Future Electric Power Systems and the Energy Transition – M. Paolone | 0’4.02.2019 1

Future Electric Power Systems and

the Energy Transition

Feb. 4th –8th, 2019

Influencing the Bulk Power System Reserve by Dispatching Power Distribution Networks using Local Energy Storage Prof. Mario PaoloneEPFL Distributed Electrical Systems Laboratory (EPFL-DESL)2nd FEPSET Champéry 2019

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Motivations – The issue of the reserve scheduling

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Motivations – The issue of the reserve scheduling

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Motivations – The issue of the reserve scheduling

Fundamental observationThe increasing connection of stochastic renewables and the upcoming penetration of distributed storage and demand-response mechanisms, are expected to affect significantly this control philosophy.

This will require, in general, an increase of the primary/secondary reserves in order to keep safe margins and maintain the grid vulnerability at acceptable levels.

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Motivations

Local dispatch of stochastic resources, such as:

PV plants [Marinelli et al., 2014], [Conte et al., 2017].

wind farms [Abu Abdullah et al., 2015], and

heterogeneous resources [Sossan et al., 2016], [Appino et al., 2018],

by leveraging forecasts and exploiting local flexibility is often advocated to reduce the amount of reserve requirements required to operate the grid.

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Dispatching heterogeneous resources via local storage

Dispatch Plan (shaded orange)Stochastic flowCorrected stochastic flow

Problem Statement

Compute a dispatch plan for a set of heterogeneous resources at the grid connection point (GCP) accounting for local grid constraints and local storage capacity (see also [Stai et al., 2017]).

Control storage devices in real-time to track the dispatch plan.

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Dispatching heterogeneous resources via local storage

1. Objective: control an energy storage asset (for instance, a battery energy storage system - BESS) in order to dispatch the operation of a MV network hosting non-controllable stochastic generation and demand.

2. Stochastic aspects: determine a set of possible consumption/generation scenarios for the stochastic resources (prosumption).

3. Problem: maximize the exploitation of the BESS capacity subject to energy and power constraints and to the uncertainty due to the stochastic nature of the resources (PV generation, loads). 2nd FEPSET Champéry 2019

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Dispatching heterogeneous resources via local storage

L↓L↑1. Objective: control an energy storage asset (for instance, a battery energy storage system - BESS) in order to dispatch the operation of a MV network hosting non-controllable stochastic generation and demand.

2. Stochastic aspects: determine a set of possible consumption/generation scenarios for the stochastic resources (prosumption).

3. Problem: maximize the exploitation of the BESS capacity subject to energy and power constraints and to the uncertainty due to the stochastic nature of the resources (PV generation, loads).2nd FEPSET Champéry 2019

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Provision of multiple ancillary services

Single service applications lead to poor exploitation of battery’s power and energy ratings.

Residual power/energy capacity can be used to provide multiple ancillary services simultaneously. 2nd FEPSET Champéry 2019

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We may provide multiple services. We define for each ancillary service the [Namor et al., 2019]:Power Budget Energy Budget

Operator to determine width of envelopes:

We seek to find the controllers’ parameters which maximize the exploitation of the battery energy capacity subject to the battery’s power and energy constraints.

parametrized vector of controller’s parameters and forecast of the unitary budgets θ.

Provision of multiple ancillary services

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Dispatch + primary frequency regulation (PFR)

Provision of multiple ancillary services

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Influencing the bulk power system reserve

Working HP: BESSs are deployed to achieve dispatch-by-design operation of multiple distribution systems [Bozorg et al., 2018]

ApproachCase study: we consider the case of the Danish transmission grid and the associated fleet of conventional power plants and compare it against local dispatched distribution grids.We perform stochastic simulations to quantify and validate the reserve requirement necessary to operate this power systems with a desired reliability level.We establish a numerical equivalence between saved conventional reserve capacity and amount of BESS storage deployed in distribution networks.Finally, we quantify the economic pay-back times of BESSs capital expenditure (CAPEX).

Key QuestionsWhat is the impact on total power system reserve requirements ?Is this integration approach economically viable compared to the centralized procurement of reserve from traditional sources ?

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Case Study – West Denmark power system

126 buses at 400 kV and 165 kV connected by 147 transmission lines and 41 high voltage transformer.227 power generation units with overall capacity of 7323,1 MW. Stochastic generation (wind) penetration is 50%.Total load (electric energy demand), during one hour, is 2071,9 MWh.

NoteTechnical details of system components are considered based on realistic data sets providedby ENTSO-E and Energinet.dk.

Interconnections with neighbouring countries isnot considered as the system is balanced in terms of overall generation and consumption.

