内蒙古500KV变电站全生命周期成本管理研究
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摘要
随着我国国民经济的快速发展,电力在能源消费结构中比重也越来越大。在巨大的电力需求的带动下,各地电力部门的电力建设步伐也都在不约而同的加快,电力投资的金额相应地也在加速上升,而变电站的投资和运行维护费用在整个电力系统投资中占相当重要的份额,因此对变电站的全生命周期工程造价进行研究有重要的经济价值。
本文结合变电站各阶段费用的特点,建立了变电站全生命周期工程造价(LCC)模型,并用鱼群神经网络及盲数理论对变电站LCC进行了计算,主要研究工作如下：
①分析了变电站LCC各组成费用的内容和特点,建立了变电站LCC的费用估算模型。该模型不仅考虑了变电站一次性投资,还考虑了今后运行和维护等支出费用,以及中断供电成本和废弃成本,更加符合实际情况,具有重要的经济意义。
②运用生态位从宏观成本的角度来分析了变电站的建设对其台区的影响。选取生态位综合评估变电站的建设对台区的环境、经济和社会等方面的影响程度和性质。考虑宏观成本,比较变电站建设前后的台区生态位,控制宏观成本的加大或减少
③运用鱼群神经网络对变电站的全生命周期工程造价了进行估算。全生命周期成本估算涉及的时间长、指标多、计算过程复杂、人为因素影响大,神经网络能够通过遴选出的具有代表意义的几个指标客观快速的估算出相应的成本。然而,神经网络本身具有训练速度较慢、容易陷入局部最优的缺点,为了解决这些问题,本文引入鱼群理论对神经网络进行了优化,使其更加合理。
④运用盲数理论对变电站全生命周期工程造价进行估了算。变电站全生命周期具有多种不确定信息,这些不确定性信息具有各种不同的特点和性质,且具有随机性、模糊性、区间性以及未确知性等多种不确定性,如果采用单一的不确定性方法来处理,显然是不准确的。采用盲数来描述和处理这类不确定信息可以在最大程度的保留有效信息的同时,计算出更真实的成本。
⑤变电站LCC不同阶段的风险不同,其损害影响程度也不同。本文运用云模型对各种风险进行的分析和归类,计算出了各种风险发生的估计值,为变电站运行过程中的风险控制提出建议。
⑥由于影响变电站LCC的各参数不是固定不变的,本文阐述了LCC灵敏度分析的含义,以及在变电站建设中进行灵敏度分析的意义,并应用敏感性函数和参数本身的大小来共同确定LCC对各参数的敏感度。
With the rapid development of China's national economy, the proportion of electric power in energy consumption structure gets greater year by year. Driven by the tremendous power demand, the pace of power construction around China happen to coincide speeding up, the amount of electricity investment is also correspondingly accelerated, because the substation investment as well as the operation and maintenance costs play an important role in the whole power system, so the study of Life Cycle Cost of the substation has important economic value.
This paper combined with the characteristics of substation costs in each stage, set up the substation Life Cycle Cost (LCC) model, using Life Cycle Cost theory, ulteriorly calculated substation Life Cycle Cost (LCC) using fish-swarm neural network algorithm and blind number theory, the main research work are as follows:
Firstly, this paper analyzed the content and characteristics of substation Life Cycle Cost, established a estimate model of the substation Life Cycle Cost, which consider not only the substation one-time investment, but also the operation and maintenance expenses as well as the cost of power supply interruption and disposal cost. So this model is more tally with the actual situation, and has more important practical economic meaning.
Secondly, this paper analyzed the impact of station on the substation supply region, which including the impact on the environment, economy and society. Consider the macroeconomic costs; this paper compared the ecological niche of station supply region before and after the substation's construction in order to control the macroeconomic costs.
Thirdly, this paper estimated the substation Life Cycle Cost using the fish-swarm neural network algorithm.The estimation of the Life Cycle Cost has some shortcomings as follows:it involves a long time, has multiple indexes and complicated computed process, at the same time it is vulnerable to the impact of human factors. Neural network can estimate the cost rapidly through selecting several representative indexes. Nevertheless, neural network has its own shortcomings, such as slowed Training speed, the problem of low efficiency and local optimum and so on, in order to solve these problems, this paper used the fish-swarm algorithm optimize the neural network model to make it more reasonable.
Fourthly, this paper estimated the substation Life Cycle Cost using blind number theory.There are a variety of uncertain information in the Substation lifecycle, which possesses different characteristics and nature as well as random, fuzzy, and interval, so it is clearly not accurate to adopt a single uncertain method to analyze the substation Life Cycle Cost. Nevertheless, using the blind number theory to describe and deal with such uncertain information can calculate a more real cost while retaining the effective information to the utmost extent.
Fifthly, the substation Life Cycle Cost has different risks at different stage, and each risk has various influenced degree. This paper classified the risks according to the analysis of these risks, calculated the estimate of these risks, and proposed some recommendations to control these risks that occurred in the process of the station operation.
