بهینه سازی سهم منابع انرژی تجدیدپذیر در سبد انرژی ایران با استفاده از الگوریتم ژنتیک (پیش بینی 25 ساله)
محورهای موضوعی : فصلنامه اقتصاد محاسباتی
زهرا پورخاقان شاهرضایی
1
,
علی اصغر اسماعیل نیاکتابی
2
,
سیدکمیل طیبی
3
,
مرجان دامن کشیده
4
1 - دانشگاه آزاد اسلامی تهران مرکزی
2 - استادیار دانشکده اقتصاد دانشگاه آزاد اسلامی واحد تهران مرکزی
3 - ستاد گروه اقتصاد، دانشگاه اصفهان
4 - استادیار گروه اقتصاد و حسابداری، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران
کلید واژه: بهینه سازی, انرژی¬های تجدیدپذیر, سبد انرژی تولید برق, الگوریتم ژنتیک,
چکیده مقاله :
با رشد جمعیت و تشدید پدیده گرمایش جهانی، نیاز به انرژی بهطور فزایندهای در حال افزایش است. در کنار این روند، نگرانیهای زیستمحیطی، محدودیت منابع فسیلی و بودجهای، توجه به انرژیهای تجدیدپذیر و اولویتبندی سرمایهگذاری در این حوزه را ضروری ساخته است. با این حال، سیاستگذاری مشخصی در مورد سهم بهینه انرژیهای تجدیدپذیر و سرعت حرکت بهسوی این سهم بهینه وجود ندارد. از آنجا که سرمایهگذاری در حوزه انرژی فرآیندی برگشتناپذیر و زمانبر است، لازم است این اولویتبندی در چارچوبی بلندمدت انجام گیرد.
در ایران به دلیل دسترسی آسان به منابع فسیلی، این منابع سهم عمدهای از سبد انرژی کشور را به خود اختصاص دادهاند. از آنجا که انرژیهای تجدیدپذیر عمدتاً برای تولید برق استفاده میشوند، بهینهسازی سهم آنها در سبد تولید برق میتواند راهکاری مناسب برای اصلاح ترکیب انرژی کشور باشد. این پژوهش با استفاده از الگوریتم ژنتیک در نرمافزار متلب، ترکیب بهینه منابع انرژی شامل فسیلی، هستهای و تجدیدپذیر (خورشیدی، بادی، زیستتوده، آبی، زمینگرمایی) را در بازه ۱۳۹۷ تا ۱۴۱۸ برای دورههای ۵ ساله، با لحاظ محدودیتهای اقتصادی، فنی و زیستمحیطی محاسبه کرده و بر اساس نتایج بهدستآمده، اولویتهایی برای سرمایهگذاری پیشنهاد داده است.
نتایج نشان میدهد ترکیب فعلی تولید برق بهینه نیست؛ بهطور مثال، سهم فعلی انرژیهای فسیلی ۹۲ درصد است، در حالی که سهم بهینه ۵۵ درصد برآورد شده است. همچنین، سهم بهینه انرژیهای هستهای و تجدیدپذیر در مجموع به حدود ۴۵ درصد میرسد که بیانگر ضرورت بازنگری در سیاستهای سرمایهگذاری انرژی کشور است.
Extended Abstract
Purpose
This paper addresses the strategic optimization of renewable energy integration into Iran’s electricity generation portfolio over a 25-year period, leveraging the computational strength of genetic algorithms. With an energy sector heavily reliant on fossil fuels, Iran faces increasing pressure to diversify its energy sources in light of global environmental commitments and domestic sustainability challenges. The study introduces a multi-objective optimization model that minimizes environmental pollution and economic costs while considering technical and policy constraints. The model simulates and predicts optimal shares of various energy sources including fossil, nuclear, solar, wind, hydro, biomass, and geothermal, offering a forward-looking investment prioritization strategy for national energy planning.
The primary objective of this study is to design an optimal mix of energy sources for electricity generation in Iran that reduces reliance on fossil fuels while maintaining economic feasibility and environmental responsibility. It seeks to answer two main policy questions: (1) What should be the optimal share of renewables in Iran’s electricity generation mix? and (2) At what rate should the transition from fossil fuels to renewables be implemented over a 25-year horizon?
Methodology
Optimization in mathematics means finding the optimal values of a function. Energy consumption optimization can be performed locally or comprehensively for a system consisting of several processes. Comprehensive optimization is more complex than the local method due to the need to understand the energy dynamics of the equipment. Optimization methods are also divided into three categories based on cost: low-cost, medium-cost, and high-cost.
Classical optimization methods are suitable for those problems that have multiple and implicit objective functions of decision variables, and for solving large-scale problems and mixed-optimization problems, classical methods are difficult and sometimes impossible; for this class of problems, meta-heuristic and intelligent stochastic methods have been developed that use the principles of natural phenomena and enable them to explore large search spaces and identify optimal solutions effectively. Given that the research problem is a multi-objective, multi-variable, constrained, dynamic, stochastic, convex, nonlinear, and continuous problem; Metaheuristic evolutionary methods are the most appropriate method for solving it. Among these methods, the genetic algorithm has been introduced as one of the most well-known and widely used evolutionary algorithms. This algorithm gradually evolves the population of solutions by using biological concepts such as inheritance, selection, and mutation and guides it towards optimal values. Considering that the problem of the present research is multi-objective, constrained, dynamic, and nonlinear, the use of evolutionary methods, and especially the genetic algorithm, is considered the most appropriate approach to solve it. In order to optimize Iran's energy production portfolio, a combination of optimal production sources is considered that has lower pollution and cost. This goal can be evaluated in two ways; one separately and the other combined. In other words, the optimization goals, which are to minimize pollution and energy production costs, can be modeled in two separate functions or considered in a combined function. By separately stating the goals, it is possible to find solutions in which even one of the criteria is optimal, and by applying the resulting weight, the optimal solution can be obtained under any conditions.
Findings
The optimized energy mix demonstrates a substantial shift from the current structure. The share of fossil fuels in electricity generation drops from 92.4% to about 55.4%, while renewable sources (especially solar and wind) and nuclear energy see significant growth. The forecasted production for 25 years ahead shows that renewable energy, particularly solar, becomes the dominant source of new energy capacity additions. The model predicts that investment in fossil fuel-based electricity should be minimized and existing capacities should only be maintained rather than expanded. This shift meets both environmental goals and supports Iran’s energy security in the long term.
Conclusion
Iran’s current electricity generation mix is suboptimal from both economic and environmental perspectives. This study provides a robust quantitative framework using genetic algorithms for forecasting and optimizing energy policy decisions. The results advocate for a strategic pivot toward renewables, especially solar and nuclear energy, over the next two and a half decades. Policymakers are urged to redirect capital investments accordingly and align energy planning with global sustainability targets. The methodology also allows for adaptability in future scenarios, supporting more resilient and responsive energy strategies.
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