Optimal Sizing and Placement of Distributed Generators in Unbalanced Distribution Networks: An Analytical Perspective

Multi-faceted sustainability improvement in low voltage power distribution network employing DG and capacitor bank

—Real-world distribution networks are generally unbalanced due to unbalanced loads and asymmetric line parameters. Proper allocation of distributed generation (DG) units in unbalanced radial distribution networks to extract their maximum potential benefits is still quite difficult. Converter-coupled DG units have individual phase real and reactive power regulating ability and provide voltage unbalance compensation. This article proposes a new technique for the suitable allocation and capacity of converter-based multiple DG units considering the unbalanced operation of distribution networks. The objective function contains power loss minimization, multiphase voltage stability augmentation, and reduction of voltage unbalances among different phases in the system. A complex multi-objective optimization problem has been synthesized with the help of a Fast and Flexible Radial Power Flow (FFRPF) and solved by employing a Weighted aggregation (WA) based particle swarm optimization (PSO) approach. The performance of the proposed technique is validated on 19-bus, 25-bus, and 34-bus unbalanced radial distribution networks that consider various power factor modes of the converter-coupled DG units. The analysis of the simulation results indicates a significant enhancement in the power delivery efficiency in all the unbalanced test systems

To minimize power losses, it is important to determine the locationand size of local generators to be placed in unbalanced power distribu-tion systems. Because of some inherent features of unbalanced distribu-tion systems, such as radial structure, large number of nodes, and awide range of X/R ratios, the conventional techniques developed forthe transmission systems generally fail to determine optimum size andlocation for distributed generation (DG). This article presents a simplemethod for investigating the problem of contemporaneously choosingthe best location and capacity of DG in three-phase unbalanced radialdistribution systems (URDS) for power loss minimization and to im-prove the voltage profile of the system. The best DG location is deter-mined by using voltage index analysis, and capacity of DG is computedby a variational technique algorithm according to the available standardcapacity of DG. This article presents the results of simulations for 25-busand IEEE 37-bus unbalanced radial distribution system.

The distribution network is generally unbalanced due to the distribution of consumers in different phases and the different status of their energy consumption. Also, in recent years, the utilization of distributed generations (DGs) has flourished in the distribution network, thanks to their favorable operational and environmental benefits. Therefore, it is predicted that the optimal energy management of DGs in the unbalanced distribution network can improve the operation state of the network. Moreover, the consumer load model is generally a combination of constant impedance, constant current, and constant power conditions. Therefore, the current study focuses on the analysis of the operation of unbalanced four-wire distribution systems containing DGs, in which load models are also involved. An optimization structure is established to make the voltage deviation, power loss, neutral wire loss, and voltage unbalance minimized. Limitations of the problem include those related to the network and those concerning operating indices of the system like voltage magnitude and phase angle of buses, power flow through the distribution lines, constraints of operation of DGs, load model, and unbalanced conditions. The problem formed in this study is nonlinear and is solved using sequential quadratic programming (SQP) by implementing it in the GAMS simulation environment. To test the benefits of the introduced approach, a 13-bus distribution system has been adopted so that the optimal operation of DGs in the distribution network has been able to reduce the unbalanced situation of the network compared to the power flow studies. Also, this scheme has improved the operation of network, where energy losses and voltage drop have been reduced compared to power flow studies, and a smooth voltage profile has been extracted.

To minimize power losses, it is important to determine the location and size of local generators to be placed in unbalanced power distribution systems. Because of some inherent features of unbalanced distribution systems, such as radial structure, large number of nodes, and a wide range of X/R ratios, the conventional techniques developed for the transmission systems generally fail to determine optimum size and location for distributed generation (DG). This article presents a simple method for investigating the problem of contemporaneously choosing the best location and capacity of DG in three-phase unbalanced radial distribution systems (URDS) for power loss minimization and to improve the voltage profile of the system. The best DG location is determined by using voltage index analysis, and capacity of DG is computed by a variational technique algorithm according to the available standard capacity of DG. This article presents the results of simulations for 25-bus and IEEE 37-bus unbalanced radial distribution system.

