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.

Multi-timescale robust dispatching for coordinated automatic generation control and energy storage

The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control(AGC).Thus,there is an urgent demand to coordinate AGC units with active equipment such as energy storage.Current dispatch decision-making methods often ignore the intermittent effects of renewable energy.This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units.This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units,improve the adjustment potential,and respond to the problems of intermittent power generation from renewable energy.A column and constraint generation algorithm is used to solve the model.In an example analysis,the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency.

Reference Point Based TR-PSO for Multi-Objective Environmental/Economic Dispatch

A reference point based multi-objective optimization using a combination between trust region (TR) algorithm and particle swarm optimization (PSO) to solve the multi-objective environmental/economic dispatch (EED) problem is presented in this paper. The EED problem is handled by Reference Point Interactive Approach. One of the main advantages of the proposed approach is integrating the merits of both TR and PSO, where TR has provided the initial set (close to the Pareto set as possible and the reference point of the decision maker) followed by PSO to improve the quality of the solutions and get all the points on the Pareto frontier. The performance of the proposed algorithm is tested on standard IEEE 30-bus 6-genrator test system and is compared with conventional methods. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions in one single run. The comparison with the classical methods demonstrates the superiority of the proposed approach and confirms its potential to solve the multi-objective EED problem.

A Novel Multi-objective Protection Coordination Scheme for a Microgrid with Optimal Deployment of Dual-setting ROCOV Based Relays

Relay coordination is crucial in electrical power systems to protect against malfunctions and damage caused by unexpected events like short circuits.To address the challenge associated with the reverse direction of fault current,dual-setting(DS)directional over-current relays have evolved but failed to provide proper coordination during changes of load,generation,and network.In the meantime,with the increasing number of DS relays,the total relay operating time tends to saturate.Therefore,this paper proposes a protection scheme based on the optimal deployment of conventional and dual-setting rate of change of voltage(DS-ROCOV)relays in distribution systems.This holds true for varying network topologies and is unaffected by variations in load and generation.The objective of the proposed scheme is to ensure reliable and efficient protection against faults in distribution systems by minimizing the overall operating time with the optimal number of DS-ROCOV relays.The proposed protection scheme’s performance is evaluated for different coordination time interval values as well as in different microgrid scenarios.This paper outlines the design and implementation of the proposed protection scheme which is validated on the modified IEEE 14-bus system using simulations in Matlab/Simulink.

Solving Multi-Area Environmental/Economic Dispatch by Pareto-Based Chemical-Reaction Optimization Algorithm

In this study, we present a Pareto-based chemicalreaction optimization(PCRO) algorithm for solving the multiarea environmental/economic dispatch optimization problems.Two objectives are minimized simultaneously, i.e., total fuel cost and emission. In the proposed algorithm, each solution is represented by a chemical molecule. A novel encoding mechanism for solving the multi-area environmental/economic dispatch optimization problems is designed to dynamically enhance the performance of the proposed algorithm. Then, an ensemble of effective neighborhood approaches is developed, and a selfadaptive neighborhood structure selection mechanism is also embedded in PCRO to increase the search ability while maintaining population diversity. In addition, a grid-based crowding distance strategy is introduced, which can obviously enable the algorithm to easily converge near the Pareto front. Furthermore,a kinetic-energy-based search procedure is developed to enhance the global search ability. Finally, the proposed algorithm is tested on sets of the instances that are generated based on realistic production. Through the analysis of experimental results, the highly effective performance of the proposed PCRO algorithm is favorably compared with several algorithms, with regards to both solution quality and diversity.

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.

