The increasing integration of artificial intelligence (AI) into the electricity grid presents both significant opportunities and challenges. This paper examines two related dimensions: "AI for the Grid," which explores the use of AIto enhance grid planning, operations, and market participation, and "AI on the Grid," which considers the rising electricity demand from AI-driven data centers and its implications for grid infrastructure and cost allocation. AI offers opportunities for the grid through advanced tools to improve system planning, operations, and resilience while also promoting affordability and reducing barriers to market entry. However, challenges remain regarding the reliability and trustworthiness of AI models, regulatory complexity, coordination across stakeholders, and potential market power concerns. Rapid load growth caused by AI on the grid will stress the bulk power system, creating challenges for reliability, infrastructure planning, and managing unpredictable loads. However, if incentivized and managed appropriately, data centers can also enhance grid flexibility and responsiveness. We establish eight specific research needs to help unleash AI innovation, while maintaining system reliability and affordability. For the grid these include, 1) evaluating productivity gains and risks of AI-assisted services, 2) advancing AI methods for operations and planning, 3) evaluating the potential of private, local, small language models (SLMs), and 4) assessing AI-driven market power risks. Research needs on the grid include, 1) establishing incentives for large load flexibility, 2) enhancing market structures and designs, 3) developing new cost-allocation strategies, and 4) balancing the costs of over-and under-planning for load growth in the face of uncertainty.

We incorporate empirical offer data from the Electric Reliability Council of Texas operating reserve market into a production cost model and assess the impacts to modeled hourly prices for regulation-up and responsive reserve service. We find that naive modeling assumptions, where units offer capacity into operating reserve markets with zero-price, underestimate the resultant market clearing prices of these services. This in turn leads to a systematic undervaluation of energy storage resources, which typically generate a significant portion of their revenues from operating reserves. Specifically, in a reference 2022 portfolio, utilizing empirically-derived offers for all resources increases total energy storage revenues by 61% relative to a reference zero-price offer scenario. We also assess three energy storage penetration levels and demonstrate that storage revenues decrease substantially as deployment increases. We conclude that technoeconomic evaluations of energy storage should align their model assumptions with realistic operating reserve market offer behavior.

We apply a least-cost generation expansion model of the continental United States to assess how optimal investments in long-duration energy storage (LDES) technologies are impacted by changes in system generation portfolios and technology costs, assessing 369 capacity expansion scenarios in total. The expansion model considers 8,760 h of chronological operations for the entire target year, 2040. We find that low-cost LDES technologies can reduce generation investments and system costs. Specifically, once the costs for 24- and 100-h storage reach $38/kWh and $14/kWh, respectively, substantial deployments are observed. The distribution of storage investments across durations is strongly influenced by the system generation portfolio. We also demonstrate that a high-fidelity temporal representation is required to capture the value of LDES in generation expansion. Finally, we conduct a regression analysis of our capacity expansion results and find that LDES deployments are positively correlated with the combined wind and solar capacity share and negatively correlated with peaking and baseload shares.

Revenue sufficiency at the resource level is critical to ensuring long-term resource adequacy in power systems. To accurately quantify and analyze revenue sufficiency in a generic U.S. market design with both energy and capacity markets, we develop a coordinated simulation framework that captures interaction between the two markets. The framework dynamically updates operating reserve demand curves (ORDCs) and capacity market demand curves (CDCs) to reflect evolving market supply and demand conditions, thereby mitigating revenue miscalculation. In this framework, system ORDCs are characterized based on reliability conditions and are used to clear daily markets for energy and reserves. CDCs and resource supply curves are represented by accounting for generation units’ investment costs and forecasted energy market profits, and capacity markets are cleared accordingly. Market coordination and curves updates can be conducted at configurable temporal resolutions. Leveraging the proposed coordinated simulation framework, we conduct a series of case studies to illustrate how energy market revenues influence capacity market demand curves, supply curves, and total resource revenues. We further analyze how key market design parameters (e.g., ORDC and CDC update frequency, and energy price caps) affect the revenue sufficiency of different generation technologies. We find that ORDC update frequency has no significant impact on overall revenue outcomes, while market efficiency may be enhanced when sub-annual CDCs allocate investment costs in a non-uniform manner.

