Smart Microgrids

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Overview On Role Of Asset Management Systems For Smart Microgrids

Overview On Role Of Asset Management Systems For Smart Microgrids

THE INCREASING supply-demand gap of the electricity for the present and future needs is a foremost concern in the current energy sector. Besides, the issues of environmental pollutions, fossil fuel depletions, and rapid economy growth of the developing countries are driving the design of local power systems, namely “microgrids”, based on alternative sources of energy [1], [2]. These are further growing as smart electric systems by the use of advanced automation and control philosophies, which typically contains sensing technology for the continuous monitoring of electrical operational parameters (such as voltage, current, frequency, etc.), maintenance parameters (such as device temperature rise, equipment health conditions, etc.), business growth planning (mainly based on forecasting), and all other key parameters that have major impact on the business [3]. These measurements activate the human machine interfaces or actuators for taking the necessary control actions based on the pre-defined logics. All these modules (e.g., energy sources, loads, monitoring and control equipment, devices used for planning, maintenance and management equipment, etc.,), which takes a part of aforesaid operations are integrated to realize a smarter distribution power system called “smart microgrid” and each of these modules are called as an “asset” of the smart microgrids [4]. The fundamental objective of smart systems/networks is to create “an interactive environment to all the plant assets for better monitoring, management, and control” [5]. An asset is “an essential element/entity/device in the plant, whose operation majorly impacts the performance of the whole plant”. It should be commendably regulated to yield a value that shows positive impression on the plant‟s economy [6], [7].
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Efficient energy dispatching in smart microgrids using an integration of fuzzy AHP and TOPSIS assisted by linear programming

Efficient energy dispatching in smart microgrids using an integration of fuzzy AHP and TOPSIS assisted by linear programming

nected mode (i.e., connected to the traditional electric- ity network) or in islanded mode (i.e., isolated from the larger power network so that it functions autonomously) [3]. In a microgrid, generation and loads are typically interconnected at low or medium voltage. The capa- bility to island distributed generators and loads together has the potential to improve local supply reliability and demand stability. Yet a microgrid can still be seen as a single entity connected to the main grid through the transmission and distribution system: at any given time, from the perspective of the main grid, the microgrid will be either a consumer or a producer. This implies that microgrids can be considered as the building blocks of a wider power grid [5][8][13]. In other words, the smart grid can be modeled as a hierarchical structure in which two-way flows of electricity and information travel be- tween the high-voltage network and smart microgrids at different hierarchical levels.
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Energy Management Storage for PV Connected Smart Microgrids

Energy Management Storage for PV Connected Smart Microgrids

Abstract— Penetration of renewable sources in the power system network has been increasing in the recent years. One of the solutions being proposed to improve the reliability and performance of these systems is to integrate energy storage device into the power system network. The main fact is that Renewable Energy Sources (RES) plants have to operate at the maximum possible output whenever technically possible. That the ancillary services could be provided by the distributed generators and the energy storage systems are expected to give great added value to the microgrid-based smart grid topologies. These services must be integrated into the control system of the microgrids. This paper presents an improved strategy for energy storage management and demand side management and its application to a microgrid-based smart grid topology that improving power supply reliability.
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Design and Implementation of a Centralized Disturbance Detection System for Smart Microgrids

Design and Implementation of a Centralized Disturbance Detection System for Smart Microgrids

DER units can be classified into two groups in terms of their interface to the microgrid [3]. The first group consists of conventional rotating electrical machines that have similar dynamic behaviors as the conventional utility grid. The second group includes electronically coupled DER units that use converters to provide an interface to the utility grid. The dynamic behavior and control concept of these two groups are fundamentally different. Therefore, the control strategies and techniques employed for an autonomous mode microgrid (islanded mode of operation) are considerably different from those of the conventional utility grid. Furthermore, contrary to the traditional controls and energy management systems of in- terconnected power systems, control and management systems for microgrids are mostly the types of control and power/energy management strategies for a microgrid are mainly designed based on the conditions of the microgrid, the adopted DER technologies, and load specifica- tions. Therefore, adequate robust controllers should be designed and utilized to guarantee voltage and frequency stability of the islanded zone despite loads uncertainties and source variations [16, 17].
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Optimized solutions for smart microgrids

