模糊不确定性优化理论在能源规划中的应用
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摘要
能源规划,不仅有利于精确描述各种能源系统的行为,而且还能量化规划方案的可能实施效果,为决策者的决策管理提供科学依据。然而,能源系统通常包含多个子系统,各子系统之间及其内部各组分之间存在着错综复杂的互动关系,使得能源系统呈现出一系列的复杂性和动态性,这就给能源系统规划提出了重大挑战：确定性的规划模型很难准确反映这些复杂性和不确定性。基于不确定性理论的能源规划模型,在综合考虑能源、环境、经济等因素的同时,将这些复杂性和不确定性也纳入规划过程,大大提高了规划方案实施效果的科学性和可信度。因此,开展不确定性优化理论在能源规划中的应用研究,将为我国的能源规划与管理提供实用的技术手段,有利于我国节能减排政策的高效实施。
本文在全面掌握能源系统特征和复杂性的基础上,以改进的模糊不确定性规划理论为基础,着重强调其与传统的区间、随机规划技术的结合,以环境和经济的最优化为目标,构建模糊不确定性的能源系统规划模型体系,为管理者提供技术支撑。具体内容包括：(1)以低端达到度理论为基础,针对能源规划过程中信息的区间模糊性、随机性等问题,分别建立基于低端达到度的模糊区间、模糊区间机会约束以及模糊区间两阶段能源系统规划模型；(2)基于模糊可行性分析概念,结合能源环境系统之间的互动性和动态性,分别建立基于可行性分析的模糊不确定性能源环境系统规划模型、发电侧节能减排机会约束规划模型以及能源环境系统中长期随机规划模型,用以解决减排政策下能源的合理配置问题;(3)分别以吉林省能源规划和北京市电源规划案例研究,验证模型的实用性。分析吉林省能源系统特征,在不确定性和复杂性的条件下构建吉林省能源系统规划模型,实现吉林省能源的优化配置和经济规划；针对北京市电力系统特点,开发基于模糊可行性分析的北京市电源规划模型,用于指导节能减排政策下北京市电源规划。
结果表明,新建的能源环境系统规划模型,在系统复杂性、规划过程中的不确定性反映以及模型计算上较之传统的规划方法有很大的改进,同时通过技术方法的耦合,各方法自身的缺陷得到了很好的弥补。新开发的规划方法,可以在不确定性、动态性和互动性条件下帮助优化配置不同形式的能源,安排各种能源活动,为能源规划提供更灵活、强健的技术和理论支持。
Energy systems planning is important in energy systems management, which can not only help accurately describe the behavior of the various activities in the systems, but also can quantify the possible effects of the planning schemes. It provides a scientific basis for decision-making management. However, energy systems usually consist of multiple subsystems, and there are complex interactions and relationships between each subsystem and among its internal components, which make energy systems present a series of complexity and uncertainty. Under this situation, a major challenge shows up in energy systems planning:the traditional deterministic programming models can hardly reflect those kinds of complexity and uncertainty. However, energy systems planning models based on uncertainty theory can comprehensively consider interactions among the factors of energy, economy and environment. At the same time, they could integrate the multiple complexity and uncertainty in energy systems into the model formulation and results computation, greatly improving the reliability and credibility of the planning schemes. Thus, researches on the application of uncertainty optimization theory in energy systems planning could provide practical technologies for energy systems planning and management, which is conducive to the efficient implementation of energy saving and emission reduction policies in China.
In this thesis, fuzzy inexact energy systems planning models with the objectives being minimization of the total system cost, would be developed based on the comprehensive analysis of the complexity and uncertainty in energy systems by incorporating the improved fuzzy programming approaches into the interval and/or stochastic model frameworks, which could provide technical support for energy systems management. Specifically, the main research components include:(1) three fuzzy inexact programming models would be generated by integrating the lower-side attainment values based fuzzy programming into the interval optimization, chance-constraint programming and two-stage stochastic programming frameworks respectively to tackle the uncertainties expressed as interval numbers, fuzzy sets, probabilistic distributions, and the combination of them during energy systems planning. Moreover, compared with the conventional fuzzy programming, the lower-side attainment values based fuzzy programming would have an advantage in computation;(2) A variety of hybrid models including a feasibility based inexact fuzzy model for regional energy systems planning, a feasibility based inexact chance-constraint model for energy saving and emission reduction on power generation grid, and a feasibility based inexact multi-stage model for long-term energy and environmental systems planning would be proposed through the combination of interval or chance-constraint or multi-stage programming approaches with feasibility based fuzzy programming, which could quantify the violation during de-fuzzination compared with the lower-side attainment values based fuzzy programming;(3) Considering the energy planning problem in Jilin Province and the electric power system planning problem in Beijing as research objectives to demonstrate the applicability of the proposed models. A lower-side attainment values based fuzzy inexact model for energy systems planning in Jilin Province would be developed under the analysis of the characteristics of energy systems Jilin Province, which could help identify the optimal energy allocations with consideration of the complexity and uncertainty. According to the feature of the electric power system in Beijing, a feasibility based fuzzy inexact model would be proposed for the electric power system planning in Beijing, which could guide the capacity expansions of electric power generation technologies and the patterns of electricity generation in Beijing under the policies of energy saving and emission reduction.
