Distributed Generation Units as Ancillary Services Providers in a Pre Smart Grid Environment

2017 ◽  
Vol 18 (5) ◽  
Author(s):  
Clainer Bravin Donadel ◽  
Jussara Farias Fardin ◽  
Lucas Frizera Encarnação
Keyword(s):  
Smart Grid ◽  
Distributed Generation ◽  
Reactive Power ◽  
Optimization Problems ◽  
Distribution Networks ◽  
Ancillary Services ◽  
Market Regulation ◽  
Electronic Market ◽  
Electrical Distribution Networks ◽  
And Control

AbstractNowadays, ancillary services in electrical distribution networks (e. g. voltage support and reactive power control), usually provided by capacitor banks, start to be performed by distributed generation units (DGs). In this way, several papers have been studying the use of DGs as reactive power providers, and the power electronic/market regulation involved in this new scenario. However, the authors commonly consider a full implementation of Smart Grid philosophy, i. e., there are appropriate communications between DGs and distribution network operator (DNO)’s control centers, but it is not a close reality in many developing countries, due to high costs involved in their implementation. Therefore, this paper proposes a new method in order to use DGs as ancillary services providers in a short and medium-term (called in the literature Pre Smart Grid), in which there are not effective communications between DGs and control centers of DNOs. The proposed method uses a non-uniform DGs distribution, obtained from local atlas of wind, solar, hydraulic and biomass power. The methodology presented accurate results when compared with a PSO-based method, widely used to solve optimization problems, but needs a complete Smart Grid philosophy implementation to work.

Distributed Generation Units as Ancillary Services Providers in a Pre Smart Grid Environment

2015 ◽  
Vol 9 (1) ◽  
pp. 432-444
Author(s):  
Clainer Bravin Donadel ◽  
Jussara Farias Fardin ◽  
Lucas Frizera Encarnação
Keyword(s):  
Smart Grid ◽  
Distributed Generation ◽  
Ancillary Services ◽  
Grid Environment

scholarly journals Integration and control of lithium-ion BESSs for active network management in smart grids: Sundom smart grid backup feeding case

2021 ◽  
Author(s):  
Chethan Parthasarathy ◽  
Hossein Hafezi ◽  
Hannu Laaksonen
Keyword(s):  
Smart Grid ◽  
Network Management ◽  
Smart Grids ◽  
Reactive Power ◽  
Renewable Energy Sources ◽  
Lithium Ion ◽  
Active Network ◽  
Li Ion ◽  
Grid Side ◽  
And Control

AbstractLithium-ion battery energy storage systems (Li-ion BESS), due to their capability in providing both active and reactive power services, act as a bridging technology for efficient implementation of active network management (ANM) schemes for land-based grid applications. Due to higher integration of intermittent renewable energy sources in the distribution system, transient instability may induce power quality issues, mainly in terms of voltage fluctuations. In such situations, ANM schemes in the power network are a possible solution to maintain operation limits defined by grid codes. However, to implement ANM schemes effectively, integration and control of highly flexible Li-ion BESS play an important role, considering their performance characteristics and economics. Hence, in this paper, an energy management system (EMS) has been developed for implementing the ANM scheme, particularly focusing on the integration design of Li-ion BESS and the controllers managing them. Developed ANM scheme has been utilized to mitigate MV network issues (i.e. voltage stability and adherence to reactive power window). The efficiency of Li-ion BESS integration methodology, performance of the EMS controllers to implement ANM scheme and the effect of such ANM schemes on integration of Li-ion BESS, i.e. control of its grid-side converter (considering operation states and characteristics of the Li-ion BESS) and their coordination with the grid side controllers have been validated by means of simulation studies in the Sundom smart grid network, Vaasa, Finland.

Optimization of Real Power Loss and Voltage Stability Index for Distribution Systems with Distributed Generation

2017 ◽  
Vol 33 ◽  
pp. 100-114 ◽  
Author(s):  
Mostafa Elshahed ◽  
Mahmoud Dawod ◽  
Zeinab H. Osman
Keyword(s):  
Genetic Algorithm ◽  
Distributed Generation ◽  
Distribution Systems ◽  
Voltage Stability ◽  
Reactive Power ◽  
Optimization Problems ◽  
Stability Index ◽  
Mixed Integer ◽  
Voltage Profile ◽  
Voltage Stability Index

Integrating Distributed Generation (DG) units into distribution systems can have an impact on the voltage profile, power flow, power losses, and voltage stability. In this paper, a new methodology for DG location and sizing are developed to minimize system losses and maximize voltage stability index (VSI). A proper allocation of DG has to be determined using the fuzzy ranking method to verify best compromised solutions and achieve maximum benefits. Synchronous machines are utilized and its power factor is optimally determined via genetic optimization to inject reactive power to decrease system losses and improve voltage profile and VSI. The Augmented Lagrangian Genetic Algorithm with nonlinear mixed-integer variables and Non-dominated Sorting Genetic Algorithm have been implemented to solve both single/multi-objective function optimization problems. For proposed methodology effectiveness verification, it is tested on 33-bus and 69-bus radial distribution systems then compared with previous works.

scholarly journals Distributed Generation Placement in Radial Distribution Networks using a Bat-inspired Algorithm

DYNA ◽  
2015 ◽  
Vol 82 (192) ◽  
pp. 60-67 ◽  
Author(s):  
John Edwin Candelo-Becerra ◽  
Helman Hernández-Riaño
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Reactive Power ◽  
Distribution Networks ◽  
Power Losses ◽  
Number Of Generators ◽  
Metaheuristic Techniques ◽  
Distributed Generation Placement ◽  
Made In

