Electrical Power Distribution Network Optimization with Distributed Generation and Smartgrids

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The effects of small distributed generation on the electrical distribution network

The effects of small distributed generation on the electrical distribution network

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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Multi timescale Collaborative Optimization of Distribution, Distributed Generation and Load in Microgrid

Multi timescale Collaborative Optimization of Distribution, Distributed Generation and Load in Microgrid

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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Distributed Generation Placement in Distribution Network using Selective Particle Swarm Optimization

Distributed Generation Placement in Distribution Network using Selective Particle Swarm Optimization

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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Distribution power loss minimization via distributed generation, capacitor and network reconfiguration

Distribution power loss minimization via distributed generation, capacitor and network reconfiguration

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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Optimum Size and Location of Distributed Generation and capacitor for Loss Reduction using different optimization technique in Power Distribution Network

Optimum Size and Location of Distributed Generation and capacitor for Loss Reduction using different optimization technique in Power Distribution Network

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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Investigation of Nigerian 330 Kv Electrical Network with Distributed Generation Penetration – Part II: Optimization Analyses

Investigation of Nigerian 330 Kv Electrical Network with Distributed Generation Penetration – Part II: Optimization Analyses

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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Multi Objective Optimization for Distributed Generation Allocation in Distribution Systems

Multi Objective Optimization for Distributed Generation Allocation in Distribution Systems

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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Network power flow analysis for a high penetration of distributed generation

Network power flow analysis for a high penetration of distributed generation

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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Particle Swarm Optimization Algorithm Based Reactive Power Optimization in Distribution Network

Particle Swarm Optimization Algorithm Based Reactive Power Optimization in Distribution Network

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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Feeder Reconfiguration and Distributed Generator Placement in Electric Power Distribution Network

Feeder Reconfiguration and Distributed Generator Placement in Electric Power Distribution Network

Distributed generation could be considered as one of the viable options to ease some of the problems (e.g. high loss, low reliability, poor power quality, congestion in transmission system) faced by the power systems, apart from meeting the energy demand of ever growing loads. Distributed generation (DG) devices can be strategically placed in power systems for grid reinforcement, reducing power losses and on-peak operating costs, improving voltage profiles and load factors, differing or eliminating for system upgrades, and improving system integrity, reliability, and efficiency. The aim of the DG placement is to provide the best locations and sizes of DGs to optimize electrical distribution network operation and planning taking into account DG capacity constraints. There are various types of DGU like solar Photovoltaic panels which can supply only real power at unity power factor. Some DGU can supply real as well as reactive power like solar thermal turbo alternators, biomass or biogas turbo-alternators, and wind turbines. There are many benefits of distributed generation like loss reduction, greener environment, and
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Voltage Control and Power Loss Reduction in Distribution Networks using Distributed Generation

Voltage Control and Power Loss Reduction in Distribution Networks using Distributed Generation

Distribution network/system is an integral part of power system that joins high voltage sub-transmission system to the low voltage consumers. With ever increasing demand for electrical energy, distribution network needs continuous expansion to meet the increasing load demand. According to Indian records 20% of total power generation is wasting at distribution level. Distributed Generation (DG) is the main and one of the alternatives to meet ever increasing load demand and decrease power loss at distribution level. Integration of DG units into the distribution network can provide numerous environmental, technical and economic benefits to the consumers as well as distribution companies. Many studies proved that the above said benefits can be achieved only if the DG units are placed at optimal locations with optimal size otherwise DG leads to negative impacts. DGs are categorized as follows [1]:
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Optimal Placement of Distributed Generation in Power System
Using Particle Swarm optimization

Optimal Placement of Distributed Generation in Power System Using Particle Swarm optimization

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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A new coordinated backup protection scheme for distribution network containing distributed generation

A new coordinated backup protection scheme for distribution network containing distributed generation

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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Optimal allocation and sizing of distributed generation using particle swarm optimization in distribution system

Optimal allocation and sizing of distributed generation using particle swarm optimization in distribution system

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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Impacts of Electrical Line Losses Comprising Mul ti Distributed Generation in Distribution System

Impacts of Electrical Line Losses Comprising Mul ti Distributed Generation in Distribution System

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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Particle Swarm Optimization Based Optimal Reactive Power Dispatch for Power Distribution Network with Distributed Generation

Particle Swarm Optimization Based Optimal Reactive Power Dispatch for Power Distribution Network with Distributed Generation

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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Optimization of Nigerian Power System Distribution using Distributed Generation

Optimization of Nigerian Power System Distribution using Distributed Generation

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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A Heuristic Approach to Distributed Generation Source Allocation for Electrical Power Distribution Systems

A Heuristic Approach to Distributed Generation Source Allocation for Electrical Power Distribution Systems

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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Coordinated Voltage Control in Distribution Network with Renewable Energy based Distributed Generation

Coordinated Voltage Control in Distribution Network with Renewable Energy based Distributed Generation

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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Distribution substation. and Integration of Distributed Generation

Distribution substation. and Integration of Distributed Generation

Distributed generation (DG) or decentralized generation is not a new industry concept. In 1882, Thomas Edison built his first commercial electric plant – “Pearl Street.” The Pearl Street station provided 110 V direct current (DC) electric power to 59 customers in lower Manhattan. By 1887, there were 121 Edison power stations in the United States delivering DC electricity to customers. These early power plants ran on coal or water. Centralized power generation became possible when it was recognized that alternating current (AC) electricity could be transported at relatively low costs with reduced power losses across great distances by taking advantage of the ability to raise the voltage at the generation station and lower the voltage near customer loads. In addition, the concepts of improved system performance (system stability) and more effective generation asset utilization provided a platform for wide-area grid integration. Recently, there has been a rapidly growing interest in wide deployment of distributed generation, which is electricity distributed to the grid from a variety of decentralized locations. Commercially available technologies for distributed generation are based on wind turbines, combustion engines, micro- and mini-gas turbines, fuel cells, photovol- taic (solar) installations, low-head hydro units, and geothermal systems.
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