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Vi t l P Pl t b SiVirtual Power Plants by Siemens

DEMS® – Decentralized Energy Management System

© Siemens AG 2013. All rights reserved.

Key Challenges Drive Implementation of Demand Response & Virtual Power Plants

Trends Customer challenges

Generation & network bottlenecks Generation & network capacity bottlenecks:E.g. California, US

Increasing peak load prices

Increasing peak load prices:E.g. Germany 6% in 2009Dispatch load as most economic power supply: A id f ti & t k b ttl k d

Increasing distributed &

Avoidance of generation & network bottlenecks and high peak load pricesIncreased grid stability through emergency load shed & selective load dispatchIncreasing distributed &

renewable generationshed & selective load dispatchNew market opportunities for distributed energy resources

Rising consumption

Infrastructure & Cities Sector – Smart Grid Division © Siemens AG 2013. All rights reserved.Page 2 Smart Grid Division

Demand Response & Virtual Power Plant –Current Portfolio of Siemens Smart Grid

Virtual Power Plant Integrated Solutions

Grid-specificEnterprise IT

Business consulting for identification & analysis of customer business modelsEnergy management system for monitoring planning

Business analytics, IT integration

Operational ITEnergy management system for monitoring, planning and optimized operation of DER, loads & storageFully automated demand response management system: DRMS platform for load aggregation and

Demand response management system (DRMS) , decentralized energy management system (DEMS)

Information &Communication

y p gg genablementForecasting system for consumption and renewable generation

f

Support of standard communication protocols like IEC 104 and OPC, etc. over public/private TCP/IP networks

Automation

Field

Linking together a number of individual plants to be combined to form a large-scale virtual power plant

Optimized operation of decentralized Optimized operation of decentralized

Distributed energy resources (DER) controller

DER controller load controllerEquipmentp penergy resources, load & storage,

enabling trading of energy flexibility at minimized risk.

p penergy resources, load & storage,

enabling trading of energy flexibility at minimized risk.

Smart GridServices

DER controller, load controller

Consulting, system installation & maintenancesite enrollment & enablement


Services site enrollment & enablement

Virtual Power Plants

A Virtual Power Plant (VPP) is a cluster of distributed energy resources (generationA Virtual Power Plant (VPP) is a cluster of distributed energy resources (generation, controllable loads and storages such as microCHP, wind-turbines, small hydro, back-up

gensets, flexible loads, batteries etc.) which are collectively run by a central control entity.

Infrastructure & Cities Sector – Smart Grid Division © Siemens AG 2013. All rights reserved.Page 4 Smart Grid Division Infrastructure & Cities Sector – Smart Grid Division © Siemens AG 2013. All rights reserved.Page 4 Smart Grid Division

Virtual Power Plants:Technical Structure and Use Cases

Network ControlSystem

EnergyExchange Billing

SchedulingLoad Forecast

Biomass Power Plant

MeteorologicalService

g

Load Balancing Block type

Flexible Loads

®Aggregation of DER1

Block-typeHeating Power Plant DEMS®

PV PowerPlants

Wind FarmsDistributed Small

Fuel Cell

Fuel CellsDistributed Loads

Storage

Automatic Generation Control

Renewable Generation Forecast


1 DER = Distributed Energy Resource Communication Unit

Three Main Target Groups for Customers for Virtual Power Plants

Use Case Target CustomersFacilitate participation in energy

trading/participate in markets for reserve capacity Aggregators and utilitiescapacity

(day ahead and reserve markets)

Operators with larger generation units with

To optimize of fleet management and ensure compliance with fleet schedule

p g g- More than one generation source¹/converter² and/or

- Different modalities of energy (e.g. electricity heat)electricity, heat)

Industries and municipalities with their own- Generation source and/orEconomic optimization of energy costs - Generation source and/or

- Load control- Storage³


¹Including Boilers, turbines, CHP, fuel cells, renewables ²Including compressors, chillers, electrolysis ³Including heat/cold storage, accumulators, e-cars

DEMS® for Load Balancing

Energy Data- Acquisition

Archiving- Archiving- Reporting- MonitoringEnergy contracts

Production plan

®

Supply Monitoring- Natural Gas- Electricity

energy$optimize

Production plan

Operator inputs

®

Load Forecast- Electricity

Steam

energycost

savings$short-term

purchasingon the market

Process control

Quality information- Steam

- Natural Gas

Optimization

Field information

Energy counterOptimization - Unit Commitment

- Fuels- Contracts




Load ForecastForecast of Renewable G ti

User InterfaceSCADA ArchiveGeneration SCADA

(Supervisory Control andData Acquisition)

SchedulingAutomatic Generation Reports Market Interfacesg

Controlp

Communication

®DEMS®


Virtual Power Plant Application for Electric Utilities

ISO (Day Ahead and Real Time Market)ISO (Day Ahead and Real Time Market)

Energy MarketEnergy Market Operating ReservesMarket

Operating ReservesMarket

Optimization of Generation and Demand Portfolio

Offer the delta generation (Gen – Load) to the Energy and Operating Reserves market (when load < PPA + DG). Aggregate and optimize the Energy Market Energy Market Energy Market Energy Market ) gg g pschedules of available generation during the bidding phase.

