8 Climate extremes like hurricanes can devastate vulnerable power lines, resulting in large-scale power outages, e.g., Hurricane Beryl (2024) with 2.6 million customers in Texas, US. In response, peer-to-peer (P2P) energy sharing has emerged as a promising strategy to create energy communi- ties (ECs) that become resilient by adopting distributed energy resources (DERs) to generate and share electricity locally, especially after disasters. We developed a validated high-fidelity model of power systems for 2,640 households, integrating geographical multi-sourced data with probabilistic risk analysis to assess the feasibility of ECs for hurricane resilience. Our study finds that ECs would have experienced shorter outages by 65.8% for Hurricane Isaias (2020) in Absecon City, New Jersey. We then utilized our power risk model to study the financial feasibility of ECs versus other measures (e.g., undergrounding lines) for resilience to future hurricanes in Absecon and compare it to Miami communities, in Florida, exposed to larger hurricanes. We show that benefits are larger in Miami, where ECs can shorten outages by 64.4%, 33%, and 50.54% than no grid upgrade, DERs without P2P sharing (non-ECs), and undergrounding. Battery backups and resilient solar panels enhanced ECs ability to operate in island mode, which would have reduced the percentage of households experiencing outages longer than a day by 74% during Hurricane Isaias (2020). Furthermore, we show that undergrounding results in a negative net present value (NPV) for communities, with households facing a 155% higher cash outflow compared to ECs, where the addition of solar panels reduces energy bills and increases savings. Our study demonstrates the importance of integrating resilience into energy policies, particularly as infrastructure evolves to meet the challenges of a changing climate.9 1. INTRODUCTION 10 Hurricanes can destroy old and vulnerable power lines, causing large-scale cascading power 11 failures [ 1,2]. For example, (a) Hurricane Isaias (2020) caused widespread blackouts for more 12 than 3 million customers across five states in the US [ 3]; (b) Hurricane Ida (2021) caused extensive 13 damage to power infrastructure in Louisiana, leaving 1.2 million customers without power [ 4]; 14 (c) Hurricane Ian (2022) left 2.7 million customers in Florida without power [ 5]; (d) more recently, 15 Hurricane Beryl (2024) left more than 2.6 million customers without power in Texas [ 6]. These 16 blackouts are often prolonged, threatening the health of the affected population as they endure 17 excessive heat after a hurricane [ 7,8]. Adding to the distress, the critical interdependence of 18 transport, water pumps, medical facilities, food supplies, and emergency aids with electricity 19 can deprive communities of critical services post-hurricane. Power outages can severely impact 20 vulnerable community members, including households with children, the elderly, and medically 21 1 fragile populations [9, 10]. 22 Resilient communities must have power systems capable of withstanding extreme weather events 23 and minimizing the extent of cascading power disruptions [ 11,12]. In recent years, rooftop solar 24 panels have supplemented the power supply of residential, office, and industrial buildings [ 13]. 25 The US Energy Information Administration predicts a 75% growth in solar energy production from 26 2023 to 2025 [ 14]. Distributed energy resources, such as rooftop solar panels, can generate electricity 27 locally and operate in island mode. Thus, DERs have great potential to enhance community 28 resilience by providing electricity access after a disaster, even when power lines fail [15]. 29 Furthermore, massive governmental and industry investments are targeting integrating DERs at 30 micro-urban scales through microgrids to create energy communities (ECs), an emerging concept 31 in energy markets [ 16]. In ECs, prosumers - households with energy-generating sources such as 32 rooftop solar panels - sell surplus electricity under peer-to-peer (P2P) sharing to local neighborhoods 33 [17,18]. One notable example of an energy community (EC) is the Brooklyn Microgrid, which 34 became operational in 2016 [ 19]. Such ECs are exhibiting resilience after large disasters. In 35 2017, Hurricane Maria battered Puerto Rico, snapping many power lines and leaving numerous 36 communities without power for several months. In response, communities have turned toward 37 building independent microgrids to solve their energy needs and resilient power infrastructure 38 [20]. To further support these initiatives, the US Department of Energy announced USD 450 million 39 in funding in July 2023 to incentivize the deployment of 30,000 to 40,000 residential solar systems 40 [20]. 41 Currently, there is limited research on the benefits of resiliency with P2P sharing at the community 42 level because most studies have focused on a single or small group (2-3) of households [ 21–24]. 43 Other studies have not considered the vulnerability of the existing grid [ 15]. In [ 25], authors showed 44 increased resilience to power outages at the household level with P2P power sharing during an 45 earthquake, but they did not consider power lines can fail during ground shaking, affecting the 46 power network connectivity of ECs. Analyzing electricity access from solar during hurricanes is 47 also more complex than for earthquakes. Unlike earthquakes, thick optical clouds during hurricanes 48 can significantly hinder the incoming solar irradiance on solar panels [ 26], further reducing the 49 electrical power generation during the disaster. 50 To understand the resilience of ECs, this paper develops a high-fidelity outage risk model (Hi-Fi 51 ORiM) for households with rooftop panels, behind-the-meter batteries, and P2P energy sharing 52 to predict electricity access during hurricane emergencies. The model integrates state-of-the-art 53 hurricane hazard [ 27], vulnerability [ 28], solar irradiance [ 29,30], and network modeling [ 31] to 54 study ECs during future hurricanes with a probabilistic framework. We combine multiple data 55 streams to create a realistic synthetic network for Hi-Fi ORiM using graph theory (see Methods) 56 [31]. Thus, the Hi-Fi ORiM can assess the failure risk from each component (poles, panels) in the 57 network, as their vulnerabilities vary, and how their failures cascade through the network. We also 58 use high-performance computing to quantify uncertainties in the risk model and thoroughly study 59 the viability of adopting different resilience mitigation strategies, including ECs [32]. 60 Current models that evaluate the viability of DERs in high hurricane hazard areas do not account 61 for solar panel failures, solar irradiance reductions, or the long duration of power recovery during 62 large hurricanes. These factors are crucial for evaluating the ability of DERs to sustain electricity 63 supply during disasters. Existing tools, such as renewable energy integration and optimization 64 tools (REopt) and solar power calculators (PVwatts), do not consider solar panel failure risks, 65 neglecting their repair and replacement costs when optimizing panel and battery size for savings 66 and resilience [ 33]. Other important studies for hurricane resilience have also neglected such failure 67 risks, in addition to significant solar irradiance decays during hurricanes [ 34]. Rooftop solar panels 68 can have a 75% failure probability for hurricanes with wind speeds over 90m/s [ 35]. An example 69 2 of a failed solar panel in the aftermath of Hurricane Ian (2022) is shown in Supplementary Figure 70 S5. Additionally, in [ 26], the authors found that solar generation could decrease by more than 70% 71 in Miami-Dade, US, during category-4 hurricanes. Moreover, scholars have mostly considered a 72 particular outage scenario with quick recoveries of 8h to 24h [ 23,36]. However, hurricanes can 73 leave populations without power for days to months [10]. 