Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance...Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance, roll wear ratio and strip shape control, is presented. To avoid the selection of weight coefficients encountered in single objective optimization, a multi-objective differential evolutionary algorithm, called MaximinDE, is proposed to solve this model. The experimental results based on practical production data indicate that MaximinDE can obtain a good pareto-optimal solution set, which consists of a series of alternative solutions to load distribution. Decision-makers can select a trade-off solution from the pareto-optimal solution set based on their experience or the importance of ob- iectives. In comparison with the empirical load distribution solution, the trade-off solution can achieve a better per- formance, which demonstrates the effectiveness of the multi-objective load distribution optimization. Moreover, the conflicting relationship among different objectives can be also found, which is another advantage of multi-objective load distribution optimization.展开更多
In this paper, a multi-objective load shedding framework on the power system is presented. The frame work is useable in any kind of smart power systems;the word of smart here refers to the availability of data transmi...In this paper, a multi-objective load shedding framework on the power system is presented. The frame work is useable in any kind of smart power systems;the word of smart here refers to the availability of data transmission infrastructure (like PLC or power line carrier) in the system, in order to carry the system data to the load shedding framework. This is an open framework that means it can optimize load shedding problem by considering unlimited number of objective functions, in other word, the number of objectives can be as much as the operator decides, finally in the end of frame work just one matrix breaker state is chosen in a way of having the most compatibility with the operator ideas which are determined by objectives importance percentage which are one input groups of the framework. A two-stage methodology is used for the optimal load shedding problem. In the first stage, Discrete Multi-objective Particle Swarm Optimization method is used to find a collection of the best states of load shedding (Pareto front). In the second stage, the fuzzy logic is used as a Pareto front inference engine. Fuzzy selection algorithm (FSA) is designed in a way that it can infer according to the operator’s opinion without the expert interference that means rule base is formed automatically by fuzzy algorithm. FSA is consisted of two parts. Membership functions and rules base are formed automatically in the first part, the former in accordance with the costs of Pareto front particles and the latter in correspondence with importance percentage of objectives which are entered to FSA by operator;in other word, decision matrix is formed automatically in the algorithm according to the cost of Pareto front particles and importance percentage of objectives. In the Second part, Mamdani inference engine scrutinizes the Pareto front particles by the use of formed membership functions and rules base to know if they are compatible to operator’s opinion or not. Getting this approach, cost functions of each particle are considered as the inputs of (FSA), then a fuzzy combined fitness (FCF) is allocated to each Pareto front particle by Mamdani inference engine. In other word, FCF shows how much the particle is compatible to the operator’s opinion. Finding minimum FCF, final inference is done. The proposed method is tested on 30-bus, and 118-bus IEEE systems by considering two or three objective functions and the results are presented.展开更多
<div style="text-align:justify;"> In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorith...<div style="text-align:justify;"> In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorithm, the current optimal scheme mechanism combined with multi-objective multi-verse algorithm is used to optimize the intelligent building load scheduling. The update mechanism is changed in updating the position of the universe, and the process of correction coding is omitted in the iterative process of the algorithm, which reduces the com-putational complexity. The feasibility and effectiveness of the proposed method are verified by the optimal scheduling experiments of residential loads. </div>展开更多
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 th...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.展开更多
针对电动汽车大规模接入配电系统导致的负荷波动增大、功率损耗上升及供电质量下降等问题,提出一种基于优先级的车辆到电网(vehicle-to-grid,V2G)协调调度方法。该方法构建了以“负荷方差”“有功功率损耗降低指标(power loss reduction...针对电动汽车大规模接入配电系统导致的负荷波动增大、功率损耗上升及供电质量下降等问题,提出一种基于优先级的车辆到电网(vehicle-to-grid,V2G)协调调度方法。该方法构建了以“负荷方差”“有功功率损耗降低指标(power loss reduction,PLR)”和“无功功率损耗降低指标(power loss reduction,QLR)”为核心的多目标优化模型,并引入最有价值球员(most valuable player,MVP)算法求解,以同时实现负荷曲线平滑化与配电系统供电质量提升。设计基于SOC的优先级充放电策略,通过MVP算法搜索最优调度方案,并与GA、ABC、PSO、CSO、OCSO等多种元启发式算法进行对比。结果表明:所提出方法能够显著降低负荷波动,PLR和QLR的最小化效果均优于对比算法,功率损耗降低幅度最高可达29.20%,计算效率亦具有明显优势。基于IEEE 69节点径向配电系统的仿真验证了该方法的有效性和鲁棒性,证明该方法能够为电动汽车有序用电规划提供更合理的非支配解集,为后续实时调度及可再生能源接入提供较为重要参考。展开更多
针对光伏发电网受日照影响,导致输出功率不确定且波动,提出基于改进多种群遗传算法(multiple population genetic algorithm,MPGA)的光伏配网多目标柔性规划研究方法。通过分析光伏配网荷载特性,构建多目标柔性规划模型。利用MPGA算法...针对光伏发电网受日照影响,导致输出功率不确定且波动,提出基于改进多种群遗传算法(multiple population genetic algorithm,MPGA)的光伏配网多目标柔性规划研究方法。通过分析光伏配网荷载特性,构建多目标柔性规划模型。利用MPGA算法的局部搜索机制,将种群划分为不同子群体独立进行遗传操作,引入多种群策略到改进的MPGA中,并结合局部搜索机制,克服局部最优解。通过迭代过程,不断更新种群并评估适应度,直到满足终止条件,最终获得满足所有目标和约束的光伏配网规划方案。实验结果表明,所获取的规划方案在保证线路平均负载率为70%,且年网损费用和建设投资总费用为最低值,证明了所获取的规划方案可以实现输出功率的稳定性和准确性。展开更多
基金Item Sponsored by National Natural Science Foundation of China(50974039)
文摘Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance, roll wear ratio and strip shape control, is presented. To avoid the selection of weight coefficients encountered in single objective optimization, a multi-objective differential evolutionary algorithm, called MaximinDE, is proposed to solve this model. The experimental results based on practical production data indicate that MaximinDE can obtain a good pareto-optimal solution set, which consists of a series of alternative solutions to load distribution. Decision-makers can select a trade-off solution from the pareto-optimal solution set based on their experience or the importance of ob- iectives. In comparison with the empirical load distribution solution, the trade-off solution can achieve a better per- formance, which demonstrates the effectiveness of the multi-objective load distribution optimization. Moreover, the conflicting relationship among different objectives can be also found, which is another advantage of multi-objective load distribution optimization.
