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Techno‐economic analysis of solar grid‐based virtual power plant in Indian
power sector: A case study
Article in International Transactions on Electrical Energy Systems · September 2019
DOI: 10.1002/2050-7038.12177
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2 authors:
Harpreet Sharma
Sachin Mishra
Punjab state power corporation limited
Lovely Professional University
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Received: 17 March 2019
Revised: 13 July 2019
Accepted: 10 August 2019
DOI: 10.1002/2050-7038.12177
RESEARCH ARTICLE
Techno‐economic analysis of solar grid‐based virtual power
plant in Indian power sector: A case study
Harpreet Sharma
| Sachin Mishra
School of Electronics and Electrical
Engineering, Lovely Professional
UniversityPhagwara, Phagwara, India
Correspondence
Harpreet Sharma, Lovely Professional
University, Phagwara, India.
Email: [email protected]
Summary
The environmental threat and rising fuel prices are the two major challenges
faced by the utility to provide clean and affordable electricity to the consumers.
The change in government policies provides assistance for large‐scale deployment of Distributed Energy Resource (DER) like rooftop solar PV, but at the
same time, the intermittent nature of this renewable‐based DER produce
adverse effects on utility stability and its economic viability. The concept of virtual power plant (VPP) can be a possible solution for these issues through coordination and scheduling of DER, storage, and flexible load, which results in
secure operation with high penetration of DER and utility peak‐load reduction.
In this paper, the Distributed Energy Resources Customer Adoption Model
(DER‐CAM) is utilized, which is a Mixed Integer Linear Programming‐based
decision‐making tool. For determining the potential of VPP and its implications, a case study of Punjab State Power Corporation Limited (PSPCL), a state
power utility, is studied. The main objectives of the paper are cost minimization, peak‐load reduction, and reliability enhancement. The DER‐CAM model
simulates different load profiles, and the optimal investment solution is determined, which ensures monetary benefits for both consumer and utility.
LIST OF ABBREVIATIONS AND SYMBOLS: Ani, annuity factor for investments in technologies i; BAU, total base case energy costs without
integrated DER investment disabled; c, continuous generation technologies; CCDPg, turnkey capital cost of generation technology g; d, day type (1,
2, 3); DER, distributed energy resource; DER‐CAM, distributed energy resources customer adoption model; DG, distributed generation; DR,
demand response; DRLoadm,t,h,u, electricity imported from PSPCL by consumer during hour h, type of day t, and month for end use u; EMS,
energy management systems; FCC(c,k), fixed capital cost of generation technology c or storage technology k; GAMS, general algebraic modeling
system; GenLi,m,t,h,u, generated power by technology i during hour h, type of day t, month m, and for end use u to supply the customer's load; h,
hour (1, 2, 3 ..., 24); i, all technologies generation or storage; InvGeni, number of units of technology i installed by customer; IR, interest rate on
DER investments; j, all generation technologies; k, storage technologies; loadm,d,h. u, consumer load at time m, d, h; Lti, expected lifetime of
technology i (y); m, month (1, 2,3 ..., 12); MaxHj, maximum number of hours technology j can operate during the year (h); MaxPg, rated capacity
of generation technology g; MCEk, minimum state of charge of battery technology k; MILP, mixed integer linear programming; MinLg, minimum
acceptable load for generation technology g; MINLP, mixed integer nonlinear programming; NREL, National Renewable Energy Laboratory; OMFi,
fixed annual operation and maintenance costs of technology i; OMVi, variable operation and maintenance costs of technology i; p, tariff period (on‐
peak, mid‐peak, off‐peak); PBP, maximum payback period allowed on the integrated DER investment decision (y); PSPCL, Punjab State Power
Corporation Limited; PV, photovoltaic; s, season (winter and summer including paddy season); S(j), set of end‐uses that can be met by technology j;
SA, available area for solar technologies; SCADA, supervisory control and data acquisition; SCEk, charging efficiency of battery technology k;
SDEk, discharging efficiency of battery technology k; SIm,d,h, solar insolation at time m, d, h; SPEc, theoretical peak solar conversion efficiency of
generation technology c; SREc.m.h, solar radiation conversion efficiency of generation technology c, in month m, and hour h; TEm,d,h, energy tariff
for electricity consumption charges at time m, d, h; TExm. d. h, energy tariff for electricity export at time m, d, h;; TFm, tariff for fixed charges for
using utility infrastructure in month m; TPs,p, maximum demand charges under the PSPCL tariff for season s and period p; VCC(c,k), variable
capital cost of generation technology c or storage technology k; VCj,m, generation cost of technology j during month m; VCSC(c,k), variable capital
cost of battery storage technology k; VPP, virtual power plant; φk, losses due to decay/self‐discharge in battery technology k
Int Trans Electr Energ Syst. 2019;e12177.
https://doi.org/10.1002/2050-7038.12177
wileyonlinelibrary.com/journal/etep
© 2019 John Wiley & Sons, Ltd.
