بهینه‌سازی چندهدفۀ بار مشترکین و منابع انرژی محلی جهت افزایش سودآوری و کاهش آلایندگی ریزشبکۀ هوشمند

در این مطالعه، یک روش بهینه‌سازی چندهدفه برای بهره‌برداری بهینه از منابع تجدیدپذیر و باتری در یک ریزشبکه در حضور برنامه‌های پاسخ‌گویی بار ارائه شده است. توربین بادی و پنل فتوولتائیک منابع تجدیدپذیر ریزشبکه هستند. از برنامه‌های پاسخ‌گویی بار زمان استفاده و قیمت‌گذاری اوج بحرانی نیز برای بهبود الگوی مصرف مشترکین ریزشبکه استفاده شده است. حداکثرسازی سود بهره‌بردار ریزشبکه از فروش برق و حداقل‌سازی میزان تولید گازهای آلاینده از منابع انرژی به‌عنوان توابع هدف مسئلۀ مدیریت سمت تقاضا در نظر گرفته شده‌اند. از الگوریتم بهینه‌سازی چندهدفۀ شیرمورچه برای بهینه‌سازی نامغلوب شاخص‌های ریزشبکه و ایجاد صفحۀ بهینۀ پارتو استفاده می‌شود. پس از اعمال الگوریتم بهینه‌سازی چندهدفه، از روش فازی برای انتخاب بهترین ذره که معادل با بهینه‌ترین برنامۀ بهره‌برداری از ریزشبکه است، استفاده می‌شود. در نهایت، کارایی روش پیشنهادی در یک ریزشبکۀ نمونه مورد ارزیابی قرار می‌گیرد. نتایج نشان‌دهندۀ این موضوع هستند که روش پیشنهادی با اصطلاح الگوی مصرف و بهینه‌سازی بهینه واحدهای تجدیدپذیر و باتری، کارایی ریزشبکه را به‌میزان قابل توجهی بهبود می‌بخشد. روش پیشنهادی برای مدیریت انرژی نه‌تنها باعث افزایش سودآوری شرکت بهره‌بردار ریزشبکه می‌شود، بلکه میزان آلایندگی زیست‌محیطی ریزشبکه نیز به‌میزان قابل توجهی کاهش می‌یابد.

Multi-objective Optimization of Customers’ Load and Local Energy Resources to Increase Profitability and Decrease Pollution of Smart Microgrid

Extended AbstractIntroduction: The concept of microgrid has been introduced as a practical way for better managing the power network. Microgrids with twoway communication between suppliers and consumers increase the flexibility and reliability of the electrical power system. Moreover, local energy sources decrease the dependence of the microgrid on the electrical energy of the upstream network. One of the main advantages of the microgrid is the ability of properly applying the demand side management programs to this type of network. The demand side management programs are the modification in the demand side energy consumption of the power network, like microgrid, to improve the efficiency and operations in overall electrical energy systems. Method: Energy sources of the microgrid: The electricity production part of a microgrid has a high effect on the performance of it. The economic and environmental issues of the microgrid can be improved when renewable distributed generation units are used for providing the demand of the system because their produced energy is ecofriendly. Wind turbine, photovoltaic panel, and electrical energy storage system are the considered energy untis in this study.Demand response programs: Nowadays, increasing the role of customers in managing power networks is one of the practical methods for increasing their efficiency. In this study, the time of use (TOU) and critical peak pricing (CPP) programs are applied to the microgrid for improving the consumption pattern. Objective functions and constraints: In this study, the profit and pollution rate of the microgrid are considered as the objective functions of the unit commitment method. Moreover, objective functions of the problem are simultaneously optimized in the presence of some constraints such as load balance constraint. Optimization algorithm: In this study, a combination of multiobjective ant lion optimizer and fuzzy decisionmaking method is used to optimize simultaneously the objective functions and to select the best unit commitment in a microgrid.Results of Simulation: The proposed method for optimizing the hourly operational schedule of renewable sources and of the battery of the microgrid is tested on a sample system based on 6 different cases. The amount of bought power from the upstream grid is 45 % of total demand in case 2 where no demand response program is used in the microgrid. After applying one of the demand response programs, the role of the upstream grid in providing the demand is reduced so that its participation power becomes almost 41 % of the total demand. Therefore, the dependence of the microgrid on the power of the upstream grid is reduced by about 60 % by utilizing the proposed operational schedule of energy sources and demand response programs. The initial profit rate of the system in the base case (case 1) is 9301 $. This profit is improved by about 25.16 % after utilizing the energy of the wind turbine, photovoltaic panel, and battery. The pollution rate of the microgrid is reduced considerably after operating renewable sources so that this index is decreased by 53.39 % in case 2 in comparison with case 1. In case 3, the TOU program causes an increase in the daily profit of the microgrid by about 42 % in comparison with case 1. The pollution rate is also reduced by about 3 % in comparison with case 1. The amounts of profit and pollution of the microgrid are 15484 $ and 441 Mg in case 4. In case 5, the CPP program increases the daily profit of the microgrid by about 35 % in comparison with case 1. The pollution rate is also reduced by about 4 % in comparison with case 1. The amounts of profit and pollution of the microgrid are also improved by about 59.21 and 58.27 % in case 6 in comparison with case 1. Conclusion: The numerical results present that renewable units and the energy storage system have practical performance in providing the electricity consumption of consumers and decreasing the dependence of microgrid on the power of the upstream grid. The wind turbine has the highest effect on the independence of the microgrid so that its production power is about 50 % of the total demand of the grid. The photovoltaic panel just injects electrical energy into the network during the middle time of the day because of the natural behavior of solar irradiance. Although the economic index is increased more in the TOU program than the CPP program and the environmental index is decreased more in the CPP program than the other one, the demand response programs have a positive effect on the indices of the microgrid. Totally, the profit and pollution rates of the microgrid are improved by about 60 % after utilizing the renewable sources, battery, and demand response programs in the microgrid. Therefore, it can be said that the proposed method can properly improve the efficiency of the microgrid.