The primary goal of this research study is to enhance energy resilience with a focus on cost e fficiency. To achieve this objective, two key objectives have been identified: 1) reducing unserved loads, and 2) implementing cost-e ffective resource allocation strategies. A high-fidelity real-time model of a solar plus storage microgrid is developed to simulate a variety of what-if scenarios. This model is based on the conceptual design of a campus microgrid facility, which is slated for commissioning at UL Lafayette in close collaboration with a local power utility. The study examines the microgrid’s performance under di fferent configurations, including both stationary battery and mobile battery storage options. To ensure the realism of the scenarios, real solar data from specific days following the occurrence of three major hurricanes in Louisiana is utilized. The analysis includes an assessment of unserved loads under various scenarios, as well as an investigation into the resilience impact of investment decisions and the planning and operation of mobile storage systems. The results indicate the proposed planning and operation will improve resilience while staying within the profitable range. The resilience is quantified and compared with other scenarios providing an insightful planning framework for decision-makers. Keywords: Energy Resilience, Renewable Energy, Microgrid 1. Introduction Enhancing energy resilience is essential for ensuring the sus- tainable provision of electricity services to customers. The in- creasing frequency and intensity of natural disasters, such as hurricanes, wildfires, and extreme weather events, pose signif- icant challenges to the resilience of energy infrastructure. Mi- crogrid formation has been a popular strategy in the literature as a means to enhance energy resilience in response to outages [1; 2; 3; 4; 5]. Microgrid formation is often suggested as a way to boost energy resilience during outages. This makes sense for a few reasons: First, microgrids mostly serve local areas, so they do not rely on long-distance power lines that may get dam- aged in disasters. This local focus helps keep the lights on for nearby homes and businesses. Second, because microgrids are relatively new, they are built with modern standards in mind. This means they’re more likely to withstand disasters and stay running when the main grid might fail. Lastly, microgrids can provide power when the main grid cannot, ensuring that critical services stay running even during emergencies. Power utilities and service providers are increasingly inter- ested in investing in microgrid formation to enhance energy resilience. However, their decision-making process is complex and is influenced by two key considerations: the extent to which resilience is improved and the associated costs. These questions are challenging to answer definitively due to several factors: Scenario Dependence : The concept of resilience varies de- pending on the scenario. For example, improving resilience against floods may not necessarily enhance resilience againsthurricanes. Therefore, the specific resilience needs of a partic- ular location or situation must be carefully evaluated. Measurement Metrics : Determining how to measure re- silience improvement is crucial. Various metrics for resilience have been proposed in the literature [6; 7], ranging from re- liability indices [8; 9; 10] to social and economic impacts [11; 12; 13; 14]. Selecting the most appropriate metrics for a given context is essential but challenging. Cost Considerations : Understanding how costs escalate with each percentage increase in resilience is vital. This in- volves evaluating the costs of implementing and maintaining microgrid systems compared to the potential benefits in terms of resilience enhancement. The investigation into the specific cause of the power outage falls outside the scope of this study. Instead, our research fo- cuses on quantifying energy resilience and assessing the associ- ated costs of enhancing resilience. Di fferent resilience metrics have been proposed recently in the literature. In [15], a quan- titative metric is proposed where the full service, worst perfor- mance, speed of degradation, and speed of recovery are factored in to develop a comprehensive resilience metric. However, load priorities are not set and the cost of resilience enhancement is not discussed. Authors in [16] developed a method for optimiz- ing network reconfiguration and mobile emergency generator (MEG) deployment in smart grids. The proposed method is optimization-based. Time domain studies and economic analy- sis are not provided. A di fferent resilience metric is proposed in [17] based on the measured voltage of the main DC bus. This metric allows for real-time resilience assessment and monitor- Preprint submitted to ... March 31, 2024 ing of microgrid resilience over time. The method is specifi- cally designed for microgrids as opposed to most of the exist- ing methods that are more applicable to larger power systems. However, the case study which is a naval ship is more mission- oriented rather than service-driven. The amount of supplied loads is a metric used in [18] to