ONDŘEJ NOVÁKControl Technology Department, Faculty of Electrical Engineering, Czech Technical University in Prague

Ondřej Novák, ČVUTondrej.novak@fel.cvut.cz

Household consumption control and secure integration of RESs [Renewable Energy Sources]

Control system concept and pilot project presentation

České vysoké učení technické v Praze

Project BIOZE

Project BIOZE

Joint Project of FAV ZČU, FEL ČVUT, FEL ZČU, Pontech s.r.o. and Cygni spol. s r.o.

Grant of TAČR (Technological Agency of the Czech Republic): Project Alfa TA01020865

Partial project: Power consumption control with regard to the integration of Renewable Energy Sources (RESs)

Project BIOZE

Power System Control Group

Research group at the Department of Control Technology, Faculty of Electrical Engineering, Czech Technical University in Prague, active in the field of modelling, simulation and optimisation in power engineering

Co-project management of the BIOZE project Since 2005, co-project management of the SESyS

project – Reliability and economy of system services for ČEPS, a.s.

Simulation of dispatching control in transmission systems Simulative optimisation of regulative ranges for auxiliary

services Routine application as an analytical tool for the

preparation of documents to the annual arrangement of transmission system operation

Project BIOZE

Power consumption control

Project BIOZE

Goal: to minimise negative impact of installed (fotovoltaic) RESs on DS Overflow into DS

Overvoltage in grid

Idea: to consume power on the spot, i.e. at the place where it has been generated

Accumulation of power generated in fotovoltaic units in household hot-water heaters

Goals of power consumption control

Project BIOZE

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Imported and exported power

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Power output balance of a transforming station

Optimised balanceOriginal balance

Principle of control algorithm

2nd step: Adherence to the calculated reference from the 1st stepOptimisation of hot-water heater switchingCalculation repeated in each 5 minutesLimits for optimisation:

Capacity of heaters (hot water quantity) Limitation for switching : prevention of

excessive wearing (of switches)

OptimiserOptimiser Controlled consumptionControlled

consumption

++

-

Resulting balance

Prediction ofuncontrolled consumption

Prediction ofuncontrolled consumption

Prediction ofuncontrolled generation

Prediction ofuncontrolled generation

Two-step calculation of switching times of hot-water heaters:1st step: Minimisation of power output flows to/from the area (= maximisation of local utilisation of generated power) In accordance with the prediction of power generation & consumption in the controlled area for 12 hoursResult: Reference power output balance of the area for the next 12 hours

Project BIOZE

Prediction of uncontrolled

consumption

Prediction of uncontrolled

consumption

Prediction of uncontrolled generation

Prediction of uncontrolled generation

Principle of control algorithm

2nd step: Adherence to the calculated reference from the 1st stepOptimisation of hot-water heater switchingCalculation repeated in each 5 minutesLimits for optimisation:

Capacity of heaters (hot water quantity) Limitation for switching: prevention of

excessive wearing (of switches)

OptimiserOptimiser Controlled consumptionControlled

consumption

Uncontrolled consumptionUncontrolled consumption

Uncontrolled generation

Uncontrolled generation

++

-

Resulting balance

Prediction ofuncontrolled consumption

Prediction ofuncontrolled consumption

Prediction ofuncontrolled generation

Prediction ofuncontrolled generation

Two-step calculation of switching times of hot-water heaters:1st step: Minimisation of power output flows to/from the area (= maximisation of local utilisation of generated power) In accordance with the prediction of power generation & consumption in the controlled areaResult: reference power output balance of the area for the next 12 hours

Project BIOZE

Implementation of control algorithmwith the measurement of output balance at DTS

Project BIOZE

cloudiness forecast (t : t + TP )

typical fotovoltaic generation (t : t + TP )

„DD“ fotovoltaic

„DD“ fotovoltaic

Prediction of power consumption (t : t + TP )

Controlled consumptionControlled

consumption

Instructions for hot waterheater control (t)

OptimiserOptimiser

output balanceDTS (t)

TDDTDD

daily consumption chart (t : t + TP)

Prediction ofuncontrolled generation

(fotovoltaic) & consumption

Prediction ofuncontrolled generation

(fotovoltaic) & consumption

Prediction of power generation fotovoltaic (t : t + TP )

Metering of output flow from DTS

Metering of output flow from DTS

TP – predicative horizon of calculation = 12 hrs

Meteo-dataMeteo-data

Uncontrolled generation and consumption

Uncontrolled generation and consumption

Input of controlledconsumption (t)

