IMPROVEMENT OF CUSTOMER BASELINES FOR THE EVALUATION OF DEMAND RESPONSE THROUGH THE USE OF PHYSICALLY-BASED LOAD MODELS.
Gabaldón A., García-Garre A., Ruiz-Abellón M.C., Guillamón A., Valero-Verdú, S., Álvarez-Bel C.and Fernandez-Jimenez, L.A.
4th Annual APEEN Conference 2019. Energy Demand-Side Management and Electricity Markets (University of Beira Interior - Covilha, Portugal, 17 y 18 de octubre de 2019)
Ed. Asociación portuguesa de economía y energía. APEEN
International Association for Energy Economics IAEE
The engagement of customers in Demand Response (DR) policies needs a fair and understandable incentive: they should receive income for no more and no less than the flexibility they actually provide. This objective means performing an accurate evaluation of demand flexibility, that is also of a great importance from the point of view of aggregators and for the operators of energy markets and systems. Moreover, System’s operators need a correct estimation of customers’
flexibility for planning the addition of new resources and manage supply-side resources. In one hand, is essential to encourage the participation of customers in Markets through DR programs, by receiving right incentives for the flexibility they actually provide for DR strategies. In the other hand, aggregators should estimate the “steady-state” load of their customers using appropriate and accurate Customer Baselines (CBL). The idea of this paper is presenting a double CBL that adjusts demand after and before control periods separately. This CBL is simple enough to be calculated, and presents benefits compared to other methodologies concerning the implementation and operation of DR policies. To illustrate this proposal, the particular case of a campus university in Spain is analysed. Calculations with standard CBLs
exhibit errors that are more significant in demand peak periods than in daily load profile. Through adjustment factors (based on information provided by PBLM models), the accuracy of the new CBL exhibits some improvements (reduces the error by 10-15%) and arises as a better CBL estimator without increasing the complexity of DR.
Customer Baseline, Physically Based Load Modelling, Demand Response, Electricity Markets, Aggregators.