# Hybrid Renewable Energy Systems--A Review of Optimization Approaches and Future Challenges
## 1 Introduction  
The configuration of energy systems has changed over decades during the 20 th century from individual energy devices and small sub-systems into complex centralized systems with enormous power generation capacities [1]. At the beginning of the 21 st century, it was recognized worldwide that we need to drastically reduce greenhouse gas emissions to avoid catastrophic consequences for our planet and humanity. The Paris Agreement is a good illustration of understanding this challenge--196 countries have committed to reduce emissions under the framework of this Agreement on climate change [2]. The Paris Agreement declares the ambitious target--to limit global temperature increases to as close as possible to 1.5 degrees Celsius. The energy transition is unavoidable; therefore, climate-neutral energy generation and energy storage technologies play a vital role in achieving this target. The combination and integration of different renewable energy generation technologies in an optimal way, considering technical, environmental, economic, and social criteria,becomes an important challenge. The development of hybrid renewable energy systems (HRESs), the robust design of various RES technologies, the algorithms of their optimization, and comprehensive considerations of the above-mentioned criteria are needed to address this challenge. This diverse body of research reveals the importance of interdisciplinary research on the evolution of HRESs and the importance of interdisciplinary approaches in achieving sustainable energy solutions. There is a lot of research investigating the performance of energy systems, but the adequacy and suitability of various analysis methods and tools for the optimization of HRESs are often questioned. The objective of this paper is to review the latest scientific papers on HRES optimization analysis and define its advantages, challenges, and future perspectives. The optimization focuses on technology selection and development assessment. This article further integrates insights from various studies on innovative approaches to HRES optimization. It provides an overview of the developments in this field and the ongoing challenges and innovations that continue to drive its evolution. It presents an extensive examination of HRES optimization, highlighting critical advancements in design, performance evaluation, and optimization methodologies. HRES optimization will remain critical for addressing the growing global demand for clean, reliable, and affordable energy. As renewable energy sources like solar and wind become more prevalent, their inherent intermittency and variability necessitate advanced optimization techniques to ensure stability and efficiency in energy systems [3,4]. By integrating energy storage solutions, such as batteries and hydrogen, with robust control strategies, HRESs can meet energy demands even during fluctuating environmental conditions [5, 6, 7]. Furthermore, as energy systems grow in complexity, combining multiple renewable sources in a single hybrid configuration offers opportunities to improve resilience, reduce dependency on fossil fuels, and optimize cost-effectiveness [8,9]. In the future, the role of HRESs will expand beyond technical performance to include broader considerations such as environmental sustainability, social acceptance, and economic feasibility. Existing multi-objective optimization frameworks, like those highlighted in [10,11], will be essential research objects for balancing diverse criteria. Moreover, the integration of advanced technologies, such as machine learning and artificial intelligence [12], will enable real-time adaptability and predictive maintenance, ensuring that these systems can evolve alongside advancements in technology and policy [13,14]. As climate change and energy equity continue to drive global energy transitions, optimizing HRESs will be pivotal for meeting sustainability goals while providing reliable energy access to underserved regions [15,16].