Page 23 - Hybrid-Renewable Energy Systems in Microgrids

8 Hybrid-Renewable Energy Systems in Microgrids

Table 1.1 Summary of the reliability index based optimization
objectives

Paper
Application schemes/reliabil- Control Choice of energy refer-
ity index algorithm sources ences
Minimizing the Loss of load Trade-off/Risk Wind, PV, battery [16]
probability (LOLP) and method
capital cost
To achieve lower system life An iterative Wind, PV, lead- [22]
cycle cost (LCC), minimiz- controlled acid battery
ing loss of power supply elitist genetic
probability (LPSP), and re- algorithm
duced environmental impact
To achieve lowest levelized A prediction Wind, PV, battery [23]
cost of energy (LCE) and approach based
reliability index, LPSP on LCE
Reduction of the annualized Genetic algorithm Wind, PV, lead- [24]
cost of system based on reli- (GA) acid battery
ability index, LPSP
Technoeconomic optimiza- A general sizing Wind, PV, battery [25]
tion based on the level of technique based
autonomy and cost of the on load require-
system ment
To estimate the long-term aver- An analytical Wind, PV [26]
age performance based on approach
reliability index, EENS
To satisfy the LPSP and deter- Adaptive Neuro- Wind, PV, battery [27]
mine the lowest cost Fuzzy Inference
System

4.2 Optimization practices in HRES

To achieve an efficient, reliable, and cost-effective size of the energy sources in a hybrid
MG system, adopting an optimization method is very essential. Proper optimization
method can find the optimal operating point of the hybrid energy sources at its lowest
cost and can maximize the benefit of the hybrid system by using the full capacity of the
individual resources. Regardless of the adopted optimization techniques, sizing primarily
depends on the availability of the renewable sources in a particular place, that is, solar
radiation, wind speed, generation-load demand pattern, maximum peak demand/peak
generation. etc. Because it is not practically possible to estimate the exact value of wind
speed or solar radiation beforehand, it is preferable to design an optimal solution with a
certain range rather than having only one limit, that is, flexibility to increase or decrease
the capacity of the sources must be considered. This will ensure the robustness of the sys-
tem, and additional capacity can be easily added to the system if needed with lower costs.