Difference between revisions of "Flexibility"

Latest revision as of 16:00, 24 May 2021

Why should grids study flexibility?

Increasing shares of renewable generation, particularly wind and solar power, as well as the proliferation of new demand side resources such as battery energy storage, demand response and electric vehicles are challenging the power system planning paradigm. Whether these are driven by climate and other energy policies, customer choice or cost reductions in technology, there will be a need to change power system planning processes to more efficiently and reliably integrate these resources. One of the key characteristics of wind and solar resources is their variability and uncertainty ^[1]. The output of these resources is weather-driven, and a significant portion of solar photovoltaic (PV) resources is expected to be behind the meter. As such, they are difficult to forecast perfectly (uncertain), and their output tends to be variable as well as only partly dispatchable. They can be dispatched if control capabilities exist, but even so are likely to only be dispatched down – while they have the potential to be curtailed to provide upwards dispatch capability, this is not often done. This has increased the need for and value of operational flexibility in power systems ^[2]. Flexibility here refers to the operational maneuverability of the set of resources available to the system. This includes the ability to ramp and dispatch resources to manage variability and uncertainty of supply and demand, which may become more challenging and relevant in systems with high shares of renewable generation.

System flexibility, like many other aspects of the bulk electric system, can be examined as a supply and demand problem. A holistic assessment of flexibility will examine the resources available to provide flexibility and the factors driving the demand or requirements for flexibility. These can either be assessed separately or at the same time within a power system dispatch simulation tool.

As an example of the need for flexibility, Figure 1‑1 shows the variability seen in Germany due to wind and solar production. Net load, the difference between the total load and the load less variable generation, is an important concept here, since it exhibits greater variability when compared against the load. The net load is typically served by dispatchable resources, although other factors also come into play, such as non-dispatchable generation like geothermal and nuclear, interties with neighboring regions, and the fact that wind and solar can be dispatched if control systems are enabled. While the focus is on managing load net of wind/solar, one may also want to consider these other aspects in planning studies, particularly if the inflexibility related to these aspects will impact on the ability to balance supply and demand. Figure 1‑2 shows the uncertainty associated with solar power, based on multiple different forecasts provided for the same day for a solar plant in Texas ^[3]; this shows how different forecasting models predicted the same day. This uncertainty also needs to be accommodated together with the variability of net load. The combined impact of variability and uncertainty of wind and solar power have been covered extensively in the past; in the context of these guidelines, the main point is that there is a need to ensure sufficient operational flexibility to manage this variability and uncertainty.

Figure ‑ Variability of Wind and Solar Power in Germany over 3-day period

Figure ‑ Uncertainty in Solar Power Output for a Plant in Texas, based on different solar forecasts

Another manner to examine the variability and uncertainty is to calculate the ramping mileage of the net load on the system. This measure, defined as the absolute ramping summed over the year, allows one to understand how much flexibility is required in different systems. To calculate mileage, the absolute ramps are determined across each interval of data and summed. For example, for a net load of [10 MW, 12 MW, 15 MW 10 MW], the total mileage would be 2 MW + 3 MW + 5 MW = 10 MW across the time period, even though the largest up ramp is 3 MW and down ramp is 5 MW. This measure allows for an understanding of how much additional ramping can be required, even if the largest ramps don’t always get significantly larger. EPRI calculated this for several European and US utility regions, as is shown in Figure 1‑3 and Figure 1‑4, and plotted against annual demand.

Figure ‑ Ramping Mileage for Selected Balancing Areas in Europe for Demand Only and Net Load

Figure ‑ Ramping Mileage for Selected Balancing Areas in the US for Demand Only and Net Load

As can be seen when looking only at demand, the variability is proportional to the size of the system; some systems such as the UK tend to exhibit slightly higher variability compared to most other regions when considering size. However, when net demand is considered, most regions see an increase in ramping mileage. This would be expected due to the variability of wind and solar PV. It can also be seen that certain regions such as the UK, Denmark and ERCOT appear to see a greater additional mileage than others; this may be due to their specific load shapes, or the nature of the VER.