Modeling of Combined Economic and Emission Dispatch Using Improved Sand Cat Optimization Algorithm

Combined Economic and Emission Dispatch(CEED)task forms multi-objective optimization problems to be resolved to minimize emission and fuel costs.The disadvantage of the conventional method is its incapability to avoid falling in local optimal,particularly when handling nonlinear and complex systems.Metaheuristics have recently received considerable attention due to their enhanced capacity to prevent local optimal solutions in addressing all the optimization problems as a black box.Therefore,this paper focuses on the design of an improved sand cat optimization algorithm based CEED(ISCOA-CEED)technique.The ISCOA-CEED technique majorly concen-trates on reducing fuel costs and the emission of generation units.Moreover,the presented ISCOA-CEED technique transforms the equality constraints of the CEED issue into inequality constraints.Besides,the improved sand cat optimization algorithm(ISCOA)is derived from the integration of tra-ditional SCOA with the Levy Flight(LF)concept.At last,the ISCOA-CEED technique is applied to solve a series of 6 and 11 generators in the CEED issue.The experimental validation of the ISCOA-CEED technique ensured the enhanced performance of the presented ISCOA-CEED technique over other recent approaches.

Multi-Objective Sizing of Solar-Wind-Hydro Hybrid Power System with Doubled Energy Storages Under Optimal Coordinated Operational Strategy

More and more attention has been paid to the high penetration of renewable energy in recent years.The randomness and intermittency of solar and wind energy make it an inevitable trend that renewables are coupled with energy storage technologies.Pumped hydro storage(PHS)is the most widelyused storage form in the power grid but the capacity is limited by geographic conditions.The concentrated solar power(CSP)plant with a thermal energy storage(TES)system can realize easier grid connections and effective peak shaving.Therefore,this paper proposes a solar-wind-hydro hybrid power system with PHS-TES double energy storages,and investigates the optimal coordinated operational strategy and multi-objective sizing.The optimal sizing problem which considers the minimum levelized cost of energy(LCOE)and loss of power supply probability(LPSP)as objectives is solved by multi-objective particle swarm optimization.Moreover,the seasonal uncertainties of renewables are considered by applying a scenario-based analysis using Kmeans clustering.Finally,a case study reveals the effectiveness of the coordinated operational strategy and double energy storages from the perspectives of economy and reliability.The comparisons of optimal sizing results show that the PV-WindCSP-PHS system decreases the LCOE by 19.1%compared to a PV-Wind-CSP system under the same LPSP,and reduces the LPSP compared to PV-Wind-PHS systems with limited reservoir capacity,which indicates that the proposed system with double energy storages has better economy and reliability performance compared to single storage.

Black Widow Optimization for Multi Area Economic Emission Dispatch

The optimizationfield has grown tremendously,and new optimization techniques are developed based on statistics and evolutionary procedures.There-fore,it is necessary to identify a suitable optimization technique for a particular application.In this work,Black Widow Optimization(BWO)algorithm is intro-duced to minimize the cost functions in order to optimize the Multi-Area Economic Dispatch(MAED).The BWO is implemented for two different-scale test systems,comprising 16 and 40 units with three and four areas.The performance of BWO is compared with the available optimization techniques in the literature to demonstrate the strategy’s efficacy.Results show that the optimized cost for four areas with 16 units is found to be 7336.76$/h,whereas it is 121,589$/h for four areas with 40 units using BWO.It is also noted that optimization algo-rithms other than BWO require higher cost value.The best-optimized solution for emission is achieved at 9.2784e+06 tones/h,and it is observed that there is a considerable difference between the worst and the best values.Also,the suggested technique is implemented for large-scale test systems successfully with high precision,and rapid convergence occurs in MAED.

Multi-objective Optimal Dispatch for Integrated Energy Systems Based on a Device Value Tag

Due to the variety of devices and operating scenarios in an integrated energy system(IES),the optimal dispatch of an IES is usually complicated.An optimal dispatch method for an IES is proposed by defining the scheduling value for each device which can be different under various scenarios.First,thinking over the private and public attributes of each operating equipment,the evaluation system is established with the actual scenarios of economic,environmental and energy-savings being considered.Secondly,the economic,environmental and energy-saving benefits of each operating equipment are quantified by Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS).Therefore,the scheduling value of the device is comprehensively assessed according to the specific scenario.Finally,decomposing the output of the device into direct available energy and indirect available energy,an optimal model is built with the maximum general production benefits as the objective,and is solved by MATLAB and CPLEX.The simulation results show that the evaluation system can reflect multiple values of devices.The proposed model can unify the modeling of optimal dispatch for different scenarios in the IES and can improve dispatch efficiency,while ensuring the accuracy of the results with high computation efficiency.

