A Modeling Framework for Future Energy Systems Göran Andersson, ETH Zürich ETH Power Systems Laboratory
Content • Energy Hub - Multi energy-carrier systems • Power Node - Incorporation of fluctuating power sources - Incorporation of demand side participation - Incorporation of storage 2 ETH Power Systems Laboratory
The Energy Hub L = Loads (Output) M = Output side storage flows C = Coupling matrix P = Input power flows Q = Input storage flows 3 ETH Power Systems Laboratory
Hub Equations and Results 4 ETH Power Systems Laboratory
Applications (so far) • Long term energy planning of the city of Bern • Energy planning of several Swiss municipalities • Analysis of e-mobility • Energy/Exergy analysis of city of Zürich • … 5 ETH Power Systems Laboratory
Status Quo in Power Systems Modelling Traditional power system modeling is “fractional“: Separate models are used for capturing information of Transmission & distribution grid (topology, voltage & frequency dynamics, voltage & line limits) Power generation (generator dynamics, ramp constraints, wind and PV in-feed predictions) Load models (dynamics, load demand predictions) Storage models (capacity, storage levels, dynamics) Modelled interaction between individual power system units and grid does not necessarily capture all relevant aspects No interaction with other energy carriers modeled (cf Energy Hub) 6 ETH Power Systems Laboratory
Status Quo in Power Systems Modelling Example: optimal power dispatch simulations do consider units that inject or absorb power from the grid. Which of these units are storages (energy-constrained)? Which of these units provide fluctuating power in-feed? What controllability (full / partial / none) does the operator have over fluctuating generation and demand processes? Power P in Grid System P out Unit 7 ETH Power Systems Laboratory
Status Quo in Power Systems Modelling Example: optimal power dispatch simulations do consider units that inject or absorb power from the grid. Which of these units are storages (energy-constrained)? Which of these units provide fluctuating power in-feed? What controllability (full / partial / none) does the operator have over fluctuating generation and demand processes? Power Energy ? P in Grid System provided / P out demanded Unit ? Storage 8 ETH Power Systems Laboratory
Motivation for Power Nodes Modeling Framework Create unified framework for modeling power system units (incl. relevant operation constraints, power supply and demand processes and the controllability) Diverse storage units (battery, pumped hydro, …) Diverse generation units (fully dispatchable conventional generators, fluctuating in-feed of wind turbines and PV) Diverse load units (conventional, interruptible, thermal, ...) Operation constraints: ramp rates, storage capacity, current storage level (SOC) Operation controllability over underlying process (=“flexibility“): fully controllable, curtailable / sheddable, non-controllable 9 ETH Power Systems Laboratory
The Power Nodes Framework Modeling of three domains and their interactions 10 ETH Power Systems Laboratory
One Power Node − = η − η + ξ − − 1 C x u u w v i i load load gen i i i i i gen i i 11 ETH Power Systems Laboratory
One Power Node Storage capacity × state-of- Internal losses charge Power out- Shedding term Power in- feed from grid feed to grid − = η − η + ξ − − 1 C x u u w v < < i i load load gen i i i i i gen i i Provided / demanded power Efficiency factors 12 ETH Power Systems Laboratory
One Power Node (including constraints) Power constraints defined by: min/max power, ramp rates, storage capacity Operation flexibility defined by: shedding term w i , storage term C i x i , ξ i 13 ETH Power Systems Laboratory
Power Node without storage (e.g. non-controllable load) Power node equation degenerates to algebraic equality constraint (for classical load: u gen , i = 0 ) Power node’s power in-feed / out-feed is Partially controllable, if shedding term adjustable ( w i (k) > 0 ) Non-controllable, if shedding term is zero ( w i (k) = 0 ) 14 ETH Power Systems Laboratory
Variety of Power Node modelling definitions Load Gener- ation Storage 16 ETH Power Systems Laboratory
