电力市场环境下的电源规划方法研究
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摘要
电力市场条件下的竞争机制给计划经济环境下电力行业组织和运营体制带来了巨大改变,同时也极大地影响到电源规划的技术实施,特别是规划主体的变化和市场化运作的要求导致了规划方案的评判标准由电力系统的经济可靠性最大化改变为各投资主体的利益最大化。电源在电力市场中扮演着十分重要的角色,各投资方的电源规划方案,如发电容量和电源结构的配置等,一方面受到各市场成员的切身利益的影响,另一方面又影响系统的安全运行,因而电源规划引人瞩目。本文分析了电力市场环境下电源规划的新特点和新要求,建立了新的电源规划模型,提出了适应于电力市场环境的电源规划方案评估方法,即各投资方的项目期权价值均尽可能达到最大化,并采用新算法对其进行求解。以下是本文的主要研究内容:
1)提出基于扩展排序算法的电源环保联合规划模型。该模型将环保指标及待退役机组的运行费用作为目标函数的一部分,并在规划过程中对问题进行合理简化,以便对环保设施的固定投资和运行费用以及机组的退役期进行全局优化。采用的扩展排序算法从根本上避免了“组合爆炸”问题。该模型便于电源规划协调者或发电投资方从电源规划角度对各自权限范围内的电力环保措施进行优化。
2)提出多发电公司的机组检修非合作博弈模型。该模型以各发电公司在检修期间的收益最大化为目标,同时计及相关的检修风险损失,以及系统允许检修容量不充裕时可中断负荷的合理利用。最后运用随机搜索方法寻找机组检修博弈问题的均衡点,从而得出发电公司的最优检修策略。应用该模型处理电源规划方案评估中的机组检修计划,使得电源规划方案更适合于电力市场环境。
3)提出发电投资决策的期权博弈模型。该模型以实物期权理论和非合作博弈理论为基础,研究了电力市场中电源规划多投资方的博弈行为,通过运行模拟计算各种策略组合下博弈各方策略的期权价值,进而求得Nash均衡点。其中,各发电公司的投资方案考虑了相应环保措施的优化配置,并在投资收益计算中嵌入了各发电公司的机组检修博弈分析。由于实物期权价值能够准确描述项目投资价值变化的不确定性,因而该模型能够有效解决市场环境下发电投资的一个基本问题,即寻求多不确定因素下,各发电商与通过发电容量充裕度等要求代表社会利益的监管机构之间的利益均衡点,为发电商在竞争的市场条件下提供灵活的发电投资决策。
4)提出发输电投资决策的期权博弈模型。其中的发电投资受到电力监管机构对发电公司提出的发电容量充裕度约束,输电投资受到因输电容量不足引起的失负荷价值影响,通过“发电容量充裕度约束”、“因输电容量不足引起的失负荷价值约束”引导和发电及输电投资。该模型可给市场各方更大的自主决策权,同时能够兼顾系统供电可靠性。另外,规划方案分别考虑了完全能量市场、不完全能量市场及容量市场等模式,推导了容量市场模式规划方案分析中AGC机组的CPS考核费用分摊公式,以便尽量准确计算出各种策略组合下发电公司和电网公司等博弈各方策略的期权价值,进而求得电力投资商的最佳投资策略,从而提高其资本使用价值。
5)结合电源规划问题的特点,对常规禁忌搜索算法(Tabu Search,简称TS)进行改进,以便提高TS在电源规划应用中的计算性能。具体是,提出分区记忆指导搜索策略提高了搜索的效率,采用动态管理禁忌频率信息策略节约了规划方案存储空间,针对3)和4)中电源规划模型进行计算,以便快速寻找“满意解”。
总体上,1)侧重于发电投资成本分析中的电力环保问题;2)侧重于发电投资收益计算中的机组检修规划问题;在1)和2)的基础上,3)讨论了电力市场环境下多投资主体的发电投资决策问题;4)延伸了3)的研究思路,探讨了电力市场环境下发电与输电联合投资决策问题;5)是3)和4)多主体投资决策模型采用的具体算法。以上各模型和算法考虑了电力市场环境下的电源规划具有的多投资主体的竞争性、项目投资价值的不确定性、各投资主体的经济效益性、以及总体投资方案的大系统性等特点,并对不同规划目标的某实际电源规划算例进行计算和比较,分析结果表明它们可行而且有效,为解决电力市场环境下电源规划问题开创了新的思路。
Great changes have been observed in the power mechanisms due to the competition of electricity market, which hence has a great influence on the generation expansion planning (GEP) technologies. Because of the alteration of the planner and request of market mechanisms, the evaluation standard for the planning schemes is changed from reliability or system-wide cost minimization to maximization of their profits. In electricity market, generation plays an important role, because the planning schemes of each planner, including generation capacity and structure, will be greatly affected by the benefit of each participator in the market, and influence the security of power system at the same time. Therefore GEP becomes essential. To solve these problems, this dissertation analyzes the new characteristics and requests of GEP for the developing electricity market; builds novel models for GEP; introduces new methods to evaluate the schemes, i.e., the equilibrium of maximal real option value among planners; and solves the models by a new algorithm. The innovations of this dissertation are as the follows.
