International Electrical Engineering Journal (IEEJ)
Vol. 6 (2015) No.9, pp. 2010-2024
ISSN 2078-2365
http://www.ieejournal.com/
A Review of Virtual power plant Definitions,
Components, Framework and Optimization
Mahmoud M. Othman*
Y. G. Hegazy**
Almoataz Y. Abdelaziz*
*Department of Electrical Power & Machines, Faculty of Engineering, Ain Shams University, Cairo,
Egypt
** Dean of information engineering and technology faculty, German university in Cairo
Abstract- This paper presents a comprehensive survey on the new
and interesting concept of virtual power plant (VPP). The survey
covers the virtual power plant definitions, components, and
framework and highlights the different techniques that can be
used for VPP operation optimization. Finally, a general
framework for the operation and the optimization of the virtual
power plant is proposed and discussed.
Keywords: Distributed energy resources, Virtual power plant, VPP
framework, optimization.
I. INTRODUCTION
The concept of integrating small generating units in the
power system has attracted great attention in the last few years.
Moreover, distributed generation (DG) plays an important
role in reinforcing the main generating power plants to satisfy
the growing power demand. DG can also be connected or
disconnected easily from the network unlike the main power
plants, thus providing higher flexibility. Properly planned and
operated DG installations have many benefits such as
economic savings due to the decrement of power losses,
higher reliability, and improved power quality.
However, the increased penetration of DG without
harmony between the generating units may lead to increment
of the grid power losses, undesirable voltage profiles,
unreliable operation of the protection devices, and unbalance
between the real consumption and the production. Therefore,
to achieve optimal economical operation of the main network,
DER units should be visible to the system operator.
The negative aspects of increased uncoordinated DG
penetration are the basic motivation for the introduction of
VPP concept. VPP is the aggregation of DG units,
controllable loads and storage devices connected to a certain
cluster in a single imaginative entity responsible for managing
the electrical energy flow within the cluster and in exchange
with the main network. The VPP concept was proposed early
in [1] with its framework. Earlier, DER was installed with a
“fit and forget” approach and they were not visible to the
system operators. VPP aggregated all DERs into a single
entity through which distributed energy resources (DERs)
would have system visibility and controllability and market
impacts as transmission-connected generators [2].
Different studies analyzed the VPP concept in three major
directions:
 First direction concerned with classifying DGs inside
the VPP structure according to their capacity and
ownership. Two categories were reported; Domestic
DG (DDG) and Public DG (PDG). Another DG
classification was presented according to their
operational nature; either stochastic or dispatchable.
 Second direction focused on the VPP structure both
technically and commercially; Technical VPP (TVPP)
and Commercial VPP (CVPP), and their
functionalities.
 Third direction slanted towards the optimization of
the VPP operation. Some of these studies focused on
VPP structure optimization by selecting the optimal
size and location of the VPP components. On the other
hand, other studies highlighted the profit
maximization of the VPP.
This paper presents a literature review of VPP definitions,
components, and framework. Furthermore, it simplifies the
relations/correlations between VPP structure entities and their
responsibilities. Finally, a survey is presented of the different
techniques that can be proposed to optimize the operation of
the VPP.
II. VPP DEFINITIONS
The most recent VPP concept has various definitions which
all agree upon the fact that VPP is an aggregation of DG units
of different technologies in order to operate as a single power
plant that has the ability to control the aggregated units and to
manage the electrical energy flow between these units in order
to obtain better operation of the system [2-6]. In [2], VPP is
defined as “A flexible representation of a portfolio of
distributed energy resources (DER) that can be used to make
contracts in the wholesale market and to offer services to the
system operator”. In [3], VPP is defined as “An information
2010
Othman et. al.,
A Review of Virtual power plant Definitions, Components, Framework and Optimization
International Electrical Engineering Journal (IEEJ)
Vol. 6 (2015) No.9, pp. 2010-2024
ISSN 2078-2365
http://www.ieejournal.com/
and communication system with centralized control over an
aggregation of DGs, controllable Loads and storage devices”.
In [4], VPP is defined as “An aggregation of DER including
different DER technologies, responsive loads and storage
devices which, when integrated have a flexibility and
controllability similar to large conventional power plants”. In
[5], VPP is defined as “A cluster of dispersed generator units,
controllable loads and storages systems, aggregated in order
to operate as a unique power plant. The generators can use
both fossil and Renewable Energy Sources (RES). The heart
of a VPP is an Energy Management System (EMS) which
coordinates the power flows coming from the generators,
controllable loads and storages”. In [6], VPP is defined as
“An aggregation of different types of distributed resources
which may be dispersed in different points of medium voltage
distribution networks”.
From the presented definitions a comprehensive definition
is proposed. VPP can be defined as “A concourse of
dispatchable and non dispatchable DGs, energy storage
elements and controllable loads accompanied by information
and communication technologies to form a single imaginary
power plant that plans, monitors the operation, and
coordinates the power flows between its component to
minimize the generation costs, minimize the production of
green house gases, maximize the profits, and enhance the
trade inside the electricity market”.
III. VPP COMPONENTS AND MODEL
VPP consists of three main components, distributed energy
resources, energy storage systems and information and
communication technologies as shown in Fig. (1).
Fig. 1 VPP simplified model
A. Distributed energy resources (DER)
DER can be either distributed generators or controllable
loads connected to the network. From the authors’ point of
view, DGs within the VPP premises can be classified
according to:
1) Type of the primary energy source:
According to the primary energy source type, DGs can be
classified into two categories;
 Generators utilizing RES (such as wind-based generators,
photovoltaic arrays, solar-thermal systems, and small
hydro-plants).
 Generators utilizing non-RES (such as Combined Heat and
Power (CHP), biomass, biogas, diesel generators, gas
turbines, and fuel cells (FC)).
2) Capacity of DG units:
According to DG units’ capacities, DGs can be classified
into two categories;
 Small-scale capacity DGs that must be connected to the
VPP in order to gain access to the electricity market; or
they could be connected together with controllable loads to
form micro grids that may or may not participate in the VPP
based on their capacities.
 Medium- and large-scale capacity DGs that can
individually participate in the electricity market but they
may choose to be connected to VPP to gain optimal steady
revenue.
3) Ownership of DG units:
DGs within the VPP premises may be;
 Residential-, Commercial-, and Industrial-owned DGs used
to supply part/all of its load in its own premises. They can
be referred to as Domestic DGs (DDG) [3].
 Utility-owned DGs that are used to support the main grid
supply shortage. They may be called Public DGs (PDG).
 Commercial company-owned DGs that aim to gain profits
from selling power production to the grid. They can be
named Independent Power Producers DGs (IPPDG).
4) DGs operational nature [7]:
DGs operational nature can be classified into two cases:
 Stochastic nature: In case of wind-based and photovoltaic
DG units, the output power is not controllable as it depends
on a variable input resource. To overcome this nature, this
type of DG must be equipped with battery storage in order
to be able to control the output power.
 Other DG technologies such as FCs and micro-turbines
have an operational dispatchable nature. They are capable
of varying their operation quickly. Therefore, in general,
VPP should include controllable loads, Energy Storage
Elements (ESE) and dispatchable DGs in order to
2011
Othman et. al.,
A Review of Virtual power plant Definitions, Components, Framework and Optimization
International Electrical Engineering Journal (IEEJ)
Vol. 6 (2015) No.9, pp. 2010-2024
ISSN 2078-2365
http://www.ieejournal.com/
compensate the vulnerability of the stochastic nature-DG
type.
B. Energy Storage systems (ESS)
ESS and its elements play a pivotal role in bridging the gap
between the generation and demand, especially in the
presence of high penetration of stochastic generation. Energy
storage elements (ESEs) can store energy during off-peak
periods and feed it during the peak periods. It also can
optimally redistribute the output power of wind turbines and
photovoltaic arrays throughout the day. ESS can be classified
according to their applications; i.e. supplying power or energy
[8], as follows:
 Energy supply class includes:
- Hydraulic Pumped Energy Storage (HPES)
- Compressed Air Energy Storage (CAES)
 Power supply class includes:
- Flywheel Energy Storage (FWES)
- Super Conductor Magnetic Energy Storage (SMES)
- Super Capacitors
C. Information and Communication systems
The energy management system (EMS) represents the heart
of the information and communication system. It manages the
operation of other VPP components through communication
technologies in bidirectional ways, as shown in Fig. (1).
The EMS has the following responsibilities [9]:

