pv and storage in communities: challenges and...

© 2017 L. Ochoa - The University of Melbourne PV and Storage in Communities, Mar 2017 1

PV and Storage in Communities: Challenges and Solutions

iiESI Workshop, Melbourne March, 2017

Prof Luis(Nando) Ochoa Professor of Smart Grids and Power Systems

[email protected]


Outline

Context: Flexibility

Challenges in PV-Rich LV Networks (Communities) – PV, Battery storage – Case Study, Control strategies – Effects on the LV Network and Customers

Future DSOs and Community-Based Flexibility

Conclusions


Flexibility Vision (UK Regulator)

TRANSMISSION NETWORK

DISTRIBUTION NETWORK

Renewable generators

conventional thermal generation

INDIVIDUAL PROVIDERS

Changing demand/supply in response to prices or direct signals from flexibility users.

LOCAL BALANCING

Community-based flexibility where consumers use local generation with DSR or storage, reducing the need to transport electricity.

THIRD PARTY AGREGGATION

Consumers, generators and storage can provide flexibility through an intermediary. Based on Ofgem’s

vision of flexibility


But before we get there…


Challenges in PV-Rich LV Networks

PV generation happens during the day, when many people are not at home Problems

V Max

Min

I I

V

Off-LTC


Challenges in PV-Rich MV Networks

Wide-spread PV adoption Challenges to HV networks

How can we address this?

Primary Sub OLTC

Off-LTC

V


… but people will buy storage.

Surely, this will sort it out (?)


Challenges in PV-Rich LV Networks

For Distribution Network Operators (DNOs) – PV impacts: voltage rise and thermal overload – Networks need to be reinforced

For householders

– Little or no incentive to export PV power – PV export limits are being adopted in some countries

(e.g., 70% of PV installed capacity is the limit in Germany) – Falling prices for household storage devices makes it more

viable

store the surplus during the day to use it later in the night!


Residential PV + Storage Systems

For householders – interested in their own benefits – want to save money with the PV + storage system – want to use as much low carbon energy as possible – do not care about the DNO’s technical problems!

For DNOs

– storage creates opportunities to mitigate technical issues and avoid/defer investments

– concerned with less grid dependency, lower revenue

… very different objectives


Storage – Basic Control and Sizing

Day (generation) Store the exports until full Night (no generation) Supplies the house load until empty Size of battery Enough to meet the average exports

Average energy production

High exports day

Storage becomes full network issues?


… Does residential storage

help mitigating PV impacts?


LV Network Impacts – Case Study

Real UK LV Network (NW England) – Residential, underground – Tx 800 kVA, 11/0.4 kV, 4 feeders – 200 single-phase customers – Voltage limit of 1.10 pu (253 V)

Summer, Weeklong Storage

– Similar to Tesla Powerwall – 2 kW, 10 kWh, 20 < SoC < 90% – Round trip efficiency of 87%

Feeder 1

Feeder 2

Feeder 3

Feeder 4


Probabilistic Approach 1/2

Monte Carlo Analysis – To cope with uncertainties (load/PV behaviour, location)

• Sets of 1,000 residential demand profiles (season/type of day) • Sets of 1,000 PV profiles (season)

– Unbalanced, time-series power flows (OpenDSS) – Different PV penetrations and storage control strategies – 100 week-long simulations per scenario

Key Metrics Voltage: EN50160 non-compliant customers Thermal: Feeder utilisation (1-hour moving avg) Effects on Customers: Grid Dependency, Self Consumption


Probabilistic Approach 2/2

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:00

Inso

latio

n [p

u]

0.0

0.1

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1.0

1.1Summer

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:00

Inso

latio

n [p

u]

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1.0

1.1Winter

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:00

Pow

er [k

W]

0.0

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1.0Load Profiles (Average)

Summer WDSummer WEWinter WDWinter WE

Tool developed by CREST (Loughborough Univ.)

1-min resolution

Number of Residents 1 2 3 4+ Probability (%) 29 35 16 20

PV Size (kWp) 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Probability (%) 1 1 8 13 14 14 12 37


Control Strategies

1. Optimal for the Customer – All PV surpluses (negative net demand) are stored

2. Delayed Charging – Surpluses are stored if the power export limit is reached

3. Store the Excess – Only PV generation beyond the export limit is stored

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:00-4

-3

-2

-1

0

1

2

3

Pow

er [k

W]

GenerationConsumption

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:00-4

-3

-2

-1

0

1

2

3

Net

Dem

and

[kW

]

PV Exports Limit

ExportsImports

70% limit


0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:00-4

-3

-2

-1

0

1

2

3

Net

Dem

and

[kW

]

PV Exports Limit

ChargeDischarge

Mode: Optimal for the Customer

Even small surpluses early morning are stored

More energy for the customer

Battery is likely to be full before PV impacts


Mode: Delayed Charging

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:00-4

-3

-2

-1

0

1

2

3

Net

Dem

and

[kW

]

PV Exports Limit

ChargeDischargeExport

Charging starts closer to peak generation

Battery is likely to have room during PV impacts

Not necessarily making the most of PV generation for the customer


Mode: Store the Excess

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:00-4

-3

-2

-1

0

1

2

3

Net

Dem

and

[kW

]