Generation mix in terms of annual energy production2nd FEPSET Champéry 2019

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Dispatched-by-Design distribution networks

Operation for three weeks at 5 minute resolution using the EPFL experimental setup

Dispatch following errorComposition of an HV bus: a dispatched-by-design distribution system (left) and a conventional distribution system (right) are connected to the bus

Required storageMethod: Monte Carlo Simulation

10,000 scenarios (1 day profile with 1-hour time resolution)Stochastic DG forecast error Load forecast error

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Reliability assessment simulator

For a given case (power plant fleet and network topology), we apply a three-stage approach based on Monte Carlo simulation [4,5]:

Scenario Generation (demand and stochastic distributed generation forecast error, power plants/line outages, and dispatch following error of distribution networks with dispatch-by-design capability)

System response (computation of system frequency and line congestion as a function of activation of automatic and manual frequency control)

Evaluation of reliability metrics(expected load not served, ELNS) [Alizadeh Mousavi et al, 2014]2nd FEPSET Champéry 2019

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Base case results

Base Case- Total Load = 2071.9 MWh- No dispatched-by-design distribution network- Stochastic DG penetarion: 50% of total load- Reserve capacity: R = 10% of total load (automatic and manual reserve activation Ra = 0.3 R and Rm = 0.7 R)- 2000 input scenarios

ELNS = 39.69 MWh/h (i.e., 0.014 of the total load). ELNS per total load is far beyond the ENTSO-E recommendation (0.001-0.002)

Solutions for improving reliability indexCase I the power reserve capacity is entirely provided by conventional power plants;Case II reduced capacity of conventional power plants to provide reserve power that is compensated for by implementing dispatched-by-design distribution networks.2nd FEPSET Champéry 2019

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Results validation

To validate the results of power system reliability assessment simulator, the value of ELNS for different levels of reserve capacity are calculated based on real measurement at 1 hour resolution of the net surplus/deficit power imbalances data for the Isolated West Denmark power system, obtained from energinet.dk.

Real measurements 2015

Simulated results agree well with the empirical distributions of measured power imbalances in 2015 and 2016. This validates the accuracy of the developed simulator and supports the realism of the conclusions obtained with respect to the case of deploying dispatched-by-design distribution networks.2nd FEPSET Champéry 2019

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Case I (reliability assessment)

The power reserve capacity is entirely provided by conventional power plants

The TSO requires to provide reserve capacities up to 28.5% and 34.5% of the total in order to satisfy the 0.001 and 0.002 ELNS target values, respectively.

Increasing reserve capacity does not change the amount of Expected Regulating Power (ERP). 2nd FEPSET Champéry 2019

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Case II (reliability assessment)

Case II Full control: All stochastic DG connected to the distribution network is under the dispatched-by-design regime. Case II Half control: 50% of the stochastic DG, in terms of power capacity, connected to the distribution network is under the dispatched-by-design regime.

To satisfy 0.001 and 0.002 ELNS target value:In Case II Full control: 44% and 36% dispatched-by-design penetration levelscorrespond to the installation of 4093 MWh and 3348 MWh BESSs within the distribution networks are required.

The Expected Regulating Power (ERP) is considerably decreasing by increasing the penetration level of dispatched.by-design distribution networks.

- An energy storage capacity of 4.49 (3.21) times the hourly peak load (in MWh) is required to ensure the full (half )DG control scheme.

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Comparison of Case I and II: reliability assessment

(a) The equivalent reserve capacity provided by conventional power plants that could be replaced by dispatching distribution network as a function of the total capacity of energy storage systems.

(b) The average hourly reduction in required upward/downward regulating power by installing dispatched-by-design distribution systems as a function of the total capacity of energy storage systems.

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Economic evaluation: Case I

Annual operational costs1) Cost of buying reserve capacity: computed based on the average reserve capacity prices in West Denmark in 2015 and 2016.2) Cost of buying upward and downward regulating power: calculated using the developed cost estimation model with respect to the West Denmark spot market hourly prices in 2015 and 2016.

Regulating power price Day-ahead power price Imbalance power

Model parameters,

λ=0.212,μ = -0.0067 €/MWh,η=0.952 €,α=0.197,γ=-0.008 €/MWh,β=0.635 €

YearELNSper totalload

Manual reserve Automatic reserve Upwardregulatingpowerannual cost(M€)

Downwardregulatingpowerannual cost(M€)

Totalannualcost(M€)

QuantityMW

Annual cost(M€)

QuantityMW

Annual cost(M€)

2015 0.001 346 1.51 148.4 39.48 13.29 -10.20 44.090.002 261.3 1.25 112 32.62 12.98 -9.74 37.11

2016 0.001 346 3.03 148.4 45.43 15.64 -11.84 52.260.002 261.3 2.50 112 37.52 15.28 -11.31 44.00

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Economic evaluation: Case IIAnnual operational costs (AOC)1) Cost of buying reserve capacity and 2) Cost of buying upward and downward regulating power, computed similar to Case I regarding 10% reserve provision.

