智能电网运营管理风险元传递模型及决策支持系统研究

发布时间：2018-05-30 12:33

本文选题：智能电网 + 电力系统运营管理　；参考：《华北电力大学》2014年博士论文

【摘要】：为了应对全球气候恶化与提高能源利用效率,各国电力工业都开始在智能电网方面做出探索与实践。智能电网实施给电力系统的各运营管理主体带来新的挑战,特别是可再生能源参与发电侧运营管理并接入电网,电力调度由电量调度转变为负荷调度,用户侧通过智能双向互动体系参与电网运营管理。相比传统电力系统,智能电网实现了电力流.信息流、资金流的高度融合,给电力系统注入了新的活力,同时在某种程度上也增加了各运营管理主体的风险性。针对这一新形势,本文基于电力风险元传递理论,从运营管理主体维入手,分别对智能电网环境下发电侧、电网侧、用户侧的运营管理风险元传递情形进行建模分析,进而探讨了基于风险元传递模型的智能电网运营管理决策支持系统。论文的研究成果主要体现在以下五个方面： (1)对智能电网运营管理风险元理论进行了初探,提出了智能电网运营管理风险元传递三维建模思路。借鉴电力风险元传递理论,对智能电网运营管理风险进行分析,从风险元传递参与主体维、风险元传递方法维以及风险元传递路径维三个方面,提出了智能电网运营管理风险元传递三维一体建模思路。 (2)考虑智能电网环境下可再生能源参与的发电侧运营管理,以风力发电为例进行研究,构建风力发电独自运营管理风险元传递模型与风-火联合运营风险元传递模型。风力发电独自运营管理抓住风电上网电价与风电上网电量两条主线,分别构建风险元传递模型进行运营管理风险分析。为了弥补风力发电的间歇性、随机性,采用风-火联合运营管理模式,从负荷预测风险元、燃料价格风险元、上网电价风险元以及风电出力风险元四个方面入手,构建风-火联合运营管理风险元传递模型,分析了多风险元波动对整体收益的影响。 (3)针对智能电网环境下电网侧运营管理面临的新挑战,从智能电网投资项目、负荷预测、市场购电、智能调度以及信息安全等,分别构建电网侧运营管理风险元传递模型。根据智能电网投资项目资金分配情况,从自然风险元、管理风险元、技术风险元、市场经济风险元以及政策风险元五方面入手,构建层次型风险元传递模型。考虑智能电网的实施对负荷预测提出更高的要求,提出了更为精确的基于MFGM的智能电网负荷风险元预测模型。针对智能电网环境下市场购电发生的新变化,同时考虑到负荷预测、上网电价以及可再生能源出力不确定性,构建智能电网下考虑风险元传递的市场购电优化模型。针对智能电网调度主观信息的不确定性对调度结果产生重要影响,构建了考虑信息不确定性的智能电网调度风险元传递模型。针对智能电网业务信息传递过程的不确定性,采用吸收马尔科夫链理论构建智能电网信息安全风险元传递模型,分析由于信息系统发生安全事件而导致的后果严重程度。 (4)构建了考虑用户侧参与的智能电网运营风险传递模型。针对基于激励与价格两类需求响应方式,分别选取可中断负荷与峰谷分时电价作为典型代表,构建了考虑风险元传递的可中断负荷参与系统备用配置模型、考虑峰谷分时电价实施的电网收益风险元传递模型,对智能电网用户通过需求响应方式参与运营管理进行风险分析。针对用户侧供电方面,以分布式电源与电动汽车参与微电网运营管理为例,构建考虑成本、排污以及风险(风险元传递)的微电网运营多目标优化模型,并提出小生境多目标粒子群算法进行求解。 (5)研究了基于风险元传递模型的面向电网侧的智能电网运营管理风险决策支持系统(SGOM-RDSS)。为提高智能电网运营管理决策效率,基于本文已提出的风险元传递模型,以电网侧作为运营管理主体为例,探讨了基于模型驱动的智能电网运营管理决策支持系统的设计。在对SGOM-RDSS功能需求分析的基础上,对该系统的架构设计与功能设计进行研究,并探讨了SGOM-RDSS的重要风险元提取、数据交互及模型库自定义等配套关键技术。
[Abstract]:In order to cope with the global climate deterioration and improve the efficiency of energy utilization, the power industry in all countries has begun to explore and practice the smart grid. The implementation of the smart grid brings new challenges to the main operators of the power system, especially the renewable energy involved in the operation management of the power generation side and the access to the power grid, and the power dispatching is dispatched by the power dispatching. It is transformed into load scheduling, and the user side participates in the operation and management of power grid through intelligent two-way interaction system. Compared with the traditional power system, the smart grid has realized the high fusion of power flow, information flow and capital flow, which inject new vitality into the power system, and at the same time, it also adds the risk of various operation management bodies to some extent. In this paper, based on the theory of the power risk element transfer, this paper, starting with the main dimension of the operation management, modeling and analyzing the risk element transmission of the operation management of the power generation side, the power grid side and the user side in the smart grid environment, and then discusses the decision support system of the operation and management of the smart grid based on the risk element transfer model. The results are mainly reflected in the following five aspects:
(1) the risk meta theory of smart grid operation management is discussed, and the three dimensional modeling idea of risk element transfer in smart grid operation management is proposed. Using the theory of power risk element transfer, the risk of smart grid operation management is analyzed, the risk element is transferred from the main dimension, the risk element transmission method dimension and the risk element transfer path dimension three. On the one hand, a three-dimensional integrated modeling method for risk management of smart grid operation management is put forward.
