Artificial immune system for dynamic economic dispatch

Dynamic economic dispatch determines the optimal scheduling of online generator outputs with predicted load demands over a certain period of time taking into consideration the ramp rate limits of the generators. This paper proposes artificial immune system based on the clonal selection principle for solving dynamic economic dispatch problem. This approach implements adaptive cloning, hyper-mutation, aging operator and tournament selection. Numerical results of a ten-unit system with nonsmooth fuel cost function have been presented to validate the performance of the proposed algorithm. The results obtained from the proposed algorithm are compared with those obtained from particle swarm optimization and evolutionary programming. From numerical results, it is found that the proposed artificial immune system based approach is able to provide better solution than particle swarm optimization and evolutionary programming in terms of minimum cost and computation time.     

A hybrid HNN-QP approach for dynamic economic dispatch problem

This paper introduces a solution of the dynamic economic dispatch (DED) problem using a hybrid approach of Hopfield neural network (HNN) and quadratic programming (QP). The hybrid algorithm is based on using enhanced HNN; to solve the static part of the problem; the QP algorithm for solving the dynamic part of the DED. This technique guarantees the global optimality of the solution due to its look-ahead capability. The new algorithm is applied and tested to an example from the literature and the solution is then compared with that obtained by some other techniques to prove the superiority and effectiveness of the proposed algorithm.     

Quantum genetic algorithm for dynamic economic dispatch with valve-point effects and including wind power system

An optimization algorithm is proposed in this paper to solve the problem of the economic dispatch that includes wind power generation using quantum genetic algorithm (QGA). In additional to the detail introduction for models of general economic dispatch as well as their associated constraints, the effect of wind power generation is also included in this paper. On the other hand, the use of quantum genetic algorithms to solve the process of economic dispatch is also discussed and real scenarios are used for simulation tests later on. After comparing the algorithm used in this paper with several other algorithms commonly used to solve optimization problems, the results show that the algorithm used in this paper is able to find the optimal solution most quickly and accurately (i.e. to obtain the minimum cost for power generation in the shortest time). At the end, the impact to the total cost saving for the power generation after adding (or not adding) wind power generation is also discussed. The actual operating results prove that the algorithm proposed in this paper is economical and practical as well as superior. They are quite valuable for further research.     

Robust economic dispatch considering automatic generation control with affine recourse process

A robust economic dispatch (ED) considering automatic generation control (AGC) with affine recourse process is proposed in this paper. The approach co-optimizes the base points and participation factors of the AGC units using preemptive goal programming and robust optimization while considering the uncertain nodal power injections and the network constraints. The proposed approach is realized by two steps. The aim of the first step is to maximize the system effective acceptable disturbance range (EADR) while minimize the generation costs and reserve costs with respect to the obtained EADR in the second step. The novelty of the approach is as follows: (a) The security of the power system is optimized by maximizing the system EADR. The approach can obtain a solution which can cover the disturbance as much as possible even when the system does not have enough adjustable capacity to cover it all. The obtained nodal EADR can quantitatively represent the anti-disturbance capability of a node. (b) The economics of the system is significantly improved by minimizing the generation costs and reserve costs while the constraint of EADR requirement is respected. (c) The conservative level of the solution can be tuned according to the user’s requirements. A simplified one-step linear model is also deduced. The effectiveness and validity of the proposed approach are demonstrated by a 6-bus system, the IEEE 118-bus system, and a real 445-bus system.     

Enhanced cross-entropy method for dynamic economic dispatch with valve-point effects

This paper proposes an enhanced cross-entropy (ECE) method to solve dynamic economic dispatch (DED) problem with valve-point effects. The cross-entropy (CE) method, originated from an adaptive variance minimization algorithm for estimating probabilities of rare events, is a generic approach to combinatorial and multi-extremal optimization. Exploration capability of CE algorithm is enhanced in this paper by using chaotic sequence and the resultant ECE is applied to DED with valve-point effects. The performance of the proposed ECE method is rigorously tested for optimality, convergence, robustness and computational efficiency on a 10-unit test system. Additional test cases with different load patterns and increased number of generators are also solved by ECE. Numerical results show that the proposed ECE approach finds high-quality solutions reliably with faster convergence. It outperforms CE and all the previous approaches.     

