Dispatching active distribution networks through ... active distribution networks through electrochemical storage systems and demand side management Prof.Mario Paolone and Prof.Colin

A dispatched-by-design architecture for distribution systems , Paolone-Jones | 06.02.2017 1

Dispatching active distribution networks through electrochemical storage

systems and demand side managementP r o f . Ma r io P a o lo ne a nd P r o f. C o lin N . J one s

D ist r ibut e d Ele c t r ica l Sy st e ms L a bo ra t or yA ut o ma t ic C o nt r o l L a bor a to r y 3

EP F L

F u t u r e E l e c t ri c P o w e r S y s te ms a n d t h e E n e r g y T r a n s i ti o nI n t e r n a ti o n al c o n fe re n ce

C h a m p é r y , S w i t ze rl an d , F e b . 5 2 0 1 7


Motivations

Definition of a balance group (from the Swiss grid code):

“A balance group is a virtual construct for the purposes of billing and accounting. [...]. Every distribution grid operator, trader, power producer, supplier and end consumer must belong to a balance group. The balance group manager (BGM) can conduct energy transactions with other balance group managers at home and abroad, offload energy from power stations or transfer energy to end consumers. To do this the BGM sends schedules within the planning phase to Swissgrid. On completion of the energy deliveries, Swissgrid balances all import/export schedules of the balance group [...] as well as all measured feed - ins and feed - outs per balance group (measuring values which Swissgrid receives from all grid operators) and, in the event of deviations, charges the purchased or sold energy to the BGM as balance energy. The BGM is responsible for ensuring that his or her balance group is as balanced as possible at all times.”


Motivations, cont’d

§ Achieving dispatched-by-design operation of traditionally

stochastic prosumption allows reducing grid reserve

requirements.

§ The dispatch plan is built to satisfy a local objective, such as

peak shaving, load levelling or minimization of the cost of

imported electricity.


The topology of a disp atchable feeder

The operation of a group of stochastic prosumers (generation + demand) is dispatched according to a profile established the day before operation (called dispatch plan) by controlling the real power injection of the battery.

Sources of flexibility:

§ flexible demand (part II)

§ physical energy storage storage systems (part I)


Formulation – A two stage process


Formulation – Day-ahead planning

The dispatch plan is a sequence at 5 minute resolution that denotes the power flow at the grid connection point that the feeder should follow.

It is the sum between prosumption point predictions and the so-called offset profile:

The latter is with the objective of restoring an adequate battery state-of-energy such that, during operation, enough up/down-flexibility is available to compensate the mismatch between presumption and realization.

Section III describes the experimental facility used to vali-date the proposed control strategy. Section IV presents anddiscusses the results from the experimental validation. Finally,Section V summarizes the outcomes of this work and proposesthe perspectives.

II. METHODS

A. Problem statement

We consider a group of prosumers, for which we would liketo smooth the consumption profile (load leveling) and dispatchtheir operation. As anticipated, the problem is formulatedaccording to a two-stage procedure: day-ahead and intra-dayphase. In the day-ahead stage, the objective is to determinethe dispatch plan, namely the power consumption profilethat the group prosumers is willing to follow during real-time operation. The dispatch plan is built as the sum of theforecasted power consumption profile, obtained through data-driven forecasting, and an offset profile. This latter quantity,which is obtained by solving a convex optimization problem,has the objective of generating a dispatch plan with minimumvariance, namely with minimum variation with respect to itsaverage value such that, during operation, the BESS willcharge (discharge) when the power profile exceeds (is below)the levelled profile and viceversa.

The intra-day operation consist in controlling the BESSactive power injection in order to track the dispatch plan,namely compensating for deviations between the dispatch planand actual consumption, which are likely to differ due to theoffset profile and to forecasting errors. This is accomplishedusing MPC, as illustrated in section II-C2.

B. Day-ahead problem

The objective is to build the dispatch plan, namely thepower consumption profile that the feeder should follow duringoperation, the day after. The dispatch plan

bP is defined as thesequence of N = 288 (i.e., the number of 5-minute intervals in24 hours) average power consumption values for the incomingday. The feeder dispatch plan is composed by the sum of theprosumers forecasted consumption profile bL

t

and the offset

profile Ft

:

bPt

= bLt

+ Ft

t = 1, . . . , N (1)

which are determined using the process illustrated in the nexttwo paragraphs.

