Analytical Framework for Market-oriented DSR Flexibility Integration and Management

doi:10.4236/epe.2013.54B259

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Energy and Power E ngineering, 2013, 5, 1367-1371

doi:10.4236/epe.2013.54B259 Published Online July 2013 (http://www.scirp.org/journal/epe)

Analytical F ramework for Market-oriented DSR

Flexibility Integration and Management

Shi You, Junjie Hu, Kai Heussen, Chunyu Zhang

Department of Electrical Engineering, Technical University of Denmark, Lyngby, Denmark

Email: [email protected], {junhu, kh, chzh}@elektro.dtu.dk

Received March, 2013

ABSTRACT

Integr atio n and manage ment o f the fle xibil ity of D ema nd Side Resour ces (DS R) in to da y’s ener gy syste ms p lays a sig-

nificant role in building up a sustainable society. However, the challenges of understanding, predicating and handling

the uncertainties associated this subj ect to a great extent hamper its develop ment. In this paper, an analytical framework

based on a multi-portfolio setup in presence of a deregulated power market is proposed to address such challenges by

adopting the thinking in modern portfolio theory (MPT). A Numerical example that targets on analyzing the risk and

return fo r vario us flexibility pric ing strategies are presented to illustrate some features of the framework.

Keywords: Analytical Framework; Distribute d Energy Res ources; Flexiblity; Deregulate d Power Market; Mor den

Portfolio Theory; Risk and Return

1. Introduction

Demand side resource (DSR) refers to the geographically

distributed modular power generation, consumption and

energy storage systems which are located on the demand

side and have the capability of altering their genera-

tion/consumption pattern. The capability of DSR, also

referred as flexibility, has been considered as perpetual

resources with significant value of both technical and

economic prospects for reducing energy consumption,

improving energy efficiency, facilitating the integration

of stochastic renewable, deferring the expansion of pow-

er ne tworks and securi ng the po wer syste m op erati on via

ancillary services’ provision, e tc . [1,2]

Conventional demand response (DR) programs are

normally organized by utilities, and can be split in two

categories. The first group requires a fast and reliable

response from DR programs, so that the DSRs are re-

motely controlled by the utilities in a master-slave man-

ner under bilateral agreements. These programs primarily

target on medium to large size commercial and industrial

DSRs and they are typically limited to interruptible DR

services. A second group of DR programs is aimed at

modifying the consumption of a large number of small-

scale DSRs by means of economic incentives. These so

called dynamic tariff programs include e.g. hourly real

time pricing (RTP) and time-of-use tariffs (TOU) but

also capacity pricing are used to shift consumption pat-

terns and achieve better system economics with ‘dynam-

ics’ in the scale of hours to years [3].

Along with the anticipated increase in penetration of

DSR in the distrib ution syste ms, both the util ities and the

emerging non-utility entities, e.g. Virtual Power Plants

(VPPs), DSR aggre gators, intent on exploiti ng the added

value of DSR flexibility [4,5]. By coordinating a fast

respo nse fr o m the se lo w cost small-scale DSRs by means

of various advanced control strategies, the aggregated

DSR flexibility could be treated the same as other de-

mand/generation resources in the context of a deregu-

lated electricity market. However, integrating the DSR

flexibility into the power system operation through the

present deregulated market setup is challenged by a

number of uncertain factors, such as

• the wide range of DSR technologies with varying

flexibility options;

• the diversity of aggression-oriented control strate-

gies with few comparisons among them due to the flex-

ibility of control architectures and the vaguely defined

evaluation criteria , etc.;

• the multiple-choice of market products (e.g. ancil-

lary services) with different risk-return spectrum and

requirements.

Using Denmark as a test field, several ongoing

projects, e.g. iPower1, Flexpower2 and EcogridEU3, are

working on understanding, predicating and handling the

uncertainties associated with market-oriented DSR flex-

ibility integration and management. In these projects,

1http://www.ipower-net.dk/

2FlexP ower homepage hosted by Ea Energianalyse

3http://www.eu-ecogrid.net/

S. YOU ET AL.

1368

solutions are investigated from technical, economic and

social perspectives, where the interests of different

stakeholder s, i.e. Transmission Syste m Operato rs (TSOs),

Distributio n Syste m Operators (DSO s), Balance Respon-

sible Parties (BRPs, here to be considered mostly equiv-

alent to ‘aggregators’) and DSR owners, are balanced in

the context of the Nordic power market.

