Sizing Of DGs Research Articles

Hybrid approach for optimal placement of multiple DGs of multiple types in distribution networks

Abstract In the present work, a hybrid approach has been proposed for optimal placement of multiple DGs of multiple types. The analytical approaches may not be appropriate for optimal placements of multiple DGs alone. In this work, hybridization of analytical method and heuristic search for the optimal placement of multiple DGs in power distribution network for reduction of power loss has been proposed. In this approach, the sizes of DGs are evaluated at each bus by analytical method while the locations are determined by PSO based technique. The objective function has been minimized under operating constraints. The improvements in bus voltage profile and optimal power factor of the DGs have also been observed. To validate the proposed hybrid approach, results have been compared with particle swarm optimization (PSO) technique and existing fast improved analytical (IA) method results. The proposed technique has been tested on 33-bus, and 69-bus test systems.

Optimal allocation of distribution generator in a radial-distribution system using self-adaptive modified firefly algorithm with voltage, power and line capacity limit constraints

As a result of the application of deregulation in the electric power sector, a new identity appeared in the electric power system map known as 'distribution generator' (DG). To meet out the peak load demand caused by increased power demand in the distribution system, DGs are greatly utilised by placing DGs in optimal location with optimal size. This paper presents a novel firefly optimisation-based approach to optimally incorporate a distribution generator into a distribution system. The proposed algorithm combines firefly optimisation with load flow algorithm to solve the problem in a single step. While developing the algorithm, by satisfying the voltage, power and line capacity limit, the objective function, i.e., the total network power losses will be minimised. Loss sensitivity factors (LSFs) are used to select the location for the multiple DG placements and self-adaptive modified firefly algorithm (SAMFA) is used to estimate the optimal size of DGs at optimal locations determined. Taking the self-adaptive modified firefly algorithm (SAMFA) and IEEE 69-node system as the example, the results show the effectiveness of the proposed algorithm.

Mesh distribution system analysis in presence of distributed generation with time varying load model

With the implementation of competitive electricity supply system all over the world, there is the need of optimal utilization of the existing generation sources at the same time to curtail the generation from the conventional power plants and add the renewable sources generation looking into the environmental concern and limitation of the fossil fuels. This has lead to the motivation for the studies on the integration of distributed resources to the grid. In this paper optimal locations and sizes for DG is determined for weakly meshed distribution networks based on the sensitivity method. Novel method based on loss sensitivity is used in this paper to determine optimal size and location of DGs. The modified Novel method is proposed for DG allocation. The main contribution of the paper is: (i) distributed generation allocation for mesh network using sensitivity approach, (ii) modified Novel method for DG allocation and sizes calculation for meshed distribution system with load variations, (iii) comparison of the results obtained with single and two DG placement with load variations, (iv) the loss savings and overall cost savings per annum with single and two DGs placement with load variations. In this paper we considered the impact of time varying load flow with realistic load model. The realistic ZIP load model has been considered for study. The results have been obtained for a distribution network of UK Distribution Corporation consisting of 38 buses. The results have also been obtained for radial distribution system for comparison.

Optimal Determination of Size and Site of DGs in Mesh System Using PSO

Optimal Determination of Size and Site of DGs in Mesh System Using PSO

Optimal sizing of DGs for a CHP-Based Agro-Industrial Microgrid with a Priority Criteria Operational Strategy

The paper presents a microgrid inspired by the needs and the potentials of its application in the agro-industrial field where the biomass and its derivatives are primary energy sources together with the solar irradiance. An optimization problem is also proposed to optimal size all generators, including stirling engines, micro gas turbines and solid oxide fuel cells for the simultaneous and combined on-site production of heat and power. In order to preliminarily consider the role of the energy management in dispatching power and heat, an operational strategy of distributed generators based on priority criteria is set and implemented as constraints in the optimization problem. Numerical experiments on a real case of a farm placed in the southern Italy confirm the feasibility of the proposed microgrid together with the optimization problem, quantifying both the economic and environmental benefits

Optimal placement and sizing of distributed generators based on a novel MPSI index

The Objective: This paper presents a method to identify the optimal location and size of DGs based on the power stability index and particle swarm optimization (PSO) algorithm.Materials and methods: First, a novel maximum power stability index (MPSI) is derived from the well-established theorem of maximum power transfer. The MPSI is utilized as an objective function to determine the optimal DG locations. Next, a PSO-based model with randomized load is developed to optimize DG sizing in view of the system’s real power losses.Results and Conclusion: Lastly, a IEEE 30-bus test system is employed in the simulation. The performance of proposed MPSI index are comparable with other voltage stability indices. The DG optimization model considering voltage stability and loss minimization provides better results compared to that obtained using only loss minimization approach.

