arXiv subjects

Jesse D. Jenkins

Publications and source records attributed to Jesse D. Jenkins.

14 recordsLinked to original sources

arXiv2026

Analyzing Performance and Scalability of Benders Decomposition for Generation and Transmission Expansion Planning Models

Generation and Transmission Expansion Planning (GTEP) problems co-optimize generation and transmission expansion, enabling them to provide better planning decisions than traditional Generation Expansion Planning or Transmission Expansion Planning problems, but GTEPs can be computationally complex or intractable. Benders Decomposition (BD) has been applied to expansion planning problems, with various methods applied to accelerate convergence. In this work, we test strategies for improving the performance of BD on GTEP models with nodal resolution and DCOPF constraints. We also present an alternative approach for handling the bilinear constraints that can result in these problems. These tests included combinations of using generalized Benders decomposition (GBD), hot-starting via a transport constrained model, using linear relaxations of the master problem, and using regularization. We test these methods on mixed-integer linear programming GTEP models with up to 146 buses (10 million continuous variables and 400 mixed-integer decisions). With selected accelerated Benders decomposition approaches, the problems can be solved to under a 1\% gap in as little as 5 hours where they were otherwise intractable. Results also suggest that using regularization on these initial hot-starting and relaxation steps and turning it off after they are complete was generally the best combination of strategies.

math.OC↗

arXiv2026

Graph-Based Modeling and Decomposition of Hierarchical Optimization Problems

We present a graph-theoretic modeling approach for hierarchical optimization that leverages the OptiGraph abstraction implemented in the Julia package Plasmo.jl. We show that the abstraction is flexible and can effectively capture complex hierarchical connectivity that arises from decision-making over multiple spatial and temporal scales (e.g., integration of planning, scheduling, and operations in manufacturing and infrastructures). We also show that the graph abstraction facilitates the conceptualization and implementation of decomposition and approximation schemes. Specifically, we propose a graph-based Benders decomposition (gBD) framework that enables the exploitation of hierarchical (nested) structures and that uses graph aggregation/partitioning procedures to discover such structures. In addition, we provide a Julia implementation of gBD, which we call PlasmoBenders.jl. We illustrate the capabilities using examples arising in the context of energy and power systems.

math.OC↗

arXiv2025

A Graph-Based, Distributed Memory, Modeling Abstraction for Optimization

We present a general, flexible modeling abstraction for building and working with distributed optimization problems called a RemoteOptiGraph. This abstraction extends the OptiGraph model in Plasmo$.$jl, where optimization problems are represented as hypergraphs with nodes that define modular subproblems (variables, constraints, and objectives) and edges that encode algebraic linking constraints between nodes. The RemoteOptiGraph allows OptiGraphs to be utilized in distributed memory environments through InterWorkerEdges, which manage linking constraints that span workers. This abstraction offers a unified approach for modeling optimization problems on distributed memory systems (avoiding bespoke modeling approaches), and provides a basis for developing general-purpose meta-algorithms that can exploit distributed memory structure such as Benders or Lagrangian decompositions. We implement this abstraction in the open-source package, Plasmo$.$jl and we illustrate how it can be used by solving a mixed integer capacity expansion model for the western United States containing over 12 million variables and constraints. The RemoteOptiGraph abstraction together with Benders decomposition performs 7.5 times faster than solving the same problem without decomposition.

cs.DC↗

arXiv2025

A Parallelized Cutting-Plane Algorithm for Computationally Efficient Modelling to Generate Alternatives

Contemporary macro energy systems modelling is characterized by the need to represent strategic and operational decisions with high temporal and spatial resolution and represent discrete investment and retirement decisions. This drive towards greater fidelity, however, conflicts with a simultaneous push towards greater model representation of inherent complexity in decision making, including methods like Modelling to Generate Alternatives (MGA). MGA aims to map the feasible space of a model within a cost slack by varying investment parameters without changing the operational constraints, a process which frequently requires hundreds of solutions. For large, detailed energy system models this is impossible with traditional methods, leading researchers to reduce complexity with linearized investments and zonal or temporal aggregation. This research presents a new solution method for MGA type problems using cutting-plane methods based on a tailored reformulation of Benders Decomposition. We accelerate the algorithm by sharing cuts between MGA master problems and grouping MGA objectives. We find that our new solution method consistently solves MGA problems times faster and requires less memory than existing monolithic Modelling to Generate Alternatives solution methods on linear problems, enabling rapid computation of a greater number of solutions to highly resolved models. We also show that our novel cutting-plane algorithm enables the solution of very large MGA problems with integer investment decisions.

