<![CDATA[The main objective of this paper is to review the challenges associated with the integration used in multiple energy generation from renewable energy sources. There are a number of obstacles that must be overcome for the successful integration of various energy sources and storage technologies in a hybrid energy generation system. Algorithms are very crucial for multiple energy generation due to the integration of renewable energy sources, optimum resource allocation, load balancing, system stability and real time decision making. Demand response, load forecasting, and intelligent decision-making algorithms are examples of successful management tactics that may be used to allocate power from various sources according to availability and cost-effectiveness. To operate effectively, algorithms must take into consideration many variables such as state of the batteries, load changes, and weather. The difficulties with circuit design, algorithm design, source management and switching control in hybrid energy generation systems with numerous sources are covered in this paper. These difficulties include maximizing power generation and usage from each source, dynamic power output adjustment based on energy availability and demand, and smooth source changeover. The paper emphasizes how crucial integration of renewable energy sources, using proper algorithm and switching control among energy sources are for successfully integrating various energy sources. Voltage compatibility, current balance, and surge protection are among the difficulties in circuit design. Switching control techniques are very important fact to guarantee smooth switching between energy sources but minimizing power disturbance during source switching and maintaining a steady power supply throughout the process are challenges in switching control. The challenges in circuit and algorithm design for hybrid energy generation systems with multiple sources are highlighted in this review. Hybrid energy generation systems can accomplish effective use of renewable energy sources and contribute to a sustainable energy future by successfully overcoming these obstacles. Algorithms for optimization could be used to weigh environmental sustainability against economic viability while accounting for energy prices, carbon emissions, and lifecycle analysis.]]>