74 Other grid-hardening measures can also increase the resilience of power systems. For example, 75 the resilience of the power grid to hurricanes can be enhanced by undergrounding overhead power 76 lines [ 37–39]. However, the cost of undergrounding can be high and result in increased consumer 77 electricity tariffs [ 38]. On the contrary, there is growing literature on the economic and financial 78 benefits, in addition to the environmental ones, that renewables can bring to reduce the billions of 79 dollars in losses caused by power blackouts [ 21,36,40,41]. Thus, evaluating and comparing the 80 cost of adopting such mitigation strategies to enhance resilience becomes essential. 81 This paper presents and develops a high-fidelity risk outage model (Hi-Fi ORiM) of a distribution 82 grid serving 2,640 households. We calibrate the model with power outage and recovery data from 83 Hurricane Isaias (2020) to serve as a validated test bed for assessing the resilience of power 84 communities prone to hurricane risks. To evaluate the finances of resilient electricity, we determine 85 the net present value (NPV) of solar panel adoption by considering installation costs, cash flows 86 (amount of electricity sold and bought), and the value of resilience [ 42]. Traditionally, value of 87 resilience is considered by multiplying the avoided outage duration with the value of lost load 88 (VoLL), expressed in dollars per kWh of unserved load [ 42,43]. However, traditional VoLL is limited 89 to qualitative analysis as it is determined through surveys based on the responses to hypothetical 90 conditions provided to the responder. This paper presents a new method to quantify the value 91 of resilience by arranging alternative energy resources during emergencies. Our study compares 92 multiple strategies for resilient electricity: (a) ECs, which involves the adoption of solar panels with 93 P2P sharing; (b) non-ECs, which involves the adoption of solar panels without P2P sharing; and 94 (c) undergrounding of power lines. Additionally, we integrate 5,018 synthetic hurricanes from a 95 state-of-the-art hurricane hazard model to study the resilience benefits against future hurricanes 96 [44]. Our combined approach of state-of-the-art Digital Twin, probabilistic risk modeling, and 97 financial analysis would allow stakeholders such as homeowners, utilities, and local governments 98 to make informed decisions and invest in building a resilient power grid for the future. 99 2. RESULTS 100 A. Validated High-Fidelity Outage Risk Model of Power Network 101 The high-fidelity outage risk model (Hi-Fi ORiM) represents the physical systems of the power grid, 102 enabling simulations of their interactions with natural hazards [ 31,45]. This approach assesses the 103 vulnerability of entire power networks by integrating risk modeling for individual components 104 with their interconnections rather than focusing on single components. This method is particularly 105 valuable in scenarios where network dynamics are crucial, such as in power networks. We lever- 106 aged high-performance computing (HPC) resources to overcome the computational challenges of 107 running multiple power systems and hurricane scenarios. 108 We developed Hi-Fi ORiM of the power distribution network serving 2,640 residential consumers 109 in Absecon City in Atlantic County, New Jersey, who could benefit as ECs. We integrated informa- 110 tion from publicly available multi-sourced datasets in the model (Figure 1), including limited pole 111 locations from OpenStreetMaps [ 46], roads and building parcels from New Jersey Open Geographic 112 Information Systems [47, 48], and building footprints from Microsoft [49]. 113 3 Fig. 1. Hi-Fi ORiM developed using open-source datasets of roads, parcels, and buildings from the New Jersey Geographic Information Network [ 50]; fragility curves for pole failure probabil- ities across classes C1-C7, modeled with a log-normal distribution for a median 3-second wind gust ( ¯w) and dispersion ( β) [28] (see Methods); and hazard information from 3-second wind gusts during Hurricane Isaias (2020). Most distribution networks in the US have a radial structure, where the failure of a single over- 114 head pole can cause cascading power failures for all downstream consumers [ 51]. We developed the 115 Hi-Fi ORiM by reproducing such a radial topology and coupling it with hazard and vulnerability 116 models of the power system’s components [ 31]. The resulting probabilistic risk-network model 117 predicts the damage to the overhead poles from wind hazards, disconnections in the power network 118 due to damages, and the recovery of damaged components back to a fully connected network (see 119 Methods). We only consider wind-driven failures of distribution poles [ 31] and do not model the 120 compound failures from falling trees on distribution poles due to lack of data [ 52]. Fragility curves 121 define the wind-dependent failure probabilities for different classes of poles (Figure 1). We used a 122 full circulating [ 27] and background [ 53] wind model to capture the complete structure of tropical 123 cyclones to assess realistic hurricane winds. Figure 1 presents all the components to build a Hi-Fi 124 ORiM, including datasets, risk modeling, and hazard information. 125 4 Fig. 2. a.Representation of constructed synthetic grid for Absecon City, New Jersey. b.Recovery simulations of Hi-Fi ORiM calibrated for the observed recovery during Hurricane Isaias (2020). Our Hi-Fi ORiM was calibrated using outage and recovery data from Hurricane Isaias (2020) 126 (see Methods) [ 54]. Isaias carried winds close to 100-year return period events for New Jersey [ 55]. 127 Even though Isaias transitioned to a tropical storm before hitting New Jersey, it still snapped power 128 lines and destroyed many power poles [ 56]. Isaias caused economic losses of 4.2 billion and 15 129 deaths and was responsible for major blackouts across five states in the Northeast United States. 130 One million customers were without power alone in New Jersey, with many suffering power loss 131 for over four days [57, 58]. 132 Our developed Hi-Fi ORiM model, as shown in Figure 2a, can simulate hurricane-induced 133 outages and the post-disaster recovery of the power network with high precision, as demonstrated 134 in Figure 2b. Our model predicts maximum percent outages of 77.42% close to the observed percent 135 outages for 74.15% of consumers. In our synthetic grid, 95% of the customers recover in 61hours 136 close to 55hours for the observed recovery of the power grid in Absecon City. Our developed Hi-Fi 137 ORiM model represents a typical suburban power network, making our framework adaptable for 138 assessing the risk of power outages across varying levels of hurricane wind intensity, from low to 139 high. 140 B. Could we have enhanced electricity resilience during Hurricane Isasias? 141 We found that communities would have benefited significantly from resilience measures during 142 Hurricane Isaias (Figure 4). To compare the effectiveness of resilience measures, we considered 143 four different cases: (a) No Upgrade, (b) ECs, (c) Non-ECs, and (d) Underground power lines. We 144 ran 400 random simulations of our probabilistic risk model during Hurricane Isaias. 145 The US is projected to have 55% of its electricity generation through solar energy [ 59]. In line with 146 this projection, we assumed that 50% of houses in a cluster would adopt solar panels and battery 147 storage of 10 kWh. We grouped the houses into clusters based on Euclidean distance to study 148 the effectiveness of energy sharing (see Methods). The solar panels are sized for net-zero energy 149 5 consumption, i.e.,energy generated by panels equals energy consumed by households. Figure 150 3a. depicts a network of houses in a power grid without solar panels, where all the houses lose 151 power during hurricane emergencies. In contrast, another network in Figure 3b. has prosumers 152 installing rooftop solar panels. These prosumers can generate electricity or use backup power from 153 a battery during a hurricane emergency. The prosumers gain resilience through solar panels and 154 can enhance the community’s resilience by sharing excess electricity in a solar EC. 155 To model ECs, we extended our Hi-Fi ORiM model (Figure 8). First, we integrated solar panel 156 vulnerability functions into our risk model [ 35] to predict damage from the hurricane winds [ 27,53] 157 We integrated a stochastic model to predict reduced solar irradiance into the Hi-Fi ORiM model, 158 using historical solar data to capture the effect of thick hurricane clouds on solar generation, as 159 shown in Supplementary Figure S4 (see Methods) [ 26]. Finally, we modeled each household’s 160 recovery based on the available solar irradiance and undamaged network components. 