دانلود ترجمه مقاله کاربرد الگوریتم کلونی مورچه ها در ارزیابی توان راکتیو در بازار آزاد برق – مجله الزویر

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چکیده :
برای انتقال توان حقیقی و تامین امنیت شبکه قدرت، مدیریت توان راکتیو ضروری است.در بازار برق ارائه یک روش صحیح و ممکن برای قیمت دهی توان راکتیو دارای اهمیت است. در مدل های مرسوم پخش بار بهینه، هزینه تولید توان راکتیو در نظر گرفته نشده است. در این مقاله هزینه تولید توان راکتیو و هزینه سرمایه گذاری بانک های خازنی در تابع هدف مساله OPF گنجانده شده است.  لذا، با استفاده از الگوریتم جستجوی جمعیت مورچگان، مساله بهینه حل شد. برای محاسبه هزینه توان اکتیو و راکتیو در هر باس در بازارهای رقابتی برق، از نظریه قیمت نهایی استفاده شد. اعمال روش ارائه شده روی سیستم ۱۴ باس  IEEE اعتبار و کارایی آن را به اثبات می رساند. نتایج بدست آمده از چندین مورد مطالعه ای نشان دهنده تاثیر عوامل مختلف روی قیمت توان راکتیو است.
۱٫    مقدمه
ساختار سنتی کنترل شده و انحصاری صنعت برق در کل دنیا در حال تبدیل شدن به یک محیط رقابتی و با دسترسی باز است. در این محیط جدید بازارهای برق ضرورتا بصورت رقابتی هستند. تاکنون، تمامی تلاش ها در وهله اول روی توسعه روش هایی برای تعیین میزان غرامت توان اکتیو ژنراتورها متمرکز بوده اند. با اینکه سرمایه گذاری در تولید برق و هزینه سوخت، مهم ترین هزینه های عملکردی سیستم قدرت به شمار می آیند اما روز به روز به اهمیت توان راکتیو افزوده می شود، بخصوص از نقطه نظر امنیت و تاثیر اقتصادی ای که در پی دارد [۱].
جبرانسازی و بهینه سازی توان راکتیو متحمل تبادل توان الکتریکی به عنوان بخشی از خدمات کمکی است. مصرف توان راکتیو دارای منحنی تقاضا برحسب زمان مشابه با توان اکتیو است، بخصوص برای بارهای موتوری و کوره ها. بنابراین، برای مدیریت شرکت های تولیدی و/یا انتقال، عملکرد و تخصیص هزینه توان راکتیو دارای اهمیت بسیار زیادی است [۱].

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۱٫ Introduction

The traditional regulated and monopoly structure of powerindustry throughout the world is eroding into an open-accessand competitive environment. Thus, planning and operation ofthe utilities are based on the economic principles of open-accessmarkets. In this new environment electric markets are essentiallycompetitive. Until now, effort has been directed primarily towarddeveloping methodologies to determine remuneration for the activepower of the generators. Although the investment in electricpower generation and the fuel cost, represent the most importantcosts of power system operation, reactive power is becoming moreand more important, especially from the security point of view andthe economic effect caused by it [1].Reactive power compensation and optimization sustains the exchangeof electric power greatly as a part of ancillary services. Theconsumption of the reactive power follows a similar demandagainst time curve as the active power, especially for motor loadsand furnaces. Therefore, the operation and cost allocation of reactivepower is very important to the running and management ofgeneration and/or transmission companies [1].A fixed tariff on the remuneration for reactive power is insufficientto provide a proper signal of reactive power cost [2]. Berget al. [3] pointed out the limitations of a reactive power price policybased on power factor penalties, and suggested the use ofeconomic principles based on marginal theory [4]. However, theseprices represent a small portion of the actual reactive power price[5–۷]. Hao and Papalexopoulos [8] note that the reactive powermarginal price is typically less than 1% of the active power marginalprice and depends strongly on the network constraints.Assessing the cost of reactive power production is difficult, becauseof differences in reactive power generation equipment and localcharacteristics of reactive power [9]. Several models for cost ofreactive power production have been developed [10–۱۸]. However,despite the complexity, these models lack a precise definition forthe cost of reactive power production. Also, the methodology toobtain the cost curves is not described adequately.In a competitive electric market the generators may provide thenecessary reactive power compensation if they are remunerated bythe service, provided the loss of opportunity in the commercializationof active power is taken into account [12]. Static compensators(capacitive and inductive) may be remunerated according to theirinvestment costs and depreciation of their useful lives [13].To address the above mentioned needs, in present paper, bothactive and reactive power production costs of generators and capitalcost of capacitors are considered in the objective function ofOPF problem.Then a new method based on the Ant Colony Optimization(ACO) and advanced sequential quadratic programming, is employedto solve the OPF problem.Currently, most works are carried out in the direction of applyingACO to the combinatorial optimization problems [19,20]. Formost of these applications, the results show that the ACO can outperformother heuristic methods. In power systems, the ACO hasbeen applied to solve the optimum generation scheduling problems[21], unit commitment [22], economic dispatch of power systems[23] and the constrained load flow [24]. It is rather difficult tofind a single search space, configuration and a parameter set of anACO that can satisfy every optimization problem. Therefore, thereis a need for the development of an improved version of the ACOtailored to solve the reactive power pricing. The ACO proposed inthis paper formulates the reactive power pricing problem as a combinatorialoptimization problem.In several case studies, the IEEE 14-bus system was used to verifythe validity of the proposed method. Different objective functionsare applied in the simulation tests to observe their impactson reactive power prices.The paper is organized as follows: in Section 2 the objective functionand constraints of reactive power pricing are presented. Section3 describes the proposed ant colony search algorithm. In Section 4the simulation results for IEEE 14-bus test system is illustrated.2. Objective function and constraints of reactive power pricingActive and reactive marginal prices are normally obtainedthrough solving the optimal power flow in which an objectivefunction subject to a set of equality and inequality constraints isminimized. The objective function is proposed as the summationof active and reactive power production costs, produced by generatorsand capacitor banks: