دانلود مقاله ترجمه شده سرمایه گذاری بر قدرت بادی و انتقال نیرو – مجله IEEE

فهرست مطالب:

 چکیده
۱- مقدمه
A- محرک و راهکار
B- بازبینی مقالات و کمک های این مقاله به مقالات موجود
۲- فرمولبندی مدل
A- مدل دو-سطحی
B- مسئله MPEC
C- مسئله MILP
۳- مثال توضیحی
A- داده ها
B- نتایج
۴- مطالعات موردی
A- مطالعه موردی IEEE RTS
B- مطالعه موردی سیستم تست ۱۱۸-باسی IEEE
۵- نتیجه گیری
ضمیمه

بخشی از ترجمه:

۵- نتیجه گیری
تصمیمات سرمایه گذاری نیروی باد بهینه توسط شرایط تقویت انتقال تعیین می گردد و بنابراین هر یک از دو اقدامات سرمایه گذاری باید بصورت همزمان توسط برنامه ریزی هماهنگ کننده در نظر گرفته شود. در محیط بازار، مدل پیشنهادی اجازه بررسی همزمان این دو تلاش توسعه توان و تحلیل روابط پیچیده در بین آنها را به ما میدهد، که شامل تأثیر کمکهای مالی برای توسعه سرمایه گذاری نیروی باد می باشد.
با در نظر گرفتن نتایج مطالعات موردی گزارش شده و فرضیات ساده شده در مدل ایجاد شده، نتیجه گیری های زیر را میتوان بیان کرد:
۱) اگرچه هزینه سرمایه گذاری در تسهیلات انتقال تا حد زیادی پایین تر از هزینه سرمایه گذاری در توان نیروی باد است، با اینحال تصمیمات سرمایه گذاری انتقال تا حد زیادی شرایط موارد جایگزین برای سرمایه گذاری توان نیروی باد را تعیین میکند.
۲) کمکهای مالی نسبتاً کم که نیازمند درصدی از هزینه های سرمایه گذاری هستند ممکن است نقش مهمی را در توسعه سرمایه گذاری های توان نیروی باد ایفا کنند.
۳) چون هم انتقال و هم سرمایه گذاری های توان نیروی باد در محیط بازار انجام می شوند، استفاده از راهکار MPEC تصادفی مناسب ترین راهکار است.
۴) مدل MPEC تصادفی پیشمهادی را میتوان به آسانی به یک مسئله MILP قابل کنترل تبدیل کرد که بصورت مؤثر با استفاده از حل کننده های branch-and-cut موجود قابل حل می باشد. مسئله MILP منتج دارای یک ساختار تجزیه پذیر است و در صورت نیاز می توان آنرا با استفاده از راهکار تجزیه حل کرد.
۵) مدل پیشنهادی، تقویت های شبکه که باید توسط TSO انجام شود را شناسایی میکند، و همچنین جالب ترین پروژه های نیروی باد برای توسعه نهایی توسط سرمایه گذاران خصوصی سود گرا را شناسایی میکند.

بخشی از مقاله انگلیسی:

I. INTRODUCTION A. Motivation and Approach Since wind power investment is generally tightly conditioned by transmission investment, we jointly consider these two related problems, henceforth denoted as wind power plus transmission investment problem. The objective is to identify the most attractive wind power projects and the required transmission reinforcements that result in minimum consumer payment and investment costs for given transmission and wind power investment budgets. In most electricity markets, transmission expansion is decided by a publicly controlled entity, called the transmission system operator (TSO) or the regional transmission operator (RTO) with the target of facilitating the energy trading. This is the case in most European markets and, particularly, in the market of the Iberian Peninsula that motivates this work. In most markets with a significant wind power penetration (e.g., Germany or Spain), wind investment has been and is either subsidized or heavily subsidized. If wind investment is subsidized with public funds, a public entity has the duty of identifying and promoting the building of wind power plants in the most appropriate locations taking into account potential network bottlenecks. Such public entity, the planner, seeks to identify a harmonious development of both wind production plants and transmission facilities. The planner, in its role as TSO, actually builds the transmission facilities and actively promotes wind investment in the most suitable sites. This is actually the situation of the TSO of the Spanish part of the electricity market of the Iberian Peninsula. Needless to say, the planner recognizes that the electricity business revolves around an electricity market and, thus, such market environment is properly represented in the decision-making model of the planner. The planner objective is therefore to identify optimal investments in transmission facilities and in wind production plants while maximizing a measure of social welfare, e.g., the minus total consumer payment, and subject to investment constraints, investment budgets, and the clearing of the market under many and diverse operating conditions within the planning horizon. The clearing of the market for any given operating condition is represented as an optimization problem that identifies the operating decisions that maximize social welfare. Thus, the planner problem is constrained by a collection of optimization problems, one per market operating condition. We select the objective function of the planner to be the total consumer payment because wind investment tends to lower market prices, and using such objective function, we efficaciously capture this effect. Since investment costs are also relevant for the planner, we add the investment costs to the objective function to be minimized. The aim of this paper is minimizing the consumer payment. However, we need to incorporate within the modeling framework the investment costs in wind power and transmission lines. Since wind power producers are considered to offer at zero price, if investment costs are not added to the objective function, the optimal solution would consist in building all possible wind power plants, which is not a realistic solution. Fig. 1. Load and wind-duration curves. The optimal solution of the planner decision-making problem results in transmission investment decisions to be actually carried out and wind investment proposals to be promoted among independent profit-oriented wind investors. Once the wind plants are built, wind power investment costs are recovered by the subsidies and by selling the wind power generation in the pool [1]. Once the most socially attractive wind projects have been identified, independent investors may undertake them. The effect of subsidies in making attractive such wind power projects is comprehensively analyzed using the proposed model. As it is customary in capacity expansion models, we adopt a static approach that focuses on a future target year [2], [3]. Within this target year, the load at each node is described by a piecewise constant load-duration curve composed of a number of demand blocks. Load uncertainty is described considering different load levels per demand block, as indicated in the lower plot of Fig. 1. Wind production uncertainty is also represented by considering different wind power intensities per demand block, as indicated in the upper plot of Fig. 1. The proposed description of the load and the wind-production uncertainty results in a number of demand blocks and a number of scenarios per demand block that should be large enough to accurately represent all possible combination of demand and wind production for the target year and throughout the nodes of the considered electric energy system. Modeling the load and the wind production through load and wind-duration curves, respectively, constitutes an appropriate approach for an investment problem such as the one proposed in this paper. However, this modeling may not allow accurately representing the working of storage units or operation constraints such as minimum up and down times of conventional units. Nevertheless, to a certain extent, the working of pumped-storage units can be represented within a load-duration curve framework [4], [5].