دانلود مقاله مدل سازی و تحلیل مدیریت انرژی برای ریزشبکه های یکپارچه
چکیده
مقدمه
توصیف و شاخص های عملکرد سیستم مدیریت انرژی ریزشبکه یکپارچه
روش شناسی و مدل سازی برای تحلیل عملکرد
تجزیه و تحلیل نتایج، یافته های کلیدی و بحث
نتیجه گیری و چشم انداز
منابع
Abstract
Introduction
Description and performance indices of integrated microgrid energy management system
Methodology and modeling for performance analysis
Result analysis, key findings and discussion
Conclusion and outlook
References
چکیده
مفهوم ریزشبکه یک رویکرد نوآورانه برای ادغام منابع انرژی هیبریدی و تجدیدپذیر در شبکه برق است. عدم قطعیت ها به دلیل ماهیت متناوب منابع انرژی تجدیدپذیر، بار و قیمت بازار چالش های مهمی هستند. در روش سنتی اکتشافی، داده ها پیش بینی می شوند اما کاملا شناخته شده نیستند. بهبود سیستم‌های ذخیره‌سازی انرژی و توسعه سیستم‌های مدیریت انرژی (EMS) با استفاده از روش‌های مبتنی بر بهینه‌سازی، یک راه‌حل ممکن برای بهبود عملکرد عملیات ریزشبکه است. EMS بخش مهمی از منابع انرژی توزیع شده در سیستم ریزشبکه است، به ویژه زمانی که تولید، انتقال، توزیع، استفاده و قیمت گذاری متغیر برق درگیر باشد. این فرآیند بهینه‌سازی توسعه‌یافته در این مقاله از هزینه‌های پیش‌بینی‌شده و شرایط بارگذاری برای ذخیره یا فروش انرژی از یک سیستم باتری شبکه یکپارچه استفاده می‌کند. دو رویکرد در این کار تحقیقاتی معرفی شده‌اند: روش اکتشافی با استفاده از جریان حالت (جریان نمودار) و روش بهینه‌سازی مبتنی بر برنامه‌ریزی خطی (LP)، که هزینه‌های عملیاتی (صرفه‌جویی در حدود 19٪ هزینه) را مشروط به محدودیت‌های عملیاتی به حداقل می‌رساند. بهینه سازی LP تقریباً 3.44-5.01٪ از انرژی اضافی شبکه را ذخیره می کند. چندین نتیجه قابل قبول این مطالعه تحقیقاتی، شبیه‌سازی ریزشبکه جامع و یکپارچه را برای اعتبارسنجی الگوریتم بهینه‌سازی EMS ساده می‌کند. سیستم مدیریت یکپارچه ریزشبکه پیشنهادی ممکن است بستری برای تحقیقات فناوری شبکه هوشمند باشد.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
A microgrid concept is an innovative approach for integrating hybrid and renewable energy sources into the utility grid. The uncertainties because of the intermittent nature of renewable energy resources, the load, and market price are significant challenges. In the traditional heuristic method, data is forecast but not known perfectly. Improving energy storage systems and energy management systems (EMS) development using optimization-based methods is a possible solution to improve the performance of microgrid operations. The EMS is an essential part of the distributed energy resources in the microgrid system, especially when power generation, transmission, distribution, utilization, and variable pricing are involved. This optimization process developed in this paper uses forecasted costs and loading conditions to store or sell the energy from an integrated grid battery system. Two approaches are introduced in this research work: the heuristic method using state flow (chart flow) and the optimization method based on linear programming (LP), which minimizes operation costs (savings of around 19% cost) subject to operational constraints. The LP optimization saves roughly 3.44–5.01% of excess grid energy. Several plausible outcomes of this research study simplify the comprehensive, integrated microgrid simulation for EMS optimization algorithm validation. The suggested integrated microgrid management system might be a testbed for smart grid technology research.
Introduction
Nowadays, most countries are focusing on their energy-based industrial and commercial revolutions. These developments underlay the uptake of sustainable and uninterrupted energy production, the first and fundamental element. Increasing electricity generation in various traditional ways (fossil fuel-based) sometimes leads to unsustainability. Scientists and policymakers have to think about the next generation of safer technology for human beings and the environment. Using too many fossil fuels to generate electricity is harmful to nature because of global warming. Therefore, renewable energy harness/utilization and proper economic management are prime concerns for energy safety and security. Although the renewable source is quickly expanding throughout the world, fossil fuels remain the source of the vast bulk of global energy consumption. Oil, coal, and natural gas accounted for 84% of global energy use in 2020 (Lee et al., 2012). These fossil fuels are not inexhaustible, and researchers mention that if fossil fuel burning is kept at the current rate, it is generally estimated that all fossil fuels will be depleted by 2060 (Gulagi et al., 2020). As a result, there have been few international commitments to encourage hybrid or self-sufficient renewable generation technology. This endeavor has resulted in the spread of renewable energy production’s ability to produce resilient and uninterrupted electricity from several renewable energy sources (Yang et al., 2019). The biggest challenge is securing and making safer energy generation from renewables and utilizing it with a proper and effective management system.
Conclusion and outlook
The main purpose of this research study is to minimize the total cost of variably priced electricity. The optimization approach based on linear programming (LP) is easy to implement, analyze, and evaluate the performance and has little computation complexity. The suggested optimum LP approach overcomes different types of limitations compared with the traditional heuristic approach. The whole process is used to develop the linear optimization routine that predicts forecast pricing and loading conditions that optimally store or sell energy from a gridscale battery system. The simulation results clearly show that the LPbased optimization approach is cost-efficient. Using this optimization method, the cost of variable-priced electricity is 19% less when compared to heuristic state machine logic. The LP optimization also reduces the extra grid energy usage by around 3.44–5.01%. In the future, this research study will investigate the performance of constrained LP-based optimization approaches for more complex nonlinear and binary energy management problems. The focus will be given to reducing the dimensionality of the decision variables of the proposed LPbased optimization EMS. Furthermore, the microgrid’s current model precision will be improved by adding the miles parameter and the element into the microgrid configuration.