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Time reconciliation Reliability criteria Space aggregation

Time-scale reconciliation to shed light on the plausibilityof long-term low carbon pathways : power system issues

Nadia Maïzi, Vincent Mazauric, Edi Assoumou et al

1MINES ParisTech, PSL Research University, CMA - Centre de mathématiques appliquées,CS 10207 rue Claude Daunesse 06904 Sophia Antipolis Cedex, France

2Chaire Modélisation prospective au service du développement durable

Short term versus long term energy planningConsidering temporal trade-offs in decarbonisation pathways

London, 2016N. Maïzi (MINES ParisTech) Short term vs long term energy planning 29 Avril 2016 1 / 32


Future Power System : generation mix

Figure : All-Renewable ElectricityGeneration in 2050.Source: DESERTEC.

Renewable and distributed energysources are attractive alternatives forpower generation

Z Major Technical and Economicschallenges

1 Intermittency: complexification ofoperation issues.

2 Costs: infrastructures and devices.

N. Maïzi (MINES ParisTech) Short term vs long term energy planning 29 Avril 2016 2 / 32


Future Power System : Reliability of electricity supply

Figure : Europe from orbit during theItalian blackout (Sept. 28th, 2003). Source:French TSO.

Technical constraints binding theoperation of the future power system arerelated to:

the given level and spatialdistribution of loads and capacities;the expected level of reliability toprevent from power outages.

Z Where reliability is the capability ofthe power system to withstandsudden disturbances.



Transient Stability and Reliability

Definition: Ability of a Power System after a Transient Period to lockback into Steady-State conditions, maintaining Synchronism.

During the Transient Period:Frequency and voltages change;The operator can’t modify the production plan;The system relies on the inertia (kinetic and magnetic) of itstransmission and production capacities;The power system must remain stable.

Transient Stability Studies assess the level of Reliability of powersystems.



Time scale of transient stability physical phenomena

Figure : Physical phenomena associated with the electrical system (according to themain domains : protection, network control and monitoring) versus time

Reference: Bruno Meyer, Michel Jerosolimski, Marc Stubbe: Outils de simulation dynamique des réseaux

électriques. Techniques de l’ingénieur,1998.



Assessing future power systems : dynamics issues

Stability studiesinvolve time scales ranging from afew milliseconds to a few hours

Long-term planning modelsdeal with several years or decades

Z This gap is the main reason why reliability requirements are oftenignored or not accurately implemented in long-term planning models.



Seeking for a plausible reliable, environmentallycompliant power mix

M. Drouineau, Modélisation prospective et analyse spatio-temporelle : intégration de ladynamique du réseau électrique, Mines ParisTech PhD Thesis under N. Maïzi direction,PhD Thesis under N. Maïzi direction, Sophia-Antipolis, France, 2011.

S. Bouckaert, Assessing Smart Grids contribution to the energy transition with long-termscenarios (Contribution des Smart Grids à la transition énergétique: évaluation dans desscénarios long terme), Mines ParisTech PhD Thesis under N. Maïzi direction,Sophia-Antipolis, France, 2013.

V. Krakowski, Intégration des renouvelables et stratégie de développement du réseau(Renewable and network development strategies) ongoing Mines ParisTech PhD Thesisunder N. Maïzi direction.



Assessment of reliable future power systemsZ This study proposes an approach combining these dynamics issues to assessfuture power mixes where

Future power mix are assessed through along-term model

TIMES, a bottom up technologicalmodel realizing the minimization of theglobal discounted cost of the RES

A reliability criteria

is established to handle the dynamicmanagement (frequency and voltagecontrols)

depending on:

the level of reliabilityrequired,the dynamic propertiesof capacities,the load profilethe grid properties

avoiding time-consumingmethods relying on Kirchhofflaws.N. Maïzi (MINES ParisTech) Short term vs long term energy planning 29 Avril 2016 8 / 32


