WO2012159544A1 - 利用泛能流序参量控制泛能网的方法 - Google Patents
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- the present invention relates to the field of performance optimization technologies for ubiquitous energy networks, and in particular, to a method for controlling a ubiquitous energy network by using a ubiquitous energy flow parameter.
- the ubiquitous energy network is an intelligent energy network system in which information, energy and matter are integrated through synergy. Based on system energy efficiency technology, ubiquitous energy network realizes real-time synergy of energy input and output across time domain through the coupling of energy and information in energy production, storage, application and regeneration cycle, and realizes mutual conversion of multi-category energy sources, each energy flow. Supply and demand matching and cascade utilization; Vertically realize the optimal configuration of energy life cycle, fundamentally achieve maximum energy efficiency and minimize emissions, and finally output a self-organized highly ordered and efficient intelligent energy source.
- a ubiquitous energy flow is a logical intelligent flow formed by the synergistic coupling of energy flow, material flow and information flow.
- the energy flow includes different secondary energy forms such as electricity, gas, and heat.
- the material flow includes water flow and logistics, and the information flow includes communication, control, data acquisition and transmission.
- the ubiquitous energy flow forms a closed-loop ubiquitous energy network system by connecting the energy efficiency gainer, the energy efficiency controller, and the four phases of the energy life cycle (ie, energy production, energy storage, energy application, and energy regeneration).
- FIG. 1 is a schematic diagram of a topology structure of a ubiquitous energy network in the prior art.
- the ubiquitous network topology consists of network relationships of "machine-machine, human-machine, human-human” and "mutual interaction, interaction, mutual intelligence".
- the daily operation is optimized by the ubiquitous energy network, the ubiquitous energy network system data exchange and the ubiquitous energy network balance optimization control, the trigger makes the machine-machine interaction, and the logic optimization achieves the machine-machine mutual intelligence.
- the human-machine level after the comprehensive optimization strategy is injected, the on-site monitoring personnel make basic adjustments to the situation beyond the scope of daily business control to meet the abnormal changes in business needs.
- the human-machine mutual inductance transmits information through the sensor and the human feeling.
- the person makes the machine action by issuing instructions, and the machine action also affects the human needs.
- the person achieves the human-machine mutual intelligence through analysis and optimization.
- decision-making meetings are held for disasters, accidents, policies, etc., resource scheduling strategies are determined, and coordinated with the corresponding plans, combined with the ubiquitous network management system to form a comprehensive optimization strategy, which is distributed to the human-machine layer.
- Human-to-human mutuality conveys information through various carriers, and communicates through various languages.
- the intelligent expert system can achieve the purpose of mutual human intelligence.
- the ubiquitous energy network forms a decision-making network through a three-layer decision-making optimization system of mutual inductance (machine-machine), interaction (human-machine), and mutual intelligence (human-human), thus forming a fusion of "intelligence" and "energy”.
- machine-machine mutual inductance
- human-machine interaction
- human-human mutual intelligence
- Streaming from time to time synergy and multi-scale intelligent interaction in the ubiquitous energy network from the input to the output achieving high-grade absorption of environmental potential energy and efficient use of resource energy, generating nonlinear effects of system energy throughout the life cycle, and thus outputting High quality and efficient smart energy.
- the variables involved in the multi-level decision optimization system are as follows: Control variable n, variable state m, time T.
- the control variable ⁇ and the variable state m make the number of states of each layer T increase exponentially at each moment, and the combination of the multi-level state space makes the state space exponentially increase, causing dimensionality disaster.
- Dimensional disasters refer to the exponential increase in the number of variables (dimensions), and the time and amount of data required for data is close to uncountable.
- the macro variable P is 0 when the system is in the disordered state, and the macro variable P is not 0 when the system is in the ordered state, the nature of the macro variable P can be used to indicate the generation and transformation of the ordered structure, then the macro variable P is an order parameter of the system.
- the ubiquitous flow order parameter is the driving force that can transform the unordered ubiquitous energy flow of the ubiquitous energy network system into the ordered ubiquitous energy flow, and can be embodied as a control strategy for controlling logical flow and flow direction.
- the optimization of the performance of the ubiquitous energy network system generally adopts two methods of integration optimization and collaborative optimization.
- the current energy system is mainly based on non-renewable energy, supplemented by renewable energy, and combines various technologies and small-scale, micro-thermoelectric cold (plant) full-energy multi-systems that are suitable for local conditions. Integrate and optimize in multiple systems such as electricity, heat, gas, refrigeration, environment, and transportation.
- energy companies have now shifted from production to service, and energy systems through smart computers and the Internet.
- the automation management, operation and scheduling implementation of the information system is similar to the Internet of Things.
- the current optimization of the performance of the ubiquitous energy network system still has the following limitations: Local energy optimization, only considering a single energy source, such as optimization of individual power grids, without considering multiple energy networked Integration optimization leads to a lack of corresponding solutions as the complexity of the problem increases.
- the main object of the present invention is to provide a method for controlling a ubiquitous energy network by using a ubiquitous stream order parameter to achieve comprehensive optimization of performance of a ubiquitous energy network system.
- the present invention provides a method for utilizing a ubiquitous energy flow parameter ubiquitous energy network, which first determines a key order parameter of each layer according to a natural order parameter rule, and then sequentially optimizes each according to a certain optimization order.
- the key order parameters of the hierarchy, and finally the rules of the matching ratio, make the ubiquitous network system self-organizing and self-evolving.
- the method is optimized by a sequential optimization method in the optimization process, and the sequential optimization method is a method using equal interval discrete optimization.
- the method for controlling a ubiquitous energy network using the ubiquitous energy stream parameter parameter provided by the invention comprises: analyzing the ubiquitous energy flow relationship of the four links of production, storage, application and regeneration in the ubiquitous energy network system, according to the natural order parameter rule
- the ubiquitous energy relationship determines the key order parameters of each level, where the key order parameters are used to quickly evaluate and optimize the overall system performance; the optimization order of the performance of the ubiquitous energy network system is determined according to the hierarchical order parameter rules; each level is optimized according to the optimization order The key order parameters are determined; and the optimal ratio of the four links is determined according to the optimal link ratio rule, wherein the optimal ratio of the four links is used to reduce the redundant ubiquitous energy flow of the system.
- the method includes: configuring the ubiquitous energy network system to indicate the production, storage, and application of the ubiquitous energy flow relationship before analyzing the ubiquitous energy flow relationship in the four steps of production, storage, application, and regeneration in the ubiquitous energy network system. And regeneration four links.
