The Electricity Journal 33 (2020) 106707
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The Electricity Journal
journal homepage: www.elsevier.com/locate/tej
Evaluation of the behind-the-meter benefits of energy storage systems with
consideration of ancillary market opportunities
T
Khashayar Mahania, Seyyed Danial Nazemib,*, Maryam Arabzadeh Jamalib, Mohsen A. Jafarib
a
b
Quanta Technology, Raleigh, NC 27607, USA
Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ 08854, USA
A R TICL E INFO
A BSTR A CT
Keywords:
Energy storage systems
Distributed energy resources
Ancillary market
Resiliency
Behind the meter benefits
In this study, we analyze behind the meter benefits and resiliency capability of the price-taking energy storage
devices in order to understand the impact of the facility's electricity and thermal demand behavior, energy
providers pricing structure, DER configuration, storage capacity, and facility criticality on the storage evaluation
assessment. We develop an integrated design considering ancillary market opportunities that accounts for different facilities with variant thermal and electrical loads, different DER configurations and energy tariff structures.
1. Introduction
The dependency on limited fossil energy resources while the price of
these fuels is growing is one of the most exigent challenges that researchers and decision-makers are needed to cope with (Geidl et al.,
2007; Nazemi and Boroushaki, 2016). In this regard, a move has begun
on the use of distributed energy resources (DERs), including demand
response (DR), Combined Heat and Power (CHP), fuel cells (FC), hybrid
power systems (solar hybrid and wind hybrid systems), microturbines,
photovoltaic (PV) systems, and reciprocating engines (Amini, 2019;
Bruni et al., 2015; Ghofrani et al., 2019a,b; Jiayi et al., 2008; Mahani
et al., 2017). Today’s DER systems such as microgrids have become
advanced management tools to provide financial and environmental
benefits. At times of main macro-grid failure, a microgrid can operate
independent of the larger grid and isolate its generation nodes and
power loads from any disturbance without affecting the larger grid.
Having the ability to change between islanded mode and grid-connected mode (Abu-sharkh et al., 2006; Driesen and Katiraei, 2008;
Hatziargyriou et al., 2007) provides resiliency solutions to the grid and
communities (Arabzadeh Jamali, 2019).
Energy generation comprises many different types of renewable and
non-renewable energy sources technologies (Hawkes and Leach, 2009;
Wolfe, 2008). Integrating DERs along with storage systems gives flexibility to the microgrid. Energy storage systems such as chemical storage (primarily battery, including electric vehicles) and thermal storage
(heating or cooling) have the ability to absorb energy from the main
grid or local generation and return it later. In this context, energy
⁎
Corresponding author.
E-mail address: danial.nazemi@rutgers.edu (S.D. Nazemi).
https://doi.org/10.1016/j.tej.2019.106707
1040-6190/ © 2019 Elsevier Inc. All rights reserved.
storage technologies play a key role as they enable the increased use of
renewable electricity generation to match energy production to energy
demand and independency from the grid by charging the storage during
off peak hours and using it in peak hours (Beaudin et al., 2010; Yu and
Foggo, 2017). Since some of the distributed generation technologies
generate excessive heat, the thermal energy storage represents a fundamental element in the management of thermal demand and results in
improving the overall efficiency of the microgrid (Kueck et al., 2003).
Energy storage allows collection of renewable energy during daytimes
and using it during night times (Khan et al., 2004). Although energy
storage devices make energy self-generation achievable by end users,
the integration of the DERs along with the thermal and electrical
storages create challenges for microgrid management (Comodi et al.,
2015). DERs including Distributed Generations (DG) and storages can
be managed and coordinated within a smart grid enabling a collection
of energy resources to lower environmental impacts and improve security of supply. DER systems within a microgrid can employ renewable
energy sources, such as solar power, wind power, solar biomass, and
biogas to reduce the amount of carbon emission significantly. However,
it should be noted that the supply of energy from renewable technologies is intermittent or stochastic in nature (Barton and Infield, 2004;
Ledesma et al., 2003) due to their reliance on weather conditions (i.e.
sunlight, wind). Aggregating different DERs and energy storage technologies will be associated with various problems and challenges in
control and operation of the microgrid which directly impact the evaluation process. One of the challenges in the smart grids is the scheduling of these integrated DERs and storages to optimize the energy
The Electricity Journal 33 (2020) 106707
K. Mahani, et al.
storages, which are installed behind the meter (BTM). The microgrid
also connects to the grid. The power and thermal energy produced by
onsite generation assets – i.e. PV and CHP – will be used to meet
electricity and thermal demand at the facility. Onsite generation output
may exceed facility demand from time to time. Electric and thermal
storage nodes absorb this excessive load. The energy charged from
excessive energy can be used to reduce the cost of purchased energy
from electricity distribution company (EDC) and gas distribution company (GDC). Moreover, DERs and electric storage can be used to increase the facility cash flow by increasing the net-metering revenue and
participating in PJM frequency regulation market. Fig. 1 depicts the
energy flow in such facility level microgrid.
