Search Results (18)

AI-driven growth is pushing data-center electricity demand from 415 TWh (2024) toward 945 TWh by 2030. Demand-response research has matured on operational levers but treats the physical envelope as largely exogenous, leaving construction-phase decisions that bound achievable flexibility unmodeled. This article integrates three previously separate bodies of literature (data-center demand response, grid-interactive efficient buildings, and stochastic optimal control under irreversibility) into a single framework that prices construction-phase flexibility as a portfolio of real options, pairing elicitation-derived (FAHP) weights with simulation-derived (Sobol) variance indices. None of the individual techniques is new; the contribution is their synthesis and the finding that architectural and site decisions carry the dominant financial leverage in the model, whereas a literature-grounded synthetic-persona prior (twenty-five LLM-simulated personas) prioritizes mechanical-electrical systems; a divergence we frame as a screening diagnostic between an LLM prior and the model. The Flex-by-Design Readiness Index (FDRI) is a nineteen-dimension taxonomy across architectural, MEP, and site-urban layers, weighted by Fuzzy AHP. The FDRI–ROV model formalizes the decision as stochastic optimal control under irreversibility. Calibrated to PJM, ERCOT, and CAISO (2024–2026) for a 100 MW plant at N = 10,000 paths over thirty years, it yields +$79 M net option value at Full FDRI for PJM (additive upper bound; substitution-corrected ≈ +$41 M, 1.7× CapEx; 2.4× CapEx PJM, 2.1× ERCOT, 2.8× CAISO); on both the additive (2.4–2.8×) and corrected (1.7×) bases the pre-registered H2 threshold of Vtotal/Ctotal ≥ 3× is not met. Sobol decomposition places architectural and site layers at ST ≈ 0.56 each versus MEP at 0.15, exposing waste-heat-export and regulatory-avoided-cost dimensions as under-recognized leverage. Out-of-sample validation against four hyperscale projects yields 11% MAPE, reported as an n = 4, single-period proof of concept.A pro-rata extrapolation across all ~43 GW of incremental U.S. capacity gives a nominal ~$34 billion through 2035, but this applies the single most optimistic scenario uniformly; applying the substitution-corrected per-plant value with competition, policy, and adoption decay multipliers, the defensible 2035 opportunity is ≈$3–$18 billion (central ≈$7 billion), with $34 billion retained only as an undecayed ceiling. Full article

As variable renewable energy (VRE) penetration increases, renewable generators face revenue risk from electricity prices, generation volumes, and their time-varying co-movement, commonly referred to as shape risk. This study evaluates whether the Quality Factor (QF) index, interpreted as a standardized capture-rate or value-factor index, can support hedging of this risk. We construct daily and weekly QF indices for solar photovoltaic generation in Kyushu, Japan, and wind generation in ERCOT, Texas, and use generalized additive model (GAM)-based hedge-effectiveness models to examine stylized settlement-index-based QF futures/forward contracts. The results show that QF-inclusive hedging can provide additional risk reduction, with effects depending strongly on technology and time granularity: Kyushu PV benefits at both daily and weekly horizons, whereas ERCOT wind benefits mainly at the weekly horizon. Bootstrap confidence intervals and yearly holdout sweeps support these main hedge-effectiveness findings. We also develop a quantile generalized additive model (QGAM) approach for probabilistic QF forecasting and indicative valuation of nonlinear QF-linked derivatives. Distributional forecasts show that QGAM specifications are competitive with empirical, ARIMAX, and GAM + GARCH benchmarks, although no single specification dominates across years and windows. These findings highlight the potential role of QF-linked settlement indices in renewable energy risk management. Full article

Traditional Virtual Power Plant (VPP) dispatch is mainly based on numerical time-series forecasting, which may respond slowly to extreme market events and early-warning signals expressed in unstructured text. This paper proposes a Retrieval-Augmented Generation Virtual Power Plant (RAG-VPP) framework that integrates ISO-style market notices, emergency alerts, weather warnings, and regulatory updates into risk-aware dispatch optimization. The framework includes a semantic perception engine, a hybrid numerical forecasting engine, and a human-in-the-loop dispatch gateway. Unstructured market text is converted into a bounded semantic uncertainty metric and embedded into stochastic MIQP dispatch through semantic-conditioned scenario generation, a semantic exposure penalty, Dynamic Semantic Reserve Margin, and Semantic Demand Response Pre-Activation constraints. The framework is evaluated using a 7-day ERCOT-style controlled stress-test with synthetic ISO-like EEA1/EEA2 alerts and 5 min market resolution. The results show that RAG-VPP achieved a total profit of $285.8 k, representing a 32% improvement over the deterministic baseline. It also improved CVaR by $85.2 k, showed a four-hour semantic lead in the controlled stress-test scenario, achieved 0.92 semantic alignment, and maintained zero reserve-margin violation hours. These results indicate the potential of linguistically informed dispatch for improving VPP resilience under extreme-event conditions. Full article

