Units In Distribution Networks Research Articles

Simultaneous Distribution Network Reconfiguration and Optimal Placement of Distributed Generation

A reliable, eco- and nature-friendly operation has been the major concern of modern power system (PS). To improve the PS reliability and reduce the adverse environmental effect of conventional thermal generation facilities, renewable energy based distributed generation (RDG) are being enormously integrated to low and medium voltage distribution networks (DN). However, if these systems are not properly deployed, the reliability and stability of the PS will be endangered and its quality can be dreadfully jeopardized. Among the measures taken to avoid such is optimizing the location and size of each RDG unit in the DNs. These networks are generally operated in a radial configuration, though they can be reconfigured to other topologies to achieve certain objectives. Both RDG placement/sizing and DN reconfiguration are highly non-linear, multi-objective, constrained and combinatorial optimization problems. In this study, a hybrid of Particle Swarm Optimization (PSO) and real-coded Genetic Algorithm (GA) techniques is employed for DN reconfiguration and optimal allocation (size and location) of multiple RDG units in primary DNs simultaneously. The objectives of the proposed technique are active power loss reduction, voltage profile (VP) and feeder load balancing (LB) improvement. It is carried out subject to some technical constraints, with the search space being the set of DN branches, DG sizes and potential locations. To ascertain the effectiveness of the technique, it is implemented on standard IEEE 16-bus, 33-bus and 69-bus test DNs. The proposed algorithm is implemented in MATLAB and MATPOWER environments. It is observed the power loss, voltage deviation and LB are found to be reduced by 32.84%, 12.33% and 24.03% of their respective inherent values in the biggest system when the system is reconfigured only. With the optimized RDGs placed in the reconfigured systems, a further reductions of 46.27%, 25.92% and 36.65% are observed respectively.

A Distributionally Robust Optimization Model for Real-Time Power Dispatch in Distribution Networks

The integration of large-scale renewable distributed generation (DG) units in distribution networks brings new challenges for real-time power dispatch, including active power and reactive power (P-Q) coupling and operational uncertainties. To reduce the conservativeness of robust optimization (RO) solutions, this paper proposes a distributionally robust realtime power dispatch model, which characterizes the uncertainty of DG output with the information of first-order and second-order moments. Furthermore, a two-stage scheme, which includes economic dispatch (ED) and corrective control, is incorporated in the model, in which ED optimizes the active power schedule and corrective control strategies of P-Q are generated to eliminate overvoltage problems. The proposed model can be transformed into an equivalent semidefinite programming problem and solved via the delayed constraint generation algorithm. Numerical test results show that the proposed model reduces operational costs and mitigates overvoltage problems significantly compared with the conventional one-stage model. The proposed model also outperforms the conventional RO model by exploiting probability information to achieve statistically optimal dispatch.

Optimal Sizing and Location of DG Units for Enhancing Voltage Profile and Minimizing Real Power Losses in the Radial Power Systems Based on PSO Technique

Distributed generation (DG) units have an important number of economic, environmental and technical features, which can contribute to the improvement of the reliability and security of the electric grid. However, all benefits that mentioned before cannot be maximised and enhanced unless the best sizing and position of distributed generation units are accurately determined. The arbitrary placement of DG units can lead to negative influences on the electrical networks. A noteworthy number of methods have been suggested to compute the optimal sizing and position of Distributed generation (DG) units in distribution networks. However, some of them focused on an analytical approach to estimate the optimum allocation of DG units in the radial distribution networks. Indeed, although this method was considered both constant and variable loads, as well as this method, overcome the problem of convergence, but the optimal sizing of DG units was not considered. The main intention of this study is to improve a technique that based on an intelligent algorithm for optimal planning and operation of DG technologies to minimise the real power losses, boost the voltage profile and enhance the overall reliability. IEEE Node-15 system has been taken to perform this study based on a MATLAB environment.in a single paragraph.

Investigating the effect of DG infeed on the effective cover of distance protection scheme in mixed-MV distribution network

The environmental and economic features of renewable energy sources have made it possible to be integrated as Distributed Generation (DG) units in distribution networks and to be widely utilized in modern distribution systems. The intermittent nature of renewable energy sources, altering operational conditions, and the complex topology of active distribution networks makes the level of fault currents significantly variable. Thus, the use of distance protection scheme instead of conventional overcurrent schemes offers an appropriate alternative for protection of modern distribution networks. In this study, the effect of integrating multiple DG units on the effective cover of distance protection schemes and the coordination between various relays in the network was studied and investigated in radiology and meshed operational topologies. Also, in cases of islanded and grid-connected modes. An adaptive distance scheme has been proposed for adequate planning of protection schemes to protect complex networks with multiple distribution sources. The simplified simulated network implemented in NEPLAN represents a benchmark IEC microgrid. The comprehensive results show an effective protection measure for secured microgrid operation.Article History: Received October 18th 2017; Received in revised form May 17th 2018; Accepted July 8th 2018; Available onlineHow to Cite This Article: Saad, S.M., Naily, N.E. and Mohamed, F.A. (2018). Investigating the Effect of DG Infeed on the Effective Cover of Distance Protection Scheme in Mixed-MV Distribution Network. International Journal of Renewable Energy Development, 7(3), 223-231.https://doi.org/10.14710/ijred.7.3.223-231

