To demonstrate how to use the Flexibility Model Recommender, we will walk through a sample scenario step by step. This example illustrates the selection process and filtering workflow, making it easier to understand how the tool helps identify suitable flexibility models based on specific needs.

Example scenario

A Virtual Power Plant (VPP) aggregates multiple small-scale flexibility resources (such as residential batteries and industrial demand response participants) to enhance grid stability while managing uncertainties in renewable energy generation.

To develop a model, the first step is defining the important parameters specific to the VPP. These parameters will guide your model selection or development process. Some of the key parameters would be:

• Asset Types: What are the flexibility resources? (e.g., flexible loads or battery storage systems)
• Uncertainty Handling: How will the model address renewable energy variability? This might involve probabilistic forecasting, storage management, and demand-side adjustments.
• Optimization Goals: What are the optimization objectives of the VPP? (e.g., minimizing costs, maximizing energy efficiency, balancing supply and demand, ensuring stability)
• Time Horizon: How quickly must decisions be made? (e.g., short-term like minutes/hours, or over a longer planning horizon)
• Economic Factors: Are there any constraints around cost or financial optimization, or is it primarily focused on stability?

Step 1: Exploring Parameter Options

Before selecting parameters, we familiarize ourselves with the available choices:

• The Parameter Explanation article on the Help page provides an overview.
• Alternatively, clicking the information icon next to each parameter within the Flexibility Model Recommender opens a popover with a description.

Step 2: Selecting Parameters for the Scenario

Next, we navigate to the Flexibility Model Recommender and define our selection. Using the three-way checkboxes next to each parameter, we categorize them as mandatory, desired, or irrelevant for our VPP scenario.

In the following, we derive a selection of relevant parameters from the initial set of important criteria, based on the available options in the recommender. To illustrate its use, we emphasize certain aspects while intentionally omitting others, without claiming that this represents the optimal parameter choice for the given VPP scenario.

Mandatory Parameters (Strictly Required)

1. Flexible Loads (Asset Type) → The VPP includes demand-side participants that adjust consumption based on market signals.
2. Battery Storage Systems (Asset Type) → Distributed batteries are used to store excess energy and discharge it when needed.
3. Uncertainty → The VPP must handle fluctuations in renewable generation and demand variations.
4. Aggregation → The model must support grouping multiple smaller flexibility resources into a unified entity.

Desired Parameters (Preferred but not mandatory)

5. Short-term Resolution → The VPP operates in short time frames, so models with short-term decision-making are beneficial.
6. Economic Constraints → The VPP aims to optimize financial outcomes but does not make it an absolute requirement.

Irrelevant Parameters (Not considered)

• Other parameters are not relevant to this example.

At this point, the parameter selection should match the intended scenario.

Step 3: Adjusting Match Requirements

We now define the minimum number of desired parameters a flexibility model must meet.

• In this example, we set the value to 1, meaning at least one of our desired parameters must be present.
• Setting this value to 2 would require both desired parameters to be included, effectively making them mandatory.

Step 4: Retrieving Matching Models

Finally, we click the "Show Models" button, and the recommender generates a list of flexibility models that best match our selected parameters, displaying the most relevant options first.

Clicking on any model in the results expands it to reveal more details.

Example of a possible result:

Conclusion

By following this process, you can efficiently use the Flexibility Model Recommender to identify models that best suit your specific needs, ensuring a streamlined selection process for various flexibility scenarios.

For a comprehensive overview of all available models and their details, navigate to the Models tab.

Flexibility

• Flexibility Potential: Represents the capability of flexibility resources (like energy storage, demand response) to adjust their power output or consumption, providing essential services like energy supply or demand balance.
• Flexibility Requirement: Refers to the overall needs of the power system for flexible resources to maintain stable operations and adapt to variability, such as that from renewable energy sources. This quantifies the adjustments necessary across the system to ensure reliability and prevent disruptions.

Asset Types

• Renewable Generation: Electricity generated from renewable sources such as wind and solar; typically variable and often requiring flexibility resources to integrate reliably.
• Conventional Generation: Dispatchable thermal or fossil-based generation units that can provide firm power and reserve capacity.
• Grid Infrastructure: Transmission and distribution assets, grid controls and interconnectors that enable power flows and system-level flexibility.
• Multi-Energy System: Systems integrating multiple energy vectors (electricity, heat, gas) enabling cross-vector flexibility and sector coupling.
• CHP Units: Combined heat and power plants that produce electricity and useful heat, offering flexible operation opportunities through co-optimization.
• Heat Pumps: Electrically driven heating/cooling devices that can be operated flexibly to shift demand across time.
• Thermal Energy Storage: Systems that store thermal energy (e.g., hot water tanks, chilled storage) to shift heating/cooling loads and provide flexibility.
• Distributed Generation: Small-scale generation located close to demand (e.g., rooftop PV, small gas engines) that can be coordinated for local flexibility.
• Electric Vehicles: Vehicles with smart charging or vehicle-to-grid capability that can act as flexible loads or distributed storage.
• Flexible Loads: Demand-side resources (industrial, commercial, or residential) that can be shifted or curtailed to provide system flexibility.
• Battery Storage Systems: Electrochemical storage units (BESS) used to absorb, store and release electricity, providing fast and controllable flexibility.