Accurate short-term electricity load forecasting is crucial for day-ahead scheduling and secure operation of power systems. However, electricity load series exhibit significant non-stationarity, with complex coupling between low-frequency trends and high-frequency fluctuations, making it difficult for conventional forecasting models to simultaneously characterize the overall trend and stochastic disturbances. To address this issue, this paper proposes a short-term load forecasting model based on STL decomposition and CNN-BiLSTM optimized by deep reinforcement learning. First, the original load series is decomposed into trend, seasonal, and residual components using the STL algorithm. Second, a dual-channel parallel forecasting architecture is constructed: the linear channel uses a linear regression model to predict the trend and seasonal components, thereby characterizing the low-frequency variations in the load; the nonlinear channel uses a CNN-BiLSTM framework optimized by deep reinforcement learning to predict the high-frequency residual component, and this process is formulated as a Markov decision process. Specifically, the attention-based CNN-BiLSTM serves as the policy network, and its forecasting strategy is dynamically optimized under the guidance of a reward function to enhance the modeling capability for high-frequency stochastic fluctuations. Finally, the load forecasting results for the next 24 h are obtained through dual-channel result reconstruction. Experimental results based on the ERCOT system-level load data show that the proposed model achieves superior forecasting performance, with a root mean square error of 976.4 MW and a mean absolute percentage error of 1.81%. Further multi-season testing, meteorological perturbation analysis, fair comparison under the same STL preprocessing, and ablation experiments demonstrate that the proposed model maintains good forecasting performance under different seasonal scenarios, meteorological input errors, and fair experimental settings, thereby validating its effectiveness for short-term load forecasting. Full article

The rapid proliferation of artificial intelligence (AI) has pushed hyperscale data center rack densities beyond 100 kW, driving facility power requirements to the gigawatt scale. As developers attempt to deploy these massive Zettascale compute loads across US wholesale electricity markets, they encounter severe transmission planning bottlenecks, multi-year interconnection delays, and escalating grid transient stability risks. This paper presents a generalizable techno-economic framework for evaluating grid-tied, behind-the-meter (BTM) energy architectures as a means of bypassing these constraints. The framework is demonstrated through a detailed case study in the Electric Reliability Council of Texas (ERCOT), selected for its rapid data center growth and evolving large-load regulatory environment. Using a scenario-based comparative approach, this study models the feasibility of transitioning from pure-grid reliance to hybrid, on-site generation across a three-phase deployment pathway scaling from 25 MW to 250 MW. Six distinct microgrid configurations are evaluated, integrating baseload technologies—including Enhanced Geothermal Systems (EGSs), Small Modular Reactors (SMRs), and Reciprocating Internal Combustion Engines (RICEs)—with a tiered-performance Battery Energy Storage System (BESS) combining high C-rate lithium-ion units and repurposed electric vehicle batteries. System viability is assessed through two primary metrics: the Levelized Cost of Energy (LCOE) and the Avoided Loss of Load Probability (ALOLP). The results indicate that the blended LCOE scenario ranges from $64.50/MWh (Geothermal + Solar PPA) to $94.20/MWh (SMR-anchored), compared to a $75.00/MWh pure-grid baseline. The 100% Geothermal configuration achieves a scenario-dependent ALOLP exceeding 99.9%, while gas-dependent configurations range from 58.0% to 91.2%. These findings suggest that geographic siting co-optimized with localized generation offers a viable pathway for balancing regulatory compliance, capital cost, and Uptime Tier IV operational resilience in early-stage data center development across constrained grid environments. Full article

Societies need a practical way to assess total costs of future energy mixes in complex power systems. To demonstrate such an approach, we assess the cost of electricity in the ERCOT system across two distinct generation mix scenarios, varying mostly by wind, solar and gas-fired generation, between 2023 and 2050. We use commercial software, also used by system operators and power plant developers, to ensure that evolving generation mixes in both scenarios can meet electricity demands at all times at all nodes. Such jurisdiction-specific, hourly nodal dispatch modeling is recognized as necessary for more accurate representation of costs to maintain reliable operations in complex electricity systems. We capture generation and some system costs, which we call consumer cost of electricity, CCOE, given that end-users pay these costs under different line items in their electricity bills. CCOEs are insightful cost estimates for system planning and policy discussions for a given power system and must be recalculated for different scenarios as technologies, market designs, policies, and more, change. This can be done as part of routine annual or bespoke analyses conducted by system operators. Full article

