435 resultados para distributed generation (DG)


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An algorithm based on the concept of Kalman filtering is proposed in this paper for the estimation of power system signal attributes, like amplitude, frequency and phase angle. This technique can be used in protection relays, digital AVRs, DSTATCOMs, FACTS and other power electronics applications. Furthermore this algorithm is particularly suitable for the integration of distributed generation sources to power grids when fast and accurate detection of small variations of signal attributes are needed. Practical considerations such as the effect of noise, higher order harmonics, and computational issues of the algorithm are considered and tested in the paper. Several computer simulations are presented to highlight the usefulness of the proposed approach. Simulation results show that the proposed technique can simultaneously estimate the signal attributes, even if it is highly distorted due to the presence of non-linear loads and noise.

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There has been a developing interest in smart grids, the possibility of significantly enhanced performance from remote measurements and intelligent controls. For transmission the use of PMU signals from remote sites and direct load shed controls can give significant enhancement for large system disturbances rather than relying on local measurements and linear controls. This lecture will emphasize what can be found from remote measurements and the mechanisms to get a smarter response to major disturbances. For distribution systems there has been a significant history in the area of distribution reconfiguration automation. This lecture will emphasize the incorporation of Distributed Generation into distribution networks and the impact on voltage/frequency control and protection. Overall the performance of both transmission and distribution will be impacted by demand side management and the capabilities built into the system. In particular, we consider different time scales of load communication and response and look to the benefits for system, energy and lines.

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In this paper, both Distributed Generators (DG) and capacitors are allocated and sized optimally for improving line loss and reliability. The objective function is composed of the investment cost of DGs and capacitors along with loss and reliability which are converted to the genuine dollar. The bus voltage and line current are considered as constraints which should be satisfied during the optimization procedure. Hybrid Particle Swarm Optimization as a heuristic based technique is used as the optimization method. The IEEE 69-bus test system is modified and employed to evaluate the proposed algorithm. The results illustrate that the lowest cost planning is found by optimizing both DGs and capacitors in distribution networks.

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Power system dynamic analysis and security assessment are becoming more significant today due to increases in size and complexity from restructuring, emerging new uncertainties, integration of renewable energy sources, distributed generation, and micro grids. Precise modelling of all contributed elements/devices, understanding interactions in detail, and observing hidden dynamics using existing analysis tools/theorems are difficult, and even impossible. In this chapter, the power system is considered as a continuum and the propagated electomechanical waves initiated by faults and other random events are studied to provide a new scheme for stability investigation of a large dimensional system. For this purpose, the measured electrical indices (such as rotor angle and bus voltage) following a fault in different points among the network are used, and the behaviour of the propagated waves through the lines, nodes, and buses is analyzed. The impact of weak transmission links on a progressive electromechanical wave using energy function concept is addressed. It is also emphasized that determining severity of a disturbance/contingency accurately, without considering the related electromechanical waves, hidden dynamics, and their properties is not secure enough. Considering these phenomena takes heavy and time consuming calculation, which is not suitable for online stability assessment problems. However, using a continuum model for a power system reduces the burden of complex calculations

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This paper presents a new approach for network upgrading to improve the penetration level of Small Scale Generators in residential feeders. In this paper, it is proposed that a common DC link can be added to LV network to alleviate the negative impact of increased export power on AC lines, allowing customers to inject their surplus power with no restrictions to the common DC link. In addition, it is shown that the proposed approach can be a pathway from current AC network to future DC network.

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Multi-Microgrids (MMGs) have been proposed to connect distributed generators (DG), microgrids (MG), and medium-voltage (MV) loads with the distribution system. A flexible protection scheme that enables an islanded MMG to continue operation during fault conditions is yet to be developed. In this paper, a protection scheme for an islanded MMG that utilises MG controllers and communication links is proposed. The MMG model used includes two MGs connected to the distribution system. Each MG consists of diesel, wind, and photovoltaic (PV) microsources. The effectiveness of the proposed protection scheme is evaluated by simulation.

