ExplanConstraints Class

The ExplanConstraints class defines constraints to be considered in the QuESt Planning optimization model. It contains methods for various constraints, including energy storage, thermal and renewable generation, policy constraints, transmission constraints, power balance, and investment constraints.

Authors: C. Newlun and W. Olis

Class Initialization

class ExplanConstraints

Initializes the ExplanConstraints class.

Parameters:

• data_handler: An instance of the ExplanDataHandler class to provide data and configurations for the constraints.

Attributes:

• _scenario: The scenario defined in the data_handler.

• data_handler: Reference to the data handler instance.

• index: Index of the data loaded by the data handler.

• start_hr, end_hr: Start and end hours for constraints.

• ini_level: Initial energy storage state of charge.

• year_gap, year_gap_array: Defines year gaps for weighting.

• tax_credit_end_year, total_itc, total_ptc: Tax credit details.

• system: System name from the data handler.

• Tuples for various generator types (e.g., storage_tuple, thermal_tuple, etc.).

Public Methods

set_expressions(model)

Sets expressions for the optimization model, grouping constraints by category.

Parameters:

• model: Pyomo optimization model instance.

Example Usage:

>>> constraints.set_expressions(model)

energy_storage_constraints(model)

Defines energy storage-related constraints for the optimization model.

thermal_generator_constraints(model)

Defines thermal generation-related constraints for the optimization model.

renewable_generator_constraints(model)

Defines renewable generation-related constraints for the optimization model.

policy_constraints(model)

Defines policy-related constraints such as CO2 emissions and renewable portfolio standards.

transmission_constraints(model)

Defines transmission-related constraints for the optimization model.

power_balance_constraints(model)

Ensures power balance across the system in the optimization model.

investment_constraints(model)

Enforces constraints on investments in generation and transmission capacity.

reliability_and_resilience_constraints(model)

Defines reliability and resilience-related constraints.

dr_and_ee_constraints(model)

Defines demand response and energy efficiency constraints.

obj_and_cost_breakdown(model)

Defines the objective function and cost breakdown for the optimization model.

Constraint Rules

The ExplanConstraints class includes numerous constraint rules, such as:

• Energy Storage: cSOC_old, cChDsch, cSOCmax, cSOCmin, cStoremax, cStoremin, etc.

• Thermal Generators: cThermMax, cThermMin, cThermRup, cThermRdwn, etc.

• Renewables: cPVExist, cPVCand, cWindExist, cWindCan, cCurtMax, etc.

• Policy Constraints: cRPS, cCO2emm, cCO2emmLimit, etc.

• Transmission: cTxFlwFw, cTxFlwBw, cDCPF, etc.

• Power Balance: cPwrBal, cPwrBal_CopperSheet, etc.

• Investment: cPCapTotal, cStoreCapTotal, cRetirements, etc.

• Objective Function: cOBJ.

API Reference

For a detailed reference of all methods, attributes, and inherited properties, refer to the autodoc-generated content below.

QuESt Planning - explan constraints to be considered in the model Authors: C. Newlun and W. Olis

class quest_planning.explan.explan_constraints.ExplanConstraints(data_handler)

Bases: object

cBusInvMaxAnnual(model, b, g, y)

Planning horizon investment constraints

cBusInvMaxTotal(model, b, g)

Maximum invested cumulative capacities of candidate technologies by bus

cCO2emm(model, b, g, y)

CO2 emission constraint - carbon intensity calculations

cCO2emmLimit(model, y)

CO2 emission-free generation (% of generation)

cCO2int(model, y)

CO2 emission constraint - carbon intensity calculations

cCO2intLimit(model, y)

CO2 emission constraint - carbon intensity calculations

cChDsch(model, b, g, y, s, i)

This enforces that the sum of the charge/discharge power and reserves does not exceed the cumulative invested power size of the ES

cCurtMax(model, b, g, y, s, i)

Limit curtailment to only renewable generators

cDCPF(model, l, y, s, i)

DC Power Flow Constraint Under construction; add self.mva_base

cDRMax(model, b, g, y, s, i)

Demand response limits

cDRSystMax(model, b, g)

Demand response limits TODO: fix constraint

cDRgenMax(model, b, g, y, s, i)

Demand response limits TODO: fix constraint

cDRnoAnc(model, b, g, y, s, i)

Demand response limits TODO: fix constraint

cDRnoInv(model, b, g, y)

Demand response limits TODO: fix constraint and remove hard code

cDRopMax(model, b, g, y)

Demand response limits TODO: fix constraint

cESReplaceCost(model, b, g)

Energy storage lifetime extension costs for each storage device

cESReplaceCostSum(model)

Total energy storage lifetime costs

cFOMCostAnnual(model, y)

Define annual fixed operating & maintenance costs

cFuelCostAnnual(model, y)

Define annual fuel costs

cGenInvCostAnnual(model, y)

Define annual generation investment costs

cITCAnnual(model, y)

Define investment tax credit incentives

cLNSCostAnnual(model, y)

Define load shed penalty cost

cLNSMax(model, b, y, s, i)

Ensure load shed does not exceed the demand for that time step

cNGH2Costconv(model, y)

Define hydrogen conversion costs Optional cost

cNonES(model, b, g, y, s, i)

