arXiv subjects

Christoph Graf

Publications and source records attributed to Christoph Graf.

5 recordsLinked to original sources

arXiv2025

Coordinated vs. Sequential Transmission Planning

Coordinated planning of generation, storage, and transmission more accurately captures the interactions among these three capacity types necessary to meet electricity demand, at least in theory. However, in practice, U.S. system operators typically follow a sequential planning approach: They first determine future generation and storage additions based on an assumed unconstrained (`copper plate') system. Next, they perform dispatch simulations of this projected generation and storage capacity mix on the existing transmission grid to identify transmission constraint violations. These violations indicate the need for transmission upgrades. We describe a multistage, multi-locational planning model that co-optimizes generation, storage, and transmission investments. The model respects reliability constraints as well as state energy and climate policies. We test the two planning approaches using a current stakeholder-informed 20-zone model of the PJM region, developed for the current FERC Order No. 1920 compliance filing process. In our most conservative model specification, we find that the co-optimized approach estimates 67% lower transmission upgrade needs than the sequential model, leading to total system costs that are .6% lower and similar reliability and climate outcomes. Our sensitivities show larger transmission and cost savings and reliability and climate benefits from co-optimized planning.

eess.SY↗

arXiv2025

Unlocking Transmission Flexibility under Uncertainty: Getting Dynamic Line Ratings into Electricity Markets

Static transmission line ratings may lead to underutilization of line capacity due to overly conservative assumptions. Grid-enhancing technologies (GETs) such as dynamic line ratings (DLRs), which adjust line capacity based on real-time conditions, are a techno-economically viable alternative to increase the utilization of existing power lines. Nonetheless, their adoption has been slow, partly due to the absence of operational tools that effectively account for simultaneous impacts on dispatch and pricing. In this paper, we represent transmission capacity with DLRs as a stock-like resource with time-variant interdependency, which is modeled via an approximation of line temperature evolution process, decoupling the impacts of ambient weather conditions and power flow on transmission line temperature and thus capacity. We integrate DLRs into a multi-period DC optimal power flow problem, with chance constrains addressing correlated uncertainty in DLRs and renewable generation. This yields non-convex problems that we transform into a tractable convex form by linearization. We derive locational marginal energy and ancillary services prices consistent with a competitive equilibrium. Numerical experiments on the 11-zone and 1814-node NYISO systems demonstrate its performance, including impacts on dispatch, pricing, and marginal carbon emissions.

eess.SY↗

arXiv2023

Multi-Objective Transmission Expansion: An Offshore Wind Power Integration Case Study

Despite ambitious offshore wind targets in the U.S. and globally, offshore grid planning guidance remains notably scarce, contrasting with well-established frameworks for onshore grids. This gap, alongside the increasing penetration of offshore wind and other clean-energy resources in onshore grids, highlights the urgent need for a coordinated planning framework. Our paper describes a multi-objective, multistage generation, storage and transmission expansion planning model to facilitate efficient and resilient large-scale adoption of offshore wind power. Recognizing regulatory emphasis and, in some cases, requirements to consider externalities, this model explicitly accounts for negative externalities: greenhouse gas emissions and local emission-induced air pollution. Utilizing an 8-zone ISO-NE test system and a 9-zone PJM test system, we explore grid expansion sensitivities such as impacts of optimizing Points of Interconnection (POIs) versus fixed POIs, negative externalities, and consideration of extreme operational scenarios resulting from offshore wind integration. Our results indicate that accounting for negative externalities necessitates greater upfront investment in clean generation and storage (balanced by lower expected operational costs). Optimizing POIs could significantly reshape offshore topology or POIs, and lower total cost. Finally, accounting for extreme operational scenarios typically results in greater operational costs and sometimes may alter onshore line investment.

eess.SY↗

arXiv2022

(Machine) Learning from the COVID-19 Lockdown about Electricity Market Performance with a Large Share of Renewables

The negative demand shock due to the COVID-19 lockdown has reduced net demand for electricity -- system demand less amount of energy produced by intermittent renewables, hydroelectric units, and net imports -- that must be served by controllable generation units. Under normal demand conditions, introducing additional renewable generation capacity reduces net demand. Consequently, the lockdown can provide insights about electricity market performance with a large share of renewables. We find that although the lockdown reduced average day-ahead prices in Italy by 45%, re-dispatch costs increased by 73%, both relative to the average of the same magnitude for the same period in previous years. We estimate a deep-learning model using data from 2017--2019 and find that predicted re-dispatch costs during the lockdown period are only 26% higher than the same period in previous years. We argue that the difference between actual and predicted lockdown period re-dispatch costs is the result of increased opportunities for suppliers with controllable units to exercise market power in the re-dispatch market in these persistently low net demand conditions. Our results imply that without grid investments and other technologies to manage low net demand conditions, an increased share of intermittent renewables is likely to increase costs of maintaining a reliable grid.

econ.GN↗

arXiv2021

Computational Performance of Deep Reinforcement Learning to find Nash Equilibria

We test the performance of deep deterministic policy gradient (DDPG), a deep reinforcement learning algorithm, able to handle continuous state and action spaces, to learn Nash equilibria in a setting where firms compete in prices. These algorithms are typically considered model-free because they do not require transition probability functions (as in e.g., Markov games) or predefined functional forms. Despite being model-free, a large set of parameters are utilized in various steps of the algorithm. These are e.g., learning rates, memory buffers, state-space dimensioning, normalizations, or noise decay rates and the purpose of this work is to systematically test the effect of these parameter configurations on convergence to the analytically derived Bertrand equilibrium. We find parameter choices that can reach convergence rates of up to 99%. The reliable convergence may make the method a useful tool to study strategic behavior of firms even in more complex settings. Keywords: Bertrand Equilibrium, Competition in Uniform Price Auctions, Deep Deterministic Policy Gradient Algorithm, Parameter Sensitivity Analysis

cs.GT↗