Vijay Modi

Abstract The speed and scale required to transition to a clean energy grid requires an integrated approach to address challenges in variability of energy resources and grid load. This research offers novel insights into how New York State (NYS) can strategically use excess renewable energy, reframing curtailment as an opportunity to advance decarbonization goals. The focus on NYS is motivated by a combination of its substantial energy demand stemming from large population centers, sizable offshore wind energy pipeline, enormous renewable energy potential, and ambitious clean energy targets. To quantify the extent to which future renewable energy supply may exceed demand, grid load data is combined with high spatial and temporal resolution wind energy datasets that include wind speeds at hub heights relevant to modern wind turbines. A capacity expansion model estimates the levelized cost of energy and battery capacity buildout needed to meet electrification, renewable generation, and emissions reduction targets set by the NYS Climate Act through 2050. It also reports the resulting quantity and temporal variation of curtailed energy for each 5 year milestone. The study further assesses the implications of using this excess energy to fulfill requirements for emerging technologies such as green hydrogen electrolysis and carbon capture systems using a set of curtailment utilization and supply curves. These results can inform grid planning and infrastructure investment decisions aimed at reducing emissions in hard-to-abate sectors. Ultimately, this study seeks to enhance grid planning to deliver reliable, low-cost clean energy and support a fully decarbonized economy.

Electricity markets are under increasing pressure to maintain reliability amidst rising renewable penetration, demand variability, and occasional price shocks. Traditional capacity market designs often fall short in addressing this by relying on expected-value metrics of energy unserved, which overlook risk exposure in such systems. In this work, we present CapOptix, a capacity pricing framework that interprets capacity commitments as reliability options, i.e., financial derivatives of wholesale electricity prices. CapOptix characterizes the capacity premia charged by accounting for structural price shifts modeled by the Markov Regime Switching Process. We apply the framework to historical price data from multiple electricity markets and compare the resulting premium ranges with existing capacity remuneration mechanisms.

Power grid extension into hitherto unconnected areas is a key policy goal in Sub-Saharan Africa. Yet, connection and usage rates remain low in rural grid-covered areas, at least in the short and medium run. This paper provides a long-term follow-up of a large grid extension program in rural Rwanda, analyzing electricity adoption over time in a panel of 41 communities electrified up to ten years ago. Using both survey and administrative data, we find that nearly half of the households in grid-covered communities remain unconnected. Even among those directly under the distribution grid, electrification rates barely exceed 80%. Electricity consumption and appliance use are low and have not increased over time. These findings suggest that, from an economic development or cost-benefit standpoint, rural grid investments are hard to justify. Instead, rights-based arguments centered on equity and fairness may offer a more compelling - albeit more controversial - justification for such investments.

A B S T R A C T Mini-grids often face challenges with economic viability due to low and inconsistent electricity demand, when they are being developed in rural areas of Sub-Saharan Africa. This study identifies load profiles that lead to high generation costs in solar PV-and battery-powered mini-grid. The analysis is based on electricity load data from 2124 households and 21 grain mills across 25 mini-grids in Uganda. Our results identify two key load profile aspects that determine the Levelized Cost of Electricity (LCOE): 1) Peak-day to average-day load ratio – Higher peak-day electricity demand increases generation costs. 2) Daytime load fraction – Higher daytime electricity use helps to lower generation costs. Integrating productive loads from grain mills into mini-grids improves both aspects and reduces the LCOE by 23 % compared to mini-grids that serve households alone. Further cost reductions of 15 % are possible by leveraging flexible productive loads scheduling by increasing daytime load fractions. To address the high peak-day to average-day load ratio, a sizing guide is proposed: for every kWh of average daily load, install 0.5 kW of solar capacity and 1.5 kWh of effective battery storage. This sizing cannot meet all loads at all hours. It results in an average load shedding of 1.7 % (maximum 5.1 %) of energy and an average 41 % LCOE reduction compared to systems designed to meet all loads at all hours, across household-only mini-grids. For mini-grids with both household and productive loads but no flexibility allowed, this sizing guide yields an 18 % LCOE reduction with 0.6 % load shedding.