evaluate optimum microgrid formation in a distribution feeder as a resilience enhancement strategy. Load prioritization, time domain analysis, and cost studies are not provided. A similar metric is also proposed in [19; 20] where the amount and cost of unserved loads are used to measure resilience. Improvements are made by incorporat- ing electric vehicles and solar PV into the residential feeders. The same research gaps are also seen in these two studies. Solar plus energy storage is the most common type of micro- grid formation at both residential and utility scales [21]. In ad- dition to stationary energy storage systems, Mobile Resources (MR) play an important role in enhancing energy resilience, particularly in the context of modern power systems facing in- creasing challenges from natural disasters, cyber-attacks, and other disruptions. The transportability of MRs helps a lot with uncertainties with respect to the place and scale of damages to the energy system. The concept of using MRs, such as repair trucks /crews [22], mobile generators [23], energy storage units [24], and microgrids [25], has gained attention due to their abil- ity to provide temporary power supply and restore electricity in disaster-stricken areas e fficiently. In recent years, mobile resources have been utilized in vari- ous disaster scenarios, including hurricanes [26], wildfires [27], and winter storm [28; 29], demonstrating their e ffectiveness in providing reliable power supply to critical infrastructure, emer- gency shelters, and essential services. These resources are of- ten deployed quickly and can be relocated as needed, mak- ing them a flexible solution for addressing immediate energy needs in disaster-a ffected areas. Mobile resources have been discussed in di fferent sets of literature within the past few years to improve energy resilience. In [30], an optimization prob- lem is solved to e ffectively dispatch repair crews and mobile resources; however, load priorities are not considered. Fur- thermore, the impact on resilience is not measured. Authors in [31], proposed a strategy to optimally dispatch MRs in a 33 bus distribution system. A similar optimization-based study is conducted in [32] on a balanced distribution feeder with non- real DER data. Although most research works in the MR area are proposed and tested for distribution systems, authors in [33] proposed a real-time strategy to dispatch MRs com- bined with microgrid forming. A real-time optimization prob- lem is proposed where di fferent MRs are incorporated includ- ing EVs, mobile generators, and mobile energy storage. To the best of our knowledge, in addition to [33], in three other re- search works [34; 35], the real-time dispatch of MRs is pro- posed. However, the proposed algorithms are not tested on any real-time simulator /processor. In [35], The real-time refers to sending resources from a pre-determined location to a target lo- cation. In none of the above-mentioned studies, the real-time operation of MR is tested. Also, it is not clear how the plan- ning/operation algorithm is implemented into the feeder or mi- crogrid controller making real-time decisions.In this study, a high-fidelity real-time model of a solar plus stor- age microgrid is created. The microgrid load is categorized into di fferent priorities. The microgrid’s resilience is evaluated under di fferent stationary /mobile resource allocation strategies. The estimated cost of resilience improvement can serve as a guideline for decision-makers. The technical contributions of this study include: •Measuring the impact of stationary and mobile resources on microgrid’s resilience •Cost-aware resource allocation along with load priorities •Incorporating real-time operation of mobile storage sys- tems into microgrid’s controller 2. Case Study This research utilized the microgrid facility situated at the University of Louisiana at Lafayette(UL) as the case study pre- sented in Fig. 1. Load data is collected from a real distribution feeder for this research study. All the load data is classified into three categories: priority load 1, priority load 2, and prior- ity load 3. Here, priority load 1 is considered the most critical load and will get the highest priority during the load serving; priority load 2 is considered a moderate critical load and will get moderate priority during the load serving; priority load 3 is considered the least critical load and will get the lowest pri- ority during the load serving. For 24-hour power outage, the load demand for the most critical, moderate, and least critical loads is 681.762 kWh, 957.701 kWh, and 638.475 kWh, respec- tively. The load demand profiles for these three categories are presented in Fig. 2. This research considered 24-hour power outages for three hurricanes that hit Louisiana in the past five years. Those hurricanes are Laura (2020), Zeta (2020), and Ida (2021). For these three hurricanes, power outages for 24 hours are considered where the microgrid is solely responsible for satisfying the load demands. The solar radiation data corre- sponding to every hurricane-a ffected day is collected from the University of Louisiana at Lafayette’s 1.1 MW solar