161 To study the effectiveness of undergrounding measures, we assumed that 50% of the most 162 vulnerable power poles are buried. For example, class 7 poles, which have the lowest median wind 163 threshold on the fragility curve (Figure 1), are undergrounded first, followed by the second most 164 vulnerable poles. 165 Supplementary Figure S7 shows the adoption of solar panels among prosumers across Absecon 166 City for ECs and non-ECs grid hardening configurations, as well as the undergrounding of power 167 poles (lines) to increase resilience against strong winds (see Methods). 168 Fig. 3. Schematic Diagram of the P2P energy sharing. The left figure shows the traditional grids where consumers suffer outages during hurricanes due to extensive damage to the power grid, and the right figure shows the adoption of solar panels so that the prosumers can generate elec- tricity in island mode after the hurricane and share excessive energy with consumers (without solar panels) to increase the resilience against power outages. 6 Fig. 4. Simulations for resilient strategies scenarios for the developed Hi-Fi ORiM in Absecon City, New Jersey, during hurricane Isaias (2020). a.Cumulative percentage of customers experi- encing power outages. b.Duration of mean power outages observed for each household under different configurations of Hi-Fi ORiM c.Representation of mean reduction in the duration of power outages for a cluster for different resilience strategies compared to the base case of no up- grades. d.Scatter plot between the reduced number of outages for a cluster and distance from the US highway. IEEE Standard 1366-2022 defines reliability indices, such as the System Average Interruption 169 Frequency Index (SAIFI). As reliability only focuses on daily interruptions, utility performance 170 ratings exclude catastrophic events, defined as ’Major Day Events,’ such as hurricanes, from SAIFI 171 calculations [ 60]. Thus, no standard index is defined for measuring the resilience of power systems. 172 7 However, any interruption to the consumers is a disturbance, whether caused by a daily outage or a 173 hurricane. The IEEE Power and Energy Sector (PES) Task Force emphasizes the resilience of power 174 systems as the ability to limit the extent, system impact, and duration of degradation following 175 an extraordinary event such as natural hazards [ 12]. Thus, we identify the resilience gained from 176 resilience strategies as the reduction in outage durations. Our proposed resilient strategies focus 177 on reducing outage durations by increasing the robustness of power systems through integrating 178 DERs and undergrounding power lines. 179 Configuration Outages duration (hours)Households (%) with long outages [ ≥24 hours] No Upgrade 13.92 [1.61-59.86] 16.86% [8.41%-31.45%] ECs 4.76 [0.00-33.11] 4.43% [2.50%-11.33%] Non-ECs 7.41 [0.00-57.56] 9.05% [4.61%-16.45%] Underground 7.46 [0.53-43.59] 8.41% [4.50%-20.76%] Table 1. Mean outage duration of households and households with longer outages for the sce- nario of Hurricane Isaias (2020) *95% C.I. in bracket Our results (Figure 4a-b and Table 1) show that ECs would achieve the highest resilience against 180 hurricanes compared to non-ECs and undergrounding configurations in the scenario of Hurricane 181 Isaias (2020). We found that households in ECs would experience a mean outage duration of 182 4.76 hours per household, which is 65.80%, 35.76%, and 36.19% shorter than in the no upgrade, 183 non-ECs, and undergrounding cases, respectively. Moreover, 97.5% of households in ECs would 184 have an outage duration of less than 33.11 hours, whereas no-upgrade scenario would be 80.80% 185 longer. Additionally, ECs would have a mean of 4.43% of households experiencing long outages 186 (>24hours), which is 73.47%, 51.04%, and 47.32% less than in the no-upgrade, non-ECs, and 187 undergrounding cases, respectively. At the 97.5thpercentile, ECs would have 11.33% of households 188 experiencing long outages, which is 177.58% higher in the no upgrade case. 189 We show the spatial distribution of mean reductions in power outage duration for different 190 resilience strategies across 264 clusters, comprising 2,640 households, in Figure 4c. We observe more 191 significant reductions in outage durations farther from the main highway across all mitigation cases 192 (Figure 4d). For instance, clusters in ECs between 1000 and 1500 meters would observe an average 193 reduction in outage duration of 24.53 hours, three times higher than the reduction for clusters 194 between 500 and 1000 meters. Typically, power grids are restored using a top-down strategy where 195 repair teams reach the poles closer to the main highway first [ 61,62]. Thus, households with more 196 remote access through local roads often recover electricity last, especially in radial distribution 197 networks. Our results show that these resilience measures, and especially ECs, can significantly 198 enhance electricity access for such households (Figure 4d). 199 C. Assessing the effectiveness of resilience measure to future hurricanes 200 Hurricane Isaias reached a maximum category of 1, but the US is exposed to more significant 201 events, e.g., the recent 2024 category 5 Hurricane Beryl with 165 mph winds [ 63]. To analyze the 202 benefits of resilience measures comprehensively, we studied multiple realistic hurricane scenarios 203 using state-of-art hurricane hazard models [ 27]. We analyzed 5,018 landfalling hurricanes that 204 8 originate in the North Atlantic Basin [ 64] representing the hurricane hazard under the current 205 climate scenario (Figure 5a). These hurricanes cover approximately 1, 485 years, as about 3.38 206 hurricanes from the Atlantic Basin make landfall each year [ 35]. We sampled the hurricanes yearly 207 as a random Poisson process (see Methods). We represent a typical year as the mean of 1, 485 years 208 of simulations. We ran a total of ∼4×105simulations to model outages and recovery in Absecon 209 and Miami. 210 We also considered a different region from Absecon City to evaluate how resilience measures 211 apply to other US communities exposed to the largest hurricane hazards. To do so, we leveraged our 212 Hi-Fi ORiM model, which represents typical communities with radial distribution lines in the US, 213 to also analyze communities in Miami, Florida (Figure 5a). Miami has recently experienced strong 214 hurricanes e.g., Matthew (2016), Irma (2017), Dorian (2019), Ian (2022) and can experience winds 215 of62m/sfor a 100-year return period, which is 38% higher than 45m/swinds for Absecon City. 216 Notice that wooden power poles can sustain winds up to 20m/s[65]. Thus, wind speeds beyond 217 20m/scan make them fail. In Absecon City, only 5.14% simulations exceeded 20m/s(Figure 218 5b). In contrast, Miami has 20.18% simulations that exceeded 20m/s(Figure 5c), highlighting 219 significantly higher hazards due to its different location (Figure 5a). 220 9 Fig. 5. Simulations for the future (synthetic) hurricanes. a.Tracks of synthetic hurricanes in the North Atlantic basin based on the historical climate [ 44].b.Distribution of wind speeds (>20 m/s) in Absecon City, New Jersey. c.Distribution of wind speeds (>20 m/s) in Miami, Florida. d. Average duration of power outages observed for each household under different configurations of Hi-Fi ORiM in Absecon City, New Jersey, for 25 years. e.Average duration of power outages observed for each household under different configurations of Hi-Fi ORiM in Miami, Florida, for 25 years. We investigated the same four cases, including a baseline and three resilience measures, under 221 future hurricanes. The average lifetime of a solar project is 25 years. We also assumed that utilities 222 would recover undergrounding costs over 25 years. Thus, we present the average duration of 223 power outages for each house across 25 years with different power grid configurations in Figures 224 5d-e and Table 2. 225 10 Similar to the results for Hurricane Isaias (2020), we found that households will gain more 226 resilience in ECs. From Figure 5d, we observe that the low-wind region of Absecon City would 227 have minimal outages across all the configurations. The observed outages for the high winds region 228 of Miami are ∼100times more than the low wind region of Absecon City. In an extreme case, a 229 household can observe an outage of 206 hours ( 8days) in Florida. In the aftermath of Hurricane 230 Ian (2022), a category 4 hurricane, nearly 30% of Lee County in Florida consumers were without 231 power after 8 days. 232 We observe more significant resilience gains for Miami than for Absecon City. For instance, 233 the mean outage durations in Miami decreased from 35.64 hours in the no upgrade scenario 234 to 12.69 hours with ECs, compared to a smaller reduction in Absecon, where the mean outage 235 durations dropped from 0.31 hours to 0.08 hours. The biggest improvement in resilience for 236 Miami is observed with ECs, where we observe a 64.4% ,33.0% , and 50.5% average reduction in 237 outage duration compared to the no-upgrade, non-ECs, and undergrounding cases, respectively. 238 Additionally, 97.5% of households in Miami will observe an average outage duration of less than 239 80.17 hours, which is 126.32% higher than the outage duration of 130.66 hours for the no upgrade 240 case. Finally, we also observe an increasing reduction in duration with the distance from the 241 highway (Supplementary Figure S9). Like the scenario analysis for Hurricane Isaias, resilience 242 gains are maximized for the population at greater risk of prolonged power outages. 243 Location Configuration Outages duration (hours) Absecon No Upgrade 0.31 [0.00-2.90] Absecon ECs 0.08 [0.00-0.69] Absecon Non-ECs 0.17 [0.00-2.34] Absecon Underground 0.08 [0.00-1.45] Miami No Upgrade 35.64 [11.42-147.09] Miami ECs 12.69 [0.00-80.17] Miami Non-ECs 18.95 [0.00-139.43] Miami Underground 25.63 [7.40-128.24] Table 2. Mean outage duration of households (95% C.I. in bracket) D. Household-scale resilience financing 244 Here, we quantify the finances for the resilience measure to weigh the cost of investments against 245 the benefits [ 66]. We evaluate the profitability of prosumers by determining their net present value 246 (NPV) for 25 years, i.e., the solar panel’s lifespan (see Methods). We studied the contributions of 247 different components towards NPV: (a) investments and operation costs for panels and batteries 248 (I&O), (b) savings from not purchasing grid electricity, (c) additional state incentives, (d) panel 249 damage from winds, and (e) the avoided costs from shorter/no outages. 250 Previous research [ 42] has considered the financial value of resilience measures as the savings 251 from avoided outages based on the value of lost load (VoLL). Historically, the VoLL for residential 252 customers has been estimated using questionnaires based on hypothetical outage scenarios [ 43]. 253 11 These questionnaires ask about customers’ willingness to pay to avoid outages, but the wide range 254 of options, such as 0 to 50 USD, can lead to underestimations of VoLL as customers tend to choose 255 a lower value. Additionally, these surveys consider outages of up to 16 hours, whereas hurricane- 256 related outages can last up to a week. In contrast, we quantified the value of avoided outages as the 257 savings from not needing alternate energy sources, such as emergency diesel generators, to supply 258 electricity when the grid is down (see Supplementary Text S3). Many communities use generators 259 for days or even weeks as a backup during significant power outages, for example, in Florida after 260 Hurricane Irma (2017) [ 7]. Thus, we use this avoided cost as a more realistic way to quantify the 261 value of shorter outages during disasters. 262 We define profitable prosumers as those with a positive NPV and study the effects of state 263 incentives. For brevity, we show results for NPV with state incentives here (Figure 6a) and without 264 them in the Supplementary Information (Figure S11 and S12). Prosumers are more profitable 265 in Miami as Florida receives nearly 25% more solar irradiance than New Jersey (Supplementary 266 Figure S3) [ 29]. Smaller-sized rooftop solar systems can generate the same amount of solar energy 267 in Florida as larger-sized solar panel systems in New Jersey. Figures 6b-c represent the breakdown 268 of profitable and unprofitable prosumers in Absecon City and Miami. While profitable prosumers 269 have similar I&O costs in Miami and Absecon City, prosumers in Miami have higher savings than 270 those in Absecon City. For example, profitable prosumers in ECs have 29% higher savings in Miami 271 than in Absecon City. We found that 99% of the prosumers are profitable in Miami compared to 272 92% in Absecon for both ECs and non-ECs. 273 Our findings show that, on average, the value of resilience, measured as the avoided cost from 274 outages, is about 16% and 23% of I&O costs for profitable and unprofitable prosumers, respectively 275 (Figure 6b-c). These avoided costs are important and can supplement other savings from reduced 276 grid electricity purchases and energy sharing. On average, prosumers have higher profits and 277 savings with ECs configuration than non-ECs. This can be attributed to higher selling electricity 278 costs in local energy markets than net-metering rates. The median NPV with state incentives in 279 Absecon and Miami for ECs is 16% and 7% higher than for non-ECs. 280 12 Fig. 6. a.NPV for each prosumer in Absecon City and Miami with state incentives. b.Average contributions of different components in NPV for profitable prosumers. c.Average contributions of different components in NPV for unprofitable prosumers. d.NPV vs. annual consumption for prosumers without state incentives. We observe from Figure 6d that the NPV for prosumers increases with higher yearly consumption 281 due to more significant savings from self-consumption. For example, in Absecon City, for ECs 282 with state incentives, the mean yearly consumption of houses with positive NPV is 144% higher 283 than that of houses with negative NPV . Similarly, in Miami, for ECs with state incentives, the mean 284 yearly power consumption of prosumers with positive NPV is 141% higher than that of prosumers 285 with negative NPV . 286 However, even unprofitable prosumers with a negative NPV can still gain significant electricity 287 resilience. For instance, an unprofitable prosumer in Absecon City can still significantly reduce up 288 to 20 hours in outage duration per year. This reduction can be up to 167 hours ( ∼7days) for an 289 unprofitable prosumer in Miami. We present the cases of unprofitable prosumers with a negative 290 NPV in Figure 6c. We find that lower savings ( e.g., 57% for unprofitable prosumers in Miami versus 291 123% for profitable ones) contribute to a negative NPV . Solar panels are sized for net-zero energy 292 consumption, and we assumed a constant battery size of 10kWh for all prosumers, leading to 293 13 higher ratios of I&O costs versus savings for smaller households. While batteries could be smaller 294 to reduce I&O costs [ 23], households would also lose resilience to more extended outages, e.g., 295 outage lasted days for Hurricane Isaias [ 3]. Given these financial challenges, further research is 296 essential to better quantify the value of resilience that could significantly boost the adoption of 297 solar panels. 