Table 4 Resource optimization and scheduling
Refs. | Objective | Method | Practical implications | Limitations |
|---|---|---|---|---|
[127] | Minimizing generation cost | Stochastic optimization, Markov-chain | Increase efficiency and utilization of RESs | Few RESs |
[128] | Maximizing net profit | MILP | Provides a model for scheduling a VPP | Perfect assumption of uncertain parameters |
[129] | Efficient utilization of energy | Fuzzy chance-constrained programming | Improve efficiency and sustainability of the power system | Data privacy |
[130] | Optimal self-scheduling plan for VPPs | Robust optimization | grid stability and reliability of power system | Assumption of perfect wind power generation |
[131] | Management and scheduling of MGs in VPPs to optimize the system efficiency | ANN | Grid decarbonization | Data privacy |
[79] | Smart energy management | PLC, IoT | Dispatching energy optimally to achieve profit | Only validated through simulations |
[132] | Optimal dispatch to minimize expected cost | MILP | Improved energy management | Computational complexity |
[116] | Optimize residential users’ energy scheduling for optimal energy management | Blockchain | For DER integration | Coordination and cooperation |
[133] | Real-time smart energy management | Hybrid PSO | Energy management | Simulation only |
[134] | Day-ahead energy management for aggregate prosumers | Column and constraint generation algorithm | Energy management | Real-world experiment |