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Understanding and reducing electricity consumption in an energy cooperative

Energy retailers want to know consumer demand with high precision in order to optimise energy purchases and reduce risks and associated penalties. In this use case, the Spanish energy cooperative Goiener will use the WHY toolkit to make better estimates and load demand forecasts, taking into account not only climatic factors but also other non-climatic factors that are difficult to simulate on a large scale.

Goiener is a non-profit citizen's energy cooperative founded in 2012. It is located in the Basque Country, in the north of Spain. Its main activity is the sale of electricity from 100% renewable energy. As the field of action (energy) is very broad, the Goiener Association was created in 2015 to raise awareness of energy cooperatives and provide training and development services. In 2018, the first generation of renewable energy projects was launched, with the main objective of promoting, building and buying decentralised, local and distributed renewable energy. Currently there are 51 staff, 14538 partners and 200 volunteers. For Goiener, it is necessary to predict the short-term consumption profile of its members in order to be able to buy energy on the daily electricity market in a more adapted way. But it is also important for Goiener to be able to make accurate consumption forecasts in the medium and long term. Based on a good demand forecast, Goiener will be able to know exactly how much energy it will need to buy in the coming months or years. In this way, Goiener will be able to stabilise the prices of customer tariffs. It will also allow Goiener to enter into long-term power purchase agreements (PPAs) with small renewable energy producers. By entering into these PPAs with small generators, some of the strategic objectives of the cooperative can be achieved. Therefore, it is crucial for Goiener to have mathematical models and tools that predict consumer behaviour with high precision.

 


The main goal of this Use Case is to use the WHY-Toolkit to simulate the behaviour of the private consumers to understand their load profile and its response to changes in external conditions.

 

One such key condition is the electric tariff. It is also one of the most powerful interventions that an energy cooperative can do to modify the behaviour of their partners. Goiener is interested in knowing how a change in its tariff structure will:

  • Modify the load profile and purchase strategy of its partners (individually and as a group)
  • Affect the achievement of long-term goals such as reduction of the energy consumption, increase of distributed renewable generation assets for self-consumption, reduction of energy poverty and increase of community empowerment.

In order to analyse these aspects, Goiener took advantage of the change of electric tariffs that was implemented in Spain on the 1st of June of 2021. The objective of this change of tariff was to shift the load curve from peak hours to flat and valley hours in order to improve the electric system. In this Use Case, we will complement this intervention with a set of information campaigns to see the joint effect of these interventions.

This Use Case will provide the following KPIs to the Sustainability Assessment:

  • The percentage of energy that is maintained in the same periods and the percentage of energy moving to different periods. The main objective of the intervention is to shift the loads from peak to flat or valley hours, so this will be one of the most relevant KPIs.

  • Change of the amount of kWh purchased by Goiener and its costs for peak, flat and valley hours.

  • Change of the kWh penalties associated with the purchase orders made by Goiener and the associated costs in peak, flat and valley hours.

Expected Results

• Reduce consumption and power in peak hours which creates a financial saving due to the new tariff structure.

• Facilitate and improve the forecast of purchase in the wholesale electricity market. This implies reduction of the penalties associated with misguided energy purchases by the system operator.

• Test if a change in the tariff could significantly change the electrical load profile of a consumer.

• Understand which incentives and policy interventions contribute to increasing investments in each of the sectors that will be analysed in T2.2: building, appliances, mobility, and flexibility.