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This paper discusses the potential use of probabilistic wind power forecasting in electricity markets, with focus on the scheduling and dispatch decisions of the system operator. We apply probabilistic kernel density forecasting with a quantile‐copula estimator to forecast the probability density function, from which forecasting quantiles and scenarios with temporal dependency of errors are derived. We show how the probabilistic forecasts can be used to schedule energy and operating reserves to accommodate the wind power forecast uncertainty. We simulate the operation of a two‐settlement electricity market with clearing of day‐ahead and real‐time markets for energy and operating reserves. At the day‐ahead stage, a deterministic point forecast is input to the commitment and dispatch procedure. Then a probabilistic forecast is used to adjust the commitment status of fast‐starting units closer to real time, on the basis of either dynamic operating reserves or stochastic unit commitment. Finally, the real‐time dispatch is based on the realized availability of wind power. To evaluate the model in a large‐scale real‐world setting, we take the power system in Illinois as a test case and compare different scheduling strategies. The results show better performance for dynamic compared with fixed operating reserve requirements. Furthermore, although there are differences in the detailed dispatch results, dynamic operating reserves and stochastic unit commitment give similar results in terms of cost. Overall, we find that probabilistic forecasts can contribute to improve the performance of the power system, both in terms of cost and reliability. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
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This paper proposes the day-ahead electricity price forecasting using the artificial neural networks (ANN) and weighted least square (WLS) technique in the restructured electricity markets. Price forecasting is very important for online trading, e-commerce and power system operation. Forecasting the hourly locational marginal prices (LMP) in the electricity markets is a very important basis for the decision making in order to maximize the profits/benefits. The novel approach proposed in this paper for forecasting the electricity prices uses WLS technique and compares the results with the results obtained by using ANNs. To perform this price forecasting, the market knowledge is utilized to optimize the selection of input data for the electricity price forecasting tool. In this paper, price forecasting for Pennsylvania-New Jersey-Maryland (PJM) interconnection is demonstrated using the ANNs and the proposed WLS technique. The data used for this price forecasting is obtained from the PJM website. The forecasting results obtained by both methods are compared, which shows the effectiveness of the proposed forecasting approach. From the simulation results, it can be observed that the accuracy of prediction has increased in both seasons using the proposed WLS technique. Another important advantage of the proposed WLS technique is that it is not an iterative method. 相似文献
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Athanasios Papakonstantinou Georgia Champeri Stefanos Delikaraoglou Pierre Pinson 《风能》2019,22(11):1487-1499
Wind power producers participating in today's electricity markets face significant variability in revenue streams, with potential high losses mostly due to wind's limited predictability and the intermittent nature of the generated electricity. In order to further expand wind power generation despite such challenges, it is important to maximize its market value and move decisively towards economically sustainable and financially viable asset management. In this paper, we introduce a decision‐making framework based on stochastic optimization that allows wind power producers to hedge their position in the market by trading physically settled options in futures markets in conjunction with their participation in the short‐term electricity markets. The proposed framework relies on a series of two‐stage stochastic optimization models that identify a combined trading strategy for wind power producers actively participating in both financial and day‐ahead electricity markets. The proposed models take into consideration penalties from potential deviations between day‐ahead market offers and real‐time operation and incorporates different preferences of risk aversion, enabling a trade‐off between the expected profit and its variability. Empirical analysis based on data from the Nordic region illustrates high efficiency of the stochastic model and reveals increased revenues for both risk neutral and risk averse wind producers opting for combined strategies. 相似文献