PV plant facility [36] to integrate more realistic approaches. The solar radiation profile for each hurricane is shown in Fig. 3. The microgrid investigation for every hurricane-caused power outage includes three scenarios utilizing three di fferent configurations of solar PV plant and fixed battery energy stor- age system (BESS) and Mobile battery energy storage system (BESS). Table 1 contains the configurations of the solar PV plant, fixed BESS, and Mobile BESS for the three scenarios of every power outage. For the BESS operations, the maximum and minimum state of charge (SOC) for BESS is set to 90% and 10%, respectively. Traditionally, we get to know when any hur- ricane is arriving from the weather forecast; it is assumed that the fixed BESS and Mobile BESS are charged and the BESS SOC is 90% when the simulation starts. For Mobile BESS, it can provide its rated power of 25 kW for three hours continu- ously. It is assumed that after providing power for 3 hours, the Mobile BESS will travel to the nearest charging station. Mo- bile BESS will be fully charged and return to supply power to 2 Table 1: Configuration of Three Scenarios for Every Hurricanes Scenario PV SizeFixed BESS Size (kW)Fixed BESS capacity(kWh)Mobile BESS Size (kW)Mobile BESS capacity(kWh) 1 50kW 50kW 100kWh 25kW 75kWh 2 150kW 150kW 300kWh 25kW 75kWh 3 250kW 250kW 500kWh 25kW 75kWh Figure 1: Overview of the UL-Cleco Microgrid Facility Figure 2: Di fferent Priorities Load Data Figure 3: Solar Radiation Data (normalized) for Di fferent Power Outages this microgrid. This movement and charging time duration is considered to be 3 hours. Optimization of probable road con-gestion and shortest traveling path of the Mobile BESS is out of the scope of this paper. Typhoon HIL’s real-time simulator is utilized to model and analyze the proposed microgrid control algorithm to study the high-fidelity performance of the micro- grid in real-time. 3. Methodology 3.1. Proposed Resilience Metric It requires a few hours to a few days to fully restore the power grid from power outages caused by natural disasters. Micro- grids can be used to enhance resilience by supplying power during power outages. It is unrealistic to fulfill all the power demands when the main power grid su ffers from a power out- age. Hence, the loads can be classified based on their urgency. Considering 24-hour power outages caused by natural disasters, the amount of critical load served can be identified and inves- tigated to determine the resilience level of the microgrid. Our proposed resilience metrics measure resilience value based on the amount of energy supplied to the load, focusing on the most critical loads. In recent literature [37; 38; 39; 40; 41; 42], re- searchers have placed five times more emphasis on the most critical loads than the least critical loads. The weighted factors of 5, 2.5, and 1 are assigned for priority load 1, priority load 2, and priority load 3, respectively. Thus, our proposed resilience metrics can be calculated utilizing the following equation. Resilience,R=5β1+2.5β2+β3 (1) Where, β1=S erved Load 1 Demand Load 1(2) β2=S erved Load 2 Demand Load 2(3) β3=S erved Load 3 Demand Load 3(4) In Eqn. (1), β1represents the ratio between the amount of served most critical load (kWh) and the total demand of the most critical load (kWh). Similarly, β2represents the ratio be- tween the amount of served moderate critical load(kWh) and the total demand of the moderate critical load (kWh), β3repre- sents the ratio between the amount of served least critical load (kWh) and the total demand of the least critical load (kWh). The maximum value of β1,β2, andβ3can be 1 (if the to- tal demands are fulfilled for the whole time duration), so 8.5 3 can be the maximum resilience value in our proposed resilience metrics. Utilizing our proposed resilience metrics, a single re- silience value of a microgrid can be determined for the whole power outage duration(considering the highest importance of the demand satisfaction on the most critical load). 3.2. Technical Analysis Our proposed microgrid control strategy is presented in Al- gorithm 1 - Resource Dispatch Algorithm. This microgrid con- trol algorithm is crafted to fulfill the load demands during a power outage, putting maximum concentration on the most crit- ical load satisfaction. This microgrid case study consists of a solar PV plant, fixed battery energy storage system (BESS), and Mobile BESS to supply the loads. During the power outage, the proposed control system will coordinate the solar PV plant, fixed BESS, and Mobile BESS to supply the critical loads e ffectively for longer duration of hours. If the solar radiation stays at a higher amount for a longer time period and the availability of Mobile BESS, it looks to be a more e fficient approach to charge the battery of fixed BESS to a certain level at first instead of satisfying the less critical load so that the microgrid achieves the capability to fulfill the priority load 1 for the greater amount of power outage hours. The pro- posed control system will continuously monitor the solar PV generation, battery state of charge (SOC), and load demands and will take necessary actions