Ensures ES has no P_gen and no gen has Pcha and Pdis Optional constraint

cOBJ(model)

Define objective function to mimimize

cPCapTotal(model, b, g, y)

Cumulative generation capacity TODO: address with lead times for technologies

cPFlexMax(model, b, g, y, s, i)

Ramping constraints - Flexibility – Upper Bound

cPFlexMin(model, y, s, i)

Ramping constraints - Flexibility – Lower Bound

cPRM(model, y)

Planning reserve margin constraint TODO: Add piecewise ELCC or dynamic elcc curves for PRM calculations OR Seasonal ELCC

cPRegMax(model, b, g, y, s, i)

Ramping constraints - Regulation - 5min – Upper Bound

cPRegMin(model, y, s, i)

Ramping constraints - Regulation - 5min – Lower Bound

cPSpinMax(model, b, g, y, s, i)

Ramping constraints - Spinning - 10min – Upper Bound

cPSpinMin(model, y, s, i)

Ramping constraints - Spinning - 10min – Lower Bound

cPTCAnnual(model, y)

Define production tax credit incentives

cPVCand(model, b, g, y, s, i)

Candidate PV generator dispatch

cPVExist(model, b, g, y, s, i)

Existing PV generator dispatch

cPwrBal(model, b, y, s, i)

Power Balance constraint TODO: remove for and if statements as they are expensive in pyomo

cPwrBal_CopperSheet(model, y, s, i)

Power Balance constraint - copper sheet

cRPS(model, y)

Renewable portfolio standard constraint

cRenAnnual(model, b, g, y)

Annual renewable generation calculation

cResourceByBus(model, b, g)

Custom constraint

cRetirements(model, b, g, y)

P_cap_total calculations & generation retirements

cSOC_old(model, b, g, y, s, i)

Energy Storage SOC calculation

TODO: add regulating reserves into this calculation

cSOCmax(model, b, g, y, s, i)

SOC has to be less than or equal to the battery’s upper limit- KEEP CJN

cSOCmin(model, b, g, y, s, i)

SOC has to be greater than or equal to the battery’s lower limit

cSlackBus(model, b, y, s, i)

Define slack bus TODO: define in data_handler

cStoreCapTotal(model, b, g, y)

Cumulative ES energy capacity TODO: address with lead times for technologies

cStoremax(model, b, g, y)

Define Max energy storage capacity

cStoremin(model, b, g, y)

Define minimum energy storage capacity

cSystGasMax(model)

Maximum system gas capacity

cSystPVMax(model)

Maximum system solar capacity

cSystWindMax(model)

Maximum system wind capacity

cTechAvail(model, b, g, y)

Ensure technology is not invested before its availability year

cThermMax(model, b, g, y, s, i)

Thermal generation limit - upper bound thermal_tuple is optional

cThermMin(model, b, g, y, s, i)

Enforce thermal generation minimum stable level

cThermRdwn(model, b, g, y, s, i)

Ramping constraint for each generator - ensures adequate ramp down - only valid for hourly simulation

cThermRup(model, b, g, y, s, i)

Ramping constraint for each generator - ensures adequate ramp up - only valid for hourly simulation

cThetaDiffMax(model, l, y, s, i)

Bounds on phase angle difference when running DC power flow

cThetaDiffMin(model, l, y, s, i)

Bounds on phase angle difference when running DC power flow

cTotalCostAnnual(model, y)

Define total annual costs

cTxCapInvMax(model, l)

Enforce candidate line investment capacity

cTxCapSystMax(model)

Enforce system-wide transmission investment capacity

cTxCapTotalnoDelay(model, l, y, y1)

Transmission total invested capacity calculation

cTxCapTotalwDelay(model, l, y)

TODO: Optional constraint

cTxFlwBw(model, l, y, s, i)

Backwards flow constraint

cTxFlwFw(model, l, y, s, i)

Transmission forward flow constraint with pipe and bubble OR DC-power flow

cTxInvCostAnnual(model, y)

Define annual transmission investment costs

cVOMCostAnnual(model, y)

Define annual variable operating & maintenance costs

cWindCan(model, b, g, y, s, i)

Candidate Wind generator dispatch

cWindExist(model, b, g, y, s, i)

Existing Wind generator dispatch

constraint1_i(model, b, g, y)

Define model.Store – the energy capacity Optional constraint

dr_and_ee_constraints(model)

Define the demand response and energy efficiency constraints of the optimization model

energy_storage_constraints(model)

Define the energy storage constraints of the optimization model

investment_constraints(model)

Define the investement constraints for generation and transmission of the optimization model

obj_and_cost_breakdown(model)

Define the objective and cost breakdown constraints of the optimization model

policy_constraints(model)

Define the policy constraints of the optimization model

power_balance_constraints(model)

Define the power balance constraints of the optimization model

reliability_and_resilience_constraints(model)

Define the reliability and resilience constraints of the optimization model

renewable_generator_constraints(model)

Define the renewable generation constraints of the optimization model

property scenario

Define scenario - check if we need

set_expressions(model)

Set expressions for optimization model

thermal_generator_constraints(model)

Define the thermal generation constraints of the optimization model

transmission_constraints(model)

Define the transmission constraints of the optimization model