The DNEP is done to ensure that enough power is available to meet all of the customers’ current and future load requirements. The existing distribution network infrastructure expansions must be shaped, reconfigured, and developed with the suitable electrical requirements of the customers, according to expansion planning. Distribution network planners’ key responsibilities are to discover predicted load capacity, develop enough distribution network capacity, and keep all distribution network components within acceptable capacity limits.
Ref. [
7] describes a multi-year DG-integrated system for distribution network expansion planning that is using a binary chaotic shark smell optimization algorithm. This study investigates and solves MEPDN (multi-year distribution network expansion planning) for the best growth of the primary distribution network to position and size the DG, as well as reinforce the primary distribution feeders in the projected term. Furthermore, in order to improve network resilience, an analytical technique based on minimal load shedding has been implemented to reduce total investment and operation expenses. In addition, binary chaotic shark smell optimization (BCSSO) was used to address the distribution network’s multi-year expansion plans. Ref. [
8] investigated optimal expansion planning of distribution system capacity with regard to distributed generations. This paper proposes a binary particle swarm optimization (PSO)-based heuristic evolutionary algorithm for solving single and multi-objective distribution system expansion planning problems, as well as DG and the traditional method. The voltage deviation, power loss, location, and size of DG units are employed as objective functions in the particle swarm optimization cost function. A multi-objective PSO algorithm for distribution network expansion planning was proposed by the authors of [
9]. In this paper, distribution network expansion planning is performed to minimize investment and operation costs and energy loss costs. The multi-objective PSO and two-phase multi-objective PSO have been put to the test on the 18-bus network to see if they are viable and effective. The authors of [
10] recommended planning for the distribution system expansion in a deregulated context with system uncertainty. The Monte Carlo simulation-based long-term distribution system planning model is investigated and tested in a competitive setting, taking into consideration technological and economic constraints. The proposed planning takes into account network equipment, experimental decisions, not-supplied energy costs, DG investments, environmental emissions, and component supply and demand forecasting uncertainties.
The authors of [
11] used particle swarm optimization to evaluate the location of dispersed generators for loss minimization and voltage augmentation. In this paper, photovoltaic cells, and synchronous compensator are used as a DG and their optimal sizes and locations were obtained to minimize power losses and improve voltage profiles. Although it is good that they have incorporated the cost-effectiveness of distributed generation integration in their objectives, they have left out branch current carrying capacity and reactive power loss. The author of [
12] proposed a novel DG planning approach with the goal of increasing renewable-based DG penetration while lowering annual losses. Reactive power planning and network reconfiguration are used in this paper to optimize DG penetration and reduce yearly DG losses while taking into account feeder capacity, short circuit level, and investment cost restrictions. To tackle the optimization problem, the PSO algorithm multi-objective method is used, and 96 scenarios and 10 various network load levels are investigated.
Particle swarm-based power loss mitigation and voltage profile optimization of a distribution system using distributed generation was presented in [
13]. On the Iraqi 30-bus distribution grid, the claimed method was evaluated, and the real power loss was reduced by 39.67 percent. Although the result obtained is encouraging, if network reconfiguration was undertaken after integrating distributed generations into the existing distribution system, the power losses might be significantly minimized. The author of [
14] focuses on the optimum sizing of distributed generation using particle swarm optimization to increase distribution network performance. By minimizing active power loss and improving the voltage profile of each bus in the system, optimal location and sizing play a significant role in enhancing system efficiency. The proposed technique was tested using IEEE 15 Bus and IEEE 33 Bus radial distribution systems. In terms of system loss reduction and voltage profile improvement, the NPSO optimized system beats both the PSO optimized and non-optimized systems. This study found no evidence of the impact of renewable DG on network reliability. The authors of [
15] proposed that keeping the voltage profile at the appropriate value will minimize power loss and running costs. By combining the loss sensitivity factor, the operational cost of installing distributed generation and the voltage stability index, a hybrid particle swarm optimization (HPSO) is employed to allocate distributed generation effectively. Weighting factors are used to create the goal function. The forward-backward technique is used to analyze load flow. The method was tested using IEEE 33 and 69 bus radial distribution networks. The HPSO technique converges quickly when compared to the genetic algorithm and particle swarm optimization. When compared to the preceding procedures, this strategy produces a better result. Environmental issues and reactive power loss are not investigated. The influence of distributed generation on the short circuit current level of protective devices was not examined in this study. In [
16], the author uses the Moth-Flame Optimization algorithm and the loss sensitivity factor to construct an effective hybrid solution for optimal DG allocation in radial distribution networks (LSF). The study’s purpose was to reduce power loss, enhance voltage profile, and increase voltage stability by optimizing PV and wind-based DG siting and sizing. The proposed approach was tested on standard IEEE 33 and 69 bus radial distribution systems. PV and wind-based DG improve system performance, according to the findings. The stochastic character of wind speed and sun irradiation was not considered in this study. The impact of solar and wind-powered DG on system reliability was not studied. Based on the voltage limitation index (VLI), the authors discuss the placement and size of DG in [
17]. This index ensures that all buses in the network have a voltage profile that is acceptable within the distribution’s allowed limits. Following the determination of the DG size and position, a full analysis of the cost of DG, energy loss, and network savings was performed. The author of [
18] used a combination of GA and IPSO to optimize the DG site and size while accounting for both real and reactive power losses. Real power, reactive power flow, and power loss sensitivity variables are used to determine the candidate buses for DG allocation. This literature has the benefit of shrinking the algorithm’s search space and enhancing its rate of convergence. However, because of the large iteration time, more work can be done to try to lower it. The authors introduced the genetic algorithm and improved particle swarm optimization (GAIPSO) for optimal DG placement and sizing for power loss reduction and voltage profile enhancement in [
19]. GA-IPSO was used to determine the ideal location and size for a DG, taking into consideration both real and reactive power losses. Real and reactive power, as well as power loss sensitivity factors, was used to identify candidate buses for DG allocation. However, by considering other power system elements, such as system update issues, the multi-objective function can be enhanced. Ref. [
20] proposed a novel DG planning approach with the goal of increasing renewable-based DG adoption while lowering annual losses. Reactive power planning and network reconfiguration are used in this paper to optimize DG penetration and reduce yearly DG losses while taking into account feeder capacity, short circuit level, and investment cost restrictions.
According to the above works of literature, most of the papers focus on only single problem solutions, such as power loss minimization, voltage profile improvement, and network expansion planning. Additionally, most of the papers focus on IEEE distribution network data.
This paper proposed an analytical method for distribution network expansion planning that takes into account future demand growth and optimal distributed generation sizing and placement. To evaluate the capability of the real existing Addis North distribution network and its capability to supply reliable power considering future expansion, the load demand forecast for the years 2020–2030GC is done using the least square method. The performance evaluation of the existing and the upgraded network considering the existing and forecasted load demand for the years 2030GC is done using ETAP software. Accordingly, the results revealed that the existing networks cannot meet the existing load demand of the town, with major problems of increased power loss and a reduced voltage profile.