In the process of preparing an article about the New York State Reliability Council (NYSRC) Executive Committee approval of the Extreme Conditions Whitepaper on July 8, 2022, I found a reference to a very nice report Resource Adequacy Modeling for a High Renewable Future. The report provides important background information necessary to understand the NYSRC whitepaper so my first thought was to include a summary of the report in the NYSRC post. It made the article too long so this post focuses exclusively on the background paper.
Everyone wants to do right by the environment to the extent that efforts will make a positive impact at an affordable level. I have written extensively on implementation of New York’s Climate Leadership and Community Protection Act (Climate Act) because I believe the ambitions for a zero-emissions economy embodied in the Climate Act outstrip available renewable technology such that it will do more harm than good. This post also addresses the mis-conception of many on the Climate Action Council that an electric system with zero-emissions is without risk. The opinions expressed in this post are based on my extensive meteorological education and background and do not reflect the position of any of my previous employers or any other company I have been associated with, these comments are mine alone.
Resource Adequacy Modeling for a High Renewable Future
The National Regulatory Research Institute (NRRI) is the research arm of the National
Association of Regulatory Utility Commissioners (NARUC). NRRI provides research, training, and technical support to State Public Utility Commissions. The June 2022 report “Resource Adequacy Modeling for a High Renewable Future “gives an excellent overview of electric resource adequacy planning as performed today and describes what they think will be needed in the future.
Traditional Resource Adequacy Planning
The report describes traditional resource adequacy planning:
Electric utilities have used the resource planning process for decades to develop long-term, least-cost generation supply plans to serve expected customer demand. Resource adequacy planning ensures that a system has enough energy generation throughout the year to serve demand with an acceptably low chance of shortfalls. Resource adequacy is measured by the metrics described in Figure 1. Reliability metrics provide an indication of the probability of a shortfall of generation to meet load (LOLP), the frequency of shortfalls (LOLE and LOLH), and the severity of the shortfalls (EUE and MW Short).
The industry has traditionally framed resource adequacy in terms of procuring enough resources (primarily generation) to meet the seasonal peak load forecast, plus some contingency reserves to address generation and transmission failures and/or derates in the system. This approach and the metric used to define it is called the “reserve margin.” Planners establish a reserve margin target based on load forecast uncertainty and the probability of generation outages. Required reserve margins vary by system and jurisdiction, but planners frequently target a reserve margin of 15 percent to 18 percent to maintain resource adequacy. Figure 2 shows the standard conceptualization of a load duration curve, rank ordering the level of a power system’s load for each hour of the year from highest to lowest on an average or median basis in a typical weather year. The installed reserve margin is a margin of safety to cover higher than expected load and/or unexpected losses in generation capacity due to outages.
New York resource planning analyses use the “one day in ten years,” criteria (LOLE), meaning that load does not exceed supply more than 24 hours in a 10-year period, or its equivalent metric of 2.4 hours loss of load hours (LOLH) per year. This analysis is performed at the “balancing authority” (BA) level. In the past New York BAs were vertically integrated utilities with defined service territories. After deregulation this responsibility passed to the state’s independent system operator (ISO). The region covered now includes many utility service territories. More importantly the New York Independent System Operator (NYISO) has to develop market or compliance-based rules to maintain sufficient system capacity which adds another layer of complexity. BA’s typically conduct resource adequacy analysis based on their own load and resources. The NYISO does their resource adequacy planning using resources within its geographic region or have firm transmission deliverability into the New York Control Area (NYCA). There is another complication in the state. New York City has limited transmission connectivity so there are specific reliability requirements for the amount of in-city generation that has to be operating and other rules to prevent blackouts.
The report goes on to note:
The standard metrics shown in Figure 1are generally reported as mean values of simulated power system outcomes over a range of potential future states, but planners also need to understand and plan for the worst-case outcomes and associated probability of such outcomes. Figure 3shows the mean and percentile values for loss of load hours for a power system over a three-year period.
In Figure 3,on average, the power system is resource adequate, remaining below the target of 2.4 hours per year. However, if the power system planner were more risk averse, she might want to bring a higher percentile line under the 2.4-hour target. She would need to add more firm capacity, adding to customer cost. The 95th percentile is the worst-case outcome, providing additional information on the upper bound risk of outages for a given portfolio. Only power systems with no recourse to import energy in a shortage, such as an island, would consider planning to the 95th percentile due to its high cost.