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SHARMA AND MISHRA
KEYWORDS
DER‐CAM, Distributed Energy Resource, solar PV, microgrid, virtual power plant
1 | INTRODUCTION
For the shifting of the power sector from the monopoly system of energy distribution to a competitive energy market,
the Distributed Energy Resource (DER) has now become the main spotlight for the energy policymakers. The small‐
scale DER's installation on consumers' premises makes their participation possible in the energy market and influences
the market prices. The consumers are no more limited to consuming electricity, but now, they can also inject their surplus DER generation into utility network and change their consumption pattern for monetary benefits. The government
also takes rigorous steps in facilitatiing the large‐scale penetration of renewable‐based generator like solar PV through
capital incentives and subsidies. In a country like India, the rooftop solar PV is very popular as most of its area are
receiving abundant solar radiations throughout the year. The small‐capacity rooftop PV panels commonly install on
the residential and commercial buildings throughout the country. The integration of DER in distribution network provides various benefits such as peak‐load reduction, reduced network losses, improved voltage profile, and reduced grid
dependency. On the contrary, with the dependency on the weather condition, the DER generation is intermittent in
nature and creates various issues for the distribution network operator to maintain systems reliability and quality.1
The high penetration of DER adversely affects the distribution grid by voltage fluctuations, reverse power flow, and
reduction of utility revenue. These issues with the intermittent generation can be rectified by utilizing the concept of
virtual power plant (VPP), which includes coordination of DER generations and consumers' load profiles so that it acts
as a single profile and which makes easier to control its electrical dispatch.2,3 To implement the VPP concept, the smart
grid technologies such as smart meter installs at consumer premises, which provides the bidirectional communication
between utility and consumers for sharing real‐time data during electrical dispatch. The Demand Response (DR) programs also introduce for effective implementation of the VPP under which the consumers charged at lower tariff for
a particular time of a day than the remaining period, which helps the load to follow DER generation.4 The small‐scale
DER's and consumers' loads located at the same or different geographical location are aggregate to form a single
operate‐able profile, which schedules for cost minimization.
To study the detailed implication of VPP on power utility and its consumers, the Punjab State Power Corporation
Limited (PSPCL) is selected for this case study. The PSPCL is state government–acquired utility and follows the regulation of Punjab Energy Development Agency (PEDA) for installing and operating DER such as rooftop solar PV. The
number of application for grid‐integrated rooftop PV installation are escalating than ever, and this high penetration
of PV will negatively affect the grid in upcoming years if the operation of this DER is not controlled properly. The
VPP can be a potential solution for this challenge by aggregating this small‐scale DER and creating a single generating
profile. Another major issue the utility faced is the peak load in the paddy season because of a sudden increase in agriculture load in the month of July, when the peak demand is significantly higher than the average load. This forced the
utility to buy power from private generating plants at a higher unit rate and fixed charges through long‐term contracts.
Again, the VPP can aggregate the loads into a single profile and provide flexibility by load shifting and load curtailments. This can reduce peak demand and which further deferred the utility investment in network augmentation.
The research5 gives attention to the implication on the utility network with high integration of solar PV by agent‐based
modeling and recognized the requirement for change in the business model to compensate the utility revenue loss. In
Calvillo et al,6 the benefits of aggregation of DER in the day‐ahead market for load following and its influence on energy
price is discussed. In Bianco et al,7 the different technologies are proposed to rectify the adverse effects of high penetration
of PV into distribution feeder. The various studies are conducted for optimal scheduling of DG for minimization of cost,8-16
but none of them considered utility benefits such as peak‐load reduction and reliability improvement. The various algorithms17-19 are proposed for optimal operation of DER within VPP framework for maximizing the profit and tested on
the IEEE bus system for its validation. These researches do not consider any real‐time parameters of the distribution system, and their scope is limited to consumer benefits only. The studies purposed simultaneous dispatching of DER and DR
2,20-22
for profit maximization of VPP. The novel methodology is introduced in Faria et al23 for the combined dispatch of
DER and DR for energy and reserve needs. Another case study24shows the feasibility of VPP in the specific island region
for self‐power support through DER without taking utility influence. The DR programs are utilized by various case studies
to increase distribution network efficiency25-30 and defer the network investment, but they are not considered DER during
SHARMA AND MISHRA
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electrical dispatch. In a study,31 a techno‐economic analysis of DER‐installed mid‐rise apartment is done with DER‐CAM
and MATLAB software without accessing the impacts on utility load and reliability parameters. In the present studies to
determine optimal DER capacity and scheduling, the simulation tools such as HOMER and RETScreen 32-35 are used, but
they have a lack of sophistication and robustness in the optimization process. Their optimization technique considered
limited technical and economic parameters and also not included sufficient technical constraints of utility and DER, which
limit their application in the distribution system planning.
The present literature studies the effects of DER high penetration into the utility grid with combined dispatch of DR
and storage. The main focuses of these studies are consumer profit maximization, and little emphasis is given on the
implication of utility benefits such as peak‐load reduction and reliability enhancement. The available tools are not sufficiently considered technical and economic parameters, so sophisticated model such as DER‐CAM can be used for
implementing VPP concept for better accuracy. The primary objective of this paper is to bridge the literature gap and
determine the economic feasibility of VPP for optimum DER penetration from both consumer and utility point of view.
In this paper, the DER‐CAM model is utilized for implementing the VPP concept.