Accurately estimating household electricity expenditures is essential for assessing energy poverty and informing subsidy and affordability policies. This study combines household survey data with administrative utility billing records for households in Rwanda to examine the extent and nature of misreporting in self-reported electricity spending, using a sample size of 650 households. We find systematic over-reporting among poorer households, primarily due to mismatches between survey recall periods and the irregular, prepaid nature of electricity purchases. To correct for this bias, we adjust reported expenditures using empirical distributions of monthly purchase frequency derived from utility data. Additionally, for unmatched households, we develop predictive models based on household characteristics, though these perform less reliably in data-sparse settings. Extending the analysis to Uganda, we apply both the frequency-based correction and predictive models. Across countries, adjusted estimates suggest electricity burdens for most households lie between 0.5% and 2.5% of total household expenditure across all wealth groups. These findings underscore the limitations of relying solely on household surveys to measure electricity spending in prepaid systems. They highlight the value of integrating administrative utility data with statistical correction methods to produce more accurate and policy-relevant assessments of electricity affordability. • Surveys overstate electricity spending by 2–3X among poor households. • Poorer households purchase electricity less frequently. • Survey recall periods often fail to align with actual electricity purchase durations. • Utility billing data enable more accurate estimation of purchase frequency and correction of survey bias. • After frequency adjustment, electricity burdens account for approximately 1.5% ±1 of household budgets.

Electricity access in rural sub-Saharan Africa re-mains a significant challenge. Settlement patterns in this region exhibit considerable heterogeneity, significantly impacting distri-bution network layouts and investment costs. This paper analyzes settlement patterns within communities using a network design model that minimizes transformer count and aggregate costs of LV and intra-community MV infrastructure. The optimization results provide key network metrics for each community, including transformer counts, low-voltage wire, and internal medium-voltage wire requirements per connection. This model builds upon an established two-level network design algorithm while incorporating significant computational improvements. Tests show a two orders of magnitude reduction in computational time by relaxing the voltage drop constraint while maintaining exceptional fidelity in distribution infrastructure cost per connection. Meanwhile, the maximum input size of connection nodes that can be computed is nearly three to four times larger, reducing boundary effects. Additionally, a geospatial outlier exclusion methodology prevents community cost metrics from being distorted by spatially distant connection nodes. These improvements enable the algorithm to be applied to large-scale networks with less time, reduced segmentation requirements, and smaller effects of geospatial outliers. These enhancements allow planners to identify prioritized electrification communities and allocate resources strategically across diverse settlement patterns.

In many parts of the tropics a prolonged dry season presents an economic opportunity for farmers to grow a second crop beyond an otherwise single crop that a shorter rainy season permits. These additional second crops can ensure food security, improve nutrition and increase incomes. The first contribution of this paper is to granularly identify regions of Sub-Saharan Africa where a prolonged dry season exists. Energy planners are also keen to assess where dry-season agriculture is being currently practiced and the extent of the area cropped in the dry season. Assuming this is carried out using irrigation, this allows planners to assess the scale of water and energy needs if these practices are to be scaled. The phenological characterization of the landscape using vegetation patterns helps to identify regions where dry season irrigation is feasible. This study operationalizes an irrigation detection methodology originally applied to the Ethiopian highlands built using visually collected labels from high resolution imagery and limited ground truth data. The second contribution of the paper lies in the application of the methodology over a range of African geographies, with the exclusive use of visually collected labels. The methodology relies on the distinct phenology of irrigated crops in the dry season that differentiates them from rain-fed agriculture and evergreen vegetation. The method is applied across different countries in sub-Saharan Africa to detect smallholder plots that are as small as a tenth of a hectare. The method is found to be viable in semi-arid areas with a prolonged dry season such as Northern Nigeria and Burkina Faso. We demonstrate how humid regions such as those in Uganda with longer duration rainfall are not well suited for the methodology. This is because the short dry season does not allow sufficient time for non-irrigated vegetation to senesce making it difficult to distinguish dry-season irrigation.

