Harry and the Accion consultants took very complex material and readily digested it for us to make the important decisions required by law. They were diligent in covering specific details and in-depth explanations of both material and the underlying rationale, as well as history of the subject matter. They are always thorough and pay close attention to detail.
- Senator Cilley, former Member, N.H. Nuclear Decommissioning Committee
To assist our clients in utility planning and management, Accion Group makes available a full dispatch model, Strategic Energy Risk Valuation Model (SERVM), which is a unique reliability planning tool that takes into consideration economic factors critical in determining a utility's optimum target reserve margin. Setting target reserve margins has historically been based on the 1-in-10 standard, or one day of firm load shed every ten years. While this approach determines the probability of physical load loss events, the 1-in-10 method does not explicitly state whether the reserve margin is cost-effective or justified economically. With today's increasing penetration of renewable and demand-side resources, legislative changes, and other structural changes in energy and capacity markets, determining the full economic value that higher reserve margins provide beyond physical reliability is essential for offering a level of reliability that is cost effective and economically efficient.
SERVM's Reserve Margin analysis provides a dramatically improved understanding of resource adequacy risks, determining not only if a reliability event could happen, but also quantifies the likelihood, magnitude, and economic cost of each event. To perform this analysis, SERVM utilizes historical weather, economic load growth forecast error, historical hydro and other energy-limited resource data, and unit performance history to perform hundreds of thousands of independent hourly chronological simulations of any system. The results of the model deliver a full distribution of expected reliability events and their costs, allowing system planners to mitigate reliability concerns and economically plan the expansion of their system.
Reserve Margin Study
SERVM allows users to balance capacity cost, unserved energy societal costs, reliability costs (costs above the marginal cost of a high heat rate gas CT), and emergency purchase costs. This graph can be created on the weighted average of all cases or at any confidence level. A reserve margin study will allow you to isolate what percentages of your reserves are assignable to load forecast error, unit outages uncertainty, energy limited resource unavailability, and weather uncertainty.
The above figure shows the probability-weighted average cost of various reliability-related cost elements as a function of planning reserve margin. The lowest-average-cost reserve margin can be determined, for example, based on the point at which total reliability-related costs plus the cost of carrying additional reserves is the lowest, ignoring the uncertainty of costs around the weighted average costs shown in the chart. In the case study shown above, this lowest-average-cost reserve margin is 12%. However, this result will vary significantly across regions based on their size, load shape, resource mix, and many other factors.
While this figure is informative, it over-simplifies the problem by only comparing fixed capacity costs with the long-term averages of very uncertain market exposures. To perform a more informed comparison, the uncertainty of market exposure needs to be considered as well. >> Read more in 'The Value of Resource Adequacy'
Capacity Benefit Margin Study
After performing a reserve margin study to set an optimum reserve margin, capacity benefit margins can be varied across ties to determine what CBM levels would cause a shift in the optimum reserve margin. This can be isolated to individual ties or consolidated across ties.
Seasonal Reliability Assessments
Using near-term load forecasts and capacity projections, a wide range of weather, economic growth, and unit outage scenarios can be modeled to determine what the relative risks are for upcoming seasons. The distribution of expected reliability purchases can be used to determine how much capacity should be purchased at what costs to minimize risk and total reliability costs.