Voltage Stabilization of A DC-Microgrid Using ANFIS Controller Considering Electrical Vehicles and Transient Storage

In this paper, we proposed a DC-microgrid with four main elements for Voltage stabilization. This research also presented a cost function that will guarantee the lifecycle of the EVs' battery because we use a Super Capacitor to damp the transient Ripples of Bus Voltage. This DCMG has four main branches: Ballast, Random Load, Random Source, and Stabilizer. The Random Source is photovoltaic, and the Random Load includes consumers. The three first branches make the DCMG go to the destabilization mode, and the last one has to stabilize its role in this DCMG. The controller consists of a fuzzy inference system optimized using PSO (Particle Swarm Optimization) algorithm, so this controller adjusts the duty cycle of three main branches in the stabilization branch of this DCMG. It is a MIMO ANFIS controller, and we compared the results of this controller with other controllers. In this research, we have designed three scenarios to verify the results: production more than consumption, vice versa, and equality between production and consumption. In this paper, the efficiency of this method -- using ANFIS controller -- in comparison with others -- using another type of controller -- will evaluate under different operating conditions, production and consumption inequality, and equality.Comment: 19 pages, 27 figure

Load frequency controllers considering renewable energy integration in power system

Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency

Fuzzy logic-based energy management system for grid-connected residential DC microgrids with multi-stack fuel cell systems: A multi-objective approach

Hybrid energy storage systems (HESS) are considered for use in renewable residential DC microgrids. This architecture is shown as a technically feasible solution to deal with the stochasticity of renewable energy sources, however, the complexity of its design and management increases inexorably. To address this problem, this paper proposes a fuzzy logic-based energy management system (EMS) for use in grid-connected residential DC microgrids with HESS. It is a hydrogen-based HESS, composed of batteries and multi-stack fuel cell system. The proposed EMS is based on a multivariable and multistage fuzzy logic controller, specially designed to cope with a multi-objective problem whose solution increases the microgrid performance in terms of efficiency, operating costs, and lifespan of the HESS. The proposed EMS considers the power balance in the microgrid and its prediction, the performance and degradation of its subsystems, as well as the main electricity grid costs. This article assesses the performance of the developed EMS with respect to three reference EMSs present in the literature: the widely used dual-band hysteresis and two based on multi-objective model predictive control. Simulation results show an increase in the performance of the microgrid from a technical and economic point of view.Thisresearchwasfundedby‘‘H2Integration&Control.IntegrationandControlofahydrogen-basedpilotplantinresidentialapplicationsforenergysupply’’SpanishGovernment,grant Ref:PID2020-116616RB-C31’’,‘‘SALTES:SmartgridwithreconfigurableArchitecturefortestingcontroLTechniquesandEnergy Storagepriority’’byAndalusianRegionalProgramofR+D+i,grant Ref:P20-00730,andbytheproject‘‘Thegreenhydrogenvector. Residentialandmobilityapplication’’,approvedinthecallfor researchprojectsoftheCepsaFoundationChairoftheUniversity ofHuelva.Fundingforopenaccesscharge:UniversidaddeHuelva /CBUA

Mixed-integer-linear-programming-based energy management system for hybrid PV-wind-battery microgrids: Modeling, design, and experimental verification

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMicrogrids are energy systems that aggregate distributed energy resources, loads, and power electronics devices in a stable and balanced way. They rely on energy management systems to schedule optimally the distributed energy resources. Conventionally, many scheduling problems have been solved by using complex algorithms that, even so, do not consider the operation of the distributed energy resources. This paper presents the modeling and design of a modular energy management system and its integration to a grid-connected battery-based microgrid. The scheduling model is a power generation-side strategy, defined as a general mixed-integer linear programming by taking into account two stages for proper charging of the storage units. This model is considered as a deterministic problem that aims to minimize operating costs and promote self-consumption based on 24-hour ahead forecast data. The operation of the microgrid is complemented with a supervisory control stage that compensates any mismatch between the offline scheduling process and the real time microgrid operation. The proposal has been tested experimentally in a hybrid microgrid at the Microgrid Research Laboratory, Aalborg University.Peer ReviewedPostprint (author's final draft

Energy management in microgrids with renewable energy sources: A literature review

Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed

Microgrid, Its Control and Stability: The State of The Art

Some of the challenges facing the power industries globally include power quality and stability, diminishing fossil fuel, climate change amongst others. The use of distributed generators however is growing at a steady pace to address these challenges. When interconnected and integrated with storage devices and controllable load, these generators operate together in a grid, which has incidental stability and control issues. The focus of this paper, therefore, is on the review and discussion of the different control approaches and the hierarchical control on a microgrid, the current practice in the literature concerning stability and the control techniques deployed for microgrid control; the weakness and strength of the different control strategies were discussed in this work and some of the areas that require further research are highlighted

Investigations into microgrid sizing and energy management strategies

PhD ThesisThe evolution of microgrids represents a significant step towards the transition to more sustainable power systems. Recent trends in microgrids include the integration of renewable energy resources (RERs), alternative energy resources (AERs) and energy storage systems (ESSs). However, the integration of these systems creates new challenges on microgrid operation because of their stochastic and intermittent nature. To mitigate these challenges, determining the appropriate size together with the best energy management strategy (EMS) systems are essential to ensure economic and optimal performance. This thesis presents an investigation into sizing and energy management of microgrids. In the first part of the thesis, an analytical and economic sizing (AES) approach is developed to find the optimal size of a grid-connected photovoltaicbattery energy storage system (PV-BESS). The proposed approach determines the optimal size based on the minimum levelised cost of energy (LCOE). Fundamental to this approach obtains an improved formula of LCOE which includes new parameters for reflecting the impact of surplus PV energy and the energy purchased from the grid. In the second part of this thesis, an integrated framework is proposed for finding the best size-EMS combination of a stand-alone hybrid energy system (HES). The HES consists of PV, BESS, diesel generator, fuel cell, electrolyser, and hydrogen tank. The proposed framework includes three consecutive steps; first, performing the AES to obtain the initial size of the HES, second, implementing the initial EMS using finite automata (FA) and instantiating multiple EMSs; and third, developing an evaluation model to assess the instantiated EMSs and extract the featured conditions to produce an improved EMS. Then the AES approach is re-exercised using the improved EMS to obtain the best size-EMS combination. The core of this framework is utilising FA to implement various EMSs and capturing the impact of selecting the best EMS on the sizing of the HES. Furthermore, a sensitivity analysis is performed to address the uncertainty in demand and solar radiation data showing their effect on the HES performance. The analysis is carried out by assuming variations in solar radiation and demand annual data. Several scenarios are generated from the sensitivity analysis, and a number of performance indices are computed for each scenario. Following that, a vii fuzzy logic controller is designed using the performance indices as fuzzy input sets. The objective of this controller is to modify the EMS obtained from the integrated framework. This can be accomplished by detecting any changes in the demand and solar radiation and accordingly modify the operating conditions of the diesel generator, fuel cell, and electrolyser. The performance of the proposed approaches is validated using real datasets for both demand and solar radiation. The results show the optimal size and EMS for both grid-connected and stand-alone microgrids. Moreover, the designed fuzzy logic controller enables the microgrid to mitigate the uncertainty in the demand and generation data. The proposed approaches can be used with various scales of microgrids to extract manifold benefits where reliability, environmental and cost requirements can not be tolerated.Applied Science Private University in Jorda