In this paper, an analytical framework based on a mul-

ti-portfolio setup is proposed to address such challenges

by adopting the thinking in modern portfolio theory

(MPT). Such a multi-portfolio framework is to analyze

the risk and return for different portfolio mixes from both

economic and technical perspectives. This paper is orga-

nized as follows. First, a brief interpretation of this ana-

lytical framework is provided in Section II. Section III

presents the relevant techniques for constructing the mul-

ti-portfolio setup. A numerical example that targets on

analyzing the risk and return for different flexibility

pricing strategies is presented in Section IV to illustrate

some features of the framework, while Section V dis-

cusses the chanc es and challe nges o f furt her development

of this framework and conc ludes.

2. A Multi-portfolio Analitical Framework

for DSR Flexibility Management and

Integration

A multi-portfolio based analytical framework for DSR

flexibility management and integration is illustrated in

Figure 1. Different stakeholders mentioned earlier can

establish their interest-oriented portfolios by the use of

experimental or model-based setups; meanwhile the cor-

responding risk-return performance indicators can be

identified or even created to satisfy the stakeholder’s

individual interest. Different techniques for portfolio

analysis, e.g. MPT, can be applied to performing the re-

levant analysis, and the analytical results will be fed back

to the stakeholders who need to decide on if further ac-

tions, e.g. reconfiguring portfolio setups, alternating the

performance indicators, are required.

Maintain &

improve portfolio

setups

Perform portoflio

analysis

Indeficiation of risk-

return performance

indicators

stakeholders

Interest-oriented multi-

portfolio establishment

Figure 1. Multi-portofolio based analytical framework for

DSR flexibility management and integration.

In general, portfolio analysis is applied to financial

portfolios which are combinations of various financial

products such as bonds, equities, indexes, funds, and

securities. It involves quantifying the expected financial

returns for different portfolios and the associated risk

which is often expressed as volatility of returns. For its

application to DSR flexibility management and integra-

tion, new mea sure s of r isk a n d return can be defined. For

instance, when a B RP with a given DSR por tfolio would

like to provide frequency control ancillary services and

are in doubt with the optimal combination of its control

strategies, in addition to using the economic metrics, the

risk associated with control performance could also be

taken into account. For another example, if the local

energy suppliers would like to investigate a portfolio

with different electricity pricing methods, e.g. fixed-price

(FP), time of use (TOU), real time price (RTP), the risk

and return on customer satisfaction might also be consi-

dered.

In Figure 2, an illustrative example with a three-

portfolio analytical setup is presented. In this setup, the

uncertainties of market-oriented DSR flexibility man-

agement and integration are treated in three closely re-

lated po rtfolios, wherein D SR flexibilit y, control stra tegy

and market-based flexibility service are grouped sepa-

rately. Each portfolio can be seen as a combination of

weighted assets those belong to the corresponding port-

folio category, so that the return of a portfolio is the

weighted combination of the assets’ returns. By changing

the percentage of one/several asset in a portfolio, the

peculiar risk associated with these assets and returns as-

sociated with them can be characterized and analyzed in

different ways. In rest of the paper, analysis and discus-

sion related to using this analytical framework to facili-

tate market-based DSR flexibility management and inte-

gration will b e r e fer r e d to this three-portfolio setup.

DSR (1)DSR (n)

Control

strategy

(1)

Control

strategy

(n)

Portfolio of individual

DSR flexibility

Portfolio of control strategies for

flexiblity aggregation

Portfolio of market-based

flexiblity products

Product (1)Product (n)

Figure 2. A three-portofolio establishment for DSR flexibil-

S. YOU ET AL.

1369

ity management and integration.

It is wort hwhile to note that, t his three-portfolio -based

analytical setup only illustrates a conceptual example of

the generic analytical framewor k. Portfolios of ICT solu-

tions, etc., can also be included. The process of investi-

gating the variability of portfolio setups and the asso-

ciated risk-return is analogous to the process of modeling

a constrained multi-objective optimization problem and

finding its optimal solutions, wherein exogenous and

endogenous variables as well as different objectives have

to be selected, characterized and regulated with care.

3. Establishing the Multi-portfolio Setup

3.1. Por t folio o f DSR Fl ex ib l ity

The quantitative flexibility of a DSR, i.e. when and how

much can its generation/consumption pattern be alter-

nated, is deter mined by its local c ontrol system. In ma ny

cases, this decision making process needs to consider

exogenous inputs (e.g. knowledge of global and local

environment) and endogenous inputs (e.g. knowledge of

the DSR plant dynamics and physical constraints), as

well the loca l objectives (e .g. utilit y maximization or cost

minimization). The generic formulation of DSR flexibil-

ity is hereby expressed, as in (1).

