The Wilkes Center for Climate Science & Policy

Team Members

• Marli Bain, (undergraduate, electrical engineering)
• Mahdis Borhani (graduate, metropolitan planning, policy & design)
• Johnathon Rodriguez (undergraduate, electrical engineering)
• Marcellus Serge-Kevin (graduate, electrical engineering from Université Côte d’Azur)

Summary (Generated with the help of AI)

The S.P.A.R.K. (Sustainable Power & Affordable Rate Kickstart) proposal addresses grid instability caused by aging transmission infrastructure, rising peak demand, and increasing climate-driven shocks. Over 70% of U.S. transmission lines are more than 25 years old, and traditional upgrades can take 7–10 years and cost up to $1 billion. At the same time, renewable energy integration reduces available spinning reserve, making the grid more vulnerable to frequency and voltage imbalances that can trigger widespread outages.

S.P.A.R.K. proposes a distributed, neighborhood-level Community Energy System (CES) that integrates battery storage with inertia-based flywheels. Flywheels provide instantaneous frequency and voltage stabilization, replicating spinning reserve traditionally supplied by fossil-fuel generators, while batteries perform peak shaving and load management. Together, they enhance grid flexibility, reduce outage risk, and improve power quality. A distinguishing feature of the proposal is its integration of technology, community engagement, and policy reform. Real-time monitoring via CT clamps and microcontrollers feeds into a mobile app that aggregates anonymous load data into a community index. Neighborhoods opt into the program and compete to reduce peak demand, using gamified incentives to drive behavioral demand response.

• Financial feasibility is supported by the Brooklyn-Queens Demand Management case, where a $69.9 million non-wires portfolio replaced a $1 billion substation project, generating net benefits. S.P.A.R.K. uses city capital investment, neighborhood subscriptions, and eventual utility participation to fund deployment, followed by energy dividends to participants.
• To scale, the proposal recommends Public Utility Commission reforms allowing regulated returns and performance incentives for non-wires alternatives. The model is modular, transferable across cities, and prioritizes deployment in vulnerable communities to improve affordability, resilience, and equity.
• Focus Within the Energy/Climate Sector: Grid modernization and stabilization - Distributed energy resources (DERs); Battery storage and inertia-based flywheel systems; Demand-side management and peak load reduction - Regulatory reform for non-wires alternatives; Renewable integration and emissions reduction
• Geographic Scope: Designed for city-level implementation
• Addressing Challenges Faced by Vulnerable Communities: Affordable Energy: Energy dividends from CES participation lower rates after city ROI is repaid (pp. 19–20, 38).
• Public Health: Reduced outages prevent food spoilage and medical device failures in low-income households (pp. 24, 38).
• Community Resilience: Targeted deployment in high-risk areas improves reliability without waiting for costly transmission upgrades.
• Economic Growth: Non-wires alternatives defer billion-dollar infrastructure, reduce ratepayer burdens, and enable positive municipal ROI (BQDM case, pp. 21, 35). Through distributed stabilization, behavioral engagement, and regulatory reform, S.P.A.R.K. reframes grid modernization as both a technical and social innovation, prioritizing equity alongside reliability and climate readiness.

Team Members

• Isabella DeBoer (undergraduate, computer science)
• Avery Hewitson (undergraduate, environmental & sustainability studies)
• Delia Leonard (undergraduate, computer science)
• Bode Packer (undergraduate, computer science)
• Jaxon Smith (undergraduate, computer science)

Summary (Generated with the help of AI)

“Slow Your Scroll” is a proposed third-party app that tackles a hidden climate problem: wasted data from short-form video platforms. The team argues that “wasted data = wasted energy,” noting that about 60% of data downloaded to phones goes unused, largely due to infinite scroll and videos skipped within seconds. Because platforms like TikTok serve billions of users through energy-intensive data centers, even small reductions in unnecessary data transfer could translate into meaningful energy and emissions savings.

