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    In this write-up, we will be considering an effective method of optimally placing Distribution Generation for minimizing power loss, voltage deviation and harmonics in electrical distribution systems. This proposed algorithm will determine the optimal location of a specified distribution network, as well as solving the various problems in the distribution system. This new technique will be a hybridized optimization technique that is aimed at solving the same distribution problems and tackling the cost function. In other words, the outcome of the simulation of this technique will be measured with those of other techniques in terms of reliability and capabilities.

  As said earlier, heuristic and meta heuristic methods have been the most common and most used way of dealing with problems associated with optimal Distribution Generation placement. This paper discusses new ways (maybe better ways) of determining optimal Distribution Generation location, power loss reduction, Voltage profile improvement and combating harmonic distortion using a combination of analytical method and other viable methods in order to  minimize cost and maximize consumer satisfaction and safety. That said! This paper may not contain all the needed solutions, but it sure takes this problems one step further in the direction of solution.

   This proposed solution algorithm would be simulated using MATLAB to code and test on industrial distribution systems

Keywords; Distributed Generation, Optimal Placement and sizing, power loss, voltage profile, Discrete Particle Swarm Optimization


Distribution generation has achieved great attention from researchers in the power sector, due to its working performance in reducing loss of power, increasing consumer reliability and minimizing the cost speculation.[1,2] (El-Khattam W et al) and (Pepermans G et al)  In recent years, maximizing power loss reduction, voltage fluctuation reduction and minimizing total harmonic distortion in order to achieve consumer satisfaction, low cost and safety has been the major goals of most power supply firms, scientific quests and political agenda. Electric distribution networks are turning out to be extensive and complex prompting higher loss of networks and poor voltage regulation. Studies show that just about 13% of the aggregate force produced is absorbed as I2R loss at the appropriation level (Ng et al. 2000) {3}. Consequently to lessen power misfortunes or loss, shunt capacitors are introduced in influence circulation systems to satisfy receptive force.

Distributed or conveyed generation is an electric force source joined straight forwardly to the dispersion networks or on the client site of the meter. Before introducing distribution generation, its belongings on line losses, voltage profile, cut off, measures of infused wavelength and unwavering quality must be assessed independently.

The arranging of the electric network with the vicinity of DG requires the meaning of a few elements, for example, the best innovation to be utilized, the number and the cutoff of the units, the best zone, the best location, the kind of system association, and so forth. The effect of DG in framework working qualities, for example, electric misfortunes, voltage profile, strength, all out wavelength bending and unwavering quality should be fittingly assessed.

The issue of DG siting and sizing is of awesome significance, its establishment at non-ideal location can bring about an increment in loss of power network, suggesting in an increment in expenses and, along these lines having an impact inverse to the visualized.

Numerous methods have been proposed in which their common goal is to reduce power loss improve voltage profiles and for settling the optimal DG problem in electrical distribution system. These methods may be grouped into the taking after classifications: conventional optimization technique and artificial optimization method. Among these strategies, the artificial optimization procedures have been broadly connected in unraveling the optimal DG issue.

The heuristic based strategy is proposed by (Huang et al. 2000) {4}   used for selecting optimal placement and sizing of DG in an electrical system and the genetics calculation is connected to discover the optimal sitting and sizing of attached DG location  at different capacity levels (Das et al. 2002) {5}. The genetics calculation is considered as one of the first meta-heuristic strategies utilized for taking care of optimal DG placement problem however it has some setbacks, for example, disparity and local optimal issues. Fussy logic has been connected to take care of the DG position issue in which the requirements are fortified and the quality are utilized to guide the inquiry procedure to guarantee that the target capacity is enhanced at every cycle process (Masoum et al. 2004) {6}.

 Other heuristic based strategies incorporate the ant colony utilization province calculation for comprehending the DG sitting and sizing planning problem (Annaluru et al. 2004) {7}. In the application of the ant colony optimization for the issue, capacitors ought to be in discrete qualities and not in persistent qualities which are typically more exact.


The utilization of a streamlining strategy equipped for demonstrating the best answer for a given dissemination system can be extremely helpful for the planner to arrange the system. The determination of the best places for establishment and the best size of the distribution generation units in extensive distribution frameworks is a complicated solution development. DGs incorporate specialized, practical, administrative, and potentially ecological challenges.

As in the larger part of arranging process, an expense capacity is typically built to speak to the general working and cost expenses of a distribution region. Designing parameters, for example, limit, dependability, power loss, voltage regulation, influence quality, load interest, are connected with the operation and investment. A general method for deciding the optimal DG area in this way gets to be fundamental to guarantee that their belongings on appropriation frameworks are sure, that they minimize electrical loss of power and they keep up a worthy voltage profile [3].

Literature review

This paper reviews the method of DG optimal location in power distribution system. It proposes that the technique can be used to determine the optimal location and to solve various problems in a distribution DG optimal placement; heuristic optimization technique is commonly used to study DG problems, there is likely to be some influence on the overall distribution system in terms of power losses, voltage profile, unwavering quality or reliability, power quality or protection and safety. The potential impacts of DG in an electrical system are described below.