1) Prosumers data-driven forecasting: The prosumers fore-casted consumption profile, denoted by bL, is produced througha nonparametric black-box method based on vector auto-regression. We assume that D daily sequences of 5 minutesaverage power consumption measurements are known fromhistorical data: these are denoted by L d 2 RN , d =0, . . . , D� 1. For any index d, are also known i) the calendarday-of-year, ii) whether the day corresponds to a working day

2In the problem formulation we do not consider the operational constraintsassociated to the grid. In other words, we assume that the battery power ratingresults in grid voltages and currents within operational bounds. This is thecase for stiff medium voltage grids.

or a holiday and iii) the mean global horizontal irradiance(GHI) during that day. The day for which the forecast profileis to be computed is said target day and is identified by d⇤.At first, a set ⌦ of indexes d that are representative scenariosof the target day is determined. ⌦ is identified by retainingfrom the complete dataset the indices of the daily sequenceswith characteristics more similar to those of the target daywith regard to three conditions:

• being a working day or a holiday;• being in the same period of the year;• having similar weather conditions.

This is done by identifying subsequent shrinking subsets ofindices, through the following heuristic procedure:

• a first subset ⌦00 is composed by selecting the indicesthat correspond to working days if the d⇤ is such and toholidays otherwise;

• a subset ⌦0 is then obtained by retaining from ⌦00 the p0

indices having day-of-year closer to the one of d⇤;• finally, the set ⌦ is obtained by retaining from ⌦0 the p

(with p < p0) indices corresponding to days with meanGHI closer to the one forecasted for d⇤. The GHI iscalculated from publicly available cloud coverage forecastdata for the Lausanne area and by means of the modeldescribed in [7].

The values for p0 and p are chosen equal to 10 and 5respectively.Summarizing, the set ⌦ is composed of p indexes correspond-ing to days which are i) of the same kind as the target day, ii)closest in time to the target-day and iii) closest in amount ofradiation to the GHI forecast for the target-day d⇤.

The sequence of point predictions for the day d⇤, denotedby bL0, . . . , bLN�1, is obtained by equally averaging the dailysequences identified by the indexes in ⌦:

bLi

=1

|⌦|X

d2⌦

L d

i

i = 0, . . . , N � 1, (2)

where L d

i

denotes the value at the discrete time interval i ofthe scenario L d and |⌦| is the cardinality of the set ⌦.

2) Dispatch plan offset profile: The objectives of the offset

profile are• altering the dispatch plan so that it is with mininum

variance;• making sure that an adequate level of charge is available

in the BESS to achieve dispatchability during intra-dayoperation.

We define the average daily power consumption value as:

bLavg

=1

N

N

X

t=1

bLt

. (3)

The offset profile F o =�

F o

1 , . . . , Fo

N

�

is determined by aconstrained optimization problem that minimizes the move-ment of the forecasted consumption sequence bL1, . . . , bLN

around its average daily value bLavg

. The optimization problemconstraints are:


Formulation – Offset profile problem (non convex)We seek for the smallest offset profile F such that the battery state-of-energy is within bounds in the scenarios with highest and lowest possible prosumption .

Note that this is a non convex problem due to the sign operators .


Formulation – Offset profile problem (convex)The previous problem can be formulated as a convex one by writing the sign operator as the sum of two mutually exclusive terms. We define:

which are used to rewrite the previous optimization problem. The cost function achieves to keep the positive and negative components mutually exclusive.


Formulation – The real-time control problem (MPC)The objective is to track the dispatch plan. Since it consists in accomplishing a certain energy throughput, we rely on MPC rather a conventional feedback control loop to determine the current evolution while respecting BESS operational constraints. MPC is actuated at 10 sec resolution on a 5 min shrinking horizon by plugging in short-term prosumption forecasts and open-loop predictions of the BESS operational constraints (voltage and current).

Two formulations are possible:1. determining the BESS power to accomplish the energy throughput subject to BESS

constraints. However, BESS constraints are nonlinear and nonconvex.2. Determining the BESS current to minimize the distance from the target energy

throughput while subject to linear voltage and current constraints. However the cost function:

is in the form q(r(x)). To be convex, it requires r(x) to be convex (it is) and q convex nondecreasing (it is not), thus it is nonconvex.


