Decentralized optimal tracking control for large-scale nonlinear systems with tracking error constraints

In this article, a decentralized optimal tracking control strategy is proposed for a class of nonlinear systems with tracking error constraints by utilizing adaptive dynamic programming (ADP). It should be noted that ADP technology cannot be directly used to solve decentralized optimal tracking problem of large-scale interconnected nonlinear system with nonzero equilibrium points, since that an infinite domain performance index function may result in an unsolvable solution. In addition, by introducing a smooth function, the constrained tracking error is transformed into an unconstrained one. Then, the error dynamics and a new infinite domain performance index function are designed, such that ADP technology can be used. Following the designed performance index function, the tracking error can be ensured within a small neighborhood of zero. Finally, the feasibility and the effectiveness of the proposed decentralized optimal control scheme are verified through two simulation examples.     

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。     

陕北浅埋煤层绿色保水开采技术研究

王家沟煤矿为陕北典型浅埋煤层矿井，地下煤炭资源开采有可能破坏萨拉乌苏组潜水含水层。为有效的控制导水裂隙发育，最大限度保护地表含水层，基于协调开采的原理，结合力学模型和相似材料模拟试验，对比研究了一次采全高和限高留煤柱两种开采方法导水裂隙发育规律。研究表明，一次采全高条件下导水裂隙呈间歇式变速发育最大高度94m，穿过含水层|限高留煤柱协调开采导水裂隙发育平缓，最大高度60m|关键层的稳定决定着含水层的稳定，限高留煤柱能够显著的控制协调关键层挠曲下沉，有效的抑制导水裂隙发育，实现含水层下绿色保水开采。     

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.     

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.     

Multiple models decentralized coordinated control of doubly fed induction generator

In this paper, a multiple model optimal tracking control (MOTC) design method for the double fed induction generator (DFIG) using correlative measured technique is proposed. The DFIG is represented by a third-order model, where electro-magnetic transients of stator are neglected. By using the correlative measured technique, the correlative measured matrix (CMM) of wind power system is obtained firstly. Then, a nonstandard state space equation of DFIG is obtained with the correlative measured vectors (CMVs), which reflect interactions between the DFIGs and grid. In order to cope with nonlinearities and continuous variation in the operating points, a multiple model design method is proposed in the discrete domain. The obtained control law, synthesized by using Bayesian probability, only depends on the local measured parameters. Hence, the MOTC can be regarded as a decentralized coordinated control, which can simplify the control structure and improve the transient stability of DFIG. To illustrate the effectiveness of the proposed MOTC strategy, simulations on a hybrid wind thermal power (HWTP) system are performed. The results show that the proposed MOTC strategy can provide acceptable performance throughout the whole operating region. Comparing to the conventional PID control, transient stability, damping, and fault ride-through capability of DFIG with the proposed MOTC design method have been improved effectively.     

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.