In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was...In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.展开更多
In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment a...In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.展开更多
The contracts worth $ 720 million for importing 1st phase project equipment of Yangcheng Coal-fired Power Plant were signed on August 22, 1996 in Beijing between the China Electric Power Technology Import & Export...The contracts worth $ 720 million for importing 1st phase project equipment of Yangcheng Coal-fired Power Plant were signed on August 22, 1996 in Beijing between the China Electric Power Technology Import & Export Corp., Shanxi’s Yangcheng International Power Generating Co. Ltd, Shanxi Provincial Power Corp., Germany’s Siemens and the US firm Foster Wheeler Energy Corp.展开更多
With the development of power systems, power grid within a control area becomes much more complicated due to increasing number of nodes and renewable energy interconnections. The role of power system control center is...With the development of power systems, power grid within a control area becomes much more complicated due to increasing number of nodes and renewable energy interconnections. The role of power system control center is more critical in maintaining system reliable and security operations. Latest developed information and communication technologies provide a platform to enhance the functions and performance of power system control center. Smart power dispatch concept will be the trend of future control center development. In this paper, we start from the human factors of control center design and propose operation indices to reduce the information presented to the system operator. The operation indices will be the important criteria in situation awareness of a power grid. Past, present, future and capability states of a power grid are also proposed to provide better visions to the operator of system conditions. The basic ideas of operation indices and operation states are discussed in the paper. In the end, the design factors for a power dispatch cockpit are discussed.展开更多
风电机组并网容量占比的不断增大为电力系统风电消纳带来了巨大挑战。数据中心作为高灵活性电负荷,具有电网风电消纳巨大潜力。因此,提出一种计及数据中心和风电不确定性的微电网经济调度模型。首先,根据数据中心的分层结构建立信息层...风电机组并网容量占比的不断增大为电力系统风电消纳带来了巨大挑战。数据中心作为高灵活性电负荷,具有电网风电消纳巨大潜力。因此,提出一种计及数据中心和风电不确定性的微电网经济调度模型。首先,根据数据中心的分层结构建立信息层和电力层之间的耦合模型;其次,针对风电出力不确定性,搭建计及数据中心和风电不确定性的微电网经济调度模型;最后,基于对偶理论和两阶段鲁棒优化算法,将调度模型转化为鲁棒优化模型并采用列和约束生成算法(column and constraint generation,C&CG)和对偶理论进行求解。算例结果表明:数据中心参与微电网经济调度可有效降低运行成本,同时系统运营商按需求可灵活调整风电出力不确定性。展开更多
基金Projects(70572090, 70373017) supported by the National Natural Science Foundation of China
文摘In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.
基金Project (70671039) supported by the National Natural Science Foundation of China
文摘In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.
文摘The contracts worth $ 720 million for importing 1st phase project equipment of Yangcheng Coal-fired Power Plant were signed on August 22, 1996 in Beijing between the China Electric Power Technology Import & Export Corp., Shanxi’s Yangcheng International Power Generating Co. Ltd, Shanxi Provincial Power Corp., Germany’s Siemens and the US firm Foster Wheeler Energy Corp.
文摘With the development of power systems, power grid within a control area becomes much more complicated due to increasing number of nodes and renewable energy interconnections. The role of power system control center is more critical in maintaining system reliable and security operations. Latest developed information and communication technologies provide a platform to enhance the functions and performance of power system control center. Smart power dispatch concept will be the trend of future control center development. In this paper, we start from the human factors of control center design and propose operation indices to reduce the information presented to the system operator. The operation indices will be the important criteria in situation awareness of a power grid. Past, present, future and capability states of a power grid are also proposed to provide better visions to the operator of system conditions. The basic ideas of operation indices and operation states are discussed in the paper. In the end, the design factors for a power dispatch cockpit are discussed.
文摘风电机组并网容量占比的不断增大为电力系统风电消纳带来了巨大挑战。数据中心作为高灵活性电负荷,具有电网风电消纳巨大潜力。因此,提出一种计及数据中心和风电不确定性的微电网经济调度模型。首先,根据数据中心的分层结构建立信息层和电力层之间的耦合模型;其次,针对风电出力不确定性,搭建计及数据中心和风电不确定性的微电网经济调度模型;最后,基于对偶理论和两阶段鲁棒优化算法,将调度模型转化为鲁棒优化模型并采用列和约束生成算法(column and constraint generation,C&CG)和对偶理论进行求解。算例结果表明:数据中心参与微电网经济调度可有效降低运行成本,同时系统运营商按需求可灵活调整风电出力不确定性。
