The voltage of the **distribution** feeders at the zone substation is generally controlled at somewhere from 101% to 103% of nominal voltage. This is achieved by automatically changing the winding ratio of the zone substation transformer incrementally to maintain these values regardless of load levels. The incremental changes of winding ratio range from 1% to 2.5% of the nominal ratio and these ratio changes can boost the output voltage by up to 20% and buck by up to 10%. The load supply is not broken by this process and the method is usually known as On Load Tap Changing (OLTC). The OLTC operation can be complicated but are essentially set up to operate when voltage deviates from the prescribed range by a set amount for a set time, for example if the voltage deviates outside the limits of 101% and 102.5% for more than 90 seconds. The majority of **distribution** feeders have only the voltage at their source (at the zone substations) regulated to a set value and rely on their construction and set up of the **distribution** substations to keep the customer voltage within an acceptable range. On some excessively loaded **distribution** feeder this arrangement does not deliver suitable voltages to the customer. These feeders require additional voltage support, which is generally provided by Voltage Regulators. A Voltage Regulator is a transformer with a winding ratio of nominally 100% and an OLTC with a tap range of up to 20% boost and 20% buck and tap step from 0.5% to 2%. The Voltage Regulator OLTC is controlled to maintain the output voltage in the same way as that done for Zone Substation transformers. These regulators can be operated to maintain the voltage on their downstream side regardless of the direction of **power** flow and so can be bi-directional. 1.2.12 Impacts of Small DG Systems

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The microgrid, a typical one as shown in Figure 1, can be summed up as follows [2]: an integrated energy sys- tem **network** consisting of **distributed** generations (DGs) and **electrical** loads and/or meters operating as a autonomous grid either in parallel to or islanded from the utility grid. DGs include photovoltaic **power** (PV), wind turbine (WT), electrochemical cell, marsh gas **generation** and so forth; are generally tied together on their own feeder, which is linked to the utility grid at a point of common coupling (PCC).

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In most of the developing countries, the centralized placed **power** plants supply to a multifaceted interconnected transmission and **distribution** networks. These networks are to transfer the **power** over long or short distances with an efficient manner at customer end [1]. In recent times a revolution of deregulation and restructured environment in centralized **power** system has confronted a challenge for **power** generating units to work independently and to meet the increasing **power** demand. This becomes a favorable opening to the dispersed generations such as **distributed** generators (DG) to present sufficient and dependable **power** release. Modernization of **power** system is enhancing the access of generated electricity & storage of **power** at **distribution** end. For consistent supply of **power**, placement of DG and demand organization is vital features as in [2]. For

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The ABC was introduced by Karaboga [32] in 2005. The concept of this **optimization** is following the behavior of a swarm of bees to find foods, which involved four stages as illustrated in Figure 1. For tuning process, a size of colony and limit is determined by using trial-and-error method before the start of the **optimization** process. The ABC process starts with initialization process, which all the bees randomly find the food location (parameter value that needs to be optimized). All the information wil be store and evaluate by using Equation 1. All these information will be used by employed bees during the second stage to find new food location by using Equation 2. Similarly for the third stage, onlooker bees will use the information obtained from the employed bees and find new food location, but, the probability the foods are chosen by the onlooker bees depends on the value of nectar by using Equation 3. For the last stage, one bee that is known as scout bee will find a new food location once the food is depleted. All the process is repeated until maximum cycle acieved.

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In past years the shunt capacitor banks were placed in **distribution** system for reactive **power** compensation, it supports improving the voltage profile, reduce the **power** losses and improve the **power** factor [1]. The capacitor is connected in series parallel combination. **Distribution** **generation** is a small scale **power** **generation** or decentralized generating unit usually connected at load side in **distribution** system but the objective behind it is not new at all. When electricity demand increase at consumer level then transmission capacity of transmission line is also increase. It is mandatory to build a certain capacity of **power** plant at load centre and **power** grid. So as to reduce the **power** losses enhance the system stability. In today world, the DG technologies are developing swiftly and it is an attractive area in energy research direction. The total **power** delivered to the load side has been calculated according to total **power** **generation** and **power** loss in transmission system. Due to low voltage and high current in **distribution** system, the largest **power** losses are occur and they goes up to 5-13% of the total **power** **generation**. The capacitor placement and DG installation are two methods for loss

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Abstract: The objective of this paper is to present the tools implemented in PowerFactory for the **optimization** of the proposed **network**. It involves the calculate optimal **power** flow analysis (OPF); optimal placement, type and size of capacitors in the net- work; the optimal type of reinforcement cables and overhead lines and lastly, **optimization** of a certain objective function in a **network**, whilst fulfilling equality constraints (the load flow equations) and inequality constraints (that is, generator reactive pow- er limits). The applications of the OPF include transmission line overload removal, transmission system control, available transfer capability calculation (ATC), real and reactive **power** pricing, transmission component valuation, and transmission system mar- ginal pricing. **Power** capacitors are very useful for **power** factor correction, loss reduction, voltage profile improvement and dis- tribution system-capacity release/increase. The conductor, which is determined by this **optimization** method, maintains acceptable voltage levels of the radial **distribution** system. Besides, it gives maximum saving in the capital cost of conducting material and cost of energy losses. The method also shows that only proper selection of optimum branch conductors reduces losses.