The rapid growth of artificial intelligence (AI) workloads and data center infrastructure is driving a surge in electricity demand, underscoring the need for robust metrics to evaluate energy generation and storage strategies. This study introduces the Bring Your Own Battery (BYOBattery) metric, a region-specific, temporally resolved indicator designed to quantify the ideal energy storage capacity required to mitigate generation-demand mismatches. The BYOBattery metric is computed as the minimum ideal battery storage required to eliminate generation-demand imbalances over a given time window, and is extended to incorporate curtailment via a convex optimization formulation to better manage peak generation and storage requirements. We applied the BYOBattery metric to wind, solar, and nuclear generation technologies across three major U.S. grid regions: the California Independent System Operator (CAISO), the Electric Reliability Council of Texas (ERCOT), and the Pennsylvania–New Jersey–Maryland Interconnection (PJM), using operational data from 2021 to 2024. Key findings are: (1) nuclear consistently requires the least storage in order to meet demand (i.e., one equivalent load hour compared with 10–25 h for wind and solar); (2) wind storage requirements decrease with increased capacity, whereas solar necessitates consistent levels of storage; and (3) the 30-year non-discounted cost per kWh for nuclear ($0.10/kWh) is substantially lower than that of wind or solar by a factor of 1–4 across all studied region. The BYOBattery metric enables comparative benchmarking of generation technologies under dynamic demand conditions and supports cost-informed planning for energy systems. This work contributes a reproducible, interpretable, and computationally efficient tool for energy system analyses and broader performance evaluations. Full article

Electricity forecasting is one of the most crucial aspects in maintaining stable, reliable, and autonomous power systems. While recently developed forecasting methods based on large language models can make accurate predictions, these models are still struggling due to their computational complexity, which requires more computing power, and their reliance on carefully designed prompts. This makes them complicated and harder to use in practice. To address this, we propose a Single-Attention Large Language Model (SA-LLM) that uses a unified attention mechanism to understand relationships between main and additional variables, without the need for manually created prompts. The proposed framework has been tested on real electricity supply and demand datasets, which are obtained from major U.S. electricity markets, including PJM, MISO, NYISO, ISO New England, ERCOT, SPP, and CAISO. Experimental results demonstrate that the proposed SA-LLM method outperforms the existing counterpart methods in terms of accuracy and associated errors. More specifically, the SA-LLM has also achieved a 22.5% improvement in the mean absolute error compared with traditional LSTM-based models, while reducing memory usage by 52.1% and training time by 38.4% relative to recent LLM-based methods. Furthermore, the SA-LLM demonstrates strong zero-shot generalization, achieving an additional 18.2% improvement in the MAE on previously unseen regions. Full article

Battery Energy Storage Systems (BESS) can benefit from price volatility in electricity markets, but frequent cycling increases degradation and reduces long-term value. This study develops a rolling-horizon dispatch framework in which battery operation is fully price-driven, while degradation is evaluated separately to isolate the effect of degradation model choice. A 48 h look-ahead window is solved repeatedly and advanced by 24 h, with only the first 24 h of decisions implemented and remaining capacity carried forward. Degradation is assessed using three widely used model classes: Linear-Calendar (LC), Energy-Throughput (ET), and Cycle-Based rainflow (CB) models. The framework is applied to Electric Reliability Council of Texas (ERCOT) 15 min real-time prices for 2024 (Houston Zone). LC and ET result in limited annual capacity loss (≈2%) and modest economic impact, while the CB model predicts substantially higher degradation and large negative valuation. Sensitivity analysis shows that CB-based results are highly dependent on parameter calibration. Overall, the results highlight the strong influence of degradation modeling choices on BESS valuation under rolling-horizon operation. Full article

The addition of battery storage to solar plants enhances the ability of those plants to deliver electricity during high-value periods. However, the value proposition of storage improves over time due to falling battery costs and increasing volatility in electricity prices, making it unclear when storage adoption should occur. In this work, we consider a 100 MW solar plant constructed in the year 2022 and build a techno-economic model to determine the optimal system design and timing of storage additions in four locations (CAISO, NYISO, ERCOT, and PJM). We find that the optimal time to add storage is 5–10 years after solar plant construction and that the optimal storage quantity is much higher than the amount selected if storage is included during the initial plant construction. Additionally, the model suggests significant upscaling in inverter capacity, allowing storage to deliver electricity during brief high-price periods. We also consider the effects of temporary and permanent subsidies for batteries, showing that a long-term subsidy encourages economically optimal delays in storage adoption. Full article

