ay not synchronize with the system. Therefore, direct connection of optimization fuzzy is often discouraged (Arritt, 2008).
1.2 Statement of the Problem
The problem of inconsistence power supply has become the order of the day in our country Nigeria. The causes of this has arisen as a result of power losses in transmission network, distortion, harmonic, short circuit, burning of feeder pillar to mention a few. This project focuses its attention in power losses in transmission network as a result of distortion and harmonic. This unfortunate situation of inconsistence power supply in our country has demoralized the moral of investors to desist from so doing thereby enhancing the rate of unemployment in our country Nigeria. This can be overcome by using optimization
1.3 Aim and Objectives of the Study
The aim of the research is to have a stable power supply
1. To determine power losses in transmission network using Newton Raphson method and to compare it with simplex optimization method.
2. To determine the phase values of the voltage at load buses
3. To determine the slack bus real and reactive powers
4. To determine power losses in transmission network using simplex optimization method.
5. To determine active power loss reduction in the buses.
6. To design a model that reduces power losses in transmission network using simplex optimization method.
1.4 Relevance of the research or Works
This dissertation provides solutions coupled with improvement on the way losses are reduced in the transmission networks. This solution will enable companies to design new power system network and expansion of the existing network.
1.5 Significant of the work
i. Enhance reliable power supply.
ii. Encourage investor to establish industries.
iii. Increase employment opportunities.
iv. Enable the field Engineers to easily detect fault in the lines.
1.6 Limitation and Scope of Work
The work focused attention in loss reduction in a transmission network to enhance regular power supply in local area power network and urban area power network coupled with wide area network.
1.7 Layout or Organization
Generally, in this research, dissertation of loss reduction in a transmission network, the topic was introduced in chapter one, review of previous related work in chapter two, the method adopted, the design and implementation on how the research was achieved are treated in chapter three. Data presentation and analysis in chapter four, while in chapter five conclusions were done and recommendations made for future work.
2.1 Literature Review
Transmission means the electric power transmitted to the generation network to fulfill the rapid energy demand of consumers. However, transmitted generation can be defined in a variety of ways.
1. The Electric Power Research Institute (EPRI) defines Transmitted generation (TG) as generation from ‘a few kilowatts up to 50MW (Ackevmann, 2001)
2. International Energy Agency (IEA) defines distributed generation as generating plant serving a customer on-site or providing support to a transmission network, connected to the grid at transmitted level voltage (IEA, 2002)
3. The international conference on large high voltage electric systems (CIGRE) defines TG as smaller than 50 – 100MW (Ackermann, 2001)
Although there are variations in definitions, however, the concept is almost same, TG can be treated as small scale power generation to mitigate the consumer energy demand. Transmitted generation can come from a variety of sources and technology. To analyze the TER impacts, different types of generator groups can be considered (McDermott, 2002). Here, we will consider induction generator as transmitted generation source for my analysis purpose.
As the technical design of each transmission network is unique, therefore, it cannot be answered what should be the optimum generation capacity or rating of TG (Ackermann, 2001). The maximum size or rating of TG which can be connected to a transmission network depends on numerous factors, such as voltage level within the transmission system, power loss profile and other technical, environmental, commercial and regulatory issues. In this project, we focused on the technical issues only.
As TG offers lots of benefit, the penetration of TG in transmission system is increasing rapidly. Therefore, TG should be allocated in an optimal way to maximize the system efficiency.
Buses reduction = (Ploss – after – Ploss – before) x 8760 x LSF x Penergy
Where Ploss – after: loss after methods were applied
Ploss – before: loss before methods were applied
LSF: loss factor
Penergy: price of energy
Essential Energy uses a combination of load-flow samples purchase and sales data, assumptions in relation to theft as a percentage of sales, and engineering data to calculate the proposed loss factors.
The relative loss through each asset category has been assessed by taking a typical subset of the relevant network at typical loadings and calculating the loss percentage. Modern load flow software or similar methodologies are used together with a scaled system load profile. The consumption and apportioned losses of Site Specific Customer (SSCs) is netted off the system totals to drive the correct TLFs at each level.