298 Meanwhile, to support solar adoption, incentives such as the 30% Solar Investment Tax Credit 299 (SITC) towards residential solar installations [ 67] and Successor Solar Incentive (SuSI) Program 300 [68] by NJ Clean Energy Programs can reduce the financial burden on households. As shown in 301 Figure 6d, state incentives can cover up to 40% and 60% of I&O costs for the profitable prosumers in 302 Absecon City and Miami. These incentives can significantly benefit the prosumers in low irradiance 303 regions, such as Absecon City, and could be crucial in helping unprofitable prosumers move toward 304 profitability. This is evident as there are only 26% profitable prosumers in Absecon City without 305 state incentives for ECs, while 80% profitable prosumers in Miami. These incentives will also attract 306 more solar panel adoption where net-metering rates are at wholesale rates, e.g., in New Jersey 307 [69], or where net-metering is expected to decrease to wholesale rates, e.g., in Florida with the 308 introduction of House Bill 741 [70]. 309 Finally, we found that damage costs are negligible in Absecon City’s low-wind region. In contrast, 310 in Miami’s high winds region, the average damage cost over 25 years equals 21% of average I&O 311 costs. Miami’s high-wind region experiences an annual mean panel failure rate of 3.3×10−2, 21 312 times higher than 1.5×10−3in Absecon. Despite this higher failure rate, solar panels can still 313 provide resilient electricity; for instance, when the chance of a panel failure is 3%, a class 7 pole 314 has a 38% chance of failure. The rate of solar panel damage was determined through hurricane 315 simulations and panel fragility assessments. Damage costs were calculated by multiplying the 316 failure rate by the cost of installing a new panel (see Methods). The "Solar Under Storm" report, 317 based on observations from hurricanes Irma and Maria in 2017 and Dorian in 2019, suggests 318 using vibration-resistant module bolted connections to enhance resilience. This would increase 319 installation costs by approximately 5%, which is still significantly lower than the 21% damage cost 320 [13]. Our results show Miami has more profitable prosumers even with approximately 100 times 321 higher damage costs than Absecon City because of higher solar irradiance. This highlights the need 322 for policies that not only incentivize solar adoption but also ensure resilience, enabling robust solar 323 energy even in high wind risk areas like Miami. 324 E. Community-scale resilience financing 325 The cost of installing new rooftop solar panels is covered by the individual house installing 326 the panels. However, the economic cost of undergrounding power lines is passed on to all the 327 consumers [ 38], requiring a financial analysis at the community scale, e.g., Florida Senate Bill 796 328 (2019) [ 71] (see methods). For a clean investment comparison of undergrounding versus ECs, we 329 analyzed NPVs at the community level, aggregating the cash flows for the 2,640 households. 330 At the community level, investments in ECs in Absecon City and Miami have positive aggregated 331 NPVs (Figure 7a). This positive NPV results from savings for the prosumers and reduced bills 332 for households without solar panels, who purchase electricity from prosumers at reduced prices. 333 Similar to NPV for individual prosumers, the NPV of the community in Miami is 188% higher than 334 the community in Absecon City with solar EC configuration. 335 In contrast, households primarily experience cash outflow during undergrounding due to 336 increased consumer bills needed to cover the cost, even though there is added value in resilience. 337 To cover the cost of undergrounding, households might observe an annual increase of up to 42%, 338 with the value of resilience being negligible in Absecon and only accounting for 0.3% of consumer 339 energy bills in Miami. Thus, we observe negative NPVs for the undergrounding of power lines in 340 14 both Absecon City and Miami in Figure 7a. 341 Fig. 7. Community-Scale Results. a.Community-wide NPV for adoption of solar panels in ECs and undergrounding cases. b.Scatter plot for the duration of outages and net cash outflow for ECs and undergrounding cases in Miami, Florida. To compare each household’s total spending, we calculated the net cash outflow over the next 342 25 years for solar EC and undergrounding in the Miami region, including state incentives. We 343 calculated the net cash outflow because NPV includes savings that do not generate any cash inflow. 344 Although Absecon City had 92% profitable prosumers, close to 99% in Miami, we focused on Miami 345 due to its high wind region, where solar power significantly enhances community resilience. We 346 present the distribution of cashout flow and duration of outages for EC and undergrounding cases 347 for 2,640 households in Miami in Figure 7b. We observe a cluster of households in the community 348 with less than 50 hours of outages for ECs. In contrast, the undergrounding case has a higher 349 duration of outages. We observe that close to 16% of customers would experience more than 50 350 hours of outages in undergrounding case, whereas less than 8% would experience the same duration 351 in ECs. Battery backup, combined with more resilient solar panels, enhances the capacity of ECs to 352 operate in island mode. However, undergrounding 50% of power poles can still result in network 353 disconnections where poles remain above ground, leading to prolonged outages. The average cash 354 outflow of households in ECs is USD 16998.77 [7593.40 −36012.95, 95% CI]while the average cash 355 outflow in the undergrounding case is 155% higher at USD 42552.67 [19164.34 −69465.29, 95% CI]. 356 Thus, ECs can gain more electricity resilience with lower net cash outflows than undergrounding 357 case. 358 3. DISCUSSION 359 We developed a high-fidelity outage risk model (Hi-Fi ORiM) of a power distribution network serv- 360 ing a city with 2,640 households. This model leverages multi-source datasets on roads, buildings 361 and power network and integrates state-of-the-art hurricane hazard model [ 27] to predict failures 362 of power network components, cascading outages, and recovery after hurricanes. We calibrated the 363 model to accurately reproduce outages from Hurricane Isaias (2020) in New Jersey [ 72], achieving 364 a 77.42% peak outage prediction compared to the actual 74.15% outages. Our validated risk model 365 15 represents a typical radial US power network. Thus, we used it as test bed for evaluating outage 366 risks and the feasibility (e.g., costs and benefits) of resilience measures across varying hurricane 367 conditions. 368 We studied resilience measures through four cases: a) No Upgrade, b) ECs, c) Non-ECs and 369 d) Undergrounding power lines. First, we analyzed how these resilience measures would have 370 enhanced electricity access for communities in Absecon City after Hurricane Isaias. We found 371 that ECs would have provided the most significant gains, with only 4.43% households facing 372 outages longer than 24hrscompared to 16.86% ,9.05% , and 8.41% for no upgrade, non-ECs, and 373 undergrounding cases, respectively. 374 We then investigated the benefits of these resilience measures for future hurricanes through 375 state-of-art disaster risk modeling. For a comprehensive analysis of hurricane conditions, we 376 extended our case study in Absecon City to include Miami, which, unlike Absecon City, is one of 377 the US regions facing the most significant hurricane hazards. Due to the higher hurricane hazards, 378 our predictions showed Miami could experience outage durations up to 100 times longer than 379 Absecon City. 380 We observed modest resilience gains in Absecon City for future hurricanes due to the lower 381 likelihood of experiencing many events like Hurricane Isaias. However, Miami is exposed to 382 stronger and more frequency hurricanes that can cause large-scale outages, and thus, we found 383 significant resilience gains. We predicted that Miami ECs would have average outage durations 384 64.4%, 33.0%, and 50.5% shorter than those in the no-upgrade, non-EC, and undergrounding cases, 385 respectively. We have already seen DERs can enhance electricity access after previous disasters. 