Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256134
Fuqiang Lu, Hualing Bi, Lin Huang, Wang Bo
This paper proposed an uncertain delivery time control model for Fourth Party Logistics (4PL). The selection of Third Party Logistics (3PL) suppliers and transportation routes, delivery time and transportation cost are included. An Improved Genetic Algorithm (I-GA) is designed to solve the resulting optimization problem. In detail, the reverse-two-point method is applied in crossover operation. In the experiment, Enumeration, Genetic Algorithm(GA) and Tabu Search Genetic Algorithm hybrid algorithm (TS-GA) are also used to compare with I-GA. The simulation results and analysis shows that the proposed model and algorithm is very useful for supporting the decision on the process of 4PL operation.
{"title":"Improved genetic algorithm based delivery time control for Fourth Party Logistics","authors":"Fuqiang Lu, Hualing Bi, Lin Huang, Wang Bo","doi":"10.1109/COASE.2017.8256134","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256134","url":null,"abstract":"This paper proposed an uncertain delivery time control model for Fourth Party Logistics (4PL). The selection of Third Party Logistics (3PL) suppliers and transportation routes, delivery time and transportation cost are included. An Improved Genetic Algorithm (I-GA) is designed to solve the resulting optimization problem. In detail, the reverse-two-point method is applied in crossover operation. In the experiment, Enumeration, Genetic Algorithm(GA) and Tabu Search Genetic Algorithm hybrid algorithm (TS-GA) are also used to compare with I-GA. The simulation results and analysis shows that the proposed model and algorithm is very useful for supporting the decision on the process of 4PL operation.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126638820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256109
Bingzhu Chen, T. Qu, M. Thürer, G. Huang, Congdong Li, Subo Xu
Warehouse operation is an important part of the production logistics process. The intelligent warehouse management not only requires hardware technical support, but also need a reasonable control technology. The growing market demand has made the manufacturing company's finished goods warehouse lack of capacity, how to use warehouse control technology to alleviate this problem is a valuable research topic. Workload control is a production planning and control technique that balances demand and capability, and its application is to solve the limited capacity of storage resources. Based on Workload Control, this paper expands the applicability of Workload control for the warehouse, including designing the sequencing rules for the temporary storage area, the order release rules for the warehouse, the storage assignment strategy for the warehouse, to control the process of warehouse operation and effectively balance demand and capacity of the warehouse, in order to reduce the average storage time and percentage of tardy order.
{"title":"Warehouse workload control for production logistic","authors":"Bingzhu Chen, T. Qu, M. Thürer, G. Huang, Congdong Li, Subo Xu","doi":"10.1109/COASE.2017.8256109","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256109","url":null,"abstract":"Warehouse operation is an important part of the production logistics process. The intelligent warehouse management not only requires hardware technical support, but also need a reasonable control technology. The growing market demand has made the manufacturing company's finished goods warehouse lack of capacity, how to use warehouse control technology to alleviate this problem is a valuable research topic. Workload control is a production planning and control technique that balances demand and capability, and its application is to solve the limited capacity of storage resources. Based on Workload Control, this paper expands the applicability of Workload control for the warehouse, including designing the sequencing rules for the temporary storage area, the order release rules for the warehouse, the storage assignment strategy for the warehouse, to control the process of warehouse operation and effectively balance demand and capacity of the warehouse, in order to reduce the average storage time and percentage of tardy order.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115502774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256204
Xiang Zhong, A. Prakash, Leanne Petty, Rita James
A specialty referral is the interface between the referring provider (typically a primary care physician) and the specialist. This process is complex and is currently mired with inefficiencies from primary care providers, referral coordinators, and insurance companies. The objective of this study is to provide a quantitative framework that will enable clinics to improve upon the existing referral process as a whole. Our initial findings obtained through shadowing referral coordinators across a representative sample of clinics at the University of Florida Health Jacksonville led us to believe that developing an analytical model that identifies bottlenecks in the referral process, particularly when reworks at all stages are frequently observed, would be beneficial. The results of the analysis would stimulate clinics to increase the capabilities of their patient records system, reduce reworks — leading ultimately to reduced referral delay and improved patient satisfaction.
