A Novel Analytical Technique for Optimal Allocation of Capacitors in Radial Distribution Systems

In this paper, a novel analytical technique is proposed to determine the optimal size and location of shunt capacitor units in radial distribution systems. An objective function is formulated to reduce real power loss, to improve thevoltage profile and to increase annual cost savings. A new constant, the Loss Sensitivity Constant (LSC), is proposed here. The value of LSC decides the location and size of candidate buses. The technique is demonstrated on an IEEE-33 bus system at different load levels and the 130-bus distribution system of Jamawa Ramgarh village, Jaipur city. The obtained results are compared with the latest optimization techniques to show the effectiveness and robustness of the proposed technique

Optimal Allocation of Capacitor Bank in Radial Distribution System using Analytical Approach

In this paper, a novel analytical technique is proposed for optimal allocation of shunt capacitor bank in radial distribution system. An objective function is formulated to determine the optimal size, number and location of capacitor bank for real & reactive power loss reduction, voltage profile enhancement and annual cost saving. A new constant, Power Voltage Sensitivity Constant (PVSC), has been proposed here. The value of PVSC constant decides the candidate bus location and size. The achievability of the proposed method has been demonstrated on IEEE-69 bus and real distribution system of Jamawaramgarh, Jaipur city. The obtained results are compared with latest optimization techniques to show the effectiveness and robustness of the proposed technique

A Genetic Algorithm Approach to Optimal Sizing and Placement of Distributed Generation on Nigerian Radial Feeders

Mitigating power loss and voltage profile problems on radial distribution networks has been a major challenge to distribution system operators. While deployment of distributed generation, as compensators, has made a suitable solution option, optimum placement and sizing of the compensators has been a concern and it has thus been receiving great attention. Meta-heuristic algorithms have been found efficacious in this respect, yet the use of the algorithms in addressing problems of radial feeders is still comparatively low in Nigeria where analytical and numerical programming methods are common. Hence; the use of genetic algorithm to site and size distributed generator for real-time power loss reduction and voltage profile improvement on the Nigerian secondary distribution networks is presented. Backward-forward sweep load flow analysis, together with loss sensitivity factor, is deployed to identify the buses suitable for the installation of the distributed generation, while the algorithm is employed in estimating the optimum size. This approach is tested on the standard IEEE 15-bus system and validated using a Nigerian 11 kV feeder. The result obtained on the IEEE test system shows 183 kW loss using the compensator, as compared to 436 kW loss without the compensator; while on the Nigerian network the loss with the compensator was 4.99 kW, in comparison with no-compensation loss of 10.47kW. By the approach of this study, real power loss on the Nigerian feeder decreased by 52.3% together with energy cost reduction from N658,789.12 to N314,227.38. Likewise the minimum bus voltage magnitude and the voltage stability index of the network are improved to acceptable limits. This approach is therefore recommended as capable of strengthening the performance of the Nigerian radial distribution system

Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm

This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature

Voltage profile improvement and losses minimization for Hayin Rigasa radial network Kaduna using distributed generation

This research work has presented the application of distributed generation (DG) units in a simultaneous placement approach on IEEE 33 radial test systems for validation of the technique with further implementation on 56-Bus Hayin Rigasa feeder. The genetic algorithm (GA) is employed in obtaining the optimal sizes and load loss sensitivity index for locations of the DGs for entire active and reactive power loss reduction. The voltage profile index is computed for each bus of the networks to ascertain the weakest voltage bus of the network before and after DG and circuit breaker allocation. The simultaneous placement approach of the DGs is tested with the IEEE 33-bus test networks and Hayin Rigasa feeder network and the results obtained are confirmed by comparing with the results gotten from separate DGs allocation on the networks. For IEEE 33-bus system, the simultaneous allocation of DGs and of optimal sizes 750 kW, 800 kW and at locations of buses 2 and 6 respectively, lead to a 66.49 % and 68.64 % drop in active and reactive power loss and 3.02 % improvement in voltage profile. For the 56-bus Hayin Rigasa network in Kaduna distribution network, the simultaneous placement of DGs of sizes 1,470 kW and 1490 kW at locations of bus 16 and 23 respectively, lead to a 79.54 % and 73.98 % drop in active and reactive power loss and 15.94 % improvement in voltage profile. From results comparison, it is evident that the allocation of DGs using the combination GA and load loss sensitivity index, gives an improved performance in relations to power loss reduction and voltage profile improvements of networks when compared to without DGs

