Safe Integration of Renewables at Transmission and Distribution … · 2019-01-18 · IVR GIS...

Safe Integration of Renewables

at Transmission and

Distribution Level

Alexander Krauss - Digital Grid Software and Solutions

siemens.com/digitalgrid Unrestricted © Siemens AG 2017

Page 2

Unrestricted © Siemens AG 2017

May 2017 Alexander Krauss / EM DG SWS S MI

Overview

Table of content

• The Masterplan - One Platform

• Active Nework Management

• Wind Power Managment

Page 3



Siemens Digital Grid masterplan architecture for a smooth transition to …

CIM – Common Information Model (IEC 61970)

Enterprise IT

IVR GIS Network planning

Asset management

WMS/mobile Weather Forecasting Web portals CIS/CRM Billing

Enterprise Service Bus

Cloud enabled applications Public cloud

Smart communication

Grid

cyb

er s

ecu

rity

Man

ag

ed

/clo

ud

serv

ices

OT

-IT in

teg

ratio

n, c

on

su

lting

Smart

transmission

Smart

distribution

Smart

consumption

and microgrids

Smart

distributed energy

systems

Smart

markets

$ € ₹

Business applications Grid control applications Grid planning and simulation

CIM CIM

Page 4



Cloud enabled applications e.g. analytics

Agility in energy:

open – adaptable – manageable, standards-based ecosystem

Enterprise IT

IVR GIS Network planning

Asset management

WMS/mobile Weather Forecasting Web portals CIS/CRM Billing


Public cloud

Smart communication

Grid

cyb

er s

ecu

rity

Man

ag

ed

/clo

ud

serv

ices

OM

NE

TR

IC G

rou

p, S

mart G

rid C

om

pass

Field area networks Substation: SICAM and SIPROTEC within ENEAS solutions

Meter Data

concen- trator

Load controls

Building/ home energy management

system

Grid sensors

Microgrid controllers

Third party …

Substation controllers

Human machine interfaces

Protection devices

Remote terminal units

Bay controllers

Power quality devices

Third party

CIM – Common Information Model (IEC 61970) | PSS – Power System Simulator

Business applications:

EnergyIP

Grid control applications:

Spectrum Power

Grid planning and simulation:

PSS Suite CIM CIM

Page 5



Spectrum Power™

Comprehensive portfolio of applications

Generic

applications

Basic

functions

Energy market

& generation

Transmission

Distribution

Multi-utility/

industry

Infrastructure

UI (User Centered Design)

Data Entry & Data Model (IMM)

Archive (HIS)

Communication

SCADA & Base System

Security

IT-Interfaces and SOA

Forecast Applications

Load Shedding

Power Applications

Resource optimization & scheduling

Energy Market Management

Transmission Network Applications

Training Simulator -Transmission

Network Stability Analysis

Distribution Network Applications

Training Simulator -Distribution

Outage Management System

GIS-Interface

Load Management Electricity, Gas

Supply-Management Water

Pipeline-Networks

Disposal Locking

Rota Switching

Renewable

generation Transmission

Energy

production Multi-utility Industry

Distribution Infrastructure

Basic Functions

Specific Applications

Page 6



Spectrum Power™ 7

Architecture


Spectrum Power High Speed Bus

HIS IMM GIS

I/F OMS CMS TCS DNA TS

Work Force Mgmt

Customer Inform. System

SAP Asset Mgmt

Meter Data Mgmt

Geo-graphical Inform. System

Weather Forecast

Network Planning

SA RO FA PA EMM

Base SCADA CFE

IFS OPC

ICCP

ELC

TNA

Base OPF DSA TS LME LMG LMW MERO

Real-time and day ahead energy market management

Communication with substation

RTU/SAS and other control

centers

Transmission load flow calculation, grid

optimization and what-if studies

Management of infeeds, switchable loads

and storages for minimum cost (power, gas,

water)

Distribution load flow calculation, grid optimization and what-if studies

Systematic outage management for faster and more restoration

Base functionality: data model, UI,

SCADA, Archive

Energy resources and production planning; load forecasting for generation and grid operation planning

Generation control for more economic and reliable operation

... ...