math.OC↗

arXiv2025

Scaling green hydrogen and CCUS via cement-methanol co-production in China

High costs of green hydrogen and of carbon capture, utilization, and sequestration (CCUS) have hindered policy ambition and slowed real-world deployment, despite their importance for decarbonizing hard-to-abate sectors, including cement and methanol. Given the economic challenges of adopting CCUS in cement and green hydrogen in methanol production separately, we propose a renewable-powered co-production system that couples electrolytic hydrogen and CCUS through molecule exchange. We optimize system configurations using an hourly-resolved, process-based model incorporating operational flexibility, and explore integrated strategies for plant-level deployment and CO2 source-sink matching across China. We find that co-production could reduce CO2 abatement costs to USD 41-53 per tonne by 2035, significantly lower than approximately USD 75 for standalone cement CCUS and over USD 120 for standalone renewable-based methanol. Co-production is preferentially deployed at cement plants in renewable-rich regions, potentially reshaping national CO2 infrastructure planning. This hydrogen-CCUS coupling paradigm could accelerate industrial decarbonization and scaling for other applications.

eess.SY↗

arXiv2025

Evaluating Advanced Nuclear Fission Technologies for Future Decarbonized Power Grids

Advanced nuclear fission, which encompasses various innovative nuclear reactor designs, could contribute to the decarbonization of the United States electricity sector. However, little is known about how cost-competitive these reactors would be compared to other technologies, or about which aspects of their designs offer the most value to a decarbonized power grid. We employ an electricity system optimization model and a case study of a decarbonized U.S. Eastern Interconnection circa 2050 to generate initial indicators of future economic value for advanced reactors and the sensitivity of future value to various design parameters, the availability of competing technologies, and the underlying policy environment. These results can inform long-term cost targets and guide near-term innovation priorities, investments, and reactor design decisions. We find that advanced reactors should cost \$5.7-\$7.3/W to gain an initial market share (assuming 30 year asset life and 3.5-6.5% real weighted average cost of capital), while those that include thermal storage in their designs can cost up to \$6.0-\$7.7/W (not including cost of storage). Since the marginal value of advanced fission reactors declines as market penetration increases, break-even costs fall 32% at 100 GW of cumulative capacity and 51% at 300 GW. Additionally, policies that provide investment tax credits for nuclear energy create the most favorable environment for advanced nuclear fission. These findings can inform near-term resource allocation decisions by stakeholders, innovators and investors working in the energy technology sector.

physics.soc-ph↗

arXiv2025

Are EVs Cleaner Than We Think? Evaluating Consequential Greenhouse Gas Emissions from EV Charging

While electrifying transportation eliminates tailpipe greenhouse gas (GHG) emissions, electric vehicle (EV) adoption can create additional electricity sector emissions. To quantify this emissions impact, prior work typically employs short-run marginal emissions or average emissions rates calculated from historical data or power systems models that do not consider changes in installed capacity. In this work, we use an electricity system capacity expansion model to consider the full consequential GHG emissions impact from large-scale EV adoption in the western United States, accounting for induced changes in generation and storage capacity. We find that the metrics described above do not accurately reflect the true emissions impact of EV adoption-average emissions rates can either under- or over-estimate emission impacts, and short-run marginal emissions rates can significantly underestimate emission reductions, especially when charging timing is flexible. Our results also show that using short-run marginal emission rates as signals to coordinate EV charging could increase emissions relative to price-based charging signals, indicating the need for alternative control strategies to minimize consequential emissions.

eess.SY↗

arXiv2025

Regularized Benders Decomposition for High Performance Capacity Expansion Models

We consider electricity capacity expansion models, which optimize investment and retirement decisions by minimizing both investment and operation costs. In order to provide credible support for planning and policy decisions, these models need to include detailed operations and time-coupling constraints, consider multiple possible realizations of weather-related parameters and demand data, and allow modeling of discrete investment and retirement decisions. Such requirements result in large-scale mixed-integer optimization problems that are intractable with off-the-shelf solvers. Hence, practical solution approaches often rely on carefully designed abstraction techniques to find the best compromise between reduced computational burden and model accuracy. Benders decomposition offers scalable approaches to leverage distributed computing resources and enable models with both high resolution and computational performance. In this study, we implement a tailored Benders decomposition method for large-scale capacity expansion models with multiple planning periods, stochastic operational scenarios, time-coupling policy constraints, and multi-day energy storage and reservoir hydro resources. Using multiple case studies, we also evaluate several level-set regularization schemes to accelerate convergence. We find that a regularization scheme that selects planning decisions in the interior of the feasible set shows superior performance compared to previously published methods, enabling high-resolution, mixed-integer planning problems with unprecedented computational performance.

