Distribution Systems
S. Panjeie; A. Fakharian; M. Sedighizadeh; A. Sheikhi fini
Abstract
Microgrid operators (MGOs) try to restore as much demand as possible when they are faced with electrical power outages corre-sponding to extreme events. This work suggests an outage management strategy (OMS) to improve microgrid resilience by using two optimal actions that are distribution feeder reconfiguration ...
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Microgrid operators (MGOs) try to restore as much demand as possible when they are faced with electrical power outages corre-sponding to extreme events. This work suggests an outage management strategy (OMS) to improve microgrid resilience by using two optimal actions that are distribution feeder reconfiguration (DFR) and scheduling of the distributed energy resources (DERs). Later happening a line fault, the radial network topology is determined by the proposed model using an evaluation of the inci-dence matrix. The presented work handles the uncertain behavior of non-dispatchable DERs and the electrical loads which model by the robust optimization approach. To expand the flexibility of the proposed model, the demand response program (DRP) is treated as the curtailed demand. The aim of optimization is the minimization of the total cost for dispatchable DER operation and electrical load decrease. The recommended robust linear problem (RLP) model is simulated by the CPLEX solver in GAMS software. Applying the suggested model in the 69-bus unbalanced test system demonstrate that the proposed model averagely decreases total operation cost and execution time by 10.62% and 22.23% on all scenarios in comparison with the de-terministic model.
S. Lotfi; M. Sedighizadeh; R. Abbasi; S.H. Hosseinian
Abstract
Vehicle to grid (V2G) is one the most important ways to effectively integrate electric vehicles (EVs) with electric power systems. The important benefits can be made by V2G such as reducing/increasing the cost/revenue of EV owners and technically sup-porting electric power systems. Concerning technical ...
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Vehicle to grid (V2G) is one the most important ways to effectively integrate electric vehicles (EVs) with electric power systems. The important benefits can be made by V2G such as reducing/increasing the cost/revenue of EV owners and technically sup-porting electric power systems. Concerning technical and regulatory constraints, EV owners must participate in electricity mar-kets via aggregators. This paper proposes a robust optimal coordinated charging (OCC) model including bidding ancillary ser-vices for regulation and spinning reserve markets. The presented work handles the uncertain behavior of the electricity market that are ancillary service prices and their deployment signals by the robust optimization approach. The aim of optimization is the maximization of the aggregator’s profits from V2G by joining the ancillary services markets. The recommended robust OCC model which is a robust linear problem (RLP) model is simulated by the CPLEX solver in GAMS software. An assumed set of 10000 EVs in the electric reliability council of Texas (ERCOT) electricity markets is considered for doing simulations. Employ-ing the presented model in this test system shows the efficacy of the proposed model in comparison to other deterministic and stochastic models.
H. Salmani; A. Rezazadeh; M. Sedighizadeh
Abstract
Fossil-fueled vehicles are being replaced by electric vehicles (EVs) around the world due to environmental pollution and high fossil fuel price. On the one hand, the electrical grid is faced with some challenges when too many EVs are improperly integrated. On the other hand, using a massive unexploited ...
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Fossil-fueled vehicles are being replaced by electric vehicles (EVs) around the world due to environmental pollution and high fossil fuel price. On the one hand, the electrical grid is faced with some challenges when too many EVs are improperly integrated. On the other hand, using a massive unexploited capability of the batteries in too many EVs makes these challenges opportunities. This unused capacity can be employed for the grid ancillary services and trading peer-to-peer (P2P) energy. However, the preference of EV users is one of the most important factors, which has to be considered within the scheduling process of EVs. Therefore, this paper proposes a stochastic model for EV bidirectional smart charging taking into account the preferences of EV users, P2P energy trading, and providing ancillary services of the grid. Considering the likings of EV users makes the proposed scheduling model adaptive against changing operating conditions. The presented model is formulated as an optimization problem aiming at optimal managing SOC of EV battery and electrical energy placement of several facilities considering the provision of ancillary services and contributing to P2P transactions. To evaluate the proposed model, real-world data collected from Tehran city are used as input data for simulation. Numerical results demonstrate the ability of the presented model. Simulation results display that considering the preferences of EV users in the proposed model can enhance the total income provided by the EV energy-planning model such that it could balance the charging cost. Moreover, this advanced user-based smart charging model increases P2P energy transactions amongst EVs and raises the ancillary services facility to the grid. Simulation results show that the yearly cost of optimal electrical charging on normal trips, light trips, and heavy trips is reduced by 32.6%, 51.2%, and 34.8% compared to non-optimal ones, respectively.