Receiving information about the status of each
element inside the VPP.

Forecasting RES primary sources and output
power.

Forecasting and management of loads.

Coordinating the power flow between the VPP
elements

Controlling the operation of DGs, storage
elements, and controllable loads.
The EMS’s aim is to achieve one of the following targets:

Minimization of generation cost.

Minimization of energy losses.

Minimization of greenhouse gases.

Maximization of profit.

Improvement of voltage profile.

Enhancement of power quality.
IV. VPP FRAMEWORK
VPP is a large entity that involves a huge number of DGs,
controllable loads, and storage elements under a layer of
Information and Communication Technologies (ICT). VPP is
responsible for controlling the supply and manages the
electrical energy flow not only within its cluster but also in
exchange with the main grid. In addition, VPP can also offer
ancillary and power quality services. To achieve these
functions, VPP must own the following tools [3]:
 ICT infrastructure.
 Monitoring and control applications.
 Smart metering and control devices installed at the
customer sites.
 Software applications to forecast the power generation of
the VPP.
For the sake of specialization, VPP is subdivided into two
entities; Technical Virtual Power Plant (TVPP) and
Commercial Virtual Power Plant (CVPP). These two entities
operate together in order to achieve the VPP functions. TVPP
and CVPP functionalities and responsibilities are as follows:
A. Technical virtual power plant (TVPP)
TVPP is responsible for the correct operation of the DER
and the ESSs in order to manage the energy flow inside the
VPP cluster, and execution of ancillary services. TVPP
receives information from the CVPP about the contractual
DGs and the controllable loads, this information must
include:
 The maximum capacity and commitment of each DG
unit.
 The production and consumption forecast.
 The location of DG units and loads.
 The capacity and the locations of the energy storage
systems.
 The available control strategy of the controllable loads at
all times during the day according to the contractual
obligations between the VPP and the loads.
Based on the information received from the CVPP in
addition to the detailed information about the distribution
network topology, TVPP ensures that the power system is
operated in an optimized and secure way taking physical
constraints and potential services offered by VPP into account.
The following functions are provided by the TVPP [10] and
[11]:
 Managing the local system for distribution system
operators (DSO)
 Providing balancing, management of the network and
execution of ancillary services.
 Providing visibility of the DERs in the distribution
network to the transmission system operator (TSO)
allowing DG and demand to contribute to the
transmission system management activities.
 Taking care of the DER operation according to
requirements obtained from CVPP and system status
information.
 Monitoring continuously the condition for the retrieval
of equipment historical loadings.
 Asset management- supported by statistical data.
2012
Othman et. al.,
A Review of Virtual power plant Definitions, Components, Framework and Optimization
International Electrical Engineering Journal (IEEJ)
Vol. 6 (2015) No.9, pp. 2010-2024
ISSN 2078-2365
http://www.ieejournal.com/




