# Copyright (c) 2019 Kelvin Say
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

library(ggplot2)
library(reshape2)
library(data.table)
library(lubridate)

# ------------------------------------------------------------------------------------
# ausgrid_data <- readRDS('_Scenarios/ConsumptionPatterns_Ausgrid/ausgrid_data.rds')
# customer_results <- readRDS('_Outputs/P2.Ausgrid_CustomerResults_Each.rds')
# all_customer_results <- readRDS('_Outputs/P2.Ausgrid_CustomerResults_All.rds')
#
#
# obs <- data.table('year' = 1:(NROW(all_customer_results$'Annual Energy Summary')),
#                   'pv' = all_customer_results$'Annual Energy Summary'$'Installed PV Capacity (kWp)',
#                   'battery' = all_customer_results$'Annual Energy Summary'$'Installed Battery Capacity (kWh)')
# mobs <- melt(obs, id.vars = "year")
# ggplot(mobs) +  aes(x = year, y = value, group = variable, fill = variable) + geom_area(alpha = 0.5, position = "identity") + geom_line() + theme_minimal()
#
# obs <- data.table('year' = 1:(NROW(all_customer_results$'Annual Energy Summary')),
#                   'self-consumed' = all_customer_results$'Annual Energy Summary'$'Self-Consumed (kWh)')#,
# #                  'imported' = all_customer_results$'Annual Energy Summary'$'Imported (kWh)')
# mobs <- melt(obs, id.vars = "year")
# ggplot(mobs) +  aes(x = year, y = value, group = variable, fill = variable) + geom_step() + theme_minimal()


# -----------------------------------------------------------------------------------------------------------------
# Combine all analysis into a single object
# -----------------------------------------------------------------------------------------------------------------
# customer_result_names <- c('P2.Ausgrid_CustomerResults_001_220',
#                            'P2.Ausgrid_CustomerResults_221_300')

customer_result_names <- c('P2.Ausgrid_ZeroRebate_CustomerResults_001_004',
                           'P2.Ausgrid_ZeroRebate_CustomerResults_005_200',
                           'P2.Ausgrid_ZeroRebate_CustomerResults_201_270')
combine_analysis <- function() {
  for (i in 1:length(customer_result_names)) {
    if (i == 1) {
      all_customer_results <- eval(parse(text=paste0(customer_result_names[i], '_All')))
      customer_imported <- data.table(Year = 1:NROW(eval(parse(text=paste0(customer_result_names[i], '_All')))$'Annual Energy Summary'))
      customer_exported <- data.table(Year = 1:NROW(eval(parse(text=paste0(customer_result_names[i], '_All')))$'Annual Energy Summary'))
    } else {
      all_customer_results$'Annual Energy Summary' <- all_customer_results$'Annual Energy Summary' + eval(parse(text=paste0(customer_result_names[i], '_All')))$'Annual Energy Summary'
      all_customer_results$'Net Power Profile (W)' <- all_customer_results$'Net Power Profile (W)' + eval(parse(text=paste0(customer_result_names[i], '_All')))$'Net Power Profile (W)'
    }
    for (j in 1:(length(eval(parse(text=paste0(customer_result_names[i], '_Each')))))) {
      customer_id <- eval(parse(text=paste0(customer_result_names[i], '_Each')))[[j]]$'Customer ID'
      col_name <- paste0("ag", sprintf("%03d", customer_id))
      customer_imported$new <- eval(parse(text=paste0(customer_result_names[i], '_Each')))[[j]]$'Annual Energy Summary'$'Imported (kWh)'
      customer_exported$new <- eval(parse(text=paste0(customer_result_names[i], '_Each')))[[j]]$'Annual Energy Summary'$'Exported (kWh)'
      setnames(customer_imported, "new", col_name)
      setnames(customer_exported, "new", col_name)
    }
  }

