In this work, the relationship between the accuracy of output prediction of the solar cell and the operation plan of the microgrid was determined. GA provides a facile method for solving optimised problem and can be easily adapted to complicated energy systems. Conventional GA requires a long runtime when the microgrid contains numerous energy sources and the solution must be highly accurate.

Chapter Contents:

• 1.1 Introduction: origin of clean energy systems
• 1.2 Dynamic operational scheduling for a microgrid with renewable energy
• 1.2.1 Introduction
• 1.2.2 Independent microgrid with renewable energy and battery
• 1.2.2.1 System configuration
• 1.2.2.2 Dynamic operation planning
• 1.2.2.3 Solar cell system
• 1.2.3 Power balance and objective function
• 1.2.3.1 Power balance
• 1.2.3.2 Objective function
• 1.2.4 Analysis method
• 1.2.4.1 Production-of-electricity prediction algorithm of solar cell (PEPA) [5]
• 1.2.4.2 Optimisation of dynamic operation using a genetic algorithm (GA)
• 1.2.4.3 Analysis flow of operation planning
• 1.2.5 Case analysis
• 1.2.5.1 Analysis system
• 1.2.5.2 Analysis conditions
• 1.2.6 Analysis results
• 1.2.6.1 Prediction of solar cell output power via PEPA
• 1.2.6.2 Prediction error of PEPA and operation method of generating equipment
• 1.2.6.3 Result of dynamic operation planning
• 1.2.7 Conclusions
• 1.3 Operation analysis of microgrids using an orthogonal array-GA hybrid method
• 1.3.1 Introduction
• 1.3.2 Analysis methods
• 1.3.2.1 Genetic algorithm
• 1.3.2.2 Orthogonal array-GA hybrid analysis
• 1.3.3 Case study
• 1.3.3.1 System outline
• 1.3.3.2 Electric power supply system
• 1.3.3.3 Heat supply system
• 1.3.3.4 Energy flow of the system
• 1.3.3.5 Analysis conditions
• 1.3.3.6 The level of the design parameters
• 1.3.3.7 Objective function (adaptive value)
• 1.3.3.8 Initial values used for the orthogonal array-GA hybrid analysis
• 1.3.3.9 Analysis parameters of the GA
• 1.3.4 Analysis results
• 1.3.4.1 Planning the equipment capacity
• 1.3.4.2 Optimal operation
• 1.3.4.3 Verification of the analysis algorithm
• 1.3.5 Conclusions
• Nomenclature
• References

Inspec keywords: distributed power generation; power generation planning; solar cells; genetic algorithms

Other keywords: clean energy systems; energy sources; GA; solar cell; microgrid operation plan; genetic algorithms

Subjects: Photoelectric conversion; solar cells and arrays; Power system planning and layout; Optimisation techniques; Distributed power generation; Solar cells and arrays