[LDES-coremodel] Update of existing and planned generation for SWITCH WECC db

Mon Sep 21 17:35:19 PDT 2020

Hi Paty,
Thanks for clarifying! Yes, I meant designating the median, max, and min
hydro "year-type" of the historical 2004 - 2018 data, based on the total
energy produced for each year across the entire WECC hydropower fleet
(summing all the generation in all months and all hydropower generators for
a given year). I agree it makes sense to keep the original monthly data for
all the historical years, and then select the different "year-types" for
scenarios, keeping the monthly pattern for that year.

Sarah, for the "baseline" scenario I think we agreed to use the median of
such "year-types." I will work on producing some comparison table to show
the variability between the year-types.

Hope this makes more sense. Please let me know if I have missed anything we
discussed in the meeting.
Best,
Julia

On Fri, Sep 18, 2020 at 8:34 AM <russ.jones at ieee.org> wrote:

> I don’t know if this is important, but it seems likely that high
> precipitation years will be negatively correlated with solar irradiance (a
> high precipitation year is likely a low irradiance year and vice versa).
>
>
>
> *Russ Jones * ☼   +1-714-206-2556 (m)  ☼   +1-310-469-9045 (VOIP
> worldwide)
>
>
>
> *From:* LDES-coremodel <ldes-coremodel-bounces at lists.ucmerced.edu> *On
> Behalf Of *Patricia Hidalgo-Gonzalez
> *Sent:* Thursday, September 17, 2020 7:31 PM
> *To:* Julia Szinai <jszinai at berkeley.edu>
> *Cc:* ldes-coremodel at lists.ucmerced.edu
> *Subject:* Re: [LDES-coremodel] Update of existing and planned generation
> for SWITCH WECC db
>
>
>
> Hey Julia,
>
>
>
> Just to clarify on 2: Did you mean to rank the hydro years by total energy
> produced? I'd advise against calculating any statistics because that will
> change hydro monthly dynamics within the "year-type". I thought it could be
> good to rank from driest year to wettest year (based on energy produced),
> and then choose for scenarios, for example: median year, driest year,
> wettest year, etc. But have the data for all the years so we can choose
> different years for different scenarios if we want. Or even different years
> for each period in a given scenario. What do you think?
>
>
>
> Thank you,
>
>
>
>
> *Patricia Hidalgo-Gonzalez *
>
> Assistant Professor, Mechanical and Aerospace Engineering
>
> University of California San Diego
>
> patricia.hidalgo.g at berkeley.edu
>
> (Pronouns: She/Her/Hers)
>
>
>
>
>
> On Thu, Sep 17, 2020 at 2:21 PM Julia Szinai <jszinai at berkeley.edu> wrote:
>
> Hi all,
>
> Thanks for your feedback on some of these questions at this morning's
> meeting.
>
> Here are notes on what we discussed for each of the points above:
>
> 1. Variable cap factors: I'll schedule a meeting with Paty to go over the
> time zones/time stamps associated with the variable capacity factors for
> the existing generation, and how it was done for the proposed generation.
>
>
>
> 2. Hydro cap factors: I'll upload the historical monthly data for 2004 -
> 2018, and construct a few scenarios based on this time series that
> calculate the monthly average, medium, maximum, and minimum generation for
> each plant and then repeat that monthly value for each future year.
>
>
>
> 3. Non-US part of WECC: I'll go through with Paty or ask Josiah where the
> Canadian and Mexican generation data came from.
>
>
>
> 4. Candidate generators: For the baseline scenario I'll start by using the
> set of generators that are environmentally constrained. We will need to ask
> E3 if they have an updated set of candidate generators and/or if they are
> still using the same "screen" to exclude generators based on environmental
> constraints.
>
>
>
> 6. Solar cap factors: For now I will average the capacity factors by load
> zone across residential, commercial, and utility scale solar. We will ask
> E3 if they have forecasts of residential solar buildout to add to the
> planned generator list.
>
>
>
> On Thu, Sep 17, 2020 at 11:02 AM Julia Szinai <jszinai at berkeley.edu>
> wrote:
>
> Hi all,
>
> I'm almost done updating the script and pushing the data of the existing
> and planned generation in the US portion of the WECC into the SWITCH
> database. The updated scripts 1) scrape the data from EIA forms 860 and
> 923, 2) process and standardize the data, and 3) upload the data into the
> database. They build on the original scripts in github here:
> https://github.com/RAEL-Berkeley/eia_scrape. (I haven't pushed my updated
> code to github yet).
>
>
>
> This data contains the generator parameters that go in the main
> generation_projects input file into SWITCH (including heat rate, capacity,
> build year, max age, etc), as well as the variable capacity factors for the
> renewable generators. The data is as of 2018 (since that is the most recent
> year for which there is both generator capacity and annual energy data),
> but I've removed generators which have been retired between 2018 - May 2020
> (since retirement data is more current). I also added planned retirement
> dates whenever available.
>
>
>
> I compared the total capacity in the WECC between the previous data of
> existing generation (as of 2015) with this update, and with the total
