Pub Date : 2017-12-01DOI: 10.1109/SGC.2017.8308835
Arman Oshnoei, Rahmat Khezri, M. Ghaderzadeh, Hasti Parang, Soroush Oshnoei, M. Kheradmandi
Load frequency control is one of the most important issues in multi area power systems. In this context, due to growing development of wind turbines in power systems, this type of renewable power sources can be used efficiently in frequency control. This paper applies doubly-fed induction generators (DFIGs) along with other conventional generation units for frequency performance enhancement after disturbances in multi-area power systems. Proportional integral (PI) controller is the conventional controller which is used in DFIGs to contribute in frequency control. The parameters of PI controller for DFIG are optimized by improved particle swarm optimization (IPSO) method. Simulation results in a three-area power system illustrate the efficiency of the DFIGs for frequency and tie-line power oscillations improvement.
{"title":"Application of IPSO algorithm in DFIG-based wind turbines for efficient frequency control of multi-area power systems","authors":"Arman Oshnoei, Rahmat Khezri, M. Ghaderzadeh, Hasti Parang, Soroush Oshnoei, M. Kheradmandi","doi":"10.1109/SGC.2017.8308835","DOIUrl":"https://doi.org/10.1109/SGC.2017.8308835","url":null,"abstract":"Load frequency control is one of the most important issues in multi area power systems. In this context, due to growing development of wind turbines in power systems, this type of renewable power sources can be used efficiently in frequency control. This paper applies doubly-fed induction generators (DFIGs) along with other conventional generation units for frequency performance enhancement after disturbances in multi-area power systems. Proportional integral (PI) controller is the conventional controller which is used in DFIGs to contribute in frequency control. The parameters of PI controller for DFIG are optimized by improved particle swarm optimization (IPSO) method. Simulation results in a three-area power system illustrate the efficiency of the DFIGs for frequency and tie-line power oscillations improvement.","PeriodicalId":346749,"journal":{"name":"2017 Smart Grid Conference (SGC)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134142714","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 : 2017-12-01DOI: 10.1109/SGC.2017.8308868
Mohammadi Mohsen, H. Siahkali
Nowadays, developments in computer science has made parallel processing feasible. One of the main control problems in power systems is the control of optimal reactive power dispatch. In this problem, we try to optimize specific objective functions using a series of control variables, while a set of constraints are met. This paper deals with multi-objective and simultaneous optimization of reactive power dispatch in power systems through parallel processing. Three objective functions are intended: reduction of active power losses, reduction of voltage deviation, and increasing voltage stability. To solve the optimization problem, Strength Pareto Multi-group Search Optimizer (SPMGSO) algorithm will be used. This algorithm employs parallel processing, and as a result, it saves the required time to solve the problem. This optimization technique also yields a set of non-dominated optimal solutions. The operator of the power system is able to utilize a multi-criteria decision technique based on M matrices to determine the best solution, and to apply the relevant control variables on the power system. A comparison of the simulation results on IEEE 30-bus system with results of NSGAII algorithm attests that SPMGSO algorithm is satisfactory.
{"title":"Multi-objective optimization of reactive power dispatch in power systems via SPMGSO algorithm","authors":"Mohammadi Mohsen, H. Siahkali","doi":"10.1109/SGC.2017.8308868","DOIUrl":"https://doi.org/10.1109/SGC.2017.8308868","url":null,"abstract":"Nowadays, developments in computer science has made parallel processing feasible. One of the main control problems in power systems is the control of optimal reactive power dispatch. In this problem, we try to optimize specific objective functions using a series of control variables, while a set of constraints are met. This paper deals with multi-objective and simultaneous optimization of reactive power dispatch in power systems through parallel processing. Three objective functions are intended: reduction of active power losses, reduction of voltage deviation, and increasing voltage stability. To solve the optimization problem, Strength Pareto Multi-group Search Optimizer (SPMGSO) algorithm will be used. This algorithm employs parallel processing, and as a result, it saves the required time to solve the problem. This optimization technique also yields a set of non-dominated optimal solutions. The operator of the power system is able to utilize a multi-criteria decision technique based on M matrices to determine the best solution, and to apply the relevant control variables on the power system. A comparison of the simulation results on IEEE 30-bus system with results of NSGAII algorithm attests that SPMGSO algorithm is satisfactory.","PeriodicalId":346749,"journal":{"name":"2017 Smart Grid Conference (SGC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133559216","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 : 2017-12-01DOI: 10.1109/SGC.2017.8308848
Seyed Naser Hashemipour, J. Aghaei
The enormous benefits of distributed generation make them used increasingly. But the shortage of proper control of these resources causes problems in the network. The interference in the performance of various network equipment is one of these problems. So, a scheduled planning for the operation of these resources is very important. In current paper, an optimized model with probable constraints is indicated that provides a suitable time scheduling for coordinating the performance of the tap-changer, distributed generation sources and batteries, by taking into account the impact of the forecast error of consuming loads and generating power of photovoltaic units. The main characteristic of the indicated method is to consider uncertainty without the need for past system information. Finally, for testing the provided method, the standard network of the IEEE 33-bus has been studied.
