Sizing Of Distributed Generation Units Research Articles

Energy loss reduction by optimal distributed generation allocation in distribution systems

Summary This paper presents a mixed-integer nonlinear optimization model for distributed generation (DG) placement in the distribution system. A multiperiod ac optimal power-flow model is developed for minimizing annual energy losses and determining the optimal site and size of DG units. Various system constraints and time-varying demands are incorporated in the model. A novel iterative method for solving the multiperiod DG placement problem based on the Benders decomposition technique is proposed, which reduces the computational burden and facilitates its practical application. Two case studies are presented to illustrate the efficacy of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.

Location and Sizing of Distributed Generation Units for Loadabilty Enhancement in Primary Feeder

Limitations of generation, transmission, and distribution of reactive power have significant impact on voltage stability of electric power systems. Moreover, the location and size of a distributed generation (DG) unit that alters reactive power flow and its path in a radial distribution network can also have a substantial influence on voltage stability. Therefore, this paper proposes an optimization methodology for identifying proper location and size of DG units in a primary distribution system to enhance the loadability of distribution system by improving static voltage stability. The optimization is implemented with the help of a particle swam optimization technique for four types of DG units. The proposed methodology is tested in two standard radial distribution test systems and one practical radial distribution of Lao Cai province, Vietnam. Results show the importance of selecting the location and sizes of DG units for enhancing the loadability of the primary distribution feeder.

An Imperialist Competitive Algorithm for Sitting and Sizing of Distributed Generation in Radial Distribution Network to Improve Reliability and Losses Reduction

Distributed Generation (DG) can help in reducing the cost of electricity to the costumer, relieve network congestion and provide environmentally friendly energy close to load centers. Its capacity is also scalable and it provides voltage support at distribution level. Hence, DG placement and penetration level is an important problem for both the utility and DG owner. The Optimal Power Flow (OPF) has been widely used for both the operation and planning of a power system. The OPF is also suited for deregulated environment. Four different objective functions are considered in this study: (1) Improvement voltage profile (2) minimization of active power loss (3) maximum capacity of conductors (4) maximization of reliability level. The site and size of DG units are assumed as design variables. The results are discussed and compared with those of traditional distribution planning and also with Imperialist competitive algorithm (ICA).

Long term scheduling for optimal allocation and sizing of DG unit considering load variations and DG type

This paper proposes a new long term scheduling for optimal allocation and sizing of different types of Distributed Generation (DG) units in the distribution networks in order to minimize power losses. The optimization process is implemented by continuously changing the load of the system in the planning time horizon. In order to make the analysis more practical, the loads are linearly changed in small steps of 1% from 50% to 150% of the actual value. In each load step, the optimal size and location for different types of DG units are evaluated. The proposed approach will help the distribution network operators (DNOs) to have a long term planning for the optimal management of DG units and reach the maximum efficiency. On the other hand, since the optimization process is implemented for the entire time period, the short term scheduling is also possible. The proposed method is applied to IEEE 33-bus test system using both the analytical approach and particle swarm optimization (PSO) algorithm. The simulation results show the effectiveness and acceptable performance of the proposed method.

Control of voltage profile with optimal control and placement of distributed generation using the refined bacterial foraging algorithm

This paper proposes a refined bacterial foraging algorithm (RBFA) for solving the multi-objective based optimal power dispatch with optimal placement of distributed generation (DG) to minimize the total real power loss, generation cost, the environmental emission and considering various controls and limits. The RBFA is based on the social foraging behavior of the Escherichia coli bacteria and its improved version of the basic bacterial foraging algorithm. The RBFA provides natural selection to eliminate poor foraging strategies for bacteria and to propagate other successful foraging strategies where foraging is proceeded using a position updating process, step length, search dimension and search direction with adaptation of basic foraging principles. Initially, the algorithm randomly generates the particle positions representing the size and location of DG and its proposal to solve the simultaneous optimization of the multi-objective problem. The proposed RBFA is used to determine the optimal sizes and locations of multi-DGs; the different types of DG are considered and the load flow is used to calculate the exact loss and to minimize simultaneously the economic cost and the emission of thermal units by changing the location and varying the sizes of the DG units. The test results indicate that the RBFA method can obtain better results than similar social behavior algorithm method on the IEEE30-bus system. The results are compared with and without DG units. The proposed method found the optimal location and sizing of DG units with control of the voltage profile, control of the cost of generation and control and reduction of environmental pollution and transmission losses.

DG allocation for benefit maximization in distribution networks

This paper proposes a method to evaluate the worth of installing renewable distributed generation (DG) in distribution networks. Moreover, the work optimally allocates these DG units in the distribution network to maximize the worth of the connection to the local distribution company (LDC), as well as the customers connected to the system. The proposed methodology helps the LDC to better assess the benefits of the renewable DG units' proposed connections and to identify the optimal buses on which to connect these DG units. The benefits considered in this paper are deferral of upgrade investments, reduction of the cost of energy losses, and reliability improvement, which is represented by the interruption cost reduction. The proposed methodology takes into consideration the uncertainty and variability associated with the output power of renewable DG as well as the load variability. The planning problem of determining the optimal location and sizes of DG units is defined as multi-objective mixed integer programming.

