发电企业成本管理与竞价优化理论与应用研究
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摘要
营销环境的不断变化导致我国发电企业面临空前的竞争压力。一方面,发电企业从资产规模竞争逐渐转变为成本竞争,成本领先战略成为发电企业的共识;另一方面,面对“竞价上网”的市场方向,电价的分析与预测成为发电企业关注的焦点。本论文在对国内发电企业的营销环境进行分析总结的基础上,通过解释结构模型最终形成风险系统的递阶有向图。对发电企业而言,成本和电价是关键性的、相对可控的风险因素。对成本管理模式的进一步研究和对市场价格的分析及预测成为本文的重点研究内容。
本论文对传统成本管理模式进行了总结,结合发电企业自身的业务特点,对发电企业运用作业成本管理模式的现实性进行了探讨。发电企业作业成本管理模型的设计主要包括以下内容:一、建立资源库,为进一步分摊到作业及成本对象建立基础;二、根据发电企业的相关业务流程,并参考国际标准,建立层次清晰、符合行业标准的电力作业库;三、确定以发电设备系统及机组为核心的成本计算对象;四、设计成本动因模型。论文针对上述各个环节展开深入讨论,并给出各个环节的具体模型设计过程。
电价分析及预测的主要技术通常归结为两种:基于统计学的方法,以及基于人工智能的方法。其中,广义自回归条件异方差模型由于能较好的适应电价的异方差特点而应用广泛,但模型所需的参数化假设条件对其应用造成了限制。本文在时间序列法的基础之上,研究了条件方差函数的非参数估计方法并将其应用在日前电价曲线的预测中。实际算例表明,本论文研究的方法能够更有效的预测电力市场中时常出现的价格尖峰。
单一模型预测电价因各自特点有不同的适应性,多模型组合预测电价能够综合各种方法所提供的有用信息,提高预测精度。在对各种模型所得结果的权重处理上,论文提出利用预测时点的环境信息来评价各单一预测模型在该时点的可信度,然后利用Dempster-Shafer证据理论的数据融合原理对单一模型的可信度进行综合处理,从而最终确定其组合权值。实际算例证明,基于Dempster-Shafer证据理论的多模型组合方法可以较好的处理波动较大的电价预测多模型组合问题,模型的预测精度较其他方法有明显的提高。
在上述理论研究基础上,论文进一步探讨了发电企业协调竞价的优化机制及系统设计,以期为发电企业的具体应用建立基础。
The marketing environment for China's power companies is continuously changing, which leads to unprecedented pressure of competition. On one hand, the competition among power plants is gradually shifting from asset scales to cost management, and the cost priority strategy becomes the consensus. On the other hand, the analysis and forecast of electricity price are greatly emphasized, under the market reform aimed at "price bidding". Based on the analysis and summaries towards the marketing environment for domestic power generation companies, this paper identifies and filters the main risk elements, which then compose the risk factors sets. The structure model is explained here, and the hierarchical directed graph is given, which is used to obtain the conclusion that both cost and price are key and relatively controllable risk factors for power companies. The further research of this paper focuses on the mode of cost management and the analysis and prediction for the market price.
The paper sums up the traditional cost management mode, and discusses the practical application situation of activity-based costing management mode in generation plants. The design mainly includes four parts. Firstly, establish the resource database, which is the base of the further apportion in activity and cost objects. Secondly, set up power activity database that is with clear levels and in accordance with industrial standards. The work refers to the relevant business processes in power enterprises and international standards. Thirdly, determining the core costing objects of generation equipment systems and units, then device-dependent maintenance cost, as well as all the unit-dependent cost. Fourthly, the design for cost driver model. Aimed at above various links, this paper goes deep into discussion, and analyzes the relevant factors which cause the important influence during the design for the activity-based costing management model. In this way, the design guideline can be made, and the specific model design process in every links.
There are many price analysis and prediction technologies, and the main methods are usually boiled down to two kinds. One is based on statistics, while the other one is based on artificial intelligence. The generalized auto-regressive conditional hetero-skedasticity model is widely used, due to its higher adaptation to the hetero-scedasticity nature of price. However, the parametric assumptions conditions required by the model restrict its application. Based on the time series method, the paper studies the nonparametric estimation in the conditional variance function, and applies it to the day-ahead electricity price forecast. Compared to the traditional generalized autoregressive conditional hetero-skedasticity and other models, practical calculation examples show that the method proposed in this paper forecasts the price spikes, which is often observed in the electricity market, more effectively.
Using a single model to predict electricity price will have its unique adaptability due to its characteristics, while using multi-model combination enables integrate useful information provided by different methods to improve the prediction accuracy. When dealing with the weight of the results from various models, a method making use of environment information is put up to evaluate the reliability at the predicting time point, then take comprehensive treatment towards the single model's credibility through data fusion principle in Dempster-Shafer's evidence theory, and eventually the combined weights are determined. Practical examples prove that multi-model combination based on the Dempster-Shafer's evidence theory is effective in volatile price forecast, whose forecasting accuracy is obviously higher than other methods.