Self-identification of system components
Determining of fault location.
Facilitating maintenance.
Optimizing of project portfolio and statistical analysis.
B. Commercial Virtual power plant (CVPP)
CVPP considers DERs as commercial entities offering the
price and amount of energy that it can deliver, optimizing
economical utilization of VPP portfolio for the electricity
market [11]. CVPP performs bilateral contracts with both the
DG units and the customers. These contracts’ information is
sent to the TVPP in order to take the amount of the contracted
power into consideration during the performance of technical
studies. Small-scale DG units are not able to participate in the
electricity market individually. Therefore, CVPP makes these
units visible to the electricity market. The CVPP
functionalities are summarized as follows [4] and [11]:

Scheduling of production based on predicted needs
of consumers.

Trading in the wholesale electricity market

Balancing and/or trading portfolios

Providing of services to the system operator

Submitting of DERs’ characteristics and costs and
maintenance

Production and consumption forecasting based on
weather forecasting and demand profiles.

Outage demand management

Constructing DER bids and submitting them to the
electricity market.

Scheduling of generation and daily optimization

Selling DER power in the electricity market.
In order to achieve the above mentioned targets, CVPP
interacts with the following entities [11]:
 DER: Its main function is to bridge the gap between
demand and production. Its production must be
planned, forecasted, and transferred that
information to the TVPP.
 Balance Responsible Party (BRP): It is an energy
trading entity with a property to make its own
production/consumption plan available to be used
by TVPP.
 Transmission System Operator (TSO): It has a main
role in maintaining the instantaneous supply and
demand balance in the network.
 TVPP: It receives information from CVPP and
takes it into consideration in optimizing the
operation of the VPP and its interaction with the
main grid.
V. VPP VERSUS MICROGRID
In regards to the power system deregulation, a new
generation of distribution networks is developed named
active distribution networks (ADN). ADN is defined as a
distribution network whose operator can remotely and
automatically control the DER units and network topology to
efficiently manage and optimally utilize the network assets
[12]. The ADN brain is the central control system that is
capable of making control actions and send control signals to
the DER units. The central control system aims to enhance the
DER controllability and the technical and economical
benefits achieved by both the DER owners and the host grid
[13] and [14].
The ADN concept radically changes the traditional
distribution system into a new distribution paradigm. Thus,
distribution systems can be decomposed into smaller,
autonomously-operated systems, called microgrid that has a
central system controller coordinating the operation of DER
units and controllable loads. Using the Virtual Power Plant
(VPP) concept, each small autonomously operated system can
be presented as aggregated controllable group. This group is
available for use in system management functions at higher
network voltage levels [14].
The ADN can operate in two distinct modes:
Fig. (2) Schematic diagram of an ADN operating as VPP
1) Utility-Connected Mode: In this mode, the distribution
network is grid connected where the ADN operator optimally
controls the ADN components to maximize the technical and
2013
Othman et. al.,
A Review of Virtual power plant Definitions, Components, Framework and Optimization
International Electrical Engineering Journal (IEEJ)
Vol. 6 (2015) No.9, pp. 2010-2024
ISSN 2078-2365
http://www.ieejournal.com/
economical benefits of the existing DER. One way of
achieving this is to coordinate the ADN apparatus to provide a
pre-specified performance profile at the Point of Common
Coupling (PCC), i.e., the ADN operates as a VPP that is
comparable to a centralized power plant [15] and [16]. A
conceptual representation of the VPP is given in Fig (2).
2) Islanded Mode: In this mode, the ADN operates in offgrid
autonomous mode where the ADN operator controls the ADN
components to achieve a safe and reliable operation of the
network independent of the utility, i.e., form an islanded
microgrid [17].
Table I VPP Vs Microgrid
Concept
Control
objective
Infrastructure
VPP
Microgrid
 Promotes market and grid Implements autonomous
of
a
service participation from operation
distribution network with
DERs
 Increases DER visibilities many DERs, of which
to
transmission
and stand-alone system is of
distribution
system particular interest
operators
 Aggregates the capacity of
many diverse DERs, and
creates a single operating
profile from a composite
of
the
parameters
characterizing each DER
to facilitate DER trading
in the wholesale energy
markets.
Information and communication technologies and software
interfaces
Othman et. al.,
Components
Features
Distributed generators, energy storage elements,
controllable loads and energy management system.
Supports grid services
Capable of
performing
automatic
islanding
Both VPP and microgrids can facilitate DER integration
into power system but with different aims. Microgrids focus
on network operation control concerning active and reactive
power using DER units existing within one local grid. On the
other hand while VPP concentrate on the provision of energy
and power system support services from DER units [18].
Table I compares the two concepts and highlighting their
differences and similarities [14] and [18].
VI. VPP OPTIMIZATION
The optimal VPP operation aims at enhancing its operation
and minimizing the cost of its produced energy. This section
surveys the publications in this field. VPP future optimization
studies can be divided into two main categories from the