{"title":"Chance constrained power-flow for voltage regulation in distribution systems","authors":"Seyed Naser Hashemipour, J. Aghaei","doi":"10.1109/SGC.2017.8308848","DOIUrl":"https://doi.org/10.1109/SGC.2017.8308848","url":null,"abstract":"The enormous benefits of distributed generation make them used increasingly. But the shortage of proper control of these resources causes problems in the network. The interference in the performance of various network equipment is one of these problems. So, a scheduled planning for the operation of these resources is very important. In current paper, an optimized model with probable constraints is indicated that provides a suitable time scheduling for coordinating the performance of the tap-changer, distributed generation sources and batteries, by taking into account the impact of the forecast error of consuming loads and generating power of photovoltaic units. The main characteristic of the indicated method is to consider uncertainty without the need for past system information. Finally, for testing the provided method, the standard network of the IEEE 33-bus has been studied.","PeriodicalId":346749,"journal":{"name":"2017 Smart Grid Conference (SGC)","volume":"432 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122997355","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 : 2017-12-01DOI: 10.1109/SGC.2017.8308838
M. Zeinaddini-Meymand, M. Rashidinejad, Mohsen Gharachedaghi
This paper presents a multi-period generation and transmission expansion planning in the presence of uncertainty in the strategies of market participations. Moreover, the effects of demand response and fixed series compensation allocation are considered for peak shaving and optimal utilization of transmission capacity, respectively. This may cut back the generating expansion capacity and transmission investment cost. The optimal expansion plan is achieved while modeling market functioning considering uncertainty in generator offers, and demand bids. In this model, DR preferences have integrated into ISO's market clearing process, which applied to the load aggregators according to locational marginal prices and market clearing. Shifting and curtailing demand peak, and onsite generation are considered as load reduction strategies in demand response program. However, ISO optimizes the decision submitted by generating companies and load aggregators in the presence of uncertainties. The proposed model is applied to the Garver system to show the effectiveness of DR and FSC in dynamic G&TEP.
{"title":"A demand-side management-based model for G&TEP problem considering FSC allocation","authors":"M. Zeinaddini-Meymand, M. Rashidinejad, Mohsen Gharachedaghi","doi":"10.1109/SGC.2017.8308838","DOIUrl":"https://doi.org/10.1109/SGC.2017.8308838","url":null,"abstract":"This paper presents a multi-period generation and transmission expansion planning in the presence of uncertainty in the strategies of market participations. Moreover, the effects of demand response and fixed series compensation allocation are considered for peak shaving and optimal utilization of transmission capacity, respectively. This may cut back the generating expansion capacity and transmission investment cost. The optimal expansion plan is achieved while modeling market functioning considering uncertainty in generator offers, and demand bids. In this model, DR preferences have integrated into ISO's market clearing process, which applied to the load aggregators according to locational marginal prices and market clearing. Shifting and curtailing demand peak, and onsite generation are considered as load reduction strategies in demand response program. However, ISO optimizes the decision submitted by generating companies and load aggregators in the presence of uncertainties. The proposed model is applied to the Garver system to show the effectiveness of DR and FSC in dynamic G&TEP.","PeriodicalId":346749,"journal":{"name":"2017 Smart Grid Conference (SGC)","volume":"404 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115320459","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 : 2017-12-01DOI: 10.1109/SGC.2017.8308842
M. Nazari-Heris, Amir Fakhim-Babaei, B. Mohammadi-ivatloo
Combined heat and power (CHP) units are able to generate power and heat, simultaneously. The main objective of CHP economic dispatch (CHPED) problem is to provide optimal heat and power production of cogeneration units with minimum operation cost of supplying heat and power demand. The CHPED problem should be studied considering several operational and electrical equality and inequality constraints consisting of valve-point loading effects of conventional thermal plants, power transmission loss of the system, power and heat capacity production limits of the plants, and heat and power load demand balance. Moreover, heat and power produced by cogeneration plants have bidirectional dependency, which results to complexity of the CHPED problem. In this study, a novel combination of harmony search (HS) algorithm and particle swarm optimization (PSO) method is proposed for the solution of non-convex non-linear CHPED problem. The proposed optimization technique is employed on two large-scale CHP systems for evaluating the performance of the proposed method. The large-scale CHPED problem is solved applying the proposed hybrid method, which demonstrates the effectiveness of the method in terms of operational cost and convergence characteristics.