The remaining paper is elaborated in the following manner: Section 2 describes the fundamentals of VPP concept and its
components. Section 3 gives methodology description of DER‐CAM model. In Section 4, a case study is discussed for analyzing the optimum dispatch in VPP for peak‐load reduction and reliability enhancement while using key data inputs. In
Section 5, the results are described and detailed techno‐economic analysis of VPP is done. The obtained results are then
also compared with HOMER approach. Section 6 concludes research major results and policy recommendations.
2 | VPP CONCEPT AND ITS COMPONENTS
The concept of VPP is driven from microgrid, but unlike the microgrid, the working of VPP is autonomous. The VPP
aggregates different type of DGs located at different points in the distribution grid and hence creates the single operating
profile. The impact of this DG on distribution network can be easily analyzed by aggregation, and this aids in decision
making while making contracts in the energy market and in generation capacity augmentation. The Figure 1 shows
detailed VPP structure below.36
The VPP not only provides generation flexibility but also can change the consumer load profile by direct load control
or by cost incentives. The VPP integrates the DER technologies with DR programs and storage devices for the efficient
operation of energy systems. The VPP needs four major components for its operation3,37:
2.1 | Distributed generation technologies
The small‐scale DGs having renewable resources are the key generation technologies of VPP. The technology advancement changes the traditional consumer to "Prosumer," which can consume or produce electricity at the same time. The
FIGURE 1
Virtual power plant (VPP) structure
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SHARMA AND MISHRA
DER provides cost‐effective and clean energy, which is the main need for a sustainable future. These generation technologies can be dispersed in different geographic locations and linked to the distribution grid at different points. The
solar PV, wind, and biomass are few generation technologies that are utilized in VPP.
2.2 | Energy storage technologies
The intermittency of DER based on renewable energy resources negatively affects power system stability. The short
period fluctuation due to cloud movement or wind speed can change the supply voltage profile significantly. To increase
the flexibility in the utility grid with DG mix generation, the various storage technologies can be used. The energy storage tackles this issue by smoothing these fluctuations by charging and discharging cycles. The capacity, duration, and
cost are some of the main parameters while selecting storage technologies. The various energy storage technologies
are as follows38:
•
•
•
•
Battery energy storage;
Super conductor energy storage;
Ultra capacitor energy storage
Flywheel energy storage.
2.3 | Controllable loads
With the intermittency of renewable resources, the DGs are incapable to follow the energy demand. In order to tackle
this issue, the flexibility is required from the consumer in its energy pattern, which is termed as DR. The electrical load
is curtailed or shifted with the utility signal during an emergency or peak period. The load shifting and curtailment of
load on peak time can be used for a significant reduction in energy and demand charges. The incentive‐based and direct
load curtailments approaches are utilized to control the demand.
2.4 | Integrated communication technologies
The coordination between DER technologies and utility can make possible participation of small‐scale DER in the
energy market.39 For this coordination, the information technologies play a vital role by providing the bidirectional
communication between consumer and utility and through which sharing critical information of energy generation
and demand is possible. The control system like SCADA and EMS are fully dependent on communication technologies.
3 | METHODOLOG Y
The DER‐CAM model is a sophisticated simulation tool proposed by Lawrence Berkeley National Laboratory, USA. It
utilizes for finding optimal DER capacity and economic dispatch of DER and DR programs within the microgrid or VPP
framework.40 DER‐CAM primarily used in the planning phase of microgrid or VPP deployment. It is based on the
optimization technique of Mixed Integer Linear Programming (MILP) and created in the General Algebraic Modeling
System (GAMS).41 Presently, it is available as a free simulation tool and also can be assessed by web interference.
The DER‐CAM version 2.3.3 is used in this research.
3.1 | Mathematical formulation
This section gives brief details of DER‐CAM mathematical formulation and its technical and economic parameters. The
main objective function is to minimize the capital and operational cost of DER annually. Continuous and discrete generation technologies like solar PV and internal combustion, respectively, are used. The optimal investment solution calculates by utilizing the complete set of tariff and economic data. The mathematical formulation of DER‐CAM describes
below.
SHARMA AND MISHRA
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3.1.1 | Input variables
continuous generation technologies: solar photovoltaic panels (PV)
all technologies generation or storage (j ‐ k)
all generation technologies (c only)
storage technologies: battery storage (ES),
tariff period (on‐peak, mid‐peak, off‐peak)
season (winter, summer including paddy season)
end‐use load: electricity only (eo)
month (1, 2,3 ..., 12), day type (1, 2, 3), hour (1, 2,3 ..., 24) loadm,d,h. u consumer load at time m, d, h for end‐use
u [kW]
c
i
j
k
p
s
u
m, d, h
3.1.2 | Tariff data
TPs,p
TEmd,h
TFm
TExm. d.
