Smart Grids as Cyber Physical Systems, 2 Volume Set E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1090

Veröffentlichungsjahr: 2024

Beschreibung

Smart Grids as Cyber Physical Systems, a new two-volume set from Wiley-Scrivener, provides a comprehensive overview of the fundamental security of supervisory control and data acquisition (SCADA) systems, offering clarity on specific operating and security issues that may arise that deteriorate the overall operation and efficiency of smart grid systems. It also provides techniques to monitor and protect systems, as well as aids for designing a threat-free system. This title discusses how artificial intelligence (AI) may be extensively deployed in the prediction of energy generation, electric grid-related line loss prediction, load forecasting, and for predicting equipment failure prevention. It also discusses power generation systems, building service systems, and explores advances in machine learning, artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms. Additionally, we will explore research contribution of experts in CPS infrastructure systems, incorporating sustainability by embedding computing and communication in day-to-day smart grid applications. This book will be of immense use to practitioners in industries focusing on adaptive configuration and optimization in smart grid systems. Through case studies, it offers a rigorous introduction to the theoretical foundations, techniques, and practical solutions CPS offers. Building CPS with effective communication, control, intelligence, and security is discussed from societal and research perspectives and a forum for researchers and practitioners to exchange ideas and achieve progress in CPS is provided by highlighting applications, advances, and research challenges. This book offers a comprehensive look at ICS cyber threats, attacks, metrics, risk, situational awareness, intrusion detection, and security testing, providing a valuable reference set for current system owners who wish to configure and operate their ICSs securely.

Leseprobe

Volume 1

Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Smart Grids as Cyber Physical Systems Volume 1

Artificial Intelligence, Cybersecurity, and Clean Energy for Next Generation Smart Grids

Edited by

O.V. Gnana Swathika

K. Karthikeyan

and

Sanjeevikumar Padmanaban

This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN: 9781394261697

Front cover images supplied by Pixabay.comCover design by Russell Richardson

Preface

Integrating artificial intelligence into a smart grid system contributes to the trajectory of growth in a knowledge-based economy suitable for the energy transition. They play a key role in smart cities, demanding intelligent energy transition that keeps the importance of energy-saving and energy management technologies. Hence, it eventually paves the way for drastic changes like smart buildings, smart transport, and smart health care systems. Information and communication technologies are a feature of any smart grid system. Smart Grids as Cyber Physical Systems is a next-generation solution with significant modules that facilitate the integration of the grid and cyberspace. It operates in a closed-loop mechanism wherein the smart grid system adapts itself based on real-time operating conditions. Smart Grid systems are designed and controlled in a framework that concentrates on clean energy source integration. This integration is done with conscious monitoring of parameters like voltage and frequency control, power quality, and storage features. The work focuses on realizing proper energy management and protection systems operating under uncertainty and prioritizes robustness and sustainable solutions. This book further offers clarity on specific operating and security issues that may arise that deteriorate the overall operation and efficiency of Smart Grid Systems. It also provides techniques to monitor and protect the system. It also aids in designing a threat-free system. It also discusses how Artificial Intelligence (AI) may be extensively deployed in prediction for energy generation, electric grid-related line loss prediction, load forecasting, and for predicting equipment failure prevention. Building CPS with effective communication, control, intelligence, and security is discussed from societal and research perspectives.

1Grid Independent Dynamic Charging of EV Batteries Using Solar Energy

P. Balamurugan1*, Tekumalla Lakshmi Sowjanya2, Manas Goyan2, J.L. Febin Daya1 and V. Ananthakrishnan3

1eVITRC, Vellore Institute of Technology - Chennai, Tamil Nadu, India

2VIT-Bhopal University, Madhya Pradesh, India

3School of Electrical Engineering, Vellore Institute of Technology - Chennai, Tamil Nadu, India

Abstract

This research aims to utilize energy emitted by the sun as a source to charge an electric car and to eliminate the need for installing more charging stations to save electricity. The proposed concept of dynamic charging from solar source minimizes carbon footprint. The concept can be applied in a wide range by installing flexible solar panels that are available commercially in the market. This concept enables embedding the flexible panels during the manufacturing stage and widespread utilization of available solar energy surplus. This will reduce the consumption of electricity generated from conventional energy sources to charge the electric vehicles. The dynamic charging will also extend the range provided by the battery per charge cycle improving the reliability of the electric vehicle.

Keywords: EV charging, dynamic charging, flexible PV panels, boost converter

1.1 Introduction

Climate changes due to emission of CO2 from remnant fuels is a major concern in recent years. This enabled electrical engineers and researchers to look out for alternate sources of energy like solar farms, wind farms, and other sources like biomass. Vehicles are the primary sources where the CO2 emission is present everywhere compared to large industries where it is localized and regulated. Hence, electric vehicles (EV) came into picture, resulting in rapid manufacturing and production of EVs. The use of additional battery storage in automotive systems for supplying the power train and internal utilities extending the range of operation influencing economic challenges is the next challenge. As a promising technology and preference, harvesting renewable energy resources is a boon for both EV industries and the transportation sector. Charging batteries and similar storage systems directly from renewable resources increases the reliability of the battery-operated vehicles. Utilizing energy from alternative energy resources as an alternative to conventional electric network to run the EV is perceived to expand the complete system efficacy and diminish carbon footprint.