Optimized solutions for smart microgrids

Better results are expected compared to the BAU energy management, using energy to warm up water should reduce the amount of excess energy and helps to maintain some storage space i[r]

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Optimal Deployment of FiWi Networks using Heuristic Method for Integration Microgrids with Smart Metering

Optimal Deployment of FiWi Networks using Heuristic Method for Integration Microgrids with Smart Metering

Abstract: The unpredictable increase in electrical demand affects the quality of the energy throughout the network. A solution to the problem is the increase of distributed generation units which burn fossil fuels. While this is an immediate solution to the problem the ecosystem gets affected by the emission of CO2. A promising solution is the integration of Distributed Renewable Energy Sources (DRES) to the conventional electrical system, thus, introducing the concept of smart microgrids (SMG) that require a safe, reliable and technically planned two-way communication system. This document presents a heuristic based on planning capable of providing a bidirectional communication near optimal route map, following the structure of an hybrid Fiber-Wireless (FiWi) with the purpose of obtaining information of electrical parameters that help us to manage the use of energy by integrating conventional electrical system to SMG. A FiWi network is based on the integration of wireless access and optical networks. This integration increases the coverage and reliability at a lower cost. The optimization model is based on clustering techniques, through the construction of balanced conglomerates. The method is used for the development of the clusters along with the Nearest-Neighbor Spanning Tree Algorithm (N-NST). Additionally, Optimal Delay Balancing (ODB) model will be used to minimize the end to end delay of each grouping. In addition, the heuristic observes real design parameters such as: capacity and coverage. Using the Dijkstra algorithm, the routes are built following the minimum shorter path. Therefore, this paper presents a heuristic able to plan the deployment of smart meters (SMs) through a tree-like hierarchical topology for the integration of SMG at the lowest cost.
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Future of Microgrids & Smart Energy Networks

Future of Microgrids & Smart Energy Networks

Building on the conversation sparked on June 2nd at [Micro] grids Today at the MaRS Discovery District, this event sought to build linkages between expert researchers, and the industry and utility leaders planning for deployment of microgrids in Canadian and global energy networks. This report presents a summary of the workshop discussion and findings, and aims to develop a roadmap for research and deeper collaboration on smart microgrids. Attendees explored the economic, public policy, and technical factors affecting deployment, and the implications for both communities and utility companies.
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Energy Management Systems of Microgrids

Energy Management Systems of Microgrids

1. Introduction 1.1. Toward Smart Grids 1.1.1. Power Distribution Systems Evolution The electrical power distribution system was designed to meet the needs of users and few large producers in a slow-changing regime. The liberalization of the electricity markets in the last decade and the proliferation of prosumers, have produced a rapid evolution by introducing elements of novelty as well as many new issues for the Distribution System Operators (DSOs). Therefore, the electrical power networks are evolving to respond dynamically, efficiently and flexibly to the increased demand (also due to the rise of the use of heat pumps and electric vehicles) and to the growing penetration of renewable energies (active users have exceeded 800,000 units in Italy, mostly equipped with solar generators). It is necessary to develop new operating strategies and smart network management algorithms in order to allow and support the complete integration of distributed generation (DG) technologies. The benefits of the DG penetration growth are:
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Optimal Energy Storage Strategies in Microgrids