{"title":"Modeling and analysis of primary care to specialty care referral process: A case study at the university of florida health Jacksonville","authors":"Xiang Zhong, A. Prakash, Leanne Petty, Rita James","doi":"10.1109/COASE.2017.8256204","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256204","url":null,"abstract":"A specialty referral is the interface between the referring provider (typically a primary care physician) and the specialist. This process is complex and is currently mired with inefficiencies from primary care providers, referral coordinators, and insurance companies. The objective of this study is to provide a quantitative framework that will enable clinics to improve upon the existing referral process as a whole. Our initial findings obtained through shadowing referral coordinators across a representative sample of clinics at the University of Florida Health Jacksonville led us to believe that developing an analytical model that identifies bottlenecks in the referral process, particularly when reworks at all stages are frequently observed, would be beneficial. The results of the analysis would stimulate clinics to increase the capabilities of their patient records system, reduce reworks — leading ultimately to reduced referral delay and improved patient satisfaction.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116576566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256253
C. Goh, G. Seet, K. Shimada
As evident in the Fukushima incident, shooting water to hit a target at a distance under a windy condition from a moving vehicle is challenging. The challenges include (i) the non-stationary condition caused by the wind and (ii) the time delay due to the distance between the water source and target. This paper proposes a model-based learning controller to address these issues. The proposed controller can adapt the water's shooting angle to different wind conditions or vehicle motions by measuring only the shooting error and angle. First, a forward model uses the current shooting error and its corresponding past shooting angle to predict the future shooting error, thereby addressing the time delay issue like the Smith Predictor. Next, the current shooting angle and its predicted future error are given to an inverse model as the augmented predictor to determine the required shooting angle necessary to achieve zero error. Both the forward and inverse models are learned using the Receptive Field-Weighted regression (RFWR) algorithm. Interpolation, cross correlation and active probing based techniques are developed to estimate the time delay adjustment needed to synchronize the shooting angle and error feedback for model learning. Experimental results obtained from computer simulations indicate that the proposed controller can adapt to non-stationary conditions and address the time-delay issue. The performance of the controller outperforms human operators and a simple PID controller in water-shooting tasks under changing wind and vehicle motion.
{"title":"A model-based learning controller with predictor augmentation for non-stationary conditions and time delay in water shooting","authors":"C. Goh, G. Seet, K. Shimada","doi":"10.1109/COASE.2017.8256253","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256253","url":null,"abstract":"As evident in the Fukushima incident, shooting water to hit a target at a distance under a windy condition from a moving vehicle is challenging. The challenges include (i) the non-stationary condition caused by the wind and (ii) the time delay due to the distance between the water source and target. This paper proposes a model-based learning controller to address these issues. The proposed controller can adapt the water's shooting angle to different wind conditions or vehicle motions by measuring only the shooting error and angle. First, a forward model uses the current shooting error and its corresponding past shooting angle to predict the future shooting error, thereby addressing the time delay issue like the Smith Predictor. Next, the current shooting angle and its predicted future error are given to an inverse model as the augmented predictor to determine the required shooting angle necessary to achieve zero error. Both the forward and inverse models are learned using the Receptive Field-Weighted regression (RFWR) algorithm. Interpolation, cross correlation and active probing based techniques are developed to estimate the time delay adjustment needed to synchronize the shooting angle and error feedback for model learning. Experimental results obtained from computer simulations indicate that the proposed controller can adapt to non-stationary conditions and address the time-delay issue. The performance of the controller outperforms human operators and a simple PID controller in water-shooting tasks under changing wind and vehicle motion.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122929056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256289
Yan Cui, Min Huang, Qin Dai
Aiming at the dynamic time changes in the logistics transportation network, the problem that a Fourth Party Logistics (4PL) supplier collaborates Third Party Logistics (3PL) suppliers to customize routes for the customers is proposed. A mathematic programming model is presented by considering the transportation, staying and transit cost with the time constraint, and the dynamic time is updated at the transit nodes. A Two-phase solution method bases on the Ant Colony Optimization (TACO) is established. In the TACO, two equations of the state are given respectively for generating the best solution and renewing the ants' pheromone distribution. The numerical analysis shows that comparing with the algorithm repeated using the ACO, TACO cannot only save the running time, but also can increase the probability of finishing the task.