> current capacity in the WECC according to the WECC website (
> https://www.wecc.org/epubs/StateOfTheInterconnection/Pages/Capacity.aspx).
> The updated data has more solar, wind and gas than the 2015 data
> (interestingly also more coal), and the numbers are pretty close to that of
> the WECC website. I've attached an Excel file of the comparison.
>
>
>
> I have a couple of questions for the group that I think need to be settled
> before I finish preparing the data for a baseline scenario:
>
> 1. The variable capacity factors in the original script are added to the
> "variable_capacity_factors" table in the database. However, the inputs for
> the WECC SWITCH runs use the capacity factors from the
> "variable_capacity_factors_historical." What is the relationship between
> these and is there an additional script I'm missing? More importantly, it
> appears that the variable capacity factors for solar are off by 7 hours
> (likely related to time zone). The adjustment in the original script
> doesn't look like it is properly correcting for this, and I think it is
> still mismatched in the final data going into SWITCH. This could be the
> reason for the infeasibility of the 100% renewables run, if for example,
> solar is not generating during the day because the hours are incorrect. Is
> there some SWITCH adjustment for timezones, or time points that I'm missing?
>
>
>
> 2. hydro capacity factors: I've added monthly hydropower data to the
> database for 2004 - 2018. For the baseline scenario, should I calculate
> average monthly capacity factors across those years? This would reflect
> "average" operations under dry and wet years. The previous data only
> included hydropower for 2010 - 2015, so not as representative of the range
> of hydroclimatic conditions.
>
>
>
> 3. Canadian + Mexican parts of WECC: The data I scraped is from the EIA,
> so it doesn't have data for existing generation in Canada (BC and Alberta)
> and Mexico (small part of Baja CA). Looking at the prior data in the
> database, the generators for these load zones were all "proposed" and there
> was no existing generation. I guess SWITCH just "built" all the
> existing generation in the first period to meet the load? I'm not sure but
> that seems a bit weird, and could cause overestimated costs for the first
> period. Am I missing something? Should I look for updated existing
> generation data for BC and Alberta, and part of Mexico? The query I ran
> below came out empty (8 and 9 are the Canadian load zones, and
> generation_plant_scenario_id 14 is the scenario used in the last CEC
> report):
>
>
>
> SELECT *
> FROM generation_plant
> JOIN generation_plant_scenario_member
> USING (generation_plant_id)
> WHERE generation_plant_scenario_id = 14
> And name != 'Proposed'
> AND load_zone_id in (8, 9)
>
>
>
> 4. This data update was just of the existing generation plants. The
> baseline scenario will need to also include the data from candidate
> generators. My plan was to append the list of candidate generators from one
> of the scenarios used in the prior CEC study. However, there are several
> scenarios of candidate generators. As we discussed some of the prior
> scenarios have candidate renewable generators excluded because of
> environmental restrictions, based on data from E3 I believe. Do you know if
> we will get an updated set of environmentally-friendly candidate generators
> from E3? If not, should I use the restricted scenario of candidate
> generators for the baseline?
>
>
>
> 5. The existing battery projects that are in the generation_plant_cost
> table for the new scenarios 19 and 20 will need to be updated in the
> storage_energy_capacity_cost_per_mwh to reflect whatever is the baseline
> assumption for battery costs. Right now I've left these as NULL in the
> table.
>
>
>
> 6. The capacity factors for solar were previously averaged by load zone
> across residential, commercial, and utility-scale solar PV. I did the same
> for this data, but not sure if we want to revisit this simplification or
> keep the residential and commercial vs. utility-scale solar capacity
> factors separate.
>
>
>
> *Julia Szinai*
>
> PhD Candidate | Energy & Resources Group | University of California,
> Berkeley
>
> Graduate Student Researcher | Lawrence Berkeley National Lab
>
> NSF InFEWS Fellow
>
> Energy & Resources Group, MS '17
>
> Goldman School of Public Policy, MPP '17
>
> University of California, Berkeley
>
> jszinai at berkeley.edu
>
>
>
>
> --
>
> *Julia Szinai*
>
> PhD Candidate | Energy & Resources Group | University of California,
> Berkeley
>
> Graduate Student Researcher | Lawrence Berkeley National Lab
>
> NSF InFEWS Fellow
>
> Energy & Resources Group, MS '17
>
> Goldman School of Public Policy, MPP '17
>
> University of California, Berkeley
>
> jszinai at berkeley.edu
>
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>
>

-- 
*Julia Szinai*
PhD Candidate | Energy & Resources Group | University of California,
Berkeley
Graduate Student Researcher | Lawrence Berkeley National Lab
NSF InFEWS Fellow
Energy & Resources Group, MS '17
Goldman School of Public Policy, MPP '17
University of California, Berkeley
jszinai at berkeley.edu
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