Optimal Energy Storage Strategies in Microgrids

In Chapter 3 , we proposed a multistage stochastic programming model to obtain viable energy procurement and storage strategies for grid-connected microgrids. The model in- cludes three sources of uncertainty: demand, renewable generation, and real-time electricity prices. This framework enables microgrid operators to determine the appropriate amount of electricity to procure from the main grid and the amount to charge to, or discharge from, local storage devices, to satisfy demand and power flow requirements during each stage of a finite planning horizon. Our extensive computational study on a realistic 4-bus microgrid revealed that the multistage stochastic programming model achieves significant cost reduc- tions as compared to myopic and non-storage policies, as well as policies obtained using a two-stage SP formulation. Moreover, our customized SDDP algorithm is able to address the computational challenges associated with the multistage structure of the problem. Our cus- tomization, which uses dynamic cut selection and a novel lower bound improvement strategy, drastically outperforms the standard SDDP algorithm and also demonstrates its scalability to potentially much larger problem instances. It is also conjectured that our improved so- lution method can be extended to address the computational issues of multistage electric generation expansion and hydropower scheduling problems.
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Microgrids: Resilience, Reliability, and Market Structure

Microgrids: Resilience, Reliability, and Market Structure

In this section, a new formulation is proposed to sectionalize the distribution network into microgrids and the Fielder eigenvalue is used as a measure to determine the number of sub-networks in a network. The problem is formulated as a bi-level mixed integer nonlinear problem (MINLP) [Bar98] in which, the binary variables were associated with each edge of the distribution network graph i.e. distribution lines. If the binary variable is 1 the distribution line remains connected, otherwise, the line will be disconnected to form microgrid. The objective of the upper-level problem is to procure the maximum number of microgrids while the lower-level problem ensures the reliability of the formed microgrids. Therefore, in the upper-level problem, the distribution lines are disconnected to increase the number of islanded microgrids in the case of a disturbance in distribution network; however, the objective of the lower-level problem is to expand the distribution network within the formed microgrids to ensure that the required level of reliability at demand buses is satisfied considering limited budget for such expansions. Employing the duality theory, the presented bi-level problem is transformed into a single-level problem in which the lower-level problem is considered as a constraint for the upper-level problem [Arr10]. The expansion decisions are further validated to avoid over-investing on the distribution assets by minimizing the cost of expansion plans considering the reliability constraints in the formed microgrids.
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A Review on Hybrid Microgrids

A Review on Hybrid Microgrids

Microgrid is a localized group of electricity sources and loads that normally operates connected to and synchronous with the traditional wide area synchronous grid (macrogrid), but can also disconnect to "island mode" — and function autonomously as physical or economic conditions dictate [3]. In this way, a microgrid can effectively integrate various sources of distributed generation (DG), especially Renewable Energy Sources (RES) - renewable electricity, and can supply emergency power, changing between island and connected modes. Microgrid also consist of energy storage systems e.g. batteries and energy generation sources like turbines and fuel cells can also be added to increase the reliability of the system. Multiple simulation tools and optimization tools [4] exist to model the economic and electric effects of Microgrids . A widely used economic optimization tool is the Distributed Energy Resources Customer Adoption Model (DER-CAM) from Lawrence Berkeley National Laboratory. Another frequently used commercial economic modelling tool is Homer Energy, originally designed by the National Renewable Energy Laboratory. There are also some power flow and electrical design tools guiding the Microgrid developers. The Pacific Northwest National Laboratory designed the public available GridLAB-D tool and the Electric Power Research Institute (EPRI) designed OpenDSS to simulate the distribution system (for Microgrids). A professional integrated DER-CAM and OpenDSS version is available via BankableEnergy . A European tool that can be used for electrical, cooling, heating, and process heat demand simulation is EnergyPLAN from the Aalborg University in Denmark.
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ADAPTIVE NETWORK PROTECTION IN MICROGRIDS

ADAPTIVE NETWORK PROTECTION IN MICROGRIDS

Microgrids comprise low voltage distribution systems with distributed energy re- sources (DER) and controllable loads which can operate connected to the medium voltage grid or islanded in a controlled coordinated way. This concept aims to move from “connect and forget” philosophy towards an integration of DER. Mi- crogrids are expected to provide environmental and economic benefits for end- customers, utilities and society. However, their implementation poses great techni- cal challenges, such as a protection of microgrid. Local generation in a combina- tion with a possible islanded operation can pose protection sensitivity and selectiv- ity problems in case of fault depending on the relay settings.
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TECHNICAL CHALLENGES ON MICROGRIDS