- the configuration of the ubiquitous energy network system is used to indicate the production, storage, application and regeneration of the ubiquitous energy relationship, including: conversion of fossil energy, biomass energy, and solar and wind energy into electrical energy, gas energy, and thermal energy that can achieve specific functions.
- cold energy configure this link as an energy production link; configure the process of storing electrical energy, thermal energy, cold energy or mechanical energy as an energy storage link; configure the process of using electrical energy, thermal energy, cold energy or mechanical energy as an energy application link;
- the process of energy system application, production or storage, and the process of re-offering to the system is the energy regeneration process.
- the ubiquitous energy flow relationship in the four stages of production, storage, application and regeneration in the ubiquitous energy network system is analyzed, and the key order parameters of each level are determined from the ubiquitous energy flow relationship according to the natural order parameter rule, which specifically includes: Comparing the energy consumption of the energy unit in the four stages of production, storage, application and regeneration in the ubiquitous energy network system, the energy unit with less energy consumption is determined as the key order parameter of each level.
- the natural order parameter rule specifically includes: Comparing the energy consumption of the energy unit in the four stages of production, storage, application and regeneration in the ubiquitous energy network system, the energy unit with less energy consumption is determined as the key order parameter of each level.
- the step of determining an optimization order of the performance of the ubiquitous energy network system according to the hierarchical order parameter rule comprises: the ubiquitous energy network system is composed of multiple layers of four links, wherein the bottom four links pass the demand upward, and the upper four links According to the hierarchical order parameter rule, the reduction of the energy consumption of the bottom four layers from the perspective of energy consumption is lower than the energy consumption of the upper four layers, which has a significant impact on the entire ubiquitous energy network system. Therefore, the bottom layer of the ubiquitous energy network system is determined.
- the four links have the highest priority in the performance optimization of the ubiquitous network system, and the four links in the upper layer have the lowest priority in the performance optimization of the ubiquitous network system, from the bottom four links to the upper four links in the ubiquitous network system. The priority is gradually reduced when performance is optimized.
- the determining the optimal ratio of the four links according to the optimal link ratio rule includes: due to the dependency of the four links, there is necessarily an optimal ratio, so that the four links remain the most in the whole life cycle operation.
- the determination of the best ratio of the four links is driven by the demand of the application link, and then selects the different parameters of the production link, the storage link and the regeneration link, and calculates the minimum four-link total energy consumption value and the maximum link satisfaction. Degree, then the best ratio of the four links.
- the best performance of the four links maintained during the entire life cycle including
- the minimum energy consumption and the highest level of satisfaction are the link satisfaction, which is the satisfaction degree of each link under normal conditions.
- the present invention specifically considers the integration optimization of multiple energy network technologies.
- the corresponding solution is given to effectively reduce the search space problem, and the following beneficial effects are obtained:
- the method for controlling a ubiquitous energy network using a ubiquitous stream order parameter is provided by the present invention, and a ubiquitous energy flow order optimization rule is adopted, and the ubiquitous energy flow order optimization is optimized at an exponential level ( ⁇ (1/2)) convergence speed, and The key parameters are quickly found through the following parameter optimization rules, thereby improving the energy efficiency level of the ubiquitous energy network and realizing the comprehensive optimization of the performance of the ubiquitous energy network system.
- the method for controlling the ubiquitous energy network using the ubiquitous energy flow parameter parameter is provided by the present invention, and the optimal link matching rule is adopted.
- the combination strategy is sought according to the optimal link ratio principle, and the search space will be From n A m to at most max(n,m), the search time is reduced to at least the original (max(n, myn A m). In fact, if there are correlations among n order parameters, the search space is also reduced. .
- the method for controlling the ubiquitous energy network by using the ubiquitous energy flow parameter parameter adopts the hierarchical order parameter rule, optimizes the order parameter from the bottom layer, and transmits the input and output relationship bidirectionally through the hierarchical association constraint, which will further reduce the search space.
- Reduce search time search space will be reduced to]"[maxC ⁇ .), search time will be reduced to the original Vri
- the method for controlling the ubiquitous energy network by using the ubiquitous energy flow parameter parameter adopts the natural order parameter rule, and the natural order parameter has the effect of significantly reducing the system energy consumption, and related research shows that the introduction of the natural order parameter can achieve the reduction of the pan. A significant level of energy consumption of 10%.
- FIG. 1 is a schematic diagram of a topology structure of a ubiquitous energy network in the prior art
- FIG. 2 is a flow chart of a method for optimizing performance of a ubiquitous energy network system by using a ubiquitous energy stream parameter according to the present invention
- 3 is a schematic diagram of optimizing the performance of a ubiquitous energy network system for a residential building using a ubiquitous flow order parameter according to a first embodiment of the present invention
- 4 is a schematic diagram of optimizing the performance of a ubiquitous energy network system for an eco-city using a ubiquitous flow order parameter in accordance with a second embodiment of the present invention.
- the ubiquitous energy flow parameter parameter technology is based on the ubiquitous energy flow sequence optimization rule
- the ubiquitous flow order parameter technique seeks key indicators through hierarchical hierarchy to quickly evaluate and optimize the overall system performance.
- the ubiquitous flow order parameter technology is mainly used to improve the synergy of enterprises.
- the synergy of enterprises is an important guarantee for the growth of enterprises. It affects the integration and utilization of enterprise resources, the coordination and cooperation of various departments of enterprises, and the adaptability of enterprises to the environment.
- the order parameter is the dominant factor of the enterprise's synergy ability, and its determination will play a vital role in the formation and improvement of the company's synergy ability.
- the order parameter refers to the change from scratch in the evolution of the system, affecting the collective cooperative behavior of each element of the system from one phase change state to another phase change state, and can indicate the parameters formed by the new structure.
- the order parameter has three basic characteristics: 1) The order parameter is a macro parameter.
- the order parameter is the product of the collective operation of the micro-subsystem, the characterization and brightness of the cooperative effect; 3) The parameters are generated by the synergy of each part, and once formed, it becomes the control center of the system, governs the behavior of each subsystem, determines the orderly structure and functional behavior of the whole system, and dominates the overall evolution process of the system.
- the ubiquitous flow order parameter is the driving force for the transformation of the disordered ubiquitous energy flow into the ordered ubiquitous energy flow in the ubiquitous energy network system, which can be expressed as a control strategy for controlling logical flow and flow direction.
- the ubiquitous flow order parameter technique is based on the ubiquitous flow order optimization rule, the optimal link proportioning rule, the hierarchical order parameter rule, and the natural order parameter rule.