Optimal Location Planning of Renewable Distributed Generation Units in Distribution Networks: An Analytical Approach

We study the optimal location planning of renewable distributed generation (RDG) units by taking into account the random uncertainties of renewable generation and load demand. In presence of the random uncertainties, location planning problem is naturally a two-stage stochastic mixed integer nonlinear programming problem, which is hard to solve efficiently. Instead of using traditional sampling methods or robust optimization methods, we propose a novel analytical approach in this paper to solve the problem efficiently and optimally. In particular, analytical expressions are derived for efficiently evaluating the performance of a candidate RDG placement decision. In this way, the stochastic mixed integer nonlinear programming problem is equivalently transformed into a deterministic integer problem, which can be solved efficiently using off-the-shelf tools. Numerical results show that the optimal RDG placement can save up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4.2\%$</tex-math></inline-formula> of the long-term average cost and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$80.59\%$</tex-math></inline-formula> of the line losses on the IEEE 13-bus test feeder. In addition, our proposed approach effectively reduces the computational time by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$99.51\%$</tex-math></inline-formula> on the IEEE 123 node test feeder compared with other traditional sampling-based metaheuristic approaches.

Development of a Decision-Making Algorithm for the Optimum Size and Placement of Distributed Generation Units in Distribution Networks

The paper presents a decision-making algorithm that has been developed for the optimum size and placement of distributed generation (DG) units in distribution networks. The algorithm that is very flexible to changes and modifications can define the optimal location for a DG unit (of any type) and can estimate the optimum DG size to be installed, based on the improvement of voltage profiles and the reduction of the network’s total real and reactive power losses. The proposed algorithm has been tested on the IEEE 33-bus radial distribution system. The obtained results are compared with those of earlier studies, proving that the decision-making algorithm is working well with an acceptable accuracy. The algorithm can assist engineers, electric utilities, and distribution network operators with more efficient integration of new DG units in the current distribution networks.

Introduction of Adaptive Clonal Differential Evolution in Allocation and Sizing of Renewable-Energy Distributed Generation Units in Distribution Networks

Introduction of Adaptive Clonal Differential Evolution in Allocation and Sizing of Renewable-Energy Distributed Generation Units in Distribution Networks

A bi-level approach for optimal contract pricing of independent dispatchable DG units in distribution networks

Distributed Generation (DG) units are increasingly installed in the power systems. Distribution Companies (DisCo) can opt to purchase the electricity from DG in an energy purchase contract to supply the customer demand and reduce energy loss. This paper proposes a framework for optimal contract pricing of independent dispatchable DG units considering competition among them. While DG units tend to increase their profit from the energy purchase contract, DisCo minimizes the demand supply cost. Multi-leader follower game theory concept is used to analyze the situation in which competing DG units offer the energy price to DisCo and DisCo determines the DG generation. A bi-level approach is used to formulate the competition in which each DG problem is the upper-level problem and the DisCo problem is considered as the lower-level one. Combining the optimality conditions ofall upper-level problems with the lower level problem results in a multi-DG equilibrium problem formulated as an equilibrium problem with equilibrium constraints (EPEC). Using a nonlinear approach, the EPEC problem is reformulated as a single nonlinear optimization model which is simultaneously solved for all independent DG units. The proposed framework was applied to the Modified IEEE 34-Bus Distribution Test System. Performance and robustness of the proposed framework in determining econo-technically fare DG contract price has been demonstrated through a series of analyses.

Optimal dispatchable DG allocation in a distribution network considering load growth with a mixed-PSO algorithm

In recent years, one of the major issues faced by distribution utilities is integrating distributed generation (DG) units in distribution networks. This paper proposes a population-based method called particle swarm optimization (PSO) for optimal planning of the location and sizing of different types of DG units in the distribution network, considering different loading conditions. The objective of this application is power loss minimization. In order tond the optimal location and size of DG units, continuous and discrete forms of PSO are deployed, respectively (mixed PSO). In addition, the optimal locations and sizes of the DG units are determined in the areas of signicant feeder load growth. This meta- heuristic approach makes little or no assumption about the issue being optimized and can search large spaces of possible solutions. The presented method is tested on the standard IEEE 33-bus and IEEE 69-bus test systems. In order to show the effectiveness of the proposed methodology, the results are compared with another method of DG allocation and another loss minimization technique.