TECHNICAL CHALLENGES ON MICROGRIDS

ABSTRACT Microgrids are becoming increasingly attractive to consumers and as such in the future, a great number of them will be installed at consumer’s sites. In this situation, conventional distribution networks that accept distributed generation connections may face serious difficulty when its control and protection functions become more complicated. This incurs a burden to the network operation and some technical limitations will appear when a great number of distributed generations are installed. One way of overcoming such problems, a micro grid system is formed to provide reliable electricity and heat delivering services by connecting distributed generations and loads together within a small area. A microgrid is usually connected to an electrical distribution network in an autonomous way and employs various distributed generation technologies such as micro-turbine, fuel cell, photovoltaic system together with energy storage devices such as battery, condenser and flywheel. Micro grids can cause several technical problems in its operation and control when operated as autonomous systems. This paper is a review of three technical challenges on micro grid with respect to voltage and frequency control, islanding and protection of microgrids.
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Power quality in microgrids

Power quality in microgrids

Microgrids are emerging as a consequence of rapidly growing distributed power gen- eration systems. Compared to a single DPGS, the microgrid has more capacity and control exibilities to full system reliability and power quality requirements. In a typical microgrid, the micro-sources may be rotating generators or Distributed Energy Resources (DER) interfaced by power electronic inverters. The installed DERs may be biomass, fuel cells, geothermal, solar, wind, steam or gas turbines. The microgrid also oers opportunities for optimising DPGS through the combined heat and power (CHP) generation, which is currently the most important means of improving energy eciency. The connected loads may be critical or non-critical. Critical loads require a reliable source of energy and good power quality [11, 15]. An example of the microgrid structure with power electronics interfaced DPGS is shown in Figure 2.1. The microgrid is connected to the utility system through a circuit breaker, also called Static Transfer Switch (STS) at the Point of Common Coupling (PCC). The circuit breaker ensures that the microgrid can be disconnected from the main grid promptly in the event of a utility interruption. As shown in Figure 2.1, two DPGS are employed in the micro- grid. Each DPGS system comprises an energy source, an energy storage system, and a grid-interfacing inverter.
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Modelling and Optimization in Microgrids

Modelling and Optimization in Microgrids

The urban network is characterized by a high settlement and building density and thus characterized by a high load density. To increase the security of supply in these areas, grids are usually realized as partly-meshed cable networks with open separation points. PV generation plants are most often encountered. Cogeneration (Combined heat and power - CHP) is occasionally present, but the energy delivered depends heavily on the season, and CHP has the ability to smooth out the feed-in of PV systems. In the basements of the buildings the installation of battery storage is also conceivable. The grid efficiency should be examined in individual cases in detail regarding the relatively low ratio of supply to consumption in urban microgrid. Since the available roof surface is low within the city compared to the number of housing units and the additional power supply partially provided by mini-CHP also estimated as low compared to the overall demand, in such microgrids very little reduction of energy consumption from the superimposed grid can be expected. Storage, however, can reduce the burden on the public grid. A completely self-sufficient supply without connection to the higher-level grid can not be realized because of the seasonal variations of infeed by photovoltaic systems.
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Frequency and Voltage Control of Islanded Microgrids

Frequency and Voltage Control of Islanded Microgrids

MG s. From the primary control perspective, an intelligent power sharing mechanism is proposed to maintain the frequency and voltage within acceptable ranges. It shares power among gener[r]

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Smart Distribution Coupled Microgrids