  intervals_per_year <- 365 * 3600 * 24 / g.scenario_simulation$'Time Step (s)'
  datetime_start <- ymd_hms("2017-01-01 00:00:00") + seconds(g.scenario_simulation$'Time Step (s)' * (0:(intervals_per_year-1)))
  datetime_start <- rep(datetime_start, g.scenario_simulation$'Number of Years')
  for (i in 2:g.scenario_simulation$'Number of Years') {
    year(datetime_start[(i-1)*intervals_per_year+(1:intervals_per_year)]) <- 2017 + (i-1)
  }
  datetime_finish <- datetime_start + seconds(g.scenario_simulation$'Time Step (s)')
  convert_W_to_Wh <- g.scenario_simulation$'Time Step (s)' / 3600
  net_grid_profile <- data.table('Start' = datetime_start,
                           'Finish' = datetime_finish,
                           'Net Power (W)' = all_customer_results$'Net Power Profile (W)',
                           'Net Energy (kWh)' = all_customer_results$'Net Power Profile (W)' * convert_W_to_Wh / 1000)

  annual_grid_profile <- data.table('Year' = 1:g.scenario_simulation$'Number of Years',
                                    'Load (MWh)' = numeric(g.scenario_simulation$'Number of Years'),
                                    'Imported (MWh)' = numeric(g.scenario_simulation$'Number of Years'),
                                    'Self-Consumed (MWh)' = numeric(g.scenario_simulation$'Number of Years'),
                                    'Exported (MWh)' = numeric(g.scenario_simulation$'Number of Years'),
                                    'Installed PV Capacity (MWp)' = numeric(g.scenario_simulation$'Number of Years'),
                                    'Installed Battery Capacity (MWh)' = numeric(g.scenario_simulation$'Number of Years'))
  for (t in 1:g.scenario_simulation$'Number of Years') {
    year_index_start <- (t-1) * intervals_per_year + 1
    year_index_end <- t * intervals_per_year

    annual_netprofile <- net_grid_profile[year_index_start:year_index_end]
    annual_load <- all_customer_results$'Annual Energy Summary'$'Load (kWh)'[t] / 1000
    annual_imported <- sum(annual_netprofile[annual_netprofile$'Net Energy (kWh)' > 0]$'Net Energy (kWh)') / 1000
    annual_selfconsumed <- annual_load - annual_imported
    annual_exported <- -1 * sum(annual_netprofile[annual_netprofile$'Net Energy (kWh)' < 0]$'Net Energy (kWh)') / 1000

    annual_grid_profile$'Load (MWh)'[t] <- annual_load
    annual_grid_profile$'Imported (MWh)'[t] <- annual_imported
    annual_grid_profile$'Self-Consumed (MWh)'[t] <- annual_selfconsumed
    annual_grid_profile$'Exported (MWh)'[t] <- annual_exported
    annual_grid_profile$'Installed PV Capacity (MWp)'[t] <- all_customer_results$'Annual Energy Summary'$'Installed PV Capacity (kWp)'[t] / 1000
    annual_grid_profile$'Installed Battery Capacity (MWh)'[t] <- all_customer_results$'Annual Energy Summary'$'Installed Battery Capacity (kWh)'[t] / 1000
  }

  return(list('Grid' = annual_grid_profile,
              'Grid NetProfile' = net_grid_profile,
              'Combined Customers' = all_customer_results,
              'Each Customer Imported (kWh)' = customer_imported,
              'Each Customer Exported (kWh)' = customer_exported))
}


get_day_netprofile <- function (i_month, i_day, i_netprofile = netprofile) {
  intervals_per_year <- 365 * 3600 * 24 / g.scenario_simulation$'Time Step (s)'
  for (t in 1:g.scenario_simulation$'Number of Years') {
    year_index_start <- (t-1) * intervals_per_year + 1
    year_index_end <- t * intervals_per_year

    annual_netprofile <- i_netprofile[year_index_start:year_index_end]
    day_netprofile <- annual_netprofile[month(annual_netprofile$'Start') == i_month & day(annual_netprofile$'Start') == i_day]

    if (t == 1) {
      collated_daily_netprofile <- data.table('Start' = day_netprofile$'Start',
                                              'Finish' = day_netprofile$'Finish',
                                              '0' = day_netprofile$'Net Energy (kWh)')
    } else {
      col_name <- sprintf("%d", t-1)
      collated_daily_netprofile$new <- day_netprofile$'Net Energy (kWh)'
      setnames(collated_daily_netprofile, "new", col_name)