The obtained results indicated that the new developed optimization models would have advantages in uncertainty reflection and model solution compare with the traditional methods. Meanwhile, the shortcomings for each single traditional inexact programming could be remedied via the combination with other inexact optimization methods. The proposed models could optimize the allocation of different forms of energy, and assign energy activities under uncertainty, dynamics and interactivity. Moreover, these approaches could offer more flexible and robust support for energy systems planning in technologies and theory
本文在全面掌握能源系统特征和复杂性的基础上,以改进的模糊不确定性规划理论为基础,着重强调其与传统的区间、随机规划技术的结合,以环境和经济的最优化为目标,构建模糊不确定性的能源系统规划模型体系,为管理者提供技术支撑。具体内容包括：(1)以低端达到度理论为基础,针对能源规划过程中信息的区间模糊性、随机性等问题,分别建立基于低端达到度的模糊区间、模糊区间机会约束以及模糊区间两阶段能源系统规划模型；(2)基于模糊可行性分析概念,结合能源环境系统之间的互动性和动态性,分别建立基于可行性分析的模糊不确定性能源环境系统规划模型、发电侧节能减排机会约束规划模型以及能源环境系统中长期随机规划模型,用以解决减排政策下能源的合理配置问题;(3)分别以吉林省能源规划和北京市电源规划案例研究,验证模型的实用性。分析吉林省能源系统特征,在不确定性和复杂性的条件下构建吉林省能源系统规划模型,实现吉林省能源的优化配置和经济规划；针对北京市电力系统特点,开发基于模糊可行性分析的北京市电源规划模型,用于指导节能减排政策下北京市电源规划。
结果表明,新建的能源环境系统规划模型,在系统复杂性、规划过程中的不确定性反映以及模型计算上较之传统的规划方法有很大的改进,同时通过技术方法的耦合,各方法自身的缺陷得到了很好的弥补。新开发的规划方法,可以在不确定性、动态性和互动性条件下帮助优化配置不同形式的能源,安排各种能源活动,为能源规划提供更灵活、强健的技术和理论支持。
Energy systems planning is important in energy systems management, which can not only help accurately describe the behavior of the various activities in the systems, but also can quantify the possible effects of the planning schemes. It provides a scientific basis for decision-making management. However, energy systems usually consist of multiple subsystems, and there are complex interactions and relationships between each subsystem and among its internal components, which make energy systems present a series of complexity and uncertainty. Under this situation, a major challenge shows up in energy systems planning:the traditional deterministic programming models can hardly reflect those kinds of complexity and uncertainty. However, energy systems planning models based on uncertainty theory can comprehensively consider interactions among the factors of energy, economy and environment. At the same time, they could integrate the multiple complexity and uncertainty in energy systems into the model formulation and results computation, greatly improving the reliability and credibility of the planning schemes. Thus, researches on the application of uncertainty optimization theory in energy systems planning could provide practical technologies for energy systems planning and management, which is conducive to the efficient implementation of energy saving and emission reduction policies in China.
In this thesis, fuzzy inexact energy systems planning models with the objectives being minimization of the total system cost, would be developed based on the comprehensive analysis of the complexity and uncertainty in energy systems by incorporating the improved fuzzy programming approaches into the interval and/or stochastic model frameworks, which could provide technical support for energy systems management. Specifically, the main research components include:(1) three fuzzy inexact programming models would be generated by integrating the lower-side attainment values based fuzzy programming into the interval optimization, chance-constraint programming and two-stage stochastic programming frameworks respectively to tackle the uncertainties expressed as interval numbers, fuzzy sets, probabilistic distributions, and the combination of them during energy systems planning. Moreover, compared with the conventional fuzzy programming, the lower-side attainment values based fuzzy programming would have an advantage in computation;(2) A variety of hybrid models including a feasibility based inexact fuzzy model for regional energy systems planning, a feasibility based inexact chance-constraint model for energy saving and emission reduction on power generation grid, and a feasibility based inexact multi-stage model for long-term energy and environmental systems planning would be proposed through the combination of interval or chance-constraint or multi-stage programming approaches with feasibility based fuzzy programming, which could quantify the violation during de-fuzzination compared with the lower-side attainment values based fuzzy programming;(3) Considering the energy planning problem in Jilin Province and the electric power system planning problem in Beijing as research objectives to demonstrate the applicability of the proposed models. A lower-side attainment values based fuzzy inexact model for energy systems planning in Jilin Province would be developed under the analysis of the characteristics of energy systems Jilin Province, which could help identify the optimal energy allocations with consideration of the complexity and uncertainty. According to the feature of the electric power system in Beijing, a feasibility based fuzzy inexact model would be proposed for the electric power system planning in Beijing, which could guide the capacity expansions of electric power generation technologies and the patterns of electricity generation in Beijing under the policies of energy saving and emission reduction.
The obtained results indicated that the new developed optimization models would have advantages in uncertainty reflection and model solution compare with the traditional methods. Meanwhile, the shortcomings for each single traditional inexact programming could be remedied via the combination with other inexact optimization methods. The proposed models could optimize the allocation of different forms of energy, and assign energy activities under uncertainty, dynamics and interactivity. Moreover, these approaches could offer more flexible and robust support for energy systems planning in technologies and theory
引文
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