حامد حسيني

In this study, an attemped has been made to investigate the use of bioenergy in sustainable energy systems to meet part of the energy demand. Therefore, two conventional systems based on bioenergy-based gas turbine and bioenergy-based gas engine are theoretically searched to eva‎luate the production, recovery, distribution and management of bioenergy in facing different conditions. In the bioenergy-based gas engine system, a smart home energy management system (SHEMS) using the DDPG algorithm to manage home appliance loads and renewable energy production is presented. The DDPG algorithm was implemented in two different scenarios for energy from PV, WT, and Bf-GE in the energy consumption of home appliancs and real data equipement to the effects of Bf-GE, which were validated, determined in energy cost coefficient (ECC). According to the results; the amount of energy consumption has been reduced from 0.466 to 0.315. Therefore, in the case of using PV and WT, 53.4% of sustainable energy is the profit of the project, by adding Bf-GE energy production to the energy obtained from PV and WT, the profit increases by 28.2%. This increase in profit increses energy production in the case of adding Bf-GE to the system. In the bioenergy-based gas turbine system, the energy production method for biomass-based poly-generation systems optimized by modern meta-heuristic algorithms, considering the amount of energy consumption of each residential area. A comprehensive analysis of energy, exergy, economic, environmental and optimized energy consumption is carried out, which provided a sufficient insight into the behavior of modern optimization algorithms in dealing with biomass-based systems and the energy consumption of these systems.The objective functions include energy efficiency, exergy and overall cost rate which design in decision variables variation. According to the results, optimization the multi-generation system by the Harris Hawk optimization algorithm increases the efficiency of the poly-generation system from 50.03% to 78.37% and increases the exergy efficiency of poly-generation system from 33.62% to 60.36%. Also, the increase in energy production from 50.072 MW to 78.437 MW is another result of using HHOA for poly-generation energy system. Optimistic-greedy algorithm is chosen as a sustainable algorithm for energy use in residential areas due to its more stable behavior and better convergence rate than ε-greedy algorithm.

بيوانرژي , يادگيري تقويتي , الگوريتم فراابتكاري , شبكه عصبي , سيستم انرژي هيبريدي

Bioenergy , Reinforcement learning , Metaheuristic algorithm , neural network , hybrid energy system

Hamed Hosseini

Dr. Sepehr Sanaye