Examining this mileage measure may provide useful insights into the flexibility needs of the system, but is still not widely understood; therefore EPRI is continuing to examine whether and how such a measure could be helpful when considering flexibility issues. Factors such as the data resolution, normalization methods and the flexibility in the wind and solar themselves will also need to be considered when assessing and comparing this metric.

EPRI’s Integrated Energy Network initiative

EPRI launched the Integrated Energy Network (IEN) initiative in 2017. This is a large initiative, focused on a range of issues crucial to the future of the energy system. In particular, focus is put on integrating different sources of energy together such as electricity, heat and transport. This will require increased coordination in planning and operating energy systems. As such, one aspect was to develop a concept called Integrated Energy Network Planning (IEN-P). This white paper, published in mid-2018 ^[4], identified the main challenges associated with the planning of future energy systems, with a particular focus on multiple aspects of the electricity system, namely generation, transmission and distribution.

Of these ten identified challenges, several are directly related to the guidelines presented here. This includes the following:

Incorporating Operational Detail – As emerging power system resources (e.g. storage, DER, PV, etc.) replace synchronous generators, which traditionally have provided needed operational reliability services, resource planners will need to consider the potential reliability impacts and operational reliability capabilities of candidate resources. From a flexibility perspective, the need for capturing operational flexibility will become increasingly important.
Increased Modeling Granularity – In terms of flexibility needs, increasing modeling granularity to represent intra-hour ramping, as well as increased spatial granularity to represent location issues (as discussed related to deliverable flexibility later), will allow flexibility to be better considered in planning tools.
Integrating Generation, Transmission and Distribution Planning – Future resource planning will require closer interaction of planners across the entire electricity supply chain to understand how decisions at one planning level may impact other levels and the ability to tradeoff potential investments between systems to optimize the future electric power system. When obtaining flexibility from distribution systems in particular, models will need to be able to consider how these resources are limited in providing flexibility.
Expanded Analysis Boundaries and Interfaces – Electric companies are beginning to be asked by regulators and external stakeholders to address issues outside of electric company service territories and even beyond the electric sector of the economy as part of their resource planning activities. Many of these changes are driven by the fact that large areas can reduce the need for or availability of flexibility resources, while flexibility may also be available from other energy systems.
Incorporating New Planning Constraints – Future resource plans will need to be optimized to achieve objectives beyond traditional least-cost resource adequacy; flexibility metrics are clearly starting to be incorporated as discussed in the examples above.
Integrating Wholesale Power Markets – Increasingly, planners will need to consider the evolution of wholesale power markets that provide opportunities for companies to buy and sell capacity, energy and ancillary services, and the impact of markets on the economic viability of resources that provide reliability services and other desired system attributes. This factor includes the need to consider market changes such as the recent development of flexible ramping products, or concepts such as that described in California above.
Addressing Uncertainty and Managing Risk – There is a growing need for resource planners to explicitly account for key uncertainties when developing resource plans and to adopt new approaches to manage evolving corporate risks. Uncertainties related to short term operations, and the flexibility required to manage that uncertainty should be included.

Other challenges were also identified related to stakeholder engagement, long term forecasting and customer modeling; these do not directly refer to flexibility issues, but still should be considered in planning. Therefore, as the IEN-P progresses, the flexibility guidelines described in this update will be coordinated with that activity to ensure consistency across the efforts.

Motivation for guidelines document

These guidelines are designed to help planners integrate the concept of flexibility and flexible resources into the planning processes. They would likely augment or improve upon the methods described in previous section. The term ‘planners’ here includes a variety of different types of planning functions, including resource adequacy assessment, transmission planning, resource planning (e.g. Integrated Resource Planning or other resource planning activities), as well as high level policy-type analysis. All of these are linked, and in some cases may involve the same analysis. However, depending on the given regulatory regime, how different aspects link may differ. For example, a vertically integrated utility may have all of these functions very tightly linked, an Independent System Operator (ISO) region may have the ISO make decisions based on proposals from stakeholders, and could include capacity markets or not, and other entities such as Generation & Transmission companies may have other processes. The guidelines here are intended to be applicable to a wide range of different end users, and as such focus more on the engineering fundamentals.

