综合全无限规划方法应用于能源系统管理
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摘要
经济的持续和快速的发展带来了工业化和城镇化的急速扩张,导致了能源消费量的不断上升,带来了越来越多的环境问题。能源系统是复杂的巨系统,其中的不确定性错综复杂、不易辨识。所以,如何表征能源系统中存在的高度不确定性因素及其互动关系,如何将这种抽象关系映射到实际的规划模型中,如何根据能源系统的特点确定规划方法等均是能源与环境系统规划面临的关键问题。针对以上问题,本文将在能源系统不确定性辨识的分析研究基础上,整合多目标、多时期、多情景、多元素的动态性和复杂性,开发一系列综合全无限规划方法应用于能源系统管理,具体包括：(1)区间全无限规划方法。该方法结合了全无限规划和区间线性规划,它可以处理存在于成本、影响因子和系统目标当中的表达为脆性区间值和函数区间的不确定问题。本方法应用于一个控制污染物排放的能源规划系统,得到了四个发电厂的能源供应、电力分配和污染物控制的决策方案。(2)区间全无限混合整数规划方法。该方法是在区间全无限规划方法的基础上,引入混合整数规划方法来处理电力系统装机扩容问题。本文以北京市能源规划为例,提出了区间全无限混合整数-城市尺度能源模型。该模型可以制定多时期、多选项环境下产能设备的容量扩充规划,同时协调经济成本、系统效率、减缓污染和能源供应安全之间的相互关系。(3)全无限模糊随机数学规划方法。该方法将模糊数学规划和随机数学规划引入全无限规划中,增加了模型对模糊集等不确定信息的表征能力。全无限模糊随机数学规划被应用到了北京市电力系统的内部碳交易建模中,以帮助系统在欧盟碳交易的框架下控制温室气体排放。(4)基于风险分析的混合整数全无限规划方法。考虑到碳交易系统的风险性,该方法直接风险区间线性规划方法引入到两阶段随机规划、全无限规划和区间参数混合整数规划中。采用北京市电力系统碳排放交易规划案例对该方法进行了验证。模型结果可以规避碳交易过程中的系统风险,有助于权衡电力供应风险、系统成本和二氧化碳减排计划之间的关系。(5)区间参数机会约束全无限规划方法。该方法整合了机会约束规划区、间数学规划,全无限规划,以及区间参数混合整数规划。以北京市能源系统为例建立的模型能够有效地处理不确定信息,得到不同概率水平下的决策方案,以此检验不确定条件下系统约束的可靠性,从而针对不同的环境、经济和能源安全条件,求得不同概率水平下的预期结果。(6)区间参数全无限联合概率混合整数规划方法。此方法援引了联合概率的理论,结合区间数学规划,全无限规划,机会约束规划以及区间参数混合整数规划方法,在处理系统不确定性的基础上,提供违背了联合概率约束的风险水平。以北京市电力系统为例建立模型,结果可以帮助管理者选取最优决策,在联合概率的约束下对系统成本,电力需求安全性和空气污染控制进行权衡折衷。(7)多阶段随机全无限整数规划方法。为应对多情景条件下的能源系统动态特性,多阶段随机全无限整数规划方法借助多阶段随机数学规划降低系统风险,用多情景树的形式反映了电能生产决策的动态特性。多阶段随机全无限整数规划方法被应用到了一个城市能源系规划模型中。MSFIP模型能够帮助制定最佳电力补救策略,降低系统故障的风险。(8)全无限区间两阶段混合整数规划方法。该方法将区间数学规划、两阶段随机规划和全无限规划方法相结合,其模型应用到了北京市电力系统的碳交易模型中。通过对多重不确定信息的表征和处理,可以得出最优碳准许排放限额和不同电厂的碳交易量。模型结果可以降低系统的碳排放量从而为电厂选择购买排放许可或者承受经济惩罚提供决策支持。
本文开发的一系列不确定性优化模型可以更好地诠释能源系统的不确定性和风险阈值的复杂性。模型结果涵盖能源系统的各个环节,包括能源的加工和供需、电力的生产和供应、污染物的减排、碳交易的实施和运行、系统的成本和投资风险等等。可以为调节现有能源供需模式提供数据支持,从而协调经济成本、系统效益、污染排放和能源供应的关系。这不仅可以为政府部门和决策者提供现有能源政策的分析和评估,以辅助制定能源系统的相关政策,进行系统设备扩容的动态分析和今后生产模式的发展规划。值得一提的是,模型结果不仅可以为政府部门提供情景分析和决策,还可以给出能源系统分析及其环境问题的前瞻性建议,分时期逐步解决或者减缓未来能源系统所可能面临的相关问题,帮助能源系统实现经济效益、社会效益和环境效益的统一。
Based on the rapid industrialization and urbanization, economy is growing fast, which leads to increasing of energy consumption. A lot of serious environmental problems have also been created. Energy systems is a complex and huge system, with various uncertainties to identify. In that case, how to express the uncertainties and their interactions, how to reflect these interactions to a optimization model and how to determine suitable methods are all key issues which planners have to face to. To solve these problems, this paper would focus on the dynamic and complexity for multi-objective, multi-period, multi-scenario, and multi-element based on the analysis of energy systems. A series of integer full-interval programming (FIP) methods would be developed:(1) an interval full-infinite programming (IFIP) method. IFIP integrates FIPinto an interval mathematical programming (IMP) framework, which is capable of addressing multiple uncertainties existing in related costs, impact factors and system objectives expressed as determinates, crisp interval values and functional intervals. Then, IFIP is applied to an energy planning system. According to the results, the amount of energy allocation, electricity supply and pollution emission of4power plants would be generated.(2)an interval full-infinite mixed-integer programming (IFMIP) method. IFMIP is based on an integration of existing IFIP and mixed-integer linear programming (MILP) techniques. IFMIP is utilized to a real case study of energy systems planning in Beijing. It can facilitate capacity-expansion planning for energy-production facilities, and coordinate the conflict interactions among economic cost, system efficiency, pollutant mitigation and energy-supply security.