PLACEMENT OF DISTRIBUTED GENERATION AND RECONFIGURATION OF RDS FOR LOSS MINIMIZATION USING PARTICLE SWARM OPTIMISATION
Anmol Pandey1, Amit Kumar2, Nitin Saxena3
1M.E. Student, Department of Electrical Engineering, Jabalpur Engineering College, Jabalpur 482011, India
2M.E. Student, Department of Electrical Engineering, Jabalpur Engineering College, Jabalpur 482011, India
3Assistant Professor, Department of Electrical Engineering, Jabalpur Engineering College, Jabalpur 482011, India
Abstract- Overloading on the transmission line with increase of demand on the load side due to increase of population and over reliant on the electricity. This research work presents an effective technique for optimal placement of Type 1 DG (Distributive Generation) i.e., Photo-Voltaic (PV) through Particle Swarm Optimization Algorithm (PSO) and network reconfiguration in Radial Distribution System (RDS) simultaneously to reduce the total real and total reactive power to enhance the voltage level of the transmission and distribution network system. IEEE 33 radial bus system with tie switches has been considered for the analysis of this paper. This whole analysis has been done on MATLAB R2021B to get the optimal power quality and preferred DG placement location.
Keywords: Distributed Generations, Network Reconfiguratiom, Particle Swarm Optimization, Radial Distribution System, Type 1 DG, Optimal Placement.
1. INTRODUCTION 1.1 Background
Energy has become the basic need of the mankind in the present scenario. It is widely used due to its flexibility of use and ease of application. Electrical energy is the clean, green and relatively safer source of energy. Sustainable development is the key to move in the future and electrical energy will be the main key.
Electrical energy distribution system has many types of losses. But, I2R losses due to high demand than the generation in developing countries like India that is why load draws more current. By which distribution system has more power loss. This loss can be minimised with the help of DG placement at the optimal location and by the network reconfiguration. The operating cost will also increase. With this regard, changing environment of power systems design and operation have necessitated the need to consider active distribution network by incorporating Distributed Generation units (DGs) sources [1].
Distributive Generation can limit the losses in the energy which may be wasted in the form of heat and in the power system. By using Distributive generation or local energy sources we can reduce the line losses in the form of heat or I2R losses. The placement and relocation of the DG also effects the losses
minimization. There are various types of DGs based on their active power and reactive power injection. There are four types of DGs named as:
Type -1 DG: It will inject only real power such as Solar PV Solar photovoltaic (PV) systems or fuel cells.
Type-2 DG: It will inject only reactive power such as variable capacitors.
Type-3 DG: It will inject both real power and reactive power such as Synchronous generators.
Type-4 DG: It will consume reactive power and inject real power such as wind mill generators.
1.2 Tie Switch
The network reconfiguration in power distribution system is done either by opening and closing of tie switches or placing the Distribution Generators (DG) on buses. In this product, we have optimized the tie switch positions and optimal positions of DGs to reduce the active power losses. The network is reconfigured when the tie switches change their positions to minimize the active power losses.
1.3 Particle Swarm Optimization
The particle swarm optimization (PSO) algorithm is a population-based search algorithm based on the simulation of the social behaviour of birds within a flock.
The initial intent of the particle swarm concept was to graphically simulate the graceful and unpredictable choreography of a bird flock, to discover patterns that govern the ability of birds to fly synchronously, and to suddenly change direction by regrouping in an optimal formation. From this initial objective, the concept evolved into a simple and efficient optimization algorithm.
1.4 Network Reconfiguration (NR) It is a process in which the topology or the states of the switches/ tie switches on the distribution system is altered to obtain the best radial structure to obtain the goals such as improving the load balance between the feeders or the branches of the distribution system, improving power supply and the voltage quality of the feeder is to be improved.
There are many general algorithms also, for example Fast decoupled method or Newton Raphson method which are very much compatible for the current transmission system but there is a problem such as high R/X ratio of the radial distribution networks, there is also another problem in solving that is the huge size of the Jacobian matrix which results in the more time consumption because of the very large numbers of the iterations. PSO optimization technique will be the best technique for finding the optimal location of the DG placement.
Using the network reconfiguration with PSO optimization will give the optimal location of the DG placement by which we can optimise the losses of the transmission system.
