Optimal Power Flow Using Differential Evolution Algorithm Essay

The feasible solutions property is necessary. It states that a minimum cost flow problem will have a feasible solution if and only if the sum of the supplies

After finishing high school I will be attending St. Clair for their fast track power engineering program learning things like the operation of steam boilers and more depending on how far i go with the tickets, and if I decide to move away or stay close to home. First off in the power engineering field I have to know things from the operation of steam boilers, to refrigeration systems and more. Second there are different classes of power engineering ranging from the fourth class to the first class ticket with different salaries and places to work with each ticket. Lastly if I end up not being able to get a job in power engineering there are other options I could branch into with this course. After finishing high school and attending St. Clair for power engineering all these things need to be taken into account when figuring out where I will finally end up with my career.

A power system is always in a state of disturbance that may lead to instability in the system. The consequences of a major power supply interruption can prove to be so disastrous, that every effort must be made to reduce the impact of such a disturbance. The process of determining the steadiness of the power system following any upset is known as security assessment. In particular, MW security assessment is a process to evaluate the security of the power system following a disturbance. It is done considering the loading conditions in respect of MW power flow on the lines. Each line has a capacity to carry MW power up to transmission line design limits beyond which the lines may trip due to overloading. In this paper MW security assessment has

So, the above issues can be applied to distributed power systems similarly, and the recent research focuses are summarized as follows:

When explaining the differences in how energy and matter travel through ecosystems and organisms, I was reminded of a diagram and teaching from when I was in grade school. The diagram is of the water cycle and how the water cycle flows through our ecosystem. This diagram I was reminded of and will refer to for the duration of this question demonstrates the process in the most simplistic of ways. The water cycle demonstrates water vapor molecules gathering in the atmosphere. As they become heavier, they form precipitation and fall to the earth. Depending on whether or not the water falls on land or a water resource, the water will either be absorbed into the ground or run-off into a body of water. Then the water will evaporate back into the atmosphere continuing this perpetual cycle.

B.K. Panigrahi [2], presents a novel heuristic optimization method to solve complex economic load dispatch problem using a hybrid method based on particle swarm optimization (PSO) and gravitational search algorithm (GSA). This algorithm named as hybrid PSOGSA combines the social thinking feature in PSO with the local search capability of GSA. To analyze the performance of the PSOGSA algorithm it has been tested on four different standard test cases of different dimensions and complexity levels arising due to practical operating constraints. The obtained results are compared with recently reported methods. The comparison confirms the robustness and efficiency of the algorithm over other existing techniques. PSOGSA is formulated by S.

To obtain the optimal solution various method such as dynamic programming, Lagrangian relaxation, mixed integer programing, and expert system were used. But mainly they worked on dynamic programming and Lagrangian relaxation and to deal with mixed integers they used mixed integer programing, Benders decomposition were also used to solve the two stage UC problem. Where after solving the unit commitment problem for generators when we check for the feasibility of transmission system and if the transmission system is not feasible then using benders cuts will be generated and this bender’s cut will be added to the master UC problem. To solve a unit commitment problem with AC constraints it creates a very complex problem which needs lots of method to linearize the system which lead to an approximate solution.

applied to find out the optimal generation of each unit when the generation cost curves are non-smooth and discontinuous in nature. Most of the PSO algorithms suffer from the problem of premature convergence in the early stages of the search and hence are unable to locate the global optimum. The idea here is to exercise proper control over the global and local exploration of the swarm during the optimization process. The PSO_TVAC based approach for practical non-convex ELD problem is tested on four test systems having different sizes and non-linearities. Out the four, two test systems are with valve point loading effects, one system has POZ and one system has a large dimension with 38 generating units. The PSO_TVAC is found to

The Lagrangian Relaxation algorithm being implemented in basically an extension of Non - Convex Optimization to a power system. In this project, we have taken up the cost functions of each of the generators under consideration and developed the cost function to be minimized keeping the mind the generator limits as well as the load balance.