authors’ point of view.
A. Selecting VPP structure by optimizing its components.
B. Optimizing the VPP operation.
These two categories are explained as follows:
A. Optimization of the VPP structure
VPP as an independent entity that has the ability to perform
two sets of contracts:
 Bilateral contracts with DG units. These contracts
include the maximum DG units’ capacities and the
obligations of these units towards the VPP.
 Contracts with customers that include the category
of the load and the possibility of controlling or even
interrupting
it.
The
corresponding
controlling/interruption duration and initiation
times.
VPP optimization methodology depends on the power
system under study; either if it is new or existing. For a
newly-established power system, VPP has the ability to
choose the capacity and location of the DG units and ESEs,
and the locations of the loads to be controlled and the
appropriate control strategies and schedules. On the other
hand for existing power systems, these options are limited as
the location and size of the DG units and ESEs, and the
locations of the controllable loads are pre-determined.
The following ideas are proposed for VPP structure
optimization:
1) DG units’ optimal sizing and siting:
Researchers investigated various optimization techniques to
determine the optimal location and size of DG in order to
reduce power loss and improve the voltage profile of the
power system. Similarly, VPP optimization can be carried out
through optimal placing of stochastic DG units (wind and
2014
A Review of Virtual power plant Definitions, Components, Framework and Optimization
International Electrical Engineering Journal (IEEJ)
Vol. 6 (2015) No.9, pp. 2010-2024
ISSN 2078-2365
http://www.ieejournal.com/
photovoltaic). Other studies can be performed to select the
optimal capacity of a conventional power plant used in
collaboration with DG units as well as purchasing energy
from the electricity market to supply the required VPP energy.
2) ESEs optimal sizing and siting:
Optimal sizing and siting of ESEs helps in reducing power
loss, improving voltage profiles, and in optimizing the
generation of stochastic DGs.
3) Optimal load control scheduling:
The VPP has the authority to control or even interrupt the
loads according to their importance in order to optimize its
operation. The loads can be divided into three categories:
 Critical loads (Class-A): These loads are the most
important loads. They are not interruptible or even
controllable. The price of energy supplied to these
loads must be the most expensive one but on the
other hand a big penalty should be paid by the VPP
operator in case of interruption.
 Emergency loads (Class-B): These loads are less
important than the critical loads. They are also not
interruptible but they are controllable. As mentioned
before, the control procedures should be well
defined and stated in the contract.
 Normal loads (Class-C): These loads are the least
important loads. They are interruptible and
controllable. The price of energy supplied to these
loads is the least one as a tribute to the possibility of
interruption. These normal loads may be further
divided into sub-categories based on the allowable
duration of interruption and control and the
corresponding time (within the peak period or
off-peak period). Undoubtedly, as the allowable
period of interruption/control increases the price of
energy decreases.
B. Optimal operation of the VPP
For an existing power system with pre-determined
capacities and locations of DG units and ESEs and with
certain allowable schedules of load control, the optimal
operation could be obtained by optimally determining the
generation of DG units, the charge and discharge rate of the
ESEs and the amount of energy to be purchased from the
electricity market.
C. Optimization of the VPP components
Although rare studies were performed to optimize the
structure and operation of the VPP as a one unit (i.e.
optimizing all the VPP components and operation
simultaneously), several studies were done to optimize each
of the VPP components of the VPP individually. The
following subsections surveyed the work done to optimize the
VPP component (i.e. DGs, ESEs and controllable loads).
C.1 Optimal sizing and placement of DGs
Distributed generators (DGs) are connected to the distribution
network for different purposes: improving the voltage profile,
reducing the power loss, enhancing of system reliability and
security, improving of power quality (supply continuity),
relieving transmission and distribution congestion, reducing
health care costs due to improved environment, reducing the
system cost, and deferral of new investments.
Optimal location and capacity of DGs plays a pivotal role in
gaining the maximum benefits from them. On the other side
non-optimal placement or sizing of DGs may cause
undesirable effects. Optimal DG sizing and siting problem
can be classified according different aspects as presented in
Fig. (3).
Fig. (3) Different classifications of DG optimization studies
C.1.1 DG optimization according to methodology
The search space of optimal location and capacity of DGs
is roomy; Different optimization methods are used in this field
for the sake of power loss minimization, cost reduction, profit
maximization and environmental emission reduction. The
optimization methods could be analytical [18-23], numerical
[24-31] and heuristic [32-46].
A) Analytical methods
The analytical method known as the “2/3 rule” was
proposed in [18] for optimal installation of a DG of 2/3
capacity of the incoming generation at 2/3 of the length of the
line. However, this technique may not be effective for
nonuniformly distributed loads. Two analytical methods for
optimal location of a single DG for radial and meshed power
systems were introduced in [19]. The first method is
2015
Othman et. al.,
