arXiv subjects

Dharik S. Mallapragada

Publications and source records attributed to Dharik S. Mallapragada.

11 recordsLinked to original sources

arXiv2026

Clustering-enhanced adaptive Benders decomposition for energy systems planning optimization

High-resolution energy system capacity expansion models (CEMs) for energy transition planning often result in large-scale mixed-integer linear programming (MILP) formulations. Benders decomposition (BD) offers a scalable solution approach by iteratively solving a master problem (MP) for investment decisions and multiple subproblems (SPs) for operational decisions. However, accumulated Benders cuts generated by the SPs can make MP solution a major computational bottleneck. Incomplete SP parallelization can also introduce further bottlenecks when SPs exceed available CPUs. We develop clustering-enhanced BD methods to address these challenges, by using clustering to group similar SPs for: a) aggregated Benders cut construction and b) identification of representative SPs to be solved most frequently. For grouped-cuts, we examine two adaptive formulations based on dual variables and a fixed-grouping formulation based on exogenous time-series inputs. We evaluate these methods in an electricity-sector CEM across varying system sizes, temporal SP lengths, inter-SP coupling strengths represented by CO2 policy, computational resources, and stochastic settings. Relative to a benchmark regularized multi-cut formulation, adaptive grouped cuts outperform fixed grouping and provide substantial benefits under weak inter-temporal coupling. The largest gains occur in larger systems with shorter SP horizons, where the MP accounts for a greater share of runtime. Their effectiveness declines under strong inter-temporal coupling, such as annual CO2 emissions limits, where the benchmark multi-cut performs best. The representative-SP method outperforms the benchmark under limited parallelization when SP solution dominates runtime. Overall, the preferred BD strategy depends on inter-SP coupling strength and whether computational burden lies in the MP or the SPs.

math.OC↗

arXiv2026

Emissions and cost tradeoffs of time-matched clean electricity procurement under inter-annual weather variability -- case study of hydrogen production

Regulators and voluntary corporate sustainability efforts are increasingly adopting time-matching requirements (TMRs) for clean electricity procurement for large loads, such as data centers, and electricity-intensive fuel production, such as hydrogen. We use a stochastic capacity expansion model (CEM) framework to assess how inter-annual weather variability affects the cost, composition, and emissions of procurement-driven infrastructure to meet annual and hourly TMRs using the case study of a grid-connected hydrogen producer in Texas. Our approach, which relies on co-optimizing investments and hourly operations over nine weather scenarios, reveals that hourly TMR comes at a higher cost premium compared to annual TMR than previously estimated by single-scenario deterministic modeling, while emissions outcomes remain directionally consistent. Demand flexibility and partial hourly TMR (80-90%) lower the cost premium while preserving emissions benefits. We further examine how binding renewable portfolio standards (RPS) interact with TMR costs and emissions outcomes. When an RPS is applied to non-H2 electricity demand, annual TMR reduces emissions comparably to hourly TMR at a lower cost. Incorporating H2-related electricity demand directly into the RPS constraint, rather than imposing a separate TMR, achieves similar emissions outcomes at still lower cost, suggesting that TMR-based clean electricity procurement, particularly hourly matching, offers limited additional value in regions with stringent grid decarbonization policies.

eess.SY↗

arXiv2025

Decarbonization pathways for liquid fuels: A multi-sector energy system perspective

Low-carbon liquid fuels play a key role in energy system decarbonization scenarios. This study uses a multi-sector capacity expansion model of the contiguous United States to examine fuels production in deeply decarbonized energy systems. Our analysis evaluates how the shares of biofuels, synthetic fuels, and fossil liquid fuels change under varying assumptions about resource constraints (biomass and CO2 sequestration availability), fuel demand distributions, and supply flexibility to produce different fuel products. Across all scenarios examined, biofuels provide a substantial share of liquid fuel supply, while synthetic fuels deploy only when biomass or CO2 sequestration is assumed to be more limited. Fossil liquid fuels remain in all scenarios examined, primarily driven by the extent to which their emissions can be offset with removals. Limiting biomass increases biogenic CO2 capture within biofuel pathways, while limiting sequestration availability increases the share of captured atmospheric (including biogenic) carbon directed toward utilization for synthetic fuel production. While varying assumptions about liquid fuel demand distributions and fuel product supply flexibility alter competition among individual fuel production technologies, broader energy system outcomes are robust to these assumptions. Biomass and CO2 sequestration availability are key drivers of energy system outcomes in deeply decarbonized energy systems.

physics.soc-ph↗

arXiv2023

Decarbonization of aviation via hydrogen propulsion: technology performance targets and energy system impacts