Their innovation combines three elements not currently unified in one tool: data-buffer minimization, screen-time management, and environmental impact feedback. The app would limit preloaded media, interrupt scrolling with prompts, display energy saved, and set usage caps. Feasibility is supported by existing screen-time apps with large user bases and proven data-saving functionality.

For implementation and scale, the team proposes a consumer-facing app targeting at least 50,000 users, projecting large aggregate energy savings, while advocating for broader, systemic adoption to ease infrastructure strain. By reducing demand on data centers—particularly in places like Brazil and Malaysia—the solution aims to lower water and energy burdens on nearby communities.

Energy/Climate Focus

Reducing energy demand and carbon emissions from data centers by minimizing unnecessary mobile data transfer

Geographic Scope

Global user base (e.g., TikTok’s 1.9 billion users) with specific attention to impacts near data centers in Brazil and Malaysia

Addressing Challenges for Vulnerable Communities

• Reduces water and electricity strain from data centers located near vulnerable communities.
• Mitigates infrastructure pressure and associated environmental harms.
• Promotes more equitable energy use by lowering systemic demand at scale

Team Members

• Caleb Black (graduate, electrical and computer engineering)
• Micah Black (undergraduate, electrical engineering)nEthan Gallup (graduate, chemical engineering)
• Turan Mammadli (graduate, chemical engineering)
• Pouya Sheikhhosseini (graduate, chemical engineering)

Summary (Generated with the help of AI)

This team addresses a growing disconnect: data centers are expanding rapidly in Utah, but they operate as isolated, high-energy facilities rather than integrated community assets. As a result, communities miss opportunities to reuse waste heat, conserve water, stabilize grids, and lower energy costs.

Their innovation reframes data centers as quasi-utilities. In urban areas, they propose capturing waste heat from a 10 MW data center and supplying it to nearby mixed-use developments through district heating, enabled by liquid-to-liquid heat exchangers and a Heat Purchase Agreement (HPA). In rural towns, they propose “load shaping,” where a data center dynamically ramps power use up or down to flatten electricity demand, avoiding grid upgrades and reducing rates.

Feasibility rests on stacked financial incentives (e.g., WattSmart programs), cooperative utility contracts, and existing policy mechanisms. In the urban case, incentives reduce payback from over 24 years to roughly 3 years, making projects financeable.

Implementation would begin with pilot integrations in both urban and rural Utah, using telemetry, smart-grid controls, and formalized heat and power agreements. The model scales by leveraging existing utility programs and replicating contractual frameworks across municipalities.

Sector Focus

• Energy efficiency, grid optimization, district heating, and water-smart cooling within the energy/climate sector.

Geographic Scope

• Urban and rural Utah, with statewide applicability.

Challenge Addressed for Vulnerable Communities

• Affordable Energy: Load shaping spreads fixed grid costs, lowering electricity rates in small towns.
• Public Health: Replacing natural gas heating with recovered waste heat reduces emissions and improves air quality.
• Economic Resilience: Avoided grid upgrades defer major capital costs for municipalities.
• Water Conservation: Shifting cooling methods saves millions of gallons annually.

Team Members

• Diya Mandot (undergrad, computer science)
• Rishabh Saini (undergrad, computer science)
• Raphael Meyer (graduate, geography – Université Côte d’Azur)

Summary (Generated with the help of AI)

VoltVault addresses a costly flaw in today’s electricity system: demand spikes in the evening drive prices from roughly $30–50/MWh to as high as $200–500/MWh, forcing utilities to fire up expensive, high-polluting “peaker” gas plants. At the same time, millions of electric vehicles (EVs)—each with a large battery—sit parked 95% of the time.

VoltVault’s innovation is to turn these idle EVs into a distributed “virtual power plant.” Using brand-agnostic aggregation, AI-driven peak forecasting, and real-time optimization, the platform coordinates thousands of vehicles to discharge small amounts of power (5–10 kW each) back to the grid during peak stress. Unlike hardware-specific pilot programs, VoltVault is a software coordination layer designed to scale from city to national levels.