Page 7



Enterprise IT

Integrated platform strategy to

ensure minimized risk and high cost efficiency

Digitalization

& Automation


Smart Communication

Smart transmission

Smart distribution

Smart consumption and microgrids

Smart distributed energy systems

Smart markets

$ € ₹

CIM – Common Information Model (IEC 61970)

IVR

GIS

Network planning

Asset management

WMS/mobile

Weather

Forecasting

Web portals

CIS/CRM

Billing

EnergyIP

Smart grid & smart market applications

• Meter Data Management

• Decentralized Energy Resource Management

• Revenue protection/Non-Technical Losses

• Prepaid Energy System

• Market Transaction Management

• Energy Engage customer web portal

• Energy Analytics

Spectrum Power

Grid control application

• Transmission & Distribution Network Analysis

• System stability and system balancing applications

• Outage and trouble call management

• Active network management

• Fleet optimization & scheduling

• Forecasting and planning applications

• Energy Market Management

CIM

Center

E-car

Operation

Page 8



Overview

Table of content




Page 9



New challenges for grid management due to growing need

for integration of renewable generation

No clear direction of power flow

Violation of voltage limits

Overload situations

Increasing installation of renewable

energies

Observability improvement

Volt-/VAR management

Capacity management

Page 10



Spectrum Power Active Network Management

Releasing hidden capacity by Active Network Management

Real time thermal rating Energy storage Voltage control device Controllable generation Controllable loads

Network state

1

Problem detection

2

Decision making

3

Set-point command

4

Active Network Management System

Page 11



Spectrum Power TM ANM

Active Network Management, Use case

Use case description

Decentralized power generation and distributed infeed causes

• voltage band violations on high, medium and low voltage level

• overload of grid components (transformers, lines, etc.)

• power quality problems due to inverters

Active network management monitors the network state to detect

network volatilities, suggests counter measures and implements

the counter measures in closed loop

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Active Network Management based on real time state estimation

Estimate

Voltage

Violations

Overloads

Significant

measurement

change

Configured

Cycle

Topology

change

Control

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Control

RDC Primary

RDC Secondary

Optimization


System components of an hierarchical solution

Data

Con

ce

ntr

ato

r Battery Capacity

Management

OLTC AVC

Management

Capacitor Control Lo

ca

l C

oo

rdin

ation

& P

rio

ritiza

tio

n Thermal Modelling

Local Thermal

Management

Local Voltage

Management

Fro

nt-

En

d

Capacity

Management

Voltage

Management

SC

AD

A

Topological

Coloring

State Estimation

Volt / Var

Optimization

Central Control

(Siemens Spectrum Power Active Network Management)

Control Center

& Assets

Local Control

(Siemens Autonomous Substation Controller)

Page 14



Spectrum Power TM ANM

References

Northern Power - Grid Grand Unified Scheme

The Grand Unified Scheme (GUS) brings together battery storage, enhanced voltage

control, demand response and real-time thermal rating in closed loop for optimal

grid operation.

Energy Northwest - Smart Street project

Voltage management at the HV level to reduce network losses and conservation

voltage reduction (CVR) at LV level to reduce energy demand, and run LV meshed

networks to release network capacity.

IREN2

Microgid with renewable generation as substitute of conventional generating units.

Migrogid providing flexibilities for the distribution grid operation

Page 15



Electricity North West, Smart Street

Loss Reduction in 11/6.6kV by increasing the

voltage level to an optimal level.

Energy Efficiency by reducing the voltage close

to the lower limit

33 kV 11 / 6.6 kV LV

+10%

-6%

+6%

-6%

Limits on DG at HV

Limits on LCTs such as

heat pumps

Low voltage leads to

unnecessary losses in

DNO network

High Voltage leads to poor

Energy Efficiency for

Customers

+10%

-6%

+6%

-6%

Increases Energy Efficiency

for CustomersIncreased scope for DG

Increased scope for

LCTs such as heat

pumps

Optimised voltage for

overall loss minimization

33 kV 11 / 6.6 kV LV

Current network situation

Optimized network situation

The coordination of the capacitor banks will optimize the voltage levels on HV network for overall loss minimization. The loss reduction will be achieved by increasing the voltage level to an optimal level.

Energy efficiency will be achieved by reducing the voltage close to the lower limit. A meshed network will be utilized using LV circuit breakers along with capacitor banks to flatten the voltage profile thus permitting ENW to drop the voltage across both feeders (using OLTC transformers) to facilitate reduction in losses and energy consumption.

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Active Network Management – Experiences from ENW Smart Street

Goal to Target

Three key steps ...

• Coordinated voltage control, using

transformers with on load tap changers and

capacitors, across HV and LV networks

• Interconnecting traditionally radial HV and

LV circuits

• Real time coordinated configuration and

separate voltage optimization targets of

HV and LV networks

… and expected targets

• Release capacity up to four times faster and

40% cheaper than traditional reinforcement

techniques for low carbon technology clusters.