The aviation sector is challenging to decarbonize since aircraft require high power and energy per unit of weight. Liquid hydrogen is an interesting solution due to its high gravimetric energy density, minimal warming impact, and low-carbon production potential. We quantify the performance targets for fuel cell systems and on-board storage to enable hydrogen-powered regional aviation. We then explore the energy infrastructure impacts of meeting this additional H2 demand in the European context under deep decarbonization scenarios. We find that minimal payload reduction would be needed for powering regional aviation up to 1000 nmi if fuel cell system specific power of 2 kW/kg and tank gravimetric index of 50% can be achieved. The energy systems analysis highlights the importance of utilizing multiple technology options: such as nuclear expansion and natural gas reforming with CCS for hydrogen production. Levelized cost of liquid hydrogen as low as 3.5 Euros/kg demonstrates pathways for Europe to achieve cost-competitive production.

cs.CE↗

arXiv2022

Decision making under uncertainty for deploying battery storage as a non-wire alternative in distribution networks

The growing demand for electricity in emerging markets and developing economies (EMDE) such as India is causing loading and congestion problems on distribution networks, particularly in urban locations, that adversely impact sustainable development and economic growth. Electric utilities in these economies face unique constraints regarding raising capital required to upgrade their congested networks. Battery storage has emerged as a non-wire alternative (NWA) to feeder upgrades. This article presents a flexible valuation framework for battery storage use in distribution networks and its application in the context of EMDE distribution network planning. We evaluate the value of storage as an NWA using a multi-stage decision making process that combines system optimization with markov-decision processes (MDP) to identify the least-cost network upgrade strategy under demand growth uncertainty. When applied to feeders in Delhi, India, the approach highlights the cost-effectiveness of battery storage to manage load growth while deferring network investments. Across the low, medium and high battery storage capital cost projections for 2030, we estimate that 18 to 29 GWh of battery storage capacity could be deployed to defer 11,752 to 15,914 km of medium voltage distribution feeder lines that are loaded at 60\% or more of their ampere capacity in 2030, resulting in 12 to 16\% capital cost savings. Interestingly, the lowering storage capital costs does not always increase NWA storage deployment, due to network capacity constraints limiting opportunities for off-peak storage charging.

eess.SY↗

arXiv2022

Representative period selection for power system planning using autoencoder-based dimensionality reduction

Power sector capacity expansion models (CEMs) that are used for studying future low-carbon grid scenarios must incorporate detailed representation of grid operations. Often CEMs are formulated to model grid operations over representative periods that are sampled from the original input data using clustering algorithms. However, such representative period selection (RPS) methods are limited by the declining efficacy of the clustering algorithm with increasing dimensionality of the input data and do not consider the relative importance of input data variations on CEM outcomes. Here, we propose a RPS method that addresses these limitations by incorporating dimensionality reduction, accomplished via neural network based autoencoders, prior to clustering. Such dimensionality reduction not only improves the performance of the clustering algorithm, but also facilitates using additional features, such as estimated outputs produced from parallel solutions of simplified versions of the CEM for each disjoint period in the input data (e.g. 1 week). The impact of incorporating dimensionality reduction as part of RPS methods is quantified through the error in outcomes of the corresponding reduced-space CEM vs. the full space CEM. Extensive numerical experimentation across various networks and range of technology and policy scenarios establish the superiority of the dimensionality-reduction based RPS methods.

cs.LG↗

arXiv2022

A Model-Adaptive Clustering Method for Low-Carbon Energy System Optimization

Intermittent renewable energy resources like wind and solar pose great uncertainty of multiple time scales, from minutes to years, on the design and operation of power systems. Energy system optimization models have been developed to find the least-cost solution to matching the uncertainty with flexibility resources. However, input data that capture such multi-time-scale uncertainty are characterized with a long time horizon and bring great difficulty to solving the optimization model. Here we propose an adaptive clustering method based on the decision variables of optimization model to alleviate the computational complexity, in which the energy system is optimized over selected representative time periods instead of the full time horizon. The proposed clustering method is adaptive to various energy system optimization models or settings, because it extracts features from the optimization models. Results show that the proposed clustering method can significantly lower the error in approximating the solution with the full time horizon, compared to traditional clustering methods.

math.OC↗

arXiv2022

Impact of demand growth on decarbonizing India's electricity sector and the role for energy storage

Global energy sector decarbonization efforts are contingent on technology choices for energy production and end-use in emerging markets such as India, where air conditioning is expected to be a major driver for electricity demand growth. Here, we use an integrated demand-supply framework to quantify the impacts of demand growth and temporal patterns on long-term electricity system evolution. Under projected renewables and Li-ion storage cost declines, our supply-demand modeling points to renewables contributing substantially (46-67%) to meet annual electricity demand in India by 2030. However, without appropriate policy measures to phase out existing coal generation, even such rapid adoption of renewable energy coupled with one or more technological levers such as low-cost energy storage and demand-side measures such as setting aggressive AC efficiency standards and deploying distribution level storage, are insufficient to reduce annual CO2 emissions in 2050 vs. 2020 because of the relatively higher growth rate of projected electricity demand over this period.