accordingly. When there is any solar PV generation, the control system will check the fixed battery SOC conditions and the availability of Mobile BESS to satisfy the load demands based on the fixed battery’s SOC. If the fixed BESS SOC is greater than 70%, solar PV , fixed BESS, and Mobile BESS will fulfill all the load demands. If Mobile BESS is available, and the total load demand is higher than 25 kW, the solar PV , Mobile BESS, and the fixed BESS will sup- ply the load demands together. If Mobile BESS is available, and the total load demand is less than 25 kW, the Mobile BESS will supply the loads, and the fixed BESS will go into charging mode. When the Mobile BESS is unavailable, if solar PV gen- eration is higher than all the load demands, solar PV will satisfy the power demands by itself, and the extra generated PV power will go to the fixed BESS for its charging. If the solar-generated power is less than the power demands of all the loads, solar PV and fixed BESS will satisfy the load demands together. When the fixed BESS SOC stays in the range between 70% to 40%, only priority loads 1 and 2 will be served, and prior- ity load 3 will be curtailed. If Mobile BESS is available, and the combined demand for priority load 1 and priority load 2 is higher than 25 kW, the solar PV , Mobile BESS, and the fixed BESS will supply the load demands together. If Mobile BESS is available, and the load demand is less than 25 kW, the Mo- bile BESS will supply the loads, and the fixed BESS will go into charging mode. When the Mobile BESS is unavailable, if solar PV generation is higher than the combined demand for priority load 1 and priority load 2, solar PV will satisfy the power de- mands by itself, and the extra generated PV power will go to the fixed BESS for its charging. If the solar-generated power is less than the power demands of the combined demand for priorityload 1 and priority load 2, solar PV and fixed BESS will satisfy the load demands together. When the fixed BESS SOC stays in the range between 40% to 10%, only priority loads 1 will be served, and priority loads 2 & 3 will be curtailed. If Mobile BESS is available, and the combined demand for priority load 1 is higher than 25 kW, the solar PV , Mobile BESS, and the fixed BESS will supply the load demands together. If Mobile BESS is available, and the load demand is less than 25 kW, the Mobile BESS will supply the loads, and the fixed BESS will go into charging mode. When the Mobile BESS is unavailable, if solar PV generation is higher than the combined demand for priority load 1, solar PV will satisfy the power demands by itself, and the extra generated PV power will go to the fixed BESS for its charging. If the solar-generated power is less than the power demands of the combined demand for priority load 1, solar PV and fixed BESS will satisfy the load demands together. When there is no solar PV generation, the fixed BESS and Mobile BESS will satisfy the loads. If the fixed BESS SOC remains higher than 80%, fixed BESS and Mobile BESS will fulfill all the load demands. If Mobile BESS is available, and the total load demand is higher than 25 kW, Mobile BESS and the fixed BESS will supply the load demands together. If Mo- bile BESS is available, and the total load demand is less than 25 kW, the Mobile BESS will supply the loads, and the fixed BESS will go into charging mode. When the Mobile BESS is unavail- able, the fixed BESS will satisfy the load demands. When the fixed BESS SOC stays in the range between 80% to 50%, only priority loads 1 and 2 will be served, and priority load 3 will be curtailed. If Mobile BESS is available, and the combined demand for priority load 1 and priority load 2 is higher than 25 kW, the Mobile BESS and the fixed BESS will supply the load demands together. If Mobile BESS is available, and the load demand is less than 25 kW, the Mobile BESS will supply the loads, and the fixed BESS will go into charging mode. When the Mobile BESS is unavailable, the fixed BESS will satisfy the load demands of priority load 1 and priority load 2. When the fixed BESS SOC stays in the range between 50% to 10%, only priority loads 1 will be served, and priority loads 2 & 3 will be curtailed. If Mobile BESS is available, and the demand for priority load 1 is higher than 25 kW, the Mobile BESS and the fixed BESS will supply the load demands together. If Mobile BESS is available, and the load demand is less than 25 kW, the Mobile BESS will supply the loads, and the fixed BESS will go into charging mode. When the Mobile BESS is unavailable, the fixed BESS will satisfy the load demands of priority load 1. 