A Review of Virtual power plant Definitions, Components, Framework and Optimization
International Electrical Engineering Journal (IEEJ)
Vol. 6 (2015) No.9, pp. 2010-2024
ISSN 2078-2365
http://www.ieejournal.com/
applicable to radial and the second one to meshed power
systems based on bus admittance matrix, generation
information and load distribution of the system. In [20] a non
iterative analytical method based on the exact loss formula to
minimize power losses by the optimal placement of DG in
radial and meshed systems was presented. In [21], the
optimum size and location of DG were defined so as to
minimize total power losses based on the equivalent current
injection technique and without the use of impedance or
Jacobian matrices for radial systems. Analytical expressions
for finding optimal size and power factor of different types of
DGs were suggested in [22]. An improved analytical method
was described in [23] for allocating four types of multiple DG
units for loss reduction in primary distribution networks.
Moreover, an approach for optimally selecting the optimal
DG power factor is also presented.
B) Numerical methods
The most common used numerical methods can be
summarized as follows
1) Linear Programming (LP): It was used to solve optimal DG
power optimization problem in [24] and [25] for achieving
maximum DG penetration and maximum DG energy
harvesting, respectively.
2) Nonlinear Programming (NLP): It was presented in [26]
for capturing the time variations of multiple renewable sites
and demands as well as the effect of innovative control
schemes
3) Gradient Search: Gradient search for the optimal sizing of
DGs in meshed networks considering fault level constraints
was proposed in [27].
4) Sequential Quadratic Programming (SQP): It was applied
in [28] to determine the optimal locations and sizes of single
and multiple DGs with specified and unspecified power
factor.
5) Dynamic Programming (DP): DP was applied to maximize
the profit of the DNO by optimal selection of DGs locations
while considering light, medium, and peak load conditions
[29].
6) Exhaustive Search: An exhaustive search was proposed for
determination of optimal DG size and locations in unbalanced
distribution networks considering the changes in the loading
conditions due to contingencies in [30] and for heavily over
loaded networks [31].
C) Heuristic methods
The most common heuristic methods used can be
summarized as follows
1) Genetic Algorithm (GA): It was applied to solve an optimal
multiple DGs sizing and siting problem with reliability
constraints in [32] where the optimization process is solved
by the combination of genetic algorithms (GA) techniques
with methods to evaluate DG impacts in system reliability,
losses and voltage profile. Authors in [33] proposed a GA
based method for optimal sizing and siting of DGs in radial as
well as networked systems for the sake of power loss
minimization. A GA was utilized in [34] to solve the
optimization problem that maximizes the profit of the system
based on nodal pricing for optimally allocating distributed
generation for profit, loss reduction, and voltage
improvement including voltage rise phenomenon. A GA
methodology was implemented to optimally allocate
renewable DG units in distribution network to maximize the
worth of the connection to the local distribution company as
well as the customers connected to the system [35]. A
value-based approach, taking into account the benefits and
costs of DGs, was developed and solved by a GA that
computes the optimal number, type, location, and size of DGs
[36].
2) Tabu Search (TS): It was used to obtain the optimal sizing
and siting of DG units simultaneously with the optimal
placement of reactive power sources in [37]. A stochastic
multiple DGs optimal sizes and locations were determined for
cost minimization by a combined TS and scatter search [38].
3) Particle Swarm Optimization (PSO): It was utilized for
optimal selection of types, locations and sizes in order to
maximize the DG penetration considering standard harmonic
limits and protection coordination constraints [39]. A PSO
based algorithm was implemented for cost minimization
through the optimal sizing and placement of multiple DG
units in [40].
4) Ant Colony Optimization: A multiobjective ant colony
system algorithm was proposed to derive the optimal recloser
and DG placement scheme for radial distribution networks in
[41]. A composite reliability index was used as the objective
function in the optimization procedure.
5) Artificial Bee Colony (ABC): DG-unit placement and
sizing process was performed with ABC algorithm in [42].
6) Harmony Search (HS): The optimal DG location is based
on loss sensitivity factors and the optimal DG size is obtained
by HS algorithm [43].
7) Cat Swarm Optimization: The authors in [44] presented a
cat swarm optimization method for optimal placement and
sizing of multiple DGs to achieve higher system reliability in
large scale primary distribution networks.
8) Big Bang Big Crunch (BB-BC): A supervised Big Bang
Big Crunch optimization method was proposed in [45] and
[46] for the optimal sizing and siting of voltage controlled
distributed generators for the sake of power loss as well as
energy losses minimization.
9) Firefly algorithm (FA): Authors in [47] proposed a firefly
based optimization algorithm for the optimal sizing and siting
of dispatchable distributed generators for power loss
reduction.
C.1.2 DG optimization according to objective
2016
Othman et. al.,
A Review of Virtual power plant Definitions, Components, Framework and Optimization
International Electrical Engineering Journal (IEEJ)
Vol. 6 (2015) No.9, pp. 2010-2024
ISSN 2078-2365
http://www.ieejournal.com/
Optimal sizing and placement of DGs is performed by
achievement of the required objective function that could be
single or multi objective.
The single objective functions may be based on [48]:
a) Maximization of:

