The Microgrid use case is based on the real-life implementation of a Microgrid in the Austrian municipality of Gniebing in south-eastern Styria. The Gniebing Microgrid was implemented to support a limited number of consumers during a blackout scenario. For that purpose, two diesel generators and a hydro-plant are being facilitated. At this point no battery storage system is installed and the diesel generators are used for the black-start and act as control reserve of the grid. The use case focuses on renewable energy sources that will replace the diesel generators whenever possible to reduce the amount of diesel consumed.
One of the most useful pieces of information for energy retailers is to know the demand of the consumers as exactly as possible as it helps to better optimize the purchase of energy, reducing deviations, risk and therefore penalties. In addition, it enables to secure futures in a more accurate way and thus fitting tariffs for the customers. Precisely knowing the demand of consumers is also necessary to be able to set the margin in the electricity rates. Finally, the financial impact is important when making purchases more tightly without expending unnecessary resources.
Currently the consumers demand is estimated based on the existing consumer portfolio (aggregate consumption profile) and extrapolating taking into account the growth forecast based on indices of previous years, the annual consumption type curve and the weather condition. It is therefore an associative level action. With the use of WHY Toolkit Goiener will be able to perform better estimations and demand forecast by taking into consideration not only climatic factors but other non-climatic factors that are difficult to simulate on a massive level.
In this use case, the WHY Toolkit will be linked with the PRIMES modelling suite (especially PRIMES-Buildings module), one of the most widely used and well-established models at the EU and MS level. It has been used to provide quantitative model-based to benchmark EU policy impact assessment in the energy and climate fields (Winter Package 2016, EE directive, Low-carbon roadmap, “Clean Planet for All” strategy). A two way interlinkages of WHY Toolkit with PRIMES model will be needed to be carried out. In particular, a data interface and a disaggregation of the PRIMES results (based on single representative households for each country) to capture consumer differences and idiosyncratic behaviors and load granularity provided by WHY Toolkit will be carried out.
In the Global Context use case the WHY Toolkit is linked to the integrated assessment model TIAM-ECN and PROMETHEUS, a global ESM, based on the widely used TIMES model generator, in which the world is disaggregated in 36 distinct regions. TIAM-ECN has been used in several research projects (e.g. TRANSRISK, LIMITS, CLIMACAP) to investigate long-term energy and climate scenarios in diverse regions in the world including the EU.
This use case has two main objectives:
- develop a plug-in to soft-link the WHY Toolkit with TIAM-ECN and other TIMES-based models from the wider TIMES modelling community
- investigate the effect of the improved representation of energy demand from the built environment on longterm global energy scenarios, in line with the Paris agreement.
The WHY tools developed will be validated in 5 very different Use Cases that ranges from a small microgrid to a global model. Every Use Case has a unique combination of geographic scope, temporal framework and policy objectives. Use Cases share a similar use context. The basic idea to develop WHY use cases is that a person (modeler) that is interested in defining a set of interventions (like a policy maker, a think tank, a manager of funds or a practitioner at an energy provider or Energy community) wanted to assess the final impact in the energy system before introducing a certain policy on energy system changes for the residential areas.
To support policy decisions, the modeler is going to support such policies by using the WHY results before the policy is implemented. First, this modeler will define the scenario:
- the geographic and temporal scale,
- the interventions to consider,
- the households segments that are going to be affected
- and any other information needed to run de model.
Then, the modeler retrieves all of this information and configures both the Energy System Model (ESM) and the WHY Toolkit.
Finally, the modeler runs the simulation. The simulations are iterative. First, the energy system model asks the WHY Toolkit for an estimation of the load profile of the residential segments under the set of interventions defined in the scenarios and provides them a price signal. Then, the WHY Toolkit performs an estimation and provides to the ESM with a load profile. Next, the ESM performs the simulation of the rest of the energy system and finally, it is advance the time step and repeated until reached the end of the simulation. At this point, the modeler will retrieve the results of the simulations in terms of a series of KPIs for the final and intermediate status of the system.