Electric Reliability Council Of Texas Research Articles

Development of a coupled agent-based generation expansion planning tool with a power dispatch model

Power companies need to adapt their generation expansion planning in response to changing market, climate and regulatory conditions as global warming, electrification, and technology breakthroughs continue. To fortify energy system resilience, it is critical to understand the collective effects of their autonomous decisions on power systems operations and reliability. To this end, we developed an integrated framework, an agent-based model (ABM) coupled with a power dispatch model (PDM) (referred to as ABM-PDM), tested on the Texas 123-bus transmission system in the Electric Reliability Council of Texas (ERCOT) region. Agents (power generation companies) can invest in natural gas, solar, and wind technologies to maximize profits from 2021 to 2050, using market information from the PDM based on their capital budget and perceived costs, financial incentives for renewable energy, and climate risks. We applied ABM-PDM to assess how power companies respond to future technological advancements and climate change. After demonstrating model credibility, we explored 25 combinations of cost and capacity factors reflecting a variety of technological evolution trajectories. Results indicated that to replace wind over solar for replacing existing fossil-fuel power plants due to lower costs and higher capacity factors. Additionally, as more agents invest, the energy market becomes more competitive, and system-wide electricity prices drop. We also analyzed the impacts of temperature increases on investments using seven projections, from 0 to 6 °C, during the modeling period. The results showed that as temperatures rise, agents invest more to accommodate the increasing loads. ABM-PDM incorporates risk attitude and learning into companies’ decision-making, providing additional information on generation expansion for the non-optimal future of power systems.

Stochastic Optimization and Uncertainty Quantification of Natrium-based Nuclear-Renewable Energy Systems for Flexible Power Applications in Deregulated Markets

Rapid integration of variable renewable energy sources (VRES) has made modeling and stochastic optimization of hybrid energy systems crucial for studying their long-term performance and viability. However, most studies have focused on just historical data, which may be unreliable for capturing short-term fluctuations, rare events, and long-term patterns of energy demand, price, and the variability of renewable energy sources. For this study, optimal synthetic time series models were developed using Wasserstein distance. The models were validated by comparing the key statistical measures against those of the historical data. They were then used to optimize the integrated Natrium-style advanced energy systems and their long-term (30 years) economics. The stochastic model performs bi-level optimization to find the optimal sizes for the balance of plant and thermal energy storage, while also optimizing energy dispatch to achieve the maximum net present value.In studies of two deregulated markets (California ISO and the Electric Reliability Council of Texas), the integrated Natrium-style system performed better in CAISO than in ERCOT, given higher and more consistent electricity prices during peak-demand periods. The potentially enlarged cost associated with the variable operation and maintenance of the TES system also plays a significant role in driving the system sizing, thus its impacts on the system are investigated in detail through comparison against a baseline case. The study also finds that the bi-level optimization results based on stochastic gradient descent closely match the grid search results. The uncertainty quantification of the stochastic signals provides further NPV-related insights and probability distributions for the case studies. The normal standard error of the mean of NPV for the case with and without TES VOM for CAISO were found to be 7.73M ± 1.09M USD and 104.99M ± 1.25M USD, respectively based on a 95% confidence. Given the relatively small NPV variance based on 150 samples, the analysis affords the most robust possible prediction of the techno-economic performance of the integrated Natrium-style energy systems.

Effects of the U.S. inflation reduction act on SMR economics

The U.S. Inflation Reduction Act (IRA) of 2022 provides a wide array of tax credits and other incentives for low-carbon energy. The technology-neutral clean generation production tax credit (PTC) (Section 45Y of the U.S. Internal Revenue Code) and the technology-neutral investment tax credit (ITC) (Section 48E) lower the net cost of new electricity generation projects with zero or negative greenhouse gas emission rates. We evaluate the impact of the IRA legislation—specifically the PTC and ITC—on the cost-competitiveness of small modular reactors (SMRs). We use the Argonne Low-carbon Energy Analysis Framework (A-LEAF) model to calculate the capacity factor of an SMR with a range of hypothetical variable operating and maintenance (O&M) costs in the Electric Reliability Council of Texas (ERCOT) electricity market. We selected ERCOT for market modeling because of its competitive structure, available data, and extensive use in prior literature. We use a discounted cash flow model to calculate the SMR’s net present value based on the market prices and capacity factors from A-LEAF, hypothetical ranges of capital and variable O&M costs, and other input parameters, with or without the IRA tax credits. We determine the SMR owner’s optimal choice of PTC or ITC for the hypothetical ranges of capital and variable O&M costs. We also evaluate potential shifts in the SMR owner’s optimal choice of PTC or ITC based on historical patterns of nuclear capital cost overruns in the United States. We also assess the sensitivity of our results to longer PTC period and electricity prices from the New England market, which tend to be higher than electricity prices in ERCOT. We find that even with the IRA tax credits, only SMRs with low capital and variable O&M costs would be economically feasible in the low-price ERCOT market scenario modeled. A longer PTC period and higher-price market such as New England, however, would significantly expand the economic feasibility of SMRs in the United States.