386 Hurricane Ian in 2022 caused widespread outages, leaving 2.6 million households in Florida without 387 electricity. Nevertheless, Babcock Ranch, a small community in the state, maintained power for 388 2,000 households using its solar panels [ 5]. Similarly, after the Tohoku Earthquake in 2011, the 389 Sendai Microgrid continued to supply electricity through solar panels and batteries [ 73]. These 390 findings highlight the need for greater investment in DERs to bolster community resilience against 391 disasters. While the energy policies are shifting towards clean energy goals [ 74], they should also 392 focus on disaster risk management. 393 Next, we assessed the financial feasibility of implementing such resilience measures. We found 394 that the avoided costs from preventing outages are 18% and 23% of the investments and operations 395 (I&O) costs of these measures for profitable and unprofitable prosumers, respectively. To estimate 396 these avoided costs, we calculated the expenses of arranging alternate energy sources, such as 397 diesel generators, to access electricity during an outage. This approach results in different costs than 398 the typical small values of lost load (VoLL) from consumer surveys [ 43], traditional VoLL estimates 399 are not suited for disaster scenarios. Traditional VoLL underestimates the costs of outages, which 400 are based on hypothetical short outage scenarios [ 43]. We observe that unprofitable prosumers can 401 significantly reduce outage duration during hurricanes. This suggests that their financial feasibility 402 might be underestimated Given these findings, future research should focus on quantifying the 403 true value of avoiding outages after disasters, as these prosumers might actually be profitable when 404 considering the broader economic benefits of resilience, especially for vulnerable groups relying on 405 electric medical equipment. 406 We also found that prosumers in Miami would observe a 21times higher failure rate for solar 407 panels than prosumers in Absecon due to their higher wind hazards. Extensive structural sur- 408 veys after Hurricanes Irma (2017), Maria (2017), and Dorian (2019) found many rooftop panels 409 experienced failures in racks and clips attaching the panels to the racks in the Caribbean Islands 410 [13]. These surveys suggested vibration-resistant module bolted connections to improve resilience 411 against storm winds. Overall, the projected increase in solar installation cost is about 5%to gain 412 resilience through stronger structural systems. This projected cost is less than the expected damage 413 16 cost for solar panels in Miami for the next 25 years, which is 21% of I&O costs, including solar 414 panels and battery, and damage cost might be an even higher proportion of only solar panels costs. 415 We found that prosumers can be profitable with higher savings and incentives and that solar 416 adoption can also help the community become resilient. State incentives could be crucial in 417 determining consumers’ willingness to adopt solar panels, especially in low-irradiance regions 418 such as New Jersey. Without state incentives, we found that the percentage of profitable prosumers 419 in Absecon City reduces from 92% to26% for ECs as those incentives can cover up to 40% of I&O 420 cost. The reduction in profitable prosumers in Miami is not as drastic as in Absecon City, as regions 421 in Florida receive 25% more year-round consistent solar irradiance than in New Jersey. Moreover, 422 prosumers in ECs can earn more profit by selling excess solar power to local neighborhood energy 423 markets at higher selling costs than net-metering costs. 424 Unlike solar adoption, where only prosumers bear installment costs, undergrounding costs are 425 distributed among all households in the community. Therefore, we calculated the NPV for the 426 entire community for both ECs and undergrounding cases. ECs had a positive NPV for both 427 Absecon City and Miami, but the NPV was negative for the undergrounding case for both locations. 428 Consumers do not benefit financially from undergrounding, except for the added resilience, as it 429 mainly leads to increased electricity bills to cover the costs. We also compared the net cash outflow 430 (NCF) and duration of power outages for ECs and undergrounding cases in the Miami region for 431 25 years. The average NCF in undergrounding is 155% higher than in ECs. Moreover, more than 432 16% of the consumers would observe more than 50hrsfor power outage duration compared to less 433 than 8%of the consumers in ECs. Thus, adopting solar panels in the P2P sharing setting not only 434 increases the community’s resilience benefits but also proves more profitable. 435 Finally, our study also has some limitations. Our Hi-Fi ORiM captures the structural failures 436 of the power grid components during hurricanes. However, this study does not consider the 437 synchronization of DERs with the main grid as required in IEEE Standard 1547-2018 [ 75]. Future 438 studies could address this limitation, as well as other requirements, such as voltage regulations, 439 since we only focus on the connectivity of power system components. Additionally, future research 440 could explore the value of inverters and the costs associated with operating a microgrid, especially 441 as additional operators might be required. 442 4. METHODS 443 A. High-Fidelity Outage Risk Model (Hi-Fi ORiM) 444 Researchers have developed probabilistic and machine-learning models to predict power outages 445 during a storm [ 51,65,76,77], but these models only provide point estimates of the total number 446 of outages in a city and cannot quantify the risk at the component level for a power grid. Another 447 stream of literature has focused on developing synthetic grids to model the risk of natural disasters 448 to power networks as individual components have different hazard-dependent failure probabilities 449 [2,31,38,78]. We developed a high-fidelity outage risk model (Hi-Fi ORiM) for the power network 450 to model hurricane risk to individual components ( e.g., poles, solar panels) at the residential level. 451 Our Hi-Fi ORiM was further employed to analyze mitigation strategies, such as rooftop solar 452 adoption and undergrounding power lines. 453 For this Hi-Fi ORiM model, we used open-source data for Absecon City, New Jersey, to create 454 a power network. We calibrated our Hi-Fi ORiM against outages recorded during Hurricane 455 Isaias (2020) by PowerOutage [ 72], as shown in Figure 2. To develop the synthetic power grid, we 456 calculated an average distance of approximately 60 meters between power poles, based on the 457 limited number of poles available in OpenStreetMap data [ 46]. We then used the roads shapefile 458 from New Jersey Geographic Information Network (NJGIN) [ 47] to place poles along five classes of 459 17 roads: US highways, state highways, county routes, other county roads, and local roads, excluding 460 ramps and inaccessible roads (see Supplementary Text S1, Table S1, and Figures S1-S2). For 461 simplicity, poles were positioned along the center of roads, and for two-way roads ( e.g., US and 462 state highways), poles were placed on only one side. We obtained the locations of 2,640 residential 463 buildings from New Jersey parcel data [ 48] and assigned each building to the nearest pole like a 464 typical radial power grid. The power network can be represented as a graph G= (N,E)where 465 poles and houses are nodes ( N), and connections between poles and from pole to houses are edges 466 (E). 467 We captured the damage to the power distribution network through failures of power poles from 468 hurricane winds. The probability of pole failure is hazard-dependent, i.e., it changes with the wind 469 and is represented through fragility curves for different classes of the pole (Figure 5c) obtained 470 from [28]. The fragility curves are represented as. 471 Pf(w) =ϕln(w/¯w) β (1) where Pf(w)is the probability for an observed 3-second wind gust of w ms−1,ϕ(.)is the standard 472 normal distribution, ¯wis the median of fragility curves ( i.e.,Pf(¯w) =0.5), and βis the dispersion 473 parameter. The parameters ¯wand βvary depending on the class of the pole (Figure 1). 