{"title":"4PL collaborative routing customization problem on the dynamic networks","authors":"Yan Cui, Min Huang, Qin Dai","doi":"10.1109/COASE.2017.8256289","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256289","url":null,"abstract":"Aiming at the dynamic time changes in the logistics transportation network, the problem that a Fourth Party Logistics (4PL) supplier collaborates Third Party Logistics (3PL) suppliers to customize routes for the customers is proposed. A mathematic programming model is presented by considering the transportation, staying and transit cost with the time constraint, and the dynamic time is updated at the transit nodes. A Two-phase solution method bases on the Ant Colony Optimization (TACO) is established. In the TACO, two equations of the state are given respectively for generating the best solution and renewing the ants' pheromone distribution. The numerical analysis shows that comparing with the algorithm repeated using the ACO, TACO cannot only save the running time, but also can increase the probability of finishing the task.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123023164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256239
Zhenjiang Wang, Zhengcai Cao, Ran Huang, Jiaqi Zhang
Attribute selection is an effective approach to improve the inference efficiency of data-based scheduling strategies system that many researchers have studied based on computational intelligence methods. Comparing to computational intelligence methods, concept lattice has the advantages in attribute selection protentially. In this paper, attribute selection for production line in job shops based on concept lattice is studied and applied in the neural network (NN) scheduling system. Firstly, owing to the many-valued characteristic of production line attributes, the method of many-valued formal context converts to single-valued formal context is given. Then, the attribute feature is discussed and a concept lattice reduction method for production line attribute selection is proposed to obtain the key production line attributes. Finally, the key attributes are used as the input of neural network scheduling system which can generate optimal scheduling strategies for job shop scheduling problem. The experimental results show that the proposed scheduling system is effective in terms of various performance criteria.
{"title":"A study on attribute selection for job shop scheduling problem","authors":"Zhenjiang Wang, Zhengcai Cao, Ran Huang, Jiaqi Zhang","doi":"10.1109/COASE.2017.8256239","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256239","url":null,"abstract":"Attribute selection is an effective approach to improve the inference efficiency of data-based scheduling strategies system that many researchers have studied based on computational intelligence methods. Comparing to computational intelligence methods, concept lattice has the advantages in attribute selection protentially. In this paper, attribute selection for production line in job shops based on concept lattice is studied and applied in the neural network (NN) scheduling system. Firstly, owing to the many-valued characteristic of production line attributes, the method of many-valued formal context converts to single-valued formal context is given. Then, the attribute feature is discussed and a concept lattice reduction method for production line attribute selection is proposed to obtain the key production line attributes. Finally, the key attributes are used as the input of neural network scheduling system which can generate optimal scheduling strategies for job shop scheduling problem. The experimental results show that the proposed scheduling system is effective in terms of various performance criteria.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114436863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256173
Chu-ge Wu, Ling Wang, Jing-jing Wang
The development of cloud computing drives the research on parallel processing. One of the important problems in parallel processing is to minimize the makespan of the tasks with precedence constraints on multiprocessors scheduling. In this paper, the property of the t-level (top-level) is analyzed, and a t-level (top level) driven search is proposed to enhance the exploitation ability of the efficient estimation of distributed algorithm (eEDA), which was developed for solving the precedence constrained scheduling problem. Numerical tests and comparisons are carried out. The results demonstrate that the t-level driven search is able to improve the optimization capacity of the eEDA under heterogeneous multiprocessor situation. Moreover, it is also shown that the eEDA with the t-level driven search on homogeneous computing systems is effective.