Power grid extension into hitherto unconnected areas is a key policy goal in Sub-Saharan Africa. Yet, connection and usage rates remain low in rural grid-covered areas, at least in the short and medium run. This paper provides a long-term follow-up of a large grid extension program in rural Rwanda, analyzing electricity adoption over time in a panel of 41 communities electrified up to ten years ago. Using both survey and administrative data, we find that nearly half of the households in grid-covered communities remain unconnected. Even among those directly under the distribution grid, electrification rates stagnate slightly above 80%. Electricity consumption and appliance use are low and have not increased over time. These findings suggest that, from an economic development or cost-benefit standpoint, rural grid investments are hard to justify. Instead, rights-based arguments centered on equity and fairness may offer a more compelling – albeit more controversial – justification for such investments.

The speed and scale needed to transition to a clean energy grid requires an integrated approach to address challenges in variability of energy resources and grid load. This research provides novel insights into the optimal use of otherwise-curtailed energy by analyzing the impacts of deploying different levels of renewable capacity on the cost, curtailment, and emissions reductions of future grid scenarios. The focus on New York State is motivated by a combination of its substantial energy demand stemming from large population centers, sizable offshore wind energy pipeline, enormous offshore wind energy potential, and ambitious clean energy targets. The extent to which supply is projected to outpace demand is quantified by employing grid load data in conjunction with high spatial and temporal resolution wind energy datasets with wind speeds at relevant hub heights for modern wind turbines. The resulting model captures a range of possible renewable capacity buildout scenarios mirroring existing state energy policy, and reports the quantity and temporal variation of curtailed energy for each. The study further identifies the conditions under which it would be most efficient to use this excess energy to fulfill requirements for technology such as green hydrogen electrolysis, carbon capture systems, or battery storage. This information can facilitate decision-making for strategic grid integration planning, including investment decisions around infrastructure that will help decarbonize hard-to-abate sectors. This study aims to enhance grid planning that will better serve end users by providing reliable and low-cost clean energy and support the burgeoning net zero carbon economy.

Recent electrification efforts in Africa have expanded household connections, but understanding of post-connection electricity consumption and affordability challenges remains limited. This study examines consumption patterns and price elasticity among newly connected households in Rwanda, utilizing consumption and billing data from the national utility. Using both descriptive and econometric analyses, we assess trends in electricity usage and estimate price elasticity specifically for low-consumption customers. Our findings show that newly connected households, particularly in rural areas, consume substantially less electricity than longer-standing, primarily urban customers. Furthermore, with each new year, the most recently connected use even less electricity than those connected in previous years. We observe that demand growth remains stagnant, with overall increases in demand driven by new connections rather than increased consumption among existing customers. Among low-consumption households, price is inelastic, suggesting limited capacity to stimulate demand growth solely through reduced tariffs. These results underscore the limitations of tariff policies in driving electricity consumption growth and emphasize the need for targeted interventions to enhance usage, especially for economically disadvantaged households. Our study offers insights applicable to other low-income countries undergoing similar electrification initiatives, providing valuable evidence for policymakers seeking to expand access to affordable electricity and promote sustainable demand growth.

Adoption of irrigation even on a small scale requires access to suitable soils, sourcing seeds and fertilizers, meeting crop water requirements, energy and infrastructure needed for pumping water and access to markets. Therefore monitoring irrigation over time provides key insight on the effectiveness of policy decisions, infrastructure successes and more importantly, disruptions caused by events such as conflicts, the COVID pandemic etc. All of these have a direct impact on food security. However most of the irrigation maps including coarse global irrigation cropland layers are created for a certain time period and do not reflect changes in the landscape over time. This study presents a simplified alternative to a dry season irrigation detection methodology developed for the Ethiopian highlands that can be implemented using Google Earth Engine's built in classifiers to create irrigation maps for each year of interest. The original methodology and the proposed simplified approach are compared for a sample region in Oromia to discuss the trade-offs of the simplifications. It is then applied to Southern Tigray across five years from 2019 to 2023 to show how the monitoring technique can be used to visualize the effects of disruptions such as the Tigray War on irrigated agriculture and the recovery efforts after.

