The Electricity Journal 33 (2020) 106707 Contents lists available at ScienceDirect 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 K. Mahani, et al. 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 The Electricity Journal 33 (2020) 106707 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 The Electricity Journal 33 (2020) 106707 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 The Electricity Journal 33 (2020) 106707 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. Mahani, K., Arabzadeh Jamali, M., Nazemi, S.D., Jafari, M.A., 2020. Economic and operational evaluation of PV and CHP combined with energy storage systems considering energy and regulation markets. 2020 IEEE Texas Power and Energy 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. Nazemi, S.D., Mahani, K., Ghofrani, A., Kose, B.E., Amini, M., Jafari, M.A., 2020. Technoeconomic analysis and optimization of a microgrid considering demand-side management. 2020 IEEE Texas Power and Energy Conference (TPEC). Wolfe, P., 2008. The implications of an increasingly decentralised energy system $. Energy Policy 36, 4509–4513. Yu, N., Foggo, B., 2017. Stochastic valuation of energy storage in wholesale power markets. Energy Econ. 64, 177–185. Zhou, Z., Levin, T., Conzelmann, G., 2016. Survey of U.S. ancillary services markets. Argonne Natl. Lab. (Technical Report). References Abu-sharkh, S., Arnold, R.J., Kohler, J., Li, R., Markvart, T., Ross, J.N., Steemers, K., Wilson, P., Yao, R., 2006. Can microgrids make a major contribution to UK energy supply? Renew. Sustain. Energy Rev. 10, 78–127. Amini, M., 2019. Optimal Dispatch of Uncertain Energy Resources. University of Vermont. Arabzadeh Jamali, M., 2019. Study of Power Recoverability through Optimal Design of Energy Storage Systems. Rutgers University - Graduate School, New Brunswick. Barton, J.P., Infield, D.G., 2004. Energy storage and its use with intermittent renewable energy. IEEE Trans. Energy Convers. 19, 441–448. Beaudin, M., Zareipour, H., Schellenberglabe, A., Rosehart, W., 2010. Energy for Sustainable Development Energy storage for mitigating the variability of renewable electricity sources: an updated review. Energy Sustain. Dev. 14, 302–314. 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Geidl, M., Koeppel, G., Favre-Perrod, P., Klockl, B., Andersson, G., Frohlich, K., 2007. Energy hubs for the future. IEEE Power Energy Mag. 5, 24–30. Ghofrani, A., Nazemi, S.D., Jafari, M.A., 2019a. Prediction of building indoor temperature response in variable air volume systems. J. Build. Perform. Simul. 13, 34–47. Ghofrani, A., Nazemi, S.D., Jafari, M.A., 2019b. HVAC load synchronization in smart building communities. Sustain. Cities Soc. 51, 101741. Hatziargyriou, N., Asano, H., Iravani, R., Marnay, C., 2007. Microgrids. IEEE Power Energy 5, 78–94. Hawkes, A.D., Leach, M.A., 2009. Modelling high level system design and unit commitment for a microgrid. Appl. Energy 86, 1253–1265. Jiayi, H., Chuanwen, J., Rong, X., 2008. A review on distributed energy resources and MicroGrid. Renew. Sustain. Energy Rev. 12, 2472–2483. Khan, K.H., Rasul, M.G., Khan, M.M.K., 2004. Energy conservation in buildings: cogeneration and cogeneration coupled with thermal-energy storage. Appl. Energy 77, 15–34. Kueck, J.D., Staunton, R.H., Labinov, S.D., Kirby, B.J., 2003. Microgrid Energy Management System. 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 13