We incorporate empirical offer data from the Electric Reliability Council of Texas operating reserve market into a production cost model and assess the impacts to modeled hourly prices for regulation-up and responsive reserve service. We find that naive modeling assumptions, where units offer capacity into operating reserve markets with zero-price, underestimate the resultant market clearing prices of these services. This in turn leads to a systematic undervaluation of energy storage resources, which typically generate a significant portion of their revenues from operating reserves. Specifically, in a reference 2022 portfolio, utilizing empirically-derived offers for all resources increases total energy storage revenues by 61% relative to a reference zero-price offer scenario. We also assess three energy storage penetration levels and demonstrate that storage revenues decrease substantially as deployment increases. We conclude that technoeconomic evaluations of energy storage should align their model assumptions with realistic operating reserve market offer behavior.

This paper introduces HydroBoost, a forecast-aware rolling-horizon optimization model for co-optimizing hydropower and battery energy storage system dispatch under realistic electricity market conditions. Hydro-Boost integrates a mixed-integer linear programming formulation with reservoir dynamics, ancillary service provision, and multi-day ahead electricity price forecasts. Unlike conventional techno-economic planning tools that assume perfect foresight of future market prices, HydroBoost explicitly distinguishes between perfect foresight, defined as full knowledge of realized future prices, and imperfect foresight, in which dispatch decisions are based on forecasted prices with uncertainty. Case studies using California Independent System Operator market data demonstrate that seven-day imperfect forecasts, implemented using a mean persistence model, capture most of the economic benefits of seven-day perfect foresight while revealing important operational differences. Across two representative years, imperfect foresight results in 1%-2% lower total revenues, approximately 25% more water spillage, around 15% more hydro unit starts, and roughly 3% more battery cycling relative to perfect seven-day foresight. These results show that even modest forecast errors can materially influence operational behavior and asset wear. By explicitly incorporating forecast uncertainty and extended planning horizons, HydroBoost provides a practical, data-driven decision-support tool for evaluating and optimizing hybrid hydropower-BESS systems in real-world markets.

As new clean energy policies continue to drive expansion of variable renewable energy deployments, long-duration energy storage will be critical for the United States in serving as a grid stabilizing technology. While pumped storage hydro (PSH) remains an attractive technology due to its low cost, high round-trip efficiency, reliable operation, and long lifetime, no new large-scale PSH plants have been built in nearly 30 years due to environmental challenges, long permitting times, limited geographic suitability, large scale/cost, long development times, and challenges in demonstrating the value of services PSH can provide in an evolving grid and market landscape. Quidnet's Geomechanical Energy Storage (GES) system provides an alternative to conventional PSH that avoids geographic challenges by storing highly pressurized water in an underground reservoir and then releasing the pressurized water through a turbine up to an upper reservoir at ground level. The team used the pumped storage hydro valuation tool to evaluate the GES system in 30 balancing areas (BAs) in seven states: California, Colorado, Illinois, Michigan, New York, Pennsylvania, and Texas. A 100 megawatt, 1 gigawatt-hour GES produced strong economic performance in California, Texas, Illinois, New York, and parts of Michigan. More detailed analysis in two BAs in Texas resulted in annual revenue of roughly ${\$}$16 million and BCRs ranging from 1.34 to 1.36.

Hydropower is an abundant, dispatchable, clean energy resource that will play an important role in supporting the clean energy transition. In particular, dispatchable hydropower can provide the operational flexibility that will be required in future systems with high variable renewable energy penetrations. However, the theoretical operational flexibility of hydropower can be restricted in practice by various non-power constraints. In this paper, we quantify how increasing the operational flexibility of dispatchable hydropower resources with reservoirs impacts least-cost generation portfolios and supports power system decarbonization. Specifically, we conduct a capacity expansion analysis of a two-zone system: a hydro-dominated region and a neighboring region with aggressive decarbonization targets that are represented by the United States Pacific Northwest and California respectively. We then introduce a quantifiable index for characterizing the operational flexibility of reservoir hydropower and assess how changes in this metric impact the system-optimal generation portfolio. We find that increasing hydropower flexibility leads to more investment in wind generation, less investment in natural gas generation, lower system costs, and lower system emissions. We further demonstrate a substitution effect between the grid services provided by flexible hydropower operation, increased transmission capacity on a congested line, and energy storage resources. Finally, we show that increasing the operational flexibility of hydropower increases the effective load carrying capability of both hydropower and wind resources. This research supports a more nuanced understanding of how hydropower can support electricity system decarbonization and may motivate reassessing the cost-benefit tradeoffs of non-power constraints that restrict operational flexibility.