- the key order parameters are focused by comparing the order values to quickly evaluate the system performance, and the key order parameters are The rapid reduction of the strategy space has an exponential convergence. Selection of order parameters Through the order value decision, the ubiquitous flow order parameter technique seeks key indicators through hierarchical grading to quickly evaluate and optimize overall system performance.
- the ubiquitous flow order parameter optimization rule The comparison of the ratio of the ubiquitous energy flow can determine the key order parameters more efficiently, and achieve the optimal result of the ubiquitous energy network with a relatively high probability.
- the order comparison here includes the hierarchical order, the natural order and Comparison of sequences at different levels, such as proportioning.
- the ubiquitous flow order parameter rule 1 The natural order parameter rule (the principle of non-sequence parameter priority)
- the so-called sham parameter refers to the order parameter (such as sunlight and natural ventilation) that does not require ubiquitous energy input. Because the power-sequence parameter will reduce the energy consumption of the ubiquitous energy network system relative to the active power parameter, the power-sequence parameter is higher than the power-order parameter.
- the ubiquitous stream order parameter rule 2 the hierarchical order parameter rule (the underlying order parameter priority principle) Because the final service object of the ubiquitous energy stream is to satisfy the underlying order parameter, the underlying order parameter determines the performance of the ubiquitous energy network system, and thus the bottom layer The order value of the order parameter is greater than the order value of the upper order parameter.
- the hierarchical order parameters are passed through the hierarchical association constraint two-way transmission input and output ubiquitous energy flow order parameter rule three: the optimal link ratio rule (optimal link ratio priority principle) optimal production, storage, application and regeneration links, It will minimize the redundant ubiquitous energy flow of the system, and thus the order value of the order parameter of the optimal link ratio is higher than the order value of the sub-optimal link ratio.
- the ubiquitous flow order optimization rule The order comparison of the ubiquitous energy stream can determine the key order parameters more efficiently than the value of the ubiquitous energy stream and reach the ubiquitous energy network optimal result with a relatively high probability.
- the order comparison here includes the hierarchical order, Comparison of different levels of order, such as natural order and proportioning order.
- the ubiquitous flow sequence optimization is optimized at the exponential level ( ⁇ -(1/2 ⁇ ) convergence rate, and the key parameters are quickly found through the following order parameter optimization rules, thereby improving the energy efficiency level of the ubiquitous energy network.
- Optimal link ratio rule The optimal production, storage, application, and regeneration links will minimize the system's redundant ubiquitous energy flow, which can lead to the optimal order ratio of the order parameters higher than the sub-optimal The order value of the order parameter of the link ratio.
- the combination strategy is sought according to the principle of optimal link ratio, then the search space will change from n A m to at most Max(n,m) , then the search time is reduced to at least the original max(n, myn A m;). In fact, if there are correlations among the n order parameters, the search space will also decrease.
- the search space is usually used in the optimization domain, and refers to the combination of all possible solutions to the problem in the process of seeking the optimal solution of the problem.
- Hierarchical order parameter rule Since the final service object of the ubiquitous energy stream is to satisfy the underlying order parameter, the underlying order parameter plays a decisive role for the performance of the ubiquitous energy network system, so the order value of the underlying order parameter is greater than the order value of the upper order parameter.
- the hierarchical order parameters pass the input and output relationships in both directions through hierarchical association constraints. From the bottom layer, the order parameter is optimized, and the input and output relationship is transmitted in both directions through the hierarchical association constraint, which will further reduce the search space and reduce the search time.
- the search space will be reduced to] "[ max , , the search time will also be reduced to the original ] ⁇ [ max( , . ) / ] ⁇ [ n i A i ) 0
- Natural order parameter rule The so-called non-sequence parameter refers to the order parameter (such as sunlight, natural ventilation) that does not require ubiquitous energy input. Because the power-sequence parameter will reduce the energy consumption of the ubiquitous energy network system relative to the active power parameter, the order value of the power-sequence parameter is higher than the order value of the power-sequence parameter. The natural order parameter has a significant effect on reducing system energy consumption. The related study (UTC-Tsmghua Institute) shows that the introduction of natural order parameters can achieve a significant level of 10% reduction in energy consumption of the ubiquitous energy network.
- the above three criteria are applied to the discrimination of key order parameters.
- the key order parameters of each layer are determined according to the natural (reactive) order parameter rules, and then each level is optimized according to a certain optimization order.
- the key order parameters that is, the underlying layer is optimized first, and the hierarchical dynamic association constraint is optimized to the upper layer.
- the system can have self-organization and self-evolution.
- FIG. 2 is a flowchart of a method for optimizing performance of a ubiquitous energy network system by using a ubiquitous energy stream parameter provided by the present invention, and the method includes the following steps:
- Step 201 Analyze the ubiquitous energy flow relationship in the four steps of production, storage, application and regeneration in the ubiquitous energy network system, and determine the key order parameters of each level from the ubiquitous energy flow relationship according to the natural order parameter rule, wherein the key sequence Parameters are used to quickly evaluate and optimize overall system performance;
- Step 202 Determine an optimization order of performance of the ubiquitous energy network system according to a hierarchical order parameter rule.
- Step 203 sequentially optimize key order parameters of each level according to the optimization order.
- Step 204 Determine the optimal ratio of the four links according to the optimal link ratio rule, wherein the optimal ratio of the four links is used to reduce the redundant ubiquitous energy flow of the system.
- the order optimization runs through the optimization process.
- the ubiquitous energy flow is optimized by the equal interval discrete optimization method. As the number of samples increases, the optimal combination will increase exponentially.
- the order optimization method can solve this problem well: (1) soften the optimization target, Good enough solution as the optimization goal; (2) Through the comparison of the order of the ubiquitous energy flow, rather than the comparison of the values quickly. Practice has proved that the sequential optimization method can reduce the strategic space exponentially and seek key indicators to quickly evaluate and optimize the overall system performance.
- the above method is called the ubiquitous flow order parameter optimization method.
- Figure 3 is a schematic illustration of the optimization of the performance of a ubiquitous energy network system for a dwelling using a ubiquitous flow order parameter in accordance with a first embodiment of the present invention.
- FS (G, S, U, R).
- FS (i3 ⁇ 4") + Solar(t), , O 2 h ⁇ t), ).
- the four-link FS contains four parameters of G (production), S (storage), U (application), and R (regeneration), some energy units may lack one or several links, for example
- the brightness system has only the production link and the application link, and there is no storage link or regeneration link.
- the space indicates that the energy unit does not exist in the link.
- the spaces appearing in the formula below are the same as here, indicating that the energy unit does not exist in the link.