“Comparing merit order effects of wind penetration across wholesale electricity markets”

Renewable energy (RE) deployment is growing rapidly in electricity industries worldwide due to climate change and energy security concerns. Increased penetration into wholesale electricity markets poses new challenges for the electric utility industry. Among the topics receiving increased attention is the decrease of the equilibrium electricity price resulting from RE penetration, commonly known as the Merit Order Effect (MOE) in wholesale electricity markets. The pricing mechanism in most wholesale electricity markets is based on marginal cost. Using data from two wholesale electricity market regions (Pennsylvania – New Jersey – Maryland (PJM) and Electric Reliability Council of Texas (ERCOT)) and two different markets (day-ahead market (DAM) and real-time market (RTM)), we examine whether the MOE exists across both the DAM and RTM and how do different market conditions as represented by quantiles of wholesale electricity prices impact the MOE. Quantile regression is used to obtain coefficient estimates that indicate wind penetration demonstrates the MOE on a relatively consistent basis across market conditions in the DAM for both the PJM and ERCOT regions. However, results from the RTM show vastly different results with the PJM region demonstrating the MOE across all quantiles with declining impacts at higher quantiles and in the ERCOT region, the lower quantiles show a positive impact from wind penetration, failing to support the MOE price-dampening hypothesis. One possible interpretation of this unusual result is linked to connections between ancillary services markets in ERCOT and DAM prices, as described and estimated by Zarnikau et al. (2019) [23].

Limiting Ramp Rate of Wind Power Output using a Battery Based on the Variance Gamma Process

A battery system is designed to reduce violations of ramping limit due to severe and long ramps of wind power output. The power rating, battery capacity, and operational policies of the battery are decided based on the assumption that the wind power output follows the Variance Gamma (V.G.) process over short time interval. The histogram of the ramp rates of wind power follows the symmetric Laplace distribution. Furthermore, ramp rates have independent and stationary increments, and their variances are proportional to the time interval. Consequently, the wind power output can be modelled as a simple form of the V.G. process. Battery operational oplicies based on the V.G. process are tested using statistics representing the sum of all wind power outputs from wind farms in the Electric Reliability Council of Texas (ERCOT). It is found that the battery can essentially eliminate violations of ramping limits due to severe ramp events. Furthermore, in this operation, it is discovered that the power rating influences the ramp limiting performance of the battery more than battery storage rating.

Ensuring reliability: What is the optimal time for power plant maintenance in Texas as the climate changes?

We analyzed data for the Electric Reliability Council of Texas (ERCOT) to assess shoulder seasons – that is, the 45 days of lowest total energy use and peak demand in the spring and fall typically used for power plant maintenance – and whether their occurrence has changed over time. Over the period 1996–2022, the shoulder seasons never started earlier than late March nor later than mid-October, corresponding well with the minimum of total degree days. In the temperature record 1959–2022, the minimum in degree days in the spring moved earlier, from early March to early February, and in the fall moved later, from early to mid-November. Warming temperatures might cause these minima in degree days to merge into a single annual minimum in December or January by the mid‐2040s, a time when there is a non-trivial risk of 1-day record energy use and peak demand from winter storms.

High temporal resolution generation expansion planning for the clean energy transition

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.