Uncontrolledconsumptionbalance (t)

meteringtransmitted data

Implementation of control algorithmwith simulated output balance of DTS (without on-line measurement of balance)

Project BIOZE

cloud forecast (t : t + TP )

typical fotovoltaic generation (t : t + TP )

„DD“ fotovoltaic

„DD“ fotovoltaic

daily consumptioncharts (t) and typicalfotovoltaicgeneration (t) revisedacc.to actual weather

TDDTDD

Prediction of power consumption (t : t + TP )

„DD“ fotovoltaic

„DD“ fotovoltaic

measured input and voltage at the places ofconsumption control (t)

Controlled consumptionControlled

consumption

instructions for hot waterheater control (t)

OptimiserOptimiser

output balanceDTS (t)

TDDTDD

daily consumption chart (t : t + TP)

Prediction ofuncontrolled generation

(fotovoltaic) & consumption

Prediction ofuncontrolled generation

(fotovoltaic) & consumption

Prediction of power generation fotovoltaic (t : t + TP )

Load Flow grid

simulation

Load Flow grid

simulation

TP – predicative horizon of calculation = 12 hrs

Meteo-data

Meteo-data

Pilot project Horušany

Horušany (region Plzeň) Small municipality

connected to DS by one supply TS (250kVA)

installed fotovoltaic power sources 120 kWp

problems of compliance with voltage limits

“export balance” of the municipality

Project BIOZE

Simulated control results

Performed while supposing optional control of all hot-water heaters in the area (40 households)

Performed in the period April - May 2011 (available measurement data of power output flow at DTS)

Consumption of hot water in households simulated according to respective data from the project IEA ECBS – measurement data on hot water consumption have not been available

Simulation in accordance with measured balance at DTS

Project BIOZE

Simulated control results Three scenarios have been simulated to demonstrate the impact of

information on the hot-water heater status (quantity of hot water): Scenario No.1:

Hot-water heaters have no hot water quantity detection Hot water volume must not be lower than 45% of the total capacity It was necessary to heat water at 100% capacity at least once a day (to

synchronise the estimation of status and the real status of respective hot-water heater)

Scenario No.2: Each hot-water heater has been equipped with a sensor sending a signal when

the quantity of hot water decreases below 25% of its capacity Hot water volume must not be lower than 20% of the total capacity

Scenario No.3: Each hot-water heater has been equipped with a sensor sending a signal when

the quantity of hot water decreases below 25% or increases above 75% of the total capacity

Hot water volume must not be lower than 20 % of the total capacity

Project BIOZE

Simulated control - results

Scenario No. 1 2 3

Reduction of power export [%]

42.4 46.9 51.1

Reduction of power import [%]

26.5 25.3 24.9

Reduction of min-max output range [%]

34.7 36.4 38

Reduction of power transferred [%](in 4 days) [MWh]

24.1 26.5 26.7

1.13 1.25 1.26

Project BIOZE

Simulated control – 24hr detailing, scenario 1

Project BIOZE

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Spotreba bojlery

SpotrebaFVE vyroba

Bilance

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EMAX = 65 kWh (maximum import)

EMIN = -84 kWh (maximum export)

E+ = 608 kWh (total consumption) E- = -503 kWh (total generation)

EMAX = 58 kWh

EMIN = -56 kWh

E+ = 496 kWh E- = -418 kWh

Blue field= hot-water heater pwr consumption

Green field = other power consumption

Red line = fotovoltaic power generation

Black line = power balance

Consumption control results – from real operation

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with heater control

without heater control

6 hot-water heaters available for control ~ 13 kW

Project BIOZE

Consumption control results – from real operation

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Detail of consumption control impact on output balance of DTS Switching-on 13 kW for 15 minutes

Project BIOZE

Summary

Created algorithm can suppress the export of power from respective area down to one half

Presented solution offers better exploitation of transmission capacity of DS

Power supply quality has been improved (voltage stabilisation)

Possible savings of investments in grid reinforcement

Project BIOZE

Further development

Implementation of the Optimiser as an embedded system (on ARM architecture)

Development of algorithms for the estimation of hot water consumption from the hot-water heater power input

Extension of the pilot project by adding the hot water consumption metering (hot-water heater operation control should be then better)

Extrapolation of the control scheme for power control at HV level Optimisation of HV area operation to generate required

power output values for the control at LV level Decentralised cooperation among more LV areas

Project BIOZE