Maximum demand charges under the PSPCL tariff for season s and period p [$/kW]
Energy tariff for electricity consumption charges at time m, d, h ($/kWh)
Tariff for fixed charges for using utility infrastructure in month m ($)
Energy tariff for electricity export at time m, d, h ($/kWh)
h
3.1.3 | Generation and storage technology data
rated capacity of generation technology g: PV (kW)
minimum acceptable load for generation technology g: PV (kW)
expected lifetime of technology i (y)
turnkey capital cost of generation technology g ($/kW)
fixed capital cost of generation technology c or storage technology k ($)
variable capital cost of generation technology c or storage technology k: battery storage ($/kW)
variable capital cost of battery storage technology k ($/kWh)
fixed annual operation and maintenance costs of technology i ($/kW)
MaxPg
MinLg
Lti
CCDPg
FCC(c,k)
VCC(c,k)
VCSC(c,k)
OMFi
3.1.4 | Decision variables
• InvGeni
• GenLi,m,t,h,u
• DRLoadm,t,h,u
•
•
•
•
•
•
•
•
•
•
OMVi
MaxHj
VCj,m
S(j)
SCEk
SDEk
φk
MCEk
SPEc
SREc. m.
h
number of units of technology i installed by customer
generated power by technology i during hour h, type of day t, month m, and for end use u to supply
the customer's load (kW)
electricity imported from PSPCL by consumer during hour h, type of day t, and month m for end use u
(kW)
variable operation and maintenance costs of technology i ($/kWh)
maximum number of hours technology j can operate during the year (h)
generation cost of technology j during month m ($/kWh)
set of end‐uses that can be met by technology j (electrical only)
charging efficiency of battery technology k (%)
discharging efficiency of battery technology k (%)
losses due to decay/self‐discharge in battery technology k (%)
minimum state of charge of battery technology k (%)
theoretical peak solar conversion efficiency of generation technology c (%)
solar radiation conversion efficiency of generation technology c, in month m, and hour h (%)
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SHARMA AND MISHRA
3.1.5 | Financial parameters
•
•
•
•
•
•
IR
Ani
SIm,d,h
SA
BAU
PBP
interest rate on DER investments (%)
annuity factor for investments in technologies i
solar insolation at time m, d, h (kW/m2)
available area for solar technologies (m2)
total base case energy costs without integrated DER investment disabled ($)
maximum payback period allowed on the integrated DER investment decision (y)
Economic objective function
min c ¼ Σm TF m þ Σm Σd Σh Σu ULm;d;h;u ·TE m;d;h
þ Σm Σm∈s Σp TP
s;p ·max Σu∈eo ULm;ðd;hÞ∈p;u
þ Σj Σm Σd Σh GSj;m;d;h þ Σu GU j;m;d;h;u · VCj;m þ OMV j þ Σg IGg ·MaxPg · CCDg ·Ang þ OMF g
þ Σi∈c;k ððFCC i ·Pur i þ VCCi ·Capi þ VCSC k ·ECapk Þ·Ani þ Capi ·OMF i Þ–Σj Σm Σd Σh GSj;m;d;h ·TEx m;d;h
(1)
Network constraints
Loadm;d;h;u þ SI nk;m;d;h =SCE k ¼ SOut k;m;d;h;u ·SDE k þ Σj GU j;m;d;h;u þ ULm;d;h;u; ∀m; d; h: k ¼ fESg ∧ u
¼ feog ½kW (2)
RGg;m;d;h ·MinLg ≤ Σu GU g;m;d;h;u þ GSj;m;d;h ≤ RGg;m;d;h ·MaxPg ∀g; m; d; h ½kW
(3)
Σm Σd Σh Σu GU j;m;d;h;u þ GSj;m;d;h ≤ IGg ·MaxPg ·MaxH g ∀g; m; d; h ½kW
(4)
Capi ≤ Pur i ·M∀i ∈ fc; k g ½kW
(5)
Σu GU j;m;d;h;u þ GSj;m;d;h ≤ Capc ·
SRE c;m;h
·SI m;d;h ∀m; d; h: c ∈ fPV g ½kW
SPE C
(6)
Σ Capc
≤ SA: c ∈ fPV g m2
cSPE c
(7)
SOC k;m;d;h ¼ Slnk k;m;d;h –Σu SOut k;m;d;h;u þ SOC k;m;d;h –1·ð1–φk Þ∀k; m; d; h ≠ 1 ½kWh
(8)
SOC k;m;d;1 ¼ SOC k;m;d;24 ∀k; m; d ½kWh
(9)
SOC k;m;d;h ≥ ECapk ·MSC k ∀k; m; d; h ½kWh
(10)
SOC k;m;d;h ≤ ECapk ∀k; m; d; h ½kWh
(11)
Slnk k;m;d;h ≤ Capk ∀k; m; d; h ½kW
(12)
Σu SOC k;m;d;h ≤ Capk ∀k; m; d; h ½kW
(13)
Slnk;m;d;h ≤ sbk;m;d;h ·M∀k; m; d; h ½kW
(14)
SHARMA AND MISHRA
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Σu SOCk;m;d;h;u ≤ 1 − sbk;m;d;h ·M∀k; m; d; h ½kW
(15)
Σu ULm;dh;u; ≤ psbm;d;h ·M∀m; d; h: u ¼ feog ½kW
(16)
GSj;m;d;h ≤ 1 − psbm;d;h ·M∀ j; m; d; h ½kW
(17)
Ani ¼
IR
1−
1
∀i ½1; 2; 3…
ð1 þ IRÞLti
C ≤ BAU þ ΣgIGg ·MaxPg ·CCDPg ·Ang þ Σi∈c;k ðFCCi ·Puri þ VCCi ·Capi þ VCSCk ·ECapk Þ·
Ani −
(18)
(19)
ΣgIGg ·MaxPg ·CCPg þ Σi∈c;k ðFCCi ·Puri þ VCCi ·Capi þ VCSCk ·ECapk
½$ PBP
The above Equation (1) shows the objective function and includes all the economical components like DER capital
cost, storage cost, operation cost, and utility charges. The network constraints for this objective function are as follows:
•
•
•
•
•
•
•
•
•
•
•
Equation 2 forced balanced constraint between load and generation.
