference document. This is required to reach a shared end goal in a controlled manner.
- Don’t rely on existing and potentially outdated documentation to gain insights into the current status of the grid. Instead use data analysis on historic data, which is often more accurate.
- Run the simulation solution in a test environment for a longer period of time. The robustness of a model can only be tested when a large number of scenarios is simulated. This will improve the quality and reliability of the solution.
- Use a professional analytics tool to bring out the discrepancies in the model for multiple parameters. This will help in the analysis and shorten the time required to resolve issues.
Integrating these best practices into your approach will allow you to unleash the full potential of simulation software into the integrated control room. A necessary step for all control rooms to survive in these turbulent times.