Feasibility is demonstrated quantitatively: replacing a 100 MW gas peaker plant for a two-hour peak would require about 10,000 EVs—well within reach as EV adoption accelerates.

Implementation involves detecting grid stress, dispatching enrolled bi-directional chargers, aggregating distributed output to avoid peaker activation, and compensating drivers through an app interface. As EV adoption grows, capacity scales automatically.

Sector Focus: Grid-scale energy storage, vehicle-to-grid (V2G), virtual power plants

Geographic Scope: Designed for city → state → national scale

Community Impact: Lowers peak electricity prices, reduces reliance on fossil-fuel peaker plants, improves grid resilience, and creates income opportunities for EV owners—supporting affordability, cleaner air, and community stability

Team MembersnnnBraden Howe, (Chemical Engineering, Biochemical Engineering Emphasis)nnnAdam Stringham, (Chemical Engineering, Minor in Nuclear Engineering, Chemistry)nnnGretchen Harris, (Chemical Engineering, Biochemical Engineering Emphasis)nnnAlexandra Niederhauser, (Chemical Engineering, Biochemical Engineering Emphasis)nnnRyunosuke Hattori, (Chemical Engineering, Minor in Nuclear Engineering)nnnSummary (Generated with the help of AI)n“The New Nuclear” (ChEnergy) proposes accelerating the clean energy transition by replacing aging coal plants with Small Modular Reactors (SMRs), a new generation of compact nuclear reactors. The problem they address is that fossil fuels still dominate electricity generation, even though renewables are expanding. Coal remains less reliable and more carbon-intensive, while nuclear provides high, steady output with far fewer emissions.nTheir innovation is the use of modular, “stackable” nuclear reactors that can be added over time like building blocks. SMRs are smaller, use advanced TRISO fuel that resists meltdown, rely on passive safety systems, and can operate 24/7 with a high reliability rate. Unlike traditional large nuclear plants, SMRs can be transported by truck or plane and deployed in rural or disaster-affected areas.nThey argue feasibility based on improving public support for nuclear energy, supportive state and federal policy reforms, and proven reactor reliability data. Implementation would begin with pilot plants—potentially in Rocky Mountain states—replacing coal infrastructure and scaling by adding modules as demand grows. Long term, they envision nationwide deployment to reduce carbon emissions, power AI data centers, provide district heating, and desalinate water.nSector FocusAdvanced nuclear energy (Small Modular Reactors) within the clean energy transition.nGeographic ScopeInitial pilots in Rocky Mountain states; scalable across the United States, with potential broader replication.nChallenge for Vulnerable CommunitiesThe proposal addresses energy reliability and resilience, particularly in rural and disaster-prone communities (e.g., Lahaina after wildfire). SMRs could provide stable electricity, emergency power, desalinated drinking water, and district heating—improving affordable access, public health, and long-term community resilience.n