- ⁇ FS (E (t) + E, h (t) + 0 ⁇ (t), , 0, h f(t), ).
- ie FS ( E 4 h 2 "(t) + O (t) + 0 , h 2 g (t) , , i3 ⁇ 4"(t)+i3 ⁇ 4"(t)+i3 ⁇ 4"(t),).
- the present invention can find key order parameters, for example, In a layer, natural light sources can be considered as key order parameters by comparing them with electrical energy because they consume less energy.
- the second and third layers can find the key order parameters, which is the first criterion of the key order parameters: natural (reactive) order parameter rules.
- natural order parameter rules the energy consumption of the ubiquitous energy network can be reduced by more than 10%.
- the order of optimization of the present invention is from low to high, and the priority is from high to low, which is the second criterion of key order parameters: hierarchical order parameter rules.
- the actual effect also shows that the energy saving at the end of the user is very significant for the load reduction of the production end of the entire ubiquitous energy network system.
- the four links of the ubiquitous energy network should maintain a certain ratio under certain conditions.
- the ubiquitous energy network runs at full life cycle. The effect is the best, so when optimizing the flow of the ubiquitous energy flow in each link, the present invention can optimize the flow of the four-link ubiquitous energy flow according to the optimal ratio of the four links, which will be self-organizing and self-evolving for the ubiquitous energy network.
- This is the third criterion for the ubiquitous flow order parameter: the link matching rule.
- the above three criteria are applied to the discrimination of key order parameters, firstly, the key order parameters of each layer are determined according to the natural (reactive) order parameter rule, and then each is optimized according to a certain optimization order.
- the key order parameters of the first level that is, the underlying layer is optimized first, and the hierarchical dynamic association constraint is optimized to the upper level).
- the system has self-organizing and self-evolving functions through the matching rules.
- the method of order optimization will run through the beginning and the end. Firstly, the ubiquitous energy flow is optimized by the equal interval discrete optimization method. As the number of samples increases, the optimal combination will increase exponentially. Then the order optimization method can be very good.
- FIG. 4 is a partial schematic diagram showing the optimization of performance of a ubiquitous energy network system for an eco-city using a ubiquitous flow order parameter in accordance with a second embodiment of the present invention.
- ⁇ FS ( E (t) + E, h (t) + 0 ⁇ (t), , 0, h f(t) , ).
- the present invention can analyze the above ubiquitous energy flow relationship through natural (reactive) order parameter rules, and can find key order parameters, for example, in the third layer, according to the natural (reactive) order parameter rule,
- the energy input is selected for the input of the multi-connection, including electric input, gas input, or electric and gas input according to a certain ratio.
