Balancing of Variable Renewable GenerationPower System Seminar at Chalmers 20140930

Mikael Amelin

1

Dept. of Electric Power Systems

• Two professors, one associate professor, two assistant professors

• Four research groups (each group has 5–10 Ph.D. students and a few postdocs)

- Power System Stability and Control

- Smart Transmission Systems Laboratory

- Power System Operation and Planning

- Electricity Market Research

2

Variable renewable generation

• Available capacity is depending on weather.

• High investment costs, negligible variable costs run at available capacity (nondispatchable).

• Examples:

- Run-of-the-river hydro power

- Wind power

- Photovoltaics

- Wave energy

- Tidal energy

3

Challenges of nondispatchable generation in power system operation and planning

• Variability. Nondispatchable generation is varying continu-ously depending on weather conditions.

• Forecast errors. Nondispatchable generation can be difficult to predict even for the next day.

4

Variability

• Nondispatchable gener-ation is not the only thing varying in a power system.

• Power systems are designed to manage large variations!

MWh

10 000

load

0

20 000

12 24

25 January 201327 July 2013

Source: Svenska kraftnät

5

Wind power variations

Storm hits western Denmark

HVDC

HVDC

HVDC

StenungsundKungälvGöteborg

Varberg

Falkenberg

Gnosjö

Halmstad

Ängelholm

Helsingborg

LundMalmö

Trelleborg

(220 k

V)

(220

kV)

(300 k

V)

Lübeck

Flensburg

RinghalsHelsing-borg

Cøpen-hagen

Gothen-burg

MalmöKarlshamn

Norrköping

Oskars-hamn

Hasle

RjukanOslo

Stockholm

Enköping

Nea

Trondheim Umeå

Sundsvall

Loviisa

Olkiluoto

Tallin

HVDC

Kristiansand

Rauma

Forsmark

0 100 200 km

Norway

Finland

Denmark

HelsingforsÅbo

Vasa

Tammerfors

Viborg

Slupsk

reda

ktör

erna

AB

2009

HVDC

HVDC

HVDC

Rostock

The Swedish Natural GasNetwork(high pressure)

6

Consequences of variability

• Some units must deviate from their optimal working point.

• Larger need for automatic frequency control.

• Larger need for real-time balancing.

7

%10

5

7

2

Forecast errors

r

• Most of the trading is done in the day-ahead market.

• Wind power generation is difficult to forecast on the day before.

Day-ahead forecast

–50 +500 +10 +40+30+20–10–20–40 –30

0

MW

forecast erro

0

5

5

Intra-day forecast

Forecast error of 100 MW wind power

8

Consequences of forecast errors

• Inefficient usage of power system resources.

• Larger need for intra-day trading.

• Larger need for real-time balancing.

9

Balancing resources

• Planning target for 2020: 30 TWh wind power

- Equals about 12 000 MW installed capacity.

- Actual wind power generation 2013: 10 TWh.

• A large-scale development of nondispatchable generation will need more balancing resources.

• There is a correlation between nondispatchable generation and reserves.

- High wind power generation less usage of conven-tional generation capacity available for up-regulation.

• How can we utilise the balancing capacity in the most efficient way?

10

Balancing using hydro power

• How flexible is the existing Swedish hydro power system considering hydrology and environment court decisions?

• How can producers use hydro power most efficiently in the future? Stochastic daily and weekly planning.

RIVER LULE ÄLVLength: 461 kmInstalled capacity: 4 345 MWMean generation: 14 TWh/yr

11

Balancing using combined heat and power

• How can we increase the flexibility of electricity generation in combined heat and power?

• How can producers use combined heat and power in the future? Stochastic daily planning.

MW power

MW

heat

12

Balancing using consumers

• How can we use new technical solutions to allow consumers to participate in balancing of variable renewable generation? How willing are consumers to participate?

• How can retailers use consumption flexibility in the future? Stochastic daily planning.

13

Market design

• How should electricity markets be designed in order to efficiently utilise the available balancing capacity?

• How will the electricity market behave under different rules? Simulation of electricity markets.

14