Battery Power Smoothing Control in a Marine Electric Power Plant Using Nonlinear Model Predictive Control

Bø, Torstein Ingebrigtsen; Johansen, Tor Arne

doi:10.1109/tcst.2016.2601301

Cited by 49 publications

(16 citation statements)

References 28 publications

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“…The DL shown in Figure 1 comprises a bank of eight three-phase resistors connected in series with Gate Turn-Off (GTO) thyristor-based power switches. The eight resistor values are R, R/2, R/2 2 , ..., R/2 7 . With V n the isolated grid rated voltage, P 0 = V 2 n /R is the rated power of the resistor of value R and the eight resistors rated powers are P 0 , 2P 0 , 2 2 P 0 , ..., 2 7 P 0 .…”

Section: The DL Modelmentioning

confidence: 99%

“…With V n the isolated grid rated voltage, P 0 = V 2 n /R is the rated power of the resistor of value R and the eight resistors rated powers are P 0 , 2P 0 , 2 2 P 0 , ..., 2 7 P 0 . If I 0 , I 1 , ..., I 7 are the opened(0)/closed(1) states of the GTO power switches in series with the resistors, the active power absorbed by the DL can be expressed as (I 0 + I 1 2 1 + ... + I 7 2 7 )P 0 (6) Equation ( 6) states a DL power variation from 0 to 255P 0 . In this article P 0 = 1.5 kW and the DL rated power P D−NOM is 382.5 kW, which is 39% greater than P T−NOM .…”

Section: The DL Modelmentioning

confidence: 99%

“…Isolated microgrids are microgrids which operate autonomously and are located in remote places [1][2][3]. Remote isolated microgrids have been mostly based on Diesel Generators (DG), which can be combined with sources of renewable energy, such as photovoltaic or wind [4] and short-term energy storage systems (ESS), mainly based on flywheels [5] or batteries [6,7]. When an isolated microgrid includes renewables and does not include a DG, it has to include another source of controlled generation, such as a fuel cell [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Study and Simulation of a Wind Hydro Isolated Microgrid

Sebastián¹,

Nevado²

2020

Energies

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Isolated microgrids are microgrids which operate autonomously. This paper presents an isolated microgrid which combines a Hydraulic Turbine Generator (HTG) with a Wind Turbine Generator (WTG) to supply consumers forming a Wind Hydro Isolated Microgrid (WHIM). The WHIM includes a Dump Load (DL) to dissipate the active power excess. The WHIM has been modeled and its operation has been simulated in two modes: Wind-Hydro (WH), where both HTG and WTG supply power, and Wind-Only (WO) mode, where the WTG is the active power supplier and the HTG keeps connected to the grid with null power to generate the grid voltage. In WO, a fast frequency regulation is achieved by means of a controller which commands the DL to consume the WTG power excess. Additionally, the simulation of the mode transition from WO to WH, which is triggered by a system active power deficit in WO mode, is shown. A kick starting system designed to speed up the HTG power production improves the transient from WO to WH mode change. Finally, the simulations in WH mode show the interaction between the HTG and WTG. The two controls proposed have been proved effective and the simulations show a good WHIM dynamic performance.

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Section: The DL Modelmentioning

confidence: 99%

Section: The DL Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Study and Simulation of a Wind Hydro Isolated Microgrid

Sebastián¹,

Nevado²

2020

Energies

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“…In this lumped-volume model the measurable surface temperature is assumed to be the temperature throughout the cell [95], [96]. Note that while we have depicted the cell geometry as cylindrical, this approach works equally well for pouch or prismatic cells.…”

