Training exposure: Difference between revisions

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[[Category:Costs and valuations]]
[[Category:Costs and valuations]]
[[Category:Opasnet training]]
[[Category:Contains R code]]
{{variable|moderator=Jouni|stub=Yes}}
{{variable|moderator=Jouni|stub=Yes}}


== Question ==
== Question ==


What costs curves should be used for different energy products?
What is the exposure in the [[Training assessment]]?


== Answer ==
== Answer ==


<t2b index="Row,Actor,Fuel,Use,Reference,Unit,Observation" locations="Result,Description" unit="-">
<rcode embed=1 graphics=1>
1|Kuopion energia|Coal and peat|Demand|Current|€,ton|0, -0.06|
 
2|Kuopio market|Coal and peat|Supply|Current|€,ton|0, 0.5|
library(OpasnetUtils)
</t2b>
library(ggplot2)


Parameter c<sub>i</sub> are estimated from data: it is the current supply or demand given the current price (used as the point where p = 0).
objects.latest("Op_en5674", code_name = "initiate")
exposure
exposure <- EvalOutput(exposure) # for printing


<rcode
oprint(summary(exposure)) # fashionable design
name="answer"
label="Run code"
include="
page:Object-oriented_programming_in_Opasnet|name:answer|
page:OpasnetBaseUtils|name:generic|
page:Cost_curves_for_energy|name:formula
"
>


out <- formula.Op_en5478(dependencies.Op_en5478)
ggplot(exposure@output, aes(x = exposureResult, weight = 1, fill = Year)) +
cat("Computed changes in energy production and consumption in a new situation.\n")
geom_density() +
print(out)
theme_grey(base_size = 24)


</rcode>
</rcode>
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== Rationale ==
== Rationale ==


For detailed rationale, see [[Energy balance]].
===Data===


<math>\Sigma f_i(p) = \Sigma (a_i p^2 + b_i p + c_i) = 0</math>
<t2b index="Exposure agent,Year" obs="Result" desc="Description" unit="µg /m3">
PM2.5|2012|1 - 4|
PM2.5|2020|10 - 11|
</t2b>


<math>p = \frac{-\Sigma b_i \pm \sqrt{(\Sigma b_i)^2 - 4 \Sigma a_i \Sigma c_i}} {2 \Sigma a_i}</math>
=== Calculations ===


Cost curves are parameterised in the way that the current price p = 0 and therefore the current supply or demand is c<sub>i</sub>.
<rcode label="Initiate ovariable" name="initiate">


When a demand or supply changes, a new price p must be calculated based on f<sub>i</sub>, and then the supplies and demands of other actors can be calculated, resulting in a new balance.
library(OpasnetUtils) # relevant libraries


{{comment|# |There is an alternative way to describe the functions:
# Define ovariable
* Zero price is considered as 0 €/product unit. The true market price must be known. This function's parameters are more difficult to estimate.|--[[User:Jouni|Jouni]] 06:16, 28 January 2012 (EET)}}


{{comment|# |There is also alternative possibilities for functional forms (p is the price of the product):
exposure <- Ovariable("exposure", ddata = "Op_en5674")
* Exponential: P0 + P1 * exp(P2*p - P3)
colnames(exposure@data) <- gsub("[ \\.]", "_", colnames(exposure@data))
|--[[User:Jouni|Jouni]] 17:04, 28 January 2012 (EET)}}
=== Dependencies ===


=== Formula ===
objects.store(exposure) # save for later use


<rcode
cat("Ovariable exposure saved.\n")
name="formula"
label="Initiate functions"
include="
page:Object-oriented_programming_in_Opasnet|name:answer|
page:OpasnetBaseUtils|name:generic
"
>
 
formula.Op_en5478 <- function(x) {
data <- tidy(op_baseGetData("opasnet_base", x$curves[1]), direction = "wide")
curves <- strsplit(as.character(data$Parameters), split = ",")
coefficients <- data.frame(array(NA, dim = c(length(curves), 10)))
 
for(j in 1:length(curves)) {
coefficients[j, 1:length(curves[[j]])] <- curves[[j]]
}
coefficients[nrow(coefficients) + 1, 1] <- x$deviation
for(k in 1:ncol(coefficients)) {
coefficients[, k] <- ifelse(is.na(coefficients[, k]), 0, as.numeric(coefficients[, k]))
}
x.new <- polyroot(as.vector(colSums(coefficients)))
x.new <- ifelse(abs(Im(x.new)) < 1e-6, Re(x.new), 1e+30) # Take only the real roots.
x.new <- x.new[abs(x.new) == min(abs(x.new))] # Pick the root that is closest to zero (=BAU).
y.new <- 0
for(k in 1:ncol(coefficients)) {
y.new <- y.new + coefficients[, k] * x.new^(k-1)
}
temp <- data[1, ]
temp[1, ] <- "Deviation"
data <- rbind(data, temp)
data <- cbind(data, data.frame(Result = y.new))
return(data)
}
 
dependencies.Op_en5478 <- list(
curves = c("Op_en5478", "Cost curves for energy"),
bau = c("Op_en5469", "Energy balance in Kuopio"),
deviation = -5
)


</rcode>
</rcode>
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==See also==
==See also==


{{urgenche}}
{{Opasnet training}}


==Keywords==
==Keywords==
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==Related files==
==Related files==
{{mfiles}}

Latest revision as of 08:18, 3 June 2015



Question

What is the exposure in the Training assessment?

Answer

+ Show code

Rationale

Data

Training exposure: Difference between revisions(µg /m3)
ObsExposure agentYearResultDescription
1PM2.520121 - 4
2PM2.5202010 - 11

Calculations

+ Show code

See also

Materials and examples for training in Opasnet and open assessment
Help pages Wiki editing • How to edit wikipages • Quick reference for wiki editing • Drawing graphs • Opasnet policies • Watching pages • Writing formulae • Word to Wiki • Wiki editing Advanced skills
Training assessment (examples of different objects) Training assessment • Training exposure • Training health impact • Training costs • Climate change policies and health in Kuopio • Climate change policies in Kuopio
Methods and concepts Assessment • Variable • Method • Question • Answer • Rationale • Attribute • Decision • Result • Object-oriented programming in Opasnet • Universal object • Study • Formula • OpasnetBaseUtils • Open assessment • PSSP
Terms with changed use Scope • Definition • Result • Tool


Keywords

References


Related files

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