{"title":"A novel hybrid harmony search and particle swarm optimization method for solving combined heat and power economic dispatch","authors":"M. Nazari-Heris, Amir Fakhim-Babaei, B. Mohammadi-ivatloo","doi":"10.1109/SGC.2017.8308842","DOIUrl":"https://doi.org/10.1109/SGC.2017.8308842","url":null,"abstract":"Combined heat and power (CHP) units are able to generate power and heat, simultaneously. The main objective of CHP economic dispatch (CHPED) problem is to provide optimal heat and power production of cogeneration units with minimum operation cost of supplying heat and power demand. The CHPED problem should be studied considering several operational and electrical equality and inequality constraints consisting of valve-point loading effects of conventional thermal plants, power transmission loss of the system, power and heat capacity production limits of the plants, and heat and power load demand balance. Moreover, heat and power produced by cogeneration plants have bidirectional dependency, which results to complexity of the CHPED problem. In this study, a novel combination of harmony search (HS) algorithm and particle swarm optimization (PSO) method is proposed for the solution of non-convex non-linear CHPED problem. The proposed optimization technique is employed on two large-scale CHP systems for evaluating the performance of the proposed method. The large-scale CHPED problem is solved applying the proposed hybrid method, which demonstrates the effectiveness of the method in terms of operational cost and convergence characteristics.","PeriodicalId":346749,"journal":{"name":"2017 Smart Grid Conference (SGC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115994491","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 : 2017-12-01DOI: 10.1109/SGC.2017.8308843
Morteza Mansouri Takantape, M. Hamzeh
This paper presents an accurate active power-sharing in low-voltage (LV) islanded microgrids by synthesizing voltage-real power droop and frequency-reactive power boost (PVD/QFB) method and distributed secondary cooperative control. In contrast to conventional droop method, which may lead to poor stability in LV microgrids, PVD/QFB method brings the stable condition to islanded LV microgrids. The utilized distributed secondary cooperative control removes the substantial active power-sharing error of PVD/QFB method. Therefore, the capability of PVD/QFB method in LV microgrids is improved and, moreover, it becomes practical for both parallel inverters and networked microgrids. The microgrid voltage restoration is also provided by aforementioned secondary control. Finally, a simulation is conducted in PLECS software to verify the presented method.
{"title":"Accurate active power-sharing in low-voltage islanded microgrids using a distributed secondary cooperative control","authors":"Morteza Mansouri Takantape, M. Hamzeh","doi":"10.1109/SGC.2017.8308843","DOIUrl":"https://doi.org/10.1109/SGC.2017.8308843","url":null,"abstract":"This paper presents an accurate active power-sharing in low-voltage (LV) islanded microgrids by synthesizing voltage-real power droop and frequency-reactive power boost (PVD/QFB) method and distributed secondary cooperative control. In contrast to conventional droop method, which may lead to poor stability in LV microgrids, PVD/QFB method brings the stable condition to islanded LV microgrids. The utilized distributed secondary cooperative control removes the substantial active power-sharing error of PVD/QFB method. Therefore, the capability of PVD/QFB method in LV microgrids is improved and, moreover, it becomes practical for both parallel inverters and networked microgrids. The microgrid voltage restoration is also provided by aforementioned secondary control. Finally, a simulation is conducted in PLECS software to verify the presented method.","PeriodicalId":346749,"journal":{"name":"2017 Smart Grid Conference (SGC)","volume":"637 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122950129","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 : 2017-12-01DOI: 10.1109/SGC.2017.8308867
Kamran Hosseini, Samad Araghi, M. Ahmadian, Vli Asadian
This paper proposes a sustainable simulation method for managing energy resources from the point of view of virtual power players (VPP) operating in a smart grid. The proposed energy resource management schedule in a micro-grid, including fuel cells, micro turbines, solar panels, wind turbines, and batteries, intelligently meets the needs of the grid. Apart from using the aforementioned resources, VPP can also purchase additional energy from upper utility to respond to the load. In addition, the proposed method plans suitably a micro-grid using a multi-objective framework, which minimizes the total operation cost and emission caused by the generating units simultaneously. To achieve this goal, the multi-objective Ant Lion Optimizer (MOALO) has been used to solve the multi-objective optimization problem and to produce Pareto optimal solutions. The fuzzy technique has been used for the decision making process. Finally, to demonstrate the effectiveness of the proposed method, the results have been compared with multi-objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which shows that the use of the MOALO method in the presence of fuzzy technique attain the superior solutions on the operation cost and the emission of pollutant.