profit

voltage limit loadability(i.e., the maximum
loading that can be supplied by the power
distribution system while the voltages at all nodes
are kept within the limits)
b) Minimization of:

energy loss

generation cost

voltage deviations

system average interruption duration index
Multiobjective formulations can be classified as:
1) Multiobjective function with weights (the weighted sum
method), where the multiobjective formulation is converted to
a single objective function using the weighted sum of
individual objectives.
2) Goal multiobjective index, where the multiobjective
formulation is transformed into a single objective function
using the goal programming method.
3) Multiobjective formulation considering more than one
contrasting objectives and selecting the best compromise
solution in a set of feasible solutions [48].
Several Multiobjective optimization algorithms were
performed for optimal sizing and placement of DG units
[49-64], the reviewed literature and their contributions are
summarized in Table 2.
[54]
Table II Summary of multiobjective optimization literature review
Contribution
Method
[61]
Ref.
[49]
[50]
[51]
[52]
[53]
A multiobjective with weight algorithm
for optimal sizing and siting of multiple
DGs considering DGs uncertainty and
power quality.
A multiobjective algorithm for optimal
sizing and siting of multiple DGs for
minimization of network costs and
improvement of power quality.
Two strategies are worked out to
achieve an integration of multiple
stochastic DG units in low-and
medium-voltage distribution grids
while optimizing several relevant
objectives.
A multiobjective algorithm for
placement of DG in which the
objectives are defined as minimization
of cost index, technical risks and
economic risk.
Time-varying approach is applied to
both load and generation to estimate the
benefits of DG insertion
GA
[55]
[44]
[56]
[57]
[58]
[59]
[60]
[62]
Double trade off
method
Monte Carlo
simulation integrated
with GA
[63]
[64]
Non-dominant
sorting GA
Exhaustive Search
A multiobjective algorithm for optimal
sizing and siting of multiple DGs.
A
multiobjective
programming
approach is applied in order to find
configurations that maximize the
integration of distributed wind power
generation while satisfying voltage and
thermal limits.
A composite reliability index is used as
the objective function in the
optimization procedure to derive the
optimal recloser and DG placement
scheme for radial distribution networks.
A
multiobjective
performance
index-based
size
and
location
determination of distributed generation
in distribution systems with different
load models.
An optimal proposed approach to
determine the optimal sitting and sizing
of DG with multi-system constraints to
achieve a single or multi-objectives.
A
multiobjective
with
weights
algorithm is proposed to lower down
both cost and loss effectively by optimal
selection of DG size and location.
A
multiobjective
with
weights
algorithm is used for optimal placement
of DG considering electricity market
price fluctuation.
An effective method to determine the
optimal size and best locations of DG
sources taking into account the system
constraints, maximizes the system
loading margin as well as the profit of
the distribution companies over the
planning period.
Transform the original objectives and
constraints into a fuzzy weighted
single-objective function in order to
optimize different types of DGs
A mathematical model of the chance
constrained programming is developed
and solved with the minimization of the
DGs’ investment cost, operating cost,
maintenance cost, network loss cost, as
well as the capacity adequacy cost as the
objective, security limitations as
constraints, and the siting and sizing of
DGs as optimization variables.
A hybrid genetic algorithm and particle
swarm is suggested for optimal sizing
and siting of DGs.
A novel application of multiobjective
optimization with the aim of
determining the optimal DGs places,
sizes, and their generated power
contract price taking into consideration
improving the voltage profile and
stability,
power-loss
reduction,
reliability enhancement and economic
issues.
GA combined with
fuzzy programming
Non-dominant
sorting GA
Ant Colony
Algorithm
GA
GA
A simple
conventional iterative
search technique
along with Newton
Raphson method
Mixed integer
nonlinear
programming
GA
Fuzzy set theory and
genetic algorithm
Monte Carlo
simulation-embedde
d genetic-algorithmbased approach
Combination of GA
and PSO
PSO
2017
Othman et. al.,
A Review of Virtual power plant Definitions, Components, Framework and Optimization
International Electrical Engineering Journal (IEEJ)
Vol. 6 (2015) No.9, pp. 2010-2024
ISSN 2078-2365
http://www.ieejournal.com/
Ref.
Table 3- Review of ESE literature
Contribution
Objective
[74]
[65]
An open-loop optimal control
scheme to incorporate the
operating constraints of the
battery storage, such as state of
charge limits, charge/discharge
current limits, and lifetime.
An ESE mathematical model was
developed
based
on
stochastic
programming, in which the
forecast error of wind power is
taken into consideration. Hybrid
intelligent algorithm based on GA
was introduced to solve the
stochastic model.
Provide as much smoothing
as possible, so that the wind
power can be dispatched on
an hourly basis based on the
forecasted wind conditions.
[67]
A
matrix
real-coded
GA
methodology
for
optimal
allocation and economic analysis
of ESS in microgrids.
Maximize the total net
profit achieved during the
system operational lifetime
period.
[68]
The unit commitment problem
with spinning reserve for
microgrid was formulated as
mixed linear integer problem.
Time series and feed forward
neural network techniques were
used for forecasting the wind
speed and solar radiations
respectively
taking
into
consideration the forecasting
errors.
An optimal energy management
scheme for active hybrid ESS.
Determine the optimal size
of ESE connected to
microgrid based on cost
benefit analysis
[66]
[69]
[70]
[71]
[72]
[73]
An economical cost-benefit
analysis has been performed
