Energy and Power E ngineering, 2013, 5, 1367-1371
doi:10.4236/epe.2013.54B259 Published Online July 2013 (
Copyright © 2013 S ciRes. 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:, {junhu, kh, chzh}
Received March, 2013
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
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,
2FlexP ower homepage hosted by Ea Energianalyse
Copyright © 2013 S ciRes. EPE
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
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
Perform portoflio
Indeficiation of risk-
return performance
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-
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)
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-
Copyright © 2013 S ciRes. EPE
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).
.. ()
st x
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 enabled
auc tion
control, e.g.
load shedding
Market clearing
on the ba s is of
bids and offers
schemes, e.g.
Probabilistic Aggregated
TCLs a controlled by
freq./temp. s et-points
Game theory
enabled stochastic
utility control
Copyright © 2013 S ciRes. EPE
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
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
T ime of day (hour )
Electricity pr ice ( eur o/ kWh)
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)
Figure 4. Daily peak load over the year under different
pricing s chemes.
Copyright © 2013 S ciRes. EPE
4.5 55.5 66.5 77.5 88.5 99.5
Stand ar d deviation of d ail y pea k load (kW h)
Mean of daily peak load( kW h)
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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