Impact of DG paper chapter 2

3.1. Power Loss

DG causes a critical effect in electric loss because of its nearness to the load focuses. DG units ought to be distributed in places where they give a higher deduction of loss. This procedure of DG sitting like capacitor designation is to minimize loss. The primary contrast is that the DG units cause impacts on both the dynamic and reactive power, while the capacitor banks just have impact in the reactive power flow .In feeders with high power loss, a little measure of DG deliberately assigned (10–20% of the feeder load) could bring about a high deduction of losses [28,71–76]. Borges CLT, Falcao DM {8}.

• With the combination of DG in a grid power misfortunes are reduced.

 â€¢   For a specific DG limit there is an area in the system such that if we join DG at that location power misfortunes are minimum in comparison, when same DG is placed at any other point.

• That specific location where power misfortunes are least, known as optimum location.

3.2. Voltage profile palgerise

The distribution networks are typically controlled through tap changing at substation transformers and by the utilization of voltage controllers and capacitors on the feeders. This type of voltage regulation accepts power flow circling from the substation to the loads. DG presents meshed force flows that may interfere with the customarily utilized regulation practices [28, 58, 86]  86 Barin Alexandre et al 2010 {9} . Since the control of voltage regulation is normally in view of radial power streams, the unsuitable DG assignment can bring about low or over-voltages in the system. Then again, the establishment of DG can have positive effects in the distribution network by empowering reactive compensation for voltage control, decreasing the misfortunes, contributing for frequency control and going about as turning store in fundamental network fault. Under voltage and over voltage conditions can emerge given the inconsistency of DG with the voltage regulation in radial power flow [78–80, 83] {10}. Singh A, Singh B 2010

   3.3. Power quality

Power quality refers to the extent to which control attributes adjust to the perfect sinusoidal voltage and current waveform, with current and voltage in parity [87, 91] {11}. To protect the networks from reduction in power quality, it is important for system administrators to ensure as determined least short circuit limit [89]. The connection between distribution generation and force quality is a questionable one. From one viewpoint, numerous researchers stretch the recuperating impacts of distributed generation for power quality issues. For instance, in a location where voltage support is troublesome, distribution generation can contribute because combining propagation generation commonly leads to increase  in voltage in the system [81,82,84–86,88,90] additionally specify the potential positive outcomes of distributed generation or voltage support and adjustments of power factor

3.3.1. Excess voltage  

if there are numerous DG connections fixed on a particular line, the space in the power stream among feeder lines enlarges because of the back flow from the DG .This difference may bring about the voltage profile or feeder lines to depart from the best possible range [81–85]. The voltage of substation propagation lines is controlled by a line drop compensator (LDC) or programmed timer. Typically, a solitary distribution transformer has a various feeder lines, and the voltage for these lines is regulated in a block.

3.3.2. Voltage fluctuation

The voltage of the local line networks is liable to vary if the output of DG changes over a short period, and this variance would bring about over or under voltage at the users receiving point [91–93]. There is specific concern while setting up systems that depend on essential conditions, for example, wind power or solar voltaic generators are interconnected to the local framework.

3.4. Unwavering quality or reliability

The objective of a power system is to supply power to its users in a conservative and reliable manner. It is necessary to plan and support dependable power networks because cost of interference and power outages can have extreme financial effect on the utility and its end users [28, 58]. Generally, reliability analysis and assessment methods at the distribution level have been far less advanced than at the generation level since dissemination outages are more restricted and less costly than transmission or generation level outages .notwithstanding, analysis of end users outages information of utilities has demonstrated that the biggest individual contribution for inaccessibility of supply originates from distribution network failure. One of the principle purposes of integrating DG to distribution network is to expand the unwavering quality of power supply [58]. DG can be utilized as a reinforcement system or as a fundamental supply. DG can likewise be operated during peak load periods in other to stay away from extra charges. A fundamental issue in distribution dependability evaluation is measuring the adequacy of past service. A typical arrangement comprises of consolidating the impacts of service disruption into indices of framework performance. Unwavering quality records are utilized by system operators and planners as an apparatus to enhance the level of service to the end users [94, 95]. Planners use them to decide the necessities for generation, transmission, and distribution size additions. Operators use them to guarantee that the system is sufficiently vigorous to withstand conceivable faults without disastrous outcomes. Unwavering quality lists are thought to be sensible and rationale approach to judge the performance of an electrical power networks.

Review of optimization techniques and their comparison

Optimal sizes of DG units are determined and are consequently placed in the best location in distribution systems. To find out the optimal size and location of DG units in power systems has been a major challenge to distribution system planners as well as researchers in the field. In tackling this problem many critical review of different methodologies employed, various formulations have been used to determine this difficult combinatorial issue (Ejal 2008). Figure 1 shows the number of published research papers that have proposed and addressed the optimal DG placement problem during the last decades.