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Plug-in electric vehicles will soon be connected to residential distribution networks in high quantities and will add to already overburdened residential feeders. However, as battery technology improves, plug-in electric vehicles will also be able to support networks as small distributed generation units by transferring the energy stored in their battery into the grid. Even though the increase in the plug-in electric vehicle connection is gradual, their connection points and charging/discharging levels are random. Therefore, such single-phase bidirectional power flows can have an adverse effect on the voltage unbalance of a three-phase distribution network. In this article, a voltage unbalance sensitivity analysis based on charging/discharging levels and the connection point of plug-in electric vehicles in a residential low-voltage distribution network is presented. Due to the many uncertainties in plug-in electric vehicle ratings and connection points and the network load, a Monte Carlo-based stochastic analysis is developed to predict voltage unbalance in the network in the presence of plug-in electric vehicles. A failure index is introduced to demonstrate the probability of non-standard voltage unbalance in the network due to plug-in electric vehicles.

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Global awareness for cleaner and renewable energy is transforming the electricity sector at many levels. New technologies are being increasingly integrated into the electricity grid at high, medium and low voltage levels, new taxes on carbon emissions are being introduced and individuals can now produce electricity, mainly through rooftop photovoltaic (PV) systems. While leading to improvements, these changes also introduce challenges, and a question that often rises is ‘how can we manage this constantly evolving grid?’ The Queensland Government and Ergon Energy, one of the two Queensland distribution companies, have partnered with some Australian and German universities on a project to answer this question in a holistic manner. The project investigates the impact the integration of renewables and other new technologies has on the physical structure of the grid, and how this evolving system can be managed in a sustainable and economical manner. To aid understanding of what the future might bring, a software platform has been developed that integrates two modelling techniques: agent-based modelling (ABM) to capture the characteristics of the different system units accurately and dynamically, and particle swarm optimization (PSO) to find the most economical mix of network extension and integration of distributed generation over long periods of time. Using data from Ergon Energy, two types of networks (3 phase, and Single Wired Earth Return or SWER) have been modelled; three-phase networks are usually used in dense networks such as urban areas, while SWER networks are widely used in rural Queensland. Simulations can be performed on these networks to identify the required upgrades, following a three-step process: a) what is already in place and how it performs under current and future loads, b) what can be done to manage it and plan the future grid and c) how these upgrades/new installations will perform over time. The number of small-scale distributed generators, e.g. PV and battery, is now sufficient (and expected to increase) to impact the operation of the grid, which in turn needs to be considered by the distribution network manager when planning for upgrades and/or installations to stay within regulatory limits. Different scenarios can be simulated, with different levels of distributed generation, in-place as well as expected, so that a large number of options can be assessed (Step a). Once the location, sizing and timing of assets upgrade and/or installation are found using optimisation techniques (Step b), it is possible to assess the adequacy of their daily performance using agent-based modelling (Step c). One distinguishing feature of this software is that it is possible to analyse a whole area at once, while still having a tailored solution for each of the sub-areas. To illustrate this, using the impact of battery and PV can have on the two types of networks mentioned above, three design conditions can be identified (amongst others): · Urban conditions o Feeders that have a low take-up of solar generators, may benefit from adding solar panels o Feeders that need voltage support at specific times, may be assisted by installing batteries · Rural conditions - SWER network o Feeders that need voltage support as well as peak lopping may benefit from both battery and solar panel installations. This small example demonstrates that no single solution can be applied across all three areas, and there is a need to be selective in which one is applied to each branch of the network. This is currently the function of the engineer who can define various scenarios against a configuration, test them and iterate towards an appropriate solution. Future work will focus on increasing the level of automation in identifying areas where particular solutions are applicable.

