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.     

微电网多目标动态优化调度模型与方法

为实现微电网系统运行的经济和环境双重优化目标,以独立的系统仿真模块和运行优化模块为核心,建立了微电网多目标动态优化调度的一般模型.仿真模块使用能量模型对系统调度方案的经济、环境指标进行评估,运行优化模块则使用多目标遗传算法NSGA-Ⅱ,结合仿真模块的评估结果对调度方案进行优化.在NSGA-Ⅱ中引入了初始点引导技术和去重操作,有效地改善了算法的收敛性能和Pareto前沿的分布特性.将该模型和方法应用于典型风光蓄柴微电网系统的日前优化调度,证实了所建模型和所提方法的有效性.     

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。     

Deterministically guided PSO for dynamic dispatch considering valve-point effect

This paper presents a deterministically guided particle swarm optimization (DGPSO) algorithm to solve the dynamic economic dispatch problem (DEDP) of generating units considering the valve-point effects. The cost function of the generating units exhibits the non-convex characteristics, as the valve-point effects are modeled and imposed as rectified sinusoid components in the cost function. The DGPSO method is a two-phase optimizer: in the first phase the PSO technique will explore the solution space freely. In the second phase, SQP (sequential quadratic programming) will be called only when there is an improvement of solution (a feasible solution) in the PSO run. Thus, SQP (deterministically) guides the PSO algorithm for better performance in the complex solution space. To validate the feasibility of the DGPSO method a 10-unit system is considered and studied under three different cases. The effectiveness of the presented method over EP (evolutionary programming) and EP-SQP methods is shown in general.     

Dynamic dispatch by constructive dynamic programming

This paper describes a new method of solving the dynamic dispatch problem. Boiler-turbine generators are characterised by variable operating costs and ramp rate constraints and costs. All system costs are piecewise linear. The method employs concepts from dynamic programming and linear programming. Dispatch decisions that minimise system variable cost and marginal costs are determined. Generator outputs are not discretised. Efficiency gains over dynamic programming are achieved by exploiting the underlying convex geometry of the problem and avoiding discretisation of the state space     

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.     

计及污染气体排放风险的多目标随机动态环境经济调度模型

在风电出力具有随机性的环境下,针对现有环境经济调度模型无法满足污染气体排放风险管理的现状,建立计及污染气体排放风险的多目标随机动态环境经济调度模型。考虑风电出力的随机性,给出环境经济调度污染气体排放风险评估指标的定义方法;借鉴经济学投资组合理论中半绝对离差风险的概念,建立环境经济调度污染气体排放风险评估指标;在多场景建模理论的框架内,建立计及污染气体排放风险的多目标随机动态环境经济调度模型。在利用后向场景削减技术对风电随机出力的大量场景进行削减后,采用内嵌目标相对占优的遗传算法求解模型。算例仿真验证了所提模型的有效性。     

集群空调负荷双层动态优化调度决策方法

为了解决常规空调调度中切除负荷带来的电网运行稳定问题,在集群空调的调度中考虑空间上的优化组合,提出一种考虑网损和调度偏差最小的自上而下双层优化动态调度决策方法。在空调的聚类建模中,研究空调参数和运行状态的差异性,分析并验证聚类中的误差约束值和分组数对调度准确性的影响;在双层优化调度中,通过最优潮流计算合理地分配空间上调度的容量,有效减小网损与电压越限的可能性;结合动态调度,提高调度的准确性,保证用户的舒适性。IEEE 33节点配电网算例结果验证了所提算法的可行性和准确性。     

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.     

华东电网调度动态计划研究

结合华东区域的电网条件和发展方向,提出电网动态计划方案.在日计划编制的基础上,结合超短期负荷预计及旋转备用监视程序,对华东电网各省市的口子及直属电厂实行经济调度,使调度在进行出力及负荷平衡时有一定时间裕度及科学依据.     

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.     

加速遗传算法在电厂动态负荷优化分配中的应用

针对目前电厂负荷分配非实时最优、能源浪费巨大的问题,提出了一种动态分配负荷的算法.在考虑电厂机组实际运行特点后,建立了包含辅机、启停等约束条件下的连续多时段动态负荷优化分配的数学模型;分析了标准遗传算法(SGA)在解决负荷分配问题时收敛速度慢的原因,据此对SGA进行改进,提出了基于实数编码、双倍初始种群、自适应遗传参数...     