(3) a full-infinite fuzzy stochastic programming (FFSP) method. FFSP combines fuzzy mathematical programming method andstochastic mathematical programming to a FIP framework, and FFSP can deal with uncertainties presented in terms of fuzzy sets, random variables, and functional interval values. FFSP is applied to a case study of Beijing for managing electric power systems (EPS), and reducing the GHG emission by introducing the European Union greenhouse gas emission trading scheme.(4) a risk-explicit mixed-integer full-infinite programming (RMFP) method. Considering high risks in carbon emission trading, RMFP is developed for risk reflection and policy analysis by introducing a risk explicit IMP and two-stage stochastic programming to a FIP framework under various uncertainties. RMFP is applied to plan carbon emission trading of EPS in Beijing. The results are useful for voiding the system-failure risk, and help gaining insight into the tradeoffs among electricity supply risk, system cost, and CO2mitigation strategy.(5) an interval-parameter chanced-constrained full-infinite mixed-integer programming (ICFMP) method. ICFMPintegrates IMP, FIP, MILP, and chanced-constrain programming in to an optimization framework. ICFMP can support the assessment of the reliability of satisfying systems constraints. ICFMP applied to energy systems planning in Beijing. The results are useful for making decisions of energy production and allocation under different probabilities as well as gaining insight into the tradeoffs between the system cost and the constraint-violation risk.(6) an interval-parameter full-infinite joint-probabilistic mixed-integer programming (IFJMP) method. By incorporation of "joint probability", IFJMPcan examine the reliability of satisfyingsystem constraints under uncertainty. IFJMP is then applied for planning EPS of Beijing. With the aid of IFJMP, tradeoffs among system costs, electricity-supply security, and air-pollution control can be obtained under joint probabilities.(7) a multistage stochastic full-infinite integer programming (MSFIP) method. For reflect the dynamics through generation of a set of representative scenarios, MSFIP is introduced to reduce the system risks, associated with multiple uncertainties. A case study for EPS is provided for demonstrating the applicability of the MSFIP, which is able to help for lowering the risk of system failure due to potential violation when determining optimal electricity remediation strategies.(8) a full-infinite interval-stochastic mixed-integer programming (FIMP) method. FIMP is applied to of EPS in Beijing for managing CO2emissions with trading scheme, and achieve optimized carbon emission permits of different power plants under uncertainty. The solutions can be used for CO2reduction and assessing the associated economic implications in purchasing emission permits or bearing economic penalties.
In this paper, a series of integrated FIP modes are developed, and they have capacity to assist decision makers better deal with the system complicated uncertainties and possible risks. Results cover all aspects of energy systems, includingenergy supply and demand, electricity production and supply, pollutant emission control, carbon trading's implementation, minimize of system cost, etc. These solutions can support the adjustment of the existing plans and policies, and facilitation of dynamic analysis for decisions of capacity expansion and development plans. It is worth to mention that the solutions not only can provide scenario analysis for government departments and decision-makers, but also could offer forward-looking policies of energy systems, and gradually resolve problems which decision maker may encounter in the future. It would realize the unification of economic gains, social improvements and environmental benefits.
