Resilient Demand Management Algorithms and Their … · Resilient Demand Management Algorithms and...

transcript

Resilient Demand Management

Algorithms and Their Impact

European Utility Week 2015, Vienna, Nov 3

Session: Smart Distribution System Control Over Heterogeneous

Communication Networks– SmartC2Net

SmartC2Net Consortium

Sandford Bessler, FTW

Rationale of this work

Main concept: exchange energy flexibility information

Demand management system architecture

Energy planning algorithms

Experimental results

Concluding remarks

Outline

Higher loads in households, which include electric house heating/HVAC,

electric cars, etc. may produce overflow in LV grid.

High PV deployment in households may create excessive generation.

Because of flexible loads, households demand can be remotely controlled

to reduce energy costs, without reducing comfort.

The heavy use of communication requires however resilience mechanisms

against communication failures

Rationale

Energy flexibility: minimum and maximum consumption

trajectory

Flexibility models examples

Main Concept: exchange flexibility plans

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Energy[kWh]

TimeperiodsEflexmin Eflexmax

EV charging flexible load Electric house heating flexible load

0 1 2 3 4 5 6 7

Energy[kWh]

Timeperiods

Eflexmaxatt Eflexminatt

Components

Aggregation controller (DMC)

Demand response head-end (OADR VTN)

Many CEMS’s (OADR VENs)

Operation principle

CEMS sends a load profile based on local

objectives and external set points, and

adds flexibility information

The aggregation controller adapts the

individual plans by computing set point

profiles (plans) for each CEMS, only if

- the load limit (given) or

- the generated power limit are exceeded,

CEMS Demand Control

System Architecture

Demand Response platform

energy

controller

Demand

response

Head-end

setpoint

profile

flexibility,

load profile

energy

controller

Charging

events ext.

CEMS setpoints,

PV irradiation,

prices, vg-load

CEMS flexibility,

load profile

Aggregated

energy

function

irradiation

forecast

bg-load

profile

prices

OADR2.0b

energy

controller

Demand

response

Head-end

setpoint

profile

flexibility,

load profile

energy

controller

Charging

events ext.

CEMS setpoints,

PV irradiation,

prices, vg-load

CEMS flexibility,

load profile

Aggregated

energy

function

irradiation

forecast

bg-load

profile

prices

OADR2.0b

energy

controller

Demand

response

Head-end

setpoint

profile

flexibility,

load profile

energy

controller

Charging

events ext.

CEMS setpoints,

PV irradiation,

prices, vg-load

CEMS flexibility,

load profile

Aggregated

energy

function

irradiation

forecast

bg-load

profile

prices

OADR2.0b

energy

controller

Demand

response

Head-end

setpoint

profile

flexibility,

load profile

energy

controller

Charging

events ext.

CEMS setpoints,

PV irradiation,

prices, vg-load

CEMS flexibility,

load profile

Aggregated

energy

function

irradiation

forecast

bg-load

profile

prices

OADR2.0b

Central site

Demand

response

Head-end

setpoint

profile

flexibility,

load profile

Charging

generation

Modelnon-flexible

availab.

CEMS setpoints,

price updates

CEMS flexibility,

load profile

Aggregation

Controller

irradiation

forecast

non-flex.

forecast

day-ahead

pricesoutdoor

Customer

Energy

Management

controller (CEMS)

internet

OADR 2.0b

Controller interactions

15 min

DMC - Demand Management Control

CEMS - Customer Energy

Management System

Investigation of CEMS

on a large scale

Simulations based on

traffic measurements

of the developed

monitoring and control

algorithems

Focus on impact of ICT

performance on Use

Case viability

CEMS – ICT in large scale deployments

CEMS Use Case in Simulator – Individual Household

CEMS Use Case in Simulator (4G) – Benchmark Area (Støvring, DK)

Comparison of (non-) exclusive wireline/wireless access networks for DSL /

UMTS based Households in (sub)urban area

UMTS shows significantly higher delays compared to DSL (negligible packet loss)

No negative impact on performance of CEMS (including shared com. networks)

Use of preexisting com. infrastructure is a viable strategy

CEMS – ICT in large scale deployments

Given are transformer power limits LVmaxp , LVminp, but no details on the grid

topology

Calculate optimal setpoints for each household i, and time period:

Minimize the MSE to the requested power profile

Smooth the setpoint curve

- If the total planned consumption of the houses is below the limit

Simple solution: setpoints correspond to the required power

If loads can be linearly controlled, proportional setpoints can be used even

in overload situation

- In case of large on/off loads (heating) and overload:

Use flexibility of house i to find the limits Pmini,and Pmax

Allocate either the minimum necessary or the maximum power

Algorithms to avoid overloading the grid

xiPimin + (1- xi )Pi

å £ LVmax, xi = {0,1}, iÎ N

Test bed for Demand Management

38 houses in a low voltage

(LV) benchmark grid

- 8 electric cars charging in

1-2 intervals per day

- 5 kW electric heating in

winter

- PV generation in every

The benchmark grid

Performance indices

n(Pi - LVmax ) / LVmax

å , i |Pi > LVmax

Define as load overload index (oi) the relative occurrence of total power

exceeding nominal transformer power LVmax:

Define the daily energy costs using day-ahead prices. The saving index is

the relative difference between the costs in a fixed-price and day-ahead

price scenario.