Fig.1. Summary of citation of researches on DRG planning

(ng et al. 2000) discuss about solving this optimization problem for ease of reference and to facilitate understanding, these research paper categorized and discuss briefly about the existing approaches into different major headings in which the benefits as well as the disadvantages of each approach are thoroughly examined based on the optical DGs placement issue i.e. power losses minimization, improvement of voltage profile, improvement of reliability and power quality.

i. The analytical approaches.

ii. The artificial intelligence approaches

iii. Fuzzy Logic

iv. Hybrid Artificial Intelligent Techniques.

v. Other approaches

2.2. Analytical Method

Different analytical method (AM) having been designed for the position of DRG with optimal size in the distribution system. A large portion of the techniques depend on hypothetical, numerical calculations and analysis [15–20]. They have common objectives, which are to decrease the power losses, enhance voltage profiles, discovering optimal location and optimal size. In a work presented in 2004, Wang and Nehrir [18] presented AM to decide the optimal placement in radial as well as system frameworks to minimize the power loss of the framework.

Artificial intelligence technique (AI)

Artificial intelligence (AI) approaches inspired by evolutionary mechanism such as selection, crossover and mutations [111,112]. They are efficient optimization search techniques employed in finding the exact or near-optimal solutions in multi-objective optimization problems. Applications of AI to complex problems are found in several disciplines such as bioinformatics, computational science, engineering, chemistry, mathematics etc. A genetic search is usually preceded with a randomly generated initial population, covers the whole range of possible solutions, otherwise known as the space. The fitness of each individual in the population in each generation is then evaluated and thereafter modified to form a new population of better solutions. This new populations is then used in the next iteration of the algorithm that terminates either when a satisfactory level of fitness has been attained or when a maximum number of generation have been produced in the population. Of the literature reviewed in this study, some researches [28,57,113,115,116,117,118,119] adopted the GAs based AI approach in finding the optimal size and site of DG units power distribution systems, though Carpinelli et al. [113] did not optimize size in their work.

2.2.1 Fuzzy Logic method

Masoum et al. (2004a) applied fuzzy logic for solving the discrete optimization problem of fixed shunt capacitor placement and sizing under harmonic conditions. Power and energy losses due to installed capacitors and the cost of fixed capacitors are used as the objective function. Kannan (2008) and Saranya (2011) developed fuzzy expert system to determine suitable candidate nodes for determining the optimal capacitor sizes in distribution systems. Bhattacharya et al. (2009) formulated new fuzzy membership functions to identify probable capacitor locations in radial distribution systems. A new algorithm for selecting capacitor nodes was presented, and simulated annealing technique was employed for final sizing of the capacitors.

2.2.3 Hybrid Artificial Intelligent Techniques

To design a hybrid intelligent system, two or more AI method is utilized. Within the past decade,  hybrid intelligent systems  have been used in electrical engineering  functions.(Al-Mohammed and Elamin 2003) discuss about  a combination optimization problem with a non-differential objective function has been planned and solved utilizing GA, TS, simulating annealing and hybrid GA-fuzzy logic algorithms.  According to Hsiao et al. (2004) the capacitor placement problem in distribution system is solved using combined fuzzy GA method. Three particular goals were considered; improve the voltage profile, minimize the total cost of energy loss and capacitor and maximize the margin loading of feeders.

Others methods

The heuristic based strategy is proposed by (Huang et al. 2000)   used for selecting optimal placement and sizing of DG in an electrical system and the genetics algorithm is connected to discover the optimal sitting and sizing of attached DG location  at different capacity levels (Das et al. 2002). The genetics algorithm is considered as one of the first meta-heuristic strategies utilized for taking care of optimal DG placement problem however it has some setbacks, for example, disparity and local optimal issues. The evolutionary programming is a simulation of the development procedure of a populace of people along various generations. In this population, every individual serve as only possible solution of the optimal placement problem.

 Other heuristic based strategies incorporate the ant colony utilization province calculation for comprehending the DG sitting and sizing planning problem (Annaluru et al. 2004). In the application of the ant colony optimization for the issue, capacitors ought to be in discrete qualities and not in persistent qualities which are typically more exact.  

Comparison of Various DG Optimization Methods

The rates of publications on optimization methods proposed to solve the optimal DG placement issue in the specified period are shown in Figure 2.3.

 Figure 2.3 Number of papers presented and published by various optimization methods for optimal DG placement.

From the figure, it is known that the GA, PSO, hybrid technique and fussy logic are the most well-known optimization method utilized for determining the optimal sitting position issue in the last years. From the graph chart it shows that PSO is the most broadly utilized optimization method due to its preferences which incorporate small computational, fast convergence and simple implementation.

PSO is efficient for determining the numerous issues which are not easy to find proper mathematical models. According to (Khajehzadeh et al. 2011) the PSO method can sometimes be relapse into local minima and premature convergence when determining optimization issue. GA which is one of the early heuristic optimization methods utilized for determining the optimal DG placement issue and it also has a few set back as well, such as divergence and local optimal issue.

 Because of the constraints of GA and PSO, Lately (Passino 2002) presented that numerous hybrid method or multi-stage techniques have been proposed and designed to discover optimal sitting and sizing of DG. Out of this hybrid method, a fussy techniques is utilized to solve the optimal DG location, while other optimization method for example, PSO, GA are utilized to locate the optimal DG location and size.

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