To quantify and confirm the benefits of BTM DERs and energy
storage, an integrated operation model has been developed to illustrate
the microgrid operation. To determine the key factors in microgrid
evaluation, the proposed methodology is demonstrated and verified
through use cases along with a sensitivity analysis. The following factors are considered:
Fig. 1. Energy flow in a facility level microgrid.
flows within the microgrids to minimize costs.
Currently, energy and regulation markets become very beneficial for
the consumers to own and operate their own electricity (Nazemi et al.,
2020). In this regard, utilities are exploring opportunities to provide
ancillary services and innovative legislation like incentives, tax credits
which are all moving in a direction that makes energy services more
beneficiary and attractive. Energy storage is effective in providing
services to each segment of the power system, from demand charge
reduction to frequency regulation (Forrester et al., 2017). Ancillary
service markets include frequency up and down-regulation, net-metering, reserve market and others each of which have a bidding structure and different rules, such as the time to react to a utility signal and
the minimum asset size, depending on the ISO that the microgrids are
operating within (Cappers et al., 2013; Zhou et al., 2016).
While several studies performed mathematical modeling of DERs, a
few focused on assessing the optimal value of DERs and storage systems
with respect to the consumer’s territory. This is because different energy providers have different pricing rate structures, facility demand
load profile, and the right sizing for the specific consumers. Each of
these principals has been studied separately. From the literature reviewed, it is clear that an area of opportunity exists for a practical tool
that can assist decision-makers to evaluate the DER projects prior to the
investment.
The main objective of this study is to determine the key factors of
behind the meter benefits, generated by the individual and combined
DERs and energy storage for various consumers, by weighing the costs
against financial gains and other benefits. It is also our objective to
investigate the effects of these selected DERs on generation scheduling
and total cost of the system considering ancillary services in energy
market. In this context, critical principals such as configuration of DERs
(sizing), different demand load profiles and pricing elements of energy
providers have been studied to determine their impacts on DERs’ values
and total cost of the system. We suggest an integrated design approach
in which electrical and heating loads and the generation sources are
modeled as mixed-integer linear programming (MILP). We will focus on
the following applications: (i) Energy Bill Management (EBM), (ii)
frequency regulation, and (iii) resiliency enhancement.
1) Facility type: Two different facilities; namely, “hospital” and “fullservice restaurant” are considered in this study. These are commercial building benchmark models developed by US DOE. For
compliance with geographical scope of this project (the state of New
Jersey), buildings’ load data is simulated using New Jersey weather
data. An overview of these facilities along with their energy consumption characteristics are provided in Table 1:
These two facilities have different hourly energy profiles, which are
illustrated in Fig. 2. As shown in this figure, contrary to full-service
restaurant, hospital has uncorrelated electricity and thermal demand. Also, hospital has the electricity profile with prolonged peak.
On the other hand, full-service has energy profiles with lots of hills
and valleys.
In each facility, the critical load is defined as the portion of the load
that should be severed at emergency incidents during grid outage.
Critical load is calculated based on break-down of end-use electricity load using EnergyPlus building simulation tool and assigning
a percentage for critical portion of each end-use. In Table 2, critical
load for each building segment is provided. It should be stated that
defined percentages are not exclusive.
2) Energy tariff: Different locations based on major electricity and gas
providers’ territories in New Jersey are defined in the set of scenarios. Different electricity and gas provider companies have different rating structures which affect the calculation in financial
evaluation process. Three EDCs and two GDCs in New Jersey are
considered in this study. EDC billing components considered for
analysis are delivery and supply charges. For supply charges, it is
assumed that all customers have elected Rider BGS-CIEP indicating
that they will be charged according to PJM hourly locational marginal prices (LMPs) for commodity. These three EDCs have completely different rating structures for delivery charges (both energy
and demand charges). While EDC1 and EDC2 have seasonal tiered
demand charge structure according to customer’s peak shared level
(PSL), EDC3′s seasonal demand charge structure is not sensitive to
customers’ PSL. Moreover, EDC1 assigns time-of-use (TOU) demand
charge for their customers with PSL > 150 kW. Table 3 summarizes
the rating structure across the three EDCs .
2. Problem statement
In this study, we consider a facility level microgrid consisting of a
renewable resource (e.g. PV), CHP system, electric and thermal
Table 1
Customer segments information.
Segment
Hospital
Full-Service Restaurant
Floor Area
241,351
5,500
# Floors
5
1
Electricity
Natural Gas
Annual Consumption (kWh)
Peak Load (kW)
Annual Consumption (Therm)
Peak Load (Therm)
6,500,906
314,700
1,262
68
97,684
9,914
38
6.5
2
The Electricity Journal 33 (2020) 106707
K. Mahani, et al.
Fig. 2. Hourly energy profiles for an average day.
