Finite-time economic model predictive control for optimal load dispatch and frequency regulation in interconnected power systems

This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power system to ensure frequency stability,real-time economic optimization,control of the system and optimal load dispatch from it.A generalized terminal penalty term was used,and the finite-time convergence of the system was guaranteed.The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system,which had two areas connected by an AC tie line.The simulation results demonstrated the effectiveness of the algorithm.

Multiple objective particle swarm optimization technique for economic load dispatch

A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Com- parison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.

Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch

The hydro unit economic load dispatch （ELD） is of great importance in energy conservation and emission reduction. Dynamic programming （DP） and genetic algorithm （GA） are two representative algorithms for solving ELD problems. The goal of this study was to examine the performance of DP and GA while they were applied to ELD. We established numerical experiments to conduct performance comparisons between DP and GA with two given schemes. The schemes included comparing the CPU time of the algorithms when they had the same solution quality, and comparing the solution quality when they had the same CPU time. The numerical experiments were applied to the Three Gorges Reservoir in China, which is equipped with 26 hydro generation units. We found the relation between the performance of algorithms and the number of units through experiments. Results show that GA is adept at searching for optimal solutions in low-dimensional cases. In some cases, such as with a number of units of less than 10, GA＇s performance is superior to that of a coarse-grid DP. However, GA loses its superiority in high-dimensional cases. DP is powerful in obtaining stable and high-quality solutions. Its performance can be maintained even while searching over a large solution space. Nevertheless, due to its exhaustive enumerating nature, it costs excess time in low-dimensional cases.

Efficient Dynamic Economic Load Dispatch Using Parallel Process of Enhanced Optimization Approach

In Dynamic Economic Load Dispatch (DELD), optimization and evolution computation become a major part with the strategy for solving the issues. From various algorithms Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms are used to encode in a vector form and in sharing information and both approaches are based on the master-apprentice mechanism for the Dual Evolution Strategy. In order to overcome the challenges like the clustering of PSO, optimization problems and maximum and minimum searching, a new approach is developed with the improvement of searching and efficient process. In this paper, an Enhanced Hybrid Differential Evolution and Particle Swarm Optimization (EHDE-PSO) is proposed with Dynamic Sigmoid Weight using parallel procedures. A hybrid form of the proposed approach combines the optimizing algorithm of Enhanced PSO with the Differential Evolution (DE) for the improvement of computation using parallel process. The implementation and the parallel process are analyzed and discussed to gather relevant data to show the performance enhancement which is better than the existing algorithm.

Modified Shuffled Frog Leaping Algorithm for Solving Economic Load Dispatch Problem

In the recent restructured power system scenario and complex market strategy, operation at absolute minimum cost is no longer the only criterion for dispatching electric power. The economic load dispatch (ELD) problem which accounts for minimization of both generation cost and power loss is itself a multiple conflicting objective function problem. In this paper, a modified shuffled frog-leaping algorithm (MSFLA), which is an improved version of memetic algorithm, is proposed for solving the ELD problem. It is a relatively new evolutionary method where local search is applied during the evolutionary cycle. The idea of memetic algorithm comes from memes, which unlike genes can adapt themselves. The performance of MSFLA has been shown more efficient than traditional evolutionary algorithms for such type of ELD problem. The application and validity of the proposed algorithm are demonstrated for IEEE 30 bus test system as well as a practical power network of 203 bus 264 lines 23 machines system.

A Hybrid Optimization Technique Coupling an Evolutionary and a Local Search Algorithm for Economic Emission Load Dispatch Problem

This paper presents an optimization technique coupling two optimization techniques for solving Economic Emission Load Dispatch Optimization Problem EELD. The proposed approach integrates the merits of both genetic algorithm (GA) and local search (LS), where it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε-dominance. To improve the solution quality, local search technique was applied as neighborhood search engine, where it intends to explore the less-crowded area in the current archive to possibly obtain more non-dominated solutions. TOPSIS technique can incorporate relative weights of criterion importance, which has been implemented to identify best compromise solution, which will satisfy the different goals to some extent. Several optimization runs of the proposed approach are carried out on the standard IEEE 30-bus 6-genrator test system. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EELD problem.