Classification

• Metric: Uses predefined parameters to either deterministically quantify flexibility without considering uncertainties or Measures the likelihood of various flexibility scenarios using statistical methods.
• Machine Learning Model: Employs machine learning techniques to predict and optimize flexibility based on historical data.
• Envelope: Defines the operational boundaries or limits within which flexibility can be effectively measured or maintained. This includes the range of acceptable inputs, outputs, and constraints on flexibility metrics or predictions.

Type

• Deterministic: Using specific, fixed parameters and conditions to calculate flexibility needs and potentials. These models operate under the assumption that all inputs (such as demand forecasts, generation capacity, and operational constraints) are known and remain constant, leading to predictable and consistent outcomes.
• Probabilistic: Accounting for the uncertainty inherent in energy systems by using probability distributions and stochastic processes to determine flexibility requirements and resources. These models consider variations in input data like renewable energy output, consumer demand, and equipment failures, providing a range of possible outcomes rather than a single deterministic result.

Time

• Discrete: Using specific, fixed parameters and conditions to calculate flexibility needs and potentials. These models operate under the assumption that all inputs (such as demand forecasts, generation capacity, and operational constraints) are known and remain constant, leading to predictable and consistent outcomes.
• Continuous: Accounting for the uncertainty inherent in energy systems by using probability distributions and stochastic processes to determine flexibility requirements and resources. These models consider variations in input data like renewable energy output, consumer demand, and equipment failures, providing a range of possible outcomes rather than a single deterministic result.

Metric

• Active Power: Measures the real power (in watts or megawatts) that flexibility resources can deliver or consume. Critical for assessing how much instantaneous load or generation adjustment a resource can provide to balance supply and demand in real-time.
• Ramp-Rate: Quantifies how quickly a flexibility resource can change its power output (measured in MW/min). Essential for modeling fast-response capabilities needed for frequency regulation and handling rapid fluctuations in renewable generation.
• Ramp-Duration: Specifies the time period required to reach a target power level from a starting point. Important for understanding the sustained flexibility capabilities and planning operational timelines for flexibility deployment.
• Energy: Measures the total amount of electricity (in kWh or MWh) that a flexibility resource can shift or store over a period. Critical for energy-based services and determining the capacity of flexibility resources to address longer-duration imbalances.
• Reactive Power: Quantifies the reactive power (in VAR) that flexibility resources can provide to support voltage stability and grid control. Important for models addressing voltage support and maintaining power quality during grid disturbances.
• Voltage: Measures voltage support capabilities (in volts) that flexibility resources provide. Relevant for transmission and distribution network-level flexibility models requiring voltage regulation and stability assessment.
• Cost: Quantifies the economic dimension of flexibility, measuring operational or activation costs (in currency units). Essential for economic optimization and cost-benefit analysis of flexibility deployment strategies.
• Time: Represents temporal aspects of flexibility such as response time, availability windows, or planning horizons. Critical for time-dependent flexibility models and scheduling optimization.

Constraints

• Technical: Define the physical limits of power system components, such as maximum power output and ramp rates.
• Service Guarantee: Ensure that flexibility resources meet specific performance and reliability standards, such as response times and availability.
• Economic: Focus on minimizing operational costs and optimizing financial outcomes from managing flexibility resources.

Resolution

Refers to the time granularity considered for analyzing power system operations and planning.

• Short-term: Focuses on immediate operational decisions, covering minutes to a day, essential for dispatching resources and managing fast fluctuations in power supply.
• Long-term: Used for strategic planning over weeks to years, crucial for infrastructure development, integration of renewables, and long-term investment decisions.

Multi-time-scale

If checked, it implies that the specific flexibility model under consideration is capable of integrating and analyzing flexibility across multiple time scales (short-term, medium-term, long-term) simultaneously or dynamically. This means the model can handle and optimize flexibility requirements and resources across these different scales in a cohesive manner, which is essential for comprehensive energy system planning and operation.

Mediator

Facilitates the matching of flexibility requirements with flexibility potentials within an energy system. It acts as an intermediary that helps integrate and optimize the use of available flexibility resources, ensuring that the power system can efficiently respond to fluctuations in demand and supply. Common examples of flexibility mediators include the power grid itself, which redistributes energy, and market mechanisms that allow for the trading of flexibility services to balance the system.

Uncertainty

Refers to the unpredictability associated with various factors that affect the balance between electricity supply and demand. This includes variability in renewable energy production due to weather conditions, fluctuations in consumer demand, and potential equipment malfunctions or failures. Addressing uncertainty in flexibility models is crucial for ensuring that the power system can reliably handle unexpected changes and maintain stability under diverse operational conditions.

Aggregation

Refers to the model's ability to combine multiple smaller units of flexibility resources (like residential batteries, electric vehicles, or demand response participants) into a single, manageable entity. This aggregation allows for more effective coordination and utilization of distributed resources, enhancing their overall impact on grid stability and efficiency. By treating multiple small-scale assets as a unified group, operators can deploy flexibility more strategically, optimizing responses to grid demands and reducing operational complexities.