1 3 5 7 9 11131517192123252729313335373941434547495153555759616365676971737577798183858789919395

rela vepricestoavg.44.23€/Mwh

si =1- ciPiin

å / c Piin

No control (no DMC)

Baseline DMC: no load limit at the transformer, prices.

Excessive demand: reduce load if demand exceeds the limit

Excessive generation: limit the overall injected power into the LV grid

Interruption – communication failures

Changing the energy price is used for demand control

Summary of tests

1 4 7 1013161922252831343740434649525558616467707376798285889194

totalload totalsetpoint

Energy costs comparison

Scenario Constant price Day-ahead price Day-ahead price

excessive demand

Total energy/day 765kWh 1011kWh 1118 kWh

Total cost 33,75€ 39,70€ 48,25 €

Resulting avg.

price 44,23 €/MWh 39,28 €/MWh 43,15 €/MWh

Savings (si) 0 11% 2,5%

1 3 5 7 9111315171921232527293133353739414345474951535557596163656769717375777981838587899193

NoDMCscenario

Totalload

No demand management

control, constant price.

Demand limit set to 70 kW

The reference power is always

below the limit

Demand management

base-line scenario, prices,

no load limit.

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91

load setpoint

1 4 7 1013161922252831343740434649525558616467707376798285889194

totalload totalsetpoint

Load performance comparison

1 3 5 7 9111315171921232527293133353739414345474951535557596163656769717375777981838587899193

NoDMCscenario

Totalload

No demand management

control, constant price.

Demand limit set to 70 kW

The reference power is always

below the limit

Demand management

base-line scenario, prices,

no load limit.

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91

load setpoint

Scenario Baseline no

limitation

Excessive

demand

Excessive

generation

Overload

index (oi) 2,6% 1,36% 0%

Using prices in the objective may create overload !

Overload index is reduced, as setting the limit leads to load shedding

Total power generation is well controlled by the limit

13 57911131517192123252729313335373941434547495153555759616365676971737577798183858789919395

Datenreihe1

Datenreihe2

Excessive generation (summer)

Limit = -100 kW

prices +10 €/MWh

Impact of temporary energy price increase

67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84

period

impactofincreasedprice+10between18:00-19:00ontotalload

load:increasedprice baselineload deltaprice

Interruption scenario:

- all CEMS become suddenly disconnected from the DMC.

- Benefit of our approach: Cached plan is followed by the local algorithm

Changes in consumption cannot however be coordinated by the DMC

Resilient operation

17 -60

525456586062646668707274767880828486889092940 2 4 6 8101214161820222426283032343638404244464850

networkfailurebetweenperiods60-76

totalload setpointdis

connecte

Qualitative Evaluation of the Demand Management Scheme

Efficient scheduling of flexible loads allows higher individual house

consumption, as well as high penetration of PV installations.

Energy cost optimization shifts the load to the low price energy periods,

and saves in the tested scenario around 11% compared to the fixed price.

Independently of its cause, excessive total demand can be controlled at

the DMC to avoid overloading the grid. The savings become in this case

negligible (measured 2,5%) .

The injection of PV generated power in the grid is efficiently limited

similarly to the excessive demand case.

If the control communication network fails, cached plans allow for resilient

operation of CEMS’s during several hours.

Concluding Remarks

The SmartC2Net results clearly show that intelligent distribution

grid operation can be realized in a robust manner over existing

communication infrastructures even despite the presence of

accidental faults and malicious attacks.

Walter Schaffer

Head of Electrical Network

Salzburg Netz

Austria

Panel Discussion: Impact and Roadmap for Utilities

Aurelio Blanquet

Director

Portugal

Nuno Silva

Deputy Director

EFACEC Energia

Portugal

… visit our exhibition at booth B.m06!

Demo Schedule

- Tuesday (Nov. 3rd):

14-16h - MV Control

16-18h - Demand management

- Wednesday (Nov. 4th):

9-11h - External Generation Site

11-13h - Demand management

13-15h - MV Control

For further discussions and demos…

Thursday (Nov. 5th) 9-11h - Demand management

13-15h - MV Control

Resilient Demand Management Algorithms and Their … · Resilient Demand Management Algorithms and...

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