تعیین پارامترهای بهینه بهره‌برداری سد علویان با استفاده از ترکیب الگوریتم‌های ژنتیک و ازدحام ذرات

یکی از مهم‌ترین فاکتورهای مدیریتی در دوران بهره‌برداری مخازن سدها، تعیین پارامترهای بهینه بهره برداری می باشد. با توجه به اینکه حجم رهاسازی در ارتباط با حجم ذخیره مخازن سدها بوده و بایستی تواماً و باهم بهینه‌سازی گردند، لذا در این تحقیق تلاش می‌گردد، پس از معرفی الگوریتم ژنتیک و الگوریتم ازدحام ذرات، عملکرد این الگوریتم‌ها به‌تنهایی و در حالت ترکیب با هم، در بهره‌برداری بهینه از مخزن سد علویان با نتایج مدل‌سازی برنامه ریزی غیرخطی مقایسه و منحنی‌های فرمان بهره‌برداری ترسیم گردند. برای ارزیابی عملکرد الگوریتم‌های مورد بررسی در بهره‌برداری بهینه از مخزن، از شاخص‌های عملکرد مخزن استفاده ‌شده است. جواب بهینه مدل‌های الگوریتم ژنتیک و الگوریتم ازدحام ذرات به ترتیب با 1.08 و 0.87 و الگوریتم ترکیب آن‌ها با مقدار 0.62 و روش برنامه‌ریزی غیرخطی جواب بهینه محلی 0.91 می‌باشند. با توجه به شاخص‌های عملکرد مخزن، الگوریتم ترکیبی توانسته است 85 درصد از نیاز آبی کشاورزی پایاب سد علویان را تامین کند. جواب‌های بهینه نشان دادند که مدل الگوریتم ترکیبی در مورد سیاست بهره‌برداری از مخزن، نتیجه مطلوب‌تری داشته و نتایج حاکی از عملکرد بالای الگوریتم ترکیبی در مقایسه با دیگر روش‌های مورد بررسی در بهره‌برداری بهینه از سیستم تک مخزنه سد علویان بود. بر این اساس، پارامترهای بهینه بهره‌برداری از مخزن سد علویان با استفاده از الگوریتم ترکیبی به‌دست‌آمد.

determining the optimal operation parameters of alavian dam using the combination of genetic and particle swarm algorithms

abstract background and objectivesoptimum operation of dam reservoirs is one of the most significant management factors in developing the annual resource and consumption plan of dam reservoirs during operation. the decisions regarding amount of water release are made by having the volume of  the reservoir, amount of demand, and the prediction of reservoir inflow in the actual operation of dam reservoirs. since the volume of release is related to the storage volume of the reservoirs of the dams and should be optimized simultaneously, after introducing the genetic algorithm and the particle swarm algorithm, the performance of these algorithms alone and in combination with each other in the optimal operation of the alavian dam reservoir are compared with the modeling results in the nonlinear programming and the rule curves of the operation are developed in this study. the performance indicators of the reservoir were been used including reliability, vulnerability and stability  to evaluate the performance of the examined algorithms in the optimal operation of the reservoir. methodologyin this study, after introducing the genetic algorithm and the particle swarm algorithm, innovatively examines the accuracy and effectiveness of modeling by comparing the performance of these algorithms both individually and in combination. this comparison focuses on optimizing the operation of the alavian dam reservoir over multi-step ahead, using modeling results from the software lingo. to enhance decision-making for improved management of the alavian dam reservoir, operation rule curves have been developed. the model utilizes a series of 25 years of data from the alavian dam, which includes the volume of inflow, the volume of release from the reservoir, storage volume, and usage data encompassing drinking, agriculture, industry, and environmental needs. additionally, information such as the volume of overflow from the dam reservoir and the volume of evaporation from the surface of the alaviyan dam reservoir has been collected on a monthly basis. findingsthe results from these optimal solutions indicate that the combined algorithm outperforms other methods, demonstrating a better correlation with the reservoir management policy. over the last 25 years, the combined algorithm met 85% of the water requirements for agriculture downstream of alavian dam, compared to 82% for the particle swarm optimization(pso)  algorithm and 78% for the genetic algorithm (ga). in contrast, thenonlinear programming (nlp) method met 80%. the total shortages over the entire 25-year operational period for the ga, pso, ga-pso, and nlp algorithms were 38, 33.7, 27.1, and 35.2 million cubic meters, respectively. the ga-pso algorithm has successfully addressed 10.87 million cubic meters more than the ga algorithm and 6.57 million cubic meters more than the pso algorithm. conclusioninvestigating the results obtained from the optimal solutions revealed that the hybrid algorithm model provides a more favorable result and shows a better correlation regarding the reservoir operation policy. the results indicate the high performance of the hybrid algorithm compared to other studied methods in the optimal operation of the single reservoir system of alavian dam. accordingly, the optimal parameters of the alavian dam reservoir were obtained using a hybrid algorithm. it was proposed to  release volume rule curves and reservoir volume for the  multi-step ahead.