From the reversibility condition to network management

The best power transaction achieves :

tertiary & secondary control ∼ mn︷︸︸︷Pmechext = P∗Joule +

primary control︷︸︸︷dE∗cindt︸︷︷︸

∼ s

+dF∗

dt︸︷︷︸∼ ms

(1)

While in steady-state :

Electricity consumption = generation

Frequency and Voltage: constant

Embedded kinetic and magnetic free-energies are time-invariant

A fluctuation occurs : during the Transient Period, Frequency and voltages change;

Magnetic energy:spread the fluctuation over the gridProvide stiffness between distributed kinetic reserves

Kinetic energy: inertia for the power system



Load fluctuation and stability

∼ mn︷︸︸︷Pmechext = PJoule +

dEcin

dt︸︷︷︸∼ s

+dF

dt︸︷︷︸∼ ms

Two events experienced by a power system: an admissible load

fluctuation is lifted by the electromagnetic coupling energy

(ΦIexc), the kinetic reserve (Ecin) and the generation

realignement during a load fluctuation (left) ; conversely, a

short circuit lowers the coupling energy and the kinetic reserve

leading to a collapse of power transmission (right).

Energy exchanges between the subsystems involved in the Thermodynamic Framework Source: V. Mazauric



Control operations

Thus time response is related to dynamic management of a suddenimbalance involving the three control levels needed for network operations :

1 primary : automatic devices re-establishes the balance between demand and generation at a

system frequency other than the frequency set-point value (50 Hz). It causes a deviation in power exchanges

between control areas from the scheduled values. (ancillary services)

2 secondary : automatic devices restore the system frequency to its set-point and restore the

power exchanges between the control areas. (system adequacy)

3 tertiary : TSO manual action to activate reactive compensation equipment. (system

adequacy)



Deriving reliability indicators (Patent FR 11 61087)

Z In order to ensure system reliability enough reserve levels must beprovided:

magnetic reserve : transmission maintenance ;kinetic reserve : frequency maintenance.

Z The higher the reserves, the more reliable the system is.

Reliability criteriaThe reserves are associated to two indicators Hcin Hmag

They refer to dynamic properties of the installed capacities, eachcontributing to the reserves level in a specific way

The level of reliability is characterized by H:the time you have to recover the stability of the system after a loadfluctuation (equivalent to the whole system capacity) by monitoring itsreserves.



Steps of the approach

1 A power mix is delivered through the prospective horizon using TIMES

2 The level of reliability of the power system can be derived fromthe dynamic properties of the installed capacitiesthe associated inertia of the system (kinetic and magnetic)the load profile.

characterized by H as a measure of the kinetic energy inertia



Path variant towards 100% renewable in France

Power mix in the six reference scenarios (nuclear 50% by 2025, RES 27% by 2020 and 40% by 2030)

This power mix ensure only system adequacy (84 Time slices, Disaggregation of each dispatchable

power plant into six processes for a better representation of the production curve, peak load factor, flexibility

options.)N. Maïzi (MINES ParisTech) Short term vs long term energy planning 29 Avril 2016 14 / 32


Path towards 40% to 100% renewable in France

Overall installed capacity from 2013 to 2050 (six reference scenarios + three variants)

No week with low wind/PV production (v1) ; VREs do not participate in peak load factor (v2)




Power production in 2050 in the reference 100% RES scenario



Kinetic reserves for peak periods in 2050

Deviation of kinetic reserves in 2050 compared to the minimum 2012 level, in the reference 100% RES scenario,

with import contribution (variant v8) / wind contribution (variant v9), and in three variants with high biomass

potential (variants v5-v7)



Seeking for a reliable, environmentally compliantpower mix

Reliability Indicators as constraints



Focusing on the Reunion Island

power production: 100%renewable in 2030

1 Blessed with high renewableenergy potentials

2 Small, weakly-meshed andremoted power system

3 Binding target in 2030:100% renewable sources inpower generation

4 Maximum : 30% EnRintermittency



The electricity sector in 2008

Electricity production: 2 546 GWh

Installed capacitiesThermal units (76%):

