استراتژی مناقصه میکرو شبکه با در نظر گرفتن عدم قطعیت برای شرکت در بازار برق
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی|
|16767||2014||13 صفحه PDF||24 صفحه WORD|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Electrical Power & Energy Systems, Volume 59, July 2014, Pages 1–13
فهرست واژگان و اطلاعات
2. استراتژی کنترل و چارچوب بازار
3. استراتژی مناقصه میکروشبکه در بازار توان پیشرو
1-3- تعهد واحد قطعی میکروشبکه
2-1-3- محدودیت ها
1-2-1-3- محدوده تعادل عرضه – تقاضا
2-2-1-3- محدوده های DG
3-2-1-3- محدوده ذخیره سازی انرژی باتری
4-2-1-3- محدوده بار قابل قطع کردن
5-2-1-3- محدوده ظرفیت اتصال درونی
6-2-1-3- محدوده کفایت MA
2-3- مدل سناریوی توان غیر قطعی میکرو شبکه
1-2-3- مدلسازی سناریوی غیر قطعی توان بار
2-2-3- مدلسازی سناریوی نامشخص قدرت فتوولتائیک
3-2-3 – مدلسازی سناریوی بار غیرخطی (نامشخص )
4-2-3- مدلسازی سناریوی توان خالص نامشخص میکروشبکه :
3-2- تولید و کاهش سناریو
1-3-3- تولید سناریو
2-3-3- کاهش سناریو
4-3- مدل مناقصه تصادفی میکروشبکه
5-3 ابزار حل و فصل مسئله
4- مطالعه موردی
Microgrid is commonly regarded as an efficient way for integration of distributed generation (DG) in low voltage network. However, the integration method of microgrid in power system for maximum benefit needs to be further promoted. In this paper, a stochastic bidding strategy of microgrid in a joint day-ahead market of energy and spinning reserve service is proposed taking into account of uncertainty of renewable DG units’ output power and load. The stochastic bidding strategy is modeled as bi-level optimization problem and can be divided into two steps. First, Latin Hypercube Sampling (LHS) is utilized for generating microgrid uncertain net power scenarios according to day-ahead uncertain power scenario models, and then reduced by backward scenario reduction technique for less computation. Second, the upper level total bidding profit including bidding revenue, expected imbalance and operation cost is optimized by interior point algorithm in MATLAB for making optimal bids. The expected imbalance and operation cost is calculated by iteratively invoking lower level deterministic unit commitment under each microgrid uncertain net power scenario. The lower level deterministic unit commitment is coded and solved by mixed integer nonlinear programming (MINLP) solver DICOPT in GAMS. Finally, the optimal energy and spinning reserve bids are given by solving the bi-level bidding model. The model is applied to a modified typical low-voltage microgrid and the effectiveness and excellence of proposed strategy is proven by comparing simulation results with traditional deterministic bidding strategy.
The worldwide trend of exploitation of distributed generation (DG) as the alternative to traditional generation keeps on increasing. Distributed generation is small scale generation units installed close to the consumers. In this way, transmission losses and network congestion can be mitigated. Moreover, the renewable DG units are widely utilized among the different types of DG units due to green, sustainable and free energy source. However, the intermittent and the uncertain power output of isolated renewable DG units adversely impact power quality and system stability and the problem becomes especially severe with high penetration of renewable DG units. To solve the problem, microgrid is promoted as an active control method to aggregate DG units and provides heat and power to local consumers . Generally, energy storage system (ESS) is included in the microgrid for balance between power production and consumption. More friendly and efficient DG integration can be achieved by microgrid with the benefit of carbon emissions reduction, power quality improvement, reliability enhancement, energy supply cost reduction and mitigation of power network expansion pressure . Even though microgrid has many benefits, the integration of microgrid into the traditional distribution system imposes technical challenges of system operation in several aspects, such as energy management strategy, protection design and so on, that have to be comprehensively investigated. So far, many investigations have been conducted in the aspect of power control strategy of DG units , , ,  and , microgrid energy management strategy , , ,  and , protection design  and , optimal sizing and placement ,  and , reliability and stability assessment ,  and  and so on. Among these issues, research of interaction paradigm between microgrid and traditional system is crucial for maximizing potential benefits of microgrid and ultimately encouraging DG or microgrid adoption. In consideration of free-market conditions, optimal operation of microgrid in energy or ancillary service market has been investigated in , , , ,  and . These research or practical application would facilitate integration of microgrid with more effectiveness and profitability. However, uncertainty including renewable DG units’ power output, market price, and lines/units reliability is lack of consideration in these models. Therefore, probabilistic energy manage strategy were proposed under uncertainty environment in , ,  and . Furthermore, the uncertainty is actually inevitable and bound up with maximum benefit of microgrid in competitive power market. The bidding strategy of microgrid with consideration of uncertainty in power market has not been specially studied yet. Therefore, this paper focuses on the paradigm of microgrid participating in power market for providing energy and spinning reserve taking into account of uncertainty. The uncertainty in wind speed, solar irradiance and load during each operation period of next day is implemented through multi-scenario technique. The main contributions of the work are as follows: 1. Presenting a stochastic bidding strategy for microgrid participating in energy and spinning reserve market in consideration of uncertainties of load and available output power of wind and photovoltaic units. 2. Backward scenario reduction method is employed to decrease the computation time. 3. In the stochastic bi-level optimization bidding model, the imbalance and operation cost is supposed to be minimized in each scenario. It implies that integration of renewable DG units in microgrid would not only maximize utilization of renewable power but also mitigate the uncertainty of renewable power. In this way, the potential benefit of renewable power and microgrid is expected to be maximized in power market.
نتیجه گیری انگلیسی
In this paper, a new bidding strategy of centralized controlled microgrid in a joint day-ahead energy and spinning reserve market is proposed while taking uncertainty of renewable power and load into consideration. The proposed bidding strategy involves day-ahead microgrid uncertain net power scenarios generation and reduction, and bi-level stochastic bidding optimization model. Application of backward scenario reduction technique leads to distinct decrease in the computation time. In the bi-level stochastic bidding model, other than maximizing total profit under forecasted scenario in deterministic bidding strategy, the expected microgrid bidding profit is maximized in consideration of multiple microgrid net power scenarios and imbalance costs. The bidding strategies were simulated for a modified typical lower voltage microgrid with several renewable DG units. The results of the proposed stochastic bidding strategy and deterministic bidding strategy without consideration of uncertainty were compared and have shown that more risky bids are generally taken by the proposed stochastic bidding strategy. At last, the total profit of microgrid under 20 stochastic scenarios with two bidding strategies were compared, and the profits of the proposed stochastic bidding strategy are mostly higher than the deterministic bidding strategy. It reveals that the proposed bidding strategy performs better than deterministic bidding strategy in financial market. Furthermore, the following disadvantages need to be improved in the future. (1) The possible network congestion is ignored based on the assumption of adequate line capacity. (2) The uncertainty of market price and imbalance price of energy and spinning reserve and units reliability would be further considered in the bidding model. (3) The microgrid is assumed to be price taker in this paper. However, if the penetration of microgrid is high in distribution system, the bids of microgrid would affect the market price and imbalance price. That should be taken into consideration as well.