math.OC↗

arXiv2024

Modelling to Generate Continuous Alternatives: Enabling Real-Time Feasible Portfolio Generation in Convex Planning Models

Decarbonization provides new opportunities to plan energy systems for improved health, resilience, equity, and environmental outcomes, but challenges in siting and social acceptance of transition goals and targets threaten progress. Modelling to Generate Alternatives (MGA) provides an optimization method for capturing many near-cost-optimal system configurations, and can provide insights into the tradeoffs between objectives and flexibility available in the system. However, MGA is currently limited in interactive applicability to these problems due to a lack of methods for allowing users to explore near-optimal feasible spaces. In this work we describe Modelling to Generate Continuous Alternatives (MGCA), a novel post-processing algorithm for convex planning problems which enables users to rapidly generate new interior solutions, incorporate new constraints, and solve within the space with convex objectives. MGCA begins with a dimensionality reduction to capacity decisions and metric values. We then take advantage of convex combinations to generate interior points by allowing user weight specification and encoding convex combinations in an optimization problem with user-defined additional constraints and objective. Dimensionality reduction enables this problem to solve in tenths of a second, suitable for analysis in interactive settings. We discuss the interpolation of capacity and operational metric values, finding capacity metrics can be perfectly interpolated while operational metrics remain within the feasible range of the points used to create them. We demonstrate interpolated solutions can be exported and re-solved with an economic dispatch model to provide operational metric values consistent with least-cost decision-making and show interpolated metric values are generally within 10% of the optimal value.

math.OC↗

arXiv2024

Reducing transmission expansion by co-optimizing sizing of wind, solar, storage and grid connection capacity

Expanding transmission capacity is likely a bottleneck that will restrict variable renewable energy (VRE) deployment required to achieve ambitious emission reduction goals. Interconnection and inter-zonal transmission buildout may be displaced by the optimal sizing of VRE to grid connection capacity and by the co-location of VRE and battery resources behind interconnection. However, neither of these capabilities is commonly captured in macro-energy system models. We develop two new functionalities to explore the substitutability of storage for transmission and the optimal capacity and siting decisions of renewable energy and battery resources through 2030 in the Western Interconnection of the United States. Our findings indicate that modeling optimized interconnection and storage co-location better captures the full value of energy storage and its ability to substitute for transmission. Optimizing interconnection capacity and co-location can reduce total grid connection and shorter-distance transmission capacity expansion on the order of 10% at storage penetration equivalent to 2.5-10% of peak system demand. The decline in interconnection capacity corresponds with greater ratios of VRE to grid connection capacity (an average of 1.5-1.6 megawatt (MW) PV:1 MW inverter capacity, 1.2-1.3 MW wind:1 MW interconnection). Co-locating storage with VREs also results in a 10-15% increase in wind capacity, as wind sites tend to require longer and more costly interconnection. Finally, co-located storage exhibits higher value than standalone storage in our model setup (22-25%). Given the coarse representation of transmission networks in our modeling, this outcome likely overstates the real-world importance of storage co-location with VREs. However, it highlights how siting storage in grid-constrained locations can maximize the value of storage and reduce transmission expansion.

eess.SY↗

arXiv2024

Reducing transmission expansion by co-optimizing sizing of wind, solar, storage and grid connection capacity

Expanding transmission capacity is likely a bottleneck that will restrict variable renewable energy (VRE) deployment required to achieve ambitious emission reduction goals. Grid interconnection and inter-regional transmission capacity may be reduced by the optimal sizing of VREs to grid connection or co-location of VRE and battery resources behind the grid interconnection, but neither of these capabilities are commonly captured in macro-energy system models. We thus develop these two new functionalities to explore the substitutability of storage for transmission and VRE resource trade-offs through 2030 in the Western Interconnection of the United States. Our findings indicate that not modeling co-location fails to capture the full substitutability of storage and solar photovoltaic (PV) resources for transmission: co-location can reduce long-distance inter-regional transmission expansion by 12-31% and decrease grid connection capacity and shorter-distance transmission interconnection by 20-25%. We also demonstrate that not modeling colocated storage does not accurately reflect competition between wind and solar PV resources and underestimates the value of energy storage: co-location of VREs and storage favors solar PV (4-5% increase) and lithium-ion battery deployment (1.7-6 times increase), while decreasing wind buildout (0.9-1.6% decline).