Smart Distribution Coupled Microgrids

micrograph and the type of DER units is no longer relevant to the transmission of its resources. For example, in a conventional model, a control center transmits the actual and reactive power output of each generator to meet the expected demands of load and can reduce the load in some situations (i.e., Smart Load). Microgrid is a separate grid resource that provides or absorbs a known range of genuine and reactive power based on micrograph details. A micrograph can respond in response to a request for load reduction by closing its internal generation and / or any significant load. It is important to implement smart distribution concepts, there is information on the range of actual and reactive power available for transmission from each micrograph. A PV microgrid can get requests for all the available renewable energy. To portray a multi-MW microgrid potential self-treatment imaginable connected to a 12 KV substation using a quick interface switch. There are several low-voltage high-power quality microgids with this microcridged fast interface switch. This example assumes that only control over each DER unit and interface switches are autonomous algorithms. The simplest MW micrograph can include many synchronous generators and storage. Low voltage, high power quality microgrids will usually be a faster inverter-based source. Each source is connected in a plug-and-pea fashion with a peer-to-peer and autonomous control. The multi-megawatt microgrid island will be with the opening of the voltage collapse interface switch on delivery substation. The storage unit will supply the lost energy within a few milliseconds, the synchronous generators slow down their output. In the end, in the collaboration with the storage generator, it will revise its production autonomously. Island system will be arranged at low frequencies based
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Modeling and Control of Diesel-Hydrokinetic Microgrids

Modeling and Control of Diesel-Hydrokinetic Microgrids

A permanent magnet synchronous generator (PMSG)-based HKECS connects to the microgrid through a full-rated back-to-back power electronics converter [90], [101]. Considering variable water conditions, if the operation of maximum power point tracking (MPPT) is required, for a fixed-pitch HKT, the shaft speed of the turbine-generator has to be variable which is realized by the control scheme of the converter. However, with the medium and high penetration level of the hydrokinetic power, if the HKT operates with MPPT, the diesel genset might work with light loading, leading to deterioration in the engine performance and efficiency due to the issue of wet stacking. Diesel engine manufactures suggest a minimum load of about 0.4 p.u. to avoid this problem [23]. Therefore, an active power curtailment of the HKECS is desirable and necessary in order to mitigate the light loading condition on the diesel genset. Furthermore, the full-rated converter also decouples the generator frequency from the grid frequency. The disadvantage is that one tends to lose the inertia of the turbine-generator which can have a positive impact on the frequency regulation aspect of the microgrid during transient events [102]. A significant frequency reduction and a much-needed boost of diesel genset output power are expected during a large disturbance such as sudden increase in the load and/or decrease of water speed. The HKECS can release the kinetic energy stored in its rotating masses (e.g., blades, generator shaft, etc.) in order to support the grid frequency control. Moreover, these problems are magnified in storage-less D-HK microgrids where a conventional energy storage system is not available to mitigate power as well as frequency deviations by controlling active power [103].
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Imitative Learning for Online Planning in Microgrids

Imitative Learning for Online Planning in Microgrids

Abstract. This paper aims to design an algorithm dedicated to opera- tional planning for microgrids in the challenging case where the scenarios of production and consumption are not known in advance. Using expert knowledge obtained from solving a family of linear programs, we build a learning set for training a decision-making agent. The empirical perfor- mances in terms of Levelized Energy Cost (LEC) of the obtained agent are compared to the expert performances obtained in the case where the scenarios are known in advance. Preliminary results are promising. Keywords: Machine learning, Planning, Imitative learning, Microgrids
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Power Management Strategies for Islanded Microgrids

Power Management Strategies for Islanded Microgrids

At the Primary Control Layer, the main objective of the proposed strategies is to achieve decentralized power management of renewable energy sources and battery stor- age in droop controlled microgrids. More specifically, the strategies are developed for Photovoltaic (PV) as an example for one of the common renewable energy sources. Two structural configurations for the PV and the battery storage are considered. In the first configuration, the PV and the battery storage are deployed as a single PV/battery hybrid unit in a droop controlled microgrid. Two decentralized power management strategies are proposed for this configuration. In the second configuration, the PV and the battery storage are deployed independently as separate units in the droop controlled microgrid. In contrast to the common approach of controlling the PV unit as a current source, in the proposed strategies, the PV unit is controlled as a voltage source that follows a multi- segment adaptive power/frequency characteristic curve. The strategies are implemented locally at the units using multi-loop controllers without relying on a central management system and communications, as most of the existing algorithms do. Small-signal models of the proposed control loops are developed to investigate system stability. The proposed strategies are validated experimentally results on a 4 kVA prototype microgrid.
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