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This paper addresses the voltage rise constraints that are initiated from increased renewable generation resources in low voltage distribution networks. In this paper, an approach which is able to mitigate these voltage rise constraints and allow for increased distributed generator penetration is presented. The proposed approach involves utilizing the distribution transformers static tap changer to reduce the distribution feeder voltage setpoint. The proposed approach is modeled on a generic low voltage distribution network using the PSS SINCAL© simulation software package and is also implemented in a real low voltage distribution network to verify its practicality. Results indicate that this approach can be implemented to mitigate the voltage rise constraint and increase small-scale embedded generator penetration in a high proportion of low voltage feeders while avoiding any substantial network costs.

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This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Ant Colony Optimization (ACO) and Simulated Annealing (SA), called ACO-SA, for distribution feeder reconfiguration (DFR) considering Distributed Generators (DGs). Due to private ownership of DGs, a cost based compensation method is used to encourage DGs in active and reactive power generation. The objective function is summation of electrical energy generated by DGs and substation bus (main bus) in the next day. The approach is tested on a real distribution feeder. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for solving DFR problem.

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This paper deals with an efficient hybrid evolutionary optimization algorithm in accordance with combining the ant colony optimization (ACO) and the simulated annealing (SA), so called ACO-SA. The distribution feeder reconfiguration (DFR) is known as one of the most important control schemes in the distribution networks, which can be affected by distributed generations (DGs) for the multi-objective DFR. In such a case, DGs is used to minimize the real power loss, the deviation of nodes voltage and the number of switching operations. The approach is carried out on a real distribution feeder, where the simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for solving the DFR problem.

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The increasing integration of Renewable Energy Resources (RER) and the role of Electric Energy Storage (EES) in distribution systems has created interest in using energy management strategies. EES has become a suitable resource to manage energy consumption and generation in smart grid. Optimize scheduling of EES can also maximize retailer’s profit by introducing energy time-shift opportunities. This paper proposes a new strategy for scheduling EES in order to reduce the impact of electricity market price and load uncertainty on retailers’ profit. The proposed strategy optimizes the cost of purchasing energy with the objective of minimizing surplus energy cost in hedging contract. A case study is provided to demonstrate the impact of the proposed strategy on retailers’ financial benefit.

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The work is a report of research on using multiple inverters of Battery Energy Storage Systems with angle droop controllers to share real power in an isolated micro grid system consisting of inertia based Distributed Generation units and variable load. The proposed angle droop control method helps to balance the supply and demand in the micro grid autonomous mode through charging and discharging of the Battery Energy Storage Systems while ensuring that the state of charge of the storage devices is within safe operating conditions. The proposed method is also studied for its effectiveness for frequency control. The proposed control system is verified and its performance validated with simulation software MATLAB/SIMULINK.

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Renewable energy resources, in particularly PV and battery storage are increasingly becoming part of residential and agriculture premises to manage their electricity consumption. This thesis addresses the tremendous technical, financial and planning challenges for utilities created by these increases, by offering techniques to examine the significance of various renewable resources in electricity network planning. The outcome of this research should assist utilities and customers for adequate planning that can be financially effective.

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In this paper, a wind energy conversion system interfaced to the grid using a dual inverter is proposed. One of the two inverters in the dual inverter is connected to the rectified output of the wind generator while the other is directly connected to a battery energy storage system (BESS). This approach eliminates the need for an additional dc-dc converter and thus reduces power losses, cost, and complexity. The main issue with this scheme is uncorrelated dynamic changes in dc-link voltages that results in unevenly distributed space vectors. A detailed analysis on the effects of these variations is presented in this paper. Furthermore, a modified modulation technique is proposed to produce undistorted currents even in the presence of unevenly distributed and dynamically changing space vectors. An analysis on the battery charging/discharging process and maximum power point tracking of the wind turbine generator is also presented. Simulation and experimental results are presented to verify the efficacy of the proposed modulation technique and battery charging/discharging process.