An Efficient Multi-objective Approach Based on Golden Jackal Search for Dynamic Economic Emission Dispatch

Dynamic Economic Emission Dispatch(DEED)aims to optimize control over fuel cost and pollution emission,two conflicting objectives,by scheduling the output power of various units at specific times.Although many methods well-performed on the DEED problem,most of them fail to achieve expected results in practice due to a lack of effective trade-off mechanisms between the convergence and diversity of non-dominated optimal dispatching solutions.To address this issue,a new multi-objective solver called Multi-Objective Golden Jackal Optimization(MOGJO)algorithm is proposed to cope with the DEED problem.The proposed algorithm first stores non-dominated optimal solutions found so far into an archive.Then,it chooses the best dispatching solution from the archive as the leader through a selection mechanism designed based on elite selection strategy and Euclidean distance index method.This mechanism can guide the algorithm to search for better dispatching solutions in the direction of reducing fuel costs and pollutant emissions.Moreover,the basic golden jackal optimization algorithm has the drawback of insufficient search,which hinders its ability to effectively discover more Pareto solutions.To this end,a non-linear control parameter based on the cosine function is introduced to enhance global exploration of the dispatching space,thus improving the efficiency of finding the optimal dispatching solutions.The proposed MOGJO is evaluated on the latest CEC benchmark test functions,and its superiority over the state-of-the-art multi-objective optimizers is highlighted by performance indicators.Also,empirical results on 5-unit,10-unit,IEEE 30-bus,and 30-unit systems show that the MOGJO can provide competitive compromise scheduling solutions compared to published DEED methods.Finally,in the analysis of the Pareto dominance relationship and the Euclidean distance index,the optimal dispatching solutions provided by MOGJO are the closest to the ideal solutions for minimizing fuel costs and pollution emissions simultaneously,compared to the latest published DEED solutions.

Solving multi-objective optimal power flow problem considering wind-STATCOM using differential evolution

In this paper, a simple strategy based differential evolution was proposed for solving the problem of multi-objective environmental optimal power flow considering a hybrid model （Wind-Shunt-FACTS）. The DE algorithm optimized simultaneously a combined vector control based active power of wind sources and reactive power of multi STATCOM exchanged with the electrical power system to minimize fuel cost and emissions. The proposed strategy was examined and applied to the standard IEEE 30-bus with smooth cost function to solve the problem of security environmental economic dispatch considering multi distributed hybrid model based wind and STATCOM controllers. In addition, the proposed approach was validated on a large practical electrical power system 40 generating units considering valve point effect. Simulation results demonstrate that choosing the installation of multi type of FACTS devices in coordination with many distributed wind sources is a vital research area.

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。

计及区间预测信息的含风电电力系统有功多时间尺度协调调度优化方法

间歇性、集群式的风电并入大电网将会增加输电断面功率越限风险,将点预测信息纳入含风电的电力系统调度将不可避免出现“过调度”和“欠调度”。为此,该文提出计及区间预测信息的含风电电力系统有功多时间尺度协调调度优化方法。首先,利用点模型及其预测误差分布构建风电功率区间预测模型;其次,建立具有区间预测信息的风电场有功功率动态分群策略;再次,构建风电集群和自动发电控制(automatic generation control,AGC)机组与非AGC机组等多时间尺度协调的调度策略,通过滚动优化和反馈校正环节协调日前-日内以及实时调度;最后,采用实际风电集群有功功率数据和改进的IEEE-118节点标准测试系统验证所提方法的有效性。研究表明,所提方法不仅可以合理协调风电集群内各个风电场输出功率,还可以协调风电集群功率和AGC机组与非AGC之间的输出功率,有效解决了调度指令与实际输出功率不协调问题。