Utilizing energy storage solutions to reduce the need for traditional transmission investments has been recognized by system planners and supported by federal policies in recent years. This work demonstrates the need for detailed reliability assessment for quantitative comparison of the reliability benefits of energy storage and traditional transmission investments. First, a mixed-integer linear programming expansion planning model considering candidate transmission lines and storage technologies is solved to find the least-cost investment decisions. Next, operations under the resulting system configuration are simulated in a probabilistic reliability assessment which accounts for weatherdependent forced outages. The outcome of this work, when applied to TPPs, is to further equalize the consideration of energy storage compared to traditional transmission assets by capturing the value of storage for system reliability.

The complexity of traditional power system analysis workflows presents significant barriers to efficient decision-making in modern electric grids. This paper presents GridMind, a multi-agent AI system that integrates Large Language Models (LLMs) with deterministic engineering solvers to enable conversational scientific computing for power system analysis. The system employs specialized agents coordinating AC Optimal Power Flow and N-1 contingency analysis through natural language interfaces while maintaining numerical precision via function calls. GridMind addresses workflow integration, knowledge accessibility, context preservation, and expert decision-support augmentation. Experimental evaluation on IEEE test cases demonstrates that the proposed agentic framework consistently delivers correct solutions across all tested language models, with smaller LLMs achieving comparable analytical accuracy with reduced computational latency. This work establishes agentic AI as a viable paradigm for scientific computing, demonstrating how conversational interfaces can enhance accessibility while preserving numerical rigor essential for critical engineering applications.

Hybrid generation and energy storage systems can enhance asset flexibility, enabling various services and optimizing financial performance. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary or too complex for most asset owners to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run).This work presents a deep learning-based battery sizing optimization tool for hybridizing generation facilities. The tool uses deep learning technique to predict revenue over a broad search space of potential battery sizes, estimate capital and operations costs (including accounting for battery degradation), and calculate financial performance of each potential battery system investment; an output is a recommendation of battery that maximizes financial performance. The tool is tested and validated for hydropower generation and is publicly available on Idaho National Laboratory's GitHub page (https://github.com/idaholab/Hydro_Hybrids), documented in Zenodo (https://zenodo.org/record/7562692#.Y9Q7anbMKUm), and accessible through an intuitive web app (hydrohybrids.inl.gov). This tool will help a greater cross-section of generation owners consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets.

for both functions, which reduces the capital costs; (3) the reactants for chemical and electricity generation are stored outside the cell which appears beneficial for a weekly cycle. A preliminary technical and economic feasibility assessment of the hydrogen-halogen battery as an electric energy storage system was undertaken and results are presented.

As power systems integrate increasing quantities of wind, solar and energy storage resources, it is important to revisit power system capacity expansion modeling methods and assumptions that have been utilized in thermal-dominated systems. We conduct a series of case study analyses using a simplified representation of the Electric Reliability Council of Texas (ERCOT) system to demonstrate how least-cost capacity expansion outcomes are impacted by changes in model resolution across two temporal dimensions: 1) the number of considered representative periods, and 2) the system dispatch interval. First, we find that the least-cost generation portfolio can differ significantly for small changes in the number of representative days, but largely converges to the 365-day result once 104 representative days are considered. Furthermore, systems with wind, solar and storage resources were more sensitive to changes in the number of representative days than a thermal-dominated system. Second, we find that considering five-minute dispatch resolution consistently results in least-cost generation portfolios with less solar capacity and more energy storage capacity than corresponding scenarios with hourly dispatch intervals. This suggests that hourly dispatch representation fails to capture the intra-hour volatility of solar generation, and therefore also overlooks opportunities for storage resources to provide system value by balancing this volatility. Collectively these results indicate that capacity expansion modelers should revisit conventional approaches to temporal representation when conducting analyses of deeply decarbonized power systems to ensure that such analyses are robust and actionable. To our knowledge, this is the first study to analyze capacity expansion outcomes with five-minute dispatch resolution in this manner.

