Browse Topic: Microgrid

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Optimal Sizing and Energy Management of a Microgrid Using Single and Multi-Objective Particle Swarm Optimization under Autonomous and Grid Connected Mode2019-28-015810/11/2019
The conventional energy sources are getting depleted while at the same time the energy demand keeps growing. Hence, it is important to consider non-conventional energy sources to meet future energy demands. The renewable energy based microgrid system is one of the promising solutions to meet this increasing energy demand. The major parameters under consideration in a micro-grid system are cost-effectiveness, quality of service and energy management. This work concentrates on the energy management of the Photovoltaic/Wind based microgrid system connected to the fuel cell, microturbine and battery under Islanding (or) Autonomous mode and Grid-Connected Mode. The current model of PV, Wind and Battery systems are employed. The Wind, PV and Battery types are chosen from i-HOGA. The optimal combination of these sources with the aim of minimizing the operating cost, pollutant treatment cost and maximizing reliability using both single and multi-objective particle swarm optimization (PSO) has been considered. This microgrid has also been analyzed under three different strategies for both grids connected and islanded mode and the best energy management strategy is obtained after analysis. In addition to this, the type and number of PV, Wind, and Battery to meet the forecasted demand are determined under islanding mode using Multi-Objective Particle Swarm Optimization (MOPSO). A solitary best-accepted solution is attained from Fuzzy membership function. The algorithm proposed decides the optimal number of units and types of units selected to achieve the optimal cost. The simulation has been performed in MATLAB environment.
Dayalan, Suchitra, Rathinam, Rajarajeswari, Valliappan, Subramaniyan
Reliability and Resiliency Definitions for Smart Microgrids Based on Utility Theory2017-01-02053/28/2017
Reliability and resiliency (R&R) definitions differ depending on the system under consideration. Generally, each engineering sector defines relevant R&R metrics pertinent to their system. While this can impede cross-disciplinary engineering projects as well as research, it is a necessary strategy to capture all the relevant system characteristics. This paper highlights the difficulties associated with defining performance of such systems while using smart microgrids as an example. Further, it develops metrics and definitions that are useful in assessing their performance, based on utility theory. A microgrid must not only anticipate load conditions but also tolerate partial failures and remain optimally operating. Many of these failures happen infrequently but unexpectedly and therefore are hard to plan for. We discuss real life failure scenarios and show how the proposed definitions and metrics are beneficial.
Skowronska, Annette, Pandey, Vijitashwa, Weinert, Kevin, Gorsich, David, Mourelatos, Zissimos
Model-Based Analysis of V2G Impact on Battery Degradation2017-01-16993/28/2017
Vehicle-to-Grid (V2G) service has a potential to improve the reliability and stability of the electrical grid due to the ability of providing bi-directional power flow from/to the grid. However, frequent charging/discharging may impact the battery lifetime. This paper presents the analysis of battery degradation in three scenarios. In the first scenario, different battery capacities are considered. In the second scenario, the battery degradation with various depth of discharge (DOD) are studied. In the third scenario, the capacity loss due to different charging regime are compared. The charging/discharging of plug-in electric vehicles (PEVs) are simulated in a single-phase microgrid system integrated with a photovoltaics (PV) farm, an energy storage system (ESS), and ten electric vehicle service equipment (EVSE). The battery degradation model is an energy throughput model, which is developed based on the Arrhenius equation and a power law relationship between time and capacity fading. The simulation results show that V2G service potentially increases the PEV battery degradation. With the same DOD, higher battery capacity can increase the degradation degree. For a specific PEV battery, the higher DOD, higher temperature, and more frequent discharging accelerate the battery degradation to some extent.
Wang, Luting, Chen, Bo
Fuel-Optimal Strategies for Vehicle Supported Military Microgrids2016-01-03124/5/2016
Vehicles with power exporting capability are microgrids since they possess electrical power generation, onboard loads, energy storage, and the ability to interconnect. The unique load and silent watch requirements of some military vehicles make them particularly well-suited to augment stationary power grids to increase power resiliency and capability. Connecting multiple vehicles in a peer-to-peer arrangement or to a stationary grid requires scalable power management strategies to accommodate the possibly large numbers of assets. This paper describes a military ground vehicle power management scheme for vehicle-to-grid applications. The particular focus is overall fuel consumption reduction of the mixed asset inventory of military vehicles with diesel generators typically used in small unit outposts. By exploiting peak efficiency operation of the diesel generators and the vehicle’s energy storage systems, the total fuel consumption can be reduced over a typical daily outpost load cycle. While military vehicle energy storage requirements are dominated by silent watch objectives, this work adds another dimension to the objective set to include outpost fuel consumption. Motivated by these requirements, an extensible MATLAB/Simulink simulation of the system was constructed using the complex nodal admittance matrix approach to connect stationary diesel generators and vehicles with two-way power flow for either supporting the power grid or acting as a load for charging their batteries. After describing the model and energy management strategy, simulation studies are used to illustrate the approach using both idealized and measured outpost load data.
Jane, Robert S., Parker, Gordon G., Weaver, Wayne, Rizzo, Denise M.
Spotlight on Design: Automotive Charging Infrastructure: Vehicle and Grid IntegrationSOD-010S1/30/2016
“Spotlight on Design” features video interviews and case studies, focusing on technology breakthroughs, hands-on testimonials, and the importance of fundamentals. Viewers are virtually taken to industry labs and research centers to learn how design engineers solve real-life problems. These challenges include enhancing product performance, reducing cost, improving quality and safety, while decreasing environmental impact, and achieving regulatory compliance. In the episode “Automotive Charging Infrastructure: Vehicle and Grid Integration” (21:00), engineers from NextEnergy and an infrastructure expert from General Motors explain how technologies are rapidly converging to power electric vehicles and support the overall electric grid. This episode highlights: How the fast expansion of charging infrastructure is changing the way electric and hybrid-electric vehicles are gaining the confidence of consumers. The strategic work being developed to expand the presence of charging stations where they are most effective. The new models being developed to make the best possible use of batteries once they are no longer suitable for car use. The importance of the secondary use of car batteries to power local micro-grids, enabling communities to be more energy independent. The need to bring together engineers, grid experts and consumers to establish the best technology and implementation solutions to support a faster adoption of electric cars.
Flexible Design and Operation of a Smart Charging Microgrid2014-01-07164/1/2014
The reliability theory of repairable systems is vastly different from that of non-repairable systems. The authors have recently proposed a ‘decision-based’ framework to design and maintain repairable systems for optimal performance and reliability using a set of metrics such as minimum failure free period, number of failures in planning horizon (lifecycle), and cost. The optimal solution includes the initial design, the system maintenance throughout the planning horizon, and the protocol to operate the system. In this work, we extend this idea by incorporating flexibility and demonstrate our approach using a smart charging electric microgrid architecture. The flexibility is realized by allowing the architecture to change with time. Our approach “learns” the working characteristics of the microgrid. We use actual load and supply data over a short time to quantify the load and supply random processes and also establish the correlation between them. The quantified processes are then used to generate load and supply realizations over the long planning horizon. We show how this can reduce the computational effort when simulating microgrids for the entire planning horizon without impeding on their design under various operating scenarios considering uncertainty.
Pandey, Vijitashwa, Skowronska, Annette, Mourelatos, Zissimos, Gorsich, David, Castanier, Matthew
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