Smart Grids, Distributed Energy, and Electric Mobility

Introduction

Electric power systems are undergoing a transition from centrally generated, one-directional electricity networks to interactive systems in which consumers, renewable generators, storage devices, electric vehicles, sensors, and automated controls exchange information and energy. The smart grid is the digital and electrical infrastructure that enables this transition.

Traditional distribution networks were designed for predictable, one-way power flow: electricity moved from large power stations through transmission and distribution networks to customers. Modern networks increasingly contain rooftop photovoltaic systems, battery energy storage, controllable loads, electric vehicles, and local energy communities. Power may therefore flow in either direction, while demand and generation can change rapidly. Internet of Things (IoT) technologies provide the measurement, communication, computation, and control required to operate such systems safely and economically.

This chapter examines the application of IoT to advanced metering, demand response, distribution automation, microgrids, renewable generation, energy storage, electric mobility, charging infrastructure, power quality, fault detection, asset monitoring, and energy management. The focus is not only on devices, but also on the engineering relationships among sensing, communication, decision-making, and physical control.

Learning Objectives

After studying this chapter, a student should be able to:

1. Explain the architecture and operating principles of a smart grid.
2. Distinguish advanced metering, demand response, distribution automation, microgrids, and energy management.
3. Calculate basic energy, power, storage, charging, and efficiency quantities.
4. Analyze how distributed renewable generation and electric vehicles affect distribution networks.
5. Describe IoT-based approaches to power-quality monitoring, fault detection, and asset condition assessment.
6. Evaluate cybersecurity, privacy, safety, regulatory, and interoperability issues.
7. Propose an integrated smart-energy solution for a practical application.

1. From Conventional Networks to Smart Grids

A smart grid is an electricity system that combines electrical infrastructure with sensing, communications, data processing, automation, and coordinated control. Its objectives commonly include improved reliability, efficient operation, integration of low-carbon resources, customer participation, and resilience.

The principal layers are:

• Physical energy layer: generators, transformers, lines, switchgear, meters, loads, batteries, and chargers.
• Sensing layer: voltage, current, frequency, temperature, irradiance, state-of-charge, breaker position, and power-quality sensors.
• Communication layer: wired or wireless networks that transport measurements and commands.
• Data and platform layer: databases, device registries, digital twins, analytics platforms, and application programming interfaces.
• Application and control layer: outage management, demand response, voltage control, energy management, asset monitoring, and billing.
• Human and governance layer: operators, customers, aggregators, regulators, market rules, privacy policies, and safety procedures.

IoT does not replace power-system engineering. A sensor reading is useful only when its accuracy, timestamp, location, communication reliability, and operational meaning are understood. Likewise, an automated command must respect electrical limits, protection coordination, cybersecurity requirements, and human authority.

A useful distinction is between monitoring, supervisory control, and closed-loop control. Monitoring records or displays conditions. Supervisory control allows an operator or application to issue commands. Closed-loop control automatically measures a variable, compares it with a target, and changes an actuator to reduce the error. Fast protection functions generally require deterministic, highly reliable systems and should not depend solely on a general-purpose cloud connection.

2. Advanced Metering Infrastructure

Advanced metering infrastructure (AMI) is an integrated system of smart meters, communications, head-end systems, meter-data management systems, and utility applications. A smart meter measures energy and power at defined intervals and can often report voltage, outage status, tamper events, and two-way communications. A remotely controlled service switch may permit connection or disconnection, subject to safety and regulatory rules.

AMI supports:

• interval billing and time-of-use tariffs;
• remote meter reading and reduced manual visits;
• outage detection and restoration verification;
• voltage and power-quality visibility at customer premises;
• theft and tamper investigation;
• customer feedback and energy conservation;
• distributed-energy-resource coordination.

For active energy, the basic relationship is:

Here, E is energy in joules or kilowatt-hours, P(t) is instantaneous real power in watts or kilowatts, and t is time. If power is constant, the relationship becomes:

with consistent units. For example, a 2 kW load operating for 3 h consumes 6 kWh. Interval meters approximate the integral by summing measured power over successive intervals:

where P_k is the measured power during interval k and ∆t_k is that interval’s duration. The approximation is affected by sampling, measurement error, clock synchronization, and rapidly changing loads.

AMI data are operationally valuable but personally sensitive. A detailed load profile can reveal occupancy patterns, appliance use, or business activity. Data minimization, encryption, role-based access, retention limits, and transparent customer policies are therefore essential.

3. Demand Response and Energy Management

Demand response (DR) changes electricity consumption in response to prices, reliability signals, direct control, or incentives. It differs from simple energy efficiency: energy efficiency reduces consumption for a service over the long term, whereas DR generally changes the timing or short-term level of consumption.

Common DR mechanisms include:

• Time-of-use pricing: different prices at predefined periods.
• Critical-peak pricing: very high prices during declared system stress.
• Direct load control: an authorized operator controls equipment such as water heaters or air conditioners.
• Incentive-based response: customers receive compensation for reducing demand when requested.
• Automated demand response: an energy-management system receives a digital signal and executes preconfigured actions.

Important measures include peak reduction, rebound, customer comfort, baseline accuracy, and persistence. A simplified demand reduction is:

where P_base is the estimated demand without the event and P_event is demand during the event. The energy reduction over an event is:

The baseline is difficult because it is an estimate of a counterfactual: what would have happened without the DR event. Weather, occupancy, production schedules, and prior control actions must be considered.