Data-driven optimization for wind farm siting

Both increasing power output and minimizing the variability of that output are key factors in a highly utilized wind farm. Finding a location that offers this type of wind resource requires the use of stochastic methods to explicitly model the uncertainty of wind power output. While the literature is rich with wind speed resource assessments, this work moves the field to wind farm power assessment at undeveloped locations. A wind farm power prediction is achieved with improved spatial process representation. The power prediction results are utilized directly in a wind farm location siting optimization model which endogenously incorporates uncertainty in wind power output. The overall framework for siting new wind farms, termed the Farm Finder tool, is demonstrated in a case study in the Electric Reliability Council of Texas (ERCOT). It is shown that the formulation of this model will find an optimal solution to the siting problem on the Pareto front of high power output, with high reliability. Such location decisions improve the overall social welfare in the electricity market.

Market analysis for the integration of new power technologies: A case study of the deployment of hybrid fossil-based generator plus energy storage (ES-FE)

This study examines the national landscape of hybridized fossil energy (FE) power plants with energy storage (ES) technologies (“ES-FE”) and presents the compilation of an ES-FE dataset, which includes over 65 ES-FE projects and concepts in the United States, comprising approximately 500 MWh of co-located ES capacity with FE power plants. This study also estimates the economic feasibility of adding ES to existing FE power plants by characterizing the potential revenues that can be generated by the ES component through flexibility and capacity value. The analysis focuses on ES technologies with 2- to 10-h. durations located in four U.S. independent system operators (ISOs): Midcontinent ISO (MISO), Electric Reliability Council of Texas (ERCOT), PJM Interconnection (PJM), and California ISO (CAISO), which have +70,000 MW of combined FE power capacity that could add ES. Annual revenues are estimated for the ES component using a what-if-analysis approach, for capacity value, price arbitrage, or ancillary services provision. The results show that annual revenues depend on the end-use storage service, wholesale electricity and capacity market prices, and ES technology operation parameters such as discharging duration and cycling frequency. When performing a sensitivity analysis, ES accrues $7–178/kW-yr. via price arbitrage and ancillary services provision in the four ISOs, and $13–92/kW-yr. when providing capacity value only in MISO and PJM. A cash flow analysis is performed to estimate the net present value (NPV) of the ES addition using a range of ES costs. The study finds that for most ES technologies considered, these revenues alone are insufficient to achieve economic feasibility. Of the 1645 total runs analyzed, 115 had positive NPVs (7%). Therefore, other revenue streams or monetizable benefits are necessary to achieve the break-even point.

The Impact of Renewable Energy Tax Incentives on Electricity Pricing in Texas

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.

Regional revenues of solar and wind generation in Texas

Effective regulatory policies, ample renewable energy potential, and a favorable business climate have led to a boom in wind farms and solar energy projects in Texas. Using a large sample of 15-min data for the six-year period of 2016–2021, we analyze per MWh revenues from solar generation and wind generation in the renewable regions of the Electric Reliability Council of Texas (ERCOT). We find that average regional revenues from variable renewable energy (VRE) for the daytime hours of 07:00–19:00 exceed the average levelized price of recently signed solar and wind power purchase agreements (PPAs), although average regional revenues for wind generation for the nighttime hours of 19:00–07:00 were lower than recent PPA prices. Moreover, VRE's 15-min regional revenues move with ERCOT's cap on the real-time market's energy price offers, regulatory price adder, daily wholesale natural gas price, 15-min nuclear energy generation, 15-min system load, 15-min regional solar energy and 15-min regional wind energy. While continued VRE development may suppress wholesale market prices, continued growth in electricity demand and increasing natural gas prices provide offsetting effects. Nevertheless, new policies are under development to mitigate the adverse effects of further VRE development on grid reliability and generation investment incentive.

2021 Texas Electricity Black-out Crisis: Root-cause Analysis and Recommendations

In this paper, we discuss the structure of the U.S power grid and various sources of electricity generation and potential vulnerabilities that the grid can face. We also discuss the role of Electric Reliability Council of Texas (ERCOT), the organization that is primarily responsible for managing electricity in Texas, and the sequence of events that led to the power crisis in Texas during February 2021. Finally, we do a root cause analysis for the power crisis and provide recommendations to minimize the impact of these type events on the Texas grid in the future.
 The paper is organized as follows: In section one, we discuss the energy sources, how the electricity grid in America works, and some potential vulnerabilities of the power grid. Section two is entirely devoted to an explanation of how ERCOT works in Texas. In section three we discuss the events that took place in February 2021 that led to blackout. In section four we discuss why did the events unfold and break down the causes. We also discuss advantages and disadvantages of the current structural elements of Texas electricity industry. In section five we offer some recommendations to remedy the situation.

