Showing 4 results for Sedighizadeh
M. Kalantar, M. Sedighizadeh,
Volume 1, Issue 1 (January 2005)
Abstract
A dynamic reduced order model using integral manifold theory has been derived,
which can be used to simulate the DOIG wind turbine using a double-winding
representation of the generator rotor. The model is suitable for use in transient stability
programs that can be used to investigate large power systems. The behavior of a wind farm
and the network under various system disturbances was studied using this dynamic model.
Simulation results of the proposed method represents that integral manifold method results
fit the detailed model results with a higher precision than other methods.
M. Sedighizadeh, M. Esmaili, M. M. Mahmoodi,
Volume 13, Issue 3 (September 2017)
Abstract
Distribution systems can be operated in multiple configurations since they are possible combinations of radial and loop feeders. Each configuration leads to its own power losses and reliability level of supplying electric energy to customers. In order to obtain the optimal configuration of power networks, their reconfiguration is formulated as a complex optimization problem with different objective functions and network operating constraints. In this paper, a multi-objective framework is proposed for optimal network reconfiguration with objective functions of minimization of power losses, System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Average Energy Not Supplied (AENS), and Average Service Unavailability Index (ASUI). The optimization problem is solved by the Imperialist Competitive Algorithm (ICA) as one of the most modern heuristic tools. Since objective functions have different scales, a fuzzy membership is utilized here to transform objective functions into a same scale and then to determine the satisfaction level of the afforded solution using the fuzzy fitness. The efficiency of the proposed method is confirmed by testing it on 32-bus and 69-bus distribution test systems. Simulation results demonstrate that the proposed method not only presents intensified exploration ability but also has a better converge rate compared with previous methods.
M. Sedighizadeh, S. M. M. Alavi, A. Mohammadpour,
Volume 16, Issue 3 (September 2020)
Abstract
Regarding the advances in technology and anxieties around high and growing prices of fossil fuels, government incentives increase to produce cleaner and sustainable energy through distributed generations. This makes trends in the using microgrids which consist of electric demands and different distributed generations and energy storage systems. The optimum operation of microgrids with considering demand-side management increases efficiency and reliability and maximize the advantages of using distributed generations. In this paper, the optimal operation scheduling and unit commitment of generation units installed in a microgrid are investigated. The microgrid consists of technologies based on natural gas that are microturbine and phosphoric acid fuel cell and technologies based on renewable energy, including wind turbine and photovoltaic unit along with battery energy storage system and plug-in electric vehicle commercial parking lot. The goal of the paper is to solve a multi-objective problem of maximizing revenues of microgrid operator and minimizing emissions. This paper uses an augmented epsilon constraint method for solving the multi-objective problem in a stochastic framework and also implements a fuzzy-based decision-maker for choosing the suitable optimal solution amid Pareto front solutions. This new model implements the three type of the price-based and incentive-based demand response program. It also considers the generation reserve in order to enhance the flexibility of operations. The presented model is tested on a microgrid and the results demonstrate the efficacy of the proposed model economically and environmentally compared to other methods.
M. Khajevand, A. Fakharian, M. Sedighizadeh,
Volume 16, Issue 3 (September 2020)
Abstract
Using distributed generations (DGs) with optimal scheduling and optimal distribution feeder reconfiguration (DFR) are two aspects that can improve efficiency as well as technical and economic features of microgrids (MGs). This work presents a stochastic copula scenario-based framework to jointly carry out optimal scheduling of DGs and DFR. This framework takes into account non-dispatchable and dispatchable DGs. In this paper, the dispatchable DG is a fuel cell unit and the non-dispatchable DGs with stochastic generation are wind turbines and photovoltaic cells. The uncertainties of wind turbine and photovoltaic generations, as well as electrical demand, are formulated by a copula-based method. The generation of scenarios is carried out by the scenario tree method and representative scenarios are nominated with scenario reduction techniques. To obtain a weighted solution among the various solutions made by several scenarios, the average stochastic output (ASO) index is used. The objective functions are minimization of the operational cost of the MG, minimization of active power loss, maximization of voltage stability index, and minimization of emissions. The best-compromised solution is then chosen by using the fuzzy technique. The capability of the proposed model is investigated on a 33-bus MG. The simulation results show the efficiency of the proposed model to optimize objective functions, while the constraints are satisfied.