Coordinate between participation in energy market$ MWh $ MWh

gyPurchase

gyPurchase

gySellinggy

Selling

Base Load Generation Resources

IntermittentGeneration Resources

Coordinate between participation in energy market and/or operating reserve market

Maximize the benefit (Revenue – Cost): Cost of

Electric Utility

Generation Resources(PPA and/or own)

Peak LoadG ti R

Generation Resources(PV/Wind: PPA or own)

Load Following

operating own generation (vs.) purchasing from energy market

Curtailment of interruptible load as per theGeneration Resources(Distributed Generation)

gGeneration Resources

Retail Consumers(Load)

Curtailment of interruptible load as per the available load reduction programs


(Load)

DEMS® – Data Model

ConsumerElectricity

ContractsDelivery

ConsumerGas

ConsumerHeat

ConsumerHeat

EmissionCO2

BalanceElectricity

BalanceHeat

ConnectionHeat

BalanceHeat

BalanceCOElectricity Heat Heat Heat CO2

CHP FuelcellWind PhotovoltaicCHP

BalanceGas

BalanceBiomass Gas

AcquisitionGas

Biomass

AcquisitionBiomass

AcquisitionElectricity

DEMS®


GasBiomass Electricity


Energy / Media Purchase / Sales Contracts Primary energy consumption Bilateral electricity purchase / sales Energy markets (day ahead and reserve markets)

Energy / Media Demands Non-flexible loads Switchable loadsSwitchable loads Time controllable loads

RenewablesRenewables Wind power Photovoltaic Small hydro power Solar thermal Geothermal



Energy / Media Converters Boilers, Turbines, CHP, Fuel Cells Compressors, Chillers, Electrolysis

Energy / Media Storages Heat / Cold Storage Accumulators, E-Cars Media StorageMedia Storage

Emissions CO SOX NOX CO2, SOX, NOX, …

Electric system reserve considerationF t t i ti Forecast uncertainties

Own reserve capacity Sellable reserve or imbalance risk


DEMS® – Forecast Function

Import from meteorological service

Multi Area Weather Forecast

Refine imported forecasts with local online measurements

Forecast model: Day types, calendar, weather data, production schedules trends

Load ForecastTime Grid:

15/30/60 Minschedules, trends Continuous self adapting model coefficient training Kalman filter allows dynamic, partly static or fully static forecast models

15/30/60 Min.

Horizon: Up to 7 days ahead

Forecast uncertainty (bandwidth) calculation

Renewable Generation

7 days ahead

Plant characteristic (power as function of weather) is analyzed in offline stepF t t i t (b d idth) l l ti


Forecast uncertainty (bandwidth) calculation

DEMS® – Scheduling Functions

Scheduling Cost / revenue optimized scheduling of all flexible resources

C id ti f / di fl t l Consideration of energy / media flow topology Consideration of:

Reserve / risk strategygy Technical constraints of all modeled elements Environmental constraint of all modeled elements Time Grid:

15/30/60 Min.Time Grid:

15/30/60 Min. Contractual constraints of all modeled elements Fuel prices, contract prices and market options Actual process status and operating point

15/30/60 Min.

Horizon: Up to 7 days ahead

15/30/60 Min.

Horizon: Up to 7 days ahead Actual process status and operating point

Includes DSM concepts already in the operations planning phase Problem solution algorithm MILP is used

yy

Calculation of Power and commitment schedules

R l ti t d th h d l d b i t


Regulation costs around the scheduled power base points

DEMS® – Online Functions

General Cycle Time: typically 1 Minute or lower

Multi Area Exchange Monitoring Supervision of electrical “interchange” of areap g Comparison with scheduled commitment for area Energy (15/30/60 Minutes interval) or flat power area regulation mode Minute reserve monitoring Minute reserve monitoring Reaction on market reserve call Calculation of required area power correction term

Online Optimization Distribution of required area power correction term to all objects in regulating modeDistribution of required area power correction term to all objects in regulating mode Usage of storage, flexible demands and flexible generating units Preference for elements with lowest regulation costs calculated by the scheduling