476 MWFuels: coal, fuel oil,sugarcane bagasse

Hydroelectricity (20%):Dams: 109,4 MWRun-of-the-river: 11,6 MW

Others (4%):Wind: 16,8 MWSolar PV: 10 MWMunicipal Waste: 2 MW



Existing power plants



BAU Scenario : production (GWh)



100% Renewable Energy in 2030, production (GWh)



Is 30% the maximum share ?Electricity production mix in 2030

of a typical day during summer

0 5 7 9 12 17 20 22 240

5

10

15

20

25

30

35

t (h)

Hcin(s)

of a typical day during winter

0 5 7 9 12 17 20 22 240

5

10

15

20

25

30

35

t (h)

Hcin(s)

0 20 40

100 % EnR PV−OCE REF−2008

100 % EnR : limitation of 30 % of instantaneous power production issuedfrom intermittent sources

PV-OCE : no constraint on intermittency

REF-2008 : kinetic reserve level in 2008



Storage contribution to dynamic supportDedicated storage devices must be : distributed (Supercapacities, Batteries NaS,

Li-ion, PbA, etc) in order to contribute to increase the kinetic indicator

Short time response6 15s (timecaracteristic to enableprimary reserve)

High Power Capacity

103 MW

102 MW

10 MW

1 MW

102 kW

10 kW

1 kW

1 kWh 10 kWh 102 kWh 1 MWh 10 MWh 102 MWh 103 MWh 104 MWh 105 MWh

Energie stockée

Pu

issa

nce

fo

urn

ie

10-3 heures (3.6 s)

103 heures (41 jours)

1 heure

Supercondensateurs

Batteries

CAES (petite taille)

Volant d’inertie haute vitesse

Volant d’inertie basse vitesse

Micro SMES

SMES

Batteries à circulation

CAES grande taille

STEP

P. Wang, 2014, From Ibrahim et al, 2008



Impact on Demand Side Management and StorageDSM = postponing demand from peak to off-peak periods + EE

Electricity production mix of a typical day during summer in 2030

Without reliability constraint∀t : Hcin,t >min(Hcin,2008)

Reliability + DSM+ Storage (24MW)

0 5 7 9 12 17 20 22 240

200

400

600

800

t (h)

Puissance

(MW

)

0 5 7 9 12 17 20 22 240

200

400

600

800

t (h)

Puissance

(MW

)

0 5 7 9 12 17 20 22 240

200

400

600

800

t (h)

Puissance

(MW

)

BAGWOO COB DAM GEO OCE−ETM RUN SOL OCE−WAV WIN

share of intermittentsources > 50%

↗ total installedcapacities of 9.4 %

share of intermittentsources > 50%

↘ total installedcapacities of 6 %



Seeking for a reliable, environmentally compliantpower mix

Do we meet synchronism conditions ?



Synchronism issues

So far Ecin is implicitely added. This assumes that synchronism isinconditionally achieved, whatever the regime and the fluctuation!

However we have some concerns about synchronism coming from phase transition theory where 2d-DoF (DoF

physical) objects (here the rotors of the generators) coupled within a 2D lattice (the grid) is not inconditionally

ordered (synchronized) and may experience a disordering process under long-range soft modes.

Z The factors for desynchronization are: N large, weak and sparsecouplings between generators;Z The factors favouring synchronism are: strong and highly correlatedcouplings between generators (copper plate).