Equation 3 forced generation and energy export constraint.
Equation 4 forced constraint of maximum DER generation.
Equation 5 forced constraint of generation and consumer energy purchased, where M = arbitrarily large number.
Equation 6 forced solar PV generation constraint.
Equation 7 forced solar PV area constraint.
Equations 8 to 15 forced battery capacity and investment constraints.
Equation 16 forced energy purchased or selling constraint.
Equation 17 forced energy export constraint.
Equation 18 annuity rate calculation factors.
Equation 19 forced payback constraint.
The PSPCL is the only power utility in the Punjab state of India, which assumes responsibility for both distribution
and generation of energy. The PSPCL installed generation capacity by end of the year 2017 was 52204.9 MW. The generation is the mix of hydro, thermal, and cogeneration42 plants located at various parts of the state. Apart from this generation, the DER in the form of solar PV is now become an emerging resource of energy because of clean generation and
its weather suitability. The government gives subsidies and capital incentives for installing rooftop PV on the residential
and commercial buildings. These DERs are mostly grid connected, and from its present growth, it is clearly visualized
that these DER's grid penetration is expected to increase at a very high pace in upcoming years. The VPP concept could
be a promising solution for efficiently and reliably integrate these DERs into the grid without any adverse effect. The
FIGURE 2
Annual average solar radiation and clearness index data
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TABLE 1
SHARMA AND MISHRA
Load specification
Load Type
Grocery Store
Residential Apartment
Secondary School
Hospital
Aggregated
Average demand
117 kW
109 kW
102 kW
60 kW
388 kW
Peak demand
191 kW
254 kW
233 kW
96 kW
585 kW
Annual energy demand
1 010 264 kWh
923 512 kWh
987 752 kWh
546 726 kWh
3 468 255 kWh
Peak Month
June
July
July
July
July
present policy of PSPCL allows solar capacity ranging from 1 kW to 1 MW to install at consumer premises for grid‐tied
operation. In this case study, the potential of VPP is analyzed for PSPCL in a selected area.
4 | CASE STUDY OF U TILITY
4.1 | Location data of study area and resource data assessment
The selected site is under the jurisdiction of East Division, PSPCL situated (31.3260°N, 75.5762°E) in Jalandhar district
of Punjab (India). In this area, the solar PV already showed great results in reducing energy bills for consumers. The
annual average solar radiation and clearness index data are taken from the National Renewable Energy Lab Database
is shown in Figure 2 below:
FIGURE 3
Average daily load profile
TABLE 2
Technical parameters of VPP
PV Cost ($/kW)
$1200
PV Lifetime
25 y
PV inverter Cost 100 kW
$500
PV Maximum Efficiency
14.9%
Battery Cost ($/kW)
218
Battery Lifetime
5y
Efficiency Charge (%)
90%
Efficiency Discharge (%)
90%
Max Charge Rate (%/h)
30%
Max Discharge Rate (%/h)
30%
Static Switch Cost ($/kW)
$75
Static Switch Lifetime
10 y
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4.2 | Load assessment of study area
The SCADA integrated 66/11‐kV substation located within the division boundary the fed up the load. The 11‐kV distribution feeders leaving from substation are moving along the residential, commercial, and industrial area of the city.
This case study selects the DER‐installed residential and commercial buildings for the load assessment. Table 1 shows
load specification data of these buildings collected from respected distribution subdivision.
From the above table, it can be concluded that there is a significant difference between average demand and peak
demand, and the peak month comes to be July. Figure 3 shows the average daily aggregated load profile for the month
of January.
4.3 | Techno‐economic data
The capital cost of DG installation includes the cost of PV, batteries, and static switch are varying from region to region.
This case study selects the market available typical solar PV panel without any subsidy. The PV cost expects to reduce
further with technology advancement. Table 2 below shows technical and economic parameters such as efficiency, lifetime, and cost for VPP modeling.
