考虑大用户直购电的动态环境经济调度

动态环境经济调度在环境经济调度的基础上考虑发电机组的爬坡约束,增强了调度时段内各时段间机组出力的强耦合约束,如何有效地解决此类问题至关重要。另外,大用户直购电如何影响电网动态环境经济运行调度也具有一定研究价值。为此,对考虑大用户直购电的动态环境经济调度问题进行建模,并采用内点法多约束处理策略改进多目标细菌群体趋药性(MOBCC)优化算法进行求解,旨在从系统发电成本的角度研究大用户直购电对经济调度的影响。最后,通过仿真验证明所提方法的有效性,并对仿真结果进行分析总结,说明所采用方法对大用户直购电策略、电网调度影响分析具有一定指导意义。     

考虑环境污染的机组出力经济分配

对传统意义下经济调度模型进行修正,同时考虑最小化燃料费用和污染排放量,提出了多目标环境经济调度模型,并应用多目标蚁群算法(MOACA)加以求解。指出MOACA将信息素交流和基于全局最优经验指导两种寻优方式相结合,以指导蚂蚁向更好解的方向前进,可以获得分布良好的Pareto最优解。利用文内算法对IEEE-30节点系统的机组出力进行环境经济调度,并与现有一些算法进行比较。     

Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm

The environmental issues that arise from the pollutant emissions produced by fossil-fueled electric power plants have become a matter of concern more recently. The conventional economic power dispatch cannot meet the environmental protection requirements, since it only considers minimizing the total fuel cost. The multi-objective generation dispatch in electric power systems treats economic and emission impact as competing objectives, which requires some reasonable tradeoff among objectives to reach an optimal solution. In this paper, a fuzzified multi-objective particle swarm optimization (FMOPSO) algorithm is proposed and implemented to dispatch the electric power considering both economic and environmental issues. The effectiveness of the proposed approach is demonstrated by comparing its performance with other approaches including weighted aggregation (WA) and evolutionary multi-objective optimization algorithms. All the simulations are conducted based on a typical test power system.     

Combined heat and power economic emission dispatch using nondominated sorting genetic algorithm-II

This paper presents nondominated sorting genetic algorithm-II for solving combined heat and power economic emission dispatch problem. The problem is formulated as a nonlinear constrained multi-objective optimization problem. Nondominated sorting genetic algorithm-II is proposed to handle economic emission dispatch as a true multi-objective optimization problem with competing and noncommensurable objectives. The proposed algorithm is illustrated for two test systems and the test results are compared with those obtained from strength pareto evolutionary algorithm 2.     

Environmental/economic power dispatch problem using multi-objective differential evolution algorithm

This paper presents a multi-objective differential evolution (MODE) algorithm for environmental/economic power dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multi-objective problem with competing and non-commensurable objectives of fuel cost, emission and system loss. The proposed MODE approach adopts an external elitist archive to retain non-dominated solutions found during the evolutionary process. In order to preserve the diversity of Pareto optimality, a crowding entropy diversity measure tactic is proposed. The crowding entropy strategy is able to measure the crowding degree of the solutions more accurately. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed approach have been carried out on the IEEE 30- and 118-bus test system. The results demonstrate the capability of the proposed MODE approach to generate well-distributed Pareto optimal non-dominated solutions of multi-objective EED problem. The comparison with reported results of other MOEAs reveals the superiority of the proposed MODE approach and confirms its potential for solving other power systems multi-objective optimization problems.     

Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem

This paper proposes an efficient optimization approach, namely quasi-oppositional teaching learning based optimization (QOTLBO) for solving non-linear multi-objective economic emission dispatch (EED) problem of electric power generation with valve point loading. In this article, a non-dominated sorting QOTLBO is employed to approximate the set of Pareto solution through the evolutionary optimization process. The proposed approach is carried out to obtain EED solution for 6-unit, 10-unit and 40-unit systems. For showing the superiority of this optimization technique, numerical results of the four test systems are compared with several other EED based recent optimization methods. The simulation results show that the proposed algorithm gives comparatively better operational fuel cost and emission in less computational time compared to other optimization techniques.     

考虑柔性负荷响应的含风电场电力系统多目标经济调度

根据模糊随机理论对风电场功率预测误差和负荷预测误差进行模糊处理,并建立旋转备用模糊机会条件,将其转化为清晰等价类。在碳交易机制下考虑柔性负荷响应对电力系统经济调度的影响,在目标函数中引入柔性负荷响应的非线性成本和碳排放补偿成本,建立了碳交易收益最大和综合发电成本最低的多目标模型,并增加了柔性负荷约束条件。采用离散细菌群体趋药性算法对模型进行处理,以基于偏小型满意度的折中策略求解多目标函数最优解,并通过4种调度模型的对比验证了该模型的可行性。     

