Topic: LEAP model optimizationSubscribe | Previous | Next
Mawunyo Dzobo 7/19/2017
1 Like Mr. Taylor, I want to thank you for your response to my previous queries. I am having some few challenges which are as following: (i) how does LEAP model decide what amount of new capacities of candidate plants must be added annually; (ii) how does the model select between, which plants (e.g. coal, RE, CC and CT) to build; (iii) is the dispatch based on just the running cost (i.e. variable cost and fuel) and does not include the capital cost of the plant? (iv) my model was building CT plant too quickly so I increased the capital cost of the plant by five fold however, nothing happened. (v) on the other hand when I increased the availability of the plant from 30 to 80%, the capacity of the plant coming online decreased; and I used a RE target of 1% by 2030, the model did run but when I used 10% it became infeasible. can you explain the cause of the in-feasibility to me.
Taylor Binnington 7/24/2017
Hi Mawunyo,
When using the optimization capabilities, LEAP will the select the mixture of capacity and energy (i.e. power plants, and power plant dispatch) which minimizes the net present value of the electricity generation module. This means that the unconstrained dispatch would be based on running cost as you point out (so long as capacity is available), but in deciding whether to deploy a particular technology, LEAP evaluates the capital, fixed O&M and running cost of the plant during its lifetime, selecting the mixture of plants and dispatch that give the lowest overall cost. The minimum amount of capacity that must exist in each year is dictated by two constraints: the Planning Reserve Margin, which uses the Capacity Credit of the candidate power plants to ensure that the planning reserve margin is met, and also the condition that all electricity requirements are satisfied (* see my note, below).
I can't comment on specific results from your model without seeing the model itself. Even if you do increase the capital cost of a plant by 5X, using that power plant to satisfy energy requirements may still give the lowest net present value among all candidate plants. It makes sense that less capacity would be built as you increase the Maximum Availability of the plants, since less capacity would be needed to ensure that all requirements are satisfied. An "infeasible solution" means that no solution could be found - this is almost always because you have accidentally specified two or more constraints which conflict with on another. In your case, the 10% renewable target may be impossible to satisfy while also satisfying other constraints in your model. Have you entered any data for the Maximum Capacity or Maximum Capacity Addition variables (specifically for plants designated as renewable, using the Renewable Qualified variable), which might conflict with a 10% renewable target?
Hope this is useful,
Taylor
* I "starred" this part of my response because this is not truly a constraint in the specification of the optimization problem. Instead, requirements which are not satisfied are assigned an extremely high cost, and so there is little chance that any requirements would remain unmet in a truly least-cost solution.
Mawunyo Dzobo 7/25/2017
Thanks so much Mr. Taylor for your response. In the case of the 10% RE target, I specified Unlimited for Maximum Capacity and Maximum Capacity Addition for the candidate RE power plants, which were specified by the RE qualified of 1. Despite these inputs, the run still turns out as infeasible solution. I would like to send the file to you by Outlook because of its size so that you can kindly have a look at it. I would like to know the cause of the problem and how to fix it. The scenarios are (i) Without RE obligation 8_6; (ii) Low RE penetration; and (iii) High RE penetration.
Taylor Binnington 11/8/2017
Hi Mawunyo,
Sorry for any confusion on this - the help file for the Renewable Qualified variable in LEAP is actually not correct (we'll resolve this as soon as we can).
The Renewable Qualified variable expects a number from 0 to 100, indicating the percentage of the technology's output which is classified as renewable. I would suggest first making this change in your model, backing out all constraints and ensuring that your model calculates. If it does, you can then add them back in one-by-one.
Hope this helps,
Taylor