4.4 | Tariff data
This study selects the Time of Day (ToD) tariff from PSPCL energy tariff 2018. The main purpose of this tariff is to
reduce the load in peak hours by encouraging the consumer to shift their load from on‐peak to off‐peak hours, which
ultimately deferred the utility investment in the generation capacity. Table 3 below shows the main features of this
tariff43:
4.5 | Load shifting data
With the introduction of ToD tariff, the variable cost of energy encourages the consumer to shift their consumption during off‐peak hours from peak hours or during PV generation time. Depending upon the flexible load profile, the load
shifts to save on energy bills. It is to be noted that a load of the hospital is critical so there is no possibility of shifting
TABLE 3
PSPCL ToD tariff
Period
Time
1‐4‐18 to 31‐5‐18
06:00
06:00
10:00
AM
06:00
06:00
10:00
AM
06:00
06:00
10:00
AM
1‐6‐18 to 30‐9‐18
1‐10‐18 to 31‐3‐18
TABLE 4
Cost
PM
PM
PM
PM
PM
PM
to 06:00 PM
to 10:00 PM
to 06:00 AM
$0.0907
to 06:00 PM
to 10:00 PM
to 06:00 AM
$0.0907
$0.1220
$0.0907
to 06:00 PM
to 10:00 PM
to 06:00 AM
$0.0907
$0.0713
$0.0713
Schedulable load data
Load
%Schedulable Peak
%Schedulable week
%Schedulable weekend
Maximum load in an hour (kW)
Grocery store
12
15
10
50
Residential Apartment
32
28
15
85
Secondary school
15
15
15
30
0
0
0
0
Hospital
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TABLE 5
SHARMA AND MISHRA
Load curtailment parameters
Variable Cost, $/kWh
Maximum Curtailment of
Load, %
Low priority
15
15
8
Medium priority
21
18
5
Grocery store
High priority
43
10
2
Residential Apartment
Low priority
0.5
35
12
Residential Apartment
Medium priority
2
40
8
Residential Apartment
High priority
8
25
4
Secondary school
Low priority
2
20
3
Secondary school
Medium priority
7
27
2
Load
Priority Level
Grocery store
Grocery store
Maximum Curtailment Time
Period, h
Secondary School
High priority
10
18
1
Hospital
Low priority
Emergency service
0
0
Hospital
Medium priority
Emergency service
0
0
Hospital
High priority
Emergency service
0
0
TABLE 6
Financial parameters of VPP
Discount Rate
3%
Maximum Payback Period
10 y
Base Case Annualized Cost
$329 000
that load in any day. There is a maximum possibility of load shifting in a residential apartment. Table 4 summarizes the
schedulable load, which can schedules during peak, week, and weekend days.
4.6 | Load curtailment parameters
In the period of fault on feeder or grid failure, the reliability of VPP is a matter of concern. The VPP has the potential to
continue supplying the portion of a load directly from batteries and curtailing the remaining portion of the load depending upon the priority level. Table 5 below shows the load curtailment parameters.
TABLE 7
Utility normal and emergency days
Month
Peak
Days
Weekdays
Weekend
Days
Emergency Days of the
Week
Emergency Days of
Peak
Emergency Days
Weekend
January
2
21
5
1
0
2
February
2
19
5
0
1
1
March
2
19
7
1
0
2
April
2
20
5
0
1
2
May
2
21
5
1
0
2
June
2
18
7
1
0
2
July
2
17
7
2
1
2
August
2
18
6
2
1
2
September
2
18
6
1
1
2
October
2
21
5
1
1
1
November
2
19
6
1
1
1
December
2
19
6
1
1
2
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4.7 | Financial parameters
The financial inputs for decision making in the VPP model are shown in Table 6. To compute the savings from VPP, the
base case cost is calculated, which is the cost incurred without implementing VPP concept or any DER investment.
4.8 | VPP reliability data
The historical outage data of feeder tripping due to faults and schedule maintenance for the year of 2017 is collected
from the 66/11‐kV substation. Table 7 shows data regarding the utility of normal and emergency days (outages). In
the peak month period, the numbers of emergency days are the maximum because of high demand on the feeders,
which results in reduced reliability of the utility grid. The average outage duration of the emergency day is 1.5 hours.
The emergency days are mostly during the weekend due to scheduled maintenance on the feeder. The emergency days
are frequent during the month of July due to the high demand.
5 | R E S U L T S AN D D I S C U S S I O N
The DER‐CAM simulation is launch after feeding all the inputs and constraints and the results obtained from this simulation are discuss below
5.1 | VPP‐aggregated model
The model obtained includes interconnection of different loads with their designated DER. The model uses aggregated
profiles of load and DER for controlling the electrical dispatch and makes an investment decision. Figure 4 shows the
detailed VPP‐aggregated model.
The study classifies into four categories, which then simulates in DER‐CAM, and the optimum solution is determined:
•
•
•
•
Base case: the system cost without any DER investment or DR program.
With DER investment: the system cost with investment in only DER
With DER investment and DR: the system cost with DER investment and implementation of DR programs.
With DER, DR, and storage: the system cost calculation with DER and DR investment and with battery storage.
5.1.1 | Case 1: Base case
In order to set up reference cost, the base case in which DER investment and DR programs are disabled is simulating in
DER‐CAM. Figure 5 illustrates the base case electrical dispatch of the peak day of the month of July.
FIGURE 4 Virtual power plant (VPP)‐
aggregated model
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FIGURE 5
Base case electrical dispatch
FIGURE 6
Electrical dispatch with Distributed Energy Resource (DER) investment
5.1.2 | Case 2: DER investment
The DER investment in the installation of solar PV on consumer premises is the possible solution in reducing energy
charges for consumer and gives some relief to the utility feeder. The electrical dispatch with DER investment shown in
Figure 6 clearly illustrates the effectiveness of DER investment for a reduction in energy charges.