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This paper reports a study on the importance of the training criteria for wind power forecasting and calls into question the generally assumed neutrality of the ‘goodness’ of particular forecasts. The study, focused on the Spanish Electricity Market as a representative example, combines different training criteria and different users of the forecasts to compare them in terms of the benefits obtained. In addition to more classical criteria, an information theoretic learning training criterion, called parametric correntropy, is introduced as a means to correct problems detected in other criteria and achieve more satisfactory compromises among conflicting criteria, namely forecasting value and quality. We show that the interests of wind farm owners may lead to a preference for biased forecasts, which may be in conflict with the larger needs of secure operating policies. The ideas and conclusions are supported by results from three real wind farms. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
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Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost‐effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power time series. We estimate nonparametric forecast error densities, specifically using epi‐spline basis functions, allowing us to capture the skewed and nonparametric nature of error densities observed in real‐world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured. We compare the performance of our approach to the current state‐of‐the‐art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Our methodology is embodied in the joint Sandia–University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies. 相似文献
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随着市场竞争的加剧、基础设施的老化以及可再生能源整合的要求,概率负荷预测由于其丰富的结果表现形式在能源系统规划和运营中变得越来越重要.为提高区域居民概率负荷预测的精确性,文章将聚类分析作为概率负荷预测的预处理环节,构建基于单值聚类分析的区域居民概率负荷预测框架.通过探索多种概率负荷预测方法在不同聚类分组下总负荷预测精度... 相似文献
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季度用电量同时具有增长性和季节波动性的二重趋势,这使得季度用电量的变化呈现出复杂的非线性组合特征。对于这种具有复杂的非线性组合特征的时间序列,直接应用GM(1,1)灰色模型往往精度不高。GM(1,1)灰色模型只能反映时间序列的总体变化趋势,不能很好地反映其季节性波动变化的具体特征。为了提高短期用电量的预测精度,提出了用电量预测的灰色季节变动指数模型——GSVI(1,1)模型。GSVI(1,1)模型是将灰色预测方法与季节变动指数有机结合起来,对复杂的不确定性问题进行求解所建立的模型。算例计算表明,与灰色预测方法相比,GSVI(1,1)模型具有更强的适应性和更高的预测精度,适用于农村用电量预测。 相似文献
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张新松;高希;朱建峰;陈沛;郭云翔;卢成 《太阳能学报》2024,45(3):357-366
新型电力系统中,风电渗透率持续攀升,调峰需求随之增加,有必要通过日前能量-调峰联合出清调动市场主体调峰意愿,支撑大规模风电消纳。在对燃煤机组、风电场与储能等多类型市场主体调峰能力与报价规则进行分析的基础上,建立综合考虑燃煤机组深度调峰、启停调峰,风电场主动“弃风”调峰与储能充电调峰的日前能量-调峰联合市场优化模型。模型以能量与调峰辅助服务总采购成本最小为优化目标,同时考虑各市场主体技术约束、投标约束与电网潮流约束,是大规模混合整数规划问题,可采用CPLEX求解器求解。基于改进IEEE 14节点系统的仿真实验验证该文所提日前能量-调峰联合市场优化模型与求解方法的有效性。此外,结合仿真实验对各市场主体参与调峰辅助服务的收益进行详细分析。 相似文献
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研究基于函数变换与广义逆矩阵的优化新算法的一般数学表述及最优性判断方法,给出概率无功优化的解析法模型。综合运用优化算法、随机生产模拟和概率模拟方法,研究系统无功补偿合理配置。通过概率、N-1和时段的无功优化,使优化不局限于一个或几个特定方式和负荷水平,以满足负荷随机变化或随时间变化、事故及检修方式及整个时段优化运行的要求。给出计算实例。 相似文献
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Presented is a robust optimization strategy for the aerodynamic design of horizontal axis wind turbine rotors including the variability of the annual energy production because of the uncertainty of the blade geometry caused by manufacturing and assembly errors. The energy production of a rotor designed with the proposed robust optimization approach features lower sensitivity to stochastic geometry errors with respect to that of a rotor designed with the conventional deterministic optimization approach that ignores these errors. The geometry uncertainty is represented by normal distributions of the blade pitch angle, and the twist angle and chord of the airfoils. The aerodynamic module is a blade‐element momentum theory code. Both Monte Carlo sampling and the univariate reduced quadrature technique, a novel deterministic uncertainty analysis method, are used for uncertainty propagation. The performance of the two approaches is assessed in terms of accuracy and computational speed. A two‐stage multi‐objective evolution‐based optimization strategy is used. Results highlight that, for the considered turbine type, the sensitivity of the annual energy production to rotor geometry errors can be reduced by reducing the rotational speed and increasing the blade loading. The primary objective of the paper is to highlight how to incorporate an efficient and accurate uncertainty propagation strategy in wind turbine design. The formulation of the considered design problem does not include all the engineering constraints adopted in real turbine design, but the proposed probabilistic design strategy is fairly independent of the problem definition and can be easily extended to turbine design systems of any complexity. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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Does an electricity market which has been restructured to foster competition provide greater opportunities for demand response than a traditional regulated utility industry? The experiences of the restructured Electric Reliability Council of Texas (ERCOT) market over the past eight years provide some hope that it is possible to design a competitive market which will properly value and accommodate demand response. While the overall level of demand response in ERCOT is below the levels enjoyed prior to restructuring, there have nonetheless been some promising advances, including the integration of demand-side resources into competitive markets for ancillary services. ERCOT's experiences demonstrate that the degree of demand participation in a restructured market is highly sensitive to the market design. But even in a market which has been deregulated to a large degree, regulatory intervention and special demand-side programs may be needed in order to bolster demand response. 相似文献