• Deliver conservation voltage reduction to improve

the energy efficiency of customers’ electrical

appliances reducing energy up to 3.5% - 4% per

annum, and lowering network losses by up to

2.5 – 3.5% per annum across HV and LV

networks.

This will deliver recurring financial savings for

customers, without degradation to the quality of

customers’ supplies.

Page 17



Active Network Management - ANM

Solution Overview

No clear direction of

power flow

Violation of voltage

limits

Overload situations

Increasing installation of

renewable energies

Observability

improvement

Volt-/VAR management

Capacity management

Active Network Management

System

Real time

thermal rating Energy storage

Voltage control

device

Controllable

generation

Controllable

loads

Network state

1

Problem detection

2

Decision making

3

Set-point

command

4 Improves situational

awareness

Reduces voltage band

violations and equipment

overloads

Minimizes distribution

losses

Supports various network

components

Optimizes in full closed-

loop operation

Scalable and flexible

Page 18



Spectrum Power ANM

Key benefits

Efficient peak load management

provides comprehensive information

for taking decisions to avoid

problems such as voltage violations

and overloads

Reliable voltage regulation

Generic on-line optimization that can

be easily extended to cover additional

network areas and controllable

resources

High grid stability

supports operator in detecting

network volatility early enough to

react in time

1

2

3

4 Reduced costs

Helps to avoid expensive network

extensions while operating the

existing network in an optimized way

Page 19



Overview

Table of content




Page 20



Wind Power Management

Introduction

Wind power generation mainly influenced by

Weather effects, Wind Farm Structure, Region

Experience from countries in the sector shows that

advanced tools are needed to forecast wind power

generation

Principal actors: power producers, transmission

system operators and independent power generators

Uncertainty of wind hard to predict

Accurate and reliable forecasting systems of wind

power production increases wind penetration

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4 types of input data:

WPF and WPE files are given by external forecasters and are optional

21

Input Data

Real time measurements

(SCADA) are received every

15/20/30 or 60 min and give

the power production of

each telemetered wind farm

RTM

Wind Power Estimations are

received each hour:

estimation of last 24 hours of

total wind power produced

(telemetered and non

telemetered)

WPE Wind

Forecasts

Wind forecasts (speed and

direction) in a distributed set

of geographical coordinates

and close to the location of

the wind farms

Total Wind Power Forecasts

are received each hour:

forecast of total wind power

produced by the system

WPF

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Calculates predictions based on real time measurements and meteorological forecasts (wind speed and

wind direction) up to 48 hours

Ensemble forecast: 8 forecasts generated and combined internally

22

Prediction Algorithm

8 prediction

models

Nonparametric

models

More historical data

are needed to be

more accurate

Parametric

models

Less historical data is

needed to obtain

initial estimates

M1: Persistence Model

M2: Autoregressive Model

M3: Autoregressive and Linear Speed Model

M4: Autoregressive and Quadratic Speed Model

M5: Autoregressive, Linear Speed, and Wind Direction Model

M6: Autoregressive, quadratic speed and wind direction model

M7: Autoregressive and Non-parametric Prediction Based on

Speed and Direction

M8: Non-parametric Prediction Based on Speed and Direction

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May 2017 Alexander Krauss / EM DG SWS S MI 23

Prediction Algorithm

The final forecast is obtained by combining the predictions generated by

8 different models

RTM Wind Forecasts

Model 1 (accuracy 1)



Combinations

Final Prediction

…

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Benfits and Key features

Key

Features

1

4

6

5

3

Statistical and

combined

forecasting method

Total / regional wind power

forecast 1h up to 10 days

Disaggregation of

the result up to 48

hours

After the Fact Error

Analysis

15/20/30/60 minutes

time grid

2

Voltage

Pre- and Post

contingencies 7

Locational Marginal

Price for Intraday

and Day ahead

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STWPF sample display: wind farms capacities per location

25

How does it look like?

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How does it look like?

STWPF sample display: wind direction

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Questions ?

Alexander Krauss

Sr Business Developer

EM DG SWS S MI

Humboldtstr. 59

90459 Nürnberg

Germany

Phone: +49 151 54444345

E-mail:

[email protected]

siemens.com/digitalgrid

mailto:[email protected]

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Safe Integration of Renewables at Transmission and Distribution … · 2019-01-18 · IVR GIS...

Documents