A Multi-Agent Particle Swarm Optimization for Power System Economic Load Dispatch

A new versatile optimization, the particle swarm optimization based on multi-agent system (MAPSO) is presented. The economic load dispatch (ELD) problem of power system can be solved by the algorithm. By competing and cooperating with the randomly selected neighbors, and adjusting its global searching ability and local exploring ability, this algorithm achieves the goal of high convergence precision and speed. To verify the effectiveness of the proposed algorithm, this algorithm is tested by three different ELD cases, including 3, 13 and 40 units IEEE cases, and the experiment results are compared with those tested by other intelligent algorithms in the same cases. The compared results show that feasible solutions can be reached effectively, local optima can be avoided and faster solution can be applied with the proposed algorithm, the algorithm for ELD problem is versatile and efficient.

Economic Load Dispatch Based on Efficient Population Utilization Strategy for Particle Swarm Optimization

In this paper, the efficient population utilization strategy for particle swarm optimization (EPUSPSO) is proposed to solve the economic load dispatch (ELD) problem of power system. This algorithm improves the accuracy and the speed of its convergence by changing the number of particles effectively, and improving the velocity equation and position equation. In order to verify the effectiveness of the algorithm, this algorithm is tested in three different ELD cases of power system include IEEE 3-unit case, 13-unit case, and 40-unit case, and the obtained results are compared with those obtained from other algorithms using the same system parameters. The compared results show that the algorithm can find the optimal solution effectively and accurately, and avoid falling into the local optimal problem;meanwhile, faster speed can be ensured in the case.

Optimal Load Dispatch of Gas Turbine Power Generation Units based on Multiple Population Genetic Algorithm

In this paper, a multiple population genetic algorithm (MPGA) is proposed to solve the problem of optimal load dispatch of gas turbine generation units. By introducing multiple populations on the basis of Standard Genetic Algorithm (SGA), connecting each population through immigrant operator and preserving the best individuals of every generation through elite strategy, MPGA can enhance the efficiency in obtaining the global optimal solution. In this paper, MPGA is applied to optimize the load dispatch of 3×390MW gas turbine units. The results of MPGA calculation are compared with that of equal micro incremental method and AGC instruction. MPGA shows the best performance of optimization under different load conditions. The amount of saved gas consumption in the calculation is up to 2337.45m3N/h, which indicates that the load dispatch optimization of gas turbine units via MPGA approach can be effective.

Optimal short-term load dispatch strategy in wind farm

With the increasing number of wind farms in power systems, the scheduling of a single wind farm needs to be improved. For this end, this paper proposes an optimal short-term load dispatch strategy for a single wind farm. Firstly, considering the large number of wind units and the high dimensionality of the scheduling solutions, we analyze the unit load characteristics, from which we extract the unit load characteristic matrix, and then classify the wind power units with the FCM fuzzy clustering algorithm. Secondly, we define the running loss indicator and action loss indicator. Based on the prediction of wind power and the load instructions, we establish a unit commitment model in wind farm, and solve the model using a combination of the fuzzy clustering algorithm and genetic algorithm, which overcomes the difficulty of the high dimensionality of the solution in the wind farm scheduling problem, to obtain the optimal scheduling strategy. Finally, through the simulation of the scheduling strategy for a 45 MW wind farm, we demonstrate the feasibility and effectiveness of the proposed strategy.

Architecture,Key Technologies and Applications of Load Dispatching in China Power Grid