Given the range of different tools available, and the trend to model operational type issues in planning that traditionally were outside the scope of planner’s functions, this guideline document is an attempt to gather the most recent thinking, at EPRI and elsewhere, on this process and to make recommendations for the choices a planner will face. It is not intended to be a definitive process, but instead a set of potential analyses and studies that should be considered when integrating renewables. This is likely to continue to evolve over time, and will be updated accordingly. Individual planning functions within different entities will also need to adopt this general document to their own processes. As described later, the amount of work done by EPRI in this area in the past several years has been extensive. The main aim of the guidelines document is to collate the main lessons learned and outline the methods developed in one place.

References and footnotes

↑ 1. Cochran, J.; Miller, M.; Zinaman, et al., Flexibility in 21st Century Power Systems. 21st Century Power Partnership. 14 pp.; NREL Report No. TP-6A20-61721
↑ 2. Metrics for Quantifying Flexibility in Power System Planning, EPRI, Palo Alto, CA: 2014. 300200424
↑ 3. E. Lannoye, A. Tuohy, J. Sharp, V. Von Schramm, W. Callender, L. Aguirre, Solar Power Forecasting Trials and Trial Design: Experience from Texas, presented at 5th Solar Integration Workshop, Brussels, October 2015
↑ Integrated Energy Network Planning: available at https://www.epri.com/#/pages/product/000000003002010821/?lang=en

[1] 1. Cochran, J.; Miller, M.; Zinaman, et al., Flexibility in 21st Century Power Systems. 21st Century Power Partnership. 14 pp.; NREL Report No. TP-6A20-61721

[2] 2. Metrics for Quantifying Flexibility in Power System Planning, EPRI, Palo Alto, CA: 2014. 300200424

[3] 3. E. Lannoye, A. Tuohy, J. Sharp, V. Von Schramm, W. Callender, L. Aguirre, Solar Power Forecasting Trials and Trial Design: Experience from Texas, presented at 5th Solar Integration Workshop, Brussels, October 2015

[4] Integrated Energy Network Planning: available at https://www.epri.com/#/pages/product/000000003002010821/?lang=en

[1]

[2]

[3]

[4]