PV Exports Limit

ChargeDischargeExport

Charging only PV generation beyond the export limit

Battery will significantly help reducing PV impacts

Does not make the most of PV generation for the customer


PV Penetration Level [%]

0 10 20 30 40 50 60 70 80 90 100Vol

tage

Non

-Com

plia

nt C

usto

mer

s [%

]

0

10

20

30

40

50

60

70

80

Feeder 1

No Storage

"Optimal" for the Customer"Delayed" ChargingStore the Excess

Results: Voltage Issues

Optimal for the customers Largest network impacts

Store the excess Significant mitigation of impacts


Results: Thermal Issues

PV Penetration Level [%]

0 10 20 30 40 50 60 70 80 90 100

Feed

er U

tiliz

atio

n Le

vel [

%]

50

60

70

80

90

100

110

120

130

140

150Feeder 1

No Storage

"Optimal" for the Customer"Delayed" ChargingStore the Excess

Optimal for the customers Largest network impacts

Store the excess No thermal issues


… Do all control strategies

benefit customers?


Index of Grid Dependence (IGD)

Effect of using storage on the electricity bill

𝐼𝐼𝐼𝐼𝐼𝐼 =𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖

𝑐𝑐𝑖𝑖𝑐𝑐𝑖𝑖𝑐𝑐𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑐𝑐

100% - household totally grid dependent (normal bill)

0% - household totally grid independent (no bill)

Grid

Household

consumption


Grid Dependency

No Storage Optimal for the Customer Delayed Charging Store the Excess0

102030405060708090

100

Inde

x of

Grid

Dep

ende

nce

(%)

PV w/o storage Produces savings but not large

Optimal for the Customer and Delayed Charging Greatest benefits, small electricity imports

Store the Excess Almost as not having storage!


… so, it is just a matter of

‘smart’ storage control

… not really


Smart & Low Carbon

Bulk Generation

Homes, Schools, Shops, Businesses,

Communities

Distributed Generation

New Service Markets $

Storage

Distribution System Operator


Current Control Philosophy (Distribution)

Grid Supply Point (GSP)

No control of LV and part of MV

Bulk Supply Point (BSP)

Primary Sub

DNO Control Room

OLT

C

Switc

hes

OLT

C

OLTCs Coordinated Decentralised Control

Secondary Sub

Centralised Monitoring and Control Reconfiguration Manual (Remote)

Voltage Decentralised (Fixed Targets)

OLT

C

Switc

hes

Reconfiguration Manual (Local) Voltage Off-load taps


Fully Centralised vs. Coordinated Decentralised

Fully Centralised – Opportunity for holistic ‘optimisation’ – Full flexibility – Increased reliance on monitoring/comms – Increased complexity due to scale – High deployment time

Coordinated Decentralised – More localised ‘optimisation’ (static areas) – Flexibility exists to centrally react to problems – Less centralised monitoring/comms – Less complexity due to reduced scale – Less deployment time


… but there are many other barriers

Defining Distribution Markets – Voltage management, congestion management – Near or post fault management (enabling

islanding/microgrids)

DSOs should be capable of

– Quantifying operational needs and constraints wide-scale observability, forecasting

– Exchanging data with the TSO and third parties (service providers)

… all this requires advanced management systems, investment, time, training, etc.


Conclusions 1/2

Residential PV + storage might become more viable – Lower capital cost, lower electricity bills less grid dependent

However, customer-focused storage control strategies do not reduce PV impacts – Storage gets full before critical periods (around midday)

New storage control strategies can mitigate network impacts from PV without affecting customers – Customers can achieve similar levels of grid dependency


Conclusions 2/2

Complexity will increase with more controllable elements and the need for more flexibility – Computational/data complexity shouldn’t be an issue in ~15 years

Regulators need to define the roles of potential future

players and technologies in the provision of flexibility – E.g.: aggregators, storage – This will prevent new business models being hindered by

regulatory barriers

This transition of DNOs to DSOs in the next few years will

bring exciting new regulatory environments where the envisioned smart grids are likely to finally emerge


Technical Reports and Publications

Technical Reports (most publicly available): https://sites.google.com/site/luisfochoa/publications/technical-reports

List of Publications (most publicly available):

Journal Papers https://sites.google.com/site/luisfochoa/publications/journals

Conference Papers https://sites.google.com/site/luisfochoa/publications/conferences

https://sites.google.com/site/luisfochoa/publications/technical-reports

https://sites.google.com/site/luisfochoa/publications/technical-reports

https://sites.google.com/site/luisfochoa/publications/journals

https://sites.google.com/site/luisfochoa/publications/conferences


Thanks!

Questions?

Acknowledgements

• Dr. Tiago Ricciardi Post-Doctoral Research Associate • Mr. Kyriacos Petrou PhD Student • Dr. Sahban Alnaser Lecturer


PV and Storage in Communities: Challenges and Solutions

iiESI Workshop, Melbourne March, 2017

Prof Luis(Nando) Ochoa Professor of Smart Grids and Power Systems

[email protected]

pv and storage in communities: challenges and...

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