GDC billing components considered in this analysis are delivery and
supply charges. For supply charges, it is assumed that all customers
have elected Rider “A” for Basic Gas Supply Service (BGSS). GDCs
have completely different rating structures for delivery charges
(energy charges, demand charges and balancing charges). The rating
structure across two GDCs is shown in Table 4:
3
The Electricity Journal 33 (2020) 106707
K. Mahani, et al.
3) DER configuration: Different combinations and sizes of CHP, PV,
ES and TS are considered across the use cases. Moreover, since
prime-movers have significant impact on CHP efficiency, two different technologies and prime movers are considered in this study:
a) Fuel cell (SOFC) with heat recovery
b) Reciprocating engine
Table 2
Critical loads as a percentage of electricity load of major end uses in building
segments.
Full-service restaurant
Hospital
End use
% of actual load
End use
% of actual load
Heating
Cooling
Interior lights
Exterior lights
Interior equipment
Fans
Pumps
Refrigeration
80
80
50
10
50
80
80
100
Heating
Cooling
Interior lights
Exterior lights
Interior equipment
Exterior equipment
Fans
Pumps
Heat rejection
Humidifier
Refrigeration
80
80
50
10
50
80
80
100
70
80
80
Different CHP technologies/prime-movers have different operation
and financial characteristics. The parameters and characteristics of
these technologies are summarized in Table 5:
3. Methodology
The operation of facility microgrid is formulated as a mixed-integer
optimization problem to estimate the optimal value generated from
DER and storages installation compared to the base-line. This value,
along with the other cost elements such as project installation cost, will
feed to a cost-benefit analysis model to determine the cost-effectiveness
of the project. The model accounts for statistical nature of loads and
various technology features and operational conditions of DERs. The
model also considers different application scenarios.
As we discussed in Section 1, three applications are taken into account as objectives of optimization problem. Energy storage (thermal
and electrical) and CHP will be utilized to manage net energy consumption (both electrical and thermal) in the facility level. They may
participate in ancillary market such as frequency regulation to generate
revenue for the facility owner. These are all behind the meter applications, which are the focus of this study. Furthermore, during the
power outage event, the facility level microgrid will be used to serve the
critical demand in the facility, which results in resiliency enhancement.
Two objective functions are pursued here, namely, total cost optimization and resiliency enhancement. In the former objective, the total
electricity cost, the cost related to purchasing natural gas, the operation
cost, and also the revenues generated for facility owner by doing netmetering, participating in frequency regulation market and spinning
reserve market are considered. In the latter objective, the unserved
critical load is included. The operation constraints for MILP problem
include energy balance constraints at the facility, constraints on CHP
operation, PV operation, electrical and thermal storage devices, and PV
operation. We presented the complete mathematical formulation in our
conference paper (Mahani et al., 2020). Here, we utilize such a formulation for obtaining detailed results related to behind-the-meter
benefits of energy storage systems.
Table 3
EDCs rate structure.
EDC1
EDC2
EDC3
Customer differentiation
Customer
factor:
differentiation factor:
PSL (150KW)
PSL (750KW)
Supply demand charge
Supply demand charge
structure:
structure:
BGS CIEP
BGS CIEP
Supply energy charge
Supply energy charge
structure:
structure:
BGS CIEP (real-time
BGS CIEP (real-time
PJM LMP)
PJM LMP)
Delivery energy charge
Delivery energy charge
structure:
structure:
Seasonal
Seasonal
Flat
Tiered
Delivery demand charge
Delivery demand
structure:
charge structure:
Seasonal
Seasonal
Tiered
Tiered
TOU for PSL > 150KW
Aggregated KWH and KW charges ranking
KWH: EDC3 > EDC2 > EDC1
KW: EDC1 > EDC2 > EDC3
•
•
•
•
•
•
•
•
•
•
•
Customer differentiation
factor:
N/A
Supply demand charge
structure:
BGS CIEP
Supply energy charge
structure:
BGS CIEP (real-time PJM
LMP)
Delivery energy charge
structure:
Seasonal
Flat
Delivery demand charge
structure:
Seasonal
Flat
•
•
•
•
Table 4
GDCs rate structure.
GDC1
GDC2
Customer differentiation factor:
Monthly consumption peak
(3000Therm)
4. Case studies
Customer differentiation factor:
DG installation
Annual consumption
(5000Therm)
Supply charges structure:
Supply charges structure:
Rider “A” BGSS
Rider “A” BGSS
Delivery charges structure:
Delivery charges structure:
Energy: Seasonal
Energy: Seasonal
Demand & balancing: Flat
Demand & balancing: Flat
Aggregated per Therm, per demand Therm and per balancing Therm charges ranking
Per Therm: GDC1 > GDC2
Per demand therm: GDC2 > GDC1
Per balancing therm: GDC1 > GDC2
** GDC2 incentivizes distributed generation (DG) owner by assigning lower charges
•
•
•
•
•
•
•
•
•
•
•
•
In this section, two combinations of DERs are illustrated as case
studies. In each case study, the impact of DER capacity and characteristics, energy tariff rates and facility energy profiles are studied. These
two combinations are listed as below:
i) PV and electric storage (PV-ES)
ii) CHP, electric storage and thermal storage (CHP-ES-TS)
4.1. PV and electric storage (PV-ES)
Different configurations of PV-ES systems are considered across two
Table 5
CHP Prime mover characteristics.