physics.soc-ph↗

arXiv2021

Integrating Hydrogen in Single-Price Electricity Systems: The Effects of Spatial Economic Signals

Hydrogen can contribute substantially to the reduction of carbon emissions in industry and transportation. However, the production of hydrogen through electrolysis creates interdependencies between hydrogen supply chains and electricity systems. Therefore, as governments worldwide are planning considerable financial subsidies and new regulation to promote hydrogen infrastructure investments in the next years, energy policy research is needed to guide such policies with holistic analyses. In this study, we link a electrolytic hydrogen supply chain model with an electricity system dispatch model, for a cross-sectoral case study of Germany in 2030. We find that hydrogen infrastructure investments and their effects on the electricity system are strongly influenced by electricity prices. Given current uniform prices, hydrogen production increases congestion costs in the electricity grid by 17%. In contrast, passing spatially resolved electricity price signals leads to electrolyzers being placed at low-cost grid nodes and further away from consumption centers. This causes lower end-use costs for hydrogen. Moreover, congestion management costs decrease substantially, by up to 20% compared to the benchmark case without hydrogen. These savings could be transferred into according subsidies for hydrogen production. Thus, our study demonstrates the benefits of differentiating economic signals for hydrogen production based on spatial criteria.

econ.GN↗

arXiv2021

Sector coupling via hydrogen to lower the cost of energy system decarbonization

There is growing interest in hydrogen (H$_2$) use for long-duration energy storage in a future electric grid dominated by variable renewable energy (VRE) resources. Modelling the role of H$_2$ as grid-scale energy storage, often referred as "power-to-gas-to-power (P2G2P)" overlooks the cost-sharing and emission benefits from using the deployed H$_2$ production and storage assets to also supply H$_2$ for decarbonizing other end-use sectors where direct electrification may be challenged. Here, we develop a generalized modelling framework for co-optimizing energy infrastructure investment and operation across power and transportation sectors and the supply chains of electricity and H$_2$, while accounting for spatio-temporal variations in energy demand and supply. Applying this sector-coupling framework to the U.S. Northeast under a range of technology cost and carbon price scenarios, we find a greater value of power-to-H$_2$ (P2G) versus P2G2P routes. P2G provides flexible demand response, while the extra cost and efficiency penalties of P2G2P routes make the solution less attractive for grid balancing. The effects of sector-coupling are significant, boosting VRE generation by 12-55% with both increased capacities and reduced curtailments and reducing the total system cost (or levelized costs of energy) by 6-14% under 96% decarbonization scenarios. Both the cost savings and emission reductions from sector coupling increase with H$_2$ demand for other end-uses, more than doubling for a 96% decarbonization scenario as H$_2$ demand quadraples. Moreover, we found that the deployment of carbon capture and storage is more cost-effective in the H$_2$ sector because of the lower cost and higher utilization rate. These findings highlight the importance of using an integrated multi-sector energy system framework with multiple energy vectors in planning energy system decarbonization pathways.

math.OC↗

arXiv2020

Hydrogen Supply Chain Planning with Flexible Transmission and Storage Scheduling

Hydrogen is becoming an increasingly appealing energy carrier, as the costs of renewable energy generation and water electrolysis continue to decline. Developing modelling and decision tools for the H$_{2}$ supply chain that fully capture the flexibility of various resources is essential to understanding the overall cost-competitiveness of H$_{2}$ use. To address this need, we have developed a H$_{2}$ supply chain planning model that determines the least-cost mix of H$_{2}$ generation, storage, transmission, and compression facilities to meet H$_{2}$ demands and is coupled with power systems through electricity prices. We incorporate flexible scheduling for H$_{2}$ trucks and pipeline, allowing them to serve as both H$_{2}$ transmission and storage resources to shift H$_{2}$ demand/production across space and time. The case study results in the U.S. Northeast indicate that the proposed framework for flexible scheduling of H$_{2}$ transmission and storage resources is critical not only to cost minimization but also to the choice of H$_{2}$ production pathways between electrolyzer and centralized natural-gas-based production facilities. Trucks as mobile storage could make electrolyzer more competitive by providing extra spatiotemporal flexibility to respond to the electricity price variability while meeting H$_{2}$ demands. The proposed model also provides a reasonable trade-off between modeling accuracy and solution times.

math.OC↗