Page 22 - Hybrid-Renewable Energy Systems in Microgrids

Hybrid PV–wind renewable energy sources for microgrid application: an overview 7

software tools also exist and have been used in hybrid system optimization study such
as HOMER, HYBRID 2, HOGA, and HYBRIDS [20,21].
The initial installation costs of PV–wind system may be more expensive than a
conventional diesel generator. However, considering the long-term operation and
maintenance costs and fuel costs, PV–wind systems may reduce the total costs. In
addition, integrating an energy storage system (ESS) in the hybrid system will ensure
the smooth and accurate power quality of the power supply. Therefore designing an
optimal hybrid system is crucial, with many constraints to be satisfied. Generally,
design methodologies include mathematical modeling, linear–nonlinear search algo-
rithm, dispatch strategy, and case studies to validate the adopted model/algorithm.
Therefore the main design constraints for optimization in a hybrid system are the reli-
ability of the system, cost reduction, ensuring the maximum use of RES, and lower
operation costs.

4.1 Optimization objectives

Efficient, economic, and reliable operation of HRES requires optimal design of the
generating units, and this can ensure the operational reliability at the lowest possible
investments. A primary objective actually determines the size and capacity of the
HRES, storage, and other generating units. In most cases, objectives of the optimal
design are related to economy, reliability, and environment concern while satisfying
the customer reliability index. Dhaker et al. [22] have suggested a multiobjective opti-
mization, combined life cycle cost (LCC), environmental consideration as a function
of embodied energy (EE), and loss of power supply probability (LPSP) as objectives
to optimize a standalone hybrid system. An iterative controlled elitist genetic algo-
rithm is adopted to find the solution of the optimization problem. Methods to achieve
the lowest possible levelized cost of energy (LCE), as a primary objective, in a hybrid
system are proposed in Ref. [23], while ensuring the fulfillment of the reliability
index, LPSP. Annualized cost of system (ACS) based optimization is adopted in Ref.
[24] while satisfying the reliability based on the LPSP.
Technoeconomic optimization based on the design criteria of decreasing the system
cost and the level of autonomy is proposed in Ref. [25], and the author has suggested
that an alternative auxiliary source to meet the worst scenario is a better solution than
“worst-month scenario” based optimization. Energy expected not supplied (EENS),
another reliability index, is proposed in Ref. [26] to assess the long-term performance
of the studied hybrid system. Robust design to meet the design objectives under the
renewable uncertainty conditions has been proposed in Ref. [16] by using a trade-off/
risk method. Optimization methodology based on Adaptive Neuro-Fuzzy Inference
System (ANFIS) is proposed in Ref. [27] and compared with the computer software
program named HOMER (Hybrid Optimization Model for Electric Renewables) and
HOGA (Hybrid Optimization by Genetic Algorithms). The optimal size must ensure
the LPSP and determine the lowest cost operation with enough capacity to supply
the load even without any RES in the system. A short summary of the optimal sizing
based on the reliability index and environment is given in Table 1.1.