Power Quality Improvement of a Distribution Network for Sustainable Power Supply

This paper presents a heuristic technique for improving power quality of a distribution network for sustainable electric power supply using shunt capacitor placement. The issue of power loss has been a major threat to a distribution network. A distribution network is expected to operate at certain voltage level to meet consumer’s energy demand. Power flow studies has been conducted using the Newton Raphson’s technique at the 30 bus, 11 kV Onuiyi-Nsukka distribution network. It was found that the voltage profile at buses 19 and 26 were critically violated with voltage amplitudes of 0.72 pu and 0.79 pu respectively. The feeder power quality was improved using a heuristic technique and the installation of a 1200KVAr shunt capacitor to keep bus voltage amplitudes within the legal limit of (0.95-1.05) pu. The voltage profile, active and reactive power losses on the network were determined. Active power loss and reactive power loss was reduced from 0.27MW to 0.12MW and 0.76Mvar to 0.14Mvar, respectively. Therefore, the voltage profile is enhanced and the power loss significantly reduced

Swarm algorithms in dynamic optimization problem of reactive power compensation units control

Optimization of a power supply system is one of the main directions in power engineering research. The reactive power compensation reduces active power losses in transmission lines. In general, researches devoted to allocation and control of the compensation units consider this issue as a static optimization problem. However, it is dynamic and stochastic optimization problem that requires a real-time solution. To solve the dynamic optimization NP-hard problem, it is advisable to use Swarm Intelligence. This research deals with the problem of the compensation units power control as a dynamic optimization problem, considering the possible stochastic failures of the compensation units. The Particle Swarm Optimization and the Bees Algorithm were applied to solve it to compare the effectiveness of these algorithms in the dynamic optimization of a power supply system

Planning of Unbalanced Radial Distribution Systems with Reactive and Distributed Energy Sources Using Evolutionary Computing Techniques

The distribution system plays a key role in power system as it provides energy to the consumers safely, reliably, and economically. However, due to high R/X ratio, and low operating voltages, most of the losses occur in the distribution system. Moreover, distribution systems are generally unbalanced due to unequal single phase loads at the three phases of the system, and also additional unbalancing is introduced due to non-equilateral conductor spacing. This, causes the voltage, the current, and the power unbalance in the system. Further, the total neutral current of the system increases causing unwanted tripping of the relay. Hence, the service quality and the reliability of the distribution system reduces. Therefore, a suitable phase balancing strategy is required to mitigate the phase unbalancing in the unbalanced distribution systems. Also, apart from reducing the phase unbalancing in the unbalanced distribution systems, a suitable strategy is required to minimize the system power loss. In this regard, it is necessary for the distribution engineers to plan the unbalanced distribution systems in order to reduce the losses, voltage unbalances, and neutral current of the system for safe and reliable operation. Most of the approaches for the planning of the unbalanced distribution systems are based upon metaheuristic algorithms. Moreover, the recent research has focused only on either phase balancing or simultaneous phase balancing and conductor sizing optimization in unbalanced distribution systems using metaheuristic algorithms. However no work has been carried out to study the impact of the simultaneous optimization of the phase balancing, the conductor sizing, the capacitor location and sizing, the DG location and sizing, DSTATCOM location, and rating on system power loss, voltage unbalance, etc. utilizing these algorithms. As the metaheuristic algorithms are random in nature, the convergence is not guaranteed in a single simulation run. Hence, it is necessary to perform a statistical comparison among them in order to understand their relative merits and demerits for multiple simulation runs. In this thesis, the impact of the simultaneous optimization of the phase balancing and the conductor sizing on the planning problems/objective functions of the unbalanced distribution system such as; the power loss, the voltage unbalance, the total neutral current, and the complex power unbalance studied using various metaheuristic algorithms such as the DE, the CSA, the PSO, and the GA. In the first step, these objective functions are optimized separately; then they are aggregated with weights into a multi-objective optimization problem. Further, a performance comparison in terms of the mean value of the objective functions and standard deviation (SD) carried out. The reactive power compensating devices, such as the Capacitor, and the DSTATCOM has been integrated into the planning problem for the power loss minimization, the voltage profile improvement, and the voltage unbalance mitigation of the unbalanced distribution systems. Moreover, a three phase unbalanced modelling of the DSTATCOM has been developed. In this thesis, the effect of the simultaneous optimization of the phase balancing, the conductor sizing, the capacitor sizing, and the simultaneous optimization of the phase balancing, the conductor sizing, and the DSTATCOM sizing on the planning problem investigated. Both, single and multi-objective optimization approach are used in order to solve this problem. Also, statistical performance among the metaheuristic algorithms such as; the DE, the CSA, the PSO, and the GA in terms of the mean value of the objective function and SD carried out. Further, the renewable sources such as the DG and a combined DG and DSTATCOM has been incorporated into the unbalanced system in order to study their impact on various planning problems