arXiv subjects

Audun Botterud

Publications and source records attributed to Audun Botterud.

At least 19 recordsLinked to original sources

arXiv2026

Stochastic Capacity Accreditation: Incentivizing Resource Adequacy under Weather Uncertainty

High penetrations of variable renewable energy introduce significant resource adequacy challenges, particularly when weather-driven uncertainty affects renewable availability, electricity demand, and the effective capacity of thermal generators. Existing capacity credit accreditation methods often neglect these correlated weather effects, which may overstate firm capacity, distort long-term investment decisions, and weaken reliability outcomes in electricity market with price caps. This paper proposes a two-stage stochastic optimization framework for capacity accreditation that explicitly captures uncertainty in wind, solar, and temperature-dependent thermal derating. Using five years of ERCOT demand and renewable availability data, we compare the proposed stochastic capacity credit method with deterministic and average accreditation approaches, and quantify the impact of alternative accreditation methods on reliability, investment incentives, and the value of weather information. The results show that incorporating weather uncertainty yields more informative capacity credits and more reliable investment signals. In contrast, deterministic and averaged approaches can distort resource expansion decisions and produce materially worse reliability outcomes. These findings demonstrate the importance of explicitly accounting for weather uncertainty in capacity accreditation and long-term resource adequacy planning.

eess.SY↗

arXiv2026

Optimal Solar Investment and Operation under Asymmetric Net Metering

We examine the joint investment and operational decisions of a prosumer, a customer who both consumes and generates electricity, under net energy metering (NEM) tariffs. Traditional NEM schemes provide temporally flat compensation at the retail price for net energy exports over a billing period. However, ongoing reforms in several U.S. states are introducing time-varying prices and asymmetric import/export compensation to better align incentives with grid costs. While prior studies treat PV capacity as exogenous and focus primarily on consumption behavior, this work endogenizes PV investment and derives the marginal value of solar capacity for a flexible prosumer under asymmetric NEM tariffs. We characterize optimal investment and show how optimal investment changes with prices and PV costs. Through this analysis, we identify a PV effect: changes in NEM pricing in one period can influence net demand and consumption in generating periods with unchanged prices through adjustments in optimal PV investment. The PV effect weakens the ability of higher import prices to increase prosumer payments, with direct implications for NEM reform. We validate our theoretical results in a case study using simulated household and tariff data derived from historical conditions in Massachusetts.

econ.GN↗

arXiv2026

Optimal Scheduling of Electricity and Water in Renewable-Colocated Desalination Plants

We develop a mathematical framework for the optimal scheduling of flexible water desalination plants (WDPs) as hybrid generator-load resources. WDPs integrate thermal generation, membrane-based controllable loads, and renewable energy sources, offering unique operational flexibility for power system operations. They can simultaneously participate in two markets: selling desalinated water to a water utility, and bidirectionally transacting electricity with the grid based on their net electricity demand. We formulate the scheduling decision problem of a profit-maximizing WDP, capturing operational, technological, and market-based coupling between water and electricity flows. The threshold-based structure we derive provides computationally tractable coordination suitable for large-scale deployment, offering operational and economical insights into how thermal and membrane-based desalination colocated with renewables complementarily provide continuous bidirectional flexibility. The thresholds are analytically characterized in near closed form as explicit functions of technology and tariff parameters. We examine how small changes in the exogenous tariff and technology parameters affect the WDP's profit. Extensive simulations illustrate the optimal WDP's operation, profit, and water-electricity exchange, demonstrating significant improvements relative to benchmark algorithms.

eess.SY↗

arXiv2025

Watts and Drops: Co-Scheduling Power and Water in Desalination Plants

We develop a mathematical framework to jointly schedule water and electricity in a profit-maximizing renewable colocated water desalination plant that integrates both thermal and membrane based technologies. The price-taking desalination plant sells desalinated water to a water utility at a given price and engages in bidirectional electricity transactions with the grid, purchasing or selling power based on its net electricity demand. We show that the optimal scheduling policy depends on the plant's internal renewable generation and follows a simple threshold structure. Under the optimal policy, thermal based water output decreases monotonically with renewable output, while membrane based water output increases monotonically. We characterize the structure and intuition behind the threshold policy and examine key special properties.

eess.SY↗

arXiv2025

Multi-sectoral Impacts of H2 and Synthetic Fuels Adoption for Heavy-duty Transportation Decarbonization

Policies focused on deep decarbonization of regional economies emphasize electricity sector decarbonization alongside electrification of end-uses. There is growing interest in utilizing hydrogen (H2) produced via electricity to displace fossil fuels in difficult-to-electrify sectors. One such case is heavy-duty vehicles (HDV), which represent a substantial and growing share of transport emissions as light-duty vehicles electrify. Here, we assess the bulk energy system impact of decarbonizing the HDV segment via either H2, or drop-in synthetic liquid fuels produced from H2 and CO2. Our analysis soft-links two modeling approaches: (a) a bottom-up transport demand model producing a variety of final energy demand scenarios for the same service demand and (b) a multi-sectoral capacity expansion model that co-optimizes power, H2 and CO2 supply chains under technological and policy constraints to meet exogenous final energy demands. Through a case study of Western Europe in 2040 under deep decarbonization constraints, we quantify the energy system implications of different levels of H2 and synthetic fuels adoption in the HDV sector under scenarios with and without CO2 sequestration. In the absence of CO2 storage, substitution of liquid fossil fuels in HDVs is essential to meet the deep decarbonization constraint across the modeled power, H2 and transport sectors. Additionally, utilizing H2 HDVs reduces decarbonization costs and fossil liquids demand, but could increase natural gas consumption. While H2 HDV adoption reduces the need for direct air capture (DAC), synthetic fuel adoption increases DAC investments and total system costs. The study highlights the trade-offs across transport decarbonization pathways, and underscores the importance of multi-sectoral consideration in decarbonization studies.

eess.SY↗

arXiv2025

Integer-Clustering Optimization of Hydrogen and Battery EV Fleets Considering DERs

Electrified transportation leads to a tighter integration between transportation and energy distribution systems. In this work, we develop scalable optimization models to co-design hydrogen and battery electric vehicle (EV) fleets, distributed energy resources, and fast-charging and hydrogen-fueling infrastructure to efficiently meet transportation demands. A novel integer-clustering formulation is used for optimizing fleet-level EV operation while maintaining accurate individual vehicle dispatch, which significantly improves the computation efficiency with guaranteed performance. We apply the optimization model to Boston's public transit bus network using real geospatial data and cost parameters. Realistic insights are provided into the future evolution of coupled electricity-transportation-hydrogen systems, including the effects of electricity price structure, hydrogen fuel cost, carbon emission constraint, temperature effects on EV range, and distribution system upgrade cost.

eess.SY↗

arXiv2025

Optimizing Bidding Curves for Renewable Energy in Two-Settlement Electricity Markets

Coordination of day-ahead and real-time electricity markets is imperative for cost-effective electricity supply and also to provide efficient incentives for the energy transition. Although stochastic market designs feature the least-cost coordination, they are incompatible with current deterministic markets. This paper proposes a new approach for compatible coordination in two-settlement markets based on benchmark bidding curves for variable renewable energy. These curves are optimized based on a bilevel optimization problem, anticipating per-scenario responses of deterministic market-clearing problems and ultimately minimizing the expected cost across day-ahead and real-time markets. Although the general bilevel model is challenging to solve, we theoretically prove that a single-segment bidding curve with a zero bidding price is sufficient to achieve system optimality if the marginal cost of variable renewable energy is zero, thus addressing the computational challenge. In practice, variable renewable energy producers can be allowed to bid multi-segment curves with non-zero prices. We test the bilevel framework for both single- and multiple-segment bidding curves under the assumption of fixed bidding prices. We leverage duality theory and McCormick envelopes to derive the linear programming approximation of the bilevel problem, which scales to practical systems such as a 1576-bus NYISO system. We benchmark the proposed coordination and find absolute dominance over the baseline solution, which assumes that renewables agnostically bid their expected forecasts. We also demonstrate that our proposed scheme provides a good approximation of the least-cost, yet unattainable in practice, stochastic market outcome.

eess.SY↗

arXiv2024

Evaluating Offshore Electricity Market Design Considering Endogenous Infrastructure Investments: Zonal or Nodal?