The universality of phase transitions and critical phenomena theory allows to study the synchronism with a

dedicated model (instead the actual and cumbersome electromagnetic behavior) [Kuramoto,1984] enabling to derive

a new indicator :λ2,G

max(i,j)∈εG |Pi − Pj|> 1

(Dörfler, 2013)N. Maïzi (MINES ParisTech) Short term vs long term energy planning 29 Avril 2016 28 / 32



Figure : Synchronism indicator for Peak periods in 2050 (cweek is low wind andsun, no imports)



Summary and perspectives

We have developed an innovative approach in order to qualify

The level of reliability of an assessed power mix system resulting forTIMES assessment. It is derived from

the dynamic properties of the installed capacitiesthe associated inertia of the system (kinetic and magnetic)the load profilethe grid properties

This approach is dedicated to assess the technical feasibility of futurepower mix throughZ criteria reflecting kinetic energy inertia HcinZ criteria reflecting magnetic energy inertia HmagZ criteria reflecting synchronism K



http://www.modelisation-prospective.org/en

More on criteria issues :V. Mazauric and N. Maïzi, A global approach of electromagnetism dedicated to further long-term planning,Proceedings in Applied Mathematics and Mechanics, vol. 7, issue 1, 2007.M. Drouineau, V. Mazauric, N. Maïzi, Impacts of intermittent sources on the quality of power supply: Thekey role of reliability indicators, Applied Energy 2014.N. Maïzi, E. Assoumou, Future prospects for nuclear power in France, Applied Energy (2014), pp. 849-859,DOI information: 10.1016/j.apenergy.2014.03.056.S. Bouckaert, V. Mazauric, N. Maïzi, Expanding renewable energy by implementing Demand-Response,Energy Procedia (2014), pp. 1844-1847.S. Bouckaert, P. Wang, V. Mazauric, and N. Maïzi, Expanding renewable energy by implementing Dynamicsupport through storage technologies, Energy Procedia, vol. 61, pp. 2000-2003, 2014.M. Drouineau, E. Assoumou, V. Mazauric, N. Maïzi, Increasing shares of intermittent sources in ReunionIsland: impacts on the future reliability of power supply, Renewable and Sustainable Energy Reviews.06/2015; 46. DOI: 10.1016/j.rser.2015.02.024V. Krakowski, E. Assoumou, N. Maïzi, Enjeux d’une transition vers une production d’électricité 100%renouvelable en France, dans Revue de l’Energie, No 627 (Septembre/Octobre 2015), pp. 381-394, 2015.V. Krakowski, E. Assoumou, V. Mazauric, N. Maïzi, Feasible path toward 40% - 100% renewable energyshares for power supply in France by 2050: A prospective analysis. Applied Energy 171 (2016) 501-522.V. Krakowski, N. Maïzi, V. Mazauric, A magnetic model dedicated to the stability of the power grid,Advances in Magnetics, IEEE Conference, Bormio, 2016.



http://www.modelisation-prospective.org/en

More on other long term analysis subjects :

T. Le Gallic, E. Assoumou, N. Maïzi, P. Strosser, Les exercices de prospective énergétique à l’épreuve desmutations des modes de vie, VertigO, 2015.

F. Briens, N. Maïzi, Coping with the complexity of socio-ecological systems : Investigating the DegrowthParadigm through prospective Modeling,ÖkologischesWirtschaften 3.2014 (29)

S. Selosse, O. Ricci, N. Maïzi, Fukushima’s impact on the European power sector: The key role of CCStechnologies, Energy Economics, Vol. 39, pp 305-312, 2013.

A. Dubreuil, E. Assoumou, S. Bouckaert, S. Selosse, N. Maïzi, Water modeling in an energy optimizationframework - The water-scarce middle east context, Applied Energy 101, (2013), pp 268-279.

E. Assoumou, N. Maïzi, "Carbon value dynamics for France: A key driver to support mitigation pledges atcountry scale", Energy Policy, Volume 39, Issue 7, July 2011, Pages 4325-4336.

Analysis of the effect of environmental policies on the allocation of natural gas accross end-use sectors inFrance, E. Assoumou and N. Maïzi, CMA, MINES ParisTech, Working Paper n◦ 2011-02-02


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