A Survey on Particle Swarm Optimization for Use in Distributed Generation Placement and Sizing

This paper surveys the research and development of Particle Swarm Optimization (PSO) algorithm for use in selecting a suitable position for Distributed Generation (DG) units within a distribution network. Our discussion first covers the algorithm development of PSO and its use in neural networks. After establishing the foundations of PSO, we then explore its use in sizing and sitting of DG units in distribution network. Combining PSO with other optimization techniques for attaining better results is also discussed in this paper.

Sliding Mode Control of a Grid-Connected Distributed Generation Unit under Unbalanced Voltage Conditions

The increasing presence of inverter-based distributed generation (DG) units in distribution networks (DNs) requires control methods that achieve high performance not only during normal operating conditions, but also under unbalanced conditions. With a high probability, a type of voltage unbalance in DNs is unequal three-phase voltage magnitudes at the fundamental system frequency. This can occur temporarily due to faults or permanently due to uneven distribution of unbalanced loads, on the three-phases of the DN. This paper proposes a sliding mode (SM) based controller for grid-connected DG units, under unbalanced grid voltage condition. The proposed control strategy employs a nonlinear control scheme to directly cancel out the negative-sequence (NS) components of DG output current under unbalanced voltage condition; and directly regulate the positive-sequence (PS) active and reactive power injected by DG units to main-grid. The control method proposed in this paper is shown to be robust and stable system parameter uncertainties. The validity and effectiveness of the proposed controller is verified by using time-domain simulation studies, under the MATLAB/Simulink software environment.

AN MINLP APPROACH FOR OPTIMAL DG UNIT'S ALLOCATION IN RADIAL/MESH DISTRIBUTION SYSTEMS TAKE INTO ACCOUNT VOLTAGE STABILITY INDEX

Application of distributed generation (DG) units in distribution networks has increased in recent years. Sizing and sitting of DG units are two important factors which should be considered, especially for reduction of power losses in distribution system. This paper presents a new approach for DG placement in distribution systems using the mixed integer nonlinear programming (MINLP). This approach determines the optimal number, sitting and sizing of DG units in both radial and meshed distribution networks with the objectives of reducing the power losses, as well as minimizing the investment and operation costs of DG units, and the monetary value of voltage stability index improvement. The performance of the proposed method is compared with the genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO, and combined loss sensitivity factor and simulated annealing (LSFSA). Compared with the conventional methods, the proposed scheme can be applied in the meshed distribution networks as well. A 33-bus radial network and CIVANLAR meshed distribution system have been used for simulation studies. The obtained results approve the efficiency of proposed method for sitting and sizing of DG units in distribution networks.

Dynamic planning of distributed generation units in active distribution network

This study proposes long-term dynamic planning of distributed generation (DG) units in distribution networks considering active management (AM). In the AM operation mode, the distribution network equipment is controlled in real time based on the real-time measurements of system parameters (voltage and current). The proposed model determines the optimal size, location and time of investment of DG units. The uncertainty of energy price and rate of load growth is handled using a scenario-based modelling approach. To demonstrate the advantages of dynamic DG planning, its results are compared with those of static model. Also the results of optimal DG planning in active network are compared with those of the passive operation mode. In addition, a technical evaluation and comparison between conventional, passive and active networks have been performed. The proposed model is successfully applied to the 33-bus radial distribution network. The results indicate that using AM, the cost function and losses are decreased effectively compared to the passive management. Computation of the optimal DG planning in AM environment is formulated as a mixed integer non-linear problem which can be solved using commercial optimisation packages like as GAMS.

Optimal placement and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm

Abstract Distributed generation (DG) is becoming more important due to the increase in the demands for electrical energy. DG plays a vital role in reducing real power losses, operating cost and enhancing the voltage stability which is the objective function in this problem. This paper proposes a multi-objective technique for optimally determining the location and sizing of multiple distributed generation (DG) units in the distribution network with different load models. The loss sensitivity factor (LSF) determines the optimal placement of DGs. Invasive weed optimization (IWO) is a population based meta-heuristic algorithm based on the behavior of weeds. This algorithm is used to find optimal sizing of the DGs. The proposed method has been tested for different load models on IEEE-33 bus and 69 bus radial distribution systems. This method has been compared with other nature inspired optimization methods. The simulated results illustrate the good applicability and performance of the proposed method.

Two Methods for Size Selection of DG Units in Distribution Networks

Today, with advances in semiconductor technology and the ability to interface and power electronic converters and also the desires and motivations of economic and environmental another option has occurred to increase the capacity for network engineers and power systems planners. Size and place selection (planning) of distributed generation is the optimization problem that to solve it should get help from one of the optimization methods. In this paper, two methods to determine the size and location of distributed generation (DG) units are presented in distributed system. Four objective functions include, cost, power loss, voltage profile and environmental attributes are intended. The proposed algorithms are tasted on the 34- bus radial system. The results show that voltage profile and power loss improve with proposed solution.

