Using AURORAxmp to Meet Renewable Portfolio Standards (RPS)

According to the National Renewable Energy Laboratory (NREL), a renewable portfolio standard (RPS) is a “regulatory mandate to increase production of energy from renewable sources such as wind, solar, biomass and other alternatives to fossil and nuclear electric generation.” In 1983, Iowa became the first US state to adopt a renewable portfolio standard. In the last two decades, over half of the states in the country have adopted some form of RPS. Below is a chart displaying renewable capacity additions by state between 2000 and 2015:


Source: Lawrence Berkeley National Laboratory

Though RPS can be enforced in several ways, the mandates typically require utilities to provide some level of electric supply with renewable energy. The federal government, and sometimes state & local governments, provides financial incentives, often in the form of tax credits or rebates, to encourage investment in renewable energy. According to the Lawrence Berkeley National Laboratory, 60% of renewable electricity generation and 57% of renewable capacity builds since 2000 are tied to RPS mandates. The ultimate goal of these policies is to migrate away from fossil fuel generation in an attempt to reduce carbon and other various emissions.

AURORAxmp provides the flexibility to model various state RPS mandates and can be used to measure the impact of RPS standards on system cost, zonal prices, and emission reductions. The built-in constraint logic is used to easily specify the minimum amount of electric generation needed to meet any specified fleet of resources. Multiple parameters apply the constraint geographically as well; for example, RPS constraints can be applied on a local, state, and national level, and the model will solve all of them in the same run.

RPS constraints can also be defined in the form of renewable capacity rather than electric generation. For example, the Clean Power Plan (CPP), proposed by the EPA last year, contains several intricate details that specify how conventional fuel generators will be required to operate, both individually and as a group. Any capacity or generation constraints can be used in conjunction with a variety of other defined limits, such as emission rates, restrictions on fuel usage, and limitations on capacity factors. Additionally, these constraints are fluidly incorporated into various types of studies, such as long-term capacity expansion, risk, and scenario analysis. Below is a chart created using AURORAxmp to estimate the total system cost of different programs:


Generation attributed to RPS is expected to double by 2030 in the United States. As we look into the future, it is evident that the integration of renewable energy will continue to be a major point of interest in power markets. AURORAxmp offers an easy, reliable, and robust tool to analyze the impact of additional renewable generation on resources, the environment, and the electric grid.

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Reserve Margins

Discussing reserve margins is often convoluted because of the various definitions and intricacies.  The basic principle is that reserve capacity is used to ensure adequate power supply.  Different types of reserves are defined in terms of various time scales.  In the short-term, operating reserves are used to provide adequate supply in the case of sudden plant or transmission outages.  In the long-term, planning reserves are used to ensure adequate power supply given a forecasted load in the years ahead.  Both types of reserves are often expressed as a ratio of excess capacity (i.e., available capacity less demand) to demand.  In this blog post, we will discuss planning reserves; the typical values, historical trends, market-to-market differences, and modeling within AURORAxmp.

Planning Reserves

Without adequate planning reserves, new generation may not be built in time and thus ultimately cause power disruptions.  But what is adequate?  In 2005, Congress passed The Energy Policy Act of 2005 that requires the North American Reliability Corporation (NERC) to assess the reliability of the bulk power system in North America.  A part of NERCs responsibility is to periodically publish Long-Term Reliability Assessments (LTRA) which include planning reserve targets, or reference margins.  Usually these are based on information provided by each governing body (e.g., ISO, RTO, etc.) in the assessment area.  If no such information is available, NERC sets the reference margin to 15% for thermal-dominated systems and 10% for hydro-dominated systems.  For the 2015 LTRA, the NERC reference margins range from 11% to 20% across the assessment areas as shown in Figure 1.  The highest reference margin, 20% for NPCC Maritimes, is due to a disproportionate amount of load being served by large generating units.

NERC reference margins graph

Figure 1. 2016 Planning reserve margins by NERC assessment area from the 2015 LTRA.
The gold bars represent assessment areas with capacity markets.

In addition to providing reference margins, or published targets from other entities, NERC publishes yearly anticipated planning reserve margins, out 10 years, for 21 assessment areas in North America.  To do this, NERC collects data on peak demand and energy, capacity, transmission and demand response from NERC regional entities.  Data submission is usually due in the first quarter of the report year.  This strategy represents a bottom-up approach to understanding reliability.

Forecasting Anticipated Planning Reserve Margins

Forecasted anticipated planning reserve margins can vary substantially from assessment year to assessment year, area to area, and as a function of markets.  To illustrate this, one-, five-, and 10-year forecasted anticipated planning reserve margins for PJM and ERCOT are shown in Figure 2.  The variability in anticipated planning reserve margin is similar between each assessment area, and, increases with the length of the forecast.  This is presumably due to increasing uncertainty in forecasts as a function of time.  Interestingly, the number of years with shortfalls (fewer reserves than the target) is much larger in ERCOT than PJM.  PJM has a three-year forward capacity market and ERCOT is an energy only market.  Therefore, there is more incentive for long-term excess capacity in PJM.

reserve margins

Figure 2. Planning reserve margins targets (dashed line) and one-, five-, and 10-year anticipated planning reserve margin from the 2011 to 2015 NERC LTRAs.

As shown above, in both ERCOT and PJM, the year-ahead anticipated planning reserve margins are adequate, suggesting long-term planning approaches are working in both markets, however, regional complexities can pose problems.  For example, MISO recently published the 2016 Organization of MISO States (OMS) Survey to assess planning reserve margins.  In 2017, shortfalls are predicted in three zones – IL, MO, and Lower MI.  Excess capacity from other zones will be transferred to make up for the shortfall in the short term.  Similar to the NERC forecasts, uncertainty in the regional forecasted load is key to this issue, and may increase or decrease this shortfall.

In addition to regional issues, the rapid changing generation mix also poses challenges for quantifying adequate planning reserves.  NERC has recognized this and has called for new approaches for assessing reliability in both the 2014 and 2015 LTRA.  One specific issue is traditional load shape disruption with added solar resources.  A typical summer-peaking system may face reliability issues in the winter or other expected off-peak months where demand still is high but solar output is low.  Considering secondary demand peaks, and thus planning reserve margins, may be prudent in these situations.

AURORAxmp and Planning Reserve Margins

In AURORAxmp, planning reserve margins are used in the long-term capacity expansion logic to guide new resource builds.  Our Market Research and Analysis team updates planning reserve margins annually based on the latest NERC LTRA.  Planning reserve margins can be specified on the pool or zone level, thus easily facilitating varying spatial scale studies.   Risk studies can be conducted to quantify the impacts of uncertainty in each aspect of planning reserve margins on long-term resource builds.  Together these features support cutting-edge analysis surrounding the complexities of reserves.

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