(2) consider the operation management of the generation side of the renewable energy in the smart grid environment, take the wind power as an example, and construct the wind power alone operation management risk element transfer model and the wind fire joint operation risk element transfer model. The wind power alone operation management holds the two main lines of the wind power network electricity price and the wind power network electricity. In order to make up the intermittence and randomness of wind power generation, the wind fire joint operation management model is adopted to build the wind fire joint operation management model, and the wind fire combined operation management wind is constructed from four aspects: load forecasting risk element, fuel price risk element, Internet price risk element and wind power output risk element. The risk element transfer model is used to analyze the impact of multiple risk variables on the overall return.
(3) in view of the new challenges facing the operation and management of the grid side under the smart grid environment, from the smart grid investment projects, load forecasting, market purchase, intelligent scheduling and information security, the risk element transfer model of the power grid side operation management is constructed respectively. According to the allocation of investment items in the smart grid, from the natural risk element and the management risk element, In the five aspects of the technical risk element, the market economy risk element and the policy risk element, the hierarchical risk element transfer model is constructed. Considering the implementation of the smart grid, the higher requirements for the load forecasting are put forward, and a more accurate model of the MFGM based load risk prediction model for smart grid is proposed. At the same time, taking into account the load forecasting, the Internet electricity price and the uncertainty of the renewable energy output, the market purchase optimization model which considers the risk element transmission under the smart grid is constructed. The uncertainty of the subjective information of the smart grid dispatching has an important influence on the scheduling results, and the smart grid adjustment considering information uncertainty is constructed. The degree risk meta transfer model. Aiming at the uncertainty of the information transmission process of the smart grid business, using the absorption Markov chain theory, the information security risk element transfer model of the smart grid is constructed, and the severity of the consequences caused by the security events of the information system is analyzed.
(4) the risk transfer model of smart grid operation considering the user side participation is constructed. According to the response mode of incentive and price two demand response, the interruptible load and peak valley time price are selected as typical representative, and the standby configuration model of the interruptible load participation system considering the risk element transfer is constructed, and the peak valley time price is considered. The risk analysis of the power grid revenue risk element is applied to the smart grid users to participate in the operation management through the demand response mode. With regard to the power supply side of the user side, taking the distributed power and the electric vehicle to participate in the microgrid operation and management as an example, the multi-objective of the micro grid operation considering the cost, discharge and risk (risk element transfer) is constructed. Optimization model is proposed, and niche multi objective particle swarm optimization algorithm is proposed to solve it.
(5) the risk decision support system (SGOM-RDSS) for the smart grid operation management based on the risk element transfer model is studied. In order to improve the efficiency of the operation and management of the smart grid, based on the risk element transfer model proposed in this paper, the model driven smart grid based on the power grid side is taken as an example. The design of operation management decision support system (DSS) is designed. On the basis of the analysis of SGOM-RDSS function requirements, the architecture design and function design of the system are studied. The key technologies of SGOM-RDSS, such as important risk element extraction, data interaction and model library customization, are discussed.
【学位授予单位】：华北电力大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：F272.3;F426.61

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