فهرست مطالب meisam amirahmadi

One of the most important problems in distributed networks, is the loss of these networks. The function of energy annual loss is mainly one of the main functions in operation and optimum development of distributed networks problems. for calculating annual loses of energy in distributed networks need to calculate the load flow in all hours of a year, so that this matter needs high volume and time for calculating.in this article with using IEEE-RTS network load model, a new load model that named 288-h average load model for calculating annual loses of energy in distributed networks is presented. The performance of this proffered model on IEEE 33-bus test system is under study an analysis, and the forward/backward sweep technique is being used for load flow in test system. The result shows the excellence of the offered model.

In this paper, considering many advantages of distributed generation (DG) and also pay attention to the importance of optimum placement and sizing of these resources in order to reach its advantages, to determination of the optimal location and capacity of DG in distributed generation has been paid. unsuitable location and sizing of DG in distributed networks Leads to disadvantages including Increase losses, voltage profile destruction and reliability of network, stability reduce and harmonic increase. The optimizing that is noticed in this paper offered by presenting a sensitivity factor to reduce of annual energy loss in network and annual costs of DG. It used algorithm method in order to solve optimizing problem and also used IEEE 33-bus radial distributed network as system test. It used 288-h average load model and forward/backward sweep technique to calculate the annual energy loss.

In this paper, an approach is proposed for optimum placement and sizing of distributed generation (DG) and battery energy storage system (BESS) in distribution networks. The objective functions in this paper are includes voltage profile improvement of network busses, loss reduction of distribution system, and minimization of installing and operation costs of both DG and BESS. In this paper, the fuel cell-Combined heat and power (CHP) technology is used as DG and nickel/cadmium (Ni/Cd) batteries is used as BESS. It also in this paper are used genetic algorithm (GA) as a means to solve the optimization problem, IEEE 33-bus radial distribution network as test system and Forward/backward sweep technique as load flow method.

This paper proposes a method for determining the price bidding strategies of market participants consisting of Generation Companies (GENCOs) and Distribution Companies (DISCOs) in a day-ahead electricity market, while taking into consideration the load forecast uncertainty and demand response programs. The proposed algorithm tries to find a Pareto optimal point for a risk neutral participant in the market. Because of the complexity of the problem a stochastic method is used. In the proposed method, two approaches are used simultaneously. First approach is Fuzzy Genetic Algorithm for finding the best bidding strategies of market players, and another one is Mont-Carlo Method that models the uncertainty of load in price determining algorithm. It is demonstrated that with considering transmission flow constraints in the problem, load uncertainty can considerably influences the profits of companies and so using the second part of the proposed algorithm will be useful in such situation. It is also illustrated when there are no transmission flow constraints, the effect of load uncertainty can be modeled without using a stochastic model. The algorithm is finally tested on an 8 bus system.