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Electricity demand is growing in faster rate compared to the other forms of energy because it can be generated efficiently, transmitted easily and utilized ultimately at a very reasonable cost. The **electrical** energy is generated in bulk at a centralized place, called Generating Station and is transmitted over a long distance (Transmission System) to **Distribution** System, and finally is used ultimately by a large number of users. During all these processes, several technical and non-technical problems such as amount of transmission loss, transmission line congestion, increasing environmental impact etc., arise. These problems can be solved/minimized by the installation of **Distributed** **Generation** (DG) [1]. A number of studies were conducted to investigate the criteria, e.g. **power** loss reduction, improve system voltage profile, and increase system reliability, for optimal sizing and sitting of DGs units. Different techniques, such as particle swarm, genetic algorithm, and differential evolution, have been adopted to solve the problem of DG allocation in **Distribution** Systems [2] - [7]. These techniques have been applied on standard test systems, such as 33-bus and 69 bus systems, etc. Table -1 summarizes the literature results of IEEE-69 Test system including the related objectives and the used **optimization** technique.

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Abstract-- Increasing numbers of very small generators are being connected to electricity **distribution** systems around the world. Examples include photovoltaics (PV) and gas-fired domestic-scale combined heat and **power** (micro-CHP) systems, with **electrical** outputs in the region of 1 to 2 kW. These generators are normally installed within consumers’ premises and connected to the domestic electricity supply **network** (230 V single-phase in Europe, 120 V in North America). There is a growing need to understand and quantify the technical impact that high penetrations of such generators may have on the operation of **distribution** systems. This paper presents an approach to analysing this impact together with results indicating that considerable penetrations of micro-**generation** can be accommodated in a typical **distribution** system.

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Abstract: Minimization of **power** loss is the first priority of the **power** companies. Generally **power** loss is directly proportional to the reactive **power** demand and minimization of this is known as reactive **power** **optimization** (RPO). In this paper we are trying to minimize the reactive **power** loss with help of **distributed** **generation**. **Distributed** **generation** provides active as well as reactive **power** locally so, there is no need of taking the reactive **power** from the generator consequently reactive **power** loss minimizes. Now problem arises that where to place the **distributed** **generation** to have minimum **power** loss. To find the optimal location of the **distributed** **generation**, we have used particle swarm **optimization** algorithm (PSO). For that we have defined the fitness function as well as constraints. Constraints limits the value of variable within the defined range. Fitness function is sum of real **power** loss index, reactive **power** loss index and voltage deviation index. We have also used genetic algorithm just to compare the results and to find which one is better out of genetic algorithm and PSO. RPO increases the **power** transfer capability, reduces the line loss and boost the system stability therefore it can be applied in the **distribution** **network**.

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In electric **power** system, most of the **electrical** energy losses occur in the **distribution** system. **Power** loss in a **distribution** system is high because of low voltage and hence high current. The overall efficiency of the **distribution** system can be improved by integrating **distributed** **generation** (DG). However, the placement of DG unit at non optimal places can have a negative impact on the **distribution** system. This paper proposed the use of particle swarm **optimization** (PSO) for the optimal placement of **Distributed** **Generation** (DG) with the aim of reducing system losses and improving voltage profile. Etap 12.6 software was used to model the 73-bus system and the search space was reduced to 35 candidate buses using Newton-Raphson **power** flow method. The load flow result is further passed to PSO which determines the optimal DG placement. **Distributed** **generation** (DG) units of 25 MW gas turbine **power** plants were implemented on the test system The result obtained shows that 10 buses [Bus17, Bus21, Bus31, Bus37, Bus42, Bus54, Bus57, Bus59, Bus67, and Bus68] indicates the optimal location for DG placement. It was reviewed that the maximum reduction in line losses was achieved and the overall **power** losses reduced from [37.817MW, 239.832MVar] to [17.543MW,119.842MVar] using particle swarm **optimization** method. With DG integration at optimal location, the **power** demand required from the grid could be reduced thus cutting the need to strengthen the feeders connecting the **network** to the grid.