474 We used the tropical cyclone model from [ 27] to determine axis-symmetric winds and the 475 background wind model from [ 53] to capture the complete wind structure. The historical hurricane 476 paths are available from IBTRACS [ 79]. In [ 80], the authors emphasized the importance of using a 477 complete wind structure, as excluding the background winds can result in underestimating wind 478 hazards and, hence, an underprediction of risk from wind storms. 479 In [31], authors calibrated their synthetic grid for the poles of age 50 and 60 as most of the power 480 distribution systems in the United States are old. We assumed a uniform age of 50 years for all 481 poles in our synthetic power distribution network. We iteratively selected the class of pole for each 482 road class from the seven available pole classes (Figure 1). We performed 1.6×106iterations to 483 select the combination of the class of poles, which minimizes the error on the predicted percentage 484 of outages for our synthetic network with actually observed outages. 485 We obtained the observed percent of customers without power from PowerOutage[ 72]. Power- 486 Outage reports the outages for all types of customers, including industrial, commercial, and 487 residential. Since our focus is on residential customers, we matched only the percentage of cus- 488 tomers without power, not the total number of customers affected. Additionally, PowerOutage 489 compiles reports from utilities, which can delay the reporting of power outages [ 72]. Therefore, we 490 assume that maximum outages occur initially and persist until a reported decrease in outages. To 491 simulate total outages, we disconnected the network at identified failure points, forming subnet- 492 works. Since transmission networks are predominantly aligned with US highways, we assumed 493 that the subnetwork along the highway with the highest node density would retain power. The 494 proportion of outages is given as. 495 Oratio=∑Nb i=1boi Nb(2) where Oratiois the percent of customers without power, boi=1, if a building is disconnected 496 from the supply network otherwise 0, and Nbis the total number of buildings. The final selection 497 for pole classes was as follows: US and State Highways were assigned class 7 poles; County Routes 498 were assigned class 3 poles; Other County Roads were assigned class 4 poles; and Local Roads 499 were assigned class 5 poles. 500 18 We further calibrated our synthetic power distribution network to model the recovery time 501 of failed poles. We assumed that repair teams would initially focus on main highways before 502 progressing to local roads for restoration. In a radial network, this top-down strategy is expected to 503 facilitate the rapid recovery of many buildings [ 62]. Therefore, we assigned recovery times to poles 504 based on their distance from the US highway, formulated as follows. 505 trecovery =f loord 100 ∗t1+t2 (3) where f loor(.)is the greatest integer operator, dis the distance of a pole from the US highway in 506 meters normalized with 100 meters, t1follows a truncated normal distribution as t1∼N(¯t1,¯t1/2), 507 t1∈(0, 2¯t1), and t2represents an initial time to start any repair work which also follows a truncated 508 normal distribution, t2∼N(¯t2,¯t2/2),t1∈(¯t2/2, 3¯t2/2). We calibrated ¯t1and ¯t2to represent the 509 recovery during Hurricane Isaias (2020) (Figure 2b). Since recovery time depends on the number of 510 failed poles, which is influenced by wind gusts in future hurricanes and constrained by the limited 511 number of crews [62, 81], we scale ¯t1 for a future hurricane, given as. 512 ¯t1 future =wfuture·¯t1 wIsaias(4) where wfuture is the 3-second wind gust of a future hurricane, and wIsaias is the observed 3-second 513 wind gust during hurricane Isaias. 514 We performed 400 uncertainty simulations for the scenario of hurricane Isaias and 4×105515 uncertainty simulations for future hurricanes. Each simulation begins by disconnecting the failed 516 poles. Based on the recovery times, poles are reconnected to their original adjacent poles every 517 hour. The simulation continues until all nodes in the original power network are fully restored. 518 B. Future Hurricanes 519 The 5,018 landfalling synthetic hurricanes chosen for this study consider the current climate 520 scenario according to the National Center for Environmental Prediction (NCEP) reanalysis [ 44]. 521 The synthetic storm generative model involves three steps: random seeding for storm genesis, a 522 beta-advection tropical cyclone motion model, and storm development based on environmental 523 factors [44]. We show tracks of all synthetic hurricanes in Figure 5a. 524 On average, 3.38 landfalling hurricanes are expected per year in the US. Thus, the simulation of 525 5,018 hurricanes corresponds to 1,485 years. We perform Monte-Carlo Simulations (MCS) [ 82] to 526 determine the number of hurricanes in a year, assuming their occurrence follows a Poisson process, 527 given as. 528 P(k) =e−λλk k!(5) where λ=3.38/yris average hurricane occurrences per year, and P(k)is the probability of 529 knumber of hurricane events in an year. We randomly sampled without replacement yearly 530 hurricanes for 1485 years from the dataset of 5018 synthetic storms. We represent a typical year 531 with an average of 1485 years of simulation. 532 C. Solar Analysis 533 We used global horizontal irradiance (GHI), measured in watts per square meter units, to determine 534 the total solar potential. We obtained the historical GHI from the National Solar Radiation Database, 535 maintained by the National Renewable Energy Laboratory (NREL), which provides data at a spatial 536 19 resolution of 4km×4kmand a temporal resolution of 30 minutes [ 29]. We averaged GHI over a 537 window of 1 hour to obtain hourly GHI and used nearest-neighbor interpolation to assign GHI 538 to the solar energy-generating building. While historical irradiance is available for the case of 539 Hurricane Isaias (2020) scenario to measure the solar potential, we used solar irradiance for future 540 hurricanes introduced by [26], which is given as. 541 Ih=¯I×ef(R,C)(6) where Ihis the reduced irradiance during hurricane, ¯Iis the spatiotemporal average of the 542 observed irradiance, and f(R,C)≤0, is a function of the ratio of hurricane’s distance-to-site ( d) 543 to the radius-of-closed-isobars ( ROCI ) where winds are zero, R=d/ROCI and category of a 544 hurricane ( C) as can be found in [ 26]. To calculate ¯I, we used the GHI at an hourly scale during 545 2020 and averaged the irradiance at each hour across all days to get the ¯Ifor a typical 24-hour day 546 of a month. We determined the start of outages and corresponding solar irradiance ( Ihin Eq. 6) by 547 using the location and month of the year for each hour from each synthetic hurricane’s genesis to 548 its complete dissipation. 549 To share solar energy in ECs, we clustered houses using a k-means clustering algorithm based 550 on Euclidean distance, with the average cluster containing ten houses. Refer to Supplementary 551 Figure S8 for the distribution of the number of houses clustered together for energy sharing. We 552 assumed that 50% of households in a cluster adopt solar panels and behind-the-meter batteries. 553 Like overhead power lines, solar panels installed on rooftops are exposed to high hurricane winds. 554 In [35], authors developed data-driven fragility models of the rooftop-mounted panels to the 555 hurricane winds, represented similar to Eq. 1 with parameter ¯w=80m/sand β=0.32. 556 We ran simulations for any pole and solar panel failures in our Hi-Fi ORiM during a hurricane. 557 If a solar panel is damaged during a hurricane, only behind-the-meter power will provide the 558 power during the disaster, and there will be no solar generation for that panel. Energy sharing 559 during a disaster is only possible if none of the poles connecting houses in a cluster are damaged 560 and all houses belong to the same disconnected subnetwork; otherwise, houses with solar panels 561 will use the generated solar power for themselves. Also, during an emergency, households with 562 solar panels first consume energy for themselves and then share any surplus equally with those 563 without solar panels. We assume a simple power balance for household energy sharing but with 564 no transmission losses [ 81]. Our study does not consider the failure of lines due to overloading 565 [18], which could be addressed in future studies. Figure 8 outlines our approach to determining 566 generated power. 