5.1.3 | Case 3: DER investment and DR
The implementation of DR programs like load shifting can significantly enhance DER capabilities by utilizing the larger
part of their generation to reduce peak demand and hence flatten the load profile. The schedulable load of the consumer
shifts from the on‐peak period to the off‐peak period. This provides benefits for the consumer by reducing their maximum demand charges and giving a big relief to the utility feeders during the on‐peak period. The DR programs such as
load shifting based on TOD tariff increases grid integrated capacity of DER without violating technical constraints such
as reverse power flow in the distribution network. The electrical dispatch of DER and DR is illustrated in Figure 7.
5.1.4 | Case 4: DER and DR with storage
The VPP improves the reliability of electricity dispatch in the selected area and reduced the dependence of network on
the utility grid. In the outage period, the VPP can feed up its load by DER and battery storage system, and also in the
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FIGURE 7 (A) Electrical dispatch with Distributed Energy Resource (DER) and Demand Response (DR) and (B) Grocery store electricity
dispatch with load shifting and PV
worst case scenario, the VPP can use the load curtailment technique to maintain the stability. Figure 8 below shows the
electrical dispatch of VPP with DER, DR, and storage during the emergency outage.
5.2 | Techno‐economic analysis
Table 8 summarizes detailed technical and economic results of DER‐CAM simulations. In the reference to the base case,
the DER investment in solar PV aggregated capacity of 693 kW suggests, which significantly reduces annual energy cost
by 27.77% of the network by partially supplying the demand by PV. For this case, the peak load also slightly reduces by
4.61%. The further implementation of the DR program with DER investment such as load shifting reduces the annual
energy cost by 31.45% and significantly reduced the peak load by 32.99%. The demand shifts from the on‐peak period to
off‐peak period or in the duration of solar generation. The energy storage system installs to increase the reliability of the
distribution system at consumer premises so that during the outage period, the supply of the remaining network can
restore through DER and storage system. The curtailment of the load performs during the outage period if demand
exceeds the supply available. The storage capacity of 755 kWh advises by DER‐CAM simulation for this case. The installation of battery storage with DER and DR gives a maximum reduction in peak load by 38.11%, but the annual energy
cost slight increases because of capital investment. The annual energy generation from DER remains the same for all
three cases. With the maximum annual saving and lowest operational cost, the combination of DER and DR appears
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(a)
(b)
FIGURE 8 (A) Electrical dispatch with Distributed Energy Resource (DER), Demand Response (DR), and storage during an emergency
outage and (B) Electrical dispatch of a residential apartment with load shifting and PV
TABLE 8
Techno‐economic analysis
Aggregated Model
Investment
Base case with DER
Base case without Base case with
Base case with DER investment, DR, and
DER investment DER investment investment, and DR Storage
PV capacity
‐
693 kW
693 kW
693
Storage
‐
‐
‐
755 kWh
Electricity exported from PV
‐
12478 kWh
12 478 kWh
11 576 kWh
Total annual energy costs (incl. annualized
capital costs and electricity sales)
$329 456
$237 809
$225 545
$268 243
Annual savings
0
$133 892
$146 156
$139 455
Optimized operational cost
$329 456
$195 108
$182 844
$189 545
Total electric costs
$327 948
$207 024
$195 469
$188 100
Total annual electricity purchase
3 468 255 kWh
2 121 169 kWh
2 121 169 kWh
2 056 883 kWh
Total annual on‐site generation
‐
1 489 651 kWh
1 489 651 kWh
1 491 581 kWh
Peak load
585 kW
558 kW
392 kW
362 kW
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TABLE 9
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Bus wise profile
Grocery Store
Residential Apartment
Investment
With DER
Base case Without With DER With DER
Base case Without DER
investment and
investment
investment investment and DR investment
investment DR
PV capacity
‐
216 kW
309 kW
‐
139 kW
222 kW
Storage
‐
‐
‐
‐
‐
‐
On‐site DER
generation
‐
464 866
kWh
664 268 kWh
‐
299 495
kWh
475 917 kWh
Secondary school
Hospital
Base case Without
investment
DER
With DER
Base case Without
investment investment and DR investment
DER
With DER
investment investment and DR
PV capacity
‐
70 kW
96 kW
‐
268 kW
67 kW
Storage
‐
‐
‐
‐
‐
‐
On‐site DER
generation
‐
150 370
kWh
206 528 kWh
‐
574 918
kWh
142 937 kWh
to be most attractive investment solution, but the installation of storage gives clear advantage on reliability with a slight
increase in the investment cost.
To understand the VPP model in more detail, the bus wise results are analyzed, which shows in Table 9 below. With
load shifting, more capacity of DER can integrate into the grid without any adverse effects such as reverse power flow.
Figure 7 shows the grocery store electrical dispatch with load shifting and PV capacity of 309 kW. The major portion
of the peak load of the grocery store is fed up by PV generation, and only a small portion of demand is import from the
grid or other VPP agents. The excess generation of PV from 12:00 PM to 03:00 PM exports to the grid. It is clearly
observed that with DER installation, the peak load and energy export are considerably reduced. The excess/deficits
power can supply between the consumers or nodes of VPP. The PV generation reduces the peak load very effectively
as the load match with PV generation period.