改进发电调度方式实施节能、环保、经济调度的探讨

结合电力行业的发展和现状,对发电调度方式进行了简要回顾,阐述了合理改进发电调度方式的必然性。同时,立足于电网调度层面,针对尚未建立电力市场与电力市场试点2个不同区域,分析了政府政策制定和电力企业实施在调整过程中所面临的电价定价与计划制定2个方面的具体问题,并就具体问题对如何改进发电调度方式,实施节能、环保、经济调度提出了相应建议。     

A hybrid genetic algorithm and bacterial foraging approach for dynamic economic dispatch problem

Dynamic economic dispatch (DED) is one of the most significant non-linear complicated problems showing non-convex characteristic in power systems. This is due to the effect of valve-points in the generating units’ cost functions, the ramp-rate limits and transmission losses. Hence, proposing an effective solution method for this optimization problem is of great interest. The original bacterial foraging (BF) optimization algorithm suffers from poor convergence characteristics for larger constrained problems. To overcome this drawback, a hybrid genetic algorithm and bacterial foraging (HGABF) approach is presented in this paper to solve the dynamic economic dispatch problem considering valve-point effects, ramp-rate limits and transmission losses. The HGABF approach can be derived by integrating BF algorithm and genetic algorithm (GA), so that the BF’s drawback can be treated before employing it to solve the complex and high dimensioned search space of the DED problem. To illustrate the effectiveness of the HGABF approach, several test systems with different numbers of generating units are used. The results of HGABF approach are compared with those obtained by other published methods employing same test systems. These results show the effectiveness and the superiority of the introduced method over other published methods.     

华北电网多区域自动发电控制与在线经济调度的研究

华北电网先后与东北电网和华中电网实现联网。为适应电网调度体制改革和大区电网联网后的频率与联络线潮流控制,对自动发电控制AGC(Automatic Generation Control)软件功能如何能够进行多区域控制进行了研究,介绍了多区域三种控制模式、控制性能标准控制策略、电网稳定控制策略。同时利用电网能量管理系统应用软件优化AGC,进一步在华北电网实现在线经济调度。     

贵州节能发电调度经济补偿研究

节能发电调度模式下，高能耗的中小型发电企业必然在利用小时数上低于低能耗的大型发电企业。讨论相关的经济补偿办法，确保高能耗中小型发电企业能维持基本的运转，保证电网具有合理的备用容量以及电网安全稳定运行。     

Application of model predictive control to optimal dynamic dispatch of generation with emission limitations

Reducing emission from fossil-fueled electric power generating plants has received considerable attention in recent years in both regulated and deregulated power markets. Under regulated power systems, utilities solve the dynamic economic dispatch problem to determine the optimal scheduling of the committed unit's output at minimum fuel cost while satisfying a set of constraints. In this paper, we introduce the following problems where the emission effects are included in the mathematical model: (1) dynamic economic emission dispatch and (2) emission constrained dynamic economic dispatch. Under deregulated markets, the generation company can determine the optimal amounts of energy to be sold in the market by running profit-based dynamic economic dispatch problem to maximize its own profit. To take into account the emission limitations we introduced two problems: (1) profit-based dynamic economic emission dispatch problem and (2) profit-based emission constrained dynamic economic dispatch problem. In this paper we applied the model predictive control (MPC) approach proposed recently to the dynamic dispatch problems in both regulated and deregulated systems. The convergence and robustness of the MPC algorithms are demonstrated through the application of MPC to these problems with a six-unit system.     

基于PSO-BBO混合优化算法的动态经济调度问题

动态经济调度(Dynamic Economic Dispatch,DED)问题是电力系统运行与控制领域比较经典的多变量、非线性、强约束优化问题。为解决该问题,提出了将粒子群优化算法(Particle Swarm Optimization,PSO)和基本生物地理学优化算法(Biogeography-Based Optimization,BBO)相结合的改进生物地理学优化算法,并将该改进方法应用于一天24时段10机39节点标准算例。在考虑网损与不考虑网损两种情况下分别进行仿真分析,并将仿真结果与PSO和基本BBO算法以及参考文献中提出的六种智能算法进行对比,验证了该改进算法的有效性及在寻优能力上的提高。     

一种实用的电力系统在线动态经济调度模型

电力系统中的动态经济调度问题是时间上和空间上相关联的一个复杂的最优决策问题。对于一个实际运行的电力系统,如果研究周期内没有整体资源的限制,在某时刻的决策状态是否整体最优仅仅取决于未来有限的时期,此决策问题在空间上和时间上的关联强弱与这个时期长短(称为前瞻时间)关系密切。研究某时刻决策状态是否整体最优与前瞻时间的关系是核心。基于这一思想,研究前瞻时间变化对动态优化调度结果的影响,确定从可行解到最优解的前瞻时间变化范围,达到在线动态经济调度。     

Hierarchical correlated Q-learning for multi-layer optimal generation command dispatch

This paper presents a novel hierarchical correlated Q-learning (HCEQ) algorithm to solve the dynamic optimization of generation command dispatch (GCD) in the Automatic Generation Control (AGC). The GCD problem is to dynamically allocate the total AGC generation command from the central to each individual AGC generator. The proposed HCEQ is a novel multi-agent Q-learning algorithm based on the concept of correlated equilibrium point, and each AGC generator with an agent is to optimize its regulation participation factor and coordinate its decision with others for the overall GCD performance enhancement. In order to cope with the curse of dimensionality in the GCD problem with the increased number of AGC plants involved, a multi-layer optimum GCD framework is developed in this paper. In this hierarchical framework, the multiobjective design and a time-varying coordination factor have been formulated into the reward functions to improve the optimization efficiency and convergence of HCEQ. The application of the proposed approach has been fully verified on the China southern power grid (CSG) model to demonstrate its superior performance and dynamic optimization capability in various power system scenarios.     

