A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
D3.1 Models of DER Devices and associated Forecasting Algorithms
The goal of this document is to present and analyse Distributed Energy Resources (DER) device models along with the relevant processes required to define and formalise their control and response capabilities towards defining their flexibility capacity in demand response programmes. The DER models are divided into: demand, storage and generation. Demand models refer to residential loads with significant capacity to affect the building-level energy demand and provide flexibility as well as support the indoor environment optimisation in terms of comfort and health preservation. To this end, Heating, Ventilation and Air Condition (HVAC) and lighting have been identified as the most suitable loads. The FLEXCoop DER load models contain the mathematical formulas for the calculation of electric demand (consumption) of each DER type as a function of dynamic (input data) and static (configuration) parameters affecting DER operation. A training period, gathering data from the physical devices is required towards the extraction of the configuration parameters. The FLEXCoop load modelling framework will allow for continuous calibration of the respective DER models in order to account for efficient and effective dynamic adaptation to potential shifts in the “behaviour” of the corresponding physical entities (seasonal patterns, device performance degradation, etc). Storage models refer to stationary Energy Storage Systems (ESS) and Vehicle-as-ESS. Vehicle as-ESS basically means, that electrical vehicle batteries are used in a very similar manner, as stationary batteries. Therefore, it provides the same services such as: integration of renewable generation, grid services such as voltage and frequency control, supply emergency backup power, peak shaving and valley filling, to give some examples. First, the stationary system is described (incorporating both economic and thermodynamic parameters) and aligned with FLEXCoop business scenarios. Then, additional model variables are introduced, in order to consider peculiarities of the electric vehicles. The generation forecasting models presented in the document aim to describe the behaviour of small generators available in the FLEXCoop pilot dwellings and in the portfolio of energy cooperatives. For the pilot dwellings that we have currently identified only residential Photovoltaics (PVs) are available. Thus, we have chosen to analyse only PV generation forecasting in the deliverable. However, if during the project implementation, wind generation units identified and need to be incorporated in the overall FLEXCoop solution demonstration, the proposed algorithms will appropriately be adapted to fulfil this additional requirement. The models can be applied to provide short and very short-term forecasting of PV generation in an automatic and effective way, both in terms of performance and computational resources.
D3.1 Models of DER Devices and associated Forecasting Algorithms
The goal of this document is to present and analyse Distributed Energy Resources (DER) device models along with the relevant processes required to define and formalise their control and response capabilities towards defining their flexibility capacity in demand response programmes. The DER models are divided into: demand, storage and generation. Demand models refer to residential loads with significant capacity to affect the building-level energy demand and provide flexibility as well as support the indoor environment optimisation in terms of comfort and health preservation. To this end, Heating, Ventilation and Air Condition (HVAC) and lighting have been identified as the most suitable loads. The FLEXCoop DER load models contain the mathematical formulas for the calculation of electric demand (consumption) of each DER type as a function of dynamic (input data) and static (configuration) parameters affecting DER operation. A training period, gathering data from the physical devices is required towards the extraction of the configuration parameters. The FLEXCoop load modelling framework will allow for continuous calibration of the respective DER models in order to account for efficient and effective dynamic adaptation to potential shifts in the “behaviour” of the corresponding physical entities (seasonal patterns, device performance degradation, etc). Storage models refer to stationary Energy Storage Systems (ESS) and Vehicle-as-ESS. Vehicle as-ESS basically means, that electrical vehicle batteries are used in a very similar manner, as stationary batteries. Therefore, it provides the same services such as: integration of renewable generation, grid services such as voltage and frequency control, supply emergency backup power, peak shaving and valley filling, to give some examples. First, the stationary system is described (incorporating both economic and thermodynamic parameters) and aligned with FLEXCoop business scenarios. Then, additional model variables are introduced, in order to consider peculiarities of the electric vehicles. The generation forecasting models presented in the document aim to describe the behaviour of small generators available in the FLEXCoop pilot dwellings and in the portfolio of energy cooperatives. For the pilot dwellings that we have currently identified only residential Photovoltaics (PVs) are available. Thus, we have chosen to analyse only PV generation forecasting in the deliverable. However, if during the project implementation, wind generation units identified and need to be incorporated in the overall FLEXCoop solution demonstration, the proposed algorithms will appropriately be adapted to fulfil this additional requirement. The models can be applied to provide short and very short-term forecasting of PV generation in an automatic and effective way, both in terms of performance and computational resources.
D3.1 Models of DER Devices and associated Forecasting Algorithms
Relan, R. (author) / Ghasem Azar, A. (author) / Bacher, P. (author) / Madsen, H. (author) / Valalaki, K. (author) / Tsitsanis, T. (author) / Tsatsakis, K. (author) / Matina, T. (author) / Fernández Aznar, G. (author)
2018-08-17
oai:zenodo.org:4543903
Paper
Electronic Resource
English
DDC:
690