3.3. Economic Analysis This section also includes an economic analysis from di ffer- ent perspectives on economic indicators. For solar PV panels, 25 years is considered the average life duration [43; 44; 45]. This study considered a 24-year time horizon for the eco- nomic analysis of this microgrid study[46]. The time dura- tion of solar PV inverter and BESS is 12 years and 10 years, respectively[47; 48]. In this analysis, 8 years is selected as the time duration of the solar PV inverter and BESS as the advanced features (i.e., V olt-V AR control, V olt-Watt control, etc.) shorten 4 Algorithm 1 Resource Dispatch Algorithm 1:Start 2:ifPV>0then 3: ifFixed BESS SOC ≥70% then 4: ifMobile BESS is available: then 5: ifLoad demand ≥25 kW then 6: Supply all loads using Mobile BESS and Fixed BESS 7: else 8: Supply using Mobile BESS only, and charge Fixed BESS 9: end if 10: else 11: Supply all loads using Fixed BESS 12: end if 13: Fixed BESS charges until SOC reaches 90% 14: else if Fixed BESS SOC is between 40% and 70% then 15: ifMobile BESS is available then 16: ifLoad demand ≥25 kW then 17: Supply only Priority Load 1 and Priority Load 2 using Mobile BESS and Fixed BESS 18: else 19: Supply using Mobile BESS only,and charge Fixed BESS 20: end if 21: else 22: Supply only Priority Load 1 and Priority Load 2 using Fixed BESS 23: end if 24: Fixed BESS charges 25: else if Fixed BESS SOC is between 10% and 40% then 26: ifMobile BESS is available then 27: ifLoad demand ≥25 kW then 28: Supply only Priority Load 1 using Mobile BESS and Fixed BESS 29: else 30: Supply using Mobile BESS only, and charge Fixed BESS 31: end if 32: else 33: Supply only Priority 1 loads using Fixed BESS 34: end if 35: Fixed BESS charges 36: else 37: ifMobile BESS is available then 38: ifLoad demand ≥25 kW then 39: Curtail all load and charge Fixed BESS 40: else 41: Supply Priority Load 1 using Mobile BESS only, and charge Fixed BESS 42: end if 43: else 44: Curtail all load 45: end if 46: Fixed BESS will go on charging47: end if 48:else 49: ifFixed BESS SOC is greater than or equal to 80% then 50: ifMobile BESS is available then 51: ifLoad demand ≥25 kW then 52: Supply all loads using Mobile BESS and Fixed BESS 53: else 54: Supply using Mobile BESS only, and charge Fixed BESS 55: end if 56: else 57: Supply all loads using Fixed BESS 58: end if 59: else if Fixed BESS SOC is between 50% and 80% then 60: ifMobile BESS is available then 61: ifLoad demand ≥25 kW then 62: Supply Priority Load 1 and Priority Load 2 using Mobile BESS and Fixed BESS 63: else 64: Supply Priority Load 1 and Priority Load 2 using Mobile BESS only, and charge Fixed BESS 65: end if 66: else 67: Supply Priority Load 1 and Priority Load 2 us- ing Fixed BESS 68: end if 69: else if Fixed BESS SOC is between 10% and 50% then 70: ifMobile BESS is available then 71: ifLoad demand ≥25 kW then 72: Supply Priority Load 1 using Mobile BESS and Fixed BESS 73: else 74: Supply Priority Load 1 using Mobile BESS only, and charge Fixed BESS 75: end if 76: else 77: Supply Priority Load 1 using Fixed BESS 78: end if 79: else 80: ifMobile BESS is available then 81: ifLoad demand ≥25 kW then 82: Curtail all loads 83: else 84: Supply Priority Load 1 using Mobile BESS only, and charge Fixed BESS 85: end if 86: else 87: Curtail all loads 88: end if 89: end if 90:end if 91:End 5 the inverter’s conventional lifetime [49]. Economic analyses are presented for all three hurricane scenarios. Furthermore, the in- vestigation is extended to analyze the impact of the increased number of Hurricanes in a 24-year time horizon (considering 3 hurricanes in 1 set). The revenue is generated from selling solar plus storage power to priority loads 1, 2, and 3. Using 5, the revenue, Rcan be found where Eirepresents the energy supplied to the priority loads in kWh, and αis the selling price of solar plus storage energy in $ /kWh. Inflation factor, dis considered as 2.5% for this investigation [47]. During the power outages emergency supply, the value of αis considered as $10 /kWh, $5 /kWh, and $2/kWh for priority load 1 (most critical load), priority load 2 (moderate critical load), and priority load 3 (least critical load), respectively [38; 39; 40; 41; 42]. For all the remaining regular days, the value of αis considered as $0.10 /kWh in this analysis. R=nX i=1Ei·α·(1+d)i−1(5) Equation 6 calculates the cost of the solar PV system which is the algebraic summation of the market price of the solar PV panel, CMAR PV, operation and maintenance cost of the solar, COM PV, and salvage value of the solar PV , CS AL PV[46]. Equation 7 cal- culates the cost of the solar PV inverter, which is the algebraic summation of the market price of the inverter, CMAR INV, operation and maintenance cost of the inverter, COM INV, salvage value of the solar inverter, CS AL INV[46]. Equation 8 is used to compute total inverter expenditure for 24 year time period where SINVis the rating of the inverter in kV A, dis the inflation factor, and TINV R is the lifetime of the inverter. CPV=CMAR PV+COM PV−CS AL PV (6) β=CMAR PV,INV+COM PV,INV−CS AL PV,INV (7) CPV,INV=nX j=1SINV·β·(1+d)(TINV R 2)(j−1)(8) The BESS expenditure is calculated based on its power and energy ratings using equation 9 and 10, respectively. Equa- tion 9 is used to determine the BESS cost for power rating, γ, which is the algebraic summation of the market price of BESS for power rating, CMAR BES S,P, O&M cost of the BESS for power rating, COM BES S,P, and the salvage value of BESS for power rat- ing,CS AL BES S,P. Equations 10 is used to calculate the BESS cost for energy rating, η, and this calculation follows the same ap- proach of the equation 9. ηis determined using the market price of BESS for energy rating, CMAR BES S,E, O&M cost of the BESS for energy rating, COM