I need a grammar review The optimal power flow problem is a large scale, nonconvex, and nonlinear programming problem that is very complicated in realistic applications by the presence of a large number of real and integer variables. Given its importance in power systems planning and operation activities, the optimization problem has been a subject of intensive study by researchers in recent years. Practical optimal power flow applications require fast optimization algorithms like the primal-dual interior-point method. In addition, using a DC network modeling, it is quite known that real power flows are transformed to linear functions of voltage angles, so a linear programming based solution procedures can be applied to the optimization problem. In this case, the voltage magnitudes and reactive power are eliminated from the problem. In this chapter a primal-dual Interior point algorithm is applied to DC optimal power flow using DIgSILENT Programming Language (DPL). The DPL serves the purpose of offering an interface for automating tasks in the PowerFactory program. The DPL adds a new dimension to the DIgSILENT PowerFactory program by allowing the creation of new calculation functions. Such new calculation functions are written as program scripts. The contributions of this chapter proposal are as follows: 1) The study considers that the optimization problem can be solved using only the power generation as decision variables (control variables). With this proposal, the nodal power balance constraints are not needed, so the nodal constraints are reduced to one equation using the global supply demand balance constraint. 2) This modeling is accomplished using the linear sensitivity factors. These factors show the approximate change in the line flow for changes in generation on the network configuration. Such approach not only significantly improves the time of the simulation but also the main advantage of this proposal is the reduction of the dimension in the optimization problem. Therefore, an efficient algorithm has been carried out to model the DC-OPF problem. 3) The methodology is applied to IEEE standard systems (9 bus system and 30 bus system). The data of the power systems are obtained from Matpower, and the results are compared with the results published in the state of the art, and mainly with Matpower which use a primal-dual interior point algorithm as well. 4) The results and effectiveness of the proposed technique are compared in the study.

Conclusions about an optimization problem (I need an English review) In this study, we have demonstrated the better performance of the algorithm proposed. We have conducted many tests in several power systems test in order to establish the advantages along with the new characteristic of the transmission expansion planning problem. We have decided to include a new framework in the optimization problem based on sensitivity factors, which causes not only better simulation time but a new transmision model to be applied for the researchers. Another contribution was the addition of heuristic operators in the modeling stage, which was carried out base on previous author's work. In the Results section, the authors have shown the simulation time, and comparisons with MIPS algorithm. The reference algorithm was programmed using power generators and bus angles as decision variables, while the proposed algorithm was moded just with power generation variables. Finally, the transmission expansion planning problem was figured out with the proposed algorithm, and the results have been shown considering different generation and load cases. The achieved results were compared with PLEXOS results, therefore the authors consider the promising model development in the study.

What do you think about green energy? I consider that this issue is very important for all countries in the world. The solution is very complex because there are many financial along with technical factors that influenced this issue. My main idea would be the government needs to invest more money in research and development. The technology is the main activity that the countries need to figure this problem out, so the problem depends on the state of the economy. Nowadays, countries such us China, Germany, US, and many others have designated many resources to research new solution in order to find out the solution for these problems. The following question is What happen with the other countries? What happen with poor countries or developing countries?, where the government destined your budget to another aspects. Green energy needs a lot of research. There are many prototypes that should be proved and validated in order to get the right knowledge about the design. Some technologies are not mature, and mainly it could causes many technical problems to the electrical power system. The problem will be in the future, because only few places have reserves. Therefore, we need alternative technologies to supply the future consumption of the customers. For these reasons people can realize that this issue is crucial nowadays. It is not easy give a final solution, but I consider that green energy is very important, so the power systems needs that private companies will invest in these kind of technologies. These investments could increase our tariffs, but the industry needs the energy to improve the economy in each country. Finally, each country has different aspects and developing, so it is the time to change our behavior and maybe scientists could find out new technologies for better our future. We need money to do it due to the energy is crucial in the economy.

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I had programmed the DC-OPF in Digsilent using DPL. DPL is a software like C++, so we were able to develop a Newton algorithm to solve the OPF; we've gotten the same results in comparison with MIPS algorithm from Matpower. Right now, we're having a problem with the iterative algorithm, so I want to study this failed trying to solve the problem that the MIPS algorithm has. Yes, the costs are linear, and I'm studying that problem.
My future idea will be programmed a generation expansion planning algorithm using just the power generators as decision variables. In this case, I must use maybe a branch and bound algorithm. Do you have any experience with this algorithm? In the state of the art, I don't find any planning algorithm solved with the reduced model. What do you think as contribution to the state of the art?
In the same topic, I'd like to develop a transmission expansion planning algorithm in the next stage. I've published some publication about these topics applying evolutionary algorithms, but I want to change the tool that I use to solve the optimization problem, or maybe I'd like to carry out a mix tool.
With a colleague, we've used PLEXOS to include the uncertainty in the planning problem using a stochastic programming. I hope that I can develop a similar tool to model the uncertainty in the capacity expansion problem.
I wish that we can plan a presentation about optimization algorithms applied to the power system problems. What do you think about my proposal?

I hope that you can help to review the english grammar with this text.

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