Generation Management

DEMS® – Online Functions

Generation Management Unit operation modes: Independent, Manual, Scheduled, Regulating Considering technical constraints of units Considering actual unit states (disturbed, on/off, local/remote control) Start / Stop command and set point control Supervision of unit command and set point following behaviorSupervision of unit command and set point following behavior Applicable to storages and generating units Including active and reactive power set points

Load Management Load operation modes: Independent, Scheduled, Regulating Prioritization of load classes via their regulating costs Prioritization of load classes via their regulating costs One load class (continuous model) has several load groups (discrete model) Rotational load switching of load groups of one load class for continuous regulation Consideration of

Actual load state (on/off, local remote, dead time) Actual power when switching off


p g Nominal power & switching risk factor when switching on

Virtual Power Plant – RWE ProVippAggregation of Generation + Minute Reserve Market

Integration of multiple renewable energy resources

Challenge

Defining of various operation strategies Implementation of an optimal operation strategy for distributed generation

Build up a virtual power plant integrating small hydro power plants combined heat and power units

Solution Build up a virtual power plant integrating small hydro power plants, combined heat and power units,

and emergency generators based on DEMS®

DER*-Controller for innovative communication with DEMS®

Benefits

P j t t RWE

Allows market access for distributed energy resources Increases the economical benefit of distributed energy resources Provides regulating energy to reserve markets


Project partner: RWECountry: Germany *DER = Distributed Energy Resource

Page 17 Smart Grid Division

Case Study ProViPP – Virtual Power Plant for RWE

DEMS Decentralized Energy

in Operation Since 31-Oct-2008

DEMS – Decentralized Energy Management System

9 Small hydro units (8,6 MVA). Additional units will be connected in the next weeks

Hamburg

Schleswig-Holstein

Mecklenburg-Vorpommern

Project Focus: Development of a marketable Virtual Power Plant

Niedersachsen

BremenBrandenburg

Berlin

Sachsen-Anhalt

Definition of business models in different energy markets

Definition and implementation of optimalNordrhein-Westfalen

Sachen

Anhalt

ThüringenHessen

Definition and implementation of optimal operation strategies for distributed generation

Implementation of innovative communication Rheinland-

Pfalz

Saarland

pconcepts between distributed generation and DEMS

Baden-Württemberg

Bayern

P j t t RWE


Project partner: RWECountry: Germany


Stadtwerke München (SWM) –Start up Virtual Power Plant

Stadtwerke München (SWM)Key Features

Integration of 6 unit-type cogeneration modules, 5 hydropower plants and 1 wind farm to form a virtual power plantplants and 1 wind farm to form a virtual power plant

Scope is the distributed energy management system DEMS

A t t d d l t d t di h d l b d t Automated deployment and trading schedule based on exact usage and generation forecasts

C fCustomer Benefits

Opens up further marketing alternatives for distributed energy sourcessources

Minimization of generation and operational costs


Our Technology – Your Future

Already today, Siemens DEMS® and Siemens DER-Controller offer the technical basis for managing distributed energy systems.

Your Benefits:Your Benefits:

Use of synergies by aggregating distributed generation

Achievement of remarkable economical and ecological benefits

Obtaining new market opportunities for distributed generation Obtaining new market opportunities for distributed generation

Support of new operation concepts like virtual power plants

Create your future energy system with DEMS® !

Infrastructure & Cities Sector – Smart Grid Division © Siemens AG 2013. All rights reserved.Page 20 Smart Grid DivisionPage 20 Smart Grid Division

Comprehensive Modeling of power system elementsComprehensive Modeling of power system elements1

10 Good Reasons for DEMS®

Comprehensive Modeling of power system elementsComprehensive Modeling of power system elements1

Intelligent forecasting and planning using advanced mathematical techniquesIntelligent forecasting and planning using advanced mathematical techniques2

Integral consideration of all resourcesIntegral consideration of all resources3

Open interfaces for a seamless integration into the IT environmentOpen interfaces for a seamless integration into the IT environment4 Open interfaces for a seamless integration into the IT-environmentOpen interfaces for a seamless integration into the IT-environment4

Workflow support to reduce operators’ workloadWorkflow support to reduce operators’ workload5

Preparation of a solid background for energy trading decisions Preparation of a solid background for energy trading decisions 6

Simple real time operationSimple real time operation6

Clear, straightforward operationClear, straightforward operation8

Simple real-time operationSimple real-time operation6

Scalable systemScalable system9

Decentralized power generation with the character of a power plantDecentralized power generation with the character of a power plant10


Decentralized power generation with the character of a power plantDecentralized power generation with the character of a power plant10


Thank you for your attention!

8. Februar


2013

Author © Siemens AG 2013. All rights reserved.

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