Prime mover
Average electricity
efficiency
Average heat to power
ratio
Average total efficiency
Average installation and maintenance cost over lifecycle
($/kW)
Fuel Cell (FC)
Reciprocating Engine (RECIP)
47 %
38 %
0.87
1.3
87.9 %
87.4 %
9,500
5,500
4
The Electricity Journal 33 (2020) 106707
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Fig. 3. Hospital-EDC1; PV-ES; NPV/kW.
mentioned facilities. For PV system, it is assumed that installed capacity
supplies 80 % of annual electric consumption (AEC). For ES system,
rated capacities of 50 % and 100 % of peak critical load (PCL) are
considered. In addition, for each rated capacity, duration parameters
are ranging from 30 min to 5 h. Analysis of PV-ES system with respect to
both resiliency and economics objectives are conducted for each facility, configuration and EDCs. Detailed cash flow streams for all sizing
configurations and EDCs are presented for each facility. Moreover, NPV
per installed capacity of ES and resiliency evaluation across different
sizing configuration are illustrated. Detailed cash flow streams are
presented in the Appendix “A”. It is worth to mention that:
critical portion of load. Unserved critical load is penalized on $/kWh
basis.
6 It is assumed that PV system is operational during power outage.
7 The investment tax credit (ITC) is included in NPV calculation.
8 NPV calculation is based on facility cash flow improvement compared to the base case, which there is only PV system (without ES)
installed at the facility.
4.1.1. Case 1- hospital; PV-ES
Cash flow stream for different configurations of PV-ES systems in a
typical hospital facility has been analyzed. Figs. 3–5 show the net
present value of PV-ES project per installed capacity over the period of
4 years according to different EDCs’ electricity tariff.
As illustrated, increasing the duration of ES system improves the
resiliency capability of installed system. Moreover, since EDC 3 has the
higher energy charge ($/ consumed kWh) and this facility has high
level of energy consumption, PV-ES system is more beneficial for the
customers located in EDC 3 territory. Note that bigger system with
higher duration results in higher cash flow values (look at the Appendix
A); however, the additional operational value out of larger systems does
not justify the higher up-front cost for these systems. Therefore, the
“NPV/installed kW” is lower for bigger systems.
1 For ES resource, round trip efficiency (inverter and storage modules)
is set to 90 %.
2 Fixed costs associated to ES are: factory cost (∼ 400$/kWh), installation (∼ 47 % of factory cost), invertor (∼300$/kW), and O&M
(∼18$/kW w/ 2 % annual growth) costs.
3 Random outage events are generated using Monte Carlo (MC) simulations. 1000 MC simulations are performed.
4 Net-metering is done through two resources: directly from PV to
grid and discharging ES to grid. It is assumed that net-metering will
be credited back according to EDC energy tariffs.
5 In outage hours, the objective of operation optimization is to serve
Fig. 4. Hospital-EDC2; PV-ES; NPV/kW.
5
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K. Mahani, et al.
Fig. 5. Hospital-EDC3; PV-ES; NPV/kW.
4.1.2. Case 2-full-service restaurant; PV-ES
Figs. 6–8 show the NPV of PV-ES project at a typical full-service
restaurant per installed capacity. The analysis has been done over the
period of 4 years considering different rate structures.
As shown in Fig. 2, full-service restaurant demand profile has several peaks and valleys. Therefore, increasing the duration of ES system
does not have significant impact on the facility revenue. In other words,
increasing the duration of ES system results in smaller value for NPV/
installed kW.
According to the results presented for PV-ES case studies (both
NPV/kW and detailed revenues in the Appendix A), the following
findings are worth mentioning:
2
3
4
5
1 Larger ES systems (both rated capacity and energy capacity) result
in higher cash flows. However, the additional operational value out
of larger systems does not justify the higher up-front cost for these
systems.
Monetary benefits of larger ES systems depend on customers load
shape characterized by peak time, peak duration, and number of
peaks.
ES systems with higher discharging duration result in higher values
in the facilities with prolonged peak.
ES systems with higher rated capacity are more beneficial in facilities with after-hours peak.
Larger ES configurations are more capable for the resiliency purpose
during the outage events; however, they cannot be cost effective
during the normal operation. That is why critical facilities such as
hospitals need incentive for ES installation in their facility to justify
the high up-front cost for these configurations.
Fig. 6. Full-service restaurant-EDC1; PV-ES; NPV/kW.
6
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K. Mahani, et al.
Fig. 7. Full-service restaurant-EDC2; PV-ES; NPV/kW.
Fig. 8. Full-service restaurant-EDC3; PV-ES; NPV/kW.
4.2. CHP, electric storage and thermal storage (CHP-ES-TS)
engine (RECIP), and three combinations of EDC and GDC (according to
energy utilities territory map in New Jersey) are considered. Net-metering is not enabled in this case study. Value of installed configurations
and the corresponding pay-back period are illustrated for each use case.
It should be mentioned that all cost-saving and additional regulation
revenues are calculated by comparing the corresponding configuration
with the base configuration (only CHP system), therefore, these numbers represent the value of ES-TS system.