To compare the two cases from economicperspective, the yearly cumulative costs (CC)are calculated;

Unitary investment costs for BESS adapted from B. Nykvist and M. Nilsson, “Rapidly falling costs of battery packs for electric vehicles,” Nature Climate Change, vol. 5, no. 4, pp. 329–332, 2015.

Investment cost of buying BESSs (IC)

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Comparison of cases I and II: economic evaluation

The yearly cumulative costs for two desired reliability levels (Fig. a,b). The year 0 corresponds to 2016 and BESS unitary cost is 280 Euro/kWh.

Cumulative cost of Case II becomes lower than of Case I after 11 (12) years when the target ELNS per total load is 0.001 (0.002).

Sensitivity analysis is provided to quantify the pay-back time (i.e., break-even points) of investment costs associated with Case II, as a function of the unitary cost of BESS (Fig. c,d).

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Conclusions

The effect of dispatched-by-design power distribution systems on the amount of reserve required to operate the bulk grid with a certain level of reliability has been investigated.

We considered as a case study the Danish transmission grid and the associated fleet of conventional power plants. The two following cases were considered:

Case I the power reserve capacity is fully provided by conventional power plants;Case II reduced capacity of conventional power plants to provide reserve power that is compensated for by implementing dispatched-by-design distribution networks.

We performed a complete technical and economic assessment and the results showed that 1) large scale deployment of BESSs under dispatch-by-design architecture of distribution network is a viable technical solution to address flexibility requirements of power systems and 2) this solution is economically viable with a pay-back time in the range of 11-14 years (depends on deployment schemes) compared to providing flexibilities from conventional power plants. 2nd FEPSET Champéry 2019

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References

[1] M. A. Abdullah, K. M. Muttaqi, D. Sutanto and A. P. Agalgaonkar, "An Effective Power Dispatch Control Strategy to Improve Generation Schedulability and Supply Reliability of a Wind Farm Using a Battery Energy Storage System," in IEEE Transactions on Sustainable Energy, vol. 6, no. 3, pp. 1093-1102, July 2015.

[2] M. Marinelli, F. Sossan, G. T. Costanzo and H. W. Bindner, "Testing of a Predictive Control Strategy for Balancing Renewable Sources in a Microgrid," in IEEE Transactions on Sustainable Energy, vol. 5, no. 4, pp. 1426-1433, Oct. 2014.

[3] F. Conte, S. Massucco, M. Saviozzi and F. Silvestro, "A Stochastic Optimization Method for Planning and Real-Time Control of Integrated PV-Storage Systems: Design and Experimental Validation," in IEEE Transactions on Sustainable Energy, vol. 9, no. 3, pp. 1188-1197, July 2018.

[4] F. Sossan, E. Namor, R. Cherkaoui and M. Paolone, “Achieving the Dispatchability of Distribution Feeders through Prosumers Data Driven Forecasting and Model Predictive Control of Electrochemical Storage”, IEEE Trans. on Sustainable Energy, vol. 7, no. 4, pp: 1762 – 1777, Oct. 2016.

[5] Appino, R. R., Angel Gonzalez Ordiano, J., Mikut, R., Faulwasser, T., and Hagenmeyer, V. (2018). On the use of probabilistic forecasts in scheduling of renewable energy sources coupled to storages. Applied Energy, 210:1207 – 1218.2nd FEPSET Champéry 2019

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References

[6] E. Namor, F. Sossan, R. Cherkaoui and M. Paolone, “Control of Battery Storage Systems for the Simultaneous Provision of Multiple Services,” IEEE Transactions on Smart Grid, 2019 (accepted in press).

[7] E. Stai, L. Reyes-Chamorro, F. Sossan, J. Le Boudec and M. Paolone, "Dispatching Stochastic Heterogeneous Resources Accounting for Grid and Battery Losses," in IEEE Transactions on Smart Grid, vol. 9, no. 6, pp. 6522-6539, Nov. 2018.

[8] O. Alizadeh Mousavi, M. Bozorg, R. Cherkaoui, M. Paolone, “Inter-area frequency control reserve assessment regarding dynamics of cascading outages and blackouts”, Electric Power Systems Research, vol. 107, February 2014, pp. 144-152.

[9] M. Bozorg, F. Sossan, J.-Y. Le Boudec and M. Paolone, “Influencing the bulk power system reserve by dispatching power distribution networks using local energy storage,” Electric Power Systems Research, vol. 163, Part A, pp. 270-279, Oct. 2018.

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