分布式优化在互联电网中的应用分析及展望

在新能源随机性与波动性的影响下,互联电网能量与灵活性的跨区域互济重要性凸显,有效的分布式优化算法是不可或缺的技术支撑。为了在互联电网不同运行层级的重要场景中选择适宜的算法,文中归纳了分布式优化建模和求解的一般原理,并提出将现有分布式算法分为原始可行方向、对偶可行方向、原对偶、切平面、列生成和多参数法6类。针对互联电网的多区域输电网协同调度、输配协同优化调度以及配电网分布式资源协同调度不同运行层级这3个重要场景,阐明了采用分布式优化的必要性,总结了统一的变量/约束耦合问题的典型建模及变换关系,并分析了6类算法在各场景的实施情况和应用优缺点。从物理场景、数学模型以及算法整体角度分析分布式优化在互联电网3个重要场景应用存在的问题,并给出了不同场景的算法选取建议。结合配电网运行新场景、数学建模新问题、算法设计新技术,展望了未来互联电网应用分布式优化的方向。

Multi-objective optimization strategy for distribution network considering V2Genabled electric vehicles in building integrated energy system

Based on the large-scale penetration of electric vehicles(EV)into the building cluster,a multi-objective optimal strategy considering the coordinated dispatch of EV is proposed,for improving the safe and economical operation problems of distribution network.The system power loss and node voltage excursion can be effectively reduced,by taking measures of time-of-use(TOU)price mechanism bonded with the reactive compensation of energy storage devices.Firstly,the coordinate charging/discharging load model for EV has been established,to obtain a narrowed gap between load peak and valley.Next,a multi-objective optimization model of the distribution grid is also defined,and the active power loss and node voltage fluctuation are chosen to be the objective function.For improving the efficiency of optimization process,an advanced genetic algorithm associated with elite preservation policy is used.Finally,reactive compensation capacity supplied by capacitor banks is dynamically determined according to the varying building loads.The proposed strategy is demonstrated on the IEEE 33-node test case,and the simulation results show that the power supply pressure can be obviously relieved by introducing the coordinated charging/discharging behavior of EV;in the meantime,via reasonable planning of the compensation capacitor,the remarkably lower active power loss and voltage excursion can be realized,ensuring the safe and economical operation of the distribution system.

Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm

For increasing the overall performance of modem manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling （IPPS） problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS （MOIPPS） problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatch- ing rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.

Optimal Active Power Dispatching of Microgrid and Distribution Network Based on Model Predictive Control

First, a three-tier coordinated scheduling system consisting of a distribution network dispatch layer, a microgrid centralized control layer, and local control layer in the energy internet is proposed. The multi-time scale optimal scheduling of the microgrid based on Model Predictive Control（MPC） is then studied, and the optimized genetic algorithm and the microgrid multi-time rolling optimization strategy are used to optimize the datahead scheduling phase and the intra-day optimization phase. Next, based on the three-tier coordinated scheduling architecture, the operation loss model of the distribution network is solved using the improved branch current forward-generation method and the genetic algorithm. The optimal scheduling of the distribution network layer is then completed. Finally, the simulation examples are used to compare and verify the validity of the method.

电网调度AVC系统自动优化协调控制方法

由于电网调度过程中电压变化量较大,电网调度AVC系统难以精准控制电压变化,所以提出电网调度AVC系统自动优化协调控制方法。确定总-中-地调三个层次协同的电力系统架构,搭建协调调度控制模型,确定母线、机组、无功裕度、功率方面的协调约束规则。在协调约束规则下确定电网AVC系统分层分区结构,计算电压无功控制空间中任意两个待分区节点之间的距离,划分电压无功控制区域,结合灵敏度确定优化协调控制矩阵,实现AVC系统自动优化协调控制。实验结果表明,该方法控制下的电压与实际数值之间误差小,能够实现电压平衡和降低网损的目标。