فهرست مطالب parisa hajialigol

This study investigates the planning and optimal operation of demand and supply systems in a renewable energy framework. The demand is categorized into cryptocurrency exploration, hydrogen production, and national power grid, while the supply consists of a national grid and a private solar power plant. The energy flow diagram considers feeding the cryptocurrency exploration and hydrogen production systems using both the national grid and solar power plant, and the solar power plant can also supply to the national grid. A linear optimization model is used to determine the optimal capacity of the solar power plant and demand planning to maximize investor profit while considering supply and demand limitations. The study includes 43803 decision-making variables and 26281 inequality constraints. The analysis focuses on the lifetime of system components, which aligns with the lifespan of renewable technologies. The system design considers variations in electrical energy consumption per bitcoin extraction, ranging from 70 MWh/BTC to 300 MWh/BTC, as well as changes in the price of Bitcoin, ranging from 5000 $/BTC to 55 k$/BTC. Additionally, the price of hydrogen ranges from 2 $/(kg_(H2 ) ) to 12 $/(kg_(H_2 ) ), and the price of electrolyzers ranges from 1250 $/(kW_Elect ) to 3000$/(kW_Elect ), over the study's 4356 scenarios. These scenarios encompass 31 unique states of supply and demand system design, along with optimal utilization of the supply system. The variability in energy exchange tariffs between the national grid and demand sectors accounts for the differences among the 26 distinct supply and demand system designs.

This study investigates the effects of ACH, students’ number, and wall thickness, as well as different semester starting dates and energy consumption reduction. The optimal academic timetabling for reducing energy consumption considers curricula’s rules for taking courses, departments’ specific instructions, existing classes, professors’ priorities, and other related factors. This research uses simulation and demand-side management models to determine the energy consumption of holding classes during a timeslot. They can quantify the factors’ effects on energy use. ACH is between 1.5 and 12, wall thickness is up to 1.6 of its normal value, and students are 10 to 40. There are three starting dates for the semester: conventional time, one-week and two-week earlier. As long as there is no need to change cooling/heating systems, the factors’ impacts on each timeslot from the energy reduction perspective when implementing optimal timetabling are investigated. The developed model revealed that the four factors do not change classes’ priorities from the energy viewpoint but noticeably affect energy use reduction. The optimal scheduling by keeping the semester’s starting date and classes’ operational conditions decreases energy consumption between 11.5 and 24.5 %. The results show that the semester’s early start has a substantial influence on energy consumption reduction in way that if the operational conditions are the same and classes begin two weeks earlier, energy consumption will be reduced between these two ranges: 36.7 - 52.2 % and 49.4 - 63.9 %.

A combined cooling, heating and power (CCHP) system which provides power and heat demands of a five story benchmark residential building was considered. After calculation of power demand, cooling and heating loads, the best alternative for the prime mover and sizing strategy was selected. The selection was made by analytic hierarchy process (AHP). The decision making criteria were the life cycle cost, annual carbon dioxide emission and annual average of efficiencies. Limitations in available equipment and their specific capacities were considered in this research. According to the results, the national motor with the efficiency of 37.60%, sized based on the maximum electrical load, and with the score of 44.4/ 100 found to be the best alternative.