{"title":"A t-level driven search for estimation of distribution algorithm in solving task graph allocation to multiprocessors","authors":"Chu-ge Wu, Ling Wang, Jing-jing Wang","doi":"10.1109/COASE.2017.8256173","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256173","url":null,"abstract":"The development of cloud computing drives the research on parallel processing. One of the important problems in parallel processing is to minimize the makespan of the tasks with precedence constraints on multiprocessors scheduling. In this paper, the property of the t-level (top-level) is analyzed, and a t-level (top level) driven search is proposed to enhance the exploitation ability of the efficient estimation of distributed algorithm (eEDA), which was developed for solving the precedence constrained scheduling problem. Numerical tests and comparisons are carried out. The results demonstrate that the t-level driven search is able to improve the optimization capacity of the eEDA under heterogeneous multiprocessor situation. Moreover, it is also shown that the eEDA with the t-level driven search on homogeneous computing systems is effective.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123827624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256209
Teng Long, Jing-Xian Tang, Q. Jia
Due to the global environmental pollution and fossil fuel shortage, there is an increasing demand for renewable energy. In this circumstance, the wind power and the electric vehicle (EV) are an important part of the supply side and the demand side, respectively. Because of the multi-scale system dynamics, to match the random wind supply and EV charging demand to reduce the charging cost is challenging and of great practical interest. This is considered as an important problem in this paper. In order to capture the structure of this problem and to use the area information of EVs, we formulate this charging problem as a multi-scale event-based optimization (EBO) model. At the upper level, we define a series of macro events to determine the number of EVs to be charged for each aggregator. At the lower level, we finally decide every EV's charging plan based on a series of micro events and the upper level action. So as to solve this large-scale problem, we develop a multi-scale event-based policy iteration method in this paper. The numerical testing results show the effectiveness of this multi-scale EBO approach on reducing the total charging cost of all EVs.
{"title":"Multi-scale event-based optimization for matching uncertain wind supply with EV charging demand","authors":"Teng Long, Jing-Xian Tang, Q. Jia","doi":"10.1109/COASE.2017.8256209","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256209","url":null,"abstract":"Due to the global environmental pollution and fossil fuel shortage, there is an increasing demand for renewable energy. In this circumstance, the wind power and the electric vehicle (EV) are an important part of the supply side and the demand side, respectively. Because of the multi-scale system dynamics, to match the random wind supply and EV charging demand to reduce the charging cost is challenging and of great practical interest. This is considered as an important problem in this paper. In order to capture the structure of this problem and to use the area information of EVs, we formulate this charging problem as a multi-scale event-based optimization (EBO) model. At the upper level, we define a series of macro events to determine the number of EVs to be charged for each aggregator. At the lower level, we finally decide every EV's charging plan based on a series of micro events and the upper level action. So as to solve this large-scale problem, we develop a multi-scale event-based policy iteration method in this paper. The numerical testing results show the effectiveness of this multi-scale EBO approach on reducing the total charging cost of all EVs.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123940295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256089
Xiang Zhang, Y. Lou, Ran Shi
Compared with the tracking error, the contouring error has a greater impact on product quality. This paper is concerned with the contouring control of the parallel robots, whose kinematics is nonlinear, so it's hard to utilize the conventional cross-coupled control method directly. Thereby, the position loop-based cross-coupled control is applied. In this method, the contouring error is estimated as the minimum distance from the actual position to the approximated circle. The position-reference input are modified to implement the contouring error compensation. In addition, to strengthen the coordination of the two actual axes further, we proposed to add the synchronization controller to the position loop-based cross-coupled control structure. For the planar motion, the synchronization error of workspace is defined as the difference value between the tracking error of x axis and the tracking error of y axis. It is compensated in the joint space of the driven axes instead of the workspace to improve the motion accuracy more directly. A number of experiments are carried out for circular contour at different speed. The results show the proposed method really leads to improved contouring accuracy.