A mixed integer quadratic programming formulation for the economic dispatch of generators with prohibited operating zones

In this paper, an optimisation-based approach is proposed using a mixed integer quadratic programming model for the economic dispatch of electrical power generators with prohibited zones of operation. The main advantage of the proposed approach is its capability to solve case studies from the literature to global optimality quickly and without any targeting of solution procedures.     

采用分布式单纯形法的微电网分散式动态经济调度

对含多种分布式电源的微电网,基于分布式单纯形法,提出了一种求解微电网动态经济调度问题的分散式优化算法。通过对目标函数进行线性化,建立了微电网动态经济调度的线性规划模型。将每个分布式电源均视为独立的智能体,从而将线性规划模型中的成本向量和约束方程系数矩阵按智能体进行分块,进而采用分布式单纯形法求解。每次迭代过程中,智能体通过通信网络传递当前解对应的最优基和成本向量。所提算法不需要中央控制器的参与,智能体间具有一定的信息保密性,且通信次数与通信网络的直径成线性增长的关系。最后,以某实际微电网为测试系统,验证了线性化模型的准确性和算法的有效性。     

A dynamic programming based fast computation Hopfield neural network for unit commitment and economic dispatch

This paper develops a new dynamic programming based direct computation Hopfield method for solving short term unit commitment (UC) problems of thermal generators. The proposed two step process uses a direct computation Hopfield neural network to generate economic dispatch (ED). Then using dynamic programming (DP) the generator schedule is produced. The method employs a linear input–output model for neurons. Formulations for solving the UC problems are explored. Through the application of these formulations, direct computation instead of iterations for solving the problems becomes possible. However, it has been found that the UC problem cannot be tackled accurately within the framework of the conventional Hopfield network. Unlike the usual Hopfield methods which select the weighting factors of the energy function by trials, the proposed method determines the corresponding factor using formulation calculation. Hence, it is relatively easy to apply the proposed method. The Neyveli Thermal Power Station (NTPS) unit II in India with three units having prohibited operating zone has been considered as a case study and extensive study has also been performed for power system consisting of 10 generating units.     

考虑可靠性约束的含风电场电力系统动态经济调度

提出一种考虑系统可靠性约束的含风电场电力系统动态经济调度模型,在目标函数中加入了中断负荷费用.针对风速预测和负荷预测的不确定性,引入净负荷的概念,利用七分段高斯分布模拟预测误差的不确定性.在系统可靠性指标的计算过程中,考虑了系统机组的不确定性以及旋转备用.为求解模型,提出了一种改进的粒子群优化算法,引入信息分享和精英学习策略.以IEEE-RTS测试系统为算例,通过仿真分析,验证了所提模型的可行性与有效性.该模型可以在保证系统可靠性水平的基础上优化系统调度.     

基于实用化安全约束经济调度扩展建模策略

安全约束经济调度是提高电网运行可靠率,实现节能减排,提高调度计划精细化管理水平的有效技术手段。现有安全约束经济调度研究多集中在常见目标与约束建模以及优化算法等方面,较少考虑实际生产因素,因此计划结果执行率偏低,安全约束经济调度在实际生产中实用化困难。针对上述问题,针对计划编制的优化目标,“三公”调度、计划出力曲线形状以及基础数据区间处理等实际需求,提出了基于实用化的安全约束经济调度扩展模型。将所提的建模策略应用于多个生产实例,证明了所提模型在增强“三公”调度准确率、改善计划出力曲线形状以及提高计划结果执行率上有着较好的工程实用性。     

节能发电调度对华东电网规划的影响研究

建立了适应于电网规划的节能发电调度的数学模型,以及基于遗传算法的节能发电调度求解方法.在此基础上,针对2012年华东规划网架,结合华东电源布局特点和网架特性,分析了节能发电调度对电网规划的影响,包括机组出力的变化特性、输变电通道潮流受阻问题、电压稳定问题以及网损变化趋势等.     

Feasible solution for dynamic economic load dispatch using feasible operation region

High penetration of renewable energy in the future power system will pose a big problem to the load dispatch operation. The large disturbance and high forecast error must be considered when scheduling a limited number of controllable generators to follow rapid change in load. This paper proposes a dynamic economic load dispatch (DELD) problem approach based on the concept of a feasible operation region (FOR). FOR is defined as the region that committed generators may operate in to match the load profile without violating the ramp‐rate constraints. The DELD problem is solved in two stages. In the first stage, FOR of each generator is computed using recent real‐time forecasted load as well as renewable energy generation. In the second stage, a generation schedule is determined by solving the DELD problem interval by interval while considering ramp‐rate constraints and FOR constraints. The method can gives feasible solution for feasible load and specify the amount of compensation required for feasible solution for infeasible load. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.