机组负荷分配的多目标优化和多属性决策

同时计及机组运行的经济性和污染排放,将基于进化算法的多目标优化技术与多属性决策方法联合运用,针对火电厂负荷优化分配问题进行了研究。对于多目标优化问题,采用改进的非支配解排序的多目标遗传算法(NSGAⅡ),求出Pareto最优解,由这些Pareto最优解构成决策矩阵,使用客观赋权的信息熵方法对最优解的属性进行权值计算,然后用逼近理想解的排序方法(TOPSIS)进行多属性决策(MADM)研究,对Pa-reto最优解给出排序。给出了3台机组负荷分配的优化算例,计算表明所提方法适应性好,结果合理可行。     

Balancing risk and cost in fuzzy economic dispatch including wind power penetration based on particle swarm optimization

Utilization of renewable energy resources such as wind energy for electric power generation has assumed great significance in recent years. Wind power is a source of clean energy and is able to spur the reductions of both consumption of depleting fuel reserves and emissions of pollutants. However, since the availability of wind power is highly dependent on the weather conditions, the penetration of wind power into traditional utility grids may incur certain security implications. Therefore, in economic power dispatch including wind power penetration, a reasonable tradeoff between system risk and operational cost is desired. In this paper, a bi-objective economic dispatch problem considering wind penetration is formulated, which treats operational costs and security impacts as conflicting objectives. Different fuzzy membership functions are used to reflect the dispatcher’s attitude toward the wind power penetration. A modified multi-objective particle swarm optimization (MOPSO) algorithm is adopted to develop a power dispatch scheme which is able to achieve compromise between economic and security requirements. Numerical simulations including sensitivity analysis are reported based on a typical IEEE test power system to show the validity and applicability of the proposed approach.     

采用多目标纵横交叉算法的电力系统动态环境经济调度

针对带非线性约束的电力系统动态环境经济调度问题,提出一种多目标纵横交叉算法。对动态调度中燃料费用和污染排放两个相互约束、冲突的目标同时进行优化。求解过程中,结合非约束支配策略,提出一种双交叉机制,增强粒子穿越非可行区域的能力,使得生成的帕累托最优解落在可行区域内。通过边缘探索,增强算法的全局搜索能力。同时,采用外部存档集合储存非劣解,并通过拥挤度对比,保持非劣解的多样性。最后,采用模糊决策理论获得最优折中解。对10机电力系统的仿真结果验证了所提方法的有效性与优越性。     

电站多目标负荷优化分配与决策指导

对传统意义下的厂级负荷优化分配模型进行修正,同时考虑全厂供电煤耗率、污染排全放和负荷调整时间3个目标,提出厂级负荷分配的多目标优化模型。将多目标优化方法和多属性决策结合使用,研究多目标优化指导的问题。针对非劣分层遗传算法(nondominated sorting genetic algorithmII,NSGA-II)易于局部收敛的特点,提出了并行的NSGA-II多目标优化结构,增加了Pareto前沿的多样性,为决策提供丰富的信息。引入基于基点和熵的多属性决策方法,对Pareto解集进行排序,得出最优解。对某火电厂进行实例分析,结果表明该方法能准确快速地完成多目标负荷分配优化,并给出正确的指导,具有一定的实用性。     

Optimal generation dispatch with renewable energy embedded using multiple objectives

Traditional economic dispatch focuses mainly on minimizing the total operation cost of the power system. With the appearance of energy crisis and environmental pollution becoming a public issue, environmental effect of generator should be taken into consideration through the dispatch process. In this paper a multi-objective dispatch problem considering the integration of wind power is solved whose objectives include the generation cost, the reserve capacity and the environmental emission. To compromise different objectives, a coordination degree combined with a satisfaction degree are introduced in order to transform the multi-objective dispatch problem into a single-objective one, i.e., the optimal generation dispatch (OGD) model. Then the OGD is solved by a particle swarm optimization algorithm on an IEEE 30-bus system, with wind power generation and coal-fired generation embedded. The simulation results show that better results in terms of all the three objectives can be obtained from the OGD model, in comparison with two other multi-objective dispatch models. The simulation results also show that the integration of wind power will cause the increase of both the generation cost and the reserve capacity but will decrease the environmental emission.     