Team MembersnnAlexis Throop (PhD, Mechanical Engineering)nSangshin Park (PhD, Computer Science & Engineering)nSiva Viknesh (PhD, Mechanical Engineering)nnSummary (Generated with the help of AI)nECO-Grid-ε’s solution addresses a growing problem: AI-driven data centers are rapidly increasing electricity demand—projected to reach 9% of total U.S. energy consumption by 2030. Individual AI facilities require hundreds of megawatts, placing stress on power grids, increasing carbon emissions, and raising costs, especially during peak hours.nTheir innovation, ECO-Grid-ε, is a grid-interactive optimization strategy that treats data centers as flexible energy users. Instead of operating with fixed or price-only routing, it co-optimizes AI workload distribution and energy management across the edge–cloud continuum. By leveraging temporal flexibility (delaying non-urgent tasks) and spatial flexibility (routing jobs to different locations), the system reduces peak demand and carbon intensity simultaneously.nFeasibility is demonstrated using real 2023 grid data from Salt Lake County and ε-constraint optimization (Pyomo/IPOPT), achieving a 1.5 MW peak reduction, 6–7 tons of carbon reduction, and costs within 1% of optimal—with no service degradation.nImplementation begins with a Utah pilot: integrating ECO-Grid-ε as a scheduling layer, evaluating results, and aligning with utility demand-response programs. The model can scale across fleets of data centers, supporting sustainable AI growth without expanding fossil generation.nSector FocusnnEnergy–AI intersectionnGrid flexibility, demand response, carbon-aware computingnData center energy optimizationnnGeographic ScopennUtah pilot (Salt Lake County)nDesigned for regional and national scaling across data center fleetsnnAddressing Challenges for Vulnerable CommunitiesECO-Grid-ε reduces peak demand, which lowers reliance on expensive peak generation and reduces infrastructure expansion costs. Because peak-driven price volatility disproportionately burdens low-income households, stabilizing system costs can reduce energy cost pressure and improve affordability. The proposal also suggests channeling grid-flexibility incentives toward community energy assistance programs, directly linking AI infrastructure benefits to household resilience.n

Team MembersnnAmelia WaldennJames ChisholmnJulie SimonnnSummary (Generated with the help of AI)nThe team “Repurposing Nuclear Waste,” addresses two major problems: nuclear power plants waste roughly 70% of their thermal energy, and spent nuclear fuel is treated primarily as a long-term disposal challenge rather than a usable resource. They argue that nuclear energy is reliable and low-carbon but not optimized to its full potential.nTheir innovation is a “full resource utilization” model. Instead of producing electricity alone, they capture waste heat from reactors—such as Natrium HALEU systems—and redirect it to industrial users and large-scale greenhouses. At the same time, they propose recycling radioactive waste using advanced isotope separation (e.g., plasma enrichment) to recover reusable fuel (97.3%), precious metals, and medical/industrial isotopes. This reframes nuclear waste as a high-value asset.nThey argue feasibility through existing reactor infrastructure, available heat demand (industrial clusters and greenhouses), government incentives covering up to 20–60% of heat infrastructure costs, and strong projected revenues (up to ~$100M/year at full buildout). Implementation occurs in three phases: initial industrial heat integration, cluster expansion with shared heat loops and district heating, and full thermal monetization with long-term heat contracts. A Wyoming case study demonstrates food production, CO₂ reductions (~0.8 million metric tons/year), and energy cost savings with profitability projected within 4.5 years.nEnergy/Climate FocusnAdvanced nuclear energy optimization: waste heat recovery, nuclear fuel reprocessing, industrial decarbonization, and greenhouse-based food production.nGeographic ScopenInitial case study in Wyoming (U.S.), with applicability to existing nuclear plants nationwide.nAddressing Vulnerable Community ChallengesnnAffordable energy: Lower industrial and heating costs through nuclear heat reuse.nFood insecurity: Greenhouse production (~40,000 metric tons/year) and canning surplus produce to feed ~55,000 people annually.nEconomic growth: Estimated $38B+ annual reprocessing value and regional industrial development.nCommunity resilience: Reduced fossil fuel dependence and significant CO₂ reductions.nn