taking advantage of the increased
capabilities given by the
combined use of RES and storage
devices
Optimal operation of distribution
networks
with
wind-based
embedded generation and ESEs
A model for calculating the
optimal size of an ESS in a
microgrid considering reliability
criterion
where
the
ESS
investment cost and microgrid
operating cost were taken into
account.
A concept in which a DNO
controls the output of the ESEs of
commercial customers during a
specific time period in exchange
for providing a subsidy covering a
part of the initial cost of the
storage system. Further, a model
[75]
Decrease
the
bid
imbalance. Shift energy
from the cheapest to the
most expensive.
[76]
[77]
[78]
[79]
Minimize
the
magnitude/fluctuation of
the current flowing in and
out of the battery and the
energy loss
Enhance wind generation
performances and adapt it
to demand
Minimize the energy losses
Minimize the investment
cost of the ESS, and
expected
microgrid
operating cost.
Solve
the
voltage
fluctuation problem in
distribution networks
[80]
[81]
[82]
[83]
[84]
that
allows
customers
to
optimally ESSs was developed
A simple scenario in which
independent
storage
either
cooperates with an intermittent
energy producer or competes in
reserve markets.
A solution strategy that uses a
convex
optimization
based
relaxation to solve the optimal
control problem then use this
framework to illustrate the effects
of various levels of energy storage
along with both time-invariant
and demand-based cost functions.
A new algorithm to optimize the
day-ahead thermal and electrical
scheduling of a large scale VPP
using
mixed-integer
linear
programming
The optimal control of the
microgrid’s
energy
storage
devices. The suggested method
computed the globally optimal
power flow, in both the network
and time domains.
A stochastic framework to
enhance the reliability and
operability of wind integration
using optimally placed and sized
ESSs.
A double fuzzy logic control
strategy
optimizing
the
management
of
the
superconducting magnetic energy
storage was proposed by
combining the wind power
forecasting and the real-time
control of the wind power system
An adaptive optimal policy for
hourly operation of ESS in a grid
connected wind power company
to achieve the optimal operation
of ESS for wind energy time
shifting.
A Fuzzy PSO was presented to
determine the optimal sizing and
siting decisions for ESS through a
cost-benefit analysis method.
The optimal operation of a ship
electric power system comprising
full electric propulsion and ESS
was analyzed.
A probabilistic method was
proposed to determine optimal
size of ESS for wind farm.
A new evolutionary technique
named improved bat algorithm
that is used for developing
corrective strategies and for
Obtain an optimal storage
scheduling strategy
Investigate the effects of
different energy storage
capacities on generation
costs and peak-shaving
Calculate the daily optimal
operation of energy storage
devices and dispatchable
generation.
Control stored energy is
controlled to balance power
generation of RES to
optimize overall power
consumption
at
the
microgrid PCC.
Minimize the sum of
operation and interrupted
load costs over a planning
period
Smooth
the
power
fluctuations of wind turbine
and
prevent
the
superconducting magnetic
energy
storage
from
occurring of the state of
over-charge/deep-discharg
e
Maximize the expected
daily profit following
uncertainties
in
wind
generation and electricity
price through time shift
wind energy.
Track the forecasted net
demand curve, reduce the
energy
exchanged
at
distribution substation and
mitigate power output
fluctuations by installing
ESS at DG sites
Minimize the operation
cost and limit greenhouse
gas emissions
Smooth the wind power
output and make it more
dispatchable
Perform
least
cost
dispatches
2018
Othman et. al.,
A Review of Virtual power plant Definitions, Components, Framework and Optimization
International Electrical Engineering Journal (IEEJ)
Vol. 6 (2015) No.9, pp. 2010-2024
ISSN 2078-2365
http://www.ieejournal.com/
[85]
optimal sizing of ESEs.
A unit commitment formulation
for micro-grid and the optimal
sizes of different energy storage
devices are determined in the
operation problem.
Minimize generation cost
C.2 Optimal sizing and placement of ESEs
Integration of RES into power systems is a must in order to
face the swelling demand and the soaring fuel prices. Wind
and solar powers are the most dynamically growing
renewable technologies due to their primary power source
availability. However, the mounting penetration of these
resources increases the network uncertainties due to their
stochastic nature.
RES such as wind and photovoltaic (PV) power are
difficult to be accurately simulated because they are strongly
correlated to the climate, ambient temperature, season, time,
and geography. Thus, RES increase the uncertainties in the
power system operation.
In order to secure proper system operation while considering
the uncertainties of the RES and due to their importance and
developing technologies, the ESEs were integrated
extensively into power systems.
The ESE studies include [65-86]:
 Increasing the RES penetration [65], [76], and [83].
 Leveling demand curve [70], [75], [77], and [81].
 Minimizing operation cost and maximizing the
profit [67], [78], [80], [82], and [85].
 Improving voltage fluctuations [73].
 Deferring network upgrades [72].
 Minimizing the system losses [71].
 Relieving the system congestion [70] and [74].
 Shifting of energy time [66], [75], [77], and [81].
 Covering the forecast error of renewable based DGs
[68], [69], and [79].
 Ancillary services [82].
Table 3 lists a summary of ESEs literature review and their
contributions.
C.3 Optimal load controlling schedules
Unlike the two previous topics, the optimal load controlling
studies are rare. Very few publications considered the load
control problem in order to enhance the power system
performance.
The load control studies are divided into two major
categories:
1) The first category of load control studies considers the load
management from the customer point of view in order to