This research study investigates the root causes of power outages in Uganda, leveraging a unique data set obtained through a partnership with the country's electricity regulator. Covering the period from 2015 to 2022, this data set includes detailed records of each power outage, noting the duration and free-form descriptions of the incidents. The researchers used an advanced natural language processing (NLP) tool to categorize these descriptions into specific root cause groups. The findings highlight equipment failure as the most prevalent cause of power outages, suggesting a critical need to enhance or reevaluate maintenance efforts. Furthermore, the study revealed that outages resulting from vandalism and fires tend to have significantly longer resolution times, indicating areas where targeted interventions could improve response efficiency. This research sheds light on the main factors contributing to power outages in Uganda and demonstrates how NLP can be applied to quickly and accurately identify these causes. The insights gained from this study offer valuable guidance for policymakers and utility companies aiming to improve the reliability and resilience of Uganda's power grid.

Traditional cooking methods are hazardous and time- consuming, disproportionately exposing women and girls to these negative effects [2]. In rural areas, fuelwood can be gath- ered from personal trees or the surrounding landscape [2]. How- ever, urban areas lack the luxury of gathering their biomass, and commercial fuels such as LPG are expensive due to high upfront costs and lumpy recurrent payments [1]. While higher-income households can overcome these barriers, those in dense urban informal settlements find themselves in a precarious situation [1]. Measures to drive E-cooking adoption have the greatest potential to succeed in urban informal settlements because a) access to biomass is constrained, b) most consumers are connected to the grid, and c) living quarters are smaller and lack ventilation. Since informal settlements can make up a significant proportion of the urban population, investing in the transition to clean cooking in these areas may yield the highest returns on public investments.

The escalating threat of global warming has spurred an urgent need for all economic sectors to reduce anthropogenic greenhouse gas emissions. Facing this challenge, the carbon dioxide (CO 2 ) utilization assisted by green hydrogen to produce valuable hydrocarbons becomes an attractive solution for the sustainable development of the entire chemical industry. In this paper, a new conceptual design of an industrial complex has been developed, modeled, and demonstrated to simultaneously produce green hydrogen, oxygen, and various hydrocarbons based on CO 2 . The industrial complex (named FREER) includes three subsystems: (i) a Fischer–Tropsch synthesis process ( F TSP), (ii) a renewable energy-based electrolysis process ( REE P), and (iii) a reverse water–gas shift process ( R WGSP). Through effective process integration, the REEP subsystem will generate green hydrogen to support the manufacturing of both the RWGSP and FTSP subsystems. Meanwhile, the RWGSP subsystem will feed back water to help the REEP subsystem. Thus, this eco-friendly FREER complex can utilize CO 2 and renewable power to simultaneously produce hydrogen, oxygen, and various hydrocarbons, including C1 through C21 paraffins and C2–C4 olefins. The efficacy of the developed FREER complex was demonstrated via rigorous modeling and simulation. Its salient economic performance has also been comprehensively analyzed to show its excellence.

Uganda's unreliable electricity grid is a significant hurdle to its development. This paper analyzes the country's electricity situation using data from the national electricity regulator, focusing on Umeme, which supplies electricity to over 90% of the country. Our study reveals that while the median outage duration has decreased in all regions, the frequency of the outages has remained largely unchanged, except in Kampala West. Outages are now more frequent during the day rather than the evening peak demand hours, suggesting that supply constraints are not the primary cause of these outages. We identified failure of overhead equipment accessories as the most common cause of outages, with vandalism-related outages taking the longest to resolve. Additionally, our analysis reveals a connection between rainfall and increased outage intensity, underscoring the electricity grid's vulnerability to climate conditions.

Power grid extension into hitherto unconnected areas is a key policy goal in Sub-Saharan Africa. Yet, connection and usage rates remain low in rural grid-covered areas, at least in the short and medium run. This paper provides a long-term follow-up of a large grid extension program in rural Rwanda, analyzing electricity adoption over time in a panel of 41 communities electrified up to ten years ago. Using both survey and administrative data, we find that nearly half of the households in grid-covered communities remain unconnected. Even among those directly under the distribution grid, electrification rates stagnate slightly above 80%. Electricity consumption and appliance use are low and have not increased over time. These findings suggest that, from an economic development or cost-benefit standpoint, rural grid investments are hard to justify. Instead, rights-based arguments centered on equity and fairness may offer a more compelling – albeit more controversial – justification for such investments.