Team MembersnnnWilliam Lee (Chemical Engineering + Chemistry)nnnSathya Tadinada (Computer Science + Applied Math)nnnColeman Rohde (Physics + Mathematics)nnnIsaac Middlemas (QAMO)nnnMartin Keiser (Civil Engineering)nnnSummary (Generated with the help of AI)nRetroActive addresses widespread home energy inefficiency in the U.S., where millions of houses are under-insulated and homeowners face high upfront retrofit costs and confusing information. These inefficiencies drive higher energy bills, greater CO₂ emissions, and added strain on the electric grid.nTheir innovation is a no-cost, data-driven retrofit recommendation tool that combines residential inputs (ZIP code, utility bills, heating type) with geographic and financial modeling to rank the most cost-effective clean energy upgrades. Unlike approaches based on Levelized Cost of Electricity (LCOE), they use Net Present Value (NPV) to estimate return on investment over time, giving homeowners clearer, shorter-term financial insight. Sensitivity analyses show how solutions (e.g., solar vs. geothermal) vary by location.nFeasibility is grounded in established financial methods, capacity utilization metrics, and planned integration of NOAA weather data and physics-based power modeling. Implementation would occur via a backend Python analytics engine with optional PDF bill uploads. For scaling, they propose partnerships with government agencies (e.g., FHA/DOE tools) or a nonprofit model charging low-cost reports while reinvesting in model improvement.nnEnergy/Climate Focus: Residential energy efficiency and clean energy retrofits.nGeographic Scope: United States, with region-specific modeling (e.g., NYC vs. Salt Lake City).nChallenge Addressed: Improves access to affordable energy by reducing household energy bills, lowering emissions, and strengthening grid resilience—particularly beneficial for cost-burdened and climate-vulnerable communitiesnn

Team MembersnnHailey WattsnCheima HachaninMeghan TrippanynnSummary (Generated with the help of AI)nThis project addresses two escalating urban challenges: rising energy demand and growing waste accumulation. The team proposes using fungi—specifically mycelium—to break down organic and textile waste and convert it into electricity through fungi’s bioelectrochemical properties. Rather than treating waste as a liability, their system feeds waste into a controlled fungal propagation process, where decomposition releases energy that can be captured, forming a sustainable, circular cycle.nThe innovation is unique because it combines waste degradation and decentralized energy production in one biological system. It targets materials ranging from food scraps to plastics, clothing, and even toxic pollutants, while producing usable outputs such as biofuels and energy.nFeasibility is grounded in fungi’s natural decomposition abilities and established uses of mycelium in biofuels, insulation, and materials. The team proposes implementation at the local level: converting community waste streams into decentralized energy systems to improve reliability, climate resilience, and accessibility. Scaling would occur by replicating modular systems in cities, especially where waste is abundant and energy access is limited.nEnergy/Climate Sector FocusnnWaste-to-energy systemsnBioelectrochemical energy generationnCircular economy solutionsnDecentralized renewable energynnGeographic ScopennUrban communitiesnEmphasis on cities facing high waste and energy demandnParticularly relevant for vulnerable communitiesnnChallenge for Vulnerable Communities AddressednnProvides affordable, local energy from wastenReduces environmental and health harms from textile and organic wastenStrengthens climate resilience through decentralized power systemsnSupports economic sustainability by shifting from a linear to a circular modelnn

Team MembersnnAli Tasavvori (PhD, ECE, University of Utah)nShakiba Sedigh (MS, ECE, University of Utah)nSoroosh Noorzad (PhD, ECE, University of Utah)nnSummary (Generated with the help of AI)nResiliSolarAI is a wildfire-aware solar forecasting system designed to strengthen energy resilience as climate-driven wildfires become more frequent and costly. The team addresses a growing but overlooked problem: wildfire smoke can travel hundreds of miles, reducing sunlight and dirtying solar panels, which leads to significant forecasting errors and unexpected drops in solar power generation (see discussion of smoke-related forecast overestimation on page 6).nTheir innovation is unique because it combines two mechanisms in one platform: a physics-based soiling model (HSU) to estimate how particles accumulate on panels, and an AI time-series model (LSTM) to predict smoke-driven power losses. Most public solar forecasts do not explicitly integrate wildfire aerosol effects.nFeasibility comes from using publicly available national datasets (NASA POWER, EPA AQS), cloud deployment (AWS), and a web-based interface. The team proposes a Utah pilot, followed by nationwide scaling within 12–18 months, with a modest federal deployment budget and low annual operating costs.nSector FocusEnergy infrastructure resilience; wildfire-aware solar PV forecasting.nGeographic ScopePilot in Utah; scalable to all 50 U.S. states.nChallenge for Vulnerable CommunitiesBy improving forecast accuracy and quantifying energy shortfalls and bill impacts, the tool helps households and utilities prepare for smoke events, avoid unexpected energy costs, and maintain grid reliability—supporting affordable energy access and community resilience during climate extremesn