BES S,E, and the salvage value of BESS for en- ergy rating, CS AL BES S,E. In equation 11, BESS cost for 24 years is calculated where the BESS lifetime, TBES S R, is considered as 8 years, and pis the BESS depreciation rate for each year, 2%.PBES S, and EBES S represent the power capacity and energy capacity of the battery, respectively. To calculate the battery in- verter cost, equation 12 is utilized where the cost of the BESS inverter is the algebraic summation of the market price of theBESS inverter, CMAR BES S,INV, operation and maintenance cost of the BESS inverter, COM BES S,INV, salvage value of the BESS in- verter, CS AL BES S,INV. Compared to fixed BESS, Mobile BESS will likely add a cost premium of 5-10% associated with labor costs and transportation fuel costs [50]. For this case study, Mobile BESS cost is considered 7.5% higher than the fixed BESS. γ=CMAR BES S,P+COM BES S,P−CS AL BES S,P (9) η=CMAR BES S,E+COM BES S,E−CS AL BES S,E (10) CBES S =nX k=1γ·PBES S +η·EBES S·(1−p)(TBES S R−1)(k−1)(11) δ=CMAR BES S,INV+COM BES S,INV−CS AL BES S,INV (12) CBES S,INV=nX j=1SBES S,INV·δ·(1+d)(TINV R 2)(j−1)(13) In table 2, all the input parameters of economic analysis and their corresponding values are included. Here, we included some economic indicators that measure the benefit of solar plus storage systems in power distribution systems for 24 years op- eration horizon [46]. •Total cost : the Total cost, C, is the summation of costs for solar PV panel, solar PV inverter, BESS, and BESS inverter expressed in 14[46]. C=CPV+CPV,INV+CBES S +CBES S,INV (14) •Gained profit by solar system’s owner : The profit, Pis the di fference between the revenue and the total cost cal- culated using the equation 15. The revenue, R, expressed in the equation 5[46]. P=R−C. (15) •Net Profit Margin : The net profit margin NPM , or simply net margin, represents how much net income or profit is generated as a percentage of revenue made by solar system owner. The ratio represents the net profit to revenue for the owner of a solar system facility [46]. NPM =P R(16) •Net Present Value : Two terms characterize the net present value ( NPV ), the present discounted value of costs PDC in (18) and the present discounted value of revenues PDR in (17) by NPV =PDR −PDC . Let’s consider Rito be the (undiscounted) revenues (benefits) of the solar sys- tem project during the year iand we consider Cito be the (undiscounted) costs of the solar system project during the year i, afterward. We can calculate NPV using equation 6 (19). When the NPV is more than zero, the investment plan is considered profitable from the investor side [46]. PDR =TX i=1Ri (1+d)i−1(17) PDC =TX i=1Ci (1+d)i−1(18) NPV =TX i=1(Ri−Ci) (1+d)i−1(19) •Revenue-Cost Ratio : The revenue-cost ratio is the ratio of PDR toPDC which is mentioned in (20). When the RCR is greater than one, the investment plan will make revenue for the investor [46]. RCR =PDR PDC=PT i=1Ri (1+d)i−1 PT i=1Ci (1+d)i−1(20) Table 2: Di fferent Input Parameters of Economic Analysis Parameters Value Reference α 10, 5, 2, 0.1 ($ /kWh) [38; 39; 40; 41; 42] d 2.5% [47] CMAR PV400 ($ /kW) [47] COM PV1% ($ /kW) [51] CS AL PV10% ($ /kW) [52] CMAR INV60 ($/kW) [47] COM INV1% ($ /kW) CS AL INV10% ($ /kW) [52] CMAR BES S,INV50 ($/kW) [47] COM BES S,INV1% ($ /kW) CS AL BES S,INV10% ($ /kW) [52] CMAR BES S,P628 ($ /kW) [47] COM BES S,P10 ($/kW) [48] CS AL BES S,P10% ($ /kW) [52] CMAR BES S,E157 ($ /kWh) [47] COM BES S,E0.003 ($ /kW) [48] CS AL BES S,E10% ($ /kW) [52] EBES S 75, 100, 300, 500 (kWh) PBES S 25, 50, 150, 250 (kW) p 2% SINV 55, 162, 275 kV A TINV R8 Years TBES S R8 Years4. Results and Discussion 4.1. Technical Analysis In this portion, the served amount of di fferent categorized loads for all three scenarios of power outages will be presented and analyzed. In Fig. 4, the served load of Priority Loads 1, 2, and 3 are depicted for all three scenarios of the power out- ages caused by Hurricane Laura. For priority load 1, 52.48% of loads are served in scenario 1, whereas 85.15% loads are served in scenario 2. In scenario 3, all the 100% of priority load 1 is served successfully during the whole 24-hour power outage. For priority load 2, 18.97% loads are served in sce- nario 1, whereas 40.98% loads are served in scenario 2. In scenario 3, 53.64% of priority load 2 is served, which is the maximum amount for all three scenarios. For priority load 3, 7.51%, 22.45%, and 30.67% of loads are served in scenarios 1, 2, and 3, respectively. During all three scenarios, there is a tendency to increase served load for all the priority loads. Al- though around 30% priority load 1 is more served in scenario 2 than scenario 1, scenario 3 still shows the best performance by satisfying 100% priority load 1, which is the most critical load. Figure 4: Served Loads for the Hurricane Laura (In Percent) In Fig. 5, the served load of Priority Loads 1, 2, and 3 are presented for all three scenarios of power outages due to Hur- ricane Zeta. For priority load 1, 47.73% loads are satisfied in scenario 1, whereas 82.78% loads are satisfied in scenario 2. In scenario 3, 100% of priority load 1 is served successfully dur- ing the 24-hour power outage. For