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.

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 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.

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.

This paper uses a least-cost capacity expansion model to analyze system outcomes under two long-term electricity market constructs, energy-only and energy-capacity markets, for an electricity grid with hydropower in transition to a low-carbon system. We use a simplified representation of the New York power grid with three different types of hydropower technologies (reservoir, run-of-river, and pumped storage hydropower) as a case study. Both market designs are found to result in similar total system costs, average electricity prices, generation patterns, and reliability levels when realistic market parameters are used. However, the energy-capacity market design yields less volatile electricity prices and slightly greater total capacity investment, directed toward technologies with high fixed costs and low annual capacity factors. We also analyze the comparative operations and profitability of the three different hydropower technologies. Results suggest that pumped storage hydropower is more highly complementary with variable renewable energy resources than reservoir or run-of-river hydropower, and that all three hydropower technologies will experience significant operational changes in the transition to a low-carbon electricity system.

Occupant-centric control (OCC) has shown great potential in enhancing building energy performance and promoting urban sustainability. However, there have been limited large-scale field implementations due to the lack of production-ready solutions, especially in data-poor existing buildings. In this paper, we present an integrated rule-based OCC solution that does not rely on advanced sensing or computational infrastructure and is compatible with existing building management systems, thereby reaching technology readiness level 8. The proposed framework was deployed in two university buildings for 10 months, and comprehensive energy-saving analyses were cFonducted to quantify the energy impact of incremental supervisory control interventions. This data- and computationally-efficient framework showed over 40% energy savings compared with the baseline static nighttime setback control. Detailed measurement and verification highlighted the substantial potential for energy reduction in ventilation systems, especially during heating seasons. We further addressed the trade-offs of AI/ML-based supervisory control through simulation-based comparative experiments, demonstrating that the proposed OCC framework yielded results comparable to model predictive control while offering lower implementation complexity.

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.

Commercial buildings are among the largest and least intelligent energy users in the modern economy, with heating, ventilation, and air-conditioning (HVAC) systems alone often accounting for more than half of their total energy consumption. For decades, the control of HVAC systems has relied primarily on static building management systems (BMSs) and manual adjustments, using fixed setpoints and schedules and periodic readjustment. Inefficient control has led to significant energy waste. As cities decarbonize and grids integrate more variable renewable energy, buildings must transform from passive energy consumers into intelligent, flexible, and grid-supporting assets. Recent advances in sensing, optimization, and artificial intelligence (AI) pave the way for this future. This article traces the evolution from well-tuned BMSs and supervisory analytics to optimized control using model predictive control (MPC) and advanced learning and looks ahead to the next generation of HVAC control systems capable of sensing occupancy, operating across spaces, interacting with the grid, and scaling to different buildings. We illustrate how real-time AI control can dynamically manage HVAC systems across zones and buildings, adjusting setpoints based on occupancy, weather, and energy prices to reduce waste, lower costs, and create new revenue streams through services, such as demand response (DR). As a case study, we highlight an AI-based automation system developed by the Massachusetts Institute of Technology (MIT) for its campus buildings. This system utilizes graph learning and reinforcement learning to capture limited room occupancy, local weather, and thermal interactions across multiple zones. In ongoing pilot projects, it has demonstrated significant energy savings by adjusting zone-level heating and cooling setpoints. Linking such academic innovations with a deployable and BMS-compatible software layer is key to transforming commercial buildings into intelligent and sustainable players in a clean energy future.

This paper presents an analysis framework for identifying and quantifying variable renewable energy (VRE) drought events using historical operations dispatch wind and solar generation data. We apply a sliding-window methodology across multiple electricity markets (USA, Spain, Portugal, Australia) to measure VRE drought frequency, duration, and energy deficit magnitude. Results show that wind droughts are predominantly short-duration ($\lt24$ hours), while solar droughts are more sensitive to threshold definitions and often span 24–48 hours. Moreover, resource and geographic aggregation significantly mitigate VRE drought frequency. This study highlights how VRE drought sensitivity parameters influence their identification and discusses implications for operational planning. We find that most VRE drought events can be managed within the operational planning time frame, wherein long-duration energy storage (LDES) can support system operators in shoring the gap between short-duration system reserves and longer-term planning.