1) A novel joint planning model of generation expansion and environment protection based on extended ranking algorithm is presented. In this model, environmental cost and operation cost of existing units are included in the objective function. The planning procedures are reduced reasonably to obtain an optimal environment protection scheme and phase-out scheme of generating units as soon as possible. The adoption of extended ranking algorithm can avoid the problem of“combination explosion”. Thus, regulator and planners of GEP can optimize their own environment protection scheme from the view of GEP.
2) A novel noncooperative game model for maintenance scheduling of generating units (MSU) is developed. The objective is to maximize the profit of each generating company. The maintenance risk loss and the compensation fee of interruptible load when the permitted maintenance capacity in the system is insufficient are taken into consideration. A method of stochastic search is used to coordinate multiple equilibria of MSU game. The game of optimal maintenance scheduling makes the evaluation of GEP scheme fit for the environment of electricity market.
3) A novel deferment option game model of generation investment is proposed. Based on real option pricing theory and noncooperative game theory, the game behaviors of generation investors are investigated. From simulation of many strategy combinations, the real option value of each investor is calculated and Nash equilibrium is obtained. Meanwhile, the optimal scheme of environment protection is a main concern of each investor, and noncooperative game model for MSU is incorporated in the computation of its revenues. Since real option pricing can deal with the situation of investment value’s variation, flexible generation investment strategies will be provided for planners by the above model.
4) A novel deferment option game model of joint investment between generation and transmission network is put forward. Based on option game theory, with the consideration of optimal scheme of environment protection and the incorporation of MSU game, many strategy combinations are simulated. Three main modes of electricity market are analyzed, and formulation of control performance standard (CPS) fee is deduced. The real option value of each generation company or transmission company is calculated and its optimal investment scheme is obtained to maximize its profits from electricity investment.
5) According to the shortcomings in the application of Tabu Search (TS) in the GEP, some ways are proposed to improve calculation speed and global convergence performance. Based on scope partition, the adoption of remembrance-guided search method improves the efficiency of local search. The use of dynamically managed tabu frequency information reduces the demand of memory. The problem of GEP is solved by the above measures to get satisfying solution.
Briefly, model 1) focuses on the problem of environment protection in the cost analysis of generation investment. Model 2) investigates the problem of MSU game in the revenue computation of generation investment. Based on model 1) and 2), the problem of generation investment decision-making for multiple investors in electricity market is debated. As a further research, joint investment between generation and transmission network is concerned in model 4). 5) is the algorithm of model 3) and 4). The models and algorithm mentioned above take full consideration of the characters of GEP in electricity market, which can be effectively applied to game among investors, uncertainty of investment value, economic benefit of each investor and large-scale generation expansion planning. The simulation results of a real generation expansion scheme with many different objectives indicate they are feasible and efficient, and provide new ideas to solve the problem of GEP in electricity market.
1)提出基于扩展排序算法的电源环保联合规划模型。该模型将环保指标及待退役机组的运行费用作为目标函数的一部分,并在规划过程中对问题进行合理简化,以便对环保设施的固定投资和运行费用以及机组的退役期进行全局优化。采用的扩展排序算法从根本上避免了“组合爆炸”问题。该模型便于电源规划协调者或发电投资方从电源规划角度对各自权限范围内的电力环保措施进行优化。
2)提出多发电公司的机组检修非合作博弈模型。该模型以各发电公司在检修期间的收益最大化为目标,同时计及相关的检修风险损失,以及系统允许检修容量不充裕时可中断负荷的合理利用。最后运用随机搜索方法寻找机组检修博弈问题的均衡点,从而得出发电公司的最优检修策略。应用该模型处理电源规划方案评估中的机组检修计划,使得电源规划方案更适合于电力市场环境。
3)提出发电投资决策的期权博弈模型。该模型以实物期权理论和非合作博弈理论为基础,研究了电力市场中电源规划多投资方的博弈行为,通过运行模拟计算各种策略组合下博弈各方策略的期权价值,进而求得Nash均衡点。其中,各发电公司的投资方案考虑了相应环保措施的优化配置,并在投资收益计算中嵌入了各发电公司的机组检修博弈分析。由于实物期权价值能够准确描述项目投资价值变化的不确定性,因而该模型能够有效解决市场环境下发电投资的一个基本问题,即寻求多不确定因素下,各发电商与通过发电容量充裕度等要求代表社会利益的监管机构之间的利益均衡点,为发电商在竞争的市场条件下提供灵活的发电投资决策。
4)提出发输电投资决策的期权博弈模型。其中的发电投资受到电力监管机构对发电公司提出的发电容量充裕度约束,输电投资受到因输电容量不足引起的失负荷价值影响,通过“发电容量充裕度约束”、“因输电容量不足引起的失负荷价值约束”引导和发电及输电投资。该模型可给市场各方更大的自主决策权,同时能够兼顾系统供电可靠性。另外,规划方案分别考虑了完全能量市场、不完全能量市场及容量市场等模式,推导了容量市场模式规划方案分析中AGC机组的CPS考核费用分摊公式,以便尽量准确计算出各种策略组合下发电公司和电网公司等博弈各方策略的期权价值,进而求得电力投资商的最佳投资策略,从而提高其资本使用价值。
5)结合电源规划问题的特点,对常规禁忌搜索算法(Tabu Search,简称TS)进行改进,以便提高TS在电源规划应用中的计算性能。具体是,提出分区记忆指导搜索策略提高了搜索的效率,采用动态管理禁忌频率信息策略节约了规划方案存储空间,针对3)和4)中电源规划模型进行计算,以便快速寻找“满意解”。
总体上,1)侧重于发电投资成本分析中的电力环保问题;2)侧重于发电投资收益计算中的机组检修规划问题;在1)和2)的基础上,3)讨论了电力市场环境下多投资主体的发电投资决策问题;4)延伸了3)的研究思路,探讨了电力市场环境下发电与输电联合投资决策问题;5)是3)和4)多主体投资决策模型采用的具体算法。以上各模型和算法考虑了电力市场环境下的电源规划具有的多投资主体的竞争性、项目投资价值的不确定性、各投资主体的经济效益性、以及总体投资方案的大系统性等特点,并对不同规划目标的某实际电源规划算例进行计算和比较,分析结果表明它们可行而且有效,为解决电力市场环境下电源规划问题开创了新的思路。