Space-cooling is dominating building energy use in warm regions. Integrating on-site PV generation with cooling systems is a potential building-scale decarbonization solution. However, designing the system to ensure cost-effectiveness and reliability is challenging since it requires solving a highly non-linear design and dispatch problem. This paper proposes a solution strategy to the design problem of an integrated multi-chillers system with PV, and ice thermal and battery storage to reduce emissions and annual system costs. The proposed strategy adopts a bi-level optimization approach eliminating the need for simplistic models. The upper level employs particle swarm optimization to determine storage and chillers' capacities and types, while the lower level solves the dispatch problem using mixed-integer linear programming. To validate the proposed strategy and decarbonization solution, the model was applied to a generic residential building in Qatar and was exposed to a varying range of carbon pricing. The results highlight the potential for deep decarbonization in regions with abundant solar resources and high cooling needs. In Qatar, the model suggests a moderate carbon pricing range of $75–125/ton of CO 2 for deep decarbonization. The developed model demanded reasonable computational resources with an execution time of less than 1 h and exhibited stability with consistent convergence.

Abstract While most electricity systems are designed to handle peak demand during summer months, long-term energy pathways consistent with deep decarbonization generally electrify building heating, thus increasing electricity demand during winter. A key question is how climate variability and change will affect peak heating and cooling demand in an electrified future. We conduct a spatially explicit analysis of trends in temperature-based proxies of electricity demand over the past 70 years. Average annual demand for heating (cooling) decreases (increases) over most of the contiguous US. However, while climate change drives robust increases in peak cooling demand, trends in peak heating demand are generally smaller and less robust. Because the distribution of temperature exhibits a long left tail, severe cold snaps dominate the extremes of thermal demand. As building heating electrifies, system operators must account for these events to ensure reliability.

Electrification of space heating in buildings, currently dominated by on-site fossil fuel use, will be an essential element of decarbonization. Some electrification of heating is already underway, and although state-by-state adoption is highly heterogeneous, the associated impact on the grid is already being felt. The same has been true for air-conditioning except adoption levels are generally higher. As we expect rapid adoption of electrification, a model that can seamlessly disaggregate the existing electric load into that for heating, cooling and non-thermal uses is essential. We develop a model that captures the building thermal response to quantities such as building floor areas that change over years and weather that changes through the day and through the year. Complexity of occupancy, thermostat settings, diversity in building envelopes or technologies deployed are not explicitly represented, but their effects are captured as hourly changes in the response. The model can then be used to estimate the non-thermal dependent loads. Once such a disaggregation is available, it can be used to estimate new load profiles as changes in floor area or electrified loads or weather occurrences. The model is validated against actual hourly utility loads by re-aggregating the simulated hourly loads for a single year for all load zones in NYISO (boundary aligns with New York), ERCOT (covering most of Texas), CAISO (covering most of California) and some other individual balancing authorities of California (BANC, TIDC, IID, LADWP and WALC) with mean absolute percentage errors (MAPEs) across all between 3.0% and 6.0%. The obtained model parameters are further tested by backcasting hourly load for the past 10-year period without degradation in errors, which suggest the model is promising for forecasting in the long-term. While our results bridge the gap between building level energy simulation and building stock energy prediction, all source data for the present study are extracted from open-access datasets. The model is available as an open-source tool that can be easily applied to any spatial resolution at any geographical locations, as long as load profiles and building census data are available.

While most electricity systems are designed to handle peak demand during summer months, pathways to deep decarbonization generally electrify building heating, thus increasing electricity demand during winter. A key question is how climate variability and change will affect peak heating and cooling demand in an electrified future. We conduct a spatially explicit analysis of trends in temperature-based proxies of electricity demand over the past 70 years. Average annual demand for heating (cooling) decreases (increases) over most of the contiguous US. However, while climate change drives robust increases in peak cooling demand, trends in peak heating demand are generally smaller and less robust. Because the distribution of temperature exhibits a long left tail, severe cold snaps dominate the extremes of thermal demand. As building heating electrifies, system operators must account for these events to ensure reliability.