Flexnet: ‘intelligent grids in gas’

  • looptijd: 2015 - 2017
  • locatie: Groningen,
  • functie: (Groen) gas

Flexnet:Vraag en aanbod van flexibiliteit in een duurzaam, geïntegreerd energiesysteem in Nederland

‘intelligent grids in gas’ realiseert de basis technologie gericht op de mix van energiebronnen in een Flexnet Entrance in proefopstellingen.

Flexnet

Demand and supply of FLEXibility in a more sustainable energy system of the NETherlands (FLEXNET)

De mix van energiebronnen in een Flexibel Net (Energiesysteem in 2050) met zijn mogelijke en haalbare scenario’s.

Doel

to analyse (quantitatively) demand and supply of flexibility of the energy (power) system in the Netherlands up to 2050 at the national and regional level.

Het betreft hier een TKI subsidieproject uitgegeven door RVO.

Samenwerkende partners in dit project zijn: ECN, Netbeheer NL, Energie Nederland, GasTerra, Alliander, Stedin, Enexis, Gasunie, TenneT

Funding by the EDGaR project (ministry of Economics)

Subsidie: EDGaR

Partners: 

ECN
Jos Sijm
T: 06-10484843
E: sijm@ecn.nl
Grote Gasprojecten

Ontwikkelen infrastructuur voor gastoepassingen op Entrance (WKK, fuelcell, HR, warmtepomp, zonnepaneel, windmolen, etc).

Causes of the need for flexibility

  • Expected variation of the residual power load, in particular due to the variability of electricity generation from VRE sources such as sun/wind.
  • Unexpected variation of the residual power load, notably due to the uncertainty (less predictability) of electricity from VRE sources (‘forecast error’).
  • VRE surplus (‘negative residual load’)
  • Network congestion (i.e. flexibility as a means of congestion management)

2 Scenario’s

  1. Reference scenario:
    1. Based on ‘accepted policy scenario’ of NEV 2015;
    2. Major characteristics:
    3. Strong growth of installed VRE capacity up to 2030;
    4. Weak growth of additional electrification;
    5. Focal years: ‘R2015’, ‘R2023’ and ‘R2030’.
  2. Alternative scenario
    1. Similar to the reference scenario with one major exception:
    2. Strong growth of additional electrification
    3. Focal years: ‘A2023’, ‘A2030’ and ‘A2050’
    4. A2050: based on P2G-study (set of 85% GHG reduction scenarios)

Lees de presentatie Flexibility of the power system in the Netherlands, 2015-2050 – in de documentatie

 

 

 

Het project heeft enige vertraging opgelopen doordat het modeleerwerk aan beide kanten (zowel bij ECN als Alliander) meer tijd vergde dan verwacht.

(de opdrachtgever – RVO.nl – heeft derhalve enige uitstel van de einddatum en oplevering van het project verleend)

Het project vordert echter gestaag – met fraaie/interessante/relevante resultaten. In de loop van het volgende kwartaal (Q2 2017) wordt het project afgerond en de resultaten/rapportages gepubliceerd.

Bijgaand, ter informatie, een tweetal recente presentaties (‘work-in-progress’) met daarin de aanpak en resultaten van het FLEXNET project zowel door ECN (ene presentatie) als door Alliander (andere presentatie).

 

The ANDES model determines the impact of the energy transition on the Liander distribution grid using a five step process.

The approach is bottom-up: based on prediction of customer behaviour, national scenarios are translated into local adoption of which the impact on the grid is determined.

Due to differences in data resolutions of current technology adoption, the prediction of future local adoption is slightly different for each technology

Photovoltaic analysis

  • For residential PV, a logistic regression model has been built based on the currently known PV-population in the Liander service area (around 40,000 PV installations in early 2014)
  • The predictive power of a number of demographic variables regarding PV-ownership has been assessed, resulting in a model with the eight most significant demographic variables:
    House type, year of construction, house volume, living area, ownership, life phase, social class and lifestyle type

Electric Vehicle analysis

  • The current EV adoption is only known at district level; these adoptions are disaggregated to household level
  • A linear regression analysis is performed to predict the future adoption at household level
  • The impact of EV on the power grid is said to be the combination of residential and non-residential charging actions per day
  • The model therefore takes into account the following four locations for charging:

1.At/near home (53%)

2.At work (29%)

3.At public parking garages (5%)

4.At fast charging locations (13%)

Heat Pump analysis

  • For the location of heat pumps, a regression model has been developed that predicts the number of heat pumps per zip code on the basis of the type of house occupancy and the age of the houses
  • First a segmentation was made of houses in ownership and social rental houses and private rental houses
  • Each segment has a different maximum adoption rate and a different geographical distribution

Conclusies fase 1

  1. The actual number of overloaded assets is relatively low compared to the numbers predicted by previous studies on Dutch power distribution grids performed with real asset data.1,2 The main cause of this difference is most likely the absence of actual asset data in previous studies.
  2. In 2030, only 8% of the distribution transformers and 26% of the substation transformers will be overloaded in the alternative scenario. The number of substation overloads is considerable, but the lower voltage assets of the network will have sufficient capacity to facilitate the increased loads for at least the next 15 years.
  3. In the alternative case scenario in 2050, 35% of the distribution transformers and 45% of the substation transformers are expected to be overloaded. While these overload percentages are significant, they are not alarming. Most of the overloads can likely be dealt with regular network expansions if a nearly overloaded asset reaches the end of its life span. Moreover, several ‘smart solutions’ are expected to become available within this time span.
  4. Geographically, most overloads are expected to arise in city centers, because of their relatively old networks. The fact that the adaptation of PV, EV and HP is lower in the city centers is offset by the dense population, resulting in a larger increase of power load than non-urban areas.

Bekijk voor meer informatie de presentatie van Alliander onder projectdocumenten: Energy Transition and the Power of Flexibility

ECN

  Frans Nieuwenhout   https://www.ecn.nl/nl/    nieuwenhout@ecn.nl    088-5154088