Great changes have been observed in the power mechanisms due to the competition of electricity market, which hence has a great influence on the generation expansion planning (GEP) technologies. Because of the alteration of the planner and request of market mechanisms, the evaluation standard for the planning schemes is changed from reliability or system-wide cost minimization to maximization of their profits. In electricity market, generation plays an important role, because the planning schemes of each planner, including generation capacity and structure, will be greatly affected by the benefit of each participator in the market, and influence the security of power system at the same time. Therefore GEP becomes essential. To solve these problems, this dissertation analyzes the new characteristics and requests of GEP for the developing electricity market; builds novel models for GEP; introduces new methods to evaluate the schemes, i.e., the equilibrium of maximal real option value among planners; and solves the models by a new algorithm. The innovations of this dissertation are as the follows.
1) A novel joint planning model of generation expansion and environment protection based on extended ranking algorithm is presented. In this model, environmental cost and operation cost of existing units are included in the objective function. The planning procedures are reduced reasonably to obtain an optimal environment protection scheme and phase-out scheme of generating units as soon as possible. The adoption of extended ranking algorithm can avoid the problem of“combination explosion”. Thus, regulator and planners of GEP can optimize their own environment protection scheme from the view of GEP.
2) A novel noncooperative game model for maintenance scheduling of generating units (MSU) is developed. The objective is to maximize the profit of each generating company. The maintenance risk loss and the compensation fee of interruptible load when the permitted maintenance capacity in the system is insufficient are taken into consideration. A method of stochastic search is used to coordinate multiple equilibria of MSU game. The game of optimal maintenance scheduling makes the evaluation of GEP scheme fit for the environment of electricity market.
3) A novel deferment option game model of generation investment is proposed. Based on real option pricing theory and noncooperative game theory, the game behaviors of generation investors are investigated. From simulation of many strategy combinations, the real option value of each investor is calculated and Nash equilibrium is obtained. Meanwhile, the optimal scheme of environment protection is a main concern of each investor, and noncooperative game model for MSU is incorporated in the computation of its revenues. Since real option pricing can deal with the situation of investment value’s variation, flexible generation investment strategies will be provided for planners by the above model.
4) A novel deferment option game model of joint investment between generation and transmission network is put forward. Based on option game theory, with the consideration of optimal scheme of environment protection and the incorporation of MSU game, many strategy combinations are simulated. Three main modes of electricity market are analyzed, and formulation of control performance standard (CPS) fee is deduced. The real option value of each generation company or transmission company is calculated and its optimal investment scheme is obtained to maximize its profits from electricity investment.
5) According to the shortcomings in the application of Tabu Search (TS) in the GEP, some ways are proposed to improve calculation speed and global convergence performance. Based on scope partition, the adoption of remembrance-guided search method improves the efficiency of local search. The use of dynamically managed tabu frequency information reduces the demand of memory. The problem of GEP is solved by the above measures to get satisfying solution.
Briefly, model 1) focuses on the problem of environment protection in the cost analysis of generation investment. Model 2) investigates the problem of MSU game in the revenue computation of generation investment. Based on model 1) and 2), the problem of generation investment decision-making for multiple investors in electricity market is debated. As a further research, joint investment between generation and transmission network is concerned in model 4). 5) is the algorithm of model 3) and 4). The models and algorithm mentioned above take full consideration of the characters of GEP in electricity market, which can be effectively applied to game among investors, uncertainty of investment value, economic benefit of each investor and large-scale generation expansion planning. The simulation results of a real generation expansion scheme with many different objectives indicate they are feasible and efficient, and provide new ideas to solve the problem of GEP in electricity market.
引文
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