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Matthias Fripp

Publications and source records attributed to Matthias Fripp.

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Process and Policy Insights from an Intercomparison of Open Electricity System Capacity Expansion Models

This study performs a detailed intercomparison of four open-source electricity capacity expansion models - Temoa, Switch, GenX, and USENSYS - to evaluate 1) how closely the results of these models align when inputs and configurations are harmonized, and 2) the degree to which varying model configurations affect outputs. We harmonize the inputs to each model using PowerGenome and use clearly defined scenarios (policy conditions) and configurations (model setup choices). This allows us to isolate how differences in model structure affect policy outcomes and investment decisions. Our framework allows each model to be tested on identical assumptions for policy, technology costs, and operational constraints, allowing us to focus on differences that arise from inherent model structures. Key findings highlight that, when harmonized, models produce very similar capacity portfolios under current policies and net-zero scenarios, with less than 1% difference in system costs for most configurations. This agreement among models allows us to focus on how configuration choices affect model results. For instance, configurations with unit commitment constraints or economic retirement yield different investments and system costs compared to simpler configurations. Our findings underscore the importance of aligning input data and transparently defining scenarios and configurations to provide robust policy insights.

econ.GN

Optimal transmission expansion modestly reduces decarbonization costs of U.S. electricity

Major government studies and policy reports project that substantial expansion of interregional transmission will be needed to integrate clean energy and ensure reliability in decarbonized power systems. Using the open-source Switch capacity expansion model with detailed representation of existing U.S. generation and transmission infrastructure, solar, wind, and storage resources, and hourly operations, we evaluate the role of interregional transmission across least-cost, carbon-priced, and zero-emissions scenarios for 2050. An optimal nationwide plan would more than triple interregional transmission capacity, yet this reduces the cost of a zero emissions system by only 7% relative to relying on existing interregional transmission, as storage, solar and wind siting, and nuclear generation serve as close substitutes. Regional cost and rent effects vary, with transmission generally favoring wind and hydrogen resources over solar and batteries. Sensitivity analysis shows diminishing returns: one-fifth of the benefits of full expansion can be achieved with one-twelfth of the added capacity, while cost reductions for batteries and hydrogen provide comparable or greater system savings than interregional transmission. Upgrading existing interregional corridors with advanced conductors roughly doubling capacity per link at half the cost of new builds reduces system costs by only 1.6%, suggesting that reconductoring benefits are modest and that realizing their full potential likely requires pairing with new connections on key corridors or complementary reductions in battery costs. These results suggest that while substantial transmission expansion is economically justified, a diverse set of flexibility resources can substitute for large-scale grid build out, and the relative value of transmission is highly contingent on technological and cost developments.

econ.GN

Market-Based Coordination of Price-Responsive Demand Using Dantzig-Wolfe Decomposition Method

With the increased share of Distributed Generation (DG) and Demand Responsive (DR) loads in the power systems, new approaches based on the game theory framework have been proposed to tackle the problem of coordination of Price Responsive Devices (PRD). The PRDs are modeled as self-benefiting players who try to optimize their consumption based on the price. In this paper, for the first time, a new algorithm based on the Dantzig-Wolfe (DW) Decomposition method to solve the coordination problem of self-benefiting PRDs in a distributed fashion has been proposed. By utilizing the distributed nature of Dantzig-Wolfe, the PRD's self-benefiting algorithms are modeled as sub-problems of the DW, and the coordinator (or the grid operator) who collects energy consumption of PRDs (their energy bid), solves the master problem of the DW and calculate the price signal accordingly. The proposed algorithm is fast since the subproblem in DW (which could be millions of PRDs) can be solved simultaneously. Furthermore, based on the DW theory, if the PRDs subproblems are convex, reaching the optimal point (Equal to Nash Equilibrium) in limited iterations is guaranteed. A simulation with 8 participant households has been conducted to evaluate the model. Each house is equipped with two types of loads: an Electric Vehicle (EV) as a sample of interruptible loads and an Electric Water Heater (EWH) as a sample of Thermostatically Control Loads (TCA). The results show that when the algorithm reaches the optimal point, the generation cost and the user payment (based on the marginal cost of generation) decrease. Furthermore, the aggregate load's Peak to Average (PAR) reduces significantly.

eess.SY

Using Temperature Sensitivity to Estimate Shiftable Electricity Demand: Implications for power system investments and climate change

Growth of intermittent renewable energy and climate change make it increasingly difficult to manage electricity demand variability. Centralized storage can help but is costly. An alternative is to shift demand. Cooling and heating demands are substantial and can be economically shifted using thermal storage. To estimate what thermal storage, employed at scale, might do to reshape electricity loads, we pair fine-scale weather data with hourly electricity use to estimate the share of temperature-sensitive demand across 31 regions that span the continental United States. We then show how much variability can be reduced by shifting temperature-sensitive loads, with and without improved transmission between regions. We find that approximately three quarters of within-day, within-region demand variability can be eliminated by shifting just half of temperature-sensitive demand. The variability-reducing benefits of shifting temperature-sensitive demand complement those gained from improved interregional transmission, and greatly mitigate the challenge of serving higher peaks under climate change.

eess.SY

Switch 2.0: A Modern Platform for Planning High-Renewable Power Systems

This paper describes Switch 2.0, an open-source modeling platform for planning transitions to low-emission electric power grids, designed to satisfy 21st century grid planning requirements. Switch is capable of long-, medium- and short-term planning of investments and operations with conventional or smart grids, integrating large shares of renewable power, storage and/or demand response. Applications include integrated resource planning, investment planning, economic and policy analyses as well as basic research. Potential users include researchers, educators, industry and regulators. Switch formulates generation and transmission capacity planning as a mixed integer linear program where investment and operation are co-optimized across sampled time series during multiple investment periods. High-resolution production cost modeling is supported by freezing investment decisions and including longer time series and more operational details. Modeling features include unit commitment, part-load efficiency, planning and operating reserves, fuel supply curves, storage, hydroelectric networks, policy constraints and demand response. Switch has a modular architecture that allows users to flexibly compose models by choosing built-in modules 'a la carte' or writing custom modules. This paper describes the software architecture and model formulation of Switch 2.0 and provides a case study in which the model was used to identify the best options for obtaining load-shifting and reserve services from batteries and demand response in a 100% renewable power system.

math.OC