With the development of renewable energy and the changes in the characteristics of power grid,it is becoming increasingly difficult to balance power supply and demand in space and time.In addition,the requirement for improved dispatching capability of power grid is increasing.Therefore,the potential of flexible load dispatching should be realized,which can promote the large-scale consumption of renewable energy and the construction of new power grid.Based on the analysis of existing load dispatching studies and the differences in the characteristics of domestic and foreign load dispatchings,a technical architecture and several key technologies are proposed for load resources to participate in power grid dispatching under the new situation,i.e.,the autonomous collaborative control system of load dispatching.This system implements the multi-layer coordinated control of main,distribution and micro grids(load aggregators).Adjustable load resources are aggregated through an aggregator operation platform and connected with a dispatcher load regulator platform to realize real-time data interaction with dispatching agencies as well as the monitoring,con-trol,and marketing of aggregators.It supports the load resources to participate in network-wide dispatching optimization via continuous power adjustment.Several key technologies such as the control mode,load modeling,dispatching strategy,and safety protection are also elaborated.Through the closed-loop control of orderly charging piles and energy storage clusters in the North China Power Grid,the feasibility of the proposed architecture and key technologies is verified.This route has successively supported multiple adjustable load aggregators to partici-pate in the ancillary services market of North China Power Grid for peak-shaving.Finally,the technical challenges of load resources participating in power grid dispatching under the dual carbon goals are discussed and prospected.

Multi-Objective Optimal Dispatch Considering Wind Power and Interactive Load for Power System

With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to the severe wind power curtailment issue, the characteristics of interactive load are studied upon the traditional day-ahead dispatch model to mitigate the influence of wind power fluctuation. A multi-objective optimal dispatch model with the minimum operating cost and power losses is built. Optimal power flow distribution is available when both generation and demand side participate in the resource allocation. The quantum particle swarm optimization (QPSO) algorithm is applied to convert multi-objective optimization problem into single objective optimization problem. The simulation results of IEEE 30-bus system verify that the proposed method can effectively reduce the operating cost and grid loss simultaneously enhancing the consumption of wind power.

Cuckoo Search for Solving Economic Dispatch Load Problem

Economic Load Dispatch (ELD) is a process of scheduling the required load demand among available generation units such that the fuel cost of operation is minimized. The ELD problem is formulated as a nonlinear constrained optimization problem with both equality and inequality constraints. In this paper, two test systems of the ELD problems are solved by adopting the Cuckoo Search (CS) Algorithm. A comparison of obtained simulation results by using the CS is carried out against six other swarm intelligence algorithms: Particle Swarm Optimization, Shuffled Frog Leaping Algorithm, Bacterial Foraging Optimization, Artificial Bee Colony, Harmony Search and Firefly Algorithm. The effectiveness of each swarm intelligence algorithm is demonstrated on a test system comprising three-generators and other containing six-generators. Results denote superiority of the Cuckoo Search Algorithm and confirm its potential to solve the ELD problem.

考虑柔性电/热负荷的综合能源系统分布鲁棒优化调度

综合能源系统(integrated energy system,IES)的效益分析不仅取决于能源供给侧的调度方案,也受到需求侧用能方式的影响。基于此,在IES的需求侧引入柔性负荷响应,以平滑负荷曲线,进一步提升IES的风电消纳能力和经济效益;同时为尽量减小供能侧风电出力不确定性的影响、实现调度方案鲁棒性与经济性的均衡,构建了考虑柔性电负荷和柔性热负荷的IES两阶段分布鲁棒优化调度模型:预调度阶段以IES的日前综合调度成本最低为目标;再调度阶段以风电历史数据为基础,寻找最恶劣风电出力概率分布下的最优机组调节方案,并使用列约束生成算法进行求解。最后,采用算例验证了该模型的有效性。

算力-热力灵活性协同的数据中心能量管理方法

随着数字经济产业快速发展,数据中心成为电能消耗的重要部分。多样化的数据处理需求、大量的计算用电及制冷用电,在带来高能耗问题的同时也为数据中心的运行调度提供了灵活性。因此,协同考虑计算灵活性和热灵活性,制定数据中心优化运行策略,可有效提高能源利用效率,提升数据中心运行经济性。该文提出一种考虑算力-热力灵活性协同的数据中心能量管理方法。首先,考虑数据负载灵活调度、冷却系统温度变化以及冷热通道热惯性等精细化要素,实现了信息技术设备和冷却设备的协同建模。其次,进行模型的线性化转换,降低了求解难度。在此基础上,实现了基于灵活性协同的数据中心能量管理,从而降低数据中心运行能耗及成本。最后通过算例验证了所提方法的有效性。