minimize the electricity bill [90] - [92].
2) The second category of the load control studies, which are
the most common studies, adopts the system point of view in
controlling the demand. The most common load management
program of the second sector is end-use equipment control
known as Direct Load Control (DLC). The purpose of DLC is
to shape the load curve by cycling customers’ large current
drawing appliances. A number of DLC schemes have been
developed to reach both peak load shaving and operating cost
saving [87]-[90], [94] - [101].
The main objectives of the load control studies are:
 Minimizing the electricity bill [91]
 Minimizing the cost [87], [88], [92], [97], [99], and
[100]
 Leveling demand curve [88], [95], [98], [99], and
[101]
 Improving system reliability [92] and [93]
 Social welfare maximization [94]
The authors in [87] presented a mixed integer linear
programming formulation for load-side control of electrical
energy demand in order to minimize the net cost of load
shedding. In [88] a multiple-block fuzzy logic based
water-heater demand side management strategy was proposed
to shift the high electricity demand to off-peak hours. A
Relaxed Dynamic Programming (RDP) algorithm was
suggested in [89] to generate a daily control scheduling for
optimal or near-optimal air conditioner loads. The
computational scheme aids customers in restraining peak load
demand and in saving electricity costs. Authors in [90]
determined the optimal control schedules that an aggregator
should apply to the VPP controllable devices in order to
optimize load reduction over a specified control period. In
[91], an automated optimization-based residential load
control scheme in a retail electricity market was introduced.
The main goal of the control scheme is to minimize the
household’s electricity bill by optimally scheduling the
operation and energy consumption for each appliance, subject
to the special needs indicated by the users. Authors in [92]
proposed a fuzzy logic-based DLC scheme of air conditioning
loads considering nodal reliability characteristics and
considering the effect of the transmission network reliability
on the DLC scheme, fuzzy dynamic programming was
utilized to determine the optimal DLC scheme which achieves
a good tradeoff among peak load shaving, operating cost
reduction and system reliability improvement. In [93] a
control scheme was proposed. This scheme is based on the
nodal interrupted energy assessment rate which considers
both nodal reliability and customer willingness to pay for his
reliability to encourage the air conditioning loads customers
to participate in the DLC program. In [94], social welfare
maximization for energy scheduling between a utility
company and residential end-users where the utility company
adopts a cost function representing the cost of providing
energy to end-users was presented. In [95], a mixed-integer
2019
Othman et. al.,
A Review of Virtual power plant Definitions, Components, Framework and Optimization
International Electrical Engineering Journal (IEEJ)
Vol. 6 (2015) No.9, pp. 2010-2024
ISSN 2078-2365
http://www.ieejournal.com/
linear programming decision model to implement DLC on
battery charging processes at electric vehicle charging points
located at parking areas was introduced. A practical strategy
for large-scale control of domestic refrigerators for demand
peak reduction in distribution systems was proposed in [96].
Its common idea is to take advantage of the thermodynamics
of refrigerators in order to accumulate energy during a short
time interval and releasing this energy in another appropriate
interval, during which the refrigerator remains off
contributing for energy consumption reduction. A
mixed-integer programming model was presented in [97] and
applied for load shifting to minimize the overall cost of
power. Authors in [98] suggested a decentralized optimal load
control scheme that provides contingency reserve in the
presence of sudden generation drop. In [99], a control strategy
for the electricity price-load-overload was formed; the main
objective of the study is to apply demand response control
strategies to relief the distribution-line overload and to realize
improvements in terms of electricity cost. An optimization
strategy via load scheduling and control was implemented in
[100] based on PSO in order to decrease the electricity cost.
Authors in [101] concentrated on proposing a new strategy
from the perspective of an aggregator that optimally
schedules residential loads during the next day. Gaussian
copula function and Gaussian mixture model were
investigated as new efficient tools to estimate the aggregate
power demand of specific domestic appliances.
VII. CONCLUSION
VPP is a relatively new and yet an attractive concept that
needs thorough research to facilitate its implementation. This
paper presents a comprehensive literature review for the
different VPP definitions, components, and the relation
between these components. Moreover, the VPP framework is
explained and the functionalities of the TVPP and CVPP are
stated for better understanding of the VPP concept. A survey
of the different optimization techniques aims to optimize
either the VPP structure or the VPP operation discussed. The
optimization of the VPP structure included the optimal sizing
and siting of DG units and the ESEs, the optimal load control,
and the optimal measurement devices location. The required
objective functions, and optimizing algorithms for the sake of
VPP optimal operation are highlighted. The presented survey
helps the researchers in better understanding of the VPP
framework and operation and in finding the optimization tools
and objectives required to realize the VPP concept.
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International Electrical Engineering Journal (IEEJ)
Vol. 6 (2015) No.9, pp. 2010-2024
ISSN 2078-2365
http://www.ieejournal.com/
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2023
Othman et. al.,
A Review of Virtual power plant Definitions, Components, Framework and Optimization