There is increasing interest in hybridizing generation resources with batteries to improve the flexibility and value of the primary energy resource. The value propositions of hybridization are more acute with variable renewable energy (VRE) generation resources, such as wind and solar, and therefore these types of hybrids have been most studied and deployed in the real world. Here we review the state-of-the-art understanding on wind or solar plus batteries systems and compare these to value proposition opportunities for pairing hydropower with batteries. While less studied, there are also opportunities and value propositions associated with hybridizing hydropower resources. Comparing the state of hydro-hybrids research to wind and solar hybrids reveals the gaps in understanding for hydro-hybrids and provides the foundation for categorizing value propositions that are unique to hydropower. Many studies that focus on solar and wind can be adapted to inform hydro hybrids research, but wind studies were found to be the most applicable. The key lessons learned are that hydro-hybrids should take inspiration from the battery-health concerned controls and stochastic market bidding strategies, developed for solar and wind. Unlike solar and wind, modes of power generation at hydropower plants are directly linked to competing objectives around irrigation, flood control, and river ecosystem health. New studies and models that take these objectives into account are required to realize the full potential of hydropower-battery hybrids. We conclude that valuation of hydropower hybridization must go beyond an assessment of the energy arbitrage potential of the battery to include reduced cost of hydropower operations.

Well-designed electricity markets play a crucial role in maintaining reliable electric power systems, which are critical in modern society. This study examines the impact of different electricity market designs and clean energy incentive schemes on supporting renewable energy integration and achieving clean energy goals. To this end, we utilize a game-theoretical generation expansion planning model where generation companies make investment and retirement decisions to maximize their expected profit. The model is structured as an equilibrium problem with equilibrium constraints (EPEC) and solved using a diagonalization approach combined with progressive hedging. We analyze three types of electricity market designs: an energy-only market, a capacity market, and a clean energy market, and consider a wide range of market parameters resulting in 14 total sce-narios. Wind and solar capacity comprise the majority of new investments in all considered scenarios, but the resultant system planning reserve margin (PRM) can differ significantly depending on market parameters. We also find that profit-driven investments lead to lower PRMs than a traditional system cost minimization approach. These individual scenario results further demonstrate how different market designs and clean energy incentive schemes may influence investor decision-making and impact resource adequacy throughout the clean energy transition.

Building new transmission lines is not only expensive, but sometimes it has legal or social barriers, e.g. not in my backyard, preventing its deployment. While pumped storage hydro (PSH) as a transmission asset can provide an effective alternative with comparable reliability and cost, it suffers from the issue of high investment cost and low resource utilization. Therefore, a dual-use participation framework for PSH is proposed that allows PSH to provide transmission services and also participate in the energy market when no transmission service is needed, increasing resource utilization and providing financial benefits. This work proposes a computational model for PSH for dual-use operation. Specifically, the restriction on market participation is characterized by the transmission service period and state of charging, which is estimated a day ahead based on line thermal overloading risk. The risk is quantified based on a novel probabilistic power flow model. Then a cost recovery mechanism is proposed that allows the PSH operator and transmission planning authorities to share investment cost and market profit. A techno-economic case study on the WECC 240 bus test system shows that a PSH project in dual-use has the potential to provide significant financial benefits while maintaining system reliability.

The rapidly evolving electricity system with increasing variable renewable energy (VRE) resources provides both opportunities and challenges for the power sector. With the significant ramps and intermittency associated with VRE resources, the requirements and need for additional flexible resources increase. Pumped storage hydropower (PSH) provides flexibility to the electricity grid to replace fossil fuel plants, which are responsible for 25% of U.S. emissions. PSH projects support various aspects of power system operations, including flexibility, ramping capability, energy, ancillary service, black start, and others. The significant potential of hydropower requires understanding the different value drivers to the electricity system specific to the location of a project and then optimizing the plant for the different system values. Thus, determining the value of PSH projects and their many services and contributions to the electricity system can be a challenge for potential developers, system owners, regulators, policy makers, and consultants.