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| OPF for power system with renewable energy sources |
| Approach | Techniques | Application | References |
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| Distributed generation (DG) | (i) PSO
(ii) Economic/environmental dispatch (EED)
(iii) Multiobjective optimization
(iv) Probabilistic technique
(v) Dynamic optimal power flow
(vi) Affinely adjustable robust counterpart
(vii) GA and PSO
(viii) Hybrid solar wind system optimization sizing model
(ix) Probabilistic planning technique
(x) Stochastic optimal power flow (S-OPF)
(xi) Distributed and parallel OPF
(xii) Stochastic-multiperiod OPF
(xiii) PSO based optimal power management method
(xiv) Optimal control (OC) model | (i) Optimal locations and sizes
(ii) Optimal dispatch
(iii) Optimal trade-off
(iv) Optimal allocation of different types of DGs
(v) Active network management (ANM) schemes
(vi) Multiperiod OPF
(vii) Optimal location and capacity of DG
(viii) Optimizing the capacity sizes of hybrid solar wind power generation systems
(ix) Minimizing annual energy loss
(x) Optimal model of DG integration
(xi) OPF smart grid transmission system
(xii) Find trade-off cost against security
(xiii) Plug-in hybrid electric vehicles
(xiv) Energy cost minimization | [49–62] |
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| Microgrid | (i) Execution monitoring and replanning
(ii) Online energy management strategy (EMS)
(iii) Nonconvex optimal power flow
(iv) Smart energy management system (SEMS)
(v) Dynamic stochastic optimal power flow (DSOPF)
(vi) Optimal energy management system
(vii) Multiobjective adaptive modified PSO (AMPSO)
(viii) Virtual power producer (VPP)
(ix) Point estimate method (PEM) and robust optimization (RO)
(x) Improved bat algorithm (IBA)
(xi) GA
(xii) Stochastic programming
(xiii) Multiobjective bilevel optimization
(xiv) Quasi-oppositional swine influenza model based optimization with quarantine (SIMBO-Q) | (i) Optimal generation dispatch problem
(ii) Real-time operation of microgrids
(iii) Global optimal solution
(iv) Optimal power production of DG sources
(v) Wide-area measurement
(vi) Minimizing the total cost of energy
(vii) Multioperation management problem
(viii) Minimizing the generation costs and optimize storage charging and discharging time
(ix) Optimal operation of MG in islanded mode
(x) Corrective strategies
(xi) Capacitor placement
(xii) Reactive power scheduling of a MG
(xiii) Minimizing power loss, optimal operation scheme
(xiv) Reducing total operation cost of MG | [63–76] |
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| Microgrid with renewable energy sources and/or battery energy storage system | (i) Gray wolf optimization
(ii) Optimal coordinated planning strategy
(iii) Energy management scheme for MG
(iv) Bilevel structure for the optimal working of a MG | (i) Finding optimal capacity of BES
(ii) Optimization of energy sources capacity
(iii) EV parking deck | [77–80] |
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| Electric vehicle technology | (i) Residential energy management
(ii) V2G mobile energy system
(iii) Bidirectional power flow
(iv) Stochastic optimization | (i) Renewable power sources and V2G integration
(ii) Demand response management
(iii) Minimizing the total energy procurement cost
(iv) Cost aware scheduling of EV | [81–84] |
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| Solar | (i) Active power limitation strategy
(ii) Mathematical programming techniques
(iii) Selective absorption
(iv) 3D numerical simulations
(v) Newton Raphson method
(vi) pump flow-rate optimization
(vii) Optimal dispatch approach | (i) Diminishing PV power injection during peak solar irradiation
(ii) Optimization of dry cooling technologies
(iii) Minimizing thermal losses
(iv) Optimal ratio of turbine pressure drop
(v) Power flow optimization
(vi) Flow-rate optimization of solar water heating system (SWHS)
(vii) Optimal capacity and economic feasibility | [85–91] |
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| Wind | (i) Info-gap decision theory (IGDT)
(ii) Probabilistic optimal power flow (POPF)
(iii) Optimal online control
(iv) Historical data and curve fitting stochastically, GA-enhanced market-based probabilistic optimal power flow (POPF)
(v) Stochastic wind generation model
(vi) Probabilistic AC optimal power flow (POPF)
(vii) Unscented transformation (UT) technique
(viii) Evolutionary particle swarm optimization (EPSO)
(ix) Probabilistic optimal power flow
(x) Risk-limiting optimal power flow (RLOPF)
(xi) Linear quadratic (LQ) criterion
(xii) Stochastic multiobjective optimal reactive power dispatch (SMO-ORPD)
(xiii) Genetic algorithm (GA) and a modified bacteria foraging algorithm
(xiv) Modified bacteria foraging algorithm
(xv) Artificial bee colony optimization algorithm (GABC) | (i) OPF with uncertain wind power injection
(ii) OPF with chance constraints of wind power generation (WPG)
(iii) Controllable generator response to renewable fluctuation
(iv) Minimizing the hourly social cost
(v) Optimal dispatch
(vi) Optimal power flow with wind’s stochastic behaviour
(vii) Probabilistic optimal power flow (POPF)
(viii) OPF problem of a wind-thermal power system
(ix) OPF with wind uncertainty and correlated loads
(x) Security constraints violation problem
(xi) Damping and performance
(xii) Optimal reactive power dispatch
(xiii) Determining the optimal schedule
(xiv) Multiobjective optimization problem
(xv) OPF with probabilistic nature of wind power | [92–106] |
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