priority load 2, 19.18% loads are satisfied in scenario 1, whereas 36.61% loads are served in scenario 2. In scenario 3, 48.50% of priority load 2 is served. For priority load 3, 9.34%, 19.18% and 25.09% of loads are satisfied in scenarios 1, 2, and 3, respectively. During all three scenarios, there is also a similar pattern of increasing served load for all the categorized loads, like Hurricane Laura. In Fig. 6, the served load of Priority Loads 1, 2, and 3 are presented for all three scenarios of power outages caused by Hurricane Ida. For priority load 1, 47.73% loads are served in scenario 1, whereas 82.65% loads are served in scenario 2. In scenario 3, 100% of priority load 1 demand is served suc- cessfully during the whole 24 hours of power outage. For pri- ority load 2, 11.65% loads are satisfied in scenario 1, whereas 37.05% loads are served in scenario 2. In scenario 3, 49.30% of priority load 2 is satisfied. For priority load 3, 5.85%, 11.05%, 7 Figure 5: Served Loads for the Hurricane Zeta (In Percent) and 26.86% of loads are served in scenarios 1, 2, and 3, re- spectively. During all three scenarios, there is a tendency to in- crease served load for all the priority loads. Most importantly, the amount of served loads of priority load 1 is almost doubled in scenario 2 compared to scenario 1, whereas scenario 3 shows the best performance by fulfilling 100% of the most critical load demand. Figure 6: Served Loads for the Hurricane Ida (In Percent) Our proposed microgrid control algorithm concentrates on satisfying the priority load 1 (most critical load) during 24 24- hour power outage. Fig. 7 presents the time duration of the most critical load serving. For Hurricane Laura, the most crit- ical load is satisfied for 65.63% hours of the 24 hours in sce- nario 1, whereas the most critical load is served in scenario 2 for 88.54% hours of the 24 hours. Scenario 3 shows the best performance by satisfying 100% of the most critical load for 24 hours. For Hurricane Zeta, the most critical load is satisfied for 62.5% hours of the 24 hours in scenario 1, whereas the most critical load is served in scenario 2 for 86.46% hours of the 24 hours. Scenario 3 shows the best performance among all three scenarios by satisfying the most critical load for 100% hours of the whole 24 hours. For Hurricane Ida, the most critical load is satisfied for 62.5% hours of the 24 hours in scenario 1, whereas the most critical load is served in scenario 2 for 87.5% hours of the 24 hours. Scenario 3 shows the best performance among all three scenarios by fulfilling 100% of the most critical load for the whole 24 hours. In Fig. 8, Fig. 9, Fig. 10, battery SOC profiles for all the scenarios of the power outages are presented. In Fig. 8, the battery SOC for all three scenarios is provided for Hurricane Figure 7: Serving Time Duration of Most Critical Load (In Percent) Laura. In scenario 1, the battery SOC stays at 10% for around 7 hours, which is the highest duration of hours in all the sce- narios. 10% SOC indicates that no loads are served during that time horizon. In scenario 2, the battery SOC shows better char- acteristics, and SOC stays at 10% for around 2 hours. The SOC stays at 90% for around 5.5 hours, indicating that all the loads are served during that time. In scenario 3, the minimum SOC never goes below 20%, indicating that at least priority load 1 is served for 24 hours. The SOC remains 90% for around 7 hours, indicating that all the loads are served during that time. Figure 8: Battery SOC for the Hurricane Laura In Fig. 9, the battery SOC for all three scenarios of the power outage caused by Hurricane Zeta. In scenario 1, the battery SOC stays at 10% for around 8 hours, which is the highest du- ration of hours in all the scenarios. In scenario 2, the battery SOC shows better characteristics, and SOC stays at 10% for around 2.5 hours. The SOC stays at 90% for around 5.5 hours, indicating that all the loads are served during that time. In sce- nario 3, the minimum SOC never goes below 17%, indicating that at least priority load 1 is served for 24 hours. The SOC re- mains 90% for around 6 hours, indicating that all the loads are served during that time. In Fig. 10, the battery SOC for all three scenarios of the power outage caused by Hurricane Zeta. In scenario 1, the bat- tery SOC stays at 10% for around 8 hours, which is the highest duration of hours in all the scenarios. In scenario 2, the battery SOC shows better characteristics, and SOC stays at 10% for around 2 hours. The SOC stays at 90% for around 2.5 hours, indicating that all the loads are served during that time. In sce- 8 Figure 9: Battery SOC for the Hurricane Zeta Figure 10: Battery SOC for the Hurricane Ida nario 3, the minimum SOC never goes below 18%, which in- dicates that at least priority load 1 is served for the whole 24 hours. The SOC remains 90% for around 3.5 hours, indicating that all the loads are served during that time. 4.2. Resilience Value After completing the simulation for all three scenarios of power outages caused by Hurricane Laura, Zeta, and Ida and determining the amount of served load for each scenario of the power outages, resilience values have been calculated using the equations of 1, 2, 3, 4. All the resilience values are presented in Table 3. Table 3: Resilience Value for Di fferent Power Outages Hurricanes Scenarios Resilience Value, R Laura1 3.17 2 5.50 3 6.64 Zeta1 2.95 2 5.24 3 6.46 Ida1 2.73 2 5.16 3 6.50Resilience values of scenarios 1, 2, and 3 for the power out- ages caused by Hurricane Laura are 3.17, 5.50, and 6.64, re- spectively. Resilience values of scenarios 1, 2, and 3 for the power outages caused by Hurricane Zetaa are 2.95, 5.24, and 6.46, respectively. The resilience values of scenarios 1, 2, and 3 for the power outages caused by Hurricane Ida are 2.73, 5.16, and 6.50, respectively. 