- the first and second layers can follow the same principle and seek critical order parameters.
- Second, according to the hierarchical order parameter rules different levels have different effects on the entire energy application. Optimizing the energy application of the first layer is more effective than optimizing the second and third layers. Therefore, the order of optimization should be from low to high, and the priority is from high to low.
- the flow of ubiquitous energy flow in the four stages of production, regeneration, application and storage of ubiquitin network should maintain a certain ratio under certain conditions. When the optimal ratio is reached, the ubiquitous energy network is full of life. The cycle works best. Finally, the ubiquitous flow order parameter optimization method is used to soften the optimization target and the exponential reduction strategy space, and finally seek key indicators to quickly evaluate and optimize the overall system performance.
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Abstract
本发明公开了一种利用泛能流序参量控制泛能网的方法，该方法包括：分析泛能网系统中的生产、储存、应用和再生四环节的泛能流关系，根据自然序参量规则从泛能流关系确定每一层级的关键序参量，其中关键序参量用于快速评价和优化整体系统性能；根据层级序参量规则确定泛能网系统性能的优化次序；根据优化次序依次优化每一层级的关键序参量；以及根据最优环节配比规则确定四环节最佳配比，其中四环节最佳配比用于降低系统的冗余泛能流。利用本发明，实现了对泛能网系统性能的综合优化。
Description
利用泛能流序参量控制泛能网的方法 技术领域 本发明涉及泛能网性能优化技术领域， 尤其涉及一种利用泛能流序 参量控制泛能网的方法。
背景技术 本申请人在中国专利申请 201010173519.1和 201010173433.9中提出 了泛能网的方案， 以实现各种能源和物质的智能化和信息化， 以及多能 源（多种类型的能源和 /或来自多个地理位置的能源） 的耦合利用、 管理 和交易服务， 其全文内容以引用方式结合在本文中。
泛能网是一个信息、 能量和物质通过协同耦合而融为一体的智能能 源网络体系。 泛能网基于系统能效技术， 通过能源生产、 储存、 应用与 再生循环四环节能量和信息的耦合， 形成能量输入和输出跨时域的实时 协同， 横向实现多品类能源的相互转换， 各能量流的供需匹配和梯级利 用； 纵向实现能源全生命周期的优化配置， 从根本上实现能效最大化和 排放最小化， 最终输出一种自组织的高度有序的高效智能能源。
泛能流是能量流、 物质流和信息流相互协同耦合而形成的逻辑智能 流。 其中能量流包括电、 燃气、 热等不同的二次能源形式， 物质流包括 水流、 物流等， 信息流则包括通讯、 控制、 数据采集与传输等。 泛能流 通过对能效增益器、 能效控制器及能量全生命周期的四环节 （即能源生 产、 能源储存、 能源应用和能源再生） 的连接而形成一个闭环的泛能网 系统。
如图 1所示， 图 1是现有技术中泛能网的拓扑结构示意图。 泛能网 拓扑结构由 "机 -机、 人 -机、 人 -人"和 "互感、 互动、 互智" 的网络关 系构成。在机-机层， 日常运转由泛能网在线优化、 泛能网系统数据交换 与泛能网平衡优化控制， 触发器使机-机互动， 逻辑优化达到机-机互智。
在人-机层， 综合优化策略注入后，现场监控人员针对超出日常业务控制 范围的状况进行基础调整， 满足业务异常变动需求。人-机互感通过传感 器和人的感觉传递信息， 人通过发指令使机器动作， 而机器动作也会影 响人的需求， 人通过分析优化达到人-机互智。 在人-人层， 针对灾难、 事故、 政策等进行决策会议， 决定资源调度策略， 并与相应预案协调， 结合泛能网管理系统， 形成综合优化策略， 下发到人-机层。 人-人互感 通过各种载体传递信息， 通过各种语言进行交流互动， 智能的专家系统 可以达到人-人互智的目的。
泛能网通过互感 （机-机）、 互动 （人-机）、 互智 （人 -人） 三层决策 优化体系构成决策网络， 从而形成 "智"与 "能" 的融合， 通过对泛能 流从输入到输出的跨时空协同及泛能网内多尺度智能互动， 实现对环境 势能的高品位吸收和对资源能量的高效利用， 产生系统能量在全生命周 期的非线性增效， 从而输出高品质高效率的智能能源。
多层决策优化体系所涉及的变量如下： 控制变量 n， 变量状态 m， 时刻 T。控制变量 η和变量状态 m使得每一层在每一时刻 T的状态数呈 指数级增加， 加上多层状态空间的组合关系， 会使状态空间呈指数级增 力口， 引发维数灾难。 维数灾难是指变量数 （维数） 增多时， 学习的复杂 度呈指数增长， 造成数据要求的时间和数据量接近不可算。
如果当系统处于无序状态时宏观变量 P为 0， 当系统处于有序状态 时宏观变量 P不为 0， 则宏观变量 P的性质可以用于指示有序结构的产 生和转变， 则称宏观变量 P为系统的一个序参量（Order Parameter )。 泛 能流序参量是能够使泛能网系统的无序泛能流向有序泛能流转化的驱 动力， 具体可表现为控制逻辑流量和流向的控制策略。
现有技术中， 对泛能网系统性能的优化一般采用整合优化和协同优 化两种方式。其中， 对于整合优化， 当前能源系统以不可再生能源为主， 可再生能源为辅， 利用一切可以利用的资源， 将多种技术， 因地制宜的 小型、 微型热电冷 （植） 全能量多元系统进行组合， 在电力、 热力、 燃 气、 制冷、 环境、 交通等多系统中进行整合优化。 对于协同优化， 目前 能源企业已从生产型转向服务型， 能源系统通过智能计算机与互联网通
讯系统的自动化管理、 运行、 调度实现， 类似于物联网； 在因特网和智 能计算机的优化运行调度下， 进一歩与智能家用电器实现协同优化， 实 现最小范围的优化调度； 并利用低谷燃气资源和低谷电力资源为用户的 交通工具蓄电、 储氢， 实现燃气、 电力、 供暖、 制冷和生活热水的供需 平衡， 使各系统都达到最优效益状态， 以降低各能源系统代价； 最后， 将废气送入植物大棚， 实行能量和资源的综合利用， 实现零排放的环境 和资源目标。