    }
  }
  return(collated_daily_netprofile)
}


get_monthly_imported <- function(i_netprofile = netprofile) {
  collated_monthly_imported <- data.table('Month' = 1:12)

  intervals_per_year <- 365 * 3600 * 24 / g.scenario_simulation$'Time Step (s)'
  for (t in 1:g.scenario_simulation$'Number of Years') {
    year_index_start <- (t-1) * intervals_per_year + 1
    year_index_end <- t * intervals_per_year
    annual_netprofile <- i_netprofile[year_index_start:year_index_end]
    month_imported <- rep(0, 12)
    for (m in 1:12) {
      month_netprofile <- annual_netprofile[month(annual_netprofile$'Start') == m]
      month_imported[m] <- sum(month_netprofile[month_netprofile$'Net Energy (kWh)' > 0]$'Net Energy (kWh)' / 1000)
    }
    col_name <- sprintf("%d", t-1)
    collated_monthly_imported$new <- month_imported
    setnames(collated_monthly_imported, "new", col_name)
  }
  return(collated_monthly_imported)
}

get_monthly_exported <- function(i_netprofile = netprofile) {
  collated_monthly_exported <- data.table('Month' = 1:12)

  intervals_per_year <- 365 * 3600 * 24 / g.scenario_simulation$'Time Step (s)'
  for (t in 1:g.scenario_simulation$'Number of Years') {
    year_index_start <- (t-1) * intervals_per_year + 1
    year_index_end <- t * intervals_per_year
    annual_netprofile <- i_netprofile[year_index_start:year_index_end]
    month_exported <- rep(0, 12)
    for (m in 1:12) {
      month_netprofile <- annual_netprofile[month(annual_netprofile$'Start') == m]
      month_exported[m] <- -1 * sum(month_netprofile[month_netprofile$'Net Energy (kWh)' < 0]$'Net Energy (kWh)' / 1000)
    }
    col_name <- sprintf("%d", t-1)
    collated_monthly_exported$new <- month_exported
    setnames(collated_monthly_exported, "new", col_name)
  }
  return(collated_monthly_exported)
}

get_base_grid <- function() {

  first_valid_index <- which(ausgrid_data$Summary$'Data Complete' == TRUE)[1]
  datetime_start <- ausgrid_data$Customers[[first_valid_index]]$Profile$'Date Time (Start)'
  datetime_finish <- ausgrid_data$Customers[[first_valid_index]]$Profile$'Date Time (End)'
  num_intervals <- length(datetime_start)

  grid_load <- data.table('Start' = datetime_start,
                          'Finish' = datetime_finish,
                          'Load (kWh)' = numeric(num_intervals))
  for (i in 1:length(ausgrid_data$Customers)) {
    if (ausgrid_data$Summary$'Data Complete'[i]) {
      grid_load$'Load (kWh)' <- grid_load$'Load (kWh)' + ausgrid_data$Customers[[i]]$Profile$'Load (kWh)'
    }
  }

  monthly_load <- data.table('Month' = 1:12,
                             'Load (MWh)' = numeric(12))
  for (m in 1:12) {
    month_netprofile <- grid_load[month(grid_load$'Start') == m]
    monthly_load$'Load (MWh)'[m] <- sum(month_netprofile$'Load (kWh)' / 1000)
  }

  return(list('Detailed' = grid_load,
              'Monthly' = monthly_load))
}