Policy makers are formulating offshore energy infrastructure plans, including wind turbines, electrolyzers, and HVDC transmission lines. An effective market design is crucial to guide cost-efficient investments and dispatch decisions. This paper jointly studies the impact of offshore market design choices on the investment in offshore electrolyzers and HVDC transmission capacity. We present a bilevel model that incorporates investments in offshore energy infrastructure, day-ahead market dispatch, and potential redispatch actions near real-time to ensure transmission constraints are respected. Our findings demonstrate that full nodal pricing, i.e., nodal pricing both onshore and offshore, outperforms the onshore zonal combined with offshore nodal pricing or offshore zonal layouts. While combining onshore zonal with offshore nodal pricing can be considered as a second-best option, it generally diminishes the profitability of offshore wind farms. However, if investment costs of offshore electrolyzers are relatively low, they can serve as catalysts to increase the revenues of the offshore wind farms. This study contributes to the understanding of market designs for highly interconnected offshore power systems, offering insights into the impact of congestion pricing methodologies on investment decisions. Besides, it is useful towards understanding the interaction of offshore loads like electrolyzers with financial support mechanisms for offshore wind farms.

econ.GN↗

arXiv2023

Uncertainty-Informed Renewable Energy Scheduling: A Scalable Bilevel Framework

This work proposes an uncertainty-informed bid adjustment framework for integrating variable renewable energy sources (VRES) into electricity markets. This framework adopts a bilevel model to compute the optimal VRES day-ahead bids. It aims to minimize the expected system cost across day-ahead and real-time stages and approximate the cost efficiency of the stochastic market design. However, solving the bilevel optimization problem is computationally challenging for large-scale systems. To overcome this challenge, we introduce a novel technique based on strong duality and McCormick envelopes, which relaxes the problem to a linear program, enabling large-scale applications. The proposed bilevel framework is applied to the 1576-bus NYISO system and benchmarked against a myopic strategy, where the VRES bid is the mean value of the probabilistic power forecast. Results demonstrate that, under high VRES penetration levels (e.g., 40%), our framework can significantly reduce system costs and market-price volatility, by optimizing VRES quantities efficiently in the day-ahead market. Furthermore, we find that when transmission capacity increases, the proposed bilevel model will still reduce the system cost, whereas the myopic strategy may incur a much higher cost due to over-scheduling of VRES in the day-ahead market and the lack of flexible conventional generators in real time.

eess.SY↗

arXiv2023

An Integer Clustering Approach for Modeling Large-Scale EV Fleets with Guaranteed Performance

Large-scale integration of electric vehicles (EVs) leads to a tighter integration between transportation and electric energy systems. In this paper, we develop a novel integer-clustering approach to model a large number of EVs that manages vehicle charging and energy at the fleet level yet maintain individual trip dispatch. The model is then used to develop a spatially and temporally-resolved decision-making tool for optimally planning and/or operating EV fleets and charging infrastructure. The tool comprises a two-stage framework where a tractable disaggregation step follows the integer-clustering problem to recover an individually feasible solution. Mathematical relationships between the integer clustering, disaggregation, and individual formulations are analyzed. We establish theoretical lower and upper bounds on the true individual formulation which underpins a guaranteed performance of the proposed method. The optimality accuracy and computational efficiency of the integer-clustering formulation are also numerically validated on a real-world case study of Boston's public transit network under extensive test instances. Substantial speedups with minimal loss in solution quality are demonstrated.

eess.SY↗

arXiv2023

Cost-effective Planning of Decarbonized Power-Gas Infrastructure to Meet the Challenges of Heating Electrification