通过分析上述整合优化和协同优化可知， 目前对泛能网系统性能的 优化仍然具有如下局限性： 局部能源的优化， 只考虑到单一能源， 比如 单独电网的优化， 没有考虑多种能源网络化的集成优化， 因而导致当问 题的复杂度增大时， 缺少相应的解决方案。
发明内容 有鉴于此， 本发明的主要目的在于提供一种利用泛能流序参量控制 泛能网的方法， 以实现对泛能网系统性能的综合优化。
为达到上述目的， 本发明提供了一种利用泛能流序参量泛能网的方 法， 该方法首先按照自然序参量规则判断每一层的关键序参量， 然后根 据一定的优化次序依次优化每一层级的关键序参量， 最后通过环节配比 规则， 使泛能网系统具有自组织和自进化的功能。 该方法在优化过程中 采用序优化方法进行优化， 所述序优化方法是采用等间隔离散优化的方 法。
本发明提供的这种利用泛能流序参量控制泛能网的方法， 具体包 括： 分析泛能网系统中的生产、储存、应用和再生四环节的泛能流关系， 根据自然序参量规则从泛能流关系确定每一层级的关键序参量， 其中关 键序参量用于快速评价和优化整体系统性能； 根据层级序参量规则确定 泛能网系统性能的优化次序； 根据优化次序依次优化每一层级的关键序 参量； 以及根据最优环节配比规则确定四环节最佳配比， 其中四环节最 佳配比用于降低系统的冗余泛能流。
优选地， 该方法在分析泛能网系统中的生产、 储存、 应用和再生四 环节的泛能流关系之前， 还包括： 配置泛能网系统用于表明泛能流关系 的生产、 储存、 应用和再生四环节。 该配置泛能网系统用于表明泛能流 关系的生产、 储存、 应用和再生四环节， 包括： 将化石能、 生物质能源 以及太阳能和风能转化为可达到特定功能的电能、 气能、 热能或冷能， 配置此环节为能源生产环节； 配置对电能、 热能、 冷能或机械能进行储 存的过程为能源储存环节； 配置使用电能、 热能、 冷能或机械能的过程 为能源应用环节； 配置收集能源系统应用环节、 生产环节或储存环节的 余能， 并重新提供给本系统利用的过程为能源再生环节。
优选地， 分析泛能网系统中的生产、 储存、 应用和再生四环节的泛 能流关系， 根据自然序参量规则从泛能流关系确定每一层级的关键序参 量的歩骤， 具体包括： 比较泛能网系统中的生产、 储存、 应用和再生四 环节中能量单元所消耗能量的大小， 将消耗能量较小的能量单元确定为 每一层级的关键序参量。
优选地， 所述根据层级序参量规则确定泛能网系统性能的优化次序 的歩骤， 包括： 泛能网系统由多层四环节构成， 其中底层的四环节向上 传递需求， 上层的四环节向下传递能力， 根据层级序参量规则， 从能量 消耗的角度底层的四环节能量消耗的降低较上层的四环节能量消耗的 降低对于整个泛能网系统具有显著的影响， 因此确定泛能网系统底层的 四环节在泛能网系统性能优化时具有最高的优先级， 上层的四环节在泛 能网系统性能优化时具有最低的优先级， 由底层的四环节至上层的四环 节在泛能网系统性能优化时优先级逐渐降低。
优选地， 所述根据最优环节配比规则确定四环节最佳配比， 包括： 由于四环节的依存关系， 必然存在一种最佳配比， 使得四环节在全生命 周期的运行中保持最佳性能， 该四环节最佳配比的确定以应用环节的需 求量作为驱动， 然后选择生产环节、 储存环节和再生环节的不同参数， 计算出最小的四环节总能耗值和最大的环节满意度， 则得出四环节最佳 配比。
优选地， 所述四环节在全生命周期的运行中保持的最佳性能， 包括
能耗最小和环节满意度最高， 其中环节满意度是使得每一环节在约束条 件下正常运行的满意程度。
从上述技术方案可以看出， 本发明具体考虑了多种能源网络化的集 成优化， 当问题的复杂度增大时， 针对有效降低搜索空间问题， 给出相 应的解决方案， 得到以下有益效果：
1、 本发明提供的利用泛能流序参量控制泛能网的方法， 采用泛能 流序优化规则， 泛能流序优化以指数级 （^(1/2) ) 的收敛速度寻优， 并 通过如下序参量优化规则快速发现关键参量， 从而提高泛能网能效水 平， 实现了对泛能网系统性能的综合优化。
2、 本发明提供的利用泛能流序参量控制泛能网的方法， 采用最优 环节配比规则， 对于每层的序参量， 按照最优环节配比原则寻求组合策 略，那么搜索空间将会从 nAm变成至多 max(n,m)，那么搜索时间至少减 少为原来的（max(n,mynAm)， 实际上， 如果 n个序参量中有关联， 搜索 空间还会减小。
3、 本发明提供的利用泛能流序参量控制泛能网的方法， 采用层级 序参量规则， 从底层开始序参量优化， 通过层级关联约束双向传递输入 输出关系， 将进一歩减小搜索空间而减少搜索时间， 搜索空间将会减少 为]" [maxC^ .)， 搜索时间也会减少为原来的
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4、 本发明提供的利用泛能流序参量控制泛能网的方法， 采用自然 序参量规则， 自然序参量具有显著降低系统能耗的作用，相关研究表明， 自然序参量的引入能达到降低泛能网能耗 10 %的显著水平。
附图说明 图 1是现有技术中泛能网的拓扑结构示意图；
图 2是本发明提供的利用泛能流序参量对泛能网系统性能进行优化 的方法流程图；
图 3是依照本发明第一个实施例利用泛能流序参量对应用于住宅的 泛能网系统性能进行优化的示意图；
图 4是依照本发明第二个实施例利用泛能流序参量对应用于生态城 的泛能网系统性能进行优化的示意图。
具体实施方式 为使本发明的目的、 技术方案和优点更加清楚明白， 以下结合具体 实施例， 并参照附图， 对本发明进一歩详细说明。
本发明的实现原理如下： 泛能流序参量技术基于泛能流序优化规 贝 |J、 最优环节配比规则、 层级序参量规则、 自然序参量规则， 通过比较 序值聚焦关键序参量以快速评价系统性能， 而关键序参量对于迅速缩减 策略空间具有指数级收敛作用。 序参量的选择通过序值决定， 泛能流序 参量技术通过分层递阶寻求关键指标以快速评估和优化整体系统性能。
泛能流序参量技术主要用于提高企业的协同能力。 企业的协同能力 是企业成长的重要保障， 它影响到企业资源整合利用、 企业各部门的协 调合作、 企业对环境的适应力。 序参量是企业协同能力的主宰要素， 它 的确定对企业协同能力的形成与提高将起着至关重要的作用。
序参量是指在系统演化过程中从无到有的变化， 影响着系统各要素 由一种相变状态转化为另一种相变状态的集体协同行为， 并能指示出新 结构形成的参量。 序参量具有三个基本特征： 1 ) 序参量是宏观参量。 协同论研究的是由大量子系统构成的系统的宏观行为， 而仅从微观层次 上了解这些宏观行为； 2) 序参量是微观子系统集体运行的产物、 合作 效应的表征和亮度； 3 ) 序参量是通过各个部分的协同作用产生的， 而 它一旦形成， 就成为系统的控制中心， 支配各子系统的行为， 决定整个 系统的有序结构和功能行为， 主宰系统的整体演化过程。
泛能流序参量是能使泛能网系统无序泛能流向有序泛能流转化的 驱动力， 具体可表现为控制逻辑流量和流向的控制策略。 泛能流序参量 技术基于泛能流序优化规则、 最优环节配比规则、 层级序参量规则、 自 然序参量规则， 通过比较序值聚焦关键序参量以快速评价系统性能， 而 关键序参量对于迅速缩减策略空间具有指数级收敛作用。 序参量的选择
通过序值决定， 泛能流序参量技术通过分层递阶寻求关键指标以快速评 估和优化整体系统性能。
