Optimal switchable load sizing and scheduling for standalone renewable energy systems

Abstract

The variability of solar energy in off-grid systems dictates the sizing of energy storage systems along with the sizing and scheduling of loads present in the off-grid system. Unfortunately, energy storage may be costly, while frequent switching of loads in the absence of an energy storage system causes wear and tear and should be avoided. Yet, the amount of solar energy utilized should be maximized and the problem of finding the optimal static load size of a finite number of discrete electric loads on the basis of a load response optimization is considered in this paper. The objective of the optimization is to maximize solar energy utilization without the need for costly energy storage systems in an off-grid system. Conceptual and real data for solar photovoltaic power production is provided the input to the off-grid system. Given the number of units, the following analytical solutions and computational algorithms are proposed to compute the optimal load size of each unit: mixed-integer linear programming and constrained least squares. Based on the available solar power profile, the algorithms select the optimal on/off switch times and maximize solar energy utilization by computing the optimal static load sizes. The effectiveness of the algorithms is compared using one year of solar power data from San Diego, California and Thuwal, Saudi Arabia. It is shown that the annual system solar energy utilization is optimized to 73% when using two loads and can be boosted up to 98% using a six load configuration.

Optimum Design of a Standalone Solar Photovoltaic System Based on Novel Integration of Iterative-PESA-II and AHP-VIKOR Methods

“…Thus, heuristic techniques can overcome the drawback of numerical algorithms. In addition to that, some of the studies combined two or more in order to enhance the converge to the optimal values and reduce the execution time, such as the research works presented in [43,47,48]. Hussein et al [46] proposed an optimal design of the SAPV system using multi-objective particle swarm optimization (MOPSO) in Malaysia.…”

Intelligent and Optimized Microgrids for Future Supply Power from Renewable Energy Resources: A Review

“…In this study, the decision variables were the total cost of photovoltaic panels, the angle of the surface of these panels, and the continuous and stable power generated by this HRE microgrid. Habib et al [26] also designed and simulated a renewable energybased microgrid. These researchers optimized factors such as the angle of inclination and latitude of the solar panel's surface, the maximum power point of the solar panels, and the lifespan and efficiency of the battery bank and inverters.…”

“…Kiehbadroudinezhad et al [5], in their research, concluded that the reliability, total annual cost, and gas emissions' volume of the HRES are the most important factors that should be considered for the modeling, optimization, and evaluation of wind and solar-based power generation microgrids. Habib et al [26] also found that the cost of the total power generation system increases in a renewable energy system if the size of the renewable resources is considered too large; if it is too small, then the system's reliability decreases. Gupta [39] introduced an optimization algorithm to optimize the size of an HRE microgrid based on wind and solar and used the requested load hourly data to model and optimize the HRE microgrid.…”

Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia

“…Some of software tools have been utilized for sizing of a SAPV system based on economic evaluation, and planning and analysis tools such as PV.sys, Transient System Simulation (TRNSYS), Hybrid Optimization Model for Electrical Renewable (Homer), improved Hybrid Optimization by Genetic Algorithm (iHOGA), RetScreen, and System Advisor Model (SAM) [25]. Several studies have employed hybrid methods by combining two methods or more to find an optimal design of the SAPV system [26][27][28][29]. The hybrid methods overcome the drawbacks of AI methods by high ability of convergence to optimal solutions and reducing the execution time.…”