Formulation – The new (convex) MPCThe BESS energy throughput in the 5 minute interval is the integral over time of the product between BESS DC current, voltage and converter efficiency alpha:

The BESS voltage dynamic evolution depends on the charge/discharge current. It can be modelled by using a three-time-contant (TTC) model as a function of the initial BESS state x_kas the following linear relantioship.

which replaced in the first expression leads to:

The expression above is the sum of two linear expressions and a quadratic form in the current. It is therefore convex provided that ψ is SDP, which has been numerically proven for the adopted TTC model.


Formulation – The new (convex) MPCWe use the previous result to formulate a convex equivalency of the original MPC optimization problem. This consists in maximizing the current (linear cost function) subject to the energy throughput being less or equal to the target energy throughput e_k (convex inequality).

Once the current is known from the MPC, it is multiplied by the voltage to determine the real power set-point to finally submit to the BESS converter.

§ value of the prosumption set-point to match (from the dispatch plan)

§ expected average consumption with short-term point prediction


Formulation – Modelling of energy storage

We apply grey-box modelling on offline measurements of the DC voltage vs. current to identify (linear) system dynamics.

§ Measurements are from dedicated experiments where the BESS was excited by using a pseudo-random binary signal (PRBS, a two-state signal with random duration).

§ Since parameters are state-of-charge (SOC) dependent, the identification experiment is carried out for different BESS SOC intervals.


Formulation – Modelling of energy storage, cont’d

§ The model which captures the best early to middle-range time dynamics is a third order linear model, an extension of the well known TTC.

§ The autocorrelation function shows i.i.d. model residuals.

§ For the MPC, we apply model scheduling, namely we select the set of parameters corresponding to the current SOC and we assume that this does not vary in the control period.

BESS equivalent circuit (set of parameters’ values for each considered SOC range).

Model prediction errors are i.i.d. (50% SOC).


The EPFL experimental set up

§ Single measurement point at the GCP.§ 350 kW peak demand during winter.§ 95 kWp roof-top PV installation.


Parameter Value

Nominal Capacity 720 kVA/560 kWhGCP Voltage 20 kVDC Bus Voltage Range 600/800 VCell Technology(Anode/Cathode)

Lithium Titanate Oxide (LTO)Nichel Cobalt Alumnium Oxide (NCA)

Number of racks 9 in parallel

Number of modules per rack

15 in series

Cells configuration per module

20s3p

Total number of cells 8100

Cell nominal voltage 2.3 V (limits 1.7 to 2.7 V)

Cell nominal capacity 30 Ah (69 Wh)Round-trip efficiency(AC side)

94-96%

Round-trip efficiency(DC side)

97-99%

The EPFL experimental setup – The BESS sp ecs


Results – 14/01/2016, operation

• Prosumption worst-case scenarios (shaded band)

• Prosumption point predictions (dashed)

• Offset plan (black).

• Dispatch plan (gray)• Composite power realisation at

the GCP (dashed) • Prosumption realization without

the battery correction (black)


Results – 14/01/2016, BESS data

BESS state-of-charge, DC Current and DC voltage with respective limits.


Results

Dispatched operation -- 14 Jan 2016https://snapshot.raintank.io/dashboard/snapshot/PuW1Rf5d470Q0gsT7UNponM25bGDNTRA

Dispatched operation -- 13 Jan 2016https://snapshot.raintank.io/dashboard/snapshot/cDS4IDniZjRiePXvusnmQXOmMwpGLnR6

Dispatched operation + Peak Shaving -- 22/06/2016https://snapshot.raintank.io/dashboard/snapshot/LSF3bPxtWYDjHVu6siEr1VPb92EXNkd6

Dispatched Operation + Load Levelling -- 14/03/2016https://snapshot.raintank.io/dashboard/snapshot/4ztn800czpAzEFRzbGOmWc1A2pKeC9ab

Dispatched operation (continuos operation) -- 16 to 19/03/2016https://snapshot.raintank.io/dashboard/snapshot/TNbEgP7j1AWhaW7cEK1ZiK3tY1Or7P4U


Conclusions – Part I§ A bottom-up approach to tackle the challenge of increasing reserve requirements due to

integration of larger shares of renewables.

§ Suitable to operate in current vertically operated power systems.

§ Fully decentralized control mechanism with no coordination requirements: complexity is masked behind the commitment of the operator to follow the dispatch plan.

§ No pervasive monitoring/control infrastructure.

§ Inherently allows to achieve local grid operational objectives, like peak shaving or load levelling.

§ The framework is flexible to include the control of other resources (see part II).