For each CHP technology, two different sizing approaches are considered:
1 Biggest rectangular method based on the facility electricity demand.
2 Biggest rectangular method based on the facility heat demand.
To do the analysis, electricity and thermal demand values are sorted
in decreasing order and placed in a load-duration diagram. Then, the
dimensioning method (which is based on “biggest rectangle” method) is
applied. The intersection of the biggest rectangle with the vertical axis
represent the useful electricity (thermal) output of CHP system. For
Electric and thermal storage, rated capacity of 100 % CHP electricity
and useful thermal output are considered, respectively. Moreover, onehour duration is assumed for storage systems. Analysis of CHP-ES-TS
system with respect to the economics objective is conducted for each
facility, CHP technology, configuration and energy provider. Two
technologies; namely, solid-oxide fuel cell (FC) and reciprocating
4.2.1. Case 1-hospital; CHP-ES-TS
Annual cost saving and revenue for different configurations of CHPES-TS with different prime-mover technologies and sizing methods in a
typical hospital facility located in New Jersey have been analyzed.
Following table demonstrates the economics of these cases:
As illustrated in Table 6:
1 Energy storage system coupled with FC generates more value compared to energy storage coupled with RECIP. The reason is more
output power of FC due to its higher electricity efficiency. This
7
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K. Mahani, et al.
Table 6
CHP-ES-TS Economics; Hospital.
Table 7
CHP-ES-TS Economics; Full-service restaurant.
4 Revenue generated according to frequency regulation market participation is highly dependent on the capacity of electric storage
system. Therefore, bigger systems are more beneficial in frequency
regulation market.
results in lower pay-back-period of storage system coupled with FC.
2 The additional value out of larger storage systems justifies the
higher up-front cost for these systems; therefore, their pay-backperiod are lower compared to the smaller systems. The major contributor in bigger system revenue is the frequency regulation market
participation.
5. Conclusion
This study proposed an integrated design approach to model both
electrical and heating loads with generation sources. The idea is to take
full advantage of excess heat in microgrid and enhance the overall
system efficiency. Behind the meter benefits and resiliency capability of
energy storage devices located in the PJM territory were analyzed in
order to understand the impact of the facilities’ electricity and thermal
demand behavior, energy providers pricing structure, DER configuration, storage capacity, and facility criticality. Energy bill management,
frequency regulation, and resiliency enhancement were taken into account as the energy storage applications. We concluded that the economic benefits of energy storage are highly related to the technology
and configuration of other DERs within the facility. For instance, PV-ES
system is more beneficial in facilities with after-hour electricity peak,
but storage devices coupled with CHP system bring more energy cost
saving opportunities to facilities with uncorrelated thermal and electricity demand profiles. Moreover, the capacity of storage devices has
significant impact on resiliency capability and economics of the project.
For instance, larger solar-powered ES systems result in higher resiliency
enhancement; however, their higher up-front cost does not justify the
economics of project. This emphasizes the vital importance of incentive
programs for energy storage systems to increase the resiliency of power
grid during the major outage events. It is worth mentioning that pricing
structure of energy carriers also affect the economics of DER-storage
significantly.
4.2.2. Case 2-full-service restaurant; CHP-ES-TS
Table 7 summarizes the economics for this case. This analysis is
done for a typical restaurant facility in New Jersey and investigates
annual cost saving and revenue for different configurations of CHP-ESTS with different prime-mover technologies and sizing methods.
Based on Table 7, second sizing method, which is based on thermal
demand, suggests very big system. This is because full-service restaurant has high thermal demand. This bigger system increases the energy
cost saving and frequency regulation revenue significantly; therefore, it
has much lower pay-back period. It can be observed that the contribution of frequency regulation revenue is more than energy cost
saving.
According to the results presented for CHP-ES-TS case studies, the
following findings are noteworthy:
1 Larger storage projects generate higher revenue and their pay-backperiods are smaller compared to smaller projects.
2 In general, storage systems coupled with FC are more beneficial
compared to storages coupled with RECIP due to the higher output
power of FC because of its higher electric efficiency.
3 Energy storage systems bring more energy cost saving opportunities
in facilities with uncorrelated thermal and electricity demand profiles. The excessive electricity or thermal generation could be stored
in storage devices and utilized when demand is high.