eess.SY↗

arXiv2024

Quantifying the Impact of Energy System Model Resolution on Siting, Cost, Reliability, and Emissions

Energy systems models, critical for power sector decision support, incur non-linear memory and runtime penalties when scaling up under typical formulations. Even hardware improvements cannot make large models tractable, requiring omission of detail which affects siting, cost, and emission outputs to an unknown degree. Recent algorithmic innovations have enabled large scale, high resolution modeling. Newly tractable, granular systems can be compared with coarse ones for better understanding of inaccuracies from low resolution. Here we use a state of the art model to quantify the impact of resolution on results salient to policymakers and planners, affording confidence in decision quality. We find more realistic siting in recommendations from high resolution energy systems models, improving emissions, reliability, and price outcomes. Errors are generally stronger from low spatial resolution. When models have low resolution in multiple dimensions, errors are introduced by the coarser of temporal or spatial resolution. We see no diminishing returns in accuracy for several key metrics when increasing resolution. We recommend using computationally efficient techniques to maximize granularity and allocating resolution without leaving any aspect (spatial, temporal, operational) of systems unduly coarse.

eess.SP↗

arXiv2024

Measuring Exploration: Review and Systematic Evaluation of Modelling to Generate Alternatives Methods in Macro-Energy Systems Planning Models

As decarbonization agendas mature, macro-energy systems modelling studies have increasingly focused on enhanced decision support methods that move beyond least-cost modelling to improve consideration of additional objectives and tradeoffs. One candidate is Modeling to Generate Alternatives (MGA), which systematically explores new objectives without explicit stakeholder elicitation. Previous literature lacks both a comprehensive review of MGA vector selection methods in large-scale energy system models and comparative testing of their relative efficacies in this setting. To fill this gap, this paper provides a comprehensive review of the MGA literature, identifying at least seven MGA vector selection methodologies and carrying out a systematic evaluation of four: Hop-Skip-Jump, Random Vector, Variable Min/Max, and Modelling All Alternatives. We examine each method's runtime, parallelizability, new solution discovery efficiency, and spatial exploration in lower dimensional (N <= 100) spaces, as well as spatial exploration in a three-zone, 8760-hour capacity expansion model case. Through these tests, we find Random Vector provides the broadest exploration of the near-optimal feasible region and Variable Min/Max provides the most extreme results, while the two tie on computational speed. We thus propose a new Hybrid vector selection approach combining the two methods to take advantage of the strengths of each. Additional analysis is provided on MGA variable selection, in which we demonstrate MGA problems formulated over generation variables fail to retain cost-optimal dispatch and are thus not reflective of real operations of equivalent hypothetical capacity choices. As such, we recommend future studies utilize a parallelized combined vector approach over the set of capacity variables for best results in computational speed and spatial exploration while retaining optimal dispatch.

math.OC↗

arXiv2023

Inflation Reduction Act impacts on the economics of clean hydrogen and liquid fuels

The Inflation Reduction Act (IRA) in the United States provides unprecedented incentives for deploying low-carbon hydrogen and liquid fuels, among other low greenhouse gas (GHG) emissions technologies. To better understand the prospective competitiveness of low-carbon or negative-carbon hydrogen and liquid fuels under the IRA in the early 2030s, we examine the impacts of IRA provisions on costs of producing hydrogen and synthetic liquid fuel made from natural gas, electricity, short-cycle biomass (agricultural residues), and corn-ethanol. With IRA credits (45V or 45Q), but excluding incentives provided by other national or state policies, hydrogen produced by electrolysis using carbon-free electricity (green H2) and natural gas reforming with carbon capture and storage (CCS) (blue H2) are cost-competitive with the carbon-intensive benchmark gray H2 from steam methane reforming. Biomass-derived H2 with or without CCS is not cost-completive under current IRA provisions. However, if IRA allowed biomass gasification with CCS to claim a 45V credit for carbon-neutral H2 and a 45Q credit for negative biogenic-CO2 emissions, this pathway would be less costly than gray H2. The IRA credit for clean fuels (45Z), currently stipulated to end in 2027, would need to be extended, or similar policy support provided by other national or state policies, for clean synthetic liquid fuel to be cost-competitive with petroleum-derived liquid fuels. Levelized IRA subsidies per unit of CO2 mitigated for all hydrogen and synthetic liquid fuel production pathways, except electricity-derived synthetic liquid fuel, range from 65 to 384 $/t CO2, which is within or below the range in U.S. federal government estimates of the Social Cost of Carbon (SCC) in the 2030 to 2040 timeframe.

eess.SY↗