§ Grid constraints are not considered. It relies on the fact that storage can be sited and sized offline in the planning phase to mitigate localized network issues, thus without need of incorporating (which comes at the cost of much higher complexity and uncertainty).


The topology of a disp atchable feeder

The operation of a group of stochastic prosumers (generation + demand) is dispatched according to a profile established the day before operation (called dispatch plan) by controlling the real power injection of the battery.

Sources of flexibility:

§ flexible demand (part II)

§ physical energy storage storage systems (part I)


Outline – Part II

1. Dispatch plans for occupied buildings

2. Real-time control for dispatchability

3. Early experimental results (one week)

4. Optimal sizing


Dispatch Planning – Two Competing Objectives

❶ Minimize energy

❷ Maximize flexibility

Goal: Choose dispatch plan to maximize controllability during highly uncertain periods

Temp

eratur

eTe

mpera

ture

Cost

Low

High

Comfort

Low

High

Flexibility

Low

High


Optimal Dispatch Problem

Comfort metric

Day-ahead plan for the thermal (x, e) trajectory of the building and electrical dispatch d

Dispatch error

Energy cost

Thermal trajectory must be input-admissible (feasible) and comfortable C(w) for all likely

weather scenarios W

minx,u

�E

�costi ·ei + �(xi � x̄) + �ei � (di + pi)�2

�

s.t. (x, e) � C(w) �w � W

Building dispatch plan

Prosumer forecast error



Uncertainty model• Weather• Occupancy• Prosumer consumption (PV & buildings)

Model of building and HVAC equipment

minx,u

�E


�

s.t. (x, e) � C(w) �w � W



Linear time-varying model and constraints

Building Modeling Tool http://la.epfl.ch/openbuild

ui thermal energy to each building zonexi thermal stateei energy consumed

Building Information Model Measured Building Data

openBuild

Building Geometry and Time of Year / Day

xi+1 = Aixi + Biui + Tiwi

(xi , ui ) � Xi � Ui

ei = Piui



Uncertainty model• Weather• Occupancy• Prosumer consumption (PV & buildings)

minx,u

�E


�

s.t. xi+1 = Aixi + Biui + Tiwi

(xi , ui) � Xi � Ui �wi � Wi

ei = Piui



Prediction with Gaussian Processes (GP)Historical data

Estimates mean and uncertainty about the mean. Uncertainty of GP interpretation grows away from previous observations.

Train Gaussian Process



Empirical distribution with samples extracted from Gaussian Process

minx,u

�E


�

s.t. xi+1 = Aixi + Biui + Tiwi

(xi , ui) � Xi � Ui �wi � Wi

ei = Piui


Solve using standard stochastic optimization


0 5 10 15 2018

20

22

24

26

28

Tem

pera

ture

0 5 10 15 2018

20

22

24

26

28

Tem

pera

ture

Dispatch Plan vs Prediction ConfidenceSunny Day with No Clouds Possibly Cloudy Day

0 5 10 15 20

Time (h)

0

10

20

30

40

Pow

er (k

W)

0 5 10 15 20

Time (h)

0

10

20

30

40

Pow

er (k

W)

Increased uncertainty results in higher energy expenditure and higher control authority


Real-Time Control – Every 5 minBattery SOC0≈ Dispatch error integral

Weather measurementand forecast wi

Building measurements& state estimate x0

minx,u

�

i

�SOCi �SOCREFi �2 +

�

j

�Tjxi ��

k

Tkxi�2

s.t. xi+1 = Aixi + Biui + Liwi

(xi , ui) � Xi � Ui

SOCi+1 = �SOCi + �(Piui � bi)

❶ Move battery to reference SOC ❷ All zones equal temperature

Enforce comfort constraints

Simple battery model

• Dispatch errors encoded in SOC → Restore SOC to nominal• No prosumer forecast → Distributed operations


TemperatureOccupancy

LightHumidity

Power measurement

Solar radiationOutside temp

aboratoire d’ utomatique emand esponse


LADR Experimental Configuration

❶ LADR has been virtually scaled-up in all experiments

PeakConsumption(kW)

LADR 45

Battery 720

Uncontrollable prosumers(PV+Buildings) 350

Scaled up from a true peak consumption of 7.8 kW

❷ Dispatch objective : Maximize flexibility

12% of loads are controllable

17-12 00:00 17-12 06:00 17-12 12:00 17-12 18:00 18-12 00:000

20

40

60

80

100

SOC

per

cent

age

[%]