A multi-objective approach to integrate solar and wind energy sources with electrical distribution network

Solar and wind energies are the need of the hour worldwide and their high penetration in electricity power grid cause sensible amount of problems (stick to the grid code of respective country) due to randomness and uncertain generation pattern. Therefore reinforcement of intermittent renewable energy sources requires due attention to achieve optimum economical as well as operational benefit. This paper presents a novel techno-economic optimization method for proper location and size selection of multiple solar and wind generation units in distribution network. Multi-objective particle swarm optimization technique (MOPSO) is adopted to generate potential solutions by trade-off between payback year, reduction of power loss and voltage stability level of the network. The strategic planning method has been tested on a typical Indian rural distribution network. It is shown that the proposed method offers the workable solution to get the desired result.

A multi-stage MINLP-based model for sub-transmission system expansion planning considering the placement of DG units

This paper presents a new multi-stage model, based on the mixed integer nonlinear programming (MINLP) approach, to determine the optimal sub-transmission system expansion planning (SSEP). This model considers the placement of distributed generation (DG) units in distribution networks over the planning periods. The SSEP determines the facilities which should be installed and/or reinforced so that the system serves the forecasted demand at the lowest cost while satisfying the operational constraints. These constraints include the maximum allowable voltage drop, and the operation capacity of substations, MV feeders, and DG units. Meanwhile, the proposed model presents the optimal planning of existing and new sub-transmission substations, as well as the routes of medium voltage (MV) feeders. Moreover, the allocation and operation planning of DG units over the planning period is considered, as well. On the other hand, the expanding limitations of existing and candidate sub-transmission substations and DG units are considered as the expansion constraints in the SSEP problem. The effectiveness of proposed MINLP-based model is demonstrated using several case studies.

Optimal placement of dispatchable and nondispatchable renewable DG units in distribution networks for minimizing energy loss

This paper presents a methodology for the integration of dispatchable and nondispatchable renewable distributed generation (DG) units for minimizing annual energy losses. In this methodology, analytical expressions are first proposed to identify the optimal size and power factor of DG unit simultaneously for each location for minimizing power losses. These expressions are then adapted to place renewable DG units for minimizing annual energy losses while considering the time-varying characteristics of demand and generation. A combination of dispatchable and nondispatchable DG units is also proposed in this paper. The proposed methodology has been applied to a 69-bus test distribution system with different scenarios. The results demonstrate that dispatchable DG units or a combination of dispatchable and nondispatchable DG units can lead to a substantial reduction in annual energy losses when compared to nondispatchable DG units. The results also show that a maximum annual energy loss reduction is achieved for all scenarios proposed with DG operation at optimal power factor.

Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm

This paper presents an interactive fuzzy satisfying method, which is based on Hybrid Modified Shuffled Frog Leaping Algorithm, and should solve the problem of the Multi-objective optimal placement and sizing of DG (distributed generation) units in the distribution network. Minimizing total electrical energy losses, total electrical energy cost and total pollutant emissions produced are the objective functions in this problem.Also, the improvement of the voltage profile is considered as a constraint in determining the optimal placement.In the proposed method, the objective functions are modeled with fuzzy sets. The multi-objective problem is transformed into a mini–max problem, which is then handled by the proposed evolutionary algorithm.Finally, the proposed algorithm is tested on a 69-bus distribution test system based on technical, economical and environmental protection considerations. The simulation results illustrate the good performance and applicability of the proposed method.

A simple and fast approach for allocation and size evaluation of distributed generation

Penetration of distributed generation (DG) units in distribution network has increased rapidly stimulated by reduced network power loss, improved bus voltage profile, and better power quality. Appropriate size and allocation of DG units play a significant role to get beneficial effects. The objective of this study is to demonstrate a simple and fast technique to determine appropriate location and size of DG units. A voltage stability indicator (VSI) is derived which can quantify the voltage stability conditions of buses in distribution network. According to VSI, vulnerable buses of the network are arranged rank-wise to form a priority list for allocation of DG units. To determine the size of DG units, a feed forward artificial neural network is prepared in MATLAB environment (The MathWorks, Inc., Massachusetts, USA). The effectiveness of the proposed methodology has been tested on a 52-bus radial distribution network. After appropriate allocation of DG units, voltage profiles of most of the buses are increased significantly. The results also indicated that the total loss of the distribution network has reduced by nearly 76.39%, and voltage stability conditions of buses are improved considerably. Voltage stability conditions of bus-13, bus-36, and bus-44 are raised by 23.16%, 29.23%, and 37.64% respectively.