With increasing shares of renewable energy, episodes of continuously low renewable output, referred to as renewable drought (RD) events, pose growing challenges to the long-term adequacy of renewable-dominant power systems. This paper develops a meteorology-driven framework to characterize the spatiotemporal features of RD events and represent the inadequacy risk arising from their multi-day persistence and cross-regional dependence. Based on this, a risk-aware planning model is formulated to integrate typical-day operation with RD events of varying durations in long-term planning. Two case studies on the modified Garver's 6-node system and China's East Power Grid show that increasing renewable penetration amplifies inadequacy risk under RD events, while coordinated flexibility resources—including inter-regional transmission, long-duration storage, and diversified wind–solar portfolios—can effectively mitigate such risk.

We assess the bulk energy system impact of decarbonizing heavy duty vehicle (HDV) based road transportation via the use of either hydrogen (H2), or drop-in synthetic liquid fuels produced from H2 and CO2. Our analysis soft-links two modeling approaches: a) a bottom-up model of transportation energy demand that produces 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 subjected to technological and policy constraints to meet exogenous final energy demands. Through a case study of Western European countries under deep decarbonization constraints in 2040, 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 sequestration, 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 total modeled system costs and liquid fuel demand relative to synthetic fuel-based pathways. Synthetic fuel adoption generally increases DAC deployment and associated system costs. The study highlights the trade-offs associated with different transportation decarbonization pathways, and underscores the importance of multi-sectoral considerations in decarbonization studies.

Achieving global net-zero power systems by mid-century demands integrated frameworks addressing climate mitigation and energy access equity. Here we present a spatio-temporally resolved global power system model (0.25 degrees & times; 0.25 degrees, 8,760 hours) co-optimizing capacity expansion and operational strategies. Findings show that net-zero global power systems meeting universal electricity needs for decent living standards are technically feasible, requiring 15-20 TW of variable renewable energy (VRE). Abundant VRE resources offer cost-effective electricity access in low-income regions, such as Africa, promoting climate justice. Land use is critical, with solar photovoltaics alone requiring over 9 million hectares. Over 80% of VRE is within 200 km of load centres. Demand-side management could reduce system costs by 6.5% (similar to US$182 billion yr-1). Expanding international transmission and removing renewable technology trade barriers could cut costs by 5.6% (similar to US$157 billion yr-1) and 12.2% (similar to US$345 billion yr-1), underscoring the pivotal role of international collaboration in building inclusive net-zero power systems.

Capacity expansion and levelized cost of energy (LCOE) models commonly annualize capital costs using a static discount rate. In practice, however, required returns evolve across project phases as construction risk is resolved, operational performance is demonstrated, and financing structures adjust. Ignoring these shifts can distort the present value of capital costs, particularly for long-lived, capital-intensive technologies. This paper introduces a Time and Risk Adjusted Capital Cost Estimation (TRACE) method, which reformulates the capital recovery factor to incorporate discrete, phase-dependent discount rates while preserving compatibility with lifetime average modeling frameworks. Stylized screening curves, capacity expansion, and simplified LCOE examples demonstrate that static discounting assumptions can materially alter optimal generation mix and standalone technology rankings, with distortions dependent on static rate choices, capital intensity, project lifetime, and operational capacity factor. TRACE provides a representation of capital costs that is better grounded in project finance behavior.

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.

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.

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.

Building heat electrification is central to economy-wide decarbonization efforts and directly affects energy infrastructure planning through increasing electricity demand and reduces the use of gas infrastructure that also serves the power sector. However, the simultaneous effects on both the power and gas systems have yet to be rigorously evaluated. Offering two key contributions, we develop a modeling framework to project 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 62-160% and 47-65% respectively relative to business-as-usual trajectories. 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 reduce combined power and gas demand by 29-31% relative to the present day. Notably, a combination of high electrification and envelope improvements yields the lowest bulk power-gas system cost outcomes. We also find that inter-annual weather-driven variations in demand result in up to 20% variation in optimal power sector investments, which highlights the importance of capturing weather sensitivity for planning purposes.