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Patrik Sderholm 《Energy Policy》2008,36(6):2051-2062
This paper analyzes the political economy of establishing bilateral trade in green certificate markets as one step towards harmonization of European green electricity support systems. We outline some of the economic principles of an integrated bilateral green certificates market, and then discuss a number of issues that are deemed to be critical for the effectiveness, stability and legitimacy of such a market. By drawing on some of the lessons of the fairly recent intentions to integrate a future green certificate market in Norway with the existing Swedish one, we highlight, exemplify and discuss some critical policy implementation and design issues. These include, for instance, the system's connection to climate policy targets, the role of other support schemes and the definition of what green electricity technologies should be included. Furthermore, the establishment of an international market presumes that the benefits of renewable power (e.g., its impacts on the environment, diversification of the power mix, self-sufficiency, etc.) are approached and valued from an international perspective rather than from a national one, thus implying lesser emphasis on, for instance, employment and regional development impacts. A bilateral green certificate system thus faces a number of important policy challenges, but at the same time it could provide important institutional learning effects that can be useful for future attempts aiming at achieving greater policy integration in the European renewable energy sector. 相似文献
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Proliferation of wind power generation is increasingly making this power source an important asset in designs of energy and reserve markets. Intuitively, wind power producers will require the development of new offering strategies that maximize the expected profit in both energy and reserve markets while fulfilling the market rules and its operational limits. In this paper, we implement and exploit the controllability of the proportional control strategy. This strategy allows the splitting of potentially available wind power generation in energy and reserve markets. In addition, we take advantage of better forecast information from the different day‐ahead and balancing stages, allowing different shares of energy and reserve in both stages. Under these assumptions, different mathematical methods able to deal with the uncertain nature of wind power generation, namely, stochastic programming, with McCormick relaxation and piecewise linear decision rules are adapted and tested aiming to maximize the expected revenue for participating in both energy and reserve markets, while accounting for estimated balancing costs for failing to provide energy and reserve. A set of numerical examples, as well as a case study based on real data, allow the analysis and evaluation of the performance and behavior of such techniques. An important conclusion is that the use of the proposed approaches offers a degree of freedom in terms of minimizing balancing costs for the wind power producer strategically to participate in both energy and reserve markets. Copyright © 2017 John Wiley & Sons, Ltd. 相似文献
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针对可再生能源市场化进程的加快,结合中国电力市场建设现状,对促进可再生能源市场化消纳的省级中长期交易机制以及与现货市场的衔接问题进行研究。利用资源分布特点,提出以可再生能源消纳及常规机组合约完成进度偏差为目标的电量分解模型,针对可再生能源的不确定性,提出基于多场景集的ANFIS预测技术进行可再生能源中长期电量预测,以此为基础进行电量分解,提升可再生能源利用率;建立计及中长期合约分解电量及可再生能源出力不确定性的日前市场出清策略,保证中长期合约电量的物理执行,实现与现货市场的有序衔接。最后通过算例分析验证所提模型的有效性和实用性。 相似文献
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In this paper, we analyze the current trend towards a higher degree of market integration in Europe and identify those aspects that are particularly relevant for Italy. The Italian involvement in this process appears comparatively modest. A welfare analysis, which focuses specifically on the integration of the Italian market, will certainly be a useful support to any policy decision. We argue that, given the peculiarities of the Italian market design, a volume coupling solution could avoid, at the moment, the costs of what could be a significant harmonization effort and, in the end, it might constitute the best short-term strategy. This proposal should be adequately considered, taking into account the complexity of designing an efficient volume-only coordination procedure. 相似文献