@@ Line 1: / Line 1: @@
-= Motivation for flexibility to be considered more specifically in planning studies =
+{{DISPLAYTITLE: Assessing Flexibility}}
+== Why should grids study flexibility? ==
-Increasing shares of renewable generation, particularly wind and solar power, as well as the proliferation of new demand side resources such as battery energy storage, demand response and electric vehicles are challenging the power system planning paradigm. Whether these are driven by climate and other energy policies, customer choice or cost reductions in technology, there will be a need to change power system planning processes to more efficiently and reliably integrate these resources. One of the key characteristics of wind and solar resources is their variability and uncertainty [1]. The output of these resources is weather-driven, and a significant portion of solar photovoltaic (PV) resources is expected to be behind the meter. As such, they are difficult to forecast perfectly (uncertain), and their output tends to be variable as well as only partly dispatchable. They can be dispatched if control capabilities exist, but even so are likely to only be dispatched down – while they have the potential to be curtailed to provide upwards dispatch capability, this is not often done. This has increased the need for and value of operational flexibility in power systems [2]. Flexibility here refers to the operational maneuverability of the set of resources available to the system. This includes the ability to ramp and dispatch resources to manage variability and uncertainty of supply and demand, which may become more challenging and relevant in systems with high shares of renewable generation.
+Increasing shares of renewable generation, particularly wind and solar power, as well as the proliferation of new demand side resources such as battery energy storage, demand response and electric vehicles are challenging the power system planning paradigm. Whether these are driven by climate and other energy policies, customer choice or cost reductions in technology, there will be a need to change power system planning processes to more efficiently and reliably integrate these resources. One of the key characteristics of wind and solar resources is their variability and uncertainty <ref>1.	Cochran, J.; Miller, M.; Zinaman, et al., Flexibility in 21st Century Power Systems. 21st Century Power Partnership. 14 pp.; NREL Report No. TP-6A20-61721</ref>. The output of these resources is weather-driven, and a significant portion of solar photovoltaic (PV) resources is expected to be behind the meter. As such, they are difficult to forecast perfectly (uncertain), and their output tends to be variable as well as only partly dispatchable. They can be dispatched if control capabilities exist, but even so are likely to only be dispatched down – while they have the potential to be curtailed to provide upwards dispatch capability, this is not often done. This has increased the need for and value of operational flexibility in power systems <ref>2.	Metrics for Quantifying Flexibility in Power System Planning, EPRI, Palo Alto, CA: 2014. 300200424</ref>. Flexibility here refers to the operational maneuverability of the set of resources available to the system. This includes the ability to ramp and dispatch resources to manage variability and uncertainty of supply and demand, which may become more challenging and relevant in systems with high shares of renewable generation.
 System flexibility, like many other aspects of the bulk electric system, can be examined as a supply and demand problem. A holistic assessment of flexibility will examine the resources available to provide flexibility and the factors driving the demand or requirements for flexibility. These can either be assessed separately or at the same time within a power system dispatch simulation tool.
-As an example of the need for flexibility, Figure 1‑1 shows the variability seen in Germany due to wind and solar production. Net load, the difference between the total load and the load less variable generation, is an important concept here, since it exhibits greater variability when compared against the load. The net load is typically served by dispatchable resources, although other factors also come into play, such as non-dispatchable generation like geothermal and nuclear, interties with neighboring regions, and the fact that wind and solar can be dispatched if control systems are enabled. While the focus is on managing load net of wind/solar, one may also want to consider these other aspects in planning studies, particularly if the inflexibility related to these aspects will impact on the ability to balance supply and demand. Figure 1‑2 shows the uncertainty associated with solar power, based on multiple different forecasts provided for the same day for a solar plant in Texas [3]; this shows how different forecasting models predicted the same day. This uncertainty also needs to be accommodated together with the variability of net load. The combined impact of variability and uncertainty of wind and solar power have been covered extensively in the past; in the context of these guidelines, the main point is that there is a need to ensure sufficient operational flexibility to manage this variability and uncertainty.
+As an example of the need for flexibility, Figure 1‑1 shows the variability seen in Germany due to wind and solar production. Net load, the difference between the total load and the load less variable generation, is an important concept here, since it exhibits greater variability when compared against the load. The net load is typically served by dispatchable resources, although other factors also come into play, such as non-dispatchable generation like geothermal and nuclear, interties with neighboring regions, and the fact that wind and solar can be dispatched if control systems are enabled. While the focus is on managing load net of wind/solar, one may also want to consider these other aspects in planning studies, particularly if the inflexibility related to these aspects will impact on the ability to balance supply and demand. Figure 1‑2 shows the uncertainty associated with solar power, based on multiple different forecasts provided for the same day for a solar plant in Texas <ref>3.	E. Lannoye, A. Tuohy, J. Sharp, V. Von Schramm, W. Callender, L. Aguirre, Solar Power Forecasting Trials and Trial Design: Experience from Texas, presented at 5th Solar Integration Workshop, Brussels, October 2015</ref>; this shows how different forecasting models predicted the same day. This uncertainty also needs to be accommodated together with the variability of net load. The combined impact of variability and uncertainty of wind and solar power have been covered extensively in the past; in the context of these guidelines, the main point is that there is a need to ensure sufficient operational flexibility to manage this variability and uncertainty.
-[[File:./flex-assets/media/image2.png|509x394px]]
+[[File:image2.png|center|border|frame|Figure ‑ Variability of Wind and Solar Power in Germany over 3-day period]]
-Figure 1‑1<br />
+[[File:faimage3.png|center|border|frame|Figure ‑ Uncertainty in Solar Power Output for a Plant in Texas, based on different solar forecasts]]
-Variability of Wind and Solar Power in Germany over 3-day period
-[[File:./flex-assets/media/image3.emf|460x356px]]
-Figure 1‑2:<br />
-Uncertainty in Solar Power Output for a Plant in Texas, based on different solar forecasts
 Another manner to examine the variability and uncertainty is to calculate the ramping mileage of the net load on the system. This measure, defined as the absolute ramping summed over the year, allows one to understand how much flexibility is required in different systems. To calculate mileage, the absolute ramps are determined across each interval of data and summed. For example, for a net load of [10 MW, 12 MW, 15 MW 10 MW], the total mileage would be 2 MW + 3 MW + 5 MW = 10 MW across the time period, even though the largest up ramp is 3 MW and down ramp is 5 MW. This measure allows for an understanding of how much additional ramping can be required, even if the largest ramps don’t always get significantly larger. EPRI calculated this for several European and US utility regions, as is shown in Figure 1‑3 and Figure 1‑4, and plotted against annual demand.
-[[File:./flex-assets/media/image4.png|633x270px]]
+[[File:faimage4.png|center|border|frame|Figure ‑ Ramping Mileage for Selected Balancing Areas in Europe for Demand Only and Net Load]]
-Figure 1‑3<br />
-Ramping Mileage for Selected Balancing Areas in Europe for Demand Only and Net Load
-[[File:./flex-assets/media/image5.png|637x252px]]
-Figure 1‑4<br />
+[[File:faimage5.png|center|border|frame|Figure ‑ Ramping Mileage for Selected Balancing Areas in the US for Demand Only and Net Load]]
-Ramping Mileage for Selected Balancing Areas in the US for Demand Only and Net Load
 As can be seen when looking only at demand, the variability is proportional to the size of the system; some systems such as the UK tend to exhibit slightly higher variability compared to most other regions when considering size. However, when net demand is considered, most regions see an increase in ramping mileage. This would be expected due to the variability of wind and solar PV. It can also be seen that certain regions such as the UK, Denmark and ERCOT appear to see a greater additional mileage than others; this may be due to their specific load shapes, or the nature of the VER.
 Examining this mileage measure may provide useful insights into the flexibility needs of the system, but is still not widely understood; therefore EPRI is continuing to examine whether and how such a measure could be helpful when considering flexibility issues. Factors such as the data resolution, normalization methods and the flexibility in the wind and solar themselves will also need to be considered when assessing and comparing this metric.
-== Defining operational flexibility in planning studies ==
-In recent years, planners have started to consider operational flexibility explicitly in studies and decision making. This includes a number of different approaches, ranging from detailed consideration based on simulation tools, to more simplistic approaches that screen the flexibility available in the system as a starting point to further potential studies. Generally, the overall aim has been to ensure ramping capability is considered in planning, and that systems have a sufficient amount to effectively operate in the future. Examples of ongoing processes include the following.
-'''California’s Flexible Resource Adequacy Must Offer Obligation (FRAC-MOO):''' CAISO, under mandate from the California Public Utilities Commission (CPUC), developed a new resource adequacy related procurement target [4], which has been in place since 2014. Scheduling coordinators are required to not only procure sufficient capacity to meet forecasted peak load as they currently do, but also to meet additional flexibility requirements with their capacity. The objective is that the system should have sufficient flexible capacity available to meet forecasted system needs. The method for determining the need and the contribution of different resources to the need has evolved over the past few years. The need is determined based on a minute-by-minute dataset of actual load from the previous year, together with minute-by-minute data for variable generation; projected future variable generation installations are included by scaling wind and load using 1-minute historical data. The requirement is then calculated based on the largest 3-hour ramp in each month (since 3 hours is the period of most concern to CAISO), plus the maximum of either the largest contingency or 3.5% of the peak demand in that month, plus an error term to adjust for load forecast error. The three hour ramps are separated into the maximum primary and maximum secondary daily ramps of each month, with the primary being the timeframe where the maximum ramps occur and the secondary ramps being the timeframe which does not coincide with the primary timeframe where the second largest ramps occur.