(,)argmax()

.. ()

xqtux

st x

(1)

where

(,)xqt

stands for the flexibility of a DSR device

describing its output of active power q at time t;

()

stands for the DSR owner’s utility;

()

xΦ stands for all

necessary constraints.

This generic expression is more applicable to model-

ing the flexibility of an indivi dual DSR; mean while it can

be used to derive the optimal generation/consumption

schedule for a certain period. When aggregated flexibili-

ty over a certain amount of DSR is to be modeled, statis-

tical techniques, e.g. Monte Carlo method, can be applied

based on direct observations of individual DSR. It is

important for the modelers to recognize the cross-time

feature of DSR flexibilit y, particularl y for the DSRs with

deferrable characteristics, e.g. electric vehicles, thermal

storages. In other words, the DSR flexibility is not al-

ways time invariant.

3.2. Portfolio of Control-strategies

A control strate gy is a set of specific measures identified

and implemented to achieve the control objective. For an

aggregation-oriented control strategy, it is consisted of a

set of important features such as control patterns (e.g.

open-loop vs. close-loop), control structures (e.g. centra-

lized vs. decentralized) and control algorithms (e.g.

model-predictive control vs. standard PID control),

which need to be carefully designed and integrated in a

control s ystem architecture.

One conceptual way for classifying different aggrega-

tion-oriented control strategies is given in [6], wherein

the two categories, namely “direct control” and “indirect

control” are thoroughly introduced. The former alludes to

a conventional control approach which requires DSR

state information to compute reference trajectories for the

DSR consumption to follow. T he latter approach is often

associated with broadcasting of incentive signals (prices)

with an update frequency of e.g. 5 minutes to the DSRs.

Compared to the hourly dynamic tariffs, this update fre-

quency is fast, and falls into the time range of generator

ramps, for example.

This way of classification is in line with the common-

ly-understood way for classifying and modeling the con-

trollabilit y of DSR, i.e. price-re sponsive a nd controllable

(price signal free) as depicted in Table 1; me a nwhile, the

systematical structuring for “directness” and “indirect-

ness” could particularly support the active demand side

management by combining the control engineering do-

main with the value-oriented deliberation and addressing

the integration and valuation of mixed po rtfolios of direct

and indirect control as well as the further analysis of

co-existence of such control-solutions with respect to

overall control architecture of the power system.

3.3. Portfolio of Market Products

The needs which can exploit the DSR flexibility for

achieving the objectives of different stakeholders are

many and they vary in requirements on volume, location,

time and reliability, etc. In a liberalized market, ideally

these system needs are transformed into different market

products which will influence the way of flexibility inte-

gration and management. For the needs relating to

achieving the reliable, secure and efficient operation of

electric power systems, although the market-based setups

vary across countries [7], many of them have already

been reflected in existing market products such as

Table 1 . A brief overvi ew of eamples & te chniques for c on-

trol strat egies modeling.

Price-signal

free

Price-signal enabled

Double-sided

auc tion

(coordination)

Single-sided

auction

(incentive)

Deterministic

Conventional

control, e.g.

load shedding

Market clearing

on the ba s is of

bids and offers

Pricing

schemes, e.g.

TOU, RTP

Probabilistic Aggregated

TCLs a controlled by

freq./temp. s et-points

Game theory

enabled stochastic

modeling

Homeostatic

utility control

S. YOU ET AL.

1370

• Capacity products for DSOs and TSOs to ensure

the resource adequacy.

• Emergency products for DSOs and TSOs to han-

dle emergency situations.

• Ancillary service products for TSOs including

frequency regulation, voltage control, short cir-

cuit capacity, manual reserve, black-start, etc.

• Service products for DSOs, e.g. peak shaving,

voltage control, congestion management, etc.

• Energy balancing products for BRPs to maintain

their balanci ng responsibilitie s.

Techniques for modeling and analyzing the economic

performance for the electrical po wer market and its asso-

ciated products based on observed market behavior have

been extensively developed and implemented

[8].However, in addition to investigating the economic

performance of different products, it is also important to

analyze the technical requirements for each market

product in order to find appropriate portfolio mappings

among DSR flexibility, control strategies and the market

products.

4. Numerical Example

In this section, a numerical example of peak load reduc-

tion is presented to illustrate some features of the pro-

posed analytical framework. Data of residential loads and

wholesale electricity prices used in this example are pro-

vided by the Danish DSO and the Danish TSO respec-

tively, corresponding to the observations in 2011.