A. Komijani; M. Kheradmandi; M. Sedighizadeh
Abstract
Voltage drop during the fault can be effected on the performance of generation units such as wind turbines. The ability to ride through the fault is important for these generation units. Superconducting fault current limiter and superconducting magnetic energy storage can improve the fault ride through ...
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Voltage drop during the fault can be effected on the performance of generation units such as wind turbines. The ability to ride through the fault is important for these generation units. Superconducting fault current limiter and superconducting magnetic energy storage can improve the fault ride through due to fault current limiting and voltage restoring ability during the fault, respectively. This paper presents a method for optimal allocation and control of superconducting magnetic energy storage and superconducting fault current limiters in meshed microgrids. For this purpose, the doubly-fed induction generator voltage deviation, the point of common coupling power deviation, the fault current of transmission lines, and superconducting fault current limiter and superconducting magnetic energy storage characteristics were considered as objective functions. In this paper, the optimization is performed in single-step and two-step by particle swarm optimization algorithm, and the system with the optimal superconducting magnetic energy storage and superconducting fault current limiters are analyzed and compared. The results of simulations show superconducting fault current limiter and superconducting magnetic energy storage reduce 85% of voltage drop, decreases 63% of doubly fed induction generator power deviation, and limits the maximum fault current of transmission lines by 9.8 pu. Finally, the status of the studied system variables has been investigated, in two scenarios related to the different fault locations with equipment that the optimal allocated.
A. Benyaghoob sani; M. Sedighizadeh; D. Sedighizadeh; R. Abbasi
Abstract
An optimal day-ahead operation of a microgrid based on coastal energy hub is presented in this paper. The proposed CEH included wind turbine, photovoltaic unit, combined cooling, heat and power, and seawater desalination. The purpose of the optimization is minimization of the operational and environmental ...
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An optimal day-ahead operation of a microgrid based on coastal energy hub is presented in this paper. The proposed CEH included wind turbine, photovoltaic unit, combined cooling, heat and power, and seawater desalination. The purpose of the optimization is minimization of the operational and environmental costs considering several technical limitations. The CEH includes an ice storage conditioner together with an energy storage system, i.e. thermal energy storage system. Particularly, the impacts of an innovative rechargeable and emerging ESS that is solar-powered compressed air energy storage is scrutinized, on the efficiency and operational and pollution costs of the CEH. It is clear that there is an intrinsic deviation between predicted and actual uncertainty variables in MG. This paper presents a bi-level stochastic optimal operation model based on risk averse strategy of information gap decision theory to overcome this information gap and to help Microgrid operator. To reduce the complexity of the proposed model, Karush-Kuhn-Tucker method is used for converting the bi-level problem into a single level. The Augmented Epsilon Constraint method is used to deals with multi objective optimization problem to harvest the maximum horizon of the uncertainties of the parameters. The proposed model implemented the Time of Use program as a price-based demand response program. Finally, the efficacy of the SPCAES for minimizing the operational cost and pollutions in the day-ahead operation is depicted by implementation of the presented model on the typical CEH.
Mostafa Sedighizadeh; Mahdi mahmoodi
Volume 3, Issue 1 , June 2015, , Pages 56-70
Abstract
In distribution systems, in order to diminish power losses and keep voltage profiles within acceptable limits, network reconfiguration and capacitor placement are commonly used. In this paper, the Hybrid Shuffled Frog Leaping Algorithm (HSFLA) is used to optimize balanced and unbalanced radial distribution ...
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In distribution systems, in order to diminish power losses and keep voltage profiles within acceptable limits, network reconfiguration and capacitor placement are commonly used. In this paper, the Hybrid Shuffled Frog Leaping Algorithm (HSFLA) is used to optimize balanced and unbalanced radial distribution systems by means of a network reconfiguration and capacitor placement. High accuracy and fast convergence are the highlighted points of the proposed approach because of solving the multi-objective reconfiguration and capacitor placement in fuzzy frame work. These objectives are the minimization of total network real power losses, the minimization of buses voltage violation, and load balancing in the feeders. Each objective is transferred into fuzzy domain using membership function and fuzzified separately. Then, the overall fuzzy satisfaction function is formed and considered as a fitness function. To gain the optimal solution, the value of this function will be maximized. In the literature, several reconfiguration and capacitor placement methods have been investigated, which are implemented separately. However, there are few studies which simultaneously apply these two strategies. The proposed algorithm has been implemented in three IEEE test systems (two balanced and one unbalanced systems). Numerical results obtained by simulation show that the performance of the HSFLA algorithm is much higher than several other meta-heuristic algorithms.