4.3. Economic Analysis Table 4 shows the investment required for 24 years, consid- ering di fferent microgrid configuration scenarios 1, 2, and 3, respectively. Scenario 1 requires 216.97 thousand US dollars, whereas Scenario 2 requires 512.88 thousand dollars, which is more than 2.3 times the investment of Scenario 1. Scenario 3 requires the maximum investment among all three scenarios, 808.78 thousand dollars. Table 4: Investment for Three Scenarios (in thousands US $) Scenario 1 Scenario 2 Scenario 3 Investment 216.97 512.88 808.78 Table 5 depicts the profit generated during 24 years time du- ration of microgrid operation for all three configuration scenar- ios considering di fferent numbers of hurricane sets. For one hurricane set (considering 3 hurricanes in 1 set) in 24 years, the profit of scenarios 1, 2, and 3 is 406.00, 832.10, and 911.90 thousand US dollars, respectively. For five hurricane sets (con- sidering 3 hurricanes in 1 set) in 24 years, the profit of scenarios 1, 2, and 3 is 471.50, 952.90, and 1061.50 thousand US dollars, respectively. It is visible that the financial profit is increasing with the increasing number of hurricane sets for all three sce- narios, and scenario 3 is the leading profit generator among all the hurricane sets. Table 5: Profit for Three Scenarios of Di fferent Hurricane Sets (in thousands $) Hurricane SetsScenario 1 Scenario 2 Scenario 3 1 406.00 832.10 911.90 2 419.60 857.20 943.00 3 434.90 885.40 977.90 4 452.20 917.20 1017.30 5 471.50 952.90 1061.50 In order to investigate the impacts of di fferent numbers of hurricane sets in all three scenarios, NPV , NPM, and RCR, are utilized for all three scenarios considering the number of hurri- cane sets from 1 to 5. In Fig. 11, NPV is increasing for all three scenarios with the increasing number of hurricane sets. Among all the hurricane sets, scenario 1 has the lowest NPV in all three scenarios. Although scenario 2 has the highest NPV for all the hurricane sets, scenario 3 is approaching to merge on scenario 2 with the increased number of hurricane sets. In Fig. 12, NPM curves depicts the net profit margin for di fferent hurricane sets. 9 For all the di fferent hurricane sets, NPM gradually increases for all three scenarios. Furthermore, scenario 1 leads to scenarios 2 and 3 for all the hurricane sets. In Fig. 13, RCR graphs are presented for all three scenarios of di fferent numbers of hurri- cane sets. For all the hurricane sets, all three scenarios have RCR value greater than 1, and the RCR value increases with the increasing number of hurricane sets. Figure 11: Net Present Value (NPV) Figure 12: Net Profit Margin (NPM) Figure 13: Revenue-Cost Ratio (RCR) The resilience values of scenarios 1, 2, and 3 for every power outages caused by Hurricanes Laura, Zeta, and Ida are taken from Table 3 and the investment required for scenarios 1, 2, and 3 from Table 4 are utilized to present the resilience vs in- vestment curve in Fig. 14. In Fig. 14, it can be observed thatafter increasing investment from 216.97 thousand US dollars to 512.88 thousand US dollars, resilience value increased sharply for all the outages. When the investment increased from 512.88 thousand US dollars to 808.78 thousand US dollars, resilience value followed an increasing tendency with the increase of in- vestment, reaching the highest resilience level. Figure 14: Resilience vs Investment Curve 5. Summary and conclusions In conclusion, our research has demonstrated the signifi- cant potential of integrating both stationary and mobile bat- tery storage systems (BSS) within microgrids to enhance en- ergy resilience, particularly in the face of natural disasters. The study’s findings reveal that strategic deployment and real-time operation of these systems can substantially reduce unserved loads, ensuring the continuous provision of power to critical infrastructure. Moreover, our economic analysis underscores the financial viability of such investments, highlighting that with careful planning and implementation, microgrids can of- fer a cost-e ffective solution to improve resilience. The insights gained from this research provide a valuable framework for decision-makers in the energy sector, suggesting that the inte- gration of stationary and mobile BSS within microgrids is a promising avenue for enhancing energy resilience in disaster- prone areas. Acknowledgements This research work is partially supported by the Louisiana Board of Regents, ITRS program as well as Cleco Power under the grant# LEQSF(2022-25)-RD-B-05