Texas has abundant natural resources, making it a good place for renewable energy facilities to build. Unfortunately, property taxes are the highest tax on an incoming renewable energy facility in the state. In order to increase renewable energy in the state, Texas tax code Chapter 313 was introduced. Chapter 313 allows school districts the opportunity to offer a 10-year limit, ranging from USD 10 million to USD 100 million, on the taxable value of a new green energy project. With Chapter 313 ending in 2022, the following question is raised: how do tax incentives that increase the number of applications for producing renewable energy in Texas impact the wholesale, real-time pricing of electricity in the state? Skew-t regression models were implemented on a large dataset, focusing on the designated North, Houston, and West regions of the Electricity Reliability Council of Texas (ERCOT), since these regions account for 80% of the state’s energy consumption. Analysis focused on the hours ending at 3 AM, 11 AM, and 4 PM, due to the ERCOT’s time-of-day pricing. Three key findings related to the above question resulted. First, tax incentives that increase the number of active wind and solar facilities lead to a statistically significant (p < 0.0001) reduction in wholesale electricity price (USD/MWh), ranging between 2.31% and 6.6% across the ERCOT during different hours of the day. Second, for a 10% increase in tax-incentivized green energy generation, during a 24-hour period, there is a statistically significant (p < 0.0001) reduction in the generation cost (USD/MWh), ranging between 0.82% and 1.96%. Finally, electricity price reductions from solar energy are much lower than those from wind generation and/or are not statistically significant. Full article

The use of wind power has grown strongly in recent years and is expected to continue to increase in the coming decades. Solar power is also expected to increase significantly. In a power system, a continuous balance is maintained between total production and demand. This balancing is currently mainly managed with conventional power plants, but with larger amounts of wind and solar power, other sources will also be needed. Interesting possibilities include continuous control of wind and solar power, battery storage, electric vehicles, hydrogen production, and other demand resources with flexibility potential. The aim of this article is to describe and compare the different challenges and future possibilities in six systems concerning how to keep a continuous balance in the future with significantly larger amounts of variable renewable power production. A realistic understanding of how these systems plan to handle continuous balancing is central to effectively develop a carbon-dioxide-free electricity system of the future. The systems included in the overview are the Nordic synchronous area, the island of Ireland, the Iberian Peninsula, Texas (ERCOT), the central European system, and Great Britain. Full article

Motivated by the relatively infrequent but very large price spikes in the day-ahead and real-time energy markets operated by the Electric Reliability Council of Texas, this paper proposes an insurance that a small and risk-averse retailer in Texas (i.e., a retail electric provider (REP)) may buy to prevent financial insolvency caused by inadequate risk management. It also demonstrates the insurance’s practical design, pricing, and implementation. As participation in the REP’s procurement auction is voluntary, the insurance is mutually beneficial for the REP and the insurance seller. Hence, the proposed insurance is a newly developed wholesale market product that deserves consideration by REPs in Texas and competitive retailers elsewhere. Full article

Electricity price forecasts have become a fundamental factor affecting the decision-making of all market participants. Extreme price volatility has forced market participants to hedge against volume risks and price movements. Hence, getting an accurate price forecast from a few hours to a few days ahead is very important and very challenging due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority of contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of a convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and nonlinear behavior within the input data. ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region have been used to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model shows the highest accuracy and efficiency in electricity price forecasting as compared to the support vector machine (SVM) model, fully connected neural network model, LSTM model, and the traditional LRCN model without the conditional error correction stage. Full article

System strength is an important concept in the integration of renewable energy sources (RESs). However, evaluating system strength is becoming more ambiguous due to the interaction of RESs. This paper proposes a novel scheme to define the actual interaction boundaries of RESs using the power flow tracing strategy. Based on the proposed method, the interaction boundaries of RESs were identified at the southwest side of Korea Electric Power Corporation (KEPCO) systems. The test results show that the proposed approach always provides the identical interaction boundaries of RESs in KEPCO systems, compared to the Electric Reliability Council of Texas (ERCOT) method. The consistent boundaries could be a guideline for power-system planners to assess more accurate system strength, considering the actual interactions of the RESs. Full article