8
K. Mahani, et al.
Appendix A. PV-ES results
Hospital- Economic result
Hospital; PV + ES
EDC 1
9
Scenario PV cap
(kW)
ES PWR
(kW)
ES duration
(hrs)
Energy charge saving compared to
baseline
Peak demand charge compared to
baseline
Net-metering revenue
Regulation revenue
Annual Cashflow (cost savings &
revenue)
% of cash flow growth compared to
baseline
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
0
540
540
540
540
540
540
540
540
540
540
1080
1080
1080
1080
1080
1080
1080
1080
1080
1080
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
$245,191.9
$ 249,276
$ 252,030
$ 254,795
$ 257,680
$ 260,739
$ 263,920
$ 267,091
$ 270,284
$ 273,293
$ 276,173
$ 254,488
$ 260,529
$ 265,995
$ 272,001
$ 278,151
$ 284,420
$ 290,711
$ 296,637
$ 302,678
$ 308,425
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$217,435.3
$ 216,602
$ 216,094
$ 215,295
$ 213,252
$ 210,682
$ 207,949
$ 204,883
$ 201,666
$ 198,593
$ 195,491
$ 211,489
$ 209,804
$ 207,818
$ 203,341
$ 198,064
$ 192,032
$ 185,539
$ 179,248
$ 172,685
$ 166,235
$$ 52,611
$ 51,855
$ 51,312
$ 50,680
$ 49,961
$ 49,170
$ 48,411
$ 47,833
$ 47,416
$ 47,053
$ 112,666
$ 111,372
$ 110,173
$ 109,437
$ 108,314
$ 107,034
$ 105,861
$ 104,529
$ 103,378
$ 101,726
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
0%
15%
18%
21%
24%
26%
28%
29%
30%
31%
32 %
30%
36%
40%
44%
46%
48%
50 %
51%
53%
54%
4080
4080
4080
4080
4080
4080
4080
4080
4080
4080
4080
4080
4080
4080
4080
4080
4080
4080
4080
4080
4080
44,161.1
66,285
80,544
93,007
105,360
116,599
125,665
133,457
140,809
146,819
152,230
82,531
107,379
127,727
142,810
155,985
167,700
178,211
187,308
196,113
203,973
506,788
584,774
600,524
614,409
626,971
637,981
646,704
653,843
660,592
666,121
670,947
661,174
689,083
711,713
727,588
740,515
751,186
760,322
767,723
774,854
780,360
EDC 2
Scenario
Energy charge
saving compared
to
baseline
Peak demand
charge compared
to
baseline
Net-metering revenue
Regulation
revenue
Annual
Cashflow (cost
savings
& revenue)
% of cash flow
growth compared
to
baseline
Energy charge saving
compared to baseline
Peak demand charge
compared to baseline
Net-metering revenue
Regulation
revenue
Annual Cashflow
(cost savings & revenue)
% of cash flow growth
compared to baseline
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$218,766.1
$ 217,810
$ 217,287
$ 216,539
$ 214,602
$ 212,219
$ 209,680
$ 206,754
$ 203,682
$ 200,589
$ 197,400
$ 212,656
$ 210,868
$ 208,612
$ 204,408
$ 199,241
$ 193,442
$ 187,037
$ 180,798
$ 174,400
$ 167,754
$$ 53,808
$ 53,538
$ 52,736
$ 52,321
$ 51,663
$ 51,020
$ 50,423
$ 49,716
$ 49,013
$ 48,291
$ 116,254
$ 115,317
$ 114,664
$ 113,627
$ 112,328
$ 111,071
$ 109,712
$ 108,696
$ 107,205
$ 105,714
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
0%
15%
17%
18%
19%
21%
22 %
22 %
23%
24%
25%
29%
33%
35%
38 %
39%
41%
42 %
43%
43%
44%
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$$ 53,013
$ 52,958
$ 52,310
$ 51,932
$ 51,278
$ 50,555
$ 49,999
$ 49,391
$ 48,675
$ 47,869
$ 112,600
$ 111,916
$ 111,582
$ 110,882
$ 109,730
$ 108,813
$ 107,755
$ 106,575
$ 105,272
$ 103,804
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
0%
9%
9%
10%
10%
10%
10%
10%
9%
9%
9%
17%
18%
18%
18%
18%
18%
18%
18%
17%
17%
249,559
253,536
256,063
258,732
261,492
264,415
267,387
270,430
273,519
276,527
279,464
258,531
264,410
269,935
275,678
281,647
287,815
294,230
300,161
306,271
312,123
EDC 3
13,908
27,451
35,183
41,482
47,555
52,810
57,935
63,009
67,892
72,375
76,547
36,786
49,034
59,537
69,519
78,038
85,684
92,542
98,477
103,781
108,764
482,233
552,604
562,072
569,490
575,970
581,107
586,021
590,616
594,809
598,504
601,701
624,227
639,629
652,748
663,233
671,254
678,012
683,521
688,132
691,657
694,355
413,702
420,130
424,255
428,500
432,965
437,693
442,553
447,519
452,585
457,599
462,598
428,102
437,613
446,825
456,388
466,300
476,392
487,135
497,574
507,963
517,948
12,680
26,542
26,906
27,209
27,470
27,679
27,851
28,009
28,152
28,280
28,393
26,916
27,538
27,995
28,357
28,645
28,899
29,112
29,277
29,405
29,513
355,804
354,109
351,766
349,091
345,288
340,969
336,425
331,408
326,172
320,917
315,514
348,830
343,225
336,643
328,065
318,501
308,256
296,992
285,872
274,655
263,586
782,187
853,794
855,884
857,109
857,654
857,619
857,383
856,936
856,300
855,471
854,373
916,448
920,292
923,046
923,692
923,177
922,361
920,994
919,298
917,295
914,851
The Electricity Journal 33 (2020) 106707
Hospital;