计及蓄电池使用寿命的微电网经济调度模型

由于新能源发电和负荷有不确定性,为保证微电网的安全可靠运行,蓄电池作为储能装置发挥了重要作用。为充分利用蓄电池,提高微电网的经济性,建立考虑蓄电池使用寿命的微电网经济调度模型,并应用混合整数线性规划算法进行求解。以一个包含风、光、储、微型燃气轮机、柴油发电机和燃料电池的微电网为算例,对微电网并网运行方式进行经济调度优化,计算结果验证了所提模型的有效性。     

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.     

基于动态分区的配电网日前优化调度研究

为了提高对分布式可再生能源的就地消纳能力,实现配电网分层分区调度,提出了基于MOEA/D的多目标蚁群动态分区算法和基于动态分区的配电网日前优化调度模型。利用潮流追踪算法与复杂网络理论中的二分模块度,提出了量化分区间能量耦合程度的能量二分模块度指标。基于电力系统潮流方程雅克比矩阵推导蚁群算法中的启发式信息,结合预测场景集以分区的能量二分模块度与功率储备为目标函数,利用多目标蚁群算法生成动态分区方案。建立以联络线功率、灵活性不足率以及成本最低为目标的基于动态分区日前优化调度模型,并利用NSGA-II算法求解Pareto最优解集。最后基于IEEE33节点网络对所提模型与方法进行验证。结果表明,采用该方法进行动态分区与日前调度可有效提高系统应对可再生能源不确定性的能力,为就地平抑可再生能源波动奠定基础。     

Effects of ramp-rate limits on unit commitment and economicdispatch

The authors propose an algorithm to consider the ramp characteristics in starting up and shutting down the generating units as well as increasing and decreasing power generation. They consider the inclusion of ramping constraints in both unit commitment and economic dispatch. Since implementing ramp-rate constraints is a dynamic process, dynamic programming (DP) is a proper tool to treat this problem. To overcome the computational expense which is the main drawback of DP, this study initially employs artificial intelligence techniques to produce a unit commitment schedule which satisfies all system and unit operation constraints except unit ramp-rate limits. Then, a dynamic procedure is used to consider the ramp properties as units are started up and shut down. According to this adjustment, maximum generating capabilities of units will change the unit operation status instead of following a step function. Finally, a dynamic dispatch procedure is adopted to obtain a suitable power allocation which incorporates the unit generating capability information given by unit commitment and unit ramping constraints, as well as the economical considerations. Two examples are presented to demonstrate the efficiency of the method     

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.     

计及动态响应可靠性的电力负荷调度优化

当前,以卖方市场主导的需求响应方式具有较高的可靠性和可实施性。首先,以电网侧的利益最优为目标进行电力负荷的调度优化,为降低用户行为随机性对调度可靠性的影响,分析了不同类型电力负荷的响应可靠性性模型;其次,为提升用户的响应积极性,分析了不同用户的动态响应可靠性评估模型;最后构建了调度容量可靠性最高和电网侧调度运行成本最低的调度模型,以用户的最低出清利益为约束,在提升用户响应积极性的基础上,得到电网利益最优的需求侧资源调度运行方式。算例分析验证了调度优化模型的有效性。     

Short-term hydro-thermal dispatch detailed model and solutions

An efficient method is described for the solution of the short-term hydro-thermal dispatch problem including optimal power flow (OPF) as the mathematical model of the thermal subsystem. This approach has the capability of taking into account the following effects: coupling of cascaded multichannel reservoirs, water time delays, reservoir head variations, load flow, and other constraints due to security and environmental considerations. The problem is decomposed into hydro and thermal subproblems which are then solved iteratively. An effective adjustment has been proposed to take into account the nonlinear relation between the two subproblems to speed up the convergence of the iterative process. In this adjustment, as well as in solving the thermal subproblem, equations of coordination and OPF are combined for better computational efficiency. On the basis of the proposed approach, four different methods, which differ in the degree of details in modeling the thermal system, have been tested and investigated. Numerical examples are included to demonstrate the advantages of the approach