Besides these two methods, there are many other methods or hybrid methods derived from them. It has been proven that the AI based methods are more capable and much confidence of finding the global optimum than traditional methods. However, they usually require extensive numbers (population size times generation size) of individuals evaluation that are computationally time-consuming and are not suitable for real-time OPF application. Many efforts have been made in order to reduce the computation time of using AI based method. In [12], the authors used an improved power flow model which combined with GA to accelerate the overall computation time. However, this method can only show the efficiency for the particular case which is steady state OPF without considering transient stability. [13] and [14] used a new AI based method called Jaya Algorithm and Symbiotic Organisms Search Algorithm, respectively, that shown better convergence than other methods from literatures, as a consequence, the computation time is reduced. To be summarized, these methods tried to improve the convergence rate to improve the computation efficiency, however, the numbers of call of power flow are

In the last years, distribution automation has gathered a significant relevance in distribution systems planning and operation. The network operator (NOp) looks for a suitable configuration of the feeder topology as well as the system, pursuing the reliability enhancement and a full energy demand supply. Nevertheless, an efficient protection system requires an adequate investment in such devices as reclosers, fuses and sectionalizers. Thus, two conflictive objectives arise, namely, NOp investment minimization and reliability maximization. In this sense, the number and location of devices in the system are critical variables to accomplish preceding objectives.

An accurate cost function for the transmission system is formulated where both fixed and variable costs for all planned facilities are includes, in addition to the cost energy losses. The cost function is then minimized, using (BBO) algorithms. We can be used to derive algorithms for optimization. We apply the BBO on the model of IEEE of 6-bus test system.

Abstract-Increased the numbers of Plug-in Hybrid Electric Vehicles (PHEVs) can have a significant impact on distribution system performance, such as reduction in power quality and efficiency, increase in power losses and voltage variations, as well as an adverse impact on the customers’ energy price. This paper proposed approach evaluates the effect of integrating a large number of PHEV on power system operation as well as appropriate operation of PHEVs can improve the voltage profile in the network by eliminating the voltage drops in highly-loaded buses with discharging reactive power. Furthermore, the optimal placements for charging stations and renewable energy sources (RES) are identified. The objective function includes minimizing the cost of power delivery to the PHEVs, the reducing losses as well as minimizing the voltage deviation between different buses in distinct hours. For increasing the load factor of the network, special coefficient is introduced into the problem. The ratio between the hourly demand and the peak in the day-ahead load characteristic is multiplied with the electricity tariffs which lead the aggregators to charge their PHEVs in off-peak hours. For this purpose, Differential Evolution (DE) algorithm is presented for optimal planning of charging stations and RES. The proposed method is implemented in 69-bus radial distribution system (RDS).

Feeder Reconfiguration is basically an optimization problem which has several objectives and operating constraints. A. Merlin and H. Black were the first to implement optimization techniques to solve a Feeder Reconfiguration problem. Optimization is a mathematical tool to obtain an optimized solution. There are numerous techniques to solve an optimization problem where in each technique will yield a different network topology and different optimized solution. Heuristic and meta-Heuristic methods were used in the early 90’s to solve feeder reconfiguration problem. These methods uses many assumptions to yield an optimal solution. However, the solution obtained by heuristic method is considered to be sub-optimal in compared with the

The Bee Colony Optimization (BCO) is a technique based on the mutual understanding of the natural bees in food foraging process. It is a population based search algorithm for optimizing numerical problems. Bees are classic example of teamwork experience, coordination and synchronization. The way they work is remarkable. They carry out a kind of neighborhood search combined with global search by mimic the food foraging behavior. The foraging strategy of Bees is used to look for the best solution to an optimization problem. In this process each candidate solution is considered as food source and a population of Bees is used to search solution space. At each and every instant a Bee visits a source it evaluates its profitability. It allows meaningful generalization to optimize various problems by recognizing a profitable solution to complex engineering problems. It provides an adequate conceptual framework as well as a mathematical tool to depict the real world problems in an optimized way. It is one of the well know techniques with its successful applications in various domains. Bee colony optimization technique is recommended above other means of optimizations because it provides clarity and errors in case of optimal solutions. This algorithm can also be analyzed as a path structuring algorithm that structure the path from one source to another tracing the