Modern power systems present opportunities and challenges when integrating distributed generation and electric vehicle charging stations into unbalanced distribution networks. The performance and efficiency of both Distributed Generation (DG) and Electric Vehicle (EV) infrastructure are significantly affected by global temperature variation characteristics, which are taken into consideration in this study as it investigates the effects of these integrations. This scenario is further complicated by the unbalanced structure of distribution networks, which introduces inequalities that can enhance complexity and adverse effects. This paper analyzes the manner in which temperature changes influence the network operational voltage profile, power quality, energy losses, greenhouse harmful emissions, cost factor, and active and reactive power losses using analytical and heuristic techniques in the IEEE 69 bus network in both three-phase balance and modified unbalanced load conditions. In order to maximize adaptability and efficiency while minimizing the adverse impacts on the unbalanced distribution system, the findings demonstrate significant variables to take into account while locating the optimal location and size of DG and EV charging stations. To figure out the objective, three-phase distribution load flow is utilized by the particle swarm optimization technique. Greenhouse gas emissions dropped by 61.4%, 64.5%, and 60.98% in each of the three temperature case circumstances, while in the modified unbalanced condition, they dropped by 57.55%, 60.39%, and 62.79%. In balanced conditions, energy loss costs are reduced by 95.96%, 96.01%, and 96.05%, but in unbalanced conditions, they are reduced by 91.79%, 92.06%, and 92.46%. The outcomes provide valuable facts that electricity companies, decision-makers, along with other energy sector stakeholders may utilize to formulate strategies that adapt to the fluctuating patterns of electricity distribution during fluctuations in global temperature under balanced and unbalanced conditions of network.

Unbalanced active distribution networks must be carefully analyzed to minimize undesirable implications from internal unbalances and the incorporation of intermittent sources, such as DG (Distributed Generation). A coordinated voltage regulation mechanism is being created employing a MAS (Multi-Agent System) control structure to solve the difficulties mentioned earlier. The proposed technique increases coordination between DGs and Shunt capacitors (SCs) to optimize the voltage profile and reduce overall power losses, along with voltage and current unbalanced factors in the proposed unbalanced 3-phase radial distribution network. To ensure improved real-time monitoring, PMUs (Phasor Measurement Units) measure the state parameters of the above-regulated distribution network in realtime. Because it does not necessitate the placement of PMUs at all nodes for total network observability, it is a cost-effective technique for estimating network state. The IEEE standard, a 25-bus unbalanced 3-phase distribution network feeder, is used to assess the viability of the recommended technique. MATLAB R2024a programming is used to simulate the case studies.

The thrive for environment friendly sources of energy to meet the growing energy demand has driven the integration of more Distributed Generation(DG) sources into the Distribution network. In order to achieve the benefits of DG integration in terms of improvement in voltage profile, minimization of losses etc, optimal placement of DG sources is of much significance. This is to be done by using robust optimization techniques that can handle the uncertainty associated with the DG sources. Therefore the optimal placement of DG in unbalanced distribution network is a challenging issue. The paper presents the application of Reinforcement Learning(RL) for optimally allocating the Photo Voltaic (PV) units in an unbalanced distribution network. The proposed algorithm is validated for the unbalanced IEEE 13 bus distribution network.

The impact of single-phase grid-connected distributed photovoltaic systems on the distribution network using P-Q and P-V models

Two mismatch functions (power or current) and three coordinates (polar, Cartesian and complex form) result in six versions of the Newton–Raphson method for the solution of power flow problems. In this paper, five new versions of the Newton power flow method developed for single-phase problems in our previous paper are extended to three-phase power flow problems. Mathematical models of the load, load connection, transformer, and distributed generation (DG) are presented. A three-phase power flow formulation is described for both power and current mismatch functions. Extended versions of the Newton power flow method are compared with the backward-forward sweep-based algorithm. Furthermore, the convergence behavior for different loading conditions, R / X ratios, and load models, is investigated by numerical experiments on balanced and unbalanced distribution networks. On the basis of these experiments, we conclude that two versions using the current mismatch function in polar and Cartesian coordinates perform the best for both balanced and unbalanced distribution networks.