基于改进粒子群算法的多能微网多目标优化调度

为实现多能微网内部能源的灵活调用,减轻系统碳排放压力,文中提出了一种基于改进粒子群(particle swarm optimization, PSO)算法的多能微网多目标优化调度方法。建立了冷热电气多能微网模型,分析系统能源耦合机理并对设备进行数学建模;以微网运行成本与环境成本最小为目标,构建多能微网多目标优化调度模型;提出一种改进PSO算法,通过调整主要参数的迭代规则,并采用自适应粒子寻优策略加快收敛速度,提升寻优效果;仿真结果表明：与传统方法相比,所提基于改进PSO算法的多目标优化调度方法能够有效提升算法收敛速度、降低系统综合成本,兼顾其运行的经济性与环境友好性。     

A new model for optimal generation scheduling of power system considering generation units availability

The objective of the short-term combined economic–environmental dispatch is to obtain the optimal power outputs of all generating units in the system given simultaneous minimization of the corresponding fuel cost and the release of gaseous pollutants in the environment. In this paper the classical model of the dynamic combined economic–environmental power dispatch is upgraded considering the availability of the generating units present in the system. The unavailability of power generation is defined as risk index and is considered to be a function of the generating units power level. An efficient multi-objective based genetic algorithm is applied for optimization purposes. Combined hydro-thermal power system is used as a case study system. The results show increase of the availability of power generation followed by small increase of the fuel cost and the gaseous emission.     

Modeling for Dynamic Economic Emission Dispatch Under Uncertainty

This article presents a new model to solve the dynamic economic emission dispatch problem incorporating uncertainties in the process of power generation. Besides the classic dynamic economic emission dispatch factors, the constraints of both reliability and efficiency are especially considered to restrain the disturbances of uncertainties. Accordingly, the penalty and reserve cost function together with the penalty and reserve emission function are added in the multi-objective function, respectively. To obtain some quantitative results, the characterizations of the different sources of uncertainty are discussed based on statistical theory, and this optimization problem is numerically solved by the improved particle swarm optimization algorithm. Eventually, the simulation results demonstrate that improving the level of reliability will increase the operation costs and emissions of the power system, while increasing the level of efficiency will decrease the operation costs and emissions of the power system. Furthermore, there seems to be no significant linear correlation among the economic and environmental costs and the proportion of wind power generation under the requirements of reliability and efficiency.     

采用改进型多目标粒子群算法的电力系统环境经济调度

电力系统多目标环境经济调度要求在满足发电成本最小的同时发电厂的污染气体排放也最小,为此提出了基于Pareto占优策略和拥挤距离排序方法的改进型粒子群算法求解该多目标问题。采用容量可动态调整的外部存档集合存储当前Pareto最优解,利用Pareto占优策略确定个体最优位置,进而根据粒子拥挤距离确定全局最优位置,并设置了动态惯性权重,引入了小概率变异机制,提高了算法搜索能力。算例结果验证了该算法的有效性。     

基于改进多目标海樽群算法的电力系统优化调度

文中提出了一种新的多目标海樽群优化算法,将其与等式约束修正技术和可行解占优约束处理技术相结合,用于求解高度约束的电力系统环境经济优化调度问题。该算法采用高斯采样策略和变异操作增强其寻优性能;通过一种改进的基于动态拥挤距离的非支配排序方法获得分布均匀的帕累托最优前沿;应用模糊集理论为决策者提供最佳折中解。在IEEE 30节点6机组标准测试系统上进行算例仿真,并与其它优化算法进行了对比。结果表明,所提算法在求解电力系统环境经济调度问题时具有更好的优化效果。     

基于免疫遗传算法的环境经济负荷调度

介绍以目标函数为抗原,以问题解为抗体,利用进化策略进行群体更新的免疫遗传算法。讨论环境经济负荷调度多目标函数优化问题,给出利用免疫遗传算法解决这一问题的主要步骤。利用一个含有5个电力生产单元的燃煤电力系统模型验证了该算法的可行性和有效性。并与遗传算法和Hop fie ld神经网络进行比较分析,证实了该算法解决该类问题的优化性和快速收敛性。     

Differential evolution algorithm for emission constrained economic power dispatch problem

In this paper, a differential evolution (DE) algorithm is developed to solve emission constrained economic power dispatch (ECEPD) problem. Traditionally electric power systems are operated in such a way that the total fuel cost is minimized regardless of emissions produced. With increased requirements for environmental protection, alternative strategies are required. The proposed algorithm attempts to reduce the production of atmospheric emissions such as sulfur oxides and nitrogen oxides, caused by the operation of fossil-fueled thermal generation. Such reduction is achieved by including emissions as a constraint in the objective of the overall dispatching problem. A simple constraint approach to handle the system constraints is proposed. The performance of the proposed algorithm is tested on standard IEEE 30-bus system and is compared with conventional methods. The results obtained demonstrate the effectiveness of the proposed algorithm for solving the emission constrained economic power dispatch problem.     

Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization

This paper presents a newly developed teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal reactive power dispatch (ORPD) problem by minimizing real power loss, voltage deviation and voltage stability index. To accelerate the convergence speed and to improve solution quality quasi-opposition based learning (QOBL) concept is incorporated in original TLBO algorithm. The proposed TLBO and quasi-oppositional TLBO (QOTLBO) approaches are implemented on standard IEEE 30-bus and IEEE 118-bus test systems. Results demonstrate superiority in terms of solution quality of the proposed QOTLBO approach over original TLBO and other optimization techniques and confirm its potential to solve the ORPD problem.