{"title":"Multi-objective optimal scheduling of a micro-grid consisted of renewable energies using multi-objective Ant Lion Optimizer","authors":"Kamran Hosseini, Samad Araghi, M. Ahmadian, Vli Asadian","doi":"10.1109/SGC.2017.8308867","DOIUrl":"https://doi.org/10.1109/SGC.2017.8308867","url":null,"abstract":"This paper proposes a sustainable simulation method for managing energy resources from the point of view of virtual power players (VPP) operating in a smart grid. The proposed energy resource management schedule in a micro-grid, including fuel cells, micro turbines, solar panels, wind turbines, and batteries, intelligently meets the needs of the grid. Apart from using the aforementioned resources, VPP can also purchase additional energy from upper utility to respond to the load. In addition, the proposed method plans suitably a micro-grid using a multi-objective framework, which minimizes the total operation cost and emission caused by the generating units simultaneously. To achieve this goal, the multi-objective Ant Lion Optimizer (MOALO) has been used to solve the multi-objective optimization problem and to produce Pareto optimal solutions. The fuzzy technique has been used for the decision making process. Finally, to demonstrate the effectiveness of the proposed method, the results have been compared with multi-objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which shows that the use of the MOALO method in the presence of fuzzy technique attain the superior solutions on the operation cost and the emission of pollutant.","PeriodicalId":346749,"journal":{"name":"2017 Smart Grid Conference (SGC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114452571","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 : 2017-12-01DOI: 10.1109/SGC.2017.8308855
A. Lorestani, Seyed Saeed Aghaee, G. Gharehpetian, M. M. Ardehali
The objective of this study is to optimal scheduling of resources and loads in a smart home (SH) including photovoltaic (PV) panel, battery, plug-in electric vehicle (PEV) and electric heater (EH) along with electrical and thermal loads. Advantages of SH with the proposed structure is that all electrical and thermal loads can be met by electric energy and as a result, it decreases additional investment in natural gas infrastructure, balances electricity and natural gas consumption during seasons, reduces air pollution in home environment, and diminishes SH bills. To this end, an energy management system (EMS) is designed using shuffled frog leaping (SFLA) algorithm for load and resource scheduling such that SH daily energy consumption cost is minimum. Performance of the SH in different scenarios are studied, a feasibility study for the SH is conducted and the results are discussed. Simulation results show that SFLA algorithm has higher capability compared to other algorithms in solving optimal energy management problem in the SH, and it has been shown that PEV which will penetrate significantly in future, has a considerable effect on SH costs and should be considered in residential planning studies.
