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Table 8 Review of operational optimization models in power system

From: An overview of inertia requirement in modern renewable energy sourced grid: challenges and way forward

Objective function

Model description

Software and solvers

System specification

Refs.

Minimize hydropower curtailment and water spillage

Robust, MINLP Optimization

BARON solver in

GAMS 24.7.1

Not Given (NG)

[118]

Maximize the energy production

MILP

Xpress solver in Python

Intel Core i5 Processor, 8 GB of RAM

[121]

Minimize cost and peak load regulation

Robust unit commitment model

NG

NG

[132]

Minimize load changes

MILP

NG

3.3 GHz processor and 8 GB RAM

[122]

Minimize operational cost and maximize the utilization of ESS

Scenario-based stochastic model

BARONS solver and SCENRED2 tool in GAMS

Intel 2.4 GHz computer

[80]

Minimize total operating cost

Robust optimization and MILP model

Gurobi solver in MATLAB

NG

[133]

Minimize cost of market-clearing operation

MILP model

ILOG CPLEX 12.6.0.0 solver in GAMS

NG

[134]

Minimize the operating cost of power system

Mixed-integer second-order cone programming model

CVX toolbox and GUROBI 7.52 solver in Python

2.6 GHz CPU Intel Core (TM) 2 Duo, and 4 GB RAM

[135]