Good Prediction Day – Battery Only

17-12 00:00 17-12 06:00 17-12 12:00 17-12 18:00 18-12 00:000

50

100

150

200

250

Pow

er[k

W]

ConsumptionDispatch

Dispatch plan (black)

Prosumer consumption (red)

SOC reference

Battery easily compensates for prediction errors (SOC)

17-12 00:00 17-12 06:00 17-12 12:00 17-12 18:00 18-12 00:00

20

22

24

2617-12 00:00 17-12 06:00 17-12 12:00 17-12 18:00 18-12 00:00

0

20

40

60

80

100

SOC

per

cent

age

[%]

17-12 00:00 17-12 06:00 17-12 12:00 17-12 18:00 18-12 00:000

50

100

150

200

250

Pow

er[k

W]

ConsumptionDispatch

Good Prediction Day – Battery & LADR

Building dispatch plan (black)

Building consumption (red)

SOC with battery (red)

Building zone temperatures

Building reduces battery requirements significantly

14-12 00:00 14-12 06:00 14-12 12:00 14-12 18:00 15-12 00:000

20

40

60

80

100

SOC

per

cent

age

[%]

14-12 00:00 14-12 06:00 14-12 12:00 14-12 18:00 15-12 00:000

100

200

300

Pow

er[k

W]

ConsumptionDispatch

Poor Prediction Day – Battery OnlyDispatch plan (black)

Prosumer consumption (red)

Battery cannot compensate for forecast errors (SOC)

14-12 00:00 14-12 06:00 14-12 12:00 14-12 18:00 15-12 00:00

20

22

24

2614-12 00:00 14-12 06:00 14-12 12:00 14-12 18:00 15-12 00:00

0

20

40

60

80

100

SOC

per

cent

age

[%]

14-12 00:00 14-12 06:00 14-12 12:00 14-12 18:00 15-12 00:000

100

200

300

Pow

er[k

W]

ConsumptionDispatch

Poor Prediction Day – Battery & LADR

Building dispatch plan (black)

Building consumption (red)

Building zone temperatures

Building reduces battery requirements significantly (SOC)


0 5 10 15 20 25 30 35Percentage controllable loads [%]

0

100

200

300

400

500

600

700

Req

uire

d Ba

ttery

Cap

acity

[kW

h]

00

20

40

60

80

100

120

Perc

enta

ge o

f Ins

talle

d Ba

tteryExperiments run at 12%

20% controllable buildings results in 80% reduction

in battery

Impact of Building Size – Preliminary Conclusion

Current battery


0 20 40 60 80 100 120Sample Period [min]

0

100

200

300

400

500

600

Req

uire

d Ba

ttery

Cap

acity

[kW

h]

00

20

40

60

80

100

Perc

enta

ge o

f Ins

talle

d Ba

ttery

Impact of Sample Rate – Preliminary Conclusion

Experiments run at 5min

Control of average energy over 60min

requires 20% larger battery


Conclusion

Powe

r

High

Energy

Low

High

Time

Fast

Slow

✓ ✓×

×✓Slow×

Spectral Split

Tracking signal

Conclusion: Hybrid storage schemes provide much greater flexibility to offer a wide variety of services at lower cost


Acknowledgements (i.e., they who did the work!)

Laboratoire d’Automatique Distributed Electrical Systems Laboratory

Luca Fabietti

Tomasz Gorecki

Fabrizio Sossan

Emil Namor


References[1] F. Sossan; E. Namor; R. Cherkaoui; M. Paolone, Achieving the Dispatchability of Distribution Feeders through Prosumers Data Driven Forecasting and Model Predictive Control of Electrochemical Storage, in IEEE Transactions on Sustainable Energy , 10.1109/TSTE.2016.2600103.

[2] E. Namor, F. Sossan, R. Cherkaoui and M. Paolone, Load Leveling and Dispatchability of a Medium Voltage Active Feeder through Battery Energy Storage Systems: Formulation of the Control Problem and Experimental Validation, in proceedings of ISGT Europe 2016, Ljubljana, Slovenija, October 9-12, 2016.

[3] F. Sossan and M. Paolone, Integration and Operation of Utility-Scale Battery Energy Storage Systems: the EPFL's Experience, in proceedings of Control of Transmission and Distribution Smart Grids, CTDSG 2016, Praha, October 11-13, 2016.

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Dispatching active distribution networks through ... active distribution networks through electrochemical storage systems and demand side management Prof.Mario Paolone and Prof.Colin