TLFs for Site Specific Customers (SSC) are calculated individually using accurate models of the network that supplies them and forecasts of their consumption for the financial year for which the TLF will apply, as required by clause 3.6.3 of the Rules.
Finally, losses in the low voltage lines are set as the balancing item. This is done by multiplying forecast annual energy consumption at each network level, and SSC, by its TLF and comparing the total with the energy that enters the network. The low voltage line TLF is then adjusted so that the forecast losses in the network are achieved. This approach is adopted since there is virtually no direct metering of the energy that enters the low voltage network at the terminals of distribution substation transformers.
2.2 Purchase and Sales Data
The consumption data for each premise connected to Essential Energy’s network is extracted from the billing system after all billing for the period has been completed. Only invoices generated within a twelve-month period are included. Reversed invoices, demand units and service charge units are excluded. Wherever the days over which the energy was consumed is not 365 days, it is linearly prorated to 365 days.
The purchase data for each supply point is extracted from systems managed by Essential Energy’s meter data agent. The data is extracted as half hour energy, tested for missing data or other in consistencies, assigned as incoming or outgoing, and summated by voltage level.
Input data for the calculation of site specific customer (SSC) TLFs includes;
Recorded load for the most recently completed financial year:
1. For cast consumption in the financial year for the which the TLFs are to apply; and
2. Network models of the sub-transmission areas with SSC connections.
The input data above allows a calculation to determine the expected losses at forecast peak or average load on the network. Appropriate adjustments, using either loss load factors or form factors, are made to these results to calculate the expected total annual energy losses attributed to each SSC for the financial year in question method for these customers is often not practical.
Customers who are not classified as SSCs have TLFs calculated on an average in the network. The five network levels for which TLFs are calculated are:
2. High voltage Substation
3. High voltage line
4. Low voltage substation
5. Low voltage line
Calculation of the sub-transmission and high voltage substation DLFs is carried out as part of the same process. A representative sample of sub-transmission network model is analysed. This network from the bulk supply connection to the zones substation 11kv or 33kv bus bars.
Forecast zone substation peak loads, and peak load losses in both sub-transmission lines and zone substation transformers are recorded. Where the data are available, average loads and losses at average load are employed. Loss load factors (LLFs) and or form factors are calculated from recorded load data from most recently completed financial year. Forecasts of these values are then made for the financial year interest.
The TLF for the sub transmission network is calculated using the formula.
Sub-transmission TLF = 1+?(sub-trans losses) -?(sub-trans losses due to SSCs ?(sales through sub-trans – ?(sales through sub-trans to SSCs
The high voltage substation DL represents the losses in the network up to the zone substation 11kv or 33kv bus bars. Calculation is similar to that of the sub-transmission.
HV substation TLF = I+?(sub-trans + zone T x Losses) – ?(sub-trans + zone T x loss due to SSCs
?(sales through zone T x 5) – ?(sales through zone subs to SSCs)
2.3 Sustainable Optimal Reduction Of Technical power And Elimination Of Non-Technical power
Optimization of technical losses in electricity transmission and distribution grids is an engineering issue, involving classic tools of power systems planning and modeling. The driving criterion is minimization of the net present value (sum of cost over the economic life of the system discounted at a representative rate of return for the business) of the total investment cost of the transmission and distribution system plus the total cost of technical losses. Technical losses are valued at generation cost.
Technical losses represent an economic loss for the country, and its optimization should be performed from a country’s perspective regardless of the institutional organization of the sector and ownership of operating electricity utilities. Although each case has its specific characteristics depending on the current and future values of generation costs, some general comments can be made. Energy experts agree that, in the next two decades, global prices of primary energy resources (oil and other fossil fuels) will be rising in real terms. In its world Energy outlook 2008, the international Energy Agency forecasts world oil prices rebounding to about US$130 (2007 U.S dollars) per barred in 2030. Other forecasts differ in absolute values, but not in the upward tendency of energy prices. On the investment side, prices of equipment in the electricity sector (generation, transmission and distribution) steadily rose this decade until the global financial crisis that began in the 3rd quarter of 2008. Against these price trends, the total costs of technical losses tend to exceed investment costs of transmission and distribution equipment required to reduce them to their optimum value, more so where a significant portion of generation is based on fossil fuels. This tendency is accentuated of environmental costs of power generation (harmful local pollution as well as greenhouse gas emissions) and increasing difficulties in achieving social acceptance of new power plant construction (regardless of fuel type and technology) are taken into account.
