PowerGIM

Module for power grid investment analyses

class powergama.powergim.SipModel(M_const=1000)

Power Grid Investment Module - Stochastic Investment Problem

Methods

computeAreaCostBranch(model, c, stage[, …])	Investment cost for branches connected to an given area
computeAreaCostGen(model, c)	compute capital costs for new generator capacity
computeAreaEmissions(model, c[, stage, cost])	compute total emissions from a load/country
computeAreaPrice(model, area, t[, stage])	cumpute the approximate area price based on max marginal cost
computeAreaRES(model, j, shareof[, stage])	compute renewable share of demand or total generation capacity
computeAreaWelfare(model, c, t[, stage])	compute social welfare for a given area and time step
computeBranchCongestionRent(model, b[, stage])	Compute annual congestion rent for a given branch
computeCostBranch(model, b[, stage, include_om])	Investment cost of single branch NPV
computeCostGenerator(model, g[, stage, …])	Investment cost of generator NPV
computeCostNode(model, n[, include_om])	Investment cost of single node
computeCurtailment(model, g, t[, stage])	compute curtailment [MWh] per generator per hour
computeDemand(model, c, t)	compute demand at specified load ant time
computeGenerationCost(model, g, stage)	compute NPV cost of generation (+ CO2 emissions)
costBranch(model, b, stage)	Expression for cost of branch, investment cost no discounting
costGen(model, g, stage)	Expression for cost of generator, investment cost no discounting
costInvestments(model, stage[, includeOM, …])	Investment cost, including lifetime O&M costs (NPV)
costNode(model, n, stage)	Expression for cost of node, investment cost no discounting
costOperation(model, stage)	Operational costs: cost of gen, load shed (NPV)
costOperationSingleGen(model, g, stage)	Operational costs: cost of gen, load shed (NPV)
createConcreteModel(dict_data)	Create Concrete Pyomo model for PowerGIM
createModelData(grid_data, datafile, …)	Create model data in dictionary format
createScenarioTreeModel(num_scenarios[, …])	Generate model instance with data.
extractResultingGridData(grid_data[, model, …])	Extract resulting optimal grid layout from simulation results
loadResults(filename, sheet)	load results from excel into pandas dataframe
npvInvestment(model, stage, investment[, …])	NPV of investment cost including lifetime O&M and salvage value
plotAreaPrice(model[, boxplot, areas, …])	Show area price(s) TODO: incoporate samplefactor
plotBranchData(model[, stage])	Plot branch data
plotEnergyMix(model[, areas, timeMaxMin, …])	Plot energy, generation capacity or spilled energy as stacked bars
plotInvestments(filename, variable[, unit])	Plot investment bar plots
plotWelfare(model[, areas, timeMaxMin, …])	Plot welfare
saveDeterministicResults(model, excel_file)	export results to excel file
writeStochasticProblem(path, dict_data)	create input files for solving stochastic problem

computeAreaCostBranch(model, c, stage, include_om=False)

Investment cost for branches connected to an given area

computeAreaCostGen(model, c)

compute capital costs for new generator capacity

computeAreaEmissions(model, c, stage=2, cost=False)

compute total emissions from a load/country

computeAreaPrice(model, area, t, stage=2)

cumpute the approximate area price based on max marginal cost

computeAreaRES(model, j, shareof, stage=2)

compute renewable share of demand or total generation capacity

computeAreaWelfare(model, c, t, stage=2)

compute social welfare for a given area and time step

Returns: Welfare, ProducerSurplus, ConsumerSurplus,

CongestionRent, IMport, eXport

computeBranchCongestionRent(model, b, stage=1)

Compute annual congestion rent for a given branch

computeCostBranch(model, b, stage=2, include_om=False)

Investment cost of single branch NPV

corresponds to firstStageCost in abstract model

computeCostGenerator(model, g, stage=2, include_om=False)

Investment cost of generator NPV

computeCostNode(model, n, include_om=False)

Investment cost of single node

corresponds to cost in abstract model

computeCurtailment(model, g, t, stage=2)

compute curtailment [MWh] per generator per hour

computeDemand(model, c, t)

compute demand at specified load ant time

computeGenerationCost(model, g, stage)

compute NPV cost of generation (+ CO2 emissions)

This corresponds to secondStageCost in abstract model

costBranch(model, b, stage)

Expression for cost of branch, investment cost no discounting

costGen(model, g, stage)

Expression for cost of generator, investment cost no discounting

costInvestments(model, stage, includeOM=True, subtractSalvage=True)

Investment cost, including lifetime O&M costs (NPV)

costNode(model, n, stage)

Expression for cost of node, investment cost no discounting

costOperation(model, stage)

Operational costs: cost of gen, load shed (NPV)

costOperationSingleGen(model, g, stage)

Operational costs: cost of gen, load shed (NPV)

createConcreteModel(dict_data)