567 20 Fig. 8. Framework for evaluating the available energy during and after the hurricane. First, we get the cloud cover and winds during a hurricane. Then, we determine any failures for power poles and solar panels from hurricane winds. Finally, we determine the generated power during and after a hurricane. We assumed that solar panels are made of standard crystal silicone with an energy conversion 568 efficiency of 19% [33]. Further energy conversion losses could happen due to system losses such as 569 soiling, shading, and wiring issues. Hence, we considered further system losses of 14%. Finally, 570 we considered an AC-to-DC conversion ratio of 96%. Thus, the total available solar energy was 571 calculated by multiplying GHI with the energy conversion efficiency, system losses, and AC-to-DC 572 conversion factors [58]. 573 For this study, rooftop solar panels are sized according to the net-zero energy efficiency crite- 574 rion, meaning the total energy generated equals the total consumption by a house [ 25]. We also 575 constrained the panel size not to exceed the roof area. We used open-source building footprints 576 provided by Microsoft [49] to determine roof size. 577 Real-time electrical energy consumption data for individual buildings is not publicly available. 578 However, NREL’s ResStock provides a simulated dataset for thousands of residential load profiles 579 across various climate zones in the United States [ 83]. These datasets are validated against the US 580 Energy Information Administration’s survey on residential energy use [ 84]. The load profile dataset 581 includes buildings with different floor areas, construction years, and types, such as single-family 582 and multi-family residential buildings. 583 We considered diverse load profiles since larger households typically consume more electricity, 584 and newer houses might use electrical heating systems, unlike older buildings that often have 585 gas-fired heaters [ 85]. The load profiles are available at the census block level across the US. Since 586 our study focuses on residential buildings, we filtered the load profiles for single-family and 587 multi-family residences based on census block, building area, and construction year. We obtained 588 the construction year and number of floors from parcel data [ 48] and multiplied the number of 589 floors by the building footprints [ 49] to calculate the total floor area for each building. Based on the 590 different combinations of building floor area and year of construction (see Supplementary Text S2, 591 Tables S2-S3, and Figure S6), we obtained consumption profiles for our synthetic grid at an hourly 592 scale for a typical year. 593 21 D. Undergrounding 594 Undergrounding power lines can be an effective resilient strategy to hurricanes, as it reduces the 595 exposure to the high hurricane winds [ 2,39,81]. Thus, we also investigated underground energy 596 as an alternative to adopting solar energy to reduce cascading power blackouts during extreme 597 weather events. We assume that 50% of the power lines to estimate the resilience gained with the 598 undergrounding strategy. To understand the effect of undergrounding, we assume that power 599 poles are undergrounding in the decreasing order of vulnerability. For example, class 7 poles are 600 the most vulnerable (Figure 1), so they are assumed to be underground first. The poles for the 601 underground power lines were assigned a zero probability of failure, i.e.,Pf(w)→0in Eq. 1. The 602 failure analysis and recovery for the rest of the poles in the synthetic grid are similar to methods 603 for the synthetic grid without undergrounding. 604 E. Economics of resilience 605 Net present value (NPV) has been used to define prosumers’ profitability. A positive NPV represents 606 profit, while a negative NPV represents prosumers’ losses. NPV is presented as. 607 NPV =−I0+Li f etime ∑ n=1CF (1+r)n (7) where I0is the initial investment to install solar systems and behind-the-meter battery, the 608 project’s lifetime is 25 years, ris the discount rate, and CFis the cash flow, which is given as. 609 CF=−CO&M−CD−CB(n=11or n=21) +Ppv→sel f×Cgrid +Ppv→grid×Cnet−meter+Ppv→local×Clocal +Plocalpurchase ×(Cgrid−Clocal) +Davoid−outage×Coutage (8) where CO&Mis the cost of operation and maintenance, CDis the cost of damage determined 610 by multiplying the average failures of solar panels multiplied by the cost of installing a new 611 panel, CBis the cost of behind-the-meter battery assuming lifetime of a battery is 10 years, Ppv→sel f612 is the self-consumption of solar power, Ppv→local is the surplus power sold locally, Ppv→gridis 613 the surplus power sold to the grid, Plocalpurchase is the locally purchased electricity at the time of 614 underproduction of solar power, Cgridis the cost of grid purchased electricity, Cnet−meter in the 615 incentive from net-metering, Clocalis the local sell price of electricity, Davoid−outage is the duration 616 of avoided outages, and Coutage of avoided outage. Thus, Ppv→sel f×Cgridrepresents the savings 617 from self-consumption, Ppv→grid×Cnet−meter+Ppv→local×Clocalrepresents the incentives from P2P 618 sharing and net-metering, and Plocalpurchase ×(Cgrid−Clocal)represents the savings by avoiding to 619 the purchase the electricity from grid. We studied the case of ECs and non-ECs to understand the 620 profit of selling electricity locally. 621 We calculated the cost of avoided outages by considering the reduction in outage duration with 622 the adoption of solar panels in an average year (out of 1485 years of simulation). For our analysis, 623 we determined the value of resilience by calculating the cost of an alternative energy source: a 624 rental diesel generator. Consequently, the reduction in outages is rounded up to the nearest integer 625 number of days to determine the rental cost for the diesel generator as the cost of avoided outages. 626 We also evaluate the NPV for the community in the solar EC community and undergrounding 627 configurations of our synthetic power grid. We assume that the customers bear the cost of under- 628 grounding, and the utility recovers the cost over the next 25 years. Thus, we distribute the cost 629 22 to the customers uniformly based on their annual consumption to their electricity bills, which is 630 represented as. 631 ∆Cgrid,underground =Iunderground −Ounderground 25×∑Pconsumer(9) where ∆Cgrid,underground is the increase in the bill of consumers, Iunderground is the cost of under- 632 grounding, Ounderground is the reduction in operation cost of power lines after undergrounding, and 633 Pconsumer is the energy consumption of each consumer. We use Eq. 7 and cash flow for underground- 634 ing is. 635 CFunderground =−(Cgrid+∆Cgrid,underground )×Pconsumer +Coutage ,underground (10) where Coutage ,underground is the cost of saved outages with the undergrounding risk mitigation 636 strategy. We also compute the net cash outflow (NCF) for the mitigation strategies of solar EC and 637 undergrounding. For undergrounding, NCF is given as. 638 NCF underground = (Cgrid+∆Cgrid,underground )×Pconsumer (11) For solar EC, NCF is different for prosumers and consumers. For a prosumer, NCF is given as. 639 NCF solar ,prosumer =I0+CO&M+CD+CB(n=11or n=21)−Ppv→sel f×Cgrid −Ppv→grid×Cnet−meter−Ppv→local×Clocal +Plocalpurchase ×Clocal+Pgridpurchase ×Cgrid (12) NCF for a consumer is given as. 640 NCF solar ,prosumer =Plocalpurchase ×Clocal+Pgridpurchase ×Cgrid (13) The Supplementary Text S3 provides all cost details used in the above calculations. Supplemen- 641 tary Figure S10 presents an example of generated energy, energy sold, and energy purchased from 642 the grid for a house with solar panels. 643 DATA AVAILABILITY 644