In this paper, a new approach is presented to solve the electric vehicle charging coordination (EVCC) problem considering Volt-VAr control, energy storage device (ESD) operation and dispatchable distributed generation (DG) available in three-phase unbalanced electrical distribution networks (EDNs). Dynamic scheduling for the EVCC is proposed through a step-by-step methodology, which solves a mixed integer linear programming (MILP) problem for the whole time period. The objective is to minimize the total cost of energy purchased from the substation and DG units, the cost of energy curtailment on electric vehicles, the cost of energy injected from the ESDs, and the cost of energy curtailment on the ESDs. The Volt-VAr control considers the management of on-load tap changers, voltage regulators, and switchable capacitors installed along the grid. Furthermore, the formulation takes into account the voltage dependence of the loads, while the steady-state operation of the unbalanced distribution systems is modeled using linear constraints. The proposed model was tested in a 178-node three-phase unbalanced EDN considering a one-day time period.

Optimal allocation of stochastically dependent renewable energy based distributed generators in unbalanced distribution networks

The advancements in distributed generation (DG) technologies and growing concern for environmental friendly sources of energy necessitate an accurate analysis of distribution system with DG sources. The photovoltaic (PV) source is one of the most promising DG types that can be used for power generation at the distribution level because of its abundance. The efficient operation of the distribution system after the interconnection of DG sources is highly dependent on its integration to the distribution network. The uncertainty associated with the generation from the DG sources should also be considered while planning the integration. This paper presents the optimal sizing of the PV sources in the unbalanced distribution network by Reinforcement Learning, which is an efficient strategy for handling the stochastic data in practical situations. The uncertainty associated with the power output from the PV source is included in the power flow as a variable with multiple states. Here, the beta probability density function is used to model the randomness of the PV source. The seasonal variation in the power loss reduction obtained shows the effectiveness of the uncertainty model. The proposed algorithm is validated and tested for the Institute of Electrical and Electronics Engineers (IEEE) 13-bus and 37-bus distribution feeders, which shows its suitability for implementation in a real system. Copyright © 2016 John Wiley & Sons, Ltd.

This paper provides a modelling algorithm for renewable energy based distributed generators with the goal of reducing power loss and improving voltage profiles in unbalanced distribution networks. Recently, the utilization of Distributed Generation (DG) technologies in the power system is gaining the utmost importance owing to its potential benefits. The combining of the distribution system with DG put forwards a cost - effective as well as benefits to the clients. In any case the scanty of DGs might not assure the enhancement in terms of system performance. Hence In the distribution system appropriate DG units’ placement plays vital role. In this paper, the optimal DG placement problem will be discussed as a constrained nonlinear optimization problem for voltage profile enhancement and power loss minimization. As a novelty the result of Lion Algorithm (LA) compared with the supervised firefly algorithm and Bat algorithm. The voltage profile and power loss reduction in the feeder system with DGs is evaluated for two test cases: case 1(DG supplying real power alone (P), and case 2 (DG supplying reactive power alone (Q). The proposed algorithms are evaluated on the IEEE 33 and IEEE 69 node feeders in a MATLAB environment. Several case studies have been conducted, and the results of the subsequent discussions illustrate the effectiveness of the presented algorithms.

The increased penetration of renewable distributed generation (DG) such as wind power and photovoltaics (PV) in distribution networks has brought significant economic and environmental benefits to society. However, due to the intermittent nature of wind and solar, massive integration of such kinds of DG might cause severe problems, such as greater power loss, reduced power quality, and system efficiency. The integration of energy storage systems (ESS) provides an effective way to handle the above issues due to its capability of stabilizing voltage and frequency. Hence, it is necessary to optimally allocate the ESS to exert maximum support on the distribution network. This paper presents an approach for optimal allocation of ESS to improve voltage profile and reduce power losses and line loading in the unbalanced distribution system. The proposed methodology is tested on an unbalanced medium voltage IEEE-33 bus system with higher wind and PV generation. DIgSILENT PowerFactory is used for constructing and testing the system model. The simulation results demonstrate the effectiveness of the proposed approach in improving the power quality and system efficiency of unbalanced distribution systems.