Create Concrete Pyomo model for PowerGIM

Parameters:

dict_data : dictionary

dictionary containing the model data. This can be created with the createModelData(…) method

Returns:

Concrete pyomo model

createModelData(grid_data, datafile, maxNewBranchNum, maxNewBranchCap)

Create model data in dictionary format

Parameters:

grid_data : powergama.GridData object

contains grid model

datafile : string

name of XML file containing additional parameters

maxNewBranchNum : int

upper limit on parallel branches to consider (e.g. 10)

maxNewBranchCap : float (MW)

upper limit on new capacity to consider (e.g. 10000)

Returns:

dictionary with pyomo data (in pyomo format)

createScenarioTreeModel(num_scenarios, probabilities=None, stages=[1, 2])

Generate model instance with data. Alternative to .dat files

Parameters:

num_scenarios : int

number of scenarios. Each with the same probability

probabilities : list of float

probabilities of each scenario (must sum to 1). Number of elements determine number of scenarios

stages : list of stage names

NOTE: Presently only works with default value=[1,2]

Returns:

PySP 2-stage scenario tree model

This method may be called by “pysp_scenario_tree_model_callback()” in

the model input file instead of using input .dat files

extractResultingGridData(grid_data, model=None, file_ph=None, stage=1, scenario=None, newData=False)

Extract resulting optimal grid layout from simulation results

Parameters:

grid_data : powergama.GridData

grid data class

model : Pyomo model

concrete instance of optimisation model containing det. results

file_ph : string

CSV file containing results from stochastic solution

stage : int

Which stage to extract data for (1 or 2). 1: only stage one investments included (default) 2: both stage one and stage two investments included

scenario : int

which stage 2 scenario to get data for (only relevant when stage=2)

newData : Boolean

Choose whether to use only new data (True) or add new data to existing data (False)

Use either model or file_ph parameter

Returns:

GridData object reflecting optimal solution

loadResults(filename, sheet)

load results from excel into pandas dataframe

npvInvestment(model, stage, investment, includeOM=True, subtractSalvage=True)

NPV of investment cost including lifetime O&M and salvage value

Parameters:

model : object

Pyomo model

stage : int

Investment or operation stage (1 or 2)

investment :

cost of e.g. node, branch or gen

plotAreaPrice(model, boxplot=False, areas=None, timeMaxMin=None, showTitle=False, stage=1)

Show area price(s) TODO: incoporate samplefactor

Parameters:

areas (list)

list of areas to show

timeMaxMin (list) (default = None)

[min, max] - lower and upper time interval

plotBranchData(model, stage=2)

Plot branch data

plotEnergyMix(model, areas=None, timeMaxMin=None, relative=False, showTitle=True, variable='energy', gentypes=None, stage=1)

Plot energy, generation capacity or spilled energy as stacked bars

Parameters:

areas : list of sting

Which areas to include, default=None means include all

timeMaxMin : list of two integers

Time range, [min,max]

relative : boolean

Whether to plot absolute (false) or relative (true) values

variable : string (“energy”,”capacity”,”spilled”)

Which variable to plot (default is energy production)

gentypes : list

List of generator types to include. None gives all.

plotInvestments(filename, variable, unit='capacity')

Plot investment bar plots

filename: string

excel-file generated by ‘saveDeterministicResults’

variable: string

dcbranch, acbranch, node, generator

unit: string

capacity, monetary

plotWelfare(model, areas=None, timeMaxMin=None, relative=False, showTitle=False, variable='energy', gentypes=None, stage=2)

Plot welfare

Parameters:

areas : list of sting

Which areas to include, default=None means include all

timeMaxMin : list of two integers

Time range, [min,max]

relative : boolean

Whether to plot absolute (false) or relative (true) values

variable : string (“energy”,”capacity”,”spilled”)

Which variable to plot (default is energy production)

gentypes : list

List of generator types to include. None gives all.

saveDeterministicResults(model, excel_file)

export results to excel file

Parameters:

model : Pyomo model

concrete instance of optimisation model

excel_file : string

name of Excel file to create

writeStochasticProblem(path, dict_data)

create input files for solving stochastic problem

Parameters:

path : string

Where to put generated files

dict_data : dictionary

Pyomo data model in dictionary format. Output from createModelData method

Returns:

string that can be written to .dat file (reference model data)

powergama.powergim.annuityfactor(rate, years)

Net present value factor for fixed payments per year at fixed rate

powergama.powergim.computeSTOcosts(grid_data, dict_data, generation=None, include_om=True)

Compute costs as in objective function of the optimisation This function is used to analyse optimisation results.

Parameters:

grid_data : powergama.grid_data

grid object

dict_data : dict

dictionary holding the optimisation input data (as dictionary)

generation : list of dataframes, one per stage

generator operational costs, dataframe with columns [‘gen’,’time’,’value’]