A load of the residential apartment is coincided with utility peak load, in order to reduce the peak load charges and
demand charges; the load shifts to an off‐peak period and PV self‐consumption period. The detailed electrical dispatch
of a residential apartment with 222‐kW PV capacity and load shifting is shown in Figure 8. The potential of load shifting
FIGURE 9
Secondary school electricity dispatch with load shifting and PV
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FIGURE 10
TABLE 10
SHARMA AND MISHRA
Hospital electricity dispatch with PV and without load shifting
Comparison of HOMER and DER‐CAM optimization
Parameter
HOMER Optimization
DER CAM Optimization
Grid purchases
663 675 kWh
608 434 kWh
Total annual energy costs (incl. annualized capital costs and electricity sales)
$78 942
$72 788
Operational cost
$63 234
$61 002
is highest in the residential apartment because of its low priority load. The flexible load can shift in order to limit the
reverse power flow during excess PV generation period.
The peak load of secondary school also coincides with PV generation period; these reduce the peak load and the
energy charges. The schedulable load is less in comparison with other load profiles, so the load shifting does not play
a significant role in this electricity dispatch. The electricity dispatch of secondary school with 96‐kW PV, and load
shifting is shown in Figure 9 below.
Because of the 24 emergency service, the load of the hospital is critical and hence cannot be shifted or curtailed. The
PV generation still shows a decline in peak load and reduction of energy charges. The excess generation from PV is also
exported to the grid for a small duration. The detailed electrical dispatch of the hospital without load shifting with 67‐
kW PV is shown in Figure 10.
5.3 | End remarks and comparison of results
In general, the results indicated that the VPP is a promising solution for the integration of DER resources and can
reduce both demand and energy charges for the consumer point of view. On the utility side, it can enhance the network
reliability and provide relief to feeders from peak load, which ultimately deferred the investment in network augmentation. The four different aggregate load profiles are analyzed to determine the VPP implication. The DER investment
alone gives a significant decline in electrical energy cost, and its functionality can enhance by implementing load
shifting, which reduces energy cost and peak load at the same time. The utility outage due to any fault results in loss
to the industrial production and other commercial works. The impact of these outages can decrease by investing in a
battery storage system and implementing a load curtailment program. The battery storage is also used to store excess
PV generation and limit the reverse power flow into the grid. The storage also improved utility reliability and reduced
the peak load to the lowest value. The combined dispatch of DER and DR is found to be the most economical scenario
of VPP, but with the small investment in the storage system can be easily justified due to reliability improvement.
The HOMER Pro is the well‐known simulation tool available to determine the optimal DER capacity and techno‐
economic analysis of grid‐tied or standalone energy system.32,33 To determine the effectiveness of DER‐CAM optimization, the results achieve are compares with the results that obtain by utilizing the HOMER approach. The load profile of
SHARMA AND MISHRA
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grocery store selects for this comparison while utilizing identical technical and economic parameters for both of these
approaches. The results attained tabulates in Table 10 below.
The above results conclude that DER‐CAM optimization gives significantly better results in comparison to the
HOMER optimization. The grid purchases, total annual energy cost, and operational cost reductions by 8.32%, 7.79%,
and 3.52%, respectively. The DER‐CAM is also a more sophisticated tool and gives more accurate results than HOMER
approach.
6 | C O N C L U S I O N A N D FU T U R E S C O P E
The main motive of this study is to have a secure and efficient operation of the distribution grid through the coordination and scheduling of small DER and flexible demand. A case study of PSPCL, which is facing high penetration of grid‐
integrated DER, is studied for the feasibility of VPP deployment. The DER‐CAM optimize the electrical dispatch of
DER, storage, and flexible load, which results in significant annual saving subject to the number of technical and financial constraints. The scheduling of the flexible loads from peak to off‐peak also results in reduced electrical cost due to
ToD tariff, which further helps in the reduction of utility peak load. The results obtained from DER‐CAM simulation
clearly depicts that the concept of VPP is beneficial for both consumer and utility. VPP reduced the operational cost
by 44% and annualized energy cost by 31.45% with combine dispatch of DER and DR. The annual energy cost slightly
increases with investment in the battery storage system, but at the same time, the reliability of the system is significantly
improved. The overall savings encourage the consumer to be a part of VPP, and at the same time, it facilitates the utility
to defer its investment for augmentation in network capacity and rectify the adverse effects of high‐DER penetration.
The reliability of the grid improves significantly with load curtailments and battery storage system in the event of an
outage. The output results of DER‐CAM in the particular case come to be financially attractive and more accurate than
a well‐known tool such as HOMER pro.
This work can be extended with the inclusion of other renewable resources and storage system. The technical impact
includes the power quality and protection of distribution feeders with high‐penetration DER can investigate. For VPP
implementation, the different profiles of industrial and residential load could be analyzed.
A C K N O WL E D G E M E N T
The authors wish to thank Punjab State Power Corporation Limited, India for providing relevant data and their cooperation for the conduct of this study.
ORCID
Harpreet Sharma
https://orcid.org/0000-0001-7507-2189
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How to cite this article: Sharma H, Mishra S. Techno‐economic analysis of solar grid‐based virtual power plant
in Indian power sector: A case study. Int Trans Electr Energ Syst. 2019;e12177. https://doi.org/10.1002/2050‐
7038.12177
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