-Once the total procurement requirement has been calculated, the next step is to distribute the total requirements among each LSE. This is done for each of the three individual components (maximum ramp, contingency or 3.5% of peak demand and error term), based on the contribution of the LSE to that component. They must procure sufficient effective flexible capacity to meet their seasonal requirement. The Effective Flexible Capacity (EFC) is calculated for each resource based on their capability to ramp in 3 hours. The contribution from quick start units is their maximum ramp from cold start in 3 hours, whereas for longer start units, the EFC is calculated as the 3-hour ramping capability when the resources are online and at minimum generation (even if these are not online when ramps happen, it is assumed they would be if the ramps were extreme cases). While California has identified 3-hour ramping issues as the key issue for its particular circumstances, this may not be the same for all regions at all times. EFC resources must offer into the day ahead and real time markets at certain times of the day depending on which type of ramp it has cleared for (primary or secondary) – hence the must offer terminology.
-The FRAC-MOO process provides a good overview of a potential mechanism to provide flexibility in the planning time frame, however it is expected that it will need to evolve. This may include more detailed consideration of minimum generation levels, over-generation issues when solar power is high and understanding what a generator is likely to be doing when its flexibility is needed.
-'''Eirgrid (Ireland) System Services:''' EirGrid is the transmission system operator for the island of Ireland where renewable generation currently meets over 22% of annual energy demand and is set to increase further to 41% in the near future. The majority of the renewable generation consists of wind generation, with future growth in the sector all linked to wind and a small amount of solar generation. The Irish system does not have synchronous connections with other systems, but does have two HVDC links to the UK (250 MW and 500 MW, each) and another 750 MW HVDC link to France in the design stage.
-In order to address the future operational issues with high penetrations of renewable generation, EirGrid launched the DS3 initiative in 2012<ref>http://www.eirgridgroup.com/how-the-grid-works/ds3-programme/</ref>. This project was aimed at developing the system services and capabilities that would be needed by the system operator to increase the system non-synchronous generation penetration limit from 50% to 75% in operations. To date the limit on non-synchronous generation penetration has been raised to 60%, effective from November 2016. The main focus of the new system services was on three areas: frequency control, voltage control and ramping requirements. Through various studies, a set of 14 ancillary services were proposed including synchronous inertial response, fast frequency response, operating reserves dynamic reactive reserve and ramping capability.
-The ramping services have been specified to manage the expected variability and uncertainty in the upward direction that will arise at the renewable energy penetration that is expected to materialize. Three services are being implemented, differentiated by the deployment time frames: 1, 3 and 8 hours. Each of the products has an associated duration for which the response should be maintained. The durations are currently set at 2, 5 and 8 hours, respectively. Resources are contracted on an annual basis and remunerated for a payment in respect of the additional margin that they can provide in the horizon and sustain for the duration. For example, a 100 MW unit at an output of 50 MW and with a 25 MW/h ramp rate can recover the payment for 25 MWh, whereas the same unit dispatched to 90 MW can only receive payment for 10 MWh of the service. If a resource cannot technically sustain the response for the duration period, the payment is limited to the minimum potential capability.
-The products are not mutually exclusive and payment for availability for multiple services can be received in the same hour. The ramping services are currently remunerated at the rates shown in the following table for each MW of capability in each hour (settlement units are in MWh).
-Table 1‑1<br />
-EirGrid DS3 tariff arrangements for ramping margin products for Oct 2017 to April 2018
-{|
-! Service
-! 1 Hour Ramp
-! 3 Hour Ramp
-! 8 Hour Ramp
-|-
-| Rate
-| 0.11 €/MWh
-| 0.17 €/MWh
-| 0.15€/MWh
-|}
-The maximum payment that can be achieved is by a resource that remains offline but can start and reach maximum output in an hour and maintain output for 16 hours. Such a resource can earn a maximum of €72.24 per megawatt of capacity per week from ramping payments. For a 100 MW fast start resource that remains offline and provides perfect service when called upon, this equates to €72.24/MW·week×100MW×52week/year = €375,648 per annum.
-Flexibility has also begun to be considered in various Integrated Resource Plans. The following is a selection of different plans:
-'''The Oregon PUC''' has required investor owned utilities in the state of Oregon to consider flexibility as part of their Integrated Resource Plans (IRP) [6]. It is still up to each utility to determine how to quantify requirements and whether their current resources have the ability to meet those requirements. If there are insufficient resources, then all resources (including institutional/operational reforms as well as supply- or demand-side resources) must be considered. Based on this requirement, utilities in Oregon are considering flexibility in their IRPs. To the authors’ understanding, some have proposed detailed study methods using production cost tools, while most are still using relatively simple screening methods that look at large ramps over specified time horizons and compares to system resource capabilities. As an example, Portland General Electric (PGE) now include flexibility metrics in their IRP, which resulted in the need for 400 MW of flexibility in the 2016 IRP, to avoid real time imbalances<ref>More details at https://www.portlandgeneral.com/our-company/energy-strategy/resource-planning/integrated-resource-planning</ref>. This used the REFLEX tool, described later, and similar concepts to some of the EPRI flexibility metrics Oregon also has a storage mandate, providing flexibility to meet the needs of the system to manage variability and uncertainty.
-Similarly, '''Public Service of New Mexico''' (PNM) have recently begun to include the concept of flexibility in their IRP process<ref>More details can be found at https://www.pnm.com/irp</ref>. Here, traditional resource expansion tools identified least cost mix of resources to meet future demand, under various policy and other preferences. The SERVM tool, described later, was then employed to provide additional analysis as was carried out in the California CES-21 effort described later. This probabilistic production cost tool was used to examine the intra- and multi-hour flexibility needs of the system using a 5-minute dispatch model incorporating variability and uncertainty of wind and solar power. Metrics related to flexibility shortfalls with and between hours were then used to ensure the resource mixes developed by the resource expansion tool could maintain reliability. One of the challenges associated with this approach, as discussed later in the report, is to determine the level of reliability which must be maintained. Traditional planning standards such as loss of load expectation (LOLE) due to capacity shortfalls do not consider ramping or flexibility issues. Therefore, if new methods determine loss of load due to ramping shortfalls, one should not continue to just use the same reliability level (e.g. 1 day in 10 years); instead the appropriate standards need to be determined first, based on a combination of detailed studies as described in this report, and determining a baseline from current systems.
-The '''Tennessee Valley Authority''', in their 2019 IRP, included two flexibility metrics<ref>TVA 2019 Integrated Resource Plan. Pg. 129 Available: https://www.tva.gov/file_source/TVA/Site%20Content/Environment/Environmental%20Stewardship/IRP/2019%20Documents/TVA%202019%20Integrated%20Resource%20Plan%20Volume%20I%20Final%20Resource%20Plan.pdf</ref>: the Flexible Resource Coverage Ratio and the Flexibility Turn Down Factor. These metrics are defined in the IRP as follows:
-'''''Flexibility Resource Coverage Ratio:''' the ratio of flexible capacity available to meet the maximum 3 hour ramp in demand in 2038''
-'''''Flexibility Turn Down Factor:''' the ability of the system to serve low load periods as measured by the percent of must-run and non-dispatchable generation to sales''
-For the scenarios considered in the 2019 IRP, coverage ratios ranged from 0.98 to 2.22 and turn down factors ranged from ~32% to ~66%.
-The importance of flexibility is given prominence in the report with substantial reporting of the performance of each portfolio under the 6 different scenario and 5 strategies evaluated. Broadly speaking, the portfolio based metrics reflected highest flexibility in cases where inflexible or must-run generation is replaced with more flexible generation. Cases with higher levels of solar are seen to have lower coverage ratios.
-While it did not significantly impact on the specific plans being developed, it was calculated based on the ability of the fleet to follow load swings, and each scenario in the IRP was assessed against the flexibility metric. This was calculated based on the annual system regulating capacity (regulating reserve, demand response and quick start resources) expressed as a percentage of peak demand. While for this round of IRP scenarios, there was not a significant change in flexibility across scenarios, TVA will continue to monitor as they observe greater shares of variable renewable energy resources in their footprint. As well as this flexibility related metric, two other related metrics were examined though not considered part of the scoring for different scenarios. These included the variable energy resource penetration, which measures the amount of variable included in the plans; and a flexibility turn-down factor to measure the ability of the system to serve low load periods. These measures are consistent with other areas that also include renewable penetration and turn down ability in their planning processes.
-Finally, the North American Electric Reliability Corporation (NERC) recently released developed a set of measures related to Essential Reliability Services, in light of the changing generation mix expected in the coming years [7]. One of the key reliability services identified was the '''Essential Reliability Services Ramping Measure'''. Here, the purpose was to develop a measure to help identify when ramping and balancing are becoming more challenging for system operations. The measure involved identifying if system ramps are changing sufficiently such that existing operational ramping capability could be exhausted. A number of levels are proposed for the measure, with both a screening method and a more detailed method based on the Control Performance Standard (CPS1) score, which reflects the Area Control Error deviations in a balancing area.