PV + ES
The Electricity Journal 33 (2020) 106707
K. Mahani, et al.
Hospital- Resiliency result
Scenarios
Critical Load (KWh)
Total Served (KWh)
% Served Critical Load during
outage
Total outage hours
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
165058.44
78119.65
78795.04
79467.88
80137.7
80800.55
81459.08
82117
82772.66
83426.87
84080.87
84732.38
79467.88
80801.19
82117.96
83430.23
84737.18
86039.85
87340.17
88639.15
89927.15
91209.65
44%
44%
45%
45%
46%
46%
47 %
47 %
48%
48%
49%
45%
46%
47 %
48%
49%
50 %
51%
52 %
53%
54%
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
10
K. Mahani, et al.
Full-service restaurant
Full-service restaurant; PV + ES
EDC 1
Scenario PV cap (kW) ES PWR (kW) ES duration
(hrs)
Energy charge saving compared
to
baseline
Peak demand charge compared
to
baseline
Net-metering revenue
Regulation revenue
Annual Cashflow (cost savings
& revenue)
% of cash flow growth compared to
baseline
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$1,189
$2,045
$2,475
$2,795
$3,042
$3,268
$3,480
$3,662
$3,825
$3,986
$4,141
$2,764
$3,359
$3,825
$4,216
$4,589
$4,958
$5,321
$5,675
$6,028
$6,381
$ 10,741
$ 10,710
$ 10,683
$ 10,642
$ 10,559
$ 10,460
$ 10,362
$ 10,256
$ 10,150
$ 10,041
$ 9,926
$ 10,417
$ 10,329
$ 10,228
$ 10,019
$9,795
$9,546
$9,289
$9,010
$8,731
$8,441
$$1,889
$1,880
$1,868
$1,868
$1,850
$1,841
$1,827
$1,798
$1,767
$1,737
$4,840
$4,897
$4,907
$4,834
$4,750
$4,672
$4,593
$4,514
$4,436
$4,356
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
0%
13%
15%
17%
18%
19%
20%
21%
22 %
22 %
23%
30%
34%
37%
38 %
40%
41%
43%
44%
45%
46%
160
160
160
160
160
160
160
160
160
160
160
160
160
160
160
160
160
160
160
160
160
0
20
20
20
20
20
20
20
20
20
20
50
50
50
50
50
50
50
50
50
50
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
10,254
10,440
10,551
10,663
10,779
10,898
11,015
11,131
11,244
11,358
11,474
10,772
11,071
11,324
11,606
11,885
12,172
12,449
12,738
13,017
13,297
22,185
25,085
25,588
25,968
26,247
26,476
26,698
26,876
27,018
27,153
27,278
28,793
29,656
30,284
30,675
31,018
31,347
31,652
31,937
32,213
32,475
11
Full-service EDC 2
restaurant;
PV + ES
EDC 3
Energy charge
saving compared
to
baseline
Peak demand
charge compared
to
baseline
Net-metering revenue
Regulation
revenue
Annual
Cashflow (cost
savings
& revenue)
% of cash flow
growth compared
to
baseline
Energy charge saving
compared to baseline
Peak demand charge
compared to baseline
Net-metering revenue
Regulation
revenue
Annual Cashflow
(cost savings & revenue)