Team MembersnnAnnie de BrynSami ShibanCameron KatonAndrew VazqueznIan WhatleynnSummary (Generated with the help of AI)nWattWise addresses a central challenge in today’s energy transition: electricity demand never stops, but renewable sources like wind and solar are weather-dependent and variable. When wind stalls or sunlight fades, grids often rely on faster, more polluting or expensive alternatives. Because the optimal energy mix changes by location, season, and time of day, using the wrong combination wastes money and increases environmental harm.nTheir innovation is a decision-support dashboard for governments and energy companies that identifies the most cost-effective and environmentally responsible mix of energy sources for a given location. By integrating electricity data, levelized cost research, and real-time weather inputs, the tool helps users anticipate seasonal changes, prevent cost spikes during peak demand, and support infrastructure and policy decisions.nFeasibility is grounded in publicly available, credible datasets (U.S. Energy Information Administration, Lazard LCOE+, Weather API; page 6). Implementation would begin as a data-driven dashboard for utilities and regulators, with scaling achieved by expanding geographic coverage, integrating additional datasets, and tailoring outputs for regional energy planners and policymakers.nEnergy/Climate FocusnnGrid optimization and energy mix planningnCost reduction and emissions minimizationnRenewable integration and energy system stabilitynnGeographic ScopennLocation-specific modeling; initially U.S.-based datasetsnDesigned to be adaptable to different regionsnnChallenge for Vulnerable CommunitiesWattWise helps reduce energy cost volatility and inefficient infrastructure investment. By preventing cost spikes and guiding smarter long-term planning, it can support affordable electricity, reduce pollution-related health risks, and improve resilience—especially for communities that are disproportionately affected by high energy costs and unreliable grids.n

Team MembersnnCatherine DaveynNathalie PrudencionDeniz MengulluoglunMomen TakrounnKaleb Carter ShinnSummary (Generated with the help of AI)n“Wells to Watts” proposes converting abandoned oil and gas wells—of which there are roughly 3 million in the U.S.—into small-scale geothermal power plants. The problem they address is twofold: unused wells that pose environmental risks, and the need for reliable, low-carbon, locally generated electricity. Traditional geothermal projects are expensive, with up to 50% of costs tied to drilling. By reusing existing wells, the team eliminates most drilling and exploration costs while avoiding fracking-based enhanced geothermal systems.nTheir innovation is a modular, prefabricated system using commercially available small binary-cycle generators that can operate at moderate temperatures and fit on a standard freight truck. Each converted well (e.g., ~4,500 m deep, 180°C initial temperature) could produce about 199 kW—enough for roughly 140 people.nThey argue feasibility through steady-state thermal modeling, use of proven Rankine-cycle technology, minimal fuel and maintenance costs, and avoidance of new drilling. Implementation would focus on rural or low-density areas, connecting to local grids, directly powering nearby facilities, or producing hydrogen where transmission is impractical. Scaling would occur through standardized, plug-in units deployed across regions with high concentrations of abandoned wells, such as Texas.nEnergy/Climate Focus: Small-scale geothermal electricity from repurposed oil and gas wells.nGeographic Scope: U.S. focus (especially Texas, North Dakota, Louisiana, West Virginia); potential in Bolivia and ParaguaynAddressing Vulnerable Communities: Provides local, stable, low-cost electricity; reduces dependence on long-distance transmission; mitigates methane and VOC leaks from orphaned wells that disproportionately affect low-income communitiesn