A building energy-management system may coordinate HVAC equipment, lighting, refrigeration, batteries, solar generation, and electric-vehicle charging. A practical objective is often multi-criteria: reduce cost and emissions while maintaining comfort, equipment limits, and reserve capacity. An illustrative optimization objective is:

The terms represent energy cost, demand or peak cost, battery degradation cost, and an assigned cost of discomfort. This is a model rather than a universal formula; its constraints must include power limits, temperature limits, state-of-charge limits, and customer preferences.

4. Distributed Energy Resources and Microgrids

Distributed energy resources (DERs) are small or medium resources connected close to loads or within distribution networks. They include photovoltaic systems, wind turbines, combined heat and power, batteries, controllable loads, fuel cells, and electric vehicles capable of bidirectional operation.

DERs provide benefits such as reduced losses, local supply, flexibility, and resilience. They also create technical challenges:

• reverse power flow through distribution transformers;
• voltage rise during high photovoltaic output;
• variable power and forecasting uncertainty;
• changed fault-current levels;
• protection coordination problems;
• phase imbalance and harmonics from power-electronic interfaces;
• thermal overloads and congestion.

A microgrid is a coordinated group of loads and DERs with defined electrical boundaries that can operate connected to the utility or, when properly designed, in an intentional island. A microgrid controller coordinates sources, storage, loads, protection, and the point of common coupling.

Grid-connected operation can prioritize economic exchange with the utility. Islanded operation requires balancing generation and demand locally. The instantaneous active-power balance can be expressed as:

Each term is a power quantity, normally in kW or MW. P_gen is local generation, P_discharge is storage discharge, P_import is imported power, P_load is consumption, P_charge is charging power, P_export is exported power, and P_loss represents electrical losses. The equation applies over an instant or short interval; energy balance requires integration over time.

Islanded microgrids must establish voltage and frequency. Inverter-based resources may operate in grid-following mode when a voltage waveform already exists, or grid-forming mode when an inverter establishes voltage and frequency references. Protection and reconnection must be engineered carefully to prevent unsafe energization and out-of-phase closing.

5. Renewable Generation and Storage

IoT monitoring improves the operation of renewable assets by combining electrical measurements with environmental data. A photovoltaic system may measure irradiance, module temperature, string current, inverter status, and insulation resistance. A wind turbine may monitor wind speed, nacelle vibration, gearbox temperature, generator current, and yaw position. These data support forecasting, performance assessment, and predictive maintenance.

For a photovoltaic array, a simple energy estimate is:

where E_PV is estimated energy, P_r is rated power in kW, H_s is equivalent full-sun hours in h/day, and PR is the dimensionless performance ratio accounting approximately for temperature, inverter, wiring, soiling, mismatch, and other losses. The estimate is suitable for preliminary analysis, not detailed design. For a 100 kW array, 4.5 equivalent sun hours, and PR = 0.80, daily energy is approximately 360 kWh.

Battery storage is described by energy capacity, power rating, state of charge (SOC), round-trip efficiency, allowable depth of discharge, and degradation. A simplified SOC update is:

where E_N is nominal battery energy capacity, P_c and P_d are charging and discharging powers, η_c and η_d are charging and discharging efficiencies, and ∆t is the time step. This equation assumes constant power and efficiency over the interval, neglects self-discharge, and requires SOC to remain within specified minimum and maximum limits.

Worked example. A 50 kWh battery begins at 40% SOC. It charges at 10 kW for 2 h with charging efficiency 90%. The stored energy increase is:

∆E = 0.90 × 10 × 2 = 18 kWh.

The final SOC is (20 + 18)/50 = 0.76, or 76%, assuming no SOC limits are violated. If the battery instead discharged 8 kW for 2 h with 90% discharge efficiency, the energy removed from the battery would be (8 × 2)/0.90 = 17.78 kWh.

Battery management systems monitor cell voltage, temperature, current, insulation, balancing, and estimated SOC and state of health. A cloud platform can support fleet-level analytics, but local protection must remain available if communications fail.

6. Distribution Automation and Fault Detection

Distribution automation uses sensors, intelligent electronic devices, communications, and control applications to detect abnormal conditions and reconfigure the network. Typical equipment includes remotely operated switches, reclosers, sectionalizers, fault indicators, transformer monitors, voltage regulators, and capacitor controllers.

A common operational sequence is fault location, isolation, and service restoration (FLISR). Sensors identify abnormal current or voltage; the control system estimates the faulted section; switches isolate it; and an alternative feeder path may restore healthy sections. The algorithm must respect feeder capacity, protection settings, switching interlocks, and the possibility of incorrect or delayed measurements.

Fault detection methods include threshold logic, sequence components, traveling-wave measurements, waveform classification, and machine-learning models. For a balanced three-phase system, symmetrical components can help identify unbalance and faults. However, IoT analytics should not be treated as infallible. Training data may omit rare faults, sensor failures can imitate electrical events, and communication delays can make a technically correct diagnosis operationally late.

Asset monitoring applies similar principles to transformers, cables, switchgear, and overhead lines. Transformer monitoring may include oil temperature, winding hot-spot estimates, dissolved-gas analysis, moisture, bushing condition, and tap-changer operation. The value of condition monitoring lies in converting raw measurements into maintenance decisions: inspect, test, derate, repair, replace, or continue operation.

7. Power Quality and Asset Performance

Power quality concerns the suitability of voltage, current, and frequency for electrical equipment. Important phenomena include voltage sag, swell, interruption, transient, flicker, unbalance, frequency deviation, and harmonic distortion.

The total harmonic distortion of voltage is commonly defined as:

Here, V₁ is the RMS magnitude of the fundamental voltage, V_h is the RMS magnitude of harmonic order h