我国两部制电价制度对天然气发电企业盈利能力的影响

上海、浙江和江苏等地陆续出台了两部制电价制度,以期解决天然气发电(以下简称气电)企业成本高、经营困难等问题。为了研究该电价制度对气电企业盈利能力的影响,在对比分析上述三地电价政策和实施效果的基础上,基于不同类型机组的三大经营指标(项目财务内部收益率、经济净现值、动态投资回收期),采用项目经济性分析模型探究了不同类型燃气电厂的经济性。研究结果表明:①现行两部制电价与单一制电价相比,电价水平小幅度下降,给企业经营带来了一定的冲击,但从长远看,则有利于形成可持续的发展机制;②两部制电价是各地政府经过充分调研和测算后根据该区具体情况制定的,在该政策下多数企业处于盈亏平衡点附近,综合条件优、管理水平高的企业能取得一定的盈利,反之则亏损;③当电量电价一定且大于盈亏平衡点时,企业效益随年利用小时数的增加而提升,反之则随年利用小时数的增加而降低。结论认为:①两部制电价能起到促进企业管理和技术水平提升,引导和鼓励电力投资的作用,符合我国电力体制改革的方向;②政策制定时应针对不同时期建成的项目加以区别对待,实现上网电价、天然气价格和供热蒸气价格的"三个联动";③应建立大数据平台,实行气网、电网联合调度;④发电企业应创新管理,降低电厂建设及维护成本,提升机组效率,降低气耗。     

Prediction of process parameters of water-assisted injection molding based on inverse radial basis function neural network

Because of the introduction of new processing parameters in water-assisted injection molding (WAIM), processes control has become more difficult. First, design of experiment (DOE) was carried out by using optimized Latin hypercubes (Opt LHS). On the basis of this, computational fluid dynamics (CFD) method was used to simulate and calculate hollowed core ratios and wall thickness differences of cooling water pipe at different positions. Then inverse radial basis function (RBF) neural network model reflecting the fitting relationship between processing parameters and molding quality was established, and accuracy of the model was detected by cross validation. Finally, expected molding quality was applied to predict processing parameters, and the obtained molding quality under the predicted processing parameters was verified by computer aided engineering (CAE) simulation and experimental methods. The results showed that mean relative precisions of processing parameters such as melt temperature, delay time, short shot size, water pressure, and mold temperature for inverse RBF model were 98.6%, 93.6%, 98.5%, 93.9%, and 97.9%, respectively, which met the accuracy requirements. Furthermore, compared with expected values of hollowed core ratios and wall thickness differences, the average errors of CAE and experiment were 2.3% and 4.9%, respectively.     

Environmental economic dispatch in multi-area power system employing improved differential evolution with fuzzy selection

In a deregulated multi-area electrical power system the objective is to determine the most economical generation dispatch strategy that could satisfy the area load demands, the tie-line limits and other operating constraints. Usually, economic dispatch (ED) deals only with the cost minimization, but minimization of emission content has also become an equally important concern due to the mandatory requirement of pollution reduction for environmental protection. Environmental economic dispatch (EED) is a complex multi-objective optimization (MOO) problem with conflicting goals. Normally a fuzzy ranking is employed to rank the large number of Pareto solutions obtained after solving a MOO problem. But in this paper the preference of the decision maker (DM) is used to guide the search and to select the population for the next generation. An improved differential evolution (DE) method is proposed where the selection operation is modified to reduce the complexity of multi-attribute decision making with the help of a fuzzy framework. Solutions are assigned a fuzzy rank on the basis of their level of satisfaction for different objectives before the population selection and then the fuzzy rank is used to select and pass on better solutions to the next generation. A well distributed Pareto-front is obtained which presents a large number of alternate trade-off solutions for the power system operator. A momentum operation is also included to prevent stagnation and to create Pareto diversity. Studies are carried out on three test cases and results obtained are found to be better than some previous literature.     

刮膜式分子蒸馏器液体停留时间分布及壁面结构优化

在刮膜式分子蒸馏器中,可以通过液体的停留时间分布反映物料蒸发分离特性,研究其规律对改进蒸馏器内的流场结构、优化操作参数和装置的设计都具有重要意义。今采用计算流体力学的方法,建立三维CFD模型,研究了进料速率和转子转速对停留时间分布规律的影响,并且与实验值进行对比验证;对蒸馏器壁面进行了优化,包括水平圆环、倾斜圆环和网状圆环三种优化壁面,并且对其分别进行模拟计算和实验验证。模拟结果显示出与实验结果相同的规律。结果表明,在研究范围内,停留时间随着进料速率的增大而减小;随着转子转速的增大,停留时间先增大,达到一定转速后,停留时间反而减小。经过壁面优化以后,在相同的进料速率和转子转速下,水平圆环壁面和网状圆环壁面的停留时间得到延长,倾斜圆环壁面的停留时间适当缩短;壁面湍动程度增大,有利于传质传热,提高了分离效率。     

Analysis and decomposition of active and reactive power spot price in deregulated electricity markets

Electricity markets have suffered important modifications in recent decades in many countries, in which a competition framework has been established with the aim of improving market efficiency and reducing energy prices. However, this new paradigm does not assure optimal solutions, as new constraints can be introduced in optimization processes that can affect the resulting prices. An example of this situation is the establishment of power purchase agreements between producers and consumers. A wide literature can be found regarding electricity markets. Some of this literature refers to the theory of spot prices and its application to them. This paper deals with the obtaining, decomposition and deduction of behavior rules of spot prices, and their influence on established contractual relationships in a deregulated market environment which allows power purchase agreements between consumers and producers. It is performed by a deterministic modeling of the complete generation-grid system. The influence of the existence of this kind of agreements on both total costs and spot prices is discussed.     

Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

Multi-elite guide hybrid differential evolution with simulated annealing technique for dynamic economic emission dispatch

This paper proposes an improved multi-objective differential evolutionary algorithm named multi-objective hybrid differential evolution with simulated annealing technique (MOHDE-SAT) to solve dynamic economic emission dispatch (DEED) problem. The proposed MOHDE-SAT integrates the orthogonal initialization method into the differential evolution, which enlarges the population diversity at the beginning of population evolution. In addition, modified mutation operator and archive retention mechanisms are used to control convergence rate, and simulated annealing technique and entropy diversity method are utilized to adaptively monitor the population diversity as the evolution proceeds, which can properly avoid the premature convergence problem. Furthermore, the MOHDE-SAT is applied on the thermal system with a heuristic constraint handling method, and obtains more desirable results in comparison to those alternatives established recently. The obtained results also reveal that the proposed MOHDE-SAT can provide a viable way for solving DEED problems.     

Multi-period wind integrated optimal dispatch using series PSO-DE with time-varying Gaussian membership function based fuzzy selection

Wind power has emerged as the most promising option for providing sustainable eco-friendly power supply to the modern world. Due to its unpredictable nature, integration of wind power into the conventional power grid is a very challenging task having dynamic characteristics. Due to the inherent uncertainty associated with wind availability, additional spinning reserve needs to be scheduled in order to maintain security and supply reliability. Multi-period multi-objective optimal dispatch (MPMOOD) is presented for wind integrated power system with reserve constraints. The complex relationship between wind power availability, spinning reserve allocation and their impact on economic/environmental cost are analysed using an elaborate model.A new multi-objective Series PSO-DE (SPSO-DE) hybrid algorithm is proposed where the two paradigms, differential evolution (DE) and particle swarm optimization (PSO) share domain information and maintain a synergistic cooperation to overcome their individual weaknesses. For multi-objective (MO) problems, the selection operation in SPSO-DE is replaced by a 5-class time-varying fuzzy selection mechanism (TVFSM) to avoid saturation and to increase Pareto diversity. To promote convergence towards the central part of the Pareto front and to quickly isolate the boundary solutions, Guassian membership function is employed. Elitism is applied to preserve good solutions and momentum operation is used to stop premature convergence. The proposed method expedites the search for the best solution, i.e. the solution which satisfies all the objectives of the MO problems. To test the performance and computational efficiency, the proposed method is applied on two standard test power systems.     

An improved partheno genetic algorithm for multi-objective economic dispatch in cascaded hydropower systems

The multi-objective economic dispatch (MOED) problem in cascaded hydropower systems is a complicated nonlinear optimization problem with a group of complex constraints. In this paper, an improved partheno genetic algorithm (IPGA) for resolving the MOED problem in hydropower energy systems based on the non-uniform mutation operator is proposed. In the new algorithm, the crossover operator is removed and only mutation operation is made, which makes it simpler than GA in the genetic operations and not generate invalid offspring during evolution. With the help of incorporating greedy selection idea into the non-uniform mutation operator, IPGA searches the solution space uniformly at the early stage and very locally at the later stage, which makes it avoid the random blind jumping and stay at the promising solution areas. Finally, the proposed algorithm is applied to a realistic hydropower energy system with two giant scale cascaded hydropower plants in China. Compared with other algorithms, the results obtained using IPGA verify its superiority in both efficiency and precision.     

基于优化粒子数和分段权值的粒子群直线电机PID整定

以工程中直线电机伺服系统为研究对象,提出一种优化粒子数量加分段式惯性权重递减的粒子群PID控制器参数优化算法。优化粒子数量的方法可降低函数调用次数,通过对近两代的全局最优值进行比较,得到的误差值如果大于设定值,认为是在初始寻优阶段,保持粒子数量,否则在最终优化阶段,减少粒子数量,所减少的粒子特征是最接近最佳粒子的粒子,以保证在欧氏距离内实现粒子的分散性。最后再结合指数衰减曲线加线性递减曲线构成的分段式惯性权重递减策略提升算法的全局寻优和局部寻优能力。经数值验证分析,该优化算法在保证遍历性的同时,在一定程度上提高了算法的运行速度和寻优精度。实验仿真结果表明,该算法对PID控制器进行参数优化,直线电机系统响应速度快,超调量小,调节时间短。