Non- technical losses represent an avoidable financial loss of the utility. Although it’s clear that the amounts of electricity involved in non-technical losses are being consumed by users that do not pay for them, experience shows that a significant percentage of those amounts (in some cases more than 50 percent becomes reduced demand when those users have to pay for that electricity, because they adjust their consumption to their ability to pay for electricity services. The reduction in demand has exactly the same effect as a reduction in technical losses, less electricity needs to be generated. Thus, from the country’s perspective, reductions in non-technical losses are also positive. From a social point of view, non-technical losses have several perverse effects. Customers being billed for accurately measured consumption and regularly paying their bills are subsidizing those users who do not pay for electricity consumption. There is a wide range of situations creating non-technical losses. A classic case is a theft of electricity through an illegal connection to the grid or tampering of a consumption meter. But examples also include unmetered consumption by utility customers who are not accurately metered for a variety of reasons. In all the cases some level of poor management of the utility in execution of operations is present.
Electricity theft is default subsidization of those who steal by customer regularly paying bills according to their consumption. The same usually applies in the case of unmetered customers, unless this situation is explicitly and transparently defined by the competent authorities and reflected in the legal and regulatory framework of the sector. In some countries some categories of consumers (e.g. agriculture users, in India and Bangladesh even in Nigeria) are unmetered and pay a fixed amount for electricity irrespective of the amounts consumed, which means in practice that they are subsidized by consumers in other categories, tax payers or both. Depending on the financial situation of the power sector, the savings from reductions in non-technical losses could be channeled to
1. Reduce tax payers subsidies or tariffs paid by customers,
2. Achieve an average tariff level allowing recovery of costs reflecting efficient sustainable performance (critical to assure service quality),
3. Subsidize consumption of selected categories of social sensitive existing users, or
4. Extend access to electricity supply to currently unserved population (in general the
poorest and socially unprotected).
2.4 System Reliability Modeling
System reliability models are able to predict expected customer interruption statistic from component reliability data, system topology, and operational assumptions. Many of the references listed in Appendix A address system reliability modeling, but basic functionality already exists in most commercially available feeder analysis packages. These tools are sufficient to compute the expected reliability differences of overhead versus underground in non-storm conditions. However, these tools are not appropriate to assess reliability under severe storm conditions. There are almost no publications that address storm reliability modeling of electric distribution system. One suggests the use of a non-storm algorithm with different failure rate and repair times. This approach is not suitable for hurricane simulations. One paper presents a simulation methodology to compute expected performance during major wind storms. This includes the prediction of storm severity, restoration efforts during the storm, and post-storm restoration. Data used in this paper is not based on hurricanes, but the basic approach could be used as a basis for a hurricane simulation.
2.5 Failure Rate Modeling
Accurate prediction of system reliability requires accurate estimates of equipment failure rates. For example, non-storm benefits of undergrounding require information on overhead line and underground cable failure rates. There is a host of data on average equipment failure rates in a variety of publications, most of which are summarize. These are sufficient to do a basic examination of non-storm reliability, but utility-specific data often varies substantially from industry averages. Other papers discuss the relationship of equipment condition to failure rate.
The literature is consistent in its recognition that undergrounding is expensive relative to the embedded cost of existing overhead systems. However, it is often not emphasized that there are three initial costs related to undergrounding. The cost most commonly considered is the cost for a utility to remove the existing overhead electrical 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.
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
4. Positive value to nearby property and
5. More desirable during adverse weather.
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.
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