% of cash flow growth
compared to baseline
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
$9,046
$9,213
$9,312
$9,410
$9,511
$9,616
$9,720
$9,823
$9,923
$ 10,027
$ 10,131
$9,506
$9,768
$9,993
$ 10,243
$ 10,495
$ 10,745
$ 10,992
$ 11,254
$ 11,502
$ 11,757
$637
$1,139
$1,398
$1,589
$1,736
$1,872
$1,999
$2,105
$2,199
$2,292
$2,382
$1,567
$1,925
$2,196
$2,422
$2,638
$2,852
$3,063
$3,270
$3,476
$3,682
$9,357
$9,323
$9,301
$9,271
$9,201
$9,117
$9,031
$8,938
$8,844
$8,747
$8,646
$8,988
$8,928
$8,853
$8,679
$8,480
$8,264
$8,035
$7,783
$7,544
$7,285
$$1,932
$1,930
$1,921
$1,926
$1,910
$1,905
$1,890
$1,863
$1,831
$1,799
$5,062
$5,133
$5,142
$5,061
$4,981
$4,906
$4,830
$4,753
$4,672
$4,591
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
0%
13%
15%
17%
18%
18%
19%
20%
20%
20%
21%
32 %
35%
38 %
39%
40%
41%
41%
42 %
43%
43%
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$592
$1,055
$1,293
$1,469
$1,604
$1,729
$1,844
$1,943
$2,031
$2,117
$2,199
$1,448
$1,778
$2,028
$2,236
$2,436
$2,633
$2,826
$3,017
$3,207
$3,397
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$$1,914
$1,916
$1,913
$1,915
$1,902
$1,901
$1,884
$1,857
$1,827
$1,796
$4,834
$4,938
$4,987
$4,920
$4,853
$4,777
$4,708
$4,630
$4,556
$4,499
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$8%
9%
10%
11%
11%
11%
12 %
12 %
12 %
12 %
20%
22 %
23%
23%
24%
25%
25%
25%
26%
26%
19,040
21,607
21,942
22,191
22,374
22,515
22,654
22,756
22,830
22,896
22,957
25,123
25,754
26,184
26,405
26,594
26,767
26,920
27,060
27,194
27,315
14,942
15,202
15,368
15,534
15,701
15,869
16,034
16,201
16,369
16,536
16,704
15,671
16,127
16,515
16,902
17,324
17,711
18,107
18,516
18,935
19,376
15,235
15,166
15,065
14,955
14,813
14,659
14,507
14,345
14,178
14,011
13,839
14,846
14,550
14,271
13,938
13,549
13,187
12,796
12,382
11,948
11,469
30,769
33,337
33,642
33,871
34,033
34,159
34,286
34,373
34,435
34,490
34,538
36,800
37,393
37,800
37,997
38,161
38,309
38,436
38,546
38,647
38,742
The Electricity Journal 33 (2020) 106707
Scenario
The Electricity Journal 33 (2020) 106707
K. Mahani, et al.
Full-service restaurant- Resiliency result
Scenarios
Critical Load (KWh)
Total Served (KWh)
% Served Critical Load during
outage
Total outage hours
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
5676.02
2907.58
2932.53
2957.4
2982.14
3006.71
3031.1
3055.49
3079.78
3103.97
3128.16
3152.28
2969.81
3031.14
3092.04
3152.53
3212.72
3272.47
3331.88
3390.59
3448.82
3506.31
47 %
47 %
48%
49%
49%
50 %
50 %
51%
51%
52 %
53%
48%
50 %
51%
53%
54%
55%
57%
58%
59%
60%
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
248.52
Ledesma, P., Usaola, J., Rodriguez, J.L., 2003. Transient stability of a fixed speed wind
farm. Renew. Energy 28, 1341–1355.
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Conference (TPEC).
Mahani, K., Farzan, F., Jafari, M.A., 2017. Network-aware approach for energy storage
planning and control in the network with high penetration of renewables. Appl.
Energy 195, 974–990.
Nazemi, S.D., Boroushaki, M., 2016. Design, analysis and optimization of a solar dish/
stirling system. Int. J. Renew. Energy Dev. 5.
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Wolfe, P., 2008. The implications of an increasingly decentralised energy system $.
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Khashayar Mahani is an engineer in Advisory Services at Quanta Technology, LLC. He
received his B.S. degree in Electrical and Control Engineering form University of Tehran,
Tehran, Iran in 2011 and his Ph.D. degree in Industrial and System Engineering from
Rutgers, the state university of New Jersey, in 2018. His research and work interests are
in NWA solutions, Energy Storage management, Model Predictive Control and NetworkAware Planning & Control.
Seyyed Danial Nazemi is a Ph.D. student in Department of Industrial and Systems
Engineering at Rutgers University, New Jersey. He received his B.S. degree in Mechanical
Engineering form University of Tehran, Tehran, Iran in 2012 and his M.S. degree in
Energy Systems Engineering Sharif University of Technology, Tehran, Iran, in 2014. He is
also a member of the Laboratory for Energy Smart Systems (LESS) Research Group,
Rutgers University, which focuses on modeling, development and analysis of sustainable
and smart energy solutions. His research interests are in Energy Storage management,
Building Energy Management, Model Predictive Control and Non-Wire Alternative solutions.
Maryam Arabzadeh Jamali is a Ph.D. student in Department of Industrial and Systems
Engineering at Rutgers University, New Jersey. She earned a B.S. in Industrial
Engineering from Sharif University of Technology, Tehran, Iran, and her M.S. degree in
Industrial and Systems Engineering from Rutgers, the state university of New Jersey, in
2015 and 2018, respectively. She worked as a research assistant at Laboratory for Energy
Smart Systems (LESS) at Rutgers focusing on the environmental and financial impacts of
12
The Electricity Journal 33 (2020) 106707
K. Mahani, et al.
renewable energy sources integrated with storage systems in electric power systems. Her
current research focuses on machine learning algorithms with application in Energy
management.
Transportation, FHWA, DARPA, the NJ Department of Health and Senior Services, NYC
MTA, and industry in automation, system optimization, data modeling, information
systems, and cyber risk analysis. He actively collaborates with universities and research
institutes abroad. He has also been Consultant to several Fortune 500 companies as well
as local and state government agencies. He is currently a Professor and the Chair of
Industrial & Systems Engineering, Rutgers University–New Brunswick. His research applications extend to manufacturing, transportation, healthcare and energy systems. He is
a member of the IIE. He received the IEEE Excellence Award in service and research.
Mohsen Jafari (M’97) received the Ph.D. degree from Syracuse University in 1985. He
has directed or co-directed a total of over 23 million U.S. dollars in funding from various
government agencies, including the National Science Foundation, the Department of
Energy, the Office of Naval Research, the Defense Logistics Agency, the NJ Department of
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