2 LITERATURE SURVEY
Many researchers have focused many algorithms to identify or analyse the optimal DG placement to get the minimum power loss and efficient distribution system. The analysis from [2]
has suggested of employing an approach based on the heuristic algorithm to find the configuration of radial distribution networks, which will ultimately progress to the loss minimization. Shirmohammadi et al. [3] also has explained about the heuristic optimization technique for the
reconfiguration of distribution networks to minimize its resistive line losses. Nara et al. and Subburaj [4]-[5] explained about the network reconfiguration techniques for minimum loss by implying the genetic algorithm (GA). In [6], Harmony Search Algorithm (HSA) for optimal network reconfiguration for the power loss minimization of a large distribution system the HSA has been utilized. For the optimal feeder reconfiguration and capacitor placement in radial distribution networks, Ant Colony Search (ACS) optimization algorithm has been applied [7]. In [8], integration of different types of DGs such as solar, wind and biomass on the existing system for voltage profile enhancement. Combination of the Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA)has been applied to identify an optimal location and size of the DG [9].
For the maintenance of the power system the reconfiguration approach was applied to the radial power system for service restoration and the load balancing [10].
With shunt capacitors, to regulate the bus voltages, reactive power compensation is provided to reduce power and energy loss, to improve power quality, and to release feeders and system capacity [11]. Newton- Raphson load flow analysis algorithms at every load point by using Mi Power software, the voltages at different nodes and losses are determined to be carry out [12]. Distributive Generation has also applied the application of any new updated technology that is sited throughout a demand‟s service area (interconnected to the distribution or sub transmission system) to lower the cost of the system operation [13]. In [14], A new Multi Objective Particle Swarm Optimisation algorithm has utilized for the optimal placement of the wind and the solar based Distributive Generations to maximise the voltage stability. Network reconfiguration is the method by which the loss can be minimised by changing the topology of the network itself.
Research has been shown by the various authors in [15]-[18]. Feeder Reconfiguration for Loss Reduction in Distribution System with Distributed Generators has been shown by the [19].
The benefits of DG are numerous [20]-[21]
and the reasons for implementing DGs are an energy efficiency or rational use of
energy, deregulation or competition policy, diversification of the energy sources, availability of modular generating plant, ease of finding sites for smaller generators, shorter construction times and lower capital costs of smaller plants and proximity of the generation plant to heavy loads, which reduces transmission costs. To maximize power loss reduction with constraints, include current and voltage limit, size of capacitor and radial network [22]. In this paper, a particle swarm optimisation with network reconfiguration with simultaneous DG placement has been used to minimise the losses in the distributive network system by considering the IEEE 33 bus system.
2.1 Problem Formulation
To minimise the desired objective function in accordance of the multiple DGs placement for different DG type for the minimisation of the losses. Objective function is given the equation (1) and the operational constraints are discussed below.
2.2 Objective Function
The main aim of the applied approach is to the minimization of the real power and the reactive power of the distributed network for the optimal power in the system. This could be done via minimizing the main objective function constraint.
Problem is formulated below.
Min 𝑃𝑙𝑜𝑠𝑠 = 𝑁𝑖=1𝐼𝑖2𝑅𝑖 i є N …. (1) here, Rirepresents the branch of resistance, Ii represents the branch of current. The total number of branches in the network is n.
Subjects to constraints:
Radial network constraintmust be taken for the operational point of view.
Power quality constraint, voltage magnitude will be in the permissible limit of −+5%.
𝑉𝑚𝑖𝑛 ≤ 𝑉 ≤ 𝑉𝑚𝑎𝑥 …. (2)
Generator operating constraint P is DG output
𝑃𝑖𝑚𝑖𝑛 ≤ 𝑃𝑔 ≤ 𝑃𝑖𝑚𝑎𝑥 …. (3)
DG placement and size constraint
DG power factor constraint
The distribution feeder current capability limit must be within the
rated current capability of the branch 𝐼𝑖𝑟𝑎𝑡𝑒𝑑.
𝐼𝑖 ≤ 𝐼𝑖𝑟𝑎𝑡𝑒𝑑 3 METHODOLOGY
3.1 Particle Swarm Optimisation Particle swarm optimization (PSO) is a computerized technique used in computational science that attempts to constantly enhance a candidate solution with respect to a specified quality metric.
By using a population of potential solutions, here referred to as particulate, and spreading those across the state space in accordance with a straightforward mathematical formula over the particle's position and velocity, it solves problems. In spite to being led toward the most well positions in the search space, which are updated as other elements find better places, each particle's movement is also impacted by its local best-known position. The swarm should migrate toward the better answers as a result of this.
A metaheuristic, PSO may search very vast areas of candidate solutions and makes little to no assumptions about the issue being optimised. The optimization issue need not be differentiable, as is needed by traditional optimization techniques like gradient descent and quasi-newton methods, because PSO does not employ the gradient of the problem being optimised. However, the existence of an optimum solution is not guaranteed by metaheuristics like PSO.
Step 1: Bus data, bus voltage limit and line voltage.
Step 2: Utilizing a distribution load f low based backward sweep-
forward sweep approach, calculate t he loss.
Step 3: In the solution space, rando mly create an initial population (arra y) of particles with random
sizes and velocities on the dimensio ns (locations of type-I DG)
The iteration counter is set to k = 0.
Step 4: After arranging the combinat ion, discover the voltage profile for e ach particle. Using the above criteria , determine the overall loss if the bu s voltage is within the limits. Set it t o base case since that particle canno t exist otherwise.
Step 5: Compareeach particle's objec tive value to its best individual value .
When the objective value falls below Pbest, Make this the new Pbest value, and note the matching particle location.
Step 6: Pick the particle that has the lowest individual best Pbest value among all particles, and make that value the current global best Gbest.
Step7: To update the particle's weight, velocity, and location, respectively.
Step 8: Proceed to Step 9 if the iteration count exceeds the allowed number. Otherwise, return to Step 4 and set iteration index k = k + 1.
Step 9: Print the ideal response to the target issue.
The best position comprises the ideal placements and dimensions for several DGs as well as the fitness value that corresponds to the least amount of overall actual power loss.
𝑃𝑖 = (𝑝𝑖,1 , 𝑝𝑖,2 , 𝑝𝑖,3 , … … … … . , 𝑝𝑖,𝑛) 𝑉𝑖= (𝑣𝑖,1 , 𝑣𝑖,2 , 𝑣𝑖,3 , … … … … , 𝑣𝑖,𝑛)
The updated position and velocity of particle can be indicated by the following equation:
𝑝𝑖+1 = 𝑃𝑖 +𝑣𝑖+1
𝑣𝑖+1 = 𝜔𝑉𝑖 + 𝑐1𝑟1 (𝑃𝑏𝑒𝑠𝑡 −𝑃𝑖 ) + 𝑐2𝑟2(𝐺𝑏𝑒𝑠𝑡 −𝑃𝑖) The inertia weight equation can be written as:
𝜔 = 𝜔𝑚𝑎𝑥 − [(𝜔𝑚𝑎𝑥 − 𝜔𝑚𝑖𝑛) × ( 𝑖𝑡𝑒𝑟/𝑖𝑡𝑒𝑟𝑚𝑎𝑥)]
4 NETWORK RECONFIGURATION
The process of reconfiguring a distribution network involves shifting the open/closed status of the switches to move loads across feeders while maintaining the same basic architecture of the distribution systems. Feeder reconfiguration has the following advantages: (a) less power is lost; (b) load balancing; (c) better bus voltage profile;
(d) increased system security and dependability; and (e) better power quality.
4.1 DG Placement
Today‟s electricity distribution network has a difficult job to do as it tries to keep up with the steadily rising load demand.
Voltage drops as a result of the rising load
demand, and the distribution network experiences losses. Due to its prospective advantages, DG technology use has recently greatly increased on a global scale. An efficient method for lowering system losses and enhancing voltage and reliability is the optimal sizing and placement of DG units close to the load centres. The efficiency of PSO (particle swarm optimization) for DG placement and size optimization in the radial distribution system is examined in this work. The computational robustness of these strategies is their key benefit. They offer the best option in terms of voltage profile enhancement, dependability,as well as the reduction of losses. In terms of enhancing the voltage profile, dependability, and loss minimization, they offer the best resolution. Using a multi- objective function, the predicted algorithms are evaluated on IEEE 33 radial distribution systems, and the outcomes are contrasted
4.2 Optimal Places
The overall number of system buses is the number of bus combinations that can be used for a single DG deployment. So, it was easy to determine the DG size and to assess the loss at each bus.
However, there are only NCn
combinations that can be determined for a given set of N buses in the same network for „n‟DGs, where n is the number of Distributed generation and N is the set of buses. Therefore, the best position must be discovered using a search strategy or heuristic method. As a result, the PSO approach is used to find the best places to install many DGs.
For Type-I DG: ki ≠lj∀ j.