Power Node Modelling Examples PV with local storage unit, no RES feed-in tariff u PV Panel gen, PV η = ξ -1 u gen, PV gen, PV gen, PV u Power Grid gen, Bat Battery storage u = η − η − − u = ξ 1 load, Grid C x u u u u Bat Bat load, Bat load, Bat gen, Bat gen, Bat gen, Grid load, Grid Grid load, Bat (modelled as a slack u power node) u Controllable Local Load gen, Grid gen, CL = η − − + ζ C x u a x ( x ) CL CL load,CL load,CL CL CL,0 CL Non-Controllable Local Load η = − ξ u load,NCL load,NCL load,NCL u load, NCL 17 ETH Power Systems Laboratory
Power Node Modelling Examples PV with local storage unit, RES feed-in tariff u u PV Panel (subject to RES FIT) gen, PV load, Grid, RES η = ξ -1 u gen, PV gen, PV gen, PV Node 1 P 12 Node 2 Power Grid u Battery storage load, Bat − = η − η − u u 1 C x u u gen, Grid load, Grid Bat Bat load, Bat load, Bat gen, Bat gen, Bat u − = ξ u gen, Bat u load, Grid, RES Grid gen, Grid (additional variable u for FIT energy) Controllable Thermal Load u load, CL load, Grid = η − − + ζ C x u a x ( x ) CL CL load,CL load,CL CL CL,0 CL This enables the modelling of Non-Controllable Local Load u η = − ξ load, NCL differentiated feed-in tariffs u load,NCL load,NCL load,NCL incl. options for local PV energy usage (e.g. Germany). 18 ETH Power Systems Laboratory
Power Node Modelling Examples Joint Provision of Load Frequency Control Convenient representation: ∆ Control signal modelled as u Battery storage load, Bat a load to be served − ∆ = η ∆ − η ∆ 1 ∆ C x u u u Bat Bat load, Bat load, Bat gen, Bat gen, Bat gen, Bat Secondary ∆ u Controllable Thermal Load Frequency load, LFC ∆ ∆ = η ∆ − ∆ − ∆ + ∆ ζ Controller u C x u a ( x x ) CL CL load,CL load,CL CL CL,0 CL load, CL ∆ = ⋅ ˆ u P Y load, LFC LFC LFC Dispatchable Generator ∆ Y η ∆ u = ∆ ξ : control signal [-100%, +100%] -1 u LFC gen, CG gen, CG gen, CG gen, CG ˆ P : offered control band [MW] LFC Power Balance: ∆ + ∆ − ∆ − ∆ = ∆ u u u u u gen, Bat gen, CG load, Bat load, CL load, LFC 19 ETH Power Systems Laboratory
Power Node Modelling Examples Demand response (driven by dynamic electricity tariff) [ ] = − k N 1 ( ) ( ) ( ) ∑ ∗ = ⋅ + u min elec . tariff k u k v k load losses i i = k 0 ( ) = η + ξ − C x u v x , s . t . i i load demand losses i load i i i i ≤ ≤ 0 x 1 i ≥ u 0 , load i 20 ETH Power Systems Laboratory
Power Node Modeling Example: Predictive power dispatch Conventional generation unit [6] Conventional (uncontrolled) load [1] + load predictions Pumped-hydro storage units [4+5] and flexible loads (DSM) [7] Wind/PV units (curtailable) [2-3] + Wind/PV power in-feed predictions 21 ETH Power Systems Laboratory
Power Node Modeling Example: Predictive power dispatch 22 ETH Power Systems Laboratory
Optimal predictive power dispatch (Germany) T pred. = 72h, T upd. = 4h, T sample =15min. Simulation Period: May 2010 (30% Wind, 20% PV) – Calc < 4min. 23 ETH Power Systems Laboratory
Optimal predictive power dispatch (Germany, high PV ) T pred. = 72h, T upd. = 4h, T sample =15min. Simulation Period: May 2010 (30% Wind, 50% PV , no DSM ) 25 ETH Power Systems Laboratory
Power Nodes and Energy Hubs Partial transformation between Power Nodes and Energy Hubs is possible Converter: natural gas → electricity ( u load = 0, M β = 0 ) = η − η − + ξ = − η − + ξ gas el 1 el gas 1 el gas C x u u u load load gen gen in gen gen in ( ) ( ) = η ξ − ⇔ = − el gas gas u C x L c P Q β αβ α α gen gen in Q α = C gas ẋ [ ] [ ] + = − E α = C gas x L M C P Q E α P α = ξ gas L β Q α Gas Grid Converter El. Grid P α 27 ETH Power Systems Laboratory
Goals of Power Node Approach Goal is to better evaluate performance of power system operation and to improve performance Storage utilisation (What is its best use?) Integrating fluctuating power in-feed Integrating demand-side management (DSM) Reduce forced ramping of conventional generators for load following and balancing of fluctuating power in-feed Examples of performance criteria power system operation cost curtailment of RES in-feed Power system CO 2 emissions 29 ETH Power Systems Laboratory
Contributions from Kai Heussen (DTU) Stephan Koch Andreas Ulbig Martin Geidl Gaudenz Koeppel Thilo Krause Florian Kienzle …….. 30 ETH Power Systems Laboratory
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