Energy from photovoltaic (PV) panels are progressively competing with other forms of renewable sources of energy due to its splendid nature and is easily extractable. Hence, the adaptability of e-transportation is facilitated much by PV sources. In recent days, utilizing PV as a primary source of energy is adapted from small scale to large industries, airports, and residential applications. Since it exhibits lesser maneuver cost and maintenance, low greenhouse gas emanations, and self-governing capability, PV is adopted everywhere. Vast exploration of technology has been adopted lately to charge EV with energy from solar. Many countries have initiated design standards for PV systems.

Analyzing the current scenario and forthcoming challenges in the implementation of EV and charging systems, it is essential to adopt operation of EVs independent on conventional grid for charging. Several agencies analyzed the challenges and deployed several universal standards and codes for charging EVs. Considering wide-spread applications and situations, batteries are charged by PVs which are installed on the vehicles, causing the reduction in the installation of charging stations.

This work, “Solar Powered 4-Wheeler” is a dynamic system, wherein the energy from the sun is transformed into the electrical energy using the required number of solar panels of sufficient area mounted on the EV, to power up the lead–acid battery, which is used in the 4-wheeler [1].

As this is a dynamic system, due to weather conditions, the charging is influenced by parameters like irradiance and temperature. The unregulated output of the PV panel is regulated using DC-DC charge controller that is capable of boosting or stepping down the PV output with simultaneous tracking of maximum power output from the panel. The maximum power point tracking (MPPT) controller is better for the high-power range applications rather than the pulse width modulation (PWM) controller to measure the maximum power point (MPP) and keep the battery in charging mode whenever the variable parameters get changed [2]. Solar cells are implemented on the surface of the scooter by calculating the parameters of the single cell and connect those cells in the series and parallel to get the output that is helpful to charge the battery [3]. The battery plays a major role in electric vehicles. Knowing the total energy left in a battery and the driving conditions, the control system notifies the user, and the range for which the battery will perform until the next recharge, which is a measure of the intermittent ability of the battery [4]. The state-of-charge (SOC) of the battery can be resolute from the discharging characteristics of the battery. An SOC of 100% is a measure of area under the discharge characteristics of a battery from 100% charge to 0% charge in the battery [5]. The battery charging is done using three stages of charging of the battery to maintain the standard and charging takes place with respect to the SOC of battery by constant-current and constant-voltage methods [6].

1.2 Proposed Methodology

In this proposed methodology, flexible solar panels will be mounted on car surfaces, where maximum light will fall. Since the PV panels are flexible, their size and power ratings are not regular. Hence, the power rating of the panel must be calculated using the number of cells present in the section and the data sheet of the PV panel. Based on the efficiency of solar panel, the size and power are finalized. From the data sheet, the power output of a single cell is computed and based on the area of the panel, the total power of the PV is calculated. To get the desired amount of power generation, many such cells will be connected in series and parallel. Once the PV panels are designed, then it requires a DC-DC converter to extract power from the PV panel. In this work, a boost converter is chosen to extract power from the PV. The boost converter provides necessary charging current at desired voltage to the battery present in EV. The power output of PV varies depending on cell temperature and irradiance falling on the panel. Hence, it is necessary to regulate and extract maximum power output from PV with suitable control technique using boost converter. Maximum power point tracking (MPPT) is one of the classical approaches that aims at tracking and extracting the maximum power output from the PV panel and feeds the connected loads. For tracking the maximum power, the perturb and observe (P&O) algorithm is applied. The energy output of the boost converter will be given to the battery for storage or an inverter to convert the DC to AC. The algorithm for the charge controller will adjust the current and voltage with respect to the SOC of the battery. Based on the current from the boost converter, the charging time of the battery can be computed. The functional illustration of the planned system is depicted in Figure 1.1.

Figure 1.1 Functional illustration of the proposed system.

The specifications of Renogy UK monocrystalline flexible solar panels are provided in Table 1.1. The image of Renogy UK monocrystalline flexible PV panel is shown in Figure 1.2 for reference along with its dimensions specified.

Single solar cell parameters from the selected manufacturer solar panel

Cell area = 6.5 * 3.3 in = 16.51 * 8.382 cm

2

= 138.39 cm

2

= 13,839 mm

2

Open circuit voltage from the single cell = V

oc

of solar panel/ No. of cells in panel

No. of cells connected in series = PV module voltage / voltage at the operating condition

Here, PV module voltage is 52 because we need 52 V from the source to charge 48 V

Table 1.1 Renogy UK monocrystalline flexible PV panel specifications.