本文开发的一系列不确定性优化模型可以更好地诠释能源系统的不确定性和风险阈值的复杂性。模型结果涵盖能源系统的各个环节,包括能源的加工和供需、电力的生产和供应、污染物的减排、碳交易的实施和运行、系统的成本和投资风险等等。可以为调节现有能源供需模式提供数据支持,从而协调经济成本、系统效益、污染排放和能源供应的关系。这不仅可以为政府部门和决策者提供现有能源政策的分析和评估,以辅助制定能源系统的相关政策,进行系统设备扩容的动态分析和今后生产模式的发展规划。值得一提的是,模型结果不仅可以为政府部门提供情景分析和决策,还可以给出能源系统分析及其环境问题的前瞻性建议,分时期逐步解决或者减缓未来能源系统所可能面临的相关问题,帮助能源系统实现经济效益、社会效益和环境效益的统一。
Based on the rapid industrialization and urbanization, economy is growing fast, which leads to increasing of energy consumption. A lot of serious environmental problems have also been created. Energy systems is a complex and huge system, with various uncertainties to identify. In that case, how to express the uncertainties and their interactions, how to reflect these interactions to a optimization model and how to determine suitable methods are all key issues which planners have to face to. To solve these problems, this paper would focus on the dynamic and complexity for multi-objective, multi-period, multi-scenario, and multi-element based on the analysis of energy systems. A series of integer full-interval programming (FIP) methods would be developed:(1) an interval full-infinite programming (IFIP) method. IFIP integrates FIPinto an interval mathematical programming (IMP) framework, which is capable of addressing multiple uncertainties existing in related costs, impact factors and system objectives expressed as determinates, crisp interval values and functional intervals. Then, IFIP is applied to an energy planning system. According to the results, the amount of energy allocation, electricity supply and pollution emission of4power plants would be generated.(2)an interval full-infinite mixed-integer programming (IFMIP) method. IFMIP is based on an integration of existing IFIP and mixed-integer linear programming (MILP) techniques. IFMIP is utilized to a real case study of energy systems planning in Beijing. It can facilitate capacity-expansion planning for energy-production facilities, and coordinate the conflict interactions among economic cost, system efficiency, pollutant mitigation and energy-supply security.(3) a full-infinite fuzzy stochastic programming (FFSP) method. FFSP combines fuzzy mathematical programming method andstochastic mathematical programming to a FIP framework, and FFSP can deal with uncertainties presented in terms of fuzzy sets, random variables, and functional interval values. FFSP is applied to a case study of Beijing for managing electric power systems (EPS), and reducing the GHG emission by introducing the European Union greenhouse gas emission trading scheme.(4) a risk-explicit mixed-integer full-infinite programming (RMFP) method. Considering high risks in carbon emission trading, RMFP is developed for risk reflection and policy analysis by introducing a risk explicit IMP and two-stage stochastic programming to a FIP framework under various uncertainties. RMFP is applied to plan carbon emission trading of EPS in Beijing. The results are useful for voiding the system-failure risk, and help gaining insight into the tradeoffs among electricity supply risk, system cost, and CO2mitigation strategy.(5) an interval-parameter chanced-constrained full-infinite mixed-integer programming (ICFMP) method. ICFMPintegrates IMP, FIP, MILP, and chanced-constrain programming in to an optimization framework. ICFMP can support the assessment of the reliability of satisfying systems constraints. ICFMP applied to energy systems planning in Beijing. The results are useful for making decisions of energy production and allocation under different probabilities as well as gaining insight into the tradeoffs between the system cost and the constraint-violation risk.(6) an interval-parameter full-infinite joint-probabilistic mixed-integer programming (IFJMP) method. By incorporation of "joint probability", IFJMPcan examine the reliability of satisfyingsystem constraints under uncertainty. IFJMP is then applied for planning EPS of Beijing. With the aid of IFJMP, tradeoffs among system costs, electricity-supply security, and air-pollution control can be obtained under joint probabilities.(7) a multistage stochastic full-infinite integer programming (MSFIP) method. For reflect the dynamics through generation of a set of representative scenarios, MSFIP is introduced to reduce the system risks, associated with multiple uncertainties. A case study for EPS is provided for demonstrating the applicability of the MSFIP, which is able to help for lowering the risk of system failure due to potential violation when determining optimal electricity remediation strategies.(8) a full-infinite interval-stochastic mixed-integer programming (FIMP) method. FIMP is applied to of EPS in Beijing for managing CO2emissions with trading scheme, and achieve optimized carbon emission permits of different power plants under uncertainty. The solutions can be used for CO2reduction and assessing the associated economic implications in purchasing emission permits or bearing economic penalties.
In this paper, a series of integrated FIP modes are developed, and they have capacity to assist decision makers better deal with the system complicated uncertainties and possible risks. Results cover all aspects of energy systems, includingenergy supply and demand, electricity production and supply, pollutant emission control, carbon trading's implementation, minimize of system cost, etc. These solutions can support the adjustment of the existing plans and policies, and facilitation of dynamic analysis for decisions of capacity expansion and development plans. It is worth to mention that the solutions not only can provide scenario analysis for government departments and decision-makers, but also could offer forward-looking policies of energy systems, and gradually resolve problems which decision maker may encounter in the future. It would realize the unification of economic gains, social improvements and environmental benefits.
引文
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