泛能流序参量优化规则： 泛能流的序比较比值比较能够更高效地决 定关键序参量， 并以相当高的概率达到泛能网最优结果， 这里的序比较 包含层级序、 自然序和配比序等不同层面的序的比较。
泛能流序参量规则一： 自然序参量规则 （无功序参量优先原则） 所谓无功序参量， 是指不需要泛能流输入的序参量 （例如太阳光、 自然通风）。 因为无功序参量相对于有功序参量将会降低泛能网系统能 耗， 因而无功序参量要比有功序参量的序值高。
泛能流序参量规则二： 层级序参量规则 （底层序参量优先原则） 由于泛能流的最终服务对象是为了满足底层序参量， 所以底层序参 量对于泛能网系统性能起决定作用， 因而底层序参量的序值大于上层序 参量的序值。 层级序参量之间通过层级关联约束双向传递输入输出关 泛能流序参量规则三：最优环节配比规则（最优环节配比优先原则） 最优的生产、 储存、 应用和再生环节， 将会最大程度降低系统的冗余泛 能流， 因而能导致最优环节配比的序参量的序值高于次优环节配比的序 值。
下面分别对泛能流序优化规则、 最优环节配比规则、 层级序参量规 则和自然序参量规则进行详细阐述。
泛能流序优化规则： 泛能流的序比较能够比泛能流的值比较更高效 地决定关键序参量并以相当高的概率达到泛能网最优结果， 这里的序比 较包含层级序、 自然序和配比序等不同层面的序的比较。 泛能流序优化 是以指数级 (Τ-(1/2Χ)的收敛速度寻优， 并通过如下序参量优化规则来快 速发现关键参量， 从而提高泛能网的能效水平。
最优环节配比规则： 最优的生产、 储存、 应用、 再生环节， 将会最 大程度降低系统的冗余泛能流， 因而能导致最优环节配比的序参量的序 值高于次优环节配比的序参量的序值。 对于每层的序参量， 按照最优环 节配比原则寻求组合策略， 那么搜索空间将会从 nAm 变成至多
max(n,m) , 那么搜索时间至少减少为原来的 max(n,mynAm；)， 实际上， 如果 n个序参量中有关联， 搜索空间还会减小。 此处， 搜索空间通常用 于优化领域， 是指在寻求问题最优解过程中问题的所有可能解所构成的 组合。
层级序参量规则： 由于泛能流的最终服务对象是为了满足底层序参 量， 所以底层序参量对于泛能网系统性能起决定作用， 因而底层序参量 的序值大于上层序参量的序值。 层级序参量之间通过层级关联约束双向 传递输入输出关系。 从底层开始对序参量进行优化， 通过层级关联约束 双向传递输入输出关系， 将进一歩减小搜索空间进而减少搜索时间， 搜 索空间将会减少为 ] "[ max , ， 搜索时间也会减少为原来的 ]~ [ max( , . ) / ]~ [ ni A i ) 0
自然序参量规则： 所谓无功序参量， 是指不需要泛能流输入的序参 量 （例如太阳光、 自然通风）。 因为无功序参量相对于有功序参量将会 降低泛能网系统能耗， 因而无功序参量的序值较有功序参量的序值高。 自然序参量具有显著降低系统能耗的作用， 相关研究 （UTC-Tsmghua Institute)表明， 自然序参量的引入能达到降低泛能网能耗 10%的显著水 平。
在优化过程中， 将上面三个准则应用到关键序参量的判别当中， 首 先按照自然 （无功） 序参量规则判断每一层的关键序参量， 然后根据一 定的优化次序依次优化每一层级的关键序参量 （即先优化底层， 通过层 级动态关联约束逐歩优化到高层）， 最后通过环节配比规则， 可使系统 具有自组织和自进化的功能。
基于上述实现原理， 图 2示出了本发明提供的利用泛能流序参量对 泛能网系统性能进行优化的方法流程图， 该方法包括以下歩骤：
歩骤 201 : 分析泛能网系统中的生产、 储存、 应用和再生四环节的 泛能流关系， 根据自然序参量规则从该泛能流关系确定每一层级的关键 序参量， 其中该关键序参量用于快速评价和优化整体系统性能；
歩骤 202:根据层级序参量规则确定该泛能网系统性能的优化次序； 歩骤 203 : 根据该优化次序依次优化每一层级的关键序参量；
歩骤 204: 根据最优环节配比规则确定四环节最佳配比， 其中该四 环节最佳配比用于降低系统的冗余泛能流。
在上述优化方法中， 序优化贯穿优化过程始末。 首先对泛能流采用 等间隔离散优化方法进行优化， 由于样本数量的增加， 优化组合将呈指 数级增加， 采用序优化方法可以很好的解决这个问题： （1) 对优化目标 进行软化， 把足够好解作为寻优目标； （2) 通过泛能流的序的比较， 而 不是值的比较快速寻优。 实践证明， 采用序优化方法可以指数级缩减策 略空间， 寻求关键指标以快速评价和优化整体系统性能。 上面的方法称 为泛能流序参量优化方法。
实施例 1
图 3是依照本发明第一个实施例利用泛能流序参量对应用于住宅的 泛能网系统性能进行优化的示意图。
生产 （ Generation )、 储存 （ Storage )、 应用 （ Utilization )、 再生 (Regeneration) 四环节 （Four Stages) 表示为 FS= (G,S,U,R)。
对于泛能流底层， 亮度系统四环节， 输入电， 附以自然光源， 满足 亮度要求， 并输出热， 即 FS= (i¾" )+Solar(t)， ， O2 h {t), )。 在此公 式中， 由于四环节 FS中包含 G (生产）， S (储存）， U (应用）， R (再 生） 四个参数， 但有的能量单元可能缺少某个或某几个环节， 例如， 亮 度系统只有生产环节、 应用环节， 没有储存环节、 再生环节， 空格表示 该能量单元不存在于该环节。 下文公式中出现的空格同此处， 均表示该 能量单元不存在于该环节。
温度系统四环节， 输入电和热， 附以亮度系统产生的热， 满足输出 热的要求， § FS= (E (t)+E,h (t) + 0^(t), ， 0,hf(t), )。
对于泛能网第二层， 光伏输送给住宅的电， 加上储电、 储热， 满足 温度系统和亮度系统需求， 即 FS= ( E4 h 2"(t) + O (t) + 0,h 2 g(t) , ， i¾"(t)+i¾"(t)+i¾"(t)，）。
对于泛能网第三层， 光伏产生的电， 满足储电、 储热和住宅用电、 用热需求， 即 FS= (Solar(t), E4 e 2 u(t)+Eh 2 u(t) , E4 h 2 u(t) , )。
通过分析以上泛能流关系， 本发明可以发现关键序参量， 例如在第
一层中， 自然光源因为耗费较少的能量， 通过与电能的比较， 可以认为 是关键序参量。 同样原理， 第二层、 第三层都可以找出关键序参量， 这 是关键序参量的第一个准则： 自然 （无功） 序参量规则。 实践表明， 通 过利用自然序参量规则， 泛能网能耗可以降低 10%以上。
其次， 由于不同的层级对于整个能量应用的效应有所区别， 例如， 如果对第一层的能量应用进行优化，那么效果就比对高层，例如第二层、 第三层的优化效果要明显，因此，本发明优化的次序按层级的由低到高， 优先级则从高到低， 这是关键序参量的第二个准则： 层级序参量规则。 实际效果也表明， 用户末端的节能对于整个泛能网系统生产端的负荷降 低作用非常明显。
再次， 泛能网的四个环节， 生产、 储存、 应用、 再生的泛能流的流 量， 在一定条件下应保持一定配比， 当达到最佳配比时， 泛能网全生命 周期的运行效果最佳， 所以本发明在优化各环节泛能流的流量的时候， 如果能根据四环节最佳配比来优化四环节泛能流的流量， 这对于泛能网 的自组织和自进化将具有一定的意义。 这就是泛能流序参量的第三个准 则： 环节配比规则。