On the basis of the above theoretical research, this paper probes into the optimization coordination mechanism and system design of market bidding for power generation companies, to lay down the basis for practical application.
本论文对传统成本管理模式进行了总结,结合发电企业自身的业务特点,对发电企业运用作业成本管理模式的现实性进行了探讨。发电企业作业成本管理模型的设计主要包括以下内容:一、建立资源库,为进一步分摊到作业及成本对象建立基础;二、根据发电企业的相关业务流程,并参考国际标准,建立层次清晰、符合行业标准的电力作业库;三、确定以发电设备系统及机组为核心的成本计算对象;四、设计成本动因模型。论文针对上述各个环节展开深入讨论,并给出各个环节的具体模型设计过程。
电价分析及预测的主要技术通常归结为两种:基于统计学的方法,以及基于人工智能的方法。其中,广义自回归条件异方差模型由于能较好的适应电价的异方差特点而应用广泛,但模型所需的参数化假设条件对其应用造成了限制。本文在时间序列法的基础之上,研究了条件方差函数的非参数估计方法并将其应用在日前电价曲线的预测中。实际算例表明,本论文研究的方法能够更有效的预测电力市场中时常出现的价格尖峰。
单一模型预测电价因各自特点有不同的适应性,多模型组合预测电价能够综合各种方法所提供的有用信息,提高预测精度。在对各种模型所得结果的权重处理上,论文提出利用预测时点的环境信息来评价各单一预测模型在该时点的可信度,然后利用Dempster-Shafer证据理论的数据融合原理对单一模型的可信度进行综合处理,从而最终确定其组合权值。实际算例证明,基于Dempster-Shafer证据理论的多模型组合方法可以较好的处理波动较大的电价预测多模型组合问题,模型的预测精度较其他方法有明显的提高。
在上述理论研究基础上,论文进一步探讨了发电企业协调竞价的优化机制及系统设计,以期为发电企业的具体应用建立基础。
The marketing environment for China's power companies is continuously changing, which leads to unprecedented pressure of competition. On one hand, the competition among power plants is gradually shifting from asset scales to cost management, and the cost priority strategy becomes the consensus. On the other hand, the analysis and forecast of electricity price are greatly emphasized, under the market reform aimed at "price bidding". Based on the analysis and summaries towards the marketing environment for domestic power generation companies, this paper identifies and filters the main risk elements, which then compose the risk factors sets. The structure model is explained here, and the hierarchical directed graph is given, which is used to obtain the conclusion that both cost and price are key and relatively controllable risk factors for power companies. The further research of this paper focuses on the mode of cost management and the analysis and prediction for the market price.
The paper sums up the traditional cost management mode, and discusses the practical application situation of activity-based costing management mode in generation plants. The design mainly includes four parts. Firstly, establish the resource database, which is the base of the further apportion in activity and cost objects. Secondly, set up power activity database that is with clear levels and in accordance with industrial standards. The work refers to the relevant business processes in power enterprises and international standards. Thirdly, determining the core costing objects of generation equipment systems and units, then device-dependent maintenance cost, as well as all the unit-dependent cost. Fourthly, the design for cost driver model. Aimed at above various links, this paper goes deep into discussion, and analyzes the relevant factors which cause the important influence during the design for the activity-based costing management model. In this way, the design guideline can be made, and the specific model design process in every links.
There are many price analysis and prediction technologies, and the main methods are usually boiled down to two kinds. One is based on statistics, while the other one is based on artificial intelligence. The generalized auto-regressive conditional hetero-skedasticity model is widely used, due to its higher adaptation to the hetero-scedasticity nature of price. However, the parametric assumptions conditions required by the model restrict its application. Based on the time series method, the paper studies the nonparametric estimation in the conditional variance function, and applies it to the day-ahead electricity price forecast. Compared to the traditional generalized autoregressive conditional hetero-skedasticity and other models, practical calculation examples show that the method proposed in this paper forecasts the price spikes, which is often observed in the electricity market, more effectively.
Using a single model to predict electricity price will have its unique adaptability due to its characteristics, while using multi-model combination enables integrate useful information provided by different methods to improve the prediction accuracy. When dealing with the weight of the results from various models, a method making use of environment information is put up to evaluate the reliability at the predicting time point, then take comprehensive treatment towards the single model's credibility through data fusion principle in Dempster-Shafer's evidence theory, and eventually the combined weights are determined. Practical examples prove that multi-model combination based on the Dempster-Shafer's evidence theory is effective in volatile price forecast, whose forecasting accuracy is obviously higher than other methods.
On the basis of the above theoretical research, this paper probes into the optimization coordination mechanism and system design of market bidding for power generation companies, to lay down the basis for practical application.
引文
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