Perspectives on peak demand: How is ERCOT peak electric load evolving in the context of changing weather and heating electrification?

This analysis characterizes the past and future evolution of peak electricity demand in the Electric Reliability Council of Texas (ERCOT) service area and assesses how these trends may be driven by changing weather and heating electrification. To accomplish this, historical ERCOT demand data from 1997 to 2021 were utilized along with weather data for the days in which peak demand occurred and from a future climate scenario. The results inform resource planners on the relationship between peak heating and cooling load and ambient temperature and present peak demand growth scenarios for 2025 through 2050. We found that historically, summer peak demand growth has been generally stable and approximately linear with time. Conversely, the winter peak demand growth has been less consistent, varying much more around the broader trend. These phenomena are likely consequences of temperatures that were fairly constant on summer peak demand days, but varied widely on winter peak demand days. The erratic nature of winter peak demand is also likely caused by the fact that electrical heating equipment becomes increasingly inefficient at lower temperatures, as is demonstrated by the polynomial relationship identified between per capita winter peak demand and heating degree days. Additionally, historical winter peak demand was shown to be growing more quickly than summer peak demand. This phenomenon is likely the result of increases in electrical efficiency of cooling and increases in electricity consumption that result from the rising penetration of electrical heating equipment that replace gas furnaces. Future peak demand scenarios indicate that winter peak demand will remain more erratic and will sporadically surpass summer peak demand between 2025 and 2050. Thus, resource planners in ERCOT should place less certainty on winter peak demand projections and an increased level of winter preparedness on both the supply and demand sectors appears warranted for resource planners. These findings might foreshadow future resiliency challenges that other regions will face as electric heating equipment is deployed in place of boilers or furnaces for decarbonization purposes.

Analyzing at-scale distribution grid response to extreme temperatures

Threats against power grids continue to increase, as extreme weather conditions and natural disasters (extreme events) become more frequent. Hence, there is a need for the simulation and modeling of power grids to reflect realistic conditions during extreme events conditions, especially distribution systems. This paper presents a modeling and simulation platform for electric distribution grids which can estimate overall power demand during extreme weather conditions. The presented platform’s efficacy is shown by demonstrating estimation of electrical demand for (1) Electricity Reliability Council of Texas (ERCOT) during winter storm Uri in 2021, and (2) alternative hypothetical scenarios of integrating Distributed Energy Resources (DERs), weatherization, and load electrification. In comparing to the actual demand served by ERCOT during the winter storm Uri of 2021, the proposed platform estimates approximately 34 GW of peak capacity deficit.11These numbers are consistent with state-of-the-art prediction results published in the literature (Gruber et al., 2022). For the case of the future electrification of heating loads, peak capacity of 78 GW (124% increase) is estimated, which would be reduced to 47 GW (38% increase) with the adoption of efficient heating appliances and improved thermal insulation. Integrating distributed solar PV and storage into the grid causes improvement in the local energy utilization and hence reduces the potential unmet energy by 31% and 40%, respectively.

The value of concentrating solar power in ancillary services markets

Ancillary services, such as spinning reserves, can provide grid reliability and contribute to profitability of an energy resource. We exercise an existing dispatch optimization model to estimate the profitability of a concentrating solar power plant by incorporating the sale of spinning reserves in the ancillary service market using the National Renewable Energy Laboratory’s System Advisor Model to simulate operations within a 72-h rolling horizon framework. Assuming a price-taker approach with day-ahead energy and spinning reserve prices from both the California Independent System Operator and the Electricity Reliability Council of Texas, we find that selling spinning reserves in addition to electric energy increases plant profitability by up to 7% with perfect knowledge of day-ahead pricing and solar resource availability. This finding suggests that spinning reserve markets provide significant value streams to concentrating solar power plants that can leverage thermal energy storage to offer reliable production in the short-to-medium term.