Hybrid Approach for Placement of Type-III Multiple DGs in Distribution Network

This paper proposes the hybridization of analytical method and heuristic search for the optimal placement of type-III DGs in power distribution network for reduction of power loss. The type-III DG is capable injecting both real and reactive powers. In this approach the locations are determined by the application of PSO while the sizes of DGs are evaluated by using the analytical method which is based on the exact loss formula. The reduction of power distribution losses has been achieved by compensation of active and reactive powers. The improvement in bus voltage profile and the optimal power factor of the DGs have also been considered. The proposed technique has been tested on a 33-bus test system and the results are compared.

Optimal Placement of Distributed Generation Units for Constructing Virtual Power Plant Using Binary Particle Swarm Optimization Algorithm

The idea of applying distributed generation resources in distribution systems has become increasingly important due to changes in the distribution systems. Optimal sizing, location, type and installation time of DGs for constructing virtual power plant is one of the important subjects in applying distributed generation in the power system. In this paper, a new method is presented for optimal placement and type of distributed generation units and long term system planning aim to constructing virtual power plant. Minimizing the long term total cost of the system is considered as the objective function. The impacts of applying demand response on expansion planning is also investigated. The Binary particle Swarm Optimization (BPSO) method is used for solving this problem. In order to evaluate the efficiency of the proposed method, the method is tested on the IEEE 33 bus distribution system.

Placement of Distributed Generation in 33/11 kV Radial Distribution System to Minimize Power Losses, Improvement in Voltage Profile and Reliability using Genetic Algorithm

Integration of renewable energy based distributed generation units provide potential benefits to conventional distribution systems. The power injections from renewable DG units located close to the load centers provide an opportunity for system voltage support, reduction in energy losses and reliability improvement. Therefore, the allocation of DG units should be carefully determined with the consideration of different planning incentives. In this paper, a simple method for real power loss reduction, voltage profile improvement which is based on voltage sensitivity index analysis is considered. Power flow analysis is done using the forward-backward sweep method, the placement and sizing of DG in distribution system are determined using optimization.The objective is to improve the reliability indices. The placement and size of DGs are optimized using a Genetic Algorithm. To evaluate the proposed algorithm, the 10 bus distribution feeder is used. The results illustrate the efficiency of the proposed method.

Optimal Placement and Sizing of Multiple DGs in a Practical Distribution System by Considering Power Loss

The selection of placement and size for multiple distributed generators based on renewable energies becomes an important issue in operating modern power systems safely and reliably. This paper extends the previous study in our earlier paper by treating the problems for their optimal locations and sizes in more realistic way based on the power loss sensitivities and Kalman filter algorithm, respectively. In particular, the proposed method is implemented by the simulation on a part of practical distribution network of 60 MVA scale, which is located at the Do-gok area in Seoul, Korea. The new factor, called as the optimal locator index, is developed to determine the optimal locations in a systematic and effective manner by the power loss sensitivities. Also, the Kalman filter algorithm is applied to determine the optimal sizes by choosing the step size of 10 KW, which is a much smaller increment than the 10-MW step used in our earlier paper, without requiring high computational efforts. This selected step size of 10 KW is realistic and suitable to apply in practical test systems.

Optimal allocation of dispersed generators for loss minimization

AbstractDispersed generators (DGs) such as fuel cells and solar cells will be installed on the demand side of power systems. Such dispersed facilities can reduce distribution system loss by appropriate allocation. Planning and operation of distribution systems with the dispersed facilities have already been discussed. In this paper, the authors discuss the extent to which distribution system loss can be reduced if DGs are optimally allocated on the demand side of the distribution system. In order to determine the optimal allocation and size of DGs so as to minimize the distribution system loss, an algorithm based on tabu search is employed. The proposed algorithm consists of iterated nested use of the tabu search algorithm. Namely, in the proposed algorithm, in one computational iteration, after the location of the DG is temporarily determined by tabu search, the size of the DG is also determined by tabu search so as to minimize the distribution system loss for the temporarily determined allocation. Numerical simulations are carried out for two system models in order to examine the validity of the algorithm. © 2001 Scripta Technica, Electr Eng Jpn, 136(2): 1–8, 2001