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When there is a fault in the **network**, node voltages have no directionality according to fault sequence ana- lysis. This is because that voltage drops occur at the nodes at or around the fault and low voltage protections will all act. The fault point has the lowest voltage and voltage increases with the increase of the **electrical** dis- tance. Thus voltage information can reflect fault location to some extends, though when voltage data is only avail- able at the key nodes, it is difficult to diagnose the fault. Obviously, when there is a fault, most **power** from the **power** sources will go to the fault point through a mini- mum impedance path, referred to as the fault **power** path in this paper. The fault **power** flow of this path is the biggest and direction is to the fault point. Based on the selection of the fault **power** path and search tree model, together with current information comparison, fault location can be identified effectively. Thus this paper proposes a MS protection based on centralized searching method, and based on that backup protection can be realized.

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The growth of any **power** system grid in the world is and always has been on an accelerating pace, feeding the almost insatiable demand for **electrical** **power** for the past century or so [1, 2]. This in turn forces a certain level of intricacy on the **power** system and that intricacy compounds with time; to the point where the **power** systems face the inability to progress with ease due to introductions of new transmission systems and construction of generating plants near load centres. As the system grows more complex and burdened with increasing load; various issues regarding cost, pollution, **power** quality and voltage stability takes centre stage [2].

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This paper proposes to study the impacts of **electrical** line losses due to the connection of **distributed** generators (DG) to 22kV **distribution** system of Provincial Electricity Authority (PEA). Data of geographic information systems (GIS) in- cluding the distance of **distribution** line and location of load being key parameter of PEA is simulated using digital si- mulation and **electrical** **network** calculation program (DIgSILENT) to analyze **power** loss of the **distribution** system. In addition, the capacity and location of DG installed into the **distribution** system is considered. The results are shown that, when DG is installed close to the substation, the **electrical** line losses are reduced. However, if DG capacity becomes larger and the distance between DG and load is longer, the **electrical** line losses tend to increase. The results of this pa- per can be used to create the suitability and fairness of the fee for both DG and utility.

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In this paper, combined technique of particle swarm **optimization** algorithm with MATPOWER toolbox is applied to solve reactive **power** dispatch problem and to determine the optimal placement of new installed DG in existing system. The performance of the proposed technique has been tested on the 41-bus **distribution** system and compared the simulation results without and with DG. It can be observed that reactive **power** dispatch approach for **distribution** system with a **distributed** **generation** can further reduce the active **power** loss than without DG. The benefit of lower active **power** loss obtained will provide better economic dispatch and secure operation in **power** system.

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Abstract : The main aim of this paper is to enhance the performance of **power** system **distribution** in Enugu State Nigeria using **distributed** **Generation** System. The main function of **power** system **distribution** is to transfer **electrical** energy to the consumers, while maintaining an acceptable reliability and voltage quality to all customers. It is sad to know that such services is lacking from the **Electrical** **distribution** company at Enugu State Nigeria. This paper proposed to setup a centralized plants distributing electricity within the state through **Distributed** **Generation** (DG). The implemented DG was abletoreducethe **Power** Loss from the transmissiona n d **distribution** stations within the state and also improve voltage profile. The author was able to optimize the **power** generationfrom wind Energy source to the **Distribution** **network** and the DG system was able tostabilize the **network** by normalizing the fluctuating voltage profile at the **distribution** end of **power** system. In order to achieve that, the **power** system **network** wasmodeled and simulated using MATLAB/SIMULINK software. The results of the simulation with DG system and without DG system were compared. The result from **power** **Network** without DG shows instability of per- unit voltage between 0 to 5 seconds and while that from DG system shows stabilization per-unit voltage between 5 to 10 seconds. The total **power** system Loss without DG system was 2350KW while the **power** loss with DG system was 1883KW. Hence, the percentage of **power** system improvement was 11.03%. Therefore from the results, there is reduction of **power** Loss when DG is applied in the **power** system.

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The **distribution** system being the nearest link to the consumers, utmost importance is to be given for its satisfactory operation. Though this sector was neglected earlier, in recent years **distribution** sector reforms have been implemented and it is expected to improve the scenario in coming years. The reforms are aimed at improvement in **network** operation vide taking appropriate steps such as incorporation of **generation** sources at **distribution** system level and encouragement to Independent **Power** Producers. This will facilitate reduction in **power** losses of the system and the customers can be served more efficiently. There are different technologies which can be adopted for DG sources like photo voltaic cells, wind **generation**, combustion engines, fuel cells and other types of **generation** from the resources which are available in the geographical area. The concept of **distributed** **generation** helps to harness the natural resources and help the **distribution** system to get more strength in its operation

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Figure 7 displays the total **power** losses for the daily operation according to the **generation** and load pattern shown in Figure 6. It is obviously shown that based on the local approach synchronous DGs do not play a role in reducing the losses during the night times. This is be- cause they are not located close to the load center. On the other hand, the solar PV with a fixed **power** factor con- trol can help reduce losses significantly during day time. This is due to their strategic locations near to load centers in the feeders 5 and 6.

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