PEVs modeling and impacts mitigation in distribution networks

This paper proposes a novel model to estimate the electric energy consumption of light duty fleet of plug-in electric vehicles (PEVs). This model can be used to evaluate the impacts of plugging such loads in distribution networks. Both vehicles users' habits and diversity of usage are considered in the presented model, as well as different electric ranges and ambient temperature effect. Moreover, the paper proposes a method to optimally allocate distributed generation (DG) units in the distribution network to mitigate the impacts of high penetration of PEVs. The proposed model shall help the local distribution companies (LDC) to better assess the expected effects of PEVs on their networks and evaluate the required upgrades. Furthermore, the proposed DG allocation methodology helps to identify the optimal buses on which to connect these DG units in the presence of high PEVs penetration. A genetic based approach is utilized for the planning problem of determining the optimal locations and sizes of DG units, which is defined as a multi-objective mixed integer programming.

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.

Optimal placement and sizing of distributed generations in distribution systems for minimizing losses and THD$_{v}$ using evolutionary programming

Growing concerns over environmental impacts, improvement of the overall network conditions, and rebate programs offered by governments have led to an increase in the number of distributed generation (DG) units in commercial and domestic electric power production. However, a large number of DG units in a distribution system may sometimes contribute to high levels of harmonic distortion, even though the emission levels of the individual DG units comply with the harmonic standards. It is known that the nonoptimal size and nonoptimal placement of DG units may lead to high power losses, bad voltage profiles, and harmonic propagations. Therefore, this paper introduces a sensitivity analysis to determine the optimal location of DG units, as well as evolutionary programming and harmonic distribution load flow for determining the optimal size of DG units in radial distribution systems. A multiobjective function is created to minimize the total losses and average total harmonic distortion voltage (THD_v) of the distribution system. The proposed methodology is tested with a 69-bus radial distribution system. The proposed optimal placement and sizing of the DG units is found to be robust and provides higher efficiency for the improvement of the voltage profile and the minimization of the losses and THD_v.

Application of Tabu Search for Optimal Placement and Sizing of Distributed Generation for Loss Reduction

This paper presents the optimal and sizing of distributed generation (DG) placement in a radial distribution system for loss reduction. The main emphasis of this paper is to identify proper locations for installing DGs in a distribution system to reduce active power loss and improve bus voltages. Nevertheless, proper placement and sizing of DG units are not straightforward to be identified as a number of their positions and capacities need to be determined. It is therefore proposed in this paper to solve a DG placement problem based on a Tabu search algorithm. The objective function of the problem is to minimize the system loss subject to power flow constraints, bus voltage limits, pre specified number of DGs, and their allowable total installed capacity, and only one distributed generator for one installation position. The effectiveness of the methodology is demonstrated by a practical sized distribution system consisting of 69 bus and 48 load points. The results show that the optimal DG placement and sizing can be identified to give the minimum power loss while respecting all the constraints.

Imperialist competition algorithm for distributed generation connections

This study proposes an imperialist competition algorithm (ICA) to maximise the benefits of distribution network operators (DNOs) because of the existence of distributed generation (DG) units. The sum of active loss reduction and network investment deferral incentives has been considered as the objective function to be maximised in this study. The optimal location and size of DG units in the network are found considering various techno-economical issues. The application of the proposed methodology in the UK under current Ofgem financial incentives for DNOs is investigated. The ability of the proposed approach in finding the optimal solution is validated by comparing the obtained results with other methods of the literature.

English

  This paper suggests a Pareto based Multi-objective Optimization Algorithm (MOA) called Strength Pareto Evolutionary Algorithm (SPEA) for Distributed Generation (DG) planning in distribution networks. As opposed to conventional multi-objective optimization techniques that correlate different objective functions by utilizing of weighting coefficients and create one single objective function, in SPEA, each objective function is optimized separately. Since the objective functions are in conflict with each other, the SPEA produces a set of optimum solutions instead of one single optimum one. Three different objective functions are considered in this study: (1) minimization of power generation cost (2) minimization of active power loss (3) maximization of reliability level. The goal is to optimize each objective function. The site and size of DG units are assumed as design variables. The results are discussed and compared with those of traditional distribution planning and also with Partial Swarm Optimization (PSO).   Key words: Distributed generation, distribution network planning, multi-objective optimization.

Capacity Evaluation and Identification of Grid Integration Points of Distributed Generation in a Distribution System

A novel iterative method for identification of grid integration points and size of distributed generators (DGs) with simultaneous capacity evaluation of a distribution system for DG integration is proposed in this paper. In order to achieve this, various performance indices governing the optimal operation of a distribution system with DG units are strategically combined to form a multi-objective index. Identification of suitable nodes and size of each DG unit for integration in the distribution system is performed by minimising the multi-objective index such that on placement of every additional DG unit, reduction in the values of performance indices is resulted without any violation in the system operating constraints. The effects of different types of DGs and load models in this DG placement problem are also analysed. The proposed technique is tested on 33- and 25-bus systems, and is found to be very effective in identifying the suitable number of locations, size of DG units and evaluating the capacity of distribution system for DG integration.

Optimal Coordination and Penetration of Distributed Generation with Shunt FACTS Using GA/Fuzzy Rules

In recent years, integration of new distributed generation (DG) technology in distribution networks has become one of the major management concerns for professional engineers. This paper presents a dynamic methodology of optimal allocation and sizing of DG units for a given practical distribution network, so that the cost of active power can be minimized. The approach proposed is based on a combined Genetic/Fuzzy Rules. The genetic algorithm generates and optimizes combinations of distributed power generation for integration into the network in order to minimize power losses, and in second step simple fuzzy rules designs based upon practical expertise rules to control the reactive power of a multi dynamic shunt FACTS Compensator (SVC, STATCOM) in order to improve the system loadability. This proposed approach is implemented with the Matlab program and is applied to small case studies, IEEE 25-Bus and IEEE 30-Bus. The results obtained confirm the effectiveness in sizing and integration of an assigned number of DG units.