基于等微增率并计及机组功率约束的火电机组最优负荷分配精确解

火电机组最优负荷分配是电力系统经济运行的重要模型,也是电力系统本科生专业基础课《电力系统分析》的重要教学内容之一。经典教科书采用等微增率方法求解该问题,并给出了相应的物理含义。由于等微增率法是基于不考虑火电机组上下界物理约束而推导出来的,部分教科书补充了计及火电机组上下界物理约束时的情况,即如果某台机组的无约束最优解违背了上(下)界约束,则将该机组对应的最优解限制到相应的出力上(下)界,然后对其余火电机组再进行重新的等微增率分配。然而,简单算例表明,补充求解方法的适用范围是有限的。为此,该文对火电机组最优负荷分配问题进行重新探索,推导教材方法适用的一个充分条件与一个必要条件。面向本科生与研究生,分别提出考虑机组上下界约束后的最优负荷分配方法,并进行严格的理论推导。理论推导与大量的仿真算例表明,在机组数量较少时,教材中的求解方法有可能适用,而机组数较多时,可能出现不适用的情况。该文所提方法可以将适用范围扩展到机组数量较多的场景,并且进行严格理论推导。希望该文可以为《电力系统分析》教学过程与教材修订提供帮助。

风光制氢合成氨系统的多时段可调度域分析

为直观描述风光发电制氢合成氨(power-to-ammonia,P2A)系统的柔性负荷调节潜力,计及制氢、合成氨化工过程的负载调控特性,基于动态规划思想和计算几何理论,提出其多时段可调度域(multi-stagedispatchableregion,MSDR)的刻画方法。所提方法将多时段间相互耦合的鲁棒约束变换为“氢缓冲罐贮量–合成氨产率”状态空间中的确定性多面体约束,使得对于可调度域内的任意系统状态,均存在满足完整调度过程安全约束的“制氢流量–氨产率爬坡速率”控制序列,从而实现P2A系统可调度能力的可视化。所提MSDR模型可进一步用于构造多时段鲁棒优化调度,在保证可行性的前提下优化P2A系统的经济收益。最后,基于内蒙古自治区某在建示范工程构造算例,验证所提方法的有效性。

基于需求响应的用户侧柔性用能分级调度策略研究

通过需求响应计划和建筑用能的灵活调度增强电网稳定性和供需平衡。基于一栋小型办公建筑的光伏发电、储能设备及地源热泵系统进行用能调度策略的设计,引入两个参数量化建筑柔性用能潜力,根据最大化光伏利用率、优先满足用户刚需、电价低谷时段储能这3个优先级原则开发一个建筑用能管理模块。在TRNSYS中应用该模块建立有用能调度策略的案例3,并与两个参考案例进行对比分析。结果表明,在一个供暖季节内,系统运行成本分别减少了65.4%、56.7%;地源热泵机组运行COP平均值从3.97提高到4.22;建筑日平均负荷转移率高达51.38%。在峰谷电价的市场机制下,对挖掘建筑柔性用能潜力、激励用户采用需求响应计划和提高电网稳定性具有重大意义。

海上风氢系统与沿海电网能量协同优化调度

为实现海上风电的高效消纳和绿色氢能的制备,提出一种海上风氢系统与沿海电网能量协同优化调度方法。首先,通过评估海上风电场间的风能耦合效应,建立考虑风能异域强化利用的风电场群发电转移模型。其次,量化分析风电场的疲劳载荷与疲劳成本之间的关系,构建考虑检修便利性的风电场群均衡载荷模型。基于此,提出风电场群综合效能调控模型,以协调发电与疲劳寿命之间的关系。然后,考虑海上风流因素对船舶航行时间的影响,建立电氢并举的能量外送模型。最后,以系统运行成本最小为目标,提出考虑主动孤岛、制氢效率特性以及系统旋转备用的海上风氢系统与沿海电网能量协同优化调度模型。算例验证了所提方法的有效性和优越性,为海上风电制氢能量调度提供了一种新的思路。