{"title":"Position-loop based cross-coupled and synchronization control of a parallel kinematics machine","authors":"Xiang Zhang, Y. Lou, Ran Shi","doi":"10.1109/COASE.2017.8256089","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256089","url":null,"abstract":"Compared with the tracking error, the contouring error has a greater impact on product quality. This paper is concerned with the contouring control of the parallel robots, whose kinematics is nonlinear, so it's hard to utilize the conventional cross-coupled control method directly. Thereby, the position loop-based cross-coupled control is applied. In this method, the contouring error is estimated as the minimum distance from the actual position to the approximated circle. The position-reference input are modified to implement the contouring error compensation. In addition, to strengthen the coordination of the two actual axes further, we proposed to add the synchronization controller to the position loop-based cross-coupled control structure. For the planar motion, the synchronization error of workspace is defined as the difference value between the tracking error of x axis and the tracking error of y axis. It is compensated in the joint space of the driven axes instead of the workspace to improve the motion accuracy more directly. A number of experiments are carried out for circular contour at different speed. The results show the proposed method really leads to improved contouring accuracy.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123640674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256271
Z. Qiao, Bo Yang, Qimin Xu, Fei Xiong, Cailian Chen, X. Guan, Bei Chen
High penetration of renewable energy source makes microgrid (MGs) be environment friendly. However, the stochastic input from renewable energy resource brings difficulty in balancing the energy supply and demand. Purchasing extra energy from macrogrid to deal with energy shortage will increase MG energy cost. To mitigate intermittent nature of renewable energy, energy trading and energy storage which can exploit diversity of renewable energy generation across space and time are efficient and cost-effective methods. But a storage with large capacity will incur additional cost. In addition, due to MG participating energy trading as prosumer, it calls for an efficient trading mechanism. Therefore, this paper focuses on the problem of MG energy management and trading. Energy trading problem is formulated as a stochastic optimization one with both individual profit and social welfare maximization. Firstly a Lyapunov optimization based algorithm is developed to solve the stochastic problem. Secondly the double-auction based mechanism is provided to attract MGs' truthful bidding for buying and selling energy. Through theoretical analysis, we demonstrate that individual MG can achieve a time average energy cost close to offline optimum with tradeoff between storage capacity and energy trading cost. Meanwhile the social welfare is also asymptotically maximized under double auction. Simulation results based on real world data show the effectiveness of our algorithm.
{"title":"Energy trading between microgrids towards individual cost and social welfare optimization","authors":"Z. Qiao, Bo Yang, Qimin Xu, Fei Xiong, Cailian Chen, X. Guan, Bei Chen","doi":"10.1109/COASE.2017.8256271","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256271","url":null,"abstract":"High penetration of renewable energy source makes microgrid (MGs) be environment friendly. However, the stochastic input from renewable energy resource brings difficulty in balancing the energy supply and demand. Purchasing extra energy from macrogrid to deal with energy shortage will increase MG energy cost. To mitigate intermittent nature of renewable energy, energy trading and energy storage which can exploit diversity of renewable energy generation across space and time are efficient and cost-effective methods. But a storage with large capacity will incur additional cost. In addition, due to MG participating energy trading as prosumer, it calls for an efficient trading mechanism. Therefore, this paper focuses on the problem of MG energy management and trading. Energy trading problem is formulated as a stochastic optimization one with both individual profit and social welfare maximization. Firstly a Lyapunov optimization based algorithm is developed to solve the stochastic problem. Secondly the double-auction based mechanism is provided to attract MGs' truthful bidding for buying and selling energy. Through theoretical analysis, we demonstrate that individual MG can achieve a time average energy cost close to offline optimum with tradeoff between storage capacity and energy trading cost. Meanwhile the social welfare is also asymptotically maximized under double auction. Simulation results based on real world data show the effectiveness of our algorithm.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121106508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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