在本发明的优化过程中， 将上面三个准则应用到关键序参量的判别 当中， 首先按照自然 （无功） 序参量规则判断每一层的关键序参量， 然 后根据一定的优化次序依次优化每一层级的关键序参量 （即先优化底 层， 通过层级动态关联约束逐歩优化到高层）， 最后通过环节配比规则， 可使系统具有自组织和自进化的功能。 在优化过程中， 序优化的方法将 贯穿始末， 首先对泛能流采用等间隔离散优化方法进行优化， 由于样本 数量的增加， 优化组合将呈指数级增加， 那么采用序优化方法可以很好 的解决这个问题： （1 )对优化目标进行软化，把足够好解作为寻优目标; (2) 通过泛能流的序的比较， 而不是值的比较快速寻优。 实践证明， 采用序优化方法可以指数级缩减策略空间， 寻求关键指标以快速评价和 优化整体系统性能。 上面的方法称为泛能流序参量优化方法。
图 4是依照本发明第二个实施例利用泛能流序参量对应用于生态城 的泛能网系统性能进行优化的局部示意图。
生产 (Generation) 、 储存 (Storage) 、 应用 (Utilization) 、 再生 (Regeneration) 四环节 (Four Stags) 表示为： FS=(G,S,U,R)。
对于泛能流底层， 亮度系统四环节， 输入电， 附以自然光源， 满足 亮度要求， 即 FS= (i¾" )+Solar(t)， ， O2 h (t) , ) 。
温度系统四环节， 输入电和热， 附以亮度系统产生的热， 满足输出 热的要求， § FS= ( E (t)+E,h (t) + 0^(t) , ， 0,hf(t) , ) 。
对于泛能网第二层， 多联供、光伏输送给智能大厦的电， 加上储电、 储 热 ， 满 足 温 度 系 统 和 亮 度 系 统 需 求 ， 即 FS= ( (0 + E2 h;1 (0 + E4 h;" (0 + H it) + (t)， ， Ee； (t) + E2 e； (t) + i¾" (t) , ) 。
对于泛能网第三层， 多联供所需电、 气， 附以太阳能， 满足储电、 储热和智能大厦用电、 用热需求， 即 FS= (i^(0 + ¾g(0+Solar(t)， (t) + E2 e 2 u (t) + EZ (t) + (t) + E3 h 2" (t)， ¾" (t) + E2 h 2 u (t) + E4 h 2 u (t) , ) 。
类似于上面的例子， 首先， 本发明可以通过自然 （无功） 序参量规 则分析以上泛能流关系， 可以发现关键序参量， 例如在第三层中， 根据 自然（无功）序参量规则， 对多联供的输入进行能耗选择， 包括电输入， 燃气输入， 或者电与燃气按照一定配比进行输入。 第一层和第二层可以 按照相同原理， 寻求关键序参量。 其次， 根据层级序参量规则， 不同的 层级对于整个能量应用的效应有所区别。 对第一层的能量应用进行优 化， 要比对第二层、 第三层进行优化的效果明显， 因此， 优化的次序应 该按照层级的由低到高， 而优先级则是由高到低。 再次， 根据环节配比 规则， 泛能网生产、 再生、 应用和储存四个环节的泛能流的流量在一定 条件下应保持一定配比， 当达到最佳配比时， 泛能网全生命周期的运行 效果达到最佳。 最后， 综合采用泛能流序参量优化方法软化优化目标和 指数级缩减策略空间， 从而最终寻求关键指标以快速评价和优化整体系 统性能。
以上所述的具体实施例， 对本发明的目的、 技术方案和有益效果进 行了进一歩详细说明， 所应理解的是， 以上所述仅为本发明的具体实施 例而已， 并不用于限制本发明， 凡在本发明的精神和原则之内， 所做的 任何修改、 等同替换、 改进等， 均应包含在本发明的保护范围之内。
Claims
1、 一种利用泛能流序参量控制泛能网的方法， 该方法首先按照自 然序参量规则判断每一层的关键序参量， 然后根据一定的优化次序依次 优化每一层级的关键序参量， 最后通过环节配比规则， 使泛能网系统具 有自组织和自进化的功能。
2、 根据权利要求 1 所述的利用泛能流序参量控制泛能网的方法， 该方法在优化过程中采用序优化方法进行优化。
3、 根据权利要求 2 所述的利用泛能流序参量控制泛能网的方法， 所述序优化方法是采用等间隔离散优化的方法。
4、 根据权利要求 1 所述的利用泛能流序参量控制泛能网的方法， 该方法具体包括：
分析泛能网系统中的生产、储存、应用和再生四环节的泛能流关系， 根据自然序参量规则从该泛能流关系确定每一层级的关键序参量， 其中 该关键序参量用于快速评价和优化整体系统性能；
根据层级序参量规则确定该泛能网系统性能的优化次序；
根据该优化次序依次优化每一层级的关键序参量； 以及
根据最优环节配比规则确定四环节最佳配比， 其中该四环节最佳配 比用于降低系统的冗余泛能流。
5、 根据权利要求 4 所述的利用泛能流序参量控制泛能网的方法， 该方法在分析泛能网系统中的生产、 储存、 应用和再生四环节的泛能流 关系之前， 还包括：
配置泛能网系统用于表明泛能流关系的生产、 储存、 应用和再生四 环节。
6、 根据权利要求 5 所述的利用泛能流序参量控制泛能网的方法， 所述配置泛能网系统用于表明泛能流关系的生产、 储存、 应用和再生四 环节， 包括：
将化石能、 生物质能源以及太阳能和风能转化为可达到特定功能的 电能、 气能、 热能或冷能， 配置此环节为能源生产环节； 配置对电能、热能、冷能或机械能进行储存的过程为能源储存环节; 配置使用电能、 热能、 冷能或机械能的过程为能源应用环节； 以及 配置收集能源系统应用环节、 生产环节或储存环节的余能， 并重新 提供给本系统利用的过程为能源再生环节。
7、 根据权利要求 4 所述的利用泛能流序参量控制泛能网的方法， 所述根据自然序参量规则从该泛能流关系确定每一层级的关键序参量 的歩骤， 具体包括：
比较泛能网系统中的生产、 储存、 应用和再生四环节中能量单元所 消耗能量的大小， 将消耗能量较小的能量单元确定为每一层级的关键序
8、 根据权利要求 4所述的利用泛能流序参量控制泛能网的方法， 所述根据层级序参量规则确定该泛能网系统性能的优化次序的歩骤， 包 括：
泛能网系统由多层四环节构成， 其中底层的四环节向上传递需求， 上层的四环节向下传递能力， 根据层级序参量规则， 从能量消耗的角度 底层的四环节能量消耗的降低较上层的四环节能量消耗的降低对于整 个泛能网系统具有显著的影响， 因此确定泛能网系统底层的四环节在泛 能网系统性能优化时具有最高的优先级， 上层的四环节在泛能网系统性 能优化时具有最低的优先级， 由底层的四环节至上层的四环节在泛能网 系统性能优化时优先级逐渐降低。
9、 根据权利要求 4所述的利用泛能流序参量控制泛能网的方法， 所述根据最优环节配比规则确定四环节最佳配比， 包括：
由于四环节的依存关系， 必然存在一种最佳配比， 使得四环节在全 生命周期的运行中保持最佳性能， 该四环节最佳配比的确定以应用环节 的需求量作为驱动， 然后选择生产环节、 储存环节和再生环节的不同参 数， 计算出最小的四环节总能耗值和最大的环节满意度， 则得出四环节 最佳配比。
10、 根据权利要求 9所述的利用泛能流序参量控制泛能网的方法， 所述四环节在全生命周期的运行中保持的最佳性能， 包括能耗最小和环 节满意度最高， 其中该环节满意度是使得每一环节在约束条件下正常运 行的满意程度。
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