Stochastic Optimization of Hybrid Renewable Energy Systems

Author(s):  
Masoud Sharafi ◽  
Tarek Y. ElMekkawy

The stochastic nature of energy demand and renewable energy (RE) resources make the design of hybrid renewable energy systems as a complex problem. In this paper, an innovative stochastic optimization approach is proposed for optimal sizing of hybrid renewable energy systems (HRES) incorporating existing uncertainties in RE resources and energy load. The design problem is formulated based on multiobjective optimization framework with three objective functions including minimize total net present cost (NPC), maximize renewable energy ratio (RER), and minimize fuel emission. The reliability index named loss of load probability (LLP) is considered as a constraint with a desirable level. The Pareto front (PF) of developed multi-objective optimization problem is approximated with the help of the integration of dynamic multi-objective particle swarm optimization (DMOPSO) algorithm, simulation module, and sampling average method. Synthetic data generation approaches are applied to tackle the randomness in wind speed, solar irradiation, ambient temperature, and energy load. A building located in Canada is used as the case study to assess the performance of the developed model. Finally, the obtained PF by the stochastic optimization approach is examined against the deterministic PF using the most famous performance metrics.

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6223
Author(s):  
Bin Ye ◽  
Minhua Zhou ◽  
Dan Yan ◽  
Yin Li

The application of renewable energy has become increasingly widespread worldwide because of its advantages of resource abundance and environmental friendliness. However, the deployment of hybrid renewable energy systems (HRESs) varies greatly from city to city due to large differences in economic endurance, social acceptance and renewable energy endowment. Urban policymakers thus face great challenges in promoting local clean renewable energy utilization. To address these issues, this paper proposes a combined multi-objective optimization method, and the specific process of this method is described as follows. The Hybrid Optimization Model for electric energy was first used to examine five different scenarios of renewable energy systems. Then, the Technique for Order Preference by Similarity to an Ideal Solution was applied using eleven comprehensive indicators to determine the best option for the target area using three different weights. To verify the feasibility of this method, Xiongan New District (XND) was selected as an example to illustrate the process of selecting the optimal HRES. The empirical results of simulation tools and multi-objective decision-making show that the Photovoltaic-Diesel-Battery off-grid energy system (option III) and PV-Diesel-Hydrogen-Battery off-grid energy system (option V) are two highly feasible schemes for an HRES in XND. The cost of energy for these two options is 0.203 and 0.209 $/kWh, respectively, and the carbon dioxide emissions are 14,473 t/yr and 345 t/yr, respectively. Our results provide a reference for policymakers in deploying an HRES in the XND area.


2021 ◽  
Author(s):  
Salvador Alejandro Ruvalcaba Velarde

Abstract As the oil and gas industry increases its focus on sustainability, including greenhouse gases emissions reductions and carbon footprint management, it is relevant to analyze optimal solutions integrating different renewable, green and hydrogen technologies into hybrid renewable energy systems and compare them with well gas-to-power approaches for off-grid, on-site power generation in upstream applications. This paper goes through a desk review of different types of upstream facilities and an overview of potential power requirements to consider for off-grid electrification. Then, different technologies used for off-grid hybrid renewable energy systems are introduced and compared in terms of potential uses and integration requirements. Furthermore, emission targets are presented along with potential economical constraints. With those aspects introduced, system sizing and assumptions are modeled, simulated and optimized. The different modeled cases, including integrated renewable energy systems and power-to-gas systems, are presented in terms of suitability in application to the facilities under consideration. For such cases, simulation results are presented in quantitative terms of equivalent optimized value for the multiple competing objectives in the study, in terms of sustainability targets and economics. Sensitivity analysis are also presented showing main parameters of influence on the optimal energy scheme approach. This paper provides a qualitative and quantitative analytical optimization approach evaluating multiple competing objectives in terms of green, renewable, hydrogen and gas-to-power technologies, economics and carbon footprint management for consideration in facilities power systems schemes.


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