Building heat electrification is central to economy-wide decarbonization efforts and directly affects energy infrastructure planning through increasing electricity demand and reducing the building sector's use of gas infrastructure that also serves the power sector. Here, we develop a modeling framework to quantify end-use demand for electricity and gas in the buildings sector under various electrification pathways and evaluate their impact on co-optimized bulk power-gas infrastructure investments and operations under deep decarbonization scenarios. Applying the framework to study the U.S. New England region in 2050 across 20 weather scenarios, we find high electrification of the residential sector can increase sectoral peak and total electricity demands by up to 56-158% and 41-59% respectively relative to business-as-usual projections. Employing demand-side measures like building envelope improvements under high electrification, however, can reduce the magnitude and weather sensitivity of peak load as well as induce overall efficiency gains, reducing the combined residential sector energy demand for power and gas by 28-30% relative to the present day. Notably, a combination of high electrification and envelope improvements yields the lowest bulk power-gas system cost outcomes. Accounting for \bt{midstream} methane emissions from gas supply chain increase the reliance on low-carbon fuels, which indirectly improves the cost-effectiveness of end-use electrification. Similarly, we find that demand flexibility programs can reduce the total system cost by up to 6.3%.

eess.SY↗

arXiv2023

Uniform Pricing vs Pay as Bid in 100%-Renewables Electricity Markets: A Game-theoretical Analysis

This paper evaluates market equilibrium under different pricing mechanisms in a two-settlement 100%-renewables electricity market. Given general probability distributions of renewable energy, we establish game-theoretical models to analyze equilibrium bidding strategies, market prices, and profits under uniform pricing (UP) and pay-as-bid pricing (PAB). We prove that UP can incentivize suppliers to withhold bidding quantities and lead to price spikes. PAB can reduce the market price, but it may lead to a mixed-strategy price equilibrium. Then, we present a regulated uniform pricing scheme (RUP) based on suppliers' marginal costs that include penalty costs for real-time deviations. We show that RUP can achieve lower yet positive prices and profits compared with PAB in a duopoly market, which approximates the least-cost system outcome. Simulations with synthetic and real data find that under PAB and RUP, higher uncertainty of renewables and real-time shortage penalty prices can increase the market price by encouraging lower bidding quantities, thereby increasing suppliers' profits.

eess.SY↗

arXiv2023

Differentially Private Algorithms for Synthetic Power System Datasets

While power systems research relies on the availability of real-world network datasets, data owners (e.g., system operators) are hesitant to share data due to security and privacy risks. To control these risks, we develop privacy-preserving algorithms for the synthetic generation of optimization and machine learning datasets. Taking a real-world dataset as input, the algorithms output its noisy, synthetic version, which preserves the accuracy of the real data on a specific downstream model or even a large population of those. We control the privacy loss using Laplace and Exponential mechanisms of differential privacy and preserve data accuracy using a post-processing convex optimization. We apply the algorithms to generate synthetic network parameters and wind power data.

cs.CR↗

arXiv2022

Market Mechanisms for Low-Carbon Electricity Investments: A Game-Theoretical Analysis

Electricity markets are transforming from the dominance of conventional energy resources (CERs), e.g., fossil fuels, to low-carbon energy resources (LERs), e.g., renewables and energy storage. This work examines market mechanisms to incentivize LER investments, while ensuring adequate market revenues for investors, guiding investors' strategic investments towards social optimum, and protecting consumers from scarcity prices. To reduce the impact of excessive scarcity prices, we present a new market mechanism, which consists of a Penalty payment for lost load, a supply Incentive, and an energy price Uplift (PIU). We establish a game-theoretical framework to analyze market equilibrium. We prove that one Nash equilibrium under the penalty payment and supply incentive can reach the social optimum given quadratic supply costs of CERs. Although the price uplift can ensure adequate revenues, the resulting system cost deviates from the social optimum while the gap decreases as more CERs retire. Furthermore, under the traditional marginal-cost pricing (MCP) mechanism, investors may withhold investments to cause scarcity prices, but such behavior is absent under the PIU mechanism. Simulation results show that the PIU mechanism can reduce consumers' costs by over 30% compared with the MCP mechanism by reducing excessive revenues of low-cost CERs from scarcity prices.

