مجله کیفیت و بهره وری صنعت برق ایران
سال هفتم شماره 1 (پیاپی 13، بهار و تابستان 1397)

The presence of distributed generations (DGs) in the power systems is causing problems such as increasing the short circuit current levels which may exceed the rating of existing circuit breakers and can damage system equipment. The utilization of fault current limiters (FCLs) in the network can be an effective method to overcome the above problems. Furthermore, FCL has the benefits such as improving the system security and reliability. FCL benefits depend on the number, installation location, and impedance of FCL. For this end, we require a method to determine the optimum number, impedance and locations for FCL placement. In the considered method, we have modeled the FCL placement as an optimization problem while the objectives are; bus fault current difference, reliability, the number and impedance of FCLs. Moreover, to solve the proposed problem, a new multi-objective optimization algorithm based on particle swarm optimization has been implemented. In the algorithm, several iterations have been considered, and the non-dominated solutions are extracted and stored in an external repository in the iterations. Finally, a fuzzy clustering technique is used to control the size of the repository during the algorithm evolution. The proposed approach is tested on a test system, namely, RBTS 2 . The obtained results demonstrate the effectiveness and feasibility of the new method.

Sensors monitor the status of various parts of hydroelectric power station and control instruments issued instructions to operate the power plant.The experts based on the amount of numbers for which sensors and thermometers are fitted and shown, and also based on environmental conditions of the plant, and experience, make a decision for emergency power shutting down. In a hydroelectric power plant several factors such as: loaders, maintenance, signs warning sensors, physical damage to equipment or the height of the water behind the dam, may be stop the generation of the electricity.So appropriate activity or inactivity time detection of power plant according to the sensors is vital. Although the existing control systems to check the syntax of the favorable conditions but different ball fitted such as human error or equipment error may decide to continue with the emergency shutdown error or work together.In this article, using data mining techniques for a system that is designed to be fitted decision sweetheart meaningful relationships between data that sensors in all hydroelectric power plant can archive . These relationships can deduce laws such as fitted in a quick and accurate decision making experts are extremely helpful and damage to equipment in the wrong decisions or prevent late. data set is gotten from Maroon power plant-Behbahan from years 92 to 94. we extract 41 rules by association rule mining that experts have been recognized 4 of them are new knowledge.

The most prominent characteristics of the electricity market in our country are thepay as bid to market winners. However, in most countries, a uniform payment method is used to clearing the market.In this paper, the effect of these two methods on the productivity and efficiency of the power industry in the manufacturing sector is compared. For a closer look, the model is designed for three hours of low load, normal load, peak, and yearly. For this purpose, with the use of the Q-Learning method, which is a subset of multy-agent models, Iran's electricity market model with all its features and limited to Khorasan power plants is simulated in two different ways and efficiency is compared in two scenarios. The results indicate that a uniform payment method leads to the use of higher power plants to supply electricity. Therefore, in this way, the cost of electricity is lower than the pay as bid method.

Natural disasters and severe weather conditions can lead to extensive outages in power networks. In order to reduce the economic and social effects of blackouts, most electric utilities apply pre-determined instructions and procedures to recover the system and restore the loads. However, due to the high impacts and different nature of such incidents, traditional methods of load restoration in the distribution networks can not guarantee the desired performance of the system in these situations. Therefore, in this paper, a novel model based on mixed-integer linear programming is presented to load restoration in distribution networks after such disasters. In this model, by utilizing graph related theories, the topological features of the distribution network along with its electrical characteristics are formulated in the form of one linear optimization problem. The formation of microgrids, determination of their service areas, and the optimal management of different technologies such as distributed generation units and demand response resources have been also considered. Finally, by performing multiple simulations, the efficiency and applicability of the proposed integrated model have been verified.

Energy hub concept has provided new opportunities in energy management scope. Additionally, largely implementation of renewable energy resources has posed strict challenges in energy systems coordination issue. Probabilistic outputs of renewable energy sources make the energy provision coordination so complicated, in the regions where their penetration is high. Energy storage system can be proposed as a solution for mentioned problem. Power to gas units as an electrical energy storage can store the electrical energy in natural gas grid. This paper has focused on stochastic optimization of renewable based networked hubs in the presence of power to gas unit with economical target. Power to gas unit’s direct hydrogen injection and hydrogen tank facilities have been considered in this paper, as well. This paper demonstrates that, power to gas unit can participate positively in network cost and renewable energy curtailment reduction, considerably.