The report’s traditional planning section concludes with this:
Resource adequacy planning is fundamentally concerned with low probability events and planning for average outcomes; although a common practice, this planning is not sufficient and increasingly risky with more uncertain supply, such as renewables. In the past, planners only needed to worry about unusually high loads or high forced outages. Now, they must worry about unusually high loads during periods of unusually low renewable output and limited storage duration. Adding supply uncertainty and, as we discuss later, more extreme weather, compounds risks and thus requires a fundamental rethinking of planning for low probability, high impact tail events.
Problems with Traditional Resource Planning with a High-Renewable System
Despite the fact that the NYISO and the consultants for the Integration Analysis that provides the framework for the Climate Act Draft Scoping Plan have identified a serious resource adequacy problem, there are vocal members of the Climate Action Council who claim there are no reliability concerns for the future 100% zero-emissions New York electric grid. However, analyses have shown otherwise. E3 in their presentation to the Power Generation Advisory Panel on September 16, 2020 noted that firm capacity is needed to meet multi-day periods of low wind and solar output. The NYISO Climate Change Phase II Study also noted that those wind lull period would be problematic in the future.
The NRRI report opens the discussion of the new problems that have to be addressed:
With weather emerging as a fundamental driver of power system conditions, planning for resource adequacy with high renewables and storage becomes an exercise in quantifying and managing increasing uncertainty on both the supply and demand side of the equation. On the load side, building electrification, electric vehicle adoption, and expected growth in customer-sited solar and storage are likely to have pronounced effects on future electric consumption. Uncertain load growth and changing daily consumption patterns increase the challenge of making sure that future resources can serve load around the clock. Simply modeling future load based on past load with added noise does not characterize uncertainty from demand side changes.
The report goes on to explain that supply-side changes create a need for new modeling approaches. In particular, the traditional system consists mostly of dispatchable resources that operators can control as necessary to keep the generation matched with the load. In the future the system will be comprised mostly of resources with limited or no dispatchability. Table 1 compares past approaches with current needs. Note that weather impacts need to be “Incorporated as a structural variable driving system demand, renewable generation, and available thermal capacity”.
There is another fundamental change. In the past the resource adequacy modeling could use average annual generation profiles to meet expected loads. In the future, there will have to be: “multiple renewable generation simulations using historical generation and weather data”. The modeling scenarios will need to meet future expected resource development and maintain the correlation
between renewable availability and load. In particular, the highest and lowest temperatures and thus the expected high loads are typically associated with large high-pressure systems that have low wind speeds and thus low wind resource availability.
The NRRI report shows an approach that addresses these concerns in Figure 5. The report notes:
Weather, primarily in the form of temperature, but potentially including insolation, humidity, wind speed, etc., drives simulations of renewable generation and customer load. Generation outage simulations can be modeled as random (the traditional approach) or as correlated with extreme heat or cold events. Once the simulations are in place, models can compute multiple future paths on an hour-by-hour basis to determine when load cannot be fully served with the available resources. For every hour of the model time horizon, there are independent simulations of load, renewables, and forced outages to determine if load shedding must occur. If a particular model contains 100 simulations and four show a lack of resources to serve load for a particular hour, the hour in question would have a loss of load probability of 0.04 (4/100).
In my opinion, the weather drivers have to be carefully considered. In my Comment on Renewable Energy Resource Availability on the Draft Scoping Plan, I explained why an accurate and detailed evaluation of renewable energy resource availability is crucial to determine the generation and energy storage requirements of the future New York electrical system. I showed that there is a viable approach using over 70 years of data that could robustly quantify the worst-case renewable energy resources and provide the information necessary for adequate planning.
The problem however is what will be the worst case? The NRRI report brings up the issue of energy storage:
Energy storage presents a unique challenge in resource adequacy models. Unlike traditional resources, storage devices such as batteries, compressed air, or pumped-hydro act as both load and generation depending on whether they are charging or discharging. Modern resource adequacy models need to simulate this behavior when determining the capability of energy storage to serve load during periods of resource scarcity. What state of charge should we expect for energy storage at times when the storage is truly needed? Are batteries likely to be fully charged at 6:00 PM on a weekday in August? What about grid charging versus closed systems where batteries must charge from a renewable resource? At the high end of renewable penetration, how much storage would be required to cover Dunkelflaute, the “dark doldrums,” that occur in the winter when wind ceases to blow for several days. Questions surrounding the effective load-carrying capability of energy storage significantly increase the complexity in modeling resource adequacy.
The worst-case meteorology has to consider the energy storage resource. The worst-case may not be the lowest amount of wind and solar resources over a few days. Instead, it could be an extended period of conditions that prevent battery re-charging. I suspect that the long-term historical records will be used to identify potential problems and then a set of scenarios based on different meteorological regimes will be developed that can be used to address the questions raised in the previous paragraph.