<p>Distributed generation (DG) is an important issue for distribution networks due to the improvement in power losses, but the location and size of generators could be a difficult task for exact techniques. The metaheuristic techniques have become a better option to determine good solutions and in this paper the application of a bat-inspired algorithm (BA) to a problem of location and size of distributed generation in radial distribution systems is presented. A comparison between particle swarm optimization (PSO) and BA was made in the 33-node and 69-node test feeders, using as scenarios the change in active and reactive power, and the number of generators. PSO and BA found good results for small number and capacities of generators, but BA obtained better results for difficult problems and converged faster for all scenarios. The maximum active power injections to reduce power losses in the distribution networks were found for the five scenarios.</p>

scholarly journals To study the voltage stability limits of distributed generation using induction machine in distribution networks

2013 ◽  
Vol 16 (2) ◽  
pp. 43-53
Author(s):  
Chuong Trong Trinh ◽  
Anh Viet Truong ◽  
Tu Phan Vu
Keyword(s):  
Distributed Generation ◽  
Power Distribution ◽  
Power Flow ◽  
Voltage Stability ◽  
Reactive Power ◽  
Induction Machine ◽  
Distribution Networks ◽  
Distribution Grid ◽  
Asynchronous Machine ◽  
Asynchronous Machines

There are now a lot of distributed generation (DG) using asynchronous machines are connected to power distribution grid. These machines do not usually generate reactive power, even consume reactive power, so they generally affect the voltage stability of whole power grid, and can cause instability in itself it is no longer balanced by the torque to work. In this paper, we investigate the voltage stability problem of the asynchronous machine of wind turbines used in power distribution networks. From the static model of the asynchronous machine, this paper will apply the pragmatic criteria to analysis the voltage stability of the asynchronous machine based on the results of the power flow in power distribution network.

Swarm-Intelligence-Based Optimal Placement and Sizing of Distributed Generation in Distribution Network

2018 ◽  
pp. 29-48 ◽  
Author(s):  
Mahesh Kumar ◽  
Perumal Nallagownden ◽  
Irraivan Elamvazuthi ◽  
Pandian Vasant ◽  
Luqman Hakim Rahman
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Distribution System ◽  
Voltage Stability ◽  
Reactive Power ◽  
Distribution Network ◽  
Stability Index ◽  
Pso Algorithm ◽  
Distribution Networks ◽  
Optimal Placement

In the distribution system, distributed generation (DG) are getting more important because of the electricity demands, fossil fuel depletion and environment concerns. The placement and sizing of DGs have greatly impact on the voltage stability and losses in the distribution network. In this chapter, a particle swarm optimization (PSO) algorithm has been proposed for optimal placement and sizing of DG to improve voltage stability index in the radial distribution system. The two i.e. active power and combination of active and reactive power types of DGs are proposed to realize the effect of DG integration. A specific analysis has been applied on IEEE 33 bus system radial distribution networks using MATLAB 2015a software.

scholarly journals Symbiotic Organism Search Algorithm for Power Loss Minimization in Radial Distribution Systems by Network Reconfiguration and Distributed Generation Placement

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Tung Tran The ◽  
Sy Nguyen Quoc ◽  
Dieu Vo Ngoc
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Power Loss ◽  
Distribution Systems ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Distribution Networks ◽  
Network Reconfiguration ◽  
Symbiotic Organism Search

This paper proposes the Symbiotic Organism Search (SOS) algorithm to find the optimal network configuration and the placement of distributed generation (DG) units that minimize the real power loss in radial distribution networks. The proposed algorithm simulates symbiotic relationships such as mutualism, commensalism, and parasitism for solving the optimization problems. In the optimization process, the reconfiguration problem produces a large number of infeasible network configurations. To reduce these infeasible individuals and ensure the radial topology of the network, the graph theory was applied during the power flow. The implementation of the proposed SOS algorithm was carried out on 33-bus, 69-bus, 84-bus, and 119-bus distribution networks considering seven different scenarios. Simulation results and performance comparison with other optimization methods showed that the SOS-based approach was very effective in solving the network reconfiguration and DG placement problems, especially for complex and large-scale distribution networks.

Distributed Generation Placement for Power Distribution Networks

2014 ◽  
Vol 24 (01) ◽  
pp. 1550009 ◽  
Author(s):  
Xiaodao Chen ◽  
Shiyan Hu
Keyword(s):  
Smart Grid ◽  
Distributed Generation ◽  
Power Distribution ◽  
Power Loss ◽  
Distribution Systems ◽  
Optimization Technique ◽  
Electrical Power ◽  
Distribution Networks ◽  
Ocean Energy ◽  
Electrical Power System

Growing concerns on the energy crisis impose great challenges in development and deployment of the smart grid technologies into the existing electrical power system. A key enabling technology in smart grid is distributed generation, which refers to the technology that power generating sources are located in a highly distributed fashion and each customer is both a consumer and a producer for energy. An important optimization problem in distributed generation design is the insertion of distributed generators (DGs), which are often renewable resources exploiting e.g., photovoltaic, hydro, wind, ocean energy. In this paper, a new power loss filtering based sensitivity guided cross entropy (CE) algorithm is proposed for the distributed generator insertion problem. This algorithm is based on the advanced CE optimization technique which exploits the idea of importance sampling in performing optimization. Our experimental results demonstrate that on large distribution networks, our algorithm can largely reduce (up to 179.3%) power loss comparing to a state-of-the-art sensitivity guided greedy algorithm with small runtime overhead. In addition, our algorithm runs about 5× faster than the classical CE algorithm due to the integration of power loss filtering and sensitivity optimization. Moreover, all existing techniques only test on very small distribution systems (usually with < 50 nodes) while our experiments are performed on the distribution networks with up to 5000 nodes, which matches the realistic setup. These demonstrate the practicality of the proposed algorithm.

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