جستجوی مقالات مرتبط با کلیدواژه "congestion management" در نشریات گروه "برق"

Overcoming the problem of transmission lines or a region of the power system congestion is one of the important issues facing the operators. In the meantime, in addition to choosing the appropriate method to relief transmission line congestion, the process should be implemented with the aim of minimizing the imposed costs. In this article, in order to optimally control the transmission lines congestion, an integrated method including management of the production and demand side of the systems along with the use of the flexibility of multi-carrier energy systems (electricity, gas and heat) is presented. The congestion alleviation technique is analyzed in the two modes of independent operation of the power system and integrated operation of multi-carrier systems, and their results have been compared. Also, taking into consideration the scenario of transmission line congestion with the simultaneous outage of a power generation unit, the effect of energy storage in solving this problem has also been studied. The simulation of scenarios and the evaluation of the effectiveness of the proposed method have been implemented in a multi-carrier energy system including the IEEE 39 bus power system in combination with the Belgian 20-node gas network and several energy hubs. The evaluation of the obtained results shows a significant reduction in the congestion management costs in the mode of integrated operation with the rescheduling of generation and consumption compared to the independent operation of the power system. In addition, it was proved that the properly storage and discharge of all types of energy can be successful in order to optimize (at least 10%) the imposed costs.

Individual and public awareness of global warming and pollution has required the different sectors of industry to pay more attention to this issue and take some action in this regard. The electrical industry as the backbone of most other industries should devote more effort for pollution reduction. Unit commitment is the process of determining the state of the units for the day-ahead market. Therefore, managing unit commitment is the best option for contamination reduction. However, in the smart grid, customers can be part of the market via demand response programs. On the other hand, congestion is one of the main problems of the transmission network. Demand response programs could have a positive effect on congestion alleviation as they reduce the total power transmitted via the transmission system. In this paper, the effect of aggregated demand response programs as virtual power plants on operation cost, congestion and emission of network-constrained unit commitment is investigated. Moreover, the best rate of the incentive paid to the demand response programs participants is determined. In this study, according to the simultaneous implementation of the unit commitment problem and the demand response program, which is a complex nonlinear problem with continuous and discrete variables, the MILP method has been applied. Moreover, the transmission system is also considered to fully analyze the impact of demand response programs in the electricity market and its impact on reducing congestion. The recommended strategy is carried out on the IEEE 24 bus reliability test system to prove the effectiveness of demand response programs integration for cost and emission reduction.

Today, electric vehicles have been significantly considered due to the lack of air pollution. However, due to the large volume of vehicles in the city, connecting them to the distribution network without a control program may cause congestion in the network lines and substations. Therefore, a bi-level hierarchical method for connecting electric vehicles to the distribution network to coordinate services and performance constraints of the three main elements, namely fleet operator, distribution system operator, and capacity market operator in the presence of vehicle owners and taking into account driving requirements, charging and compaction costs of power lines and transformers are recommended. In this method, first car owners and fleet operators present their charging program to the distribution system operator according to the predicted market prices. In the next step, if this charging program leads to congestion in the lines or substations and power transformers, it is returned to the fleet operators and the shadow price is determined by the capacity market operator based on the congestion status. Then the new charging program is dispatch according to the new market prices. The proposed method is implemented in a sample distribution network and the simulation results show the efficiency of the proposed method.

Due to the uneven growth of demand during peak hours, network operators are willing to implement demand responses to reduce costs and manage congestion. On the other hand, increasing attention to climate change has increased the penetration of renewable energy sources (RES) in the electricity network. Due to the intermittency of RESs, gas-fired power plants could play a major role in backing up the RESs in order to maintain the supply-demand balance. As a result, the interaction between electricity and gas systems is significantly increasing. In this context, modeling and optimal implementation of demand response programs in proportion to the demand is one of the main challenges for power and gas system operators. In this paper, the effects of optimal implementation of local critical peak price (LCPP) in electricity and gas systems using linear and non-linear economic models (power, exponential and logarithmic) for LCPP in terms of cost and line congestion and risk of unserved demand are investigated. To reduce efficiently congestion of transmission lines as well as gas pipelines, CPP has been implemented locally. Reliable modeling for load estimation could be effective in selecting the most appropriate load response model for estimating the load curve with the least error. Optimal prices at critical times for each bus/node are determined based on power distribution coefficient, gas transmission distribution coefficient, available electricity/gas transmission capability, and the combination of unit commitment with local demand response during integrated operation of gas and electricity systems. According to the results, implementing the LCPP in electricity and gas systems (compared with the implementation of LCPP in electricity system only) reduces the total cost by 3%, the congestion of electricity transmission lines by 1%, and the standard deviation of the gas pipes linepacks by 1%. The proposed hybrid model is applied on a 24-bus IEEE electricity network and a 15-bus gas network to quantify the role and value of different LCPP models.

Away to decrease the costs of generation and improve the performance of the grid generator, solving the problem of OPF based on line congestion management. As the power flow equation is nonlinear, this paper has performed the PSO algorithm to solve the OPF problem. By considering two technique this paper has performed the PSO algorithm for improving the performance. The first technique is to use a chaos generator to prevent PSO particles from sticking to local minimum points and the second is to consider the GSF in the WPSO algorithm structure so that the power passing through network lines can be simultaneously calculated and real power flow. Finally, the result of  WPSO-GSF algorithm which includes the bus voltage values, line losses, injection power to b buses, power passing through lines, total generation cost, setting electricity prices in two ways, UMP or LMP, depending on filling line capacity and calculating generatorschr('39') profits has carried out .and also, to check the accuracy of the algorithm, the proposed method has been tested on IEEE 14-BUS, 30-BUS, 57-BUS standard networks, the results which indicate an increase in the speed and accuracy of the WPSO-GSF algorithm compared to other methods in improving the OPF problem.

Increased demand for electrical energy and the lack of development of the transmission network, has made transmission systems, as an intermediary between production and consumption, be more pressed. If the transmission network is not able to correct the desired interactions in the market congestion occurs in the electricity market. If this density is not controlled leading to a rise in market prices and monopolies in sales. One of the effective ways to deal with the issue is the use of FACTS devices such as TCSC, by changing the transmission line impedance, it has the ability to control the power flow of the transmission lines. Therefore, finding the capacity and location of these devices, it is a necessity for power system operators. In this thesis, an optimization method is used to find the number, the installation location and optimal capacity of the TCSC are intended to control network congestion. The goal of the optimization problem is to improve the voltage profile, improve transient stability and reduce operating costs. For this purpose, the Multi-objective particle swarm optimization based on global margin ranking has been used to solve the problem. In this research, several scenarios have been presented to illustrate the effect of adding TCSC to the network. The proposed method has been implemented on the New England Testing System, which has been used for this purpose by the Matlab software environment. The results obtained by the proposed algorithm indicate the accuracy and efficiency of the proposed method.