The NRRI report explains how this might work:
Figure 6 provides an illustration of modeling the use of batteries in resource adequacy. The figure shows battery storage in blue, load in orange, and the available thermal generation in grey. When load exceeds thermal generation, the system is forced to rely on battery discharge for capacity. If the event lasts long enough to fully discharge the battery, the green line (generation minus load) will turn negative, indicating a load shed event.
The report goes on to explain how the modeling analysis is done. It notes that:
Simulations of random variables fit Monte Carlo methods by creating multiple future time series of the random variables, while maintaining correlation across time within variables (if wind is high in hour 1, it will likely be high in hour 2) and correlations between the variables, such as the strong relationship between temperature and load. If wind tends to be higher in the spring and fall, the simulations will exhibit that trend. Monte Carlo applications differ dramatically between resource adequacy models, with some models using a sequential approach that solves the model in hourly steps whereas others use techniques that solve the models quickly without stepping through each hour. Accurate representation of energy storage in resource adequacy models necessitates sequential solution techniques to account for the time dependencies for storage state of charge inherent in models.
I believe it is necessary to use the worst-case meteorological scenarios as the primary driver of these simulations. In other words, the Monte Carlo weather parameter adjustments should be small increments on top of the observed values. The report is talking primarily about correlations in time but spatial correlations are a critical wind resource availability consideration too.
The NRRI report addresses my concerns.
When using the Monte Carlo approach with weather as a fundamental driver, individual simulations represent independent futures for weather, load, and renewables. Realistic simulations maintain the statistical properties of the underlying resource and correlation between resources and load. For example, if historic data show no correlation between load and wind generation, the simulations should maintain this relationship unless a reasonable expectation exists for correlations to change in the future
However, they use simple examples of the load and resource correlations. There are those that believe that because the wind is always blowing somewhere that transmission upgrades will ensure reliability. However, if during the worst-case conditions New York has to rely on wind resources in Iowa because the high-pressure system is huge, that may not be practical. I cannot over-emphasize the need for an analysis that simulates wind and solar resource availability over wide areas. As the report notes analyses that fail to replicate the proper correlation between wind, solar, and load for the electric grid can underestimate the risk of load shedding.
The report goes on to explain other adjustments to traditional resource planning that will be necessary to address a high renewable future. That discussion is beyond the scope of my concern. The report concludes:
The electric grid is transitioning quickly from a system of large, dispatchable generators to a system reliant on high levels of variable renewable energy, energy storage, and bi-directional flow. Against this backdrop, analytical tools used for decision making regarding resource adequacy are more important than ever and those tools need to evolve to meet the modern grid challenges outlined in this paper. Models based in realistic weather-driven simulations more accurately capture the risk of load shedding due to inadequate generation. Simulations derived from historical data ensure models include load and generation patterns as well as correlations among resources and the ability to adjust to future climate conditions. Models that do not account for these factors may lead to decisions that underinvest in resources or invest in the wrong resources. Recent events in California and Texas indicate the importance of getting these projections right to keep the grid reliable.
To model resource adequacy in future power systems with high penetration of renewables, we recommend several enhancements in modeling tools and techniques. Modeling tools should simulate key structural variables and allow for validation of the simulations by benchmarking against the historical data used to create the simulations. While maintaining statistical properties derived from historical data, simulations should also include future expectations of load growth along with changes in seasonal and daily load shapes. Generation-forced outage simulations should include the possibility of correlated outages from extreme weather. Finally, climate change will drive more weather events in the power system and this risk should be accounted for in the models, at least in the form of sensitivity cases or stress tests.
I found this report to be a very useful description of the particulars of electric grid reliability analysis now and in the future. It is clear that the transition to a high renewable future introduces issues that could cause problems.
Finally, this report and other similar studies always claim that climate change should be considered in future analyses. As I will explain in my future article on the NYSRC Extreme Conditions Whitepaper I believe that the most important future weather concern is that changing the resource mix to one relying upon weather-dependent wind and solar generation is the critical vulnerability that has to be addressed. I think that the trend of extreme weather events due to greenhouse gas concentrations in the atmosphere is much smaller than natural variability. Therefore, using a long record of data for evaluation will cover most of the potential future variability. Unfortunately, recent major blackouts due to extreme weather suggest that we haven’t even been able to plan for the past. So far New York has avoided such a blackout either due to more stringent standards and better policy development or luck.