Sixthly, because the various parameters that affected the substation Life Cycle Cost is not fixed, so this paper expounded the meaning of the LCC sensitivity analysis and the implication of LCC sensitivity analysis in the process of substation construction. The values of sensitivity function and parameters were used together to determine the sensitivity of LCC to each parameter.
本文结合变电站各阶段费用的特点,建立了变电站全生命周期工程造价(LCC)模型,并用鱼群神经网络及盲数理论对变电站LCC进行了计算,主要研究工作如下：
①分析了变电站LCC各组成费用的内容和特点,建立了变电站LCC的费用估算模型。该模型不仅考虑了变电站一次性投资,还考虑了今后运行和维护等支出费用,以及中断供电成本和废弃成本,更加符合实际情况,具有重要的经济意义。
②运用生态位从宏观成本的角度来分析了变电站的建设对其台区的影响。选取生态位综合评估变电站的建设对台区的环境、经济和社会等方面的影响程度和性质。考虑宏观成本,比较变电站建设前后的台区生态位,控制宏观成本的加大或减少
③运用鱼群神经网络对变电站的全生命周期工程造价了进行估算。全生命周期成本估算涉及的时间长、指标多、计算过程复杂、人为因素影响大,神经网络能够通过遴选出的具有代表意义的几个指标客观快速的估算出相应的成本。然而,神经网络本身具有训练速度较慢、容易陷入局部最优的缺点,为了解决这些问题,本文引入鱼群理论对神经网络进行了优化,使其更加合理。
④运用盲数理论对变电站全生命周期工程造价进行估了算。变电站全生命周期具有多种不确定信息,这些不确定性信息具有各种不同的特点和性质,且具有随机性、模糊性、区间性以及未确知性等多种不确定性,如果采用单一的不确定性方法来处理,显然是不准确的。采用盲数来描述和处理这类不确定信息可以在最大程度的保留有效信息的同时,计算出更真实的成本。
⑤变电站LCC不同阶段的风险不同,其损害影响程度也不同。本文运用云模型对各种风险进行的分析和归类,计算出了各种风险发生的估计值,为变电站运行过程中的风险控制提出建议。
⑥由于影响变电站LCC的各参数不是固定不变的,本文阐述了LCC灵敏度分析的含义,以及在变电站建设中进行灵敏度分析的意义,并应用敏感性函数和参数本身的大小来共同确定LCC对各参数的敏感度。
With the rapid development of China's national economy, the proportion of electric power in energy consumption structure gets greater year by year. Driven by the tremendous power demand, the pace of power construction around China happen to coincide speeding up, the amount of electricity investment is also correspondingly accelerated, because the substation investment as well as the operation and maintenance costs play an important role in the whole power system, so the study of Life Cycle Cost of the substation has important economic value.
This paper combined with the characteristics of substation costs in each stage, set up the substation Life Cycle Cost (LCC) model, using Life Cycle Cost theory, ulteriorly calculated substation Life Cycle Cost (LCC) using fish-swarm neural network algorithm and blind number theory, the main research work are as follows:
Firstly, this paper analyzed the content and characteristics of substation Life Cycle Cost, established a estimate model of the substation Life Cycle Cost, which consider not only the substation one-time investment, but also the operation and maintenance expenses as well as the cost of power supply interruption and disposal cost. So this model is more tally with the actual situation, and has more important practical economic meaning.
Secondly, this paper analyzed the impact of station on the substation supply region, which including the impact on the environment, economy and society. Consider the macroeconomic costs; this paper compared the ecological niche of station supply region before and after the substation's construction in order to control the macroeconomic costs.
Thirdly, this paper estimated the substation Life Cycle Cost using the fish-swarm neural network algorithm.The estimation of the Life Cycle Cost has some shortcomings as follows:it involves a long time, has multiple indexes and complicated computed process, at the same time it is vulnerable to the impact of human factors. Neural network can estimate the cost rapidly through selecting several representative indexes. Nevertheless, neural network has its own shortcomings, such as slowed Training speed, the problem of low efficiency and local optimum and so on, in order to solve these problems, this paper used the fish-swarm algorithm optimize the neural network model to make it more reasonable.
Fourthly, this paper estimated the substation Life Cycle Cost using blind number theory.There are a variety of uncertain information in the Substation lifecycle, which possesses different characteristics and nature as well as random, fuzzy, and interval, so it is clearly not accurate to adopt a single uncertain method to analyze the substation Life Cycle Cost. Nevertheless, using the blind number theory to describe and deal with such uncertain information can calculate a more real cost while retaining the effective information to the utmost extent.
Fifthly, the substation Life Cycle Cost has different risks at different stage, and each risk has various influenced degree. This paper classified the risks according to the analysis of these risks, calculated the estimate of these risks, and proposed some recommendations to control these risks that occurred in the process of the station operation.
Sixthly, because the various parameters that affected the substation Life Cycle Cost is not fixed, so this paper expounded the meaning of the LCC sensitivity analysis and the implication of LCC sensitivity analysis in the process of substation construction. The values of sensitivity function and parameters were used together to determine the sensitivity of LCC to each parameter.
引文
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