Design and techno-economical optimization for stand-alone hybrid power systems with multi-objective evolutionary algorithms

Abstract

SUMMARYThe optimal design of the hybrid energy system can significantly improve the economical and technical performance of power supply. However, the problem is formidable because of the uncertain renewable energy supplies, the uncertain load demand, the nonlinear characteristics of some components, and the conflicting techno-economical objectives. In this work, the optimal design of the hybrid energy system has been formulated as a multi-objective optimization problem. We optimize the techno-economical performance of the hybrid energy system and analyse the trade-offs between the multi-objectives using multi-objective genetic algorithms. The proposed method is tested on the widely researched hybrid PVwind power system design problem. The optimization seeks the compromise system configurations with reference to three incommensurable techno-economical criteria, and uses an hourly time-step simulation procedure to determine the design criteria with the weather resources and the load demand for one reference year. The well-known efficient multi-objective genetic algorithm, called NGAS-II (the fast elitist non-dominated sorting genetic algorithm), is applied on this problem. A hybrid PV-wind power system has been designed with this method and several methods in the literature. The numerical results demonstrate that the proposed method is superior to the other methods. It can handle the optimal design of the hybrid energy system effectively and facilitate the designer with a range of the design solutions and the trade-off information. For this particular application, the hybrid PV-wind power system using more solar panels achieves better technical performance while the one using more wind power is more economical.

Sustainability assessment of a hybrid energy system

“…Multi-criteria assessment of the energy system (Shi et al, 2007; is the method to establish a measuring parameter, which comprises different interactions between the system and its surroundings. This may lead to the development of a method which will help us to understand in deep the specific role of energy system selection and quality of our life.…”

Review of recent trends in optimization techniques for solar photovoltaic–wind based hybrid energy systems

“…A controlled elitist genetic algorithm has been applied by Abbes et al [46] to perform a multi-objective design of PV-windbattery hybrid system in order to find the best compromise between three objectives: life cycle cost (LCC), system embodied energy (EE) and loss of power supply probability (LPSP). Shi et al [47] used multi-objective genetic algorithm to study technoeconomical performance of the PV-wind hybrid energy system and optimized three objectives e.g. total system cost, autonomy level, and wasted energy rate with the PV array peak power, the wind generator rated power and the rated capacitor of the battery as decisive variables.…”

Probabilistic analysis of weather data for a hybrid solar/wind energy system

“…Many approaches have been presented in the literature for the design and optimization of HSWPSs [1][2][3][4]. Most of these approaches are deterministic methods, i.e.…”