Stochastic-Risk Based Approach for Microgrid Participation in Joint Active, Reactive, and Ancillary Services Markets Considering Demand Response

In the restructured power systems, renewable energy sources (RES) have been developed. Uncertainties of these generators reduce the reliability and stability of power systems. The frequency and voltage for the correct operation of the power systems must always be maintained within a nominal value. Ancillary services (AS), energy storage systems (ESS), and demand response programs (DRPs) can be effective solutions for mentioned problems. Microgrids (MG) can make an improvement in their profits and efficiency by participating in various markets. This paper provides an optimal scheduling for the simultaneous participation of MGs in coupled active, reactive power and AS markets (regulation, spinning reserve and non-spinning reserve) by considering ESS, DRPs, call for deploying AS, and the uncertainties of wind and solar productions. Capability diagrams; mathematical equations are used to model active and reactive power of generation units. Risk management in this paper is done by the conditional value at risk (CVaR) method and probability distribution functions (PDF) are used for modeling uncertainties of wind speed and solar radiation. The ERCOT (Electric Reliability Council of Texas) market is simulated with real world data.

Insuring a Small Retail Electric Provider’s Procurement Cost Risk in Texas

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.

Regression model-based hourly aggregated electricity demand prediction

The ability to predict ggregated electricity demand of n electrical grid on an hourly basis is crucial for energy and demand management. In this study, demand and its categorical features data for three years are segregated into four seasons and then fed to an efficient Machine Learning Categorical Boosting (ML CatBoost) Regressor model to predict the next year’s demand. It uses a new gradient boosting algorithm that handles categorical features adeptly. Also, this model uses a new scheme for estimating leaf values while choosing tree structure which reduces overfitting. Further, hourly electricity demand data from the Electricity Reliability Council of Texas (ERCOT western) is used as the benchmark data to evaluate the CatBoost model. Moreover, five other ML models were developed, analyzed, and tested for the same ERCOT data for predicting the hourly aggregated electricity demand. The suggested model is compared with the long short-term memory neural network (LSTM-NN) and five other ML models in terms of performance evaluation matrices. Here, one additional performance evaluation parameter, the Coefficient of variation Root Mean Squared (CV-RMSE) is evaluated in addition to the benchmark paper’s parameters. In addition, the importance of accurate prediction in the electric grid for clean energy is discussed.

On Mitigation of Sub-Synchronous Control Interactions in Hybrid Generation Resources

One major reliability concern regarding renewable energy resources connected in the vicinity of series-compensated power transmission lines or to weak power grids is sub-synchronous resonance (SSR). A particular type of interaction known as sub-synchronous control interaction (SSCI), with a purely electrical nature, has the potential to grow very rapidly and can cause severe damage to power grid infrastructure. This paper proposes an additional damping controller that allows mitigating the SSCI expeditiously. The proposed damping loop provides a very large impedance at the synchronous frequency and very low impedance at the sub-synchronous range. More importantly, it can be integrated into the control loop of the battery energy storage systems (BESS) without imposing a negative impact on the routine performance of the BESS. As BESSs are becoming more popular in power grids, thanks to their significant impacts on reliability and power quality, it is anticipated that the proposed approach will have a great implementation opportunity. Hence, the proposed mitigation solution is implemented in a hybrid plant and tested with a radial test system and a section of the Electric Reliability Council of Texas (ERCOT) power grid under a variety of operating conditions. The results show the efficiency and robustness of the mitigation solution even under different frequencies of oscillation and larges disturbances.

Using PV inverters for voltage support at night can lower grid costs

Areas with sparse transmission lines are common in regions with high solar energy potential and need voltage support. This may require installing expensive voltage compensators, such as static synchronous compensators (STATCOMs). This expense can increase the cost and decrease the acceptance of large-scale adoption of solar power. Unlike current photovoltaic (PV) inverter controllers, which provide voltage support only during the day, commercially available augmented voltage controllers can provide voltage support at night. We examine whether PV inverters improve nighttime voltage on the grid and how much such an operation would cost compared to a STATCOM. We ran grid contingency analyses on a model for West Texas within the Electric Reliability Council of Texas (ERCOT) jurisdiction under spring and summer conditions to determine if PV inverters can support nighttime voltage under varying reactive power demand. The cost of reactive power has not been defined previously, especially in the context of the United States. Our methods and application provide a way to determine the cost of reactive power for both PV project developers and system planners. Allowing PV inverters to provide reactive power can reduce system costs by millions of dollars, or 4–15 times less costly than installing a STATCOM. We determined inverter voltage support costs by calculating the cost of earlier inverter replacements due to increased reactive power output and voltage controllers. The net system savings argue for ERCOT changing their voltage support policies to incentivize PV plants to provide voltage support at night.