文摘In this paper, a multi-objective load shedding framework on the power system is presented. The frame work is useable in any kind of smart power systems;the word of smart here refers to the availability of data transmission infrastructure (like PLC or power line carrier) in the system, in order to carry the system data to the load shedding framework. This is an open framework that means it can optimize load shedding problem by considering unlimited number of objective functions, in other word, the number of objectives can be as much as the operator decides, finally in the end of frame work just one matrix breaker state is chosen in a way of having the most compatibility with the operator ideas which are determined by objectives importance percentage which are one input groups of the framework. A two-stage methodology is used for the optimal load shedding problem. In the first stage, Discrete Multi-objective Particle Swarm Optimization method is used to find a collection of the best states of load shedding (Pareto front). In the second stage, the fuzzy logic is used as a Pareto front inference engine. Fuzzy selection algorithm (FSA) is designed in a way that it can infer according to the operator’s opinion without the expert interference that means rule base is formed automatically by fuzzy algorithm. FSA is consisted of two parts. Membership functions and rules base are formed automatically in the first part, the former in accordance with the costs of Pareto front particles and the latter in correspondence with importance percentage of objectives which are entered to FSA by operator;in other word, decision matrix is formed automatically in the algorithm according to the cost of Pareto front particles and importance percentage of objectives. In the Second part, Mamdani inference engine scrutinizes the Pareto front particles by the use of formed membership functions and rules base to know if they are compatible to operator’s opinion or not. Getting this approach, cost functions of each particle are considered as the inputs of (FSA), then a fuzzy combined fitness (FCF) is allocated to each Pareto front particle by Mamdani inference engine. In other word, FCF shows how much the particle is compatible to the operator’s opinion. Finding minimum FCF, final inference is done. The proposed method is tested on 30-bus, and 118-bus IEEE systems by considering two or three objective functions and the results are presented.
文摘<div style="text-align:justify;"> In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorithm, the current optimal scheme mechanism combined with multi-objective multi-verse algorithm is used to optimize the intelligent building load scheduling. The update mechanism is changed in updating the position of the universe, and the process of correction coding is omitted in the iterative process of the algorithm, which reduces the com-putational complexity. The feasibility and effectiveness of the proposed method are verified by the optimal scheduling experiments of residential loads. </div>
文摘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.
文摘针对电动汽车大规模接入配电系统导致的负荷波动增大、功率损耗上升及供电质量下降等问题,提出一种基于优先级的车辆到电网(vehicle-to-grid,V2G)协调调度方法。该方法构建了以“负荷方差”“有功功率损耗降低指标(power loss reduction,PLR)”和“无功功率损耗降低指标(power loss reduction,QLR)”为核心的多目标优化模型,并引入最有价值球员(most valuable player,MVP)算法求解,以同时实现负荷曲线平滑化与配电系统供电质量提升。设计基于SOC的优先级充放电策略,通过MVP算法搜索最优调度方案,并与GA、ABC、PSO、CSO、OCSO等多种元启发式算法进行对比。结果表明:所提出方法能够显著降低负荷波动,PLR和QLR的最小化效果均优于对比算法,功率损耗降低幅度最高可达29.20%,计算效率亦具有明显优势。基于IEEE 69节点径向配电系统的仿真验证了该方法的有效性和鲁棒性,证明该方法能够为电动汽车有序用电规划提供更合理的非支配解集,为后续实时调度及可再生能源接入提供较为重要参考。
文摘针对光伏发电网受日照影响,导致输出功率不确定且波动,提出基于改进多种群遗传算法(multiple population genetic algorithm,MPGA)的光伏配网多目标柔性规划研究方法。通过分析光伏配网荷载特性,构建多目标柔性规划模型。利用MPGA算法的局部搜索机制,将种群划分为不同子群体独立进行遗传操作,引入多种群策略到改进的MPGA中,并结合局部搜索机制,克服局部最优解。通过迭代过程,不断更新种群并评估适应度,直到满足终止条件,最终获得满足所有目标和约束的光伏配网规划方案。实验结果表明,所获取的规划方案在保证线路平均负载率为70%,且年网损费用和建设投资总费用为最低值,证明了所获取的规划方案可以实现输出功率的稳定性和准确性。
