Renewable energy curtailment refers to the intentional reduction or restriction of the low-cost output of renewable energy resources even when there is sufficient available demand to absorb the renewable generation. This means that excess low-cost energy that could be generated by renewable resources is intentionally not used or sent to the grid. Rapid growth of renewable energy resources means that curtailment is likely to increase in the future. Understanding and quantifying curtailment has become one of the most common reasons to perform long range system studies such as Production Cost Modeling (PCM) Analysis.
The root-cause of most curtailment events falls into two categories: system-wide oversupply and local curtailment due to transmission line limitations. While true, this does not adequately identify why one unit was ultimately selected for curtailment over another – a concern for renewable developers and in fact anyone with interest in successfully integrating large-scale renewable energy.
Three elements must be considered to adequately assess a plant’s specific reason for curtailment:
Root cause: identification of the oversupply scenario – system-wide oversupply or local oversupply due to transmission congestion
Location: Electrical relationship of the specific facility being curtailed versus other facilities, for the root cause determined
Economics: Regardless of the technical condition requiring renewable energy to be dispatched down, economics in the form of offer prices per generator will ultimately be a coincidental deciding factor in curtailment.
In the context of long-term PCM analysis – and in most day-ahead market circumstances – we offer that all curtailment results from an often complex combination of factors. A true analysis of plant-level curtailment requires a deep dive into offer price of competing resources (generator bids) as well as level of impact they have on the root-cause requiring curtailment.
System-wide oversupply is a more straightforward curtailment to diagnose. This occurs when there is not enough demand in the modeled system for the amount of renewable generation being produced at that instance. In turn, this requires some renewable energy to be reduced. The curtailed plant in this scenario will be the highest price unit followed by the next highest price unit until the oversupply situation is resolved (i.e. until supply = demand + system losses). It is accurate to identify the root cause requiring someone to be curtailed as oversupply, but the reason a specific unit was curtailed is economics: it will be the plant with the highest (most expensive) offer price.
Renewable curtailment due to transmission constraints and local congestion, or curtailment to avoid line overloads, is more complex to analyze but still a function of the root-cause, ability of each generator to resolve the overload i.e. shift factor, and the economics of offer prices.
Contrary to system-wide oversupply, the pool of renewable generators that are candidates to resolve the potential overload are limited to only the generators that harm the constraint (those that add to the flow on the transmission branch in the binding direction). Many units in the control area will not be candidates at all. The amount of flow, per megawatt (MW), that each plant adds to the transmission branch is determined by each plant’s shift factor[1] to that specific branch. The following will be true in curtailment decisions:
Assuming equal offer prices, the unit with the highest shift factor will be curtailed first, or
Assuming there are multiple units with an identical highest shift factor (these would typically be located at the same electrical bus or node), the unit with the highest offer price will be curtailed first.
When there are many candidate units to relieve the constraint, and those units all have varying shift factors and offer prices, the price and shift factor together will determine the unit that is curtailed to avoid the overload. This is done by identifying the least-cost solution in PCM software, consistent with the optimization approach that ISOs use in daily markets. Example – Assume that a transmission branch needs two MW relief to avoid overload: UNIT A has a 25% shift factor and therefore will need to be curtailed 8 MW whereas UNIT B has a 50% shift factor and will need to be curtailed only 4 MW. Based purely on physics, UNIT B should be curtailed to relieve the overload and minimize overall curtailment
However, UNIT B with its 50% shift factor could conceivably bid a far lower price than UNIT A, making the least cost solution to curtail the unit that actually has less impact on the constraint (UNIT A). Whether or not this is a frequent occurrence, it is important to understand the role that economics and bidding behavior have in determining curtailment.
A common question is whether the existence of a financial Contract or renewable incentive (PPA, PTC, etc) guarantees that a renewable plant will not be curtailed, or at a minimum, will be the last to be curtailed. In short, there are no such guarantees – units are free to bid however they’d like in nearly all US energy markets (with some guardrails) and the same flexibility is afforded in PCM software. Further, as nearly all renewables have some form of Contract(s), it is assumed that they will all bid as price-takers with each plant operator left to determine how precisely they’d like to bid with respect to Contracts they may hold. Curtailment will still be determined by the physical and economic forces described in the previous sections – each unit with a Contract remains subject to both physics (shift factors) and their market offer price.
PowerGEM’s PROBE software models all elements of renewable energy in its production cost simulations. PROBE’s complementary Dashboard Output Analysis software stores all data required to assess curtailment, automatically categorizing hourly curtailment per-unit as system-wide overgeneration versus local congestion. Since shift factors are stored in the output databases, the Output Analysis software provides users assessment of curtailment by constraint and supplemental visuals to fully understand plant-level renewable energy curtailment.
For more information on how PROBE can support your renewable curtailment analysis, fill in the contact form on our main page.
[1] Shift factors specify the percentage amount of power from a given generation facility which will flow on a transmission facility being monitored either under base case conditions or under contingency. Shift factors are determined within complex power flow analysis computations and their calculation is beyond the scope of this post. In the context of PowerGEM PROBE software, shift factors are calculated on-the-fly within PROBE’s power flow solution.