facilities in easements and rights-of-way and install equivalent underground facilities

facilities in easements and rights-of-way and install equivalent underground facilities. The second is the cost of converting or modifying each individual customer’s “private” service equipment (service drops and entrance, meter box, etc.) to accommodate new underground electric service. This second cost can be substantial and is almost always born directly by the associated customer. The third cost is for undergrounding other utilities such as telephone, cable television, and broadband fiber. There is an offset for this third cost since the third-party utilities will no longer have to pay an attachment fee to the electric utility. Virtually all undergrounding projects place all over head utilities underground. However, many undergrounding studies do not consider the cost of undergrounding third-party attachments.
Ultimately, the cost of any undergrounding project has to be paid. Selecting the most appropriate financing option and setting the cost allocation policy (who pays what portion of the cost) is a critical part of the overall undergrounding process. Most commonly, funding for initial constructing comes from one or more of the following: increased taxes, increased electricity rates, and direct contributions from customer. Funding must also be considered for other undergrounded utilities such as telephone, cable television, and broadband fiber. Most commonly, undergrounding plans involve a specific group of customers such as a municipality or a “special assessment district.” In addition, most studies recognize that individual customers must absorb the cost of converting their own service facilities to take underground service. This can be a financial burden to the individual customer with implication of its own.
Positive Effects
The literature most commonly attributes to underground distribution systems the following improvements as compared to overhead transmission systems.
1. More reliable electric service with fewer failures
2. More economical to maintain and service
3. Safer
4. Positive value to nearby property and
5. More desirable during adverse weather.
Negative Effects
Potential negative effects of undergrounding include:
1. Possible negative impacts on sensitive environmental areas
2. Higher costs (and therefore prices) for local businesses
3. Lower life expectancy of underground system equipment
4. Reduced operational flexibility and higher costs for some types of maintenance.

2.6 Background/Mathematical
Theory of the technique used in the reduction of power loss in a transmission network of power system is the primary aim of this project. However, the following techniques were adopted to achieve perfect reduction in power loss calculation of line flow
S12 = V1I12 – 2.1
S13 = V1I13 – 2.2
S23 = V2 I23 2.3
The active power loss reduction PLR is equally used which has mathematical formula of the form
PLR12 = PL12initial– PL12final x 100%
PL12inital 1 2.4

PLR13 = PL13inital – PL13final x 100%
PL13inital 1 2.5

PLR23 = PL23inital – PL23final x 100%
PL23inital 1 2.6
Where S12, S13, and S23are line flows.
V1, V2 and bus voltages
I12, I13 and I23 are line currents
PLR12, PLR13 and PLR23 are power loss reductions at different lines
PL12, PL13 and PL23 are initial power losses at different lines
PL12f, PL13f andPL23f are final power losses at different transmitted lines.
These gaps of losses, distortions and harmonics created by TG and optimization methods have been vehemently taken care of by optimization fuzzy method in the following ways
1. The power loss reduction using optimization is efficient because its power loss reduction rate is high when compared to TG and optimization method because it finds a conversing voltages after iterations.
2. The power loss reduction in optimization method is fast
3. optimization method to find slack real and reactive powers to enable the site Engineer to know the exact real power and its loss reduction.

2.3 Summary of related Research Efforts
A good number of research work is going on TG integration with grid and its safe and reliable operation (Acharya, 2006). However only a few studies have been done on TG sizing and allocation issue. Different methodologies to determine optimum location and size have been discussed in different literatures. The 2/3rule is often used in capacitor allocation studies in power transmission network. Similar approach can be performed in TG allocation to reduce system power loss (Willis, 2000). In the project (Willis, 2000) ten authors have used this analytical method and rule of thumb for analyzing the transmission system which is radical and has uniformly transmitted loads. Rule is simple and easy to use but it cannot provide the proper solution when load transmitted type is changed. Moreover, it cannot be applied in meshed network.
In (Wang, 2004) analytical approaches for both radical and network distribution systems with different types of load configuration are given. Here, separation algorithms have been used for radical and meshed networks. To simplify the analysis, authors have considered only overhead lines for which uniformly distributed parameters like R and L per unit length are same along the feeder. Results obtained from the analysis are same along the feeder. Results obtained from the analysis are very quick however, one generalized algorithm is expected for both radial and meshed networks. Besides, in practical distribution system, conductor sizes are gradually decreased from substation to load centre, therefore, this analysis procedure would be very complex when line parameter are uniformly transmitted. One major limitation of this approach is, they have only solved the location problem for a fixed size of generation but they have not considered TG sizing issue in their analysis.
Another analytical approach has been proposed on exact loss formula (Acharya, 2006). Authors have considered the loss coefficients constant. Here they have considered both sizing and location issues. This process takes only two load flows to determine the location issues. This process takes only two load flows to determine the location and size of TG. Although the technique is very fast, however, this methodology can be applied only if DG delivers real power (Hung, 2010). This is one major limitations of this approach. For load flow, authors have considered Newton Raphson algorithm. Although Newton Raphson approach has an excellent convergence character but in distribution system because smaller X/R ratio it cannot be decoupled. Moreover, in distribution network, multi-phase, unbalanced operation, unbalanced distribution load and dispersed generation makes the Newton – Rapson approach unattractive (Srinivas, 2000).
For the selection optimum size and location of TG, several genetic algorithms (GA) and optimization based methods have been discussed by (Celli, 2001) (Amelli 2010), (Queuro, 2006) and (Sabier 2007). Although GA provides almost near optimum output they are: computationally very demanding and have a slow convergence (Acharya, 2006).
As load flow represents the system states, therefore it can be used for planning the future expansion of power system. We can calculate the system loss from the load flow result and during the load flow repeatedly, we can easily tell the location and size of TG for which we get the minimum, power loss of the system. The method is known as Exhaustive Load Flow (ELF) method. Although this ELF method gives the exact answer, however, it needs lots of load flow computation. Therefore, ELF method needs to be optimized to get accurate answer and loss computation of time.
In the previous literatures researchers have considered radial distribution system but they have not considered three phase unbalanced system.
Lastly, following the above project research procedures and recommendations, I noticed that there was a gap left to be filled during Loss reduction in transmission network using Newton Raphson method. That was the reason I had to use optimization other than ordinary Transmission statcom (TSTATCOM) or with PI controller which cannot reduce loss in transmission network faster. The optimization introduced in this report is part of the transmission system that works better than the PI controller in terms of flexibility, speed and reliability for reduction of voltage fluctuations, harmonic distortions and low power factor to its lowest minimum value.