eess.SY↗

arXiv2022

A Scalable Bilevel Framework for Renewable Energy Scheduling

Accommodating the uncertain and variable renewable energy sources (VRES) in electricity markets requires sophisticated and scalable tools to achieve market efficiency. To account for the uncertain imbalance costs in the real-time market while remaining compatible with the existing sequential market-clearing structure, our work adopts an uncertainty-informed adjustment toward the VRES contract quantity scheduled in the day-ahead market. This mechanism requires solving a bilevel problem, which is computationally challenging for practical large-scale systems. To improve the scalability, we propose a technique based on strong duality and McCormick envelopes, which relaxes the original problem to linear programming. We conduct numerical studies on both IEEE 118-bus and 1814-bus NYISO systems. Results show that the proposed relaxation can achieve good performance in accuracy (0.7%-gap in the system cost wrt. the least-cost stochastic clearing benchmark) and scalability (solving the NYISO system in minutes). Furthermore, the benefit of this bilevel VRES-quantity adjustment is more significant under higher penetration levels of VRES (e.g., 70%), under which the system cost can be reduced substantially compared to a myopic day-ahead offer strategy of VRES.

eess.SY↗

arXiv2022

Multi-Stage Decision Rules for Power Generation & Storage Investments with Performance Guarantees

We develop multi-stage linear decision rules (LDRs) for dynamic power system generation and energy storage investment planning under uncertainty and propose their chance-constrained optimization with performance guarantees. First, the optimized LDRs guarantee operational and carbon policy feasibility of the resulting dynamic investment plan even when the planning uncertainty distribution is ambiguous. Second, the optimized LDRs internalize the tolerance of the system planner towards the stochasticity (variance) of uncertain investment outcomes. They can eventually produce a quasi-deterministic investment plan, which is insensitive to uncertainty (as in deterministic planning) but robust to its realizations (as in stochastic planning). Last, we certify the performance of the optimized LDRs with the bound on their sub-optimality due to their linear functional form. Using this bound, we guarantee that the preference of LDRs over less restrictive -- yet poorly scalable -- scenario-based optimization does not lead to financial losses exceeding this bound. We use a testbed of the U.S. Southeast power system to reveal the trade-offs between the cost, stochasticity, and feasibility of LDR-based investments. We also conclude that the LDR sub-optimality depends on the amount of uncertainty and the tightness of chance constraints on operational, investment and policy variables.

math.OC↗

arXiv2022

Strategic Storage Investment in Electricity Markets

Arbitrage is one important revenue source for energy storage in electricity markets. However, a large amount of storage in the market will impact the energy price and reduce potential revenues. This can lead to strategic behaviors of profit-seeking storage investors. To study the investors' strategic storage investments, we formulate a non-cooperative game between competing investors. Each investor decides the storage investment over a long investment horizon, and operates the storage for arbitrage revenues in the daily electricity market. Different investors can deploy storage with different characteristics. Their decisions are coupled due to the market price that is determined by all the investors' decisions. We use market data from California ISO to characterize the storage impact on the market price, based on which we establish a centralized optimization problem to compute the market equilibrium. We show that an increasing number of investors will increase the market competition, which reduces investors' profits but increases the total invested storage capacity. Furthermore, we find that a slight increase in the storage efficiency (e.g., increased charge and discharge efficiency) can significantly improve an investor's profit share in the market.

q-fin.GN↗

arXiv2021

Multi-Stage Linear Decision Rules for Stochastic Control of Natural Gas Networks with Linepack

The disturbances from variable and uncertain renewable generation propagate from power systems to natural gas networks, causing gas network operators to adjust gas supply nominations to ensure operational security. To alleviate expensive supply adjustments, we develop control policies to leverage instead the flexibility of linepack -- the gas stored in pipelines -- to balance stochastic gas extractions. These policies are based on multi-stage linear decision rules optimized on a finite discrete horizon to guide controllable network components, such as compressors and valves, towards feasible operations. Our approach offers several control applications. First, it treats the linepack as a main source of flexibility to balance disturbances from power systems without substantial impacts on nominal gas supply. Second, these policies can be optimized to minimize the variability (due to intermittency of renewables) and variance (due to their uncertainty) of network state variables, such as pressures. Finally, it enables topology optimization to decouple network parts and prevent uncertainty propagation through the network. This is demonstrated using illustrative numerical experiments.

math.OC↗