Parameter

Values

Maximum power

100 W

Open circuit voltage (V

oc

)

23.5 V

Voltage at maximum power point

19.4 V

Temperature coefficient (V

oc

, I

sc

, P

max

)

–0.29%, 0.05%, –0.38%

Short circuit current (I

SC

)

5.51 A

Current at maximum power point

5.2 A

Irradiance and nominal temperature

1000 W/m

2

and 25°C

Panel dimensions in mm

1093 × 582

No. of cells

36

Figure 1.2 Renogy UK monocrystalline flexible PV panel.

Light generated current (I

L

) = 5.5453 A

Diode saturation current (I

0

) = 12.358 pA

Diode ideality factor = 0.94781

Shunt resistance = 185.7255 Ω

Series resistance = 0.26886 Ω

Series connected modules per string = 2

With all the specifications of the solar PV source, the simulation is performed in MATLAB/Simulink environment. The characteristics of the PV panel at two different temperatures are shown in Figure 1.3.

Figure 1.3 I-V and P-V characteristics of PV panel.

1.3 Design of Boost Converter

The power circuit of the boost converter is illustrated in Figure 1.4. The tendency of an inductor to store energy in the form of magnetic field in it, resulting in resisting the changes in current flow through it, is the vital principle behind the boost converter. The energy stored in the inductor in series to the voltage source retains the output voltage, exceeding the input voltage of the boost converter. The working of a boost converter is understood based on the semiconductor switch S in the circuit.

The switch ‘S’ is operated with a period T, in which it is closed for TON sec and open for TOFF sec. When the switch is closed, the energy stored in the inductor increases because of the increase in current flowing through it. The polarity of voltage is opposite to that of the supply. When opened, the stored energy in the inductor is discharged to the capacitor, with the polarity aiding the supply voltage. Hence, the voltage across the load is greater than the supply, Vin. The design specifications of boost converter are listed in Table 1.2.

The simulation model of the proposed system is shown in Figure 1.5 for reference. In this model, two PV panels are connected in parallel to achieve more current at constant voltage. The output from the PV panel is tracked using MPPT algorithm embedded in the simulation as a MATLAB function block. The output duty ration is converted into a PWM signal at 50-kHz switching frequency and is applied to the insulated-gate bipolar transistor (IGBT) switch of the boost converter. A battery of 48 V, 50 AH, with an initial SOC of 80% is taken to provide a proof of concept.

Figure 1.4 Boost converter.

Table 1.2 Boost converter specifications.

Parameter

Values

Input voltage

24 V

Output voltage

52 V

Rated power

150 W

Switching frequency

50 kHz

Output current

5.49 A

Inductance (L)

414 μH

Capacitance (C)

470 μF

Figure 1.5 Simulink model of the proposed open-loop type battery charger.

Since the panels are considered to be mounted on the top of the car, the same temperature and irradiation levels are considered for the simulation. In practice, if more such panels are provided on the sides, bonnet, etc., the net power output may vary based on partial shadow conditions. But the objective of extending the range of driving an EV using self-charging capability is satisfied with the proposed model.

1.4 Perturb and Observe Algorithm for Tracking Maximum Power

Perturb and observe (P&O) is the most common algorithm employed to extract maximum available power from the renewable energy resources. Through this algorithm, we generate the duty cycle of the PWM required to operate the switch in boost converter. The input to P&O is the output voltage, V(k), of the panel and its output current, I(k). With this voltage and current, the power at kth instant is calculated and is compared with power, p(k), during previous sampling interval. At every instant, the difference in power, Δp(k), and voltage output voltage, ΔV(k), is computed to arrive at the desired duty ratio for the switch ‘S’. The flowchart for P&O algorithm is illustrated in Figure 1.6.

If we take the power vs voltage current for solar PV panels and assume there is only an MPP, which is both local and global.

Figure 1.6 Flowchart for P&O algorithm.

There, PV curve is divided into three regions. The MPP is in the peak of the curve, so the regions left-hand side (LHS) and right-hand side (RHS) to the peak point is formed. In the LHS, change in power with respect to voltage is greater than zero, we observed from zero to right when the voltage in the region is increased, and when the voltage is decreased, the power is decreased on the right-hand side and change in power with respect to voltage is less than the zero. As a result, if the voltage increases then power decreases, and if voltage decreases then power increases. At the MPP, it is observed that the change in power is zero, hence, is the condition for maximum. The P&O algorithm examines the power-to-voltage slope. If the slope is positive, it indicates that we are in the LHS of MPPT. If the slope is negative, we are in RHS and we are at MPP if slope = 0. Hence, this logic is employed to construct the flowchart in which perturb the panel voltage and observe the change in power, determining that operating point is on LHS or RHS and proceeding to MPP. The first step is to sample the PV module instantaneous voltage and current referred to as v(k) and I(k), respectively. After that, they are multiplied to get the instantaneous power, p(k). The p(k) is then compared to the power, p(k)-p(k-1), of the previous case. We will refer to the difference as Δp. Similarly, ΔV is formed by subtracting the previous voltage from the present voltage. The numerator and denominator slope of the PV curve are Δp and ΔV, respectively.