This paper presents a novel approach for optimal planning of the multi microgrids (MMGs) under uncertainties in load and renewable power generation. The proposed approach is applied for optimally determining the size, type, number, and site of renewable and dispatchable distribution generation (DG) with optimal allocation of switch for clustering distribution systems into a number of microgrids to economical and reliable structure. The optimization aim is to minimize the totally microgrid planning cost including investment cost, operation and maintenance cost, power losses cost, the pollutants emission cost and the cost of energy not supply (ENS). The system uncertainties are considered using a set of scenarios and a scenario reduction method is applied to enhance a tradeoff between the accuracy of the solution and the computational burden. Cuckoo optimization algorithm (COA) is implemented to minimize the objective function as an optimization algorithm. Also, the effect of optimization coefficients on the planning problem and the robustness of the proposed algorithm are investigated using sensitivity analysis. The efficiency of the proposed method are validated on 33-bus distribution system and the obtained results show that the proposed framework can be considered as an efficient tool for planning of multi microgrids under uncertainty.

By increasing the number of electric vehicles (EVs) in the power system, it is necessary to manage their interaction with the grid such that to minimize the costs of the owners as well as to maximize the profit of their serving entities. Therefore, in this paper, a stochastic bi-level decision making problem for the participation of EV aggregator in a competitive environment is presented, with considering various sources of uncertainty. The sources of uncertainty for the participation of aggregators in the electricity market include day-ahead (DA) and balancing market prices as well as rival's suggested prices and charging and discharging EVs demand that are modeled with time series. In the proposed bi-level program, the goal of the first level is to maximize the aggregator's profit in interaction with the network, and the goal of the second level is to minimize the payments of the owners. Since the objective function of the first and second levels are in contradiction with each other, with using KKT optimization conditions and the duality theory, the proposed two-level problem has become a linear single-level problem. Finally, the proposed program is implemented in typical test system and the results show that by using this model for the aggregator's decision making, it can offer proper charge and discharge price signals to the EV owners to attract the them in a competitive market and also to maximize its profit.

In The deregulation in power market is lead to competition among market participant to increase efficiency. In electricity market generation is the best candidate for iterance in competition to improve productivity and efficiency in resource allocation and offer lowest price by highest quality will be yielded. In the pool-based electricity market, every Genco submits a bidding price in ten step offer to the Independent System Operator (ISO) for every hour of the next day. The ISO uses the bidding price and forecasting demand to determine the MCP. The resulting spot price series exhibit strong seasonality at the annual, weekly and daily levels, as well as mean reversion, very high volatility and abrupt, short-lived and generally unanticipated extreme price changes known as spikes or jumps. So in this article we cluster time horizon in three cluster then we applied improved neural network by genetic algorithm for all clusters. In compare of normal neural network, results of our model are more better by 95% Accuracy.

Optimal reactive power dispatch plays an important role in economic operation of network by reducing the loss. In this paper, the teaching learning based optimization algorithm is used to optimal reactive power dispatch and voltage control. This algorithm is an evolutionary and population based algorithm which has a great capability to solve the nonlinear problems. This problem is formulated as a mixed integer nonlinear problem including both continues and discrete variables. Optimal solution of problem contains set of generators voltage, tap changers and compensative reactive components. In the proposed approach, using real wind speed data and considering the wind uncertainty, the two points estimate power flow is used to model the uncertainties. The proposed method has been implemented on 57-bus IEEE test case. A comparing has been done between the teaching learning based optimization algorithm and particle swarm optimization and differential evolution algorithms in order to verify the efficiency of the proposed algorithm. The results demonstrate the efficiency of the proposed method in reducing losses and handling the constraints.

Due to the increment of power demand, diminishing resources of fossil fuels and reduction of carbon dioxide out, the use of renewable energy resources is one of the important strategies in the developed countries. In this study, we use to meet electricity demand from renewable resources by as photovoltaic, batteries and wind turbines at times of peak production. The maximum amount of energy that solar panels and wind turbines can provide during any hour of the day is a function the amount of solar radiation and wind speed per hour. The aim is to minimize the cost of utilizing resources and maximize reliability. A non-linear programming model is proposed for the considered problem. Since the objectives are in conflict, for achieving a single optimal solution is not possible, therefore, NSGAII algorithm proposed for solving the problem