Section: Temperature Model Typesmentioning

confidence: 99%

Battery Energy Storage Models for Optimal Control

et al. 2019

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As batteries become more prevalent in grid energy storage applications, the controllers that decide when to charge and discharge become critical to maximizing their utilization. Controller design for these applications is based on models that mathematically represent the physical dynamics and constraints of batteries. Unrepresented dynamics in these models can lead to suboptimal control. Our goal is to examine the state-of-the-art with respect to the models used in optimal control of battery energy storage systems (BESSs). This review helps engineers navigate the range of available design choices and helps researchers by identifying gaps in the state-of-the-art. BESS models can be classified by physical domain: state-of-charge (SoC), temperature, and degradation. SoC models can be further classified by the units they use to define capacity: electrical energy, electrical charge, and chemical concentration. Most energy based SoC models are linear, with variations in ways of representing efficiency and the limits on power. The charge based SoC models include many variations of equivalent circuits for predicting battery string voltage. SoC models based on chemical concentrations use material properties and physical parameters in the cell design to predict battery voltage and charge capacity. Temperature is modeled through a combination of heat generation and heat transfer. Heat is generated through changes in entropy, overpotential losses, and resistive heating. Heat is transferred through conduction, radiation, and convection. Variations in thermal models are based on which generation and transfer mechanisms are represented and the number and physical significance of finite elements in the model. Modeling battery degradation can be done empirically or based on underlying physical mechanisms. Empirical stress factor models isolate the impacts of time, current, SoC, temperature, and depth-of-discharge (DoD) on battery state-of-health (SoH). Through a few simplifying assumptions, these stress factors can be represented using regularization norms. Physical degradation models can further be classified into models of side-reactions and those of material fatigue. This article demonstrates the importance of model selection to optimal control by providing several example controller designs. Simpler models may overestimate or underestimate the capabilities of the battery system. Adding details can improve accuracy at the expense of model complexity, and computation time. Our analysis identifies six gaps: deficiency of real-world data in control literature, lack of understanding in how to balance modeling detail with the number of representative cells, underdeveloped model uncertainty based risk-averse and robust control of BESS, underdevelopment of nonlinear energy based SoC models, lack of hysteresis in voltage models used for control, lack of entropy heating and cooling in thermal modeling, and deficiency of knowledge in what combination of empirical degradation stress factors is most accurate. These gaps are opportunities ...

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“…The battery-based power smoothing control in a shipboard microgrids based on using non-linear predictive control is proposed in [109]. In large vessels power fluctuations are quite high, which results in frequency fluctuations and can cause wear and tear of the source power plants.…”

Section: Power Smoothingmentioning

confidence: 99%

Energy Storage Systems for Shipboard Microgrids—A Review

et al. 2018

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In recent years, concerns about severe environmental pollution and fossil fuel consumption has grabbed attention in the transportation industry, particularly in marine vessels. Another key challenge in ships is the fluctuations caused by high dynamic loads. In order to have a higher reliability in shipboard power systems, presently more generators are kept online operating much below their efficient point. Hence, to improve the fuel efficiency of shipboard power systems, the minimum generator operation with N-1 safety can be considered as a simple solution, a tradeoff between fuel economy and reliability. It is based on the fact that the fewer the number of generators that are brought online, the more load is on each generator such that allowing the generators to run on better fuel efficiency region. In all-electric ships, the propulsion and service loads are integrated to a common network in order to attain improved fuel consumption with lesser emissions in contrast to traditional approaches where propulsion and service loads are fed by separate generators. In order to make the shipboard power system more reliable, integration of energy storage system (ESS) is found out to be an effective solution. Energy storage devices, which are currently being used in several applications consist of batteries, ultra-capacitor, flywheel, and fuel cell. Among the batteries, lithium-ion is one of the most used type battery in fully electric zero-emission ferries with the shorter route (around 5 to 10 km). Hybrid energy storage systems (HESSs) are one of the solutions, which can be implemented in high power/energy density applications. In this case, two or more energy storage devices can be hybridized to achieve the benefits from both of them, although it is still a challenge to apply presently such application by a single energy storage device. The aim of this paper is to review several types of energy storage devices that have been extensively used to improve the reliability, fuel consumption, dynamic behavior, and other shortcomings for shipboard power systems. Besides, a summary is conducted to address most of the applied technologies mentioned in the literature with the aim of highlighting the challenges of integrating the ESS in the shipboard microgrids.

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Battery Power Smoothing Control in a Marine Electric Power Plant Using Nonlinear Model Predictive Control

Cited by 49 publications

References 28 publications

Study and Simulation of a Wind Hydro Isolated Microgrid

Study and Simulation of a Wind Hydro Isolated Microgrid

Battery Energy Storage Models for Optimal Control

Energy Storage Systems for Shipboard Microgrids—A Review

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