4.3 Optimal Power Factor
Optimal power factor is determined using in case of ki ¼ lj. Therefore optimal power factor can be evaluated as
𝑃𝐹𝐷𝐺𝑖= 𝑃𝐹𝐷𝐺𝑖
𝑃2𝐷𝐺𝑖+𝑄2𝐷𝐺𝑖
5 CASE STUDY AND RESULT DISCUSSION
5.1 Placement of DG’s
The lowest power loss number obtained by repeatedly executing the code in the MATLAB programming environment is shown in the table below. The tie switches
computation result using the basic case is shown in table below. Reactive and real power losses are reduced by installing type 1 DGs, respectively. The results of
using numerous DGs. As the quantity of DG grows, there is a decrease in power loss.
Table (1) By the above table we can come to know
that Active Power loss decrement with only DG Placement of 4 DG is equal to 58.080% and Reactive Power loss decrement with only DG placement of 4 DG is equal to 55.946%.
5.2 Network Reconfiguration
Using Particle Swarm Optimisation algorithm, the opening of the switch positions has been choose as such that the losses are reduced. After network reconfiguration the result has been optimised and the active and reactive power has been reduced as it shown below in the table.
PARAMETER BASE CASE
Open Switch Number
9,12,8,16,43 14,40,8,17,20 12,44,16.8,58 54,70,14,41,44 ACTIVE
POWER LOSS
(KW) 224.9606 92.0832 93.8896 93.262 93.9182
REACTIVE POWER LOSS
(KVAR) 102.147 130.0393 156.8576 126.4634 124.077
Table (2) Hence from the above table after network
reconfiguration the Active Power loss decrement is equal to 58.251 and the Reactive Power loss decrement by changing the switch and network reconfiguration is equal to 59.856%
Percentage decrement.
5.3 Simultaneous DG Placement and Network Reconfiguration DG Placement Here, the DG's position, size, and switch position have been chosen in a way that
minimises power loss. The placement of several DGs of types I and 2 reduces the actual and reactive power. When positioning the DG, analyse the switching position concurrently (e.g., by opening switches to decrease power losses). The total real loss decreased after the simultaneous insertion of 4 DGs of type 1. Table below presents the various results of type 1 DG in tabular format.
Henceit is clear from the above table that after simultaneous DG placement and network reconfiguration Active Power with
4 DG is decrement equal to 70.738% and Reactive Power decrement with 4 DG is equal to 41.418%.
DG PLACEMENT
Parameter Base Case Number Of DG (Type 1 DG)
1 DG 2 DG 3 DG 4 DG
Active Power Loss (KW) 224.9606 116.3157 102.7452 94.3374 94.3025 Reactive Power Loss (KVAR) 102.147 55.6637 48.6627 44.7032 44.9994 Tie Switch Number 69 70 71
72 73 69 70 71
72 73 69 70 71
72 73 69 70 71
72 73 69 70 71 72 73
Optimal DG Location --- 59 63 9 14 61 55 2 64 69 61
DG Size P (KW) --- 806 958 674 911 948
163 341 671 864 355
DG Size Q (KVAR) --- 0 0 0 0 0 0 0 0 0 0
SIMULTANEOUS DG PLACEMENT AND NETWORK RECONFIGURATION Parameter Base Case Number Of DG (Type 1 DG)
1 DG 2 DG 3 DG 4 DG
Active Power Loss (KW) 224.9606 86.0237 73.1822 78.7533 65.827 Reactive Power Loss (KVAR) 102.147 86.6489 57.9421 66.2098 59.8396 Tie Switch Number 69 70 71
72 73 63 57 17 7
45 69 41 54
18 64 62 44 56 42 17 45 53 18 24 36
Optimal DG Location --- 63 59 61 14 58 54 27 59 62 42
DG Size P (KW) --- 765 727 510 559 470 556 319 263 603 500
DG Size Q (KVAR) --- 0 0 0 0 0 0 0 0 0 0
Table (3)
6 VOLTAGE PROFILE ANALYSIS
Figure (1) Type 1 DG with 1 DG
Figure (2) Type 1 DG with 2 DG
Figure (3) Type 1 DG with 3 DG
7 CONCLUSION
This research uses the Particle Swarm Optimization approach together with the network reconfiguration method to analyse the various instances. The voltage profile has also been enhanced, although the major goal was to reduce active power loss. Therefore, the power loss has decreased with the deployment of DG, network reconfiguration, and simultaneous DG placement and network reconfiguration. The insertion of 4 DGs of type 1 with simultaneous network reconfiguration results in a corresponding decrease of active power loss of 70.73%.
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