Assuming in one 0.4 kV radial distribution feeder, 42

Danish single-family households constitute the electrical

load of this feeder. Local DSO would li ke to investigate,

comparing with the current FP scheme, how much can

other different pricing schemes affect the maximum

loading over the feeder system and thereby conducts the

following study.

The 42 households load profiles are modeled based on

their previo us pe rfor mance, wher ein an a nnual cons ump-

tion of 4000 kWh per household was observed under FP.

Assumption about the flexibility of each household is

made, i.e. the price elasticity is set as -1 while the maxi-

mum flexibility of each household at each hour can be

±30% of the corresponding hourly load under FP.

Regar din g the varie ty o f pr ici ng sche mes , as i n Figure

3, the hourly wholesale electricity spot is assigned as

RTP; FP is assumed to be the annual average of RTP;

while TOU is comprised of three periods: peak (7-9 and

17-19), shoulder (4-6, 10-16 and 20-21) and off-peak

(1-3 and 22-24). For calculating the TOU, the electricity

price of each period is derived by averaging the spot

prices over all those hours t belong to that period cate-

gor y over the year.

After simulating different pricing schemes, the re-

sulted daily peak load over the year under different con-

text is derived as in Figure 4. Compared with the exist-

ing FP, TOU to some extent lowers the daily peak while

RTP intro duces b oth lo wer and hig he r p e ak va lue s d ue to

the demand side e la sticity.

To further analyze the three pricing schemes consti-

tuted control strategy portfolio, an efficient frontier (i.e.

the blue curved line) is plotted as in Fig ure 5. The con-

cept of efficie nt frontiers was introduced in 1952 by No-

bel Prize winner Harry Markowitz as part of the Capital

Asset Pricing Model (CAPM) for portfolio theory[9].A

key finding of the concep t was the benefit of diversifica-

tion as depicted in Figure 5 by the random portfolio asset

combinations given in red dots, while the principle shows

that combing several stocks into a portfolio can decrease

the overall risk below that of any individual stock while

still attaining a comparable return. The efficient frontier

therefore gives the lowest level of risk needed to achieve

a given expected rate of return or the

1510 15 20 1510 15 20 24

0.04

0.045

0.05

0.055

0.06

0.065

0.07

0.075

T ime of day (hour )

Electricity pr ice ( eur o/ kWh)

RTP

TO U

Figure 3 . Different pricing shcemes i n two consecutive days.

050100 150200 250 300 350400

N umber o f day

Daily peak of e lect r ical load(kWh)

R TP

TOU

Figure 4. Daily peak load over the year under different

pricing s chemes.

S. YOU ET AL.

1371

4.5 55.5 66.5 77.5 88.5 99.5

29.5

30.5

31.5

32.5

Stand ar d deviation of d ail y pea k load (kW h)

Mean of daily peak load( kW h)

RTP

TO U

Figure 5. Mean-variance efficient frontier and random

combinations for the three different pricing schemes.

best return that can be expected for a given level of risk.

In the presented example, it can be found that FP, TOU

and RTP exhibit a decreasing order with respect to both

the mean values of daily peaks and the associated stan-

dard deviations. The optimal portfolio with minimum

risk (i.e. measured by standard deviation) is found with a

combination of 25.9% FP, 43.7% RTP and 30.4% TOU,

which results in a mean of daily peak at 30.38kWh with

standard deviation of 4.72 kWh.

5. Discussion and Conclusions

A successful deployment of the emerging technologies in

smart grid and smart market require a deep understand-

ing of the associated risk and return for different stake-

holders. In the complex electrical energy system, the

inseparable relationships across various domains and

different stakeholders make the problem even more

challenging. The paper proposes a multi-por tfolio-based

analytical framework for market-oriented DSR integra-

tion a nd mana gement. T his fra me wor k aims at cl arifyi ng

the tradeoffs in satisfying different stakeholders’ objec-

tives and acceptable risk, or variability of tradeoffs.

By itself, the proposed framework can be easily un-

derstood and might result in clear suggestions to various

stakeholders given their concerns, priorities and re-

sources. However, in reality, the stakeholders generally

have to face many choices and handle the worst cases,

which would require them to put more effort on struc-

tured thinking, detailed modeling and careful analysis

whe n to use the frame work.

6. Acknowled gements

The authors would like to thank the Danish iPower plat-

form for its financial support on thi s study.

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