-The screening method consists of two steps – in the first step, the minimum load is compared to non-dispatchable generation (which may nuclear, geothermal and renewables without dispatch capability – note all of these resources may have the capability to ramp to some extent at present or in the future if so enabled). If non-dispatchable resources are greater than a certain percentage of minimum load, then the more detailed screen is used. The percentage is chosen by the user, but should be between 30% to 50% based on NERC recommendations (this is subjective based on end user).
-If this first step shows high percentage of non-dispatchable generation during minimum net load, then in the second step the upwards and downwards ramping capability for critical hours is compared to the regulation, load following and load increase in the hour. If both of these levels fail, then more analysis is needed. This is not a prescribed analysis, but one potential option described in the report is based on the EPRI framework described in this report, where simulations of future system are carried out to assess flexibility. This second step should be done for future years, though the method to determine ramping needs and resources if left to the user- it is not proscribed to do a full simulation of what wind/solar resources may look like, or what resources may be available during critical operating periods.
-The other analysis proposed by the ERSWG is based on CPS1 scores. Here, historical ramps are assessed against CPS1 performance on an hourly basis. A number of analyses are performed, similar in nature to the flexibility requirements proposed by EPRI discussed below. These include analyzing by hour and month, and also analyzing for consecutive hours of CPS shortfall. The aim is to determine whether the balancing authority is seeing an increase in total shortfalls or consecutive shortfalls across the year, or in particular months or hours. If so, then more detailed analysis should be performed, with a recommendation to work with NERC’s Resources Subcommittee. More details on the NERC measures can be found in the “Sufficiency Guidelines” report finalized in December 2016 [8].
-The above examples show that flexibility assessment methods have evolved significantly, and now span everything from inclusion in IRPs to reliability assessment. However, methods are still evolving, and generally try to balance data and effort intensive simulation approaches with simple to use screens. Additionally, they balance economic decisions with reliability related approaches, and in the long run will need to evolve to where they can better inform planning decisions.
 '''EPRI’s Integrated Energy Network initiative'''
-EPRI launched the Integrated Energy Network (IEN) initiative in 2017. This is a large initiative, focused on a range of issues crucial to the future of the energy system. In particular, focus is put on integrating different sources of energy together such as electricity, heat and transport. This will require increased coordination in planning and operating energy systems. As such, one aspect was to develop a concept called Integrated Energy Network Planning (IEN-P). This white paper, published in mid-2018<ref>Integrated Energy Network Planning: available at [https://www.epri.com/ https://www.epri.com/#/pages/product/000000003002010821/?lang=en]</ref>, identified the main challenges associated with the planning of future energy systems, with a particular focus on multiple aspects of the electricity system, namely generation, transmission and distribution.
+EPRI launched the Integrated Energy Network (IEN) initiative in 2017. This is a large initiative, focused on a range of issues crucial to the future of the energy system. In particular, focus is put on integrating different sources of energy together such as electricity, heat and transport. This will require increased coordination in planning and operating energy systems. As such, one aspect was to develop a concept called Integrated Energy Network Planning (IEN-P). This white paper, published in mid-2018 <ref>Integrated Energy Network Planning: available at [https://www.epri.com/ https://www.epri.com/#/pages/product/000000003002010821/?lang=en]</ref>, identified the main challenges associated with the planning of future energy systems, with a particular focus on multiple aspects of the electricity system, namely generation, transmission and distribution.
 Of these ten identified challenges, several are directly related to the guidelines presented here. This includes the following:
@@ Line 117: / Line 43: @@
 Given the range of different tools available, and the trend to model operational type issues in planning that traditionally were outside the scope of planner’s functions, this guideline document is an attempt to gather the most recent thinking, at EPRI and elsewhere, on this process and to make recommendations for the choices a planner will face. It is not intended to be a definitive process, but instead a set of potential analyses and studies that should be considered when integrating renewables. This is likely to continue to evolve over time, and will be updated accordingly. Individual planning functions within different entities will also need to adopt this general document to their own processes. As described later, the amount of work done by EPRI in this area in the past several years has been extensive. The main aim of the guidelines document is to collate the main lessons learned and outline the methods developed in one place.
+== References and footnotes ==