{"title":"Energy management in smart home including PV panel, battery, electric heater with integration of plug-in electric vehicle","authors":"A. Lorestani, Seyed Saeed Aghaee, G. Gharehpetian, M. M. Ardehali","doi":"10.1109/SGC.2017.8308855","DOIUrl":"https://doi.org/10.1109/SGC.2017.8308855","url":null,"abstract":"The objective of this study is to optimal scheduling of resources and loads in a smart home (SH) including photovoltaic (PV) panel, battery, plug-in electric vehicle (PEV) and electric heater (EH) along with electrical and thermal loads. Advantages of SH with the proposed structure is that all electrical and thermal loads can be met by electric energy and as a result, it decreases additional investment in natural gas infrastructure, balances electricity and natural gas consumption during seasons, reduces air pollution in home environment, and diminishes SH bills. To this end, an energy management system (EMS) is designed using shuffled frog leaping (SFLA) algorithm for load and resource scheduling such that SH daily energy consumption cost is minimum. Performance of the SH in different scenarios are studied, a feasibility study for the SH is conducted and the results are discussed. Simulation results show that SFLA algorithm has higher capability compared to other algorithms in solving optimal energy management problem in the SH, and it has been shown that PEV which will penetrate significantly in future, has a considerable effect on SH costs and should be considered in residential planning studies.","PeriodicalId":346749,"journal":{"name":"2017 Smart Grid Conference (SGC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131793130","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 : 2017-12-01DOI: 10.1109/SGC.2017.8308875
Saeed Amiri, S. Jadid
Motivated by indispensable requirements of large penetration of electric vehicles (EVs), battery swapping is an efficient performance to exert benefits of changing batteries within a short time period and charging them during off-peak hours. This paper proposes a strategy trying to find the best charging procedure of electric vehicles in an environment toward battery swapping stations (BSSs). The goal of the strategy is to minimize the charging cost as well as to reduce energy loss. Voltage deviation of buses, power flow of network branches, and maximum power consumption of BSSs are considered as constraints of this optimization problem. In order to solve the issue, a population-based evolutionary approach, which is a modified hybrid form of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, is employed. The strategy is implemented on IEEE 33-bus distribution network test system and numerical results are illustrated.
{"title":"Optimal charging schedule of electric vehicles at battery swapping stations in a smart distribution network","authors":"Saeed Amiri, S. Jadid","doi":"10.1109/SGC.2017.8308875","DOIUrl":"https://doi.org/10.1109/SGC.2017.8308875","url":null,"abstract":"Motivated by indispensable requirements of large penetration of electric vehicles (EVs), battery swapping is an efficient performance to exert benefits of changing batteries within a short time period and charging them during off-peak hours. This paper proposes a strategy trying to find the best charging procedure of electric vehicles in an environment toward battery swapping stations (BSSs). The goal of the strategy is to minimize the charging cost as well as to reduce energy loss. Voltage deviation of buses, power flow of network branches, and maximum power consumption of BSSs are considered as constraints of this optimization problem. In order to solve the issue, a population-based evolutionary approach, which is a modified hybrid form of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, is employed. The strategy is implemented on IEEE 33-bus distribution network test system and numerical results are illustrated.","PeriodicalId":346749,"journal":{"name":"2017 Smart Grid Conference (SGC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129634141","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 : 2017-12-01DOI: 10.1109/SGC.2017.8308874
M. Salehpour, S. Tafreshi
This paper computes the optimal bids that the smart microgrid energy management system (SMEMS) submits to the day-ahead electricity market. This smart microgrid consists of dispatchable generation resources, renewable generation resources, storage system and the loads that can be participate in the demand response (DR) programs. In this study we intend to maximize the expected profit earned by trading in day-ahead electricity market as well as optimal scheduling of smart microgrid for energy dispatching on the operating day. The bidding problem can be difficult due to different uncertainties in generations, loads and market prices forecasts amounts. To deal with these uncertainties, two-stage stochastic programming is employed. Various stochastic scenarios are generated by Monte Carlo simulation and then a scenario reduction algorithm based on kantorovich distance is performed. Nonlinear terms of the objective function are recast into linear forms. Numerical results have confirmed the profitability of the proposed smart microgrid.
{"title":"Optimal bidding strategy for a smart microgrid in day-ahead electricity market with demand response programs considering uncertainties","authors":"M. Salehpour, S. Tafreshi","doi":"10.1109/SGC.2017.8308874","DOIUrl":"https://doi.org/10.1109/SGC.2017.8308874","url":null,"abstract":"This paper computes the optimal bids that the smart microgrid energy management system (SMEMS) submits to the day-ahead electricity market. This smart microgrid consists of dispatchable generation resources, renewable generation resources, storage system and the loads that can be participate in the demand response (DR) programs. In this study we intend to maximize the expected profit earned by trading in day-ahead electricity market as well as optimal scheduling of smart microgrid for energy dispatching on the operating day. The bidding problem can be difficult due to different uncertainties in generations, loads and market prices forecasts amounts. To deal with these uncertainties, two-stage stochastic programming is employed. Various stochastic scenarios are generated by Monte Carlo simulation and then a scenario reduction algorithm based on kantorovich distance is performed. Nonlinear terms of the objective function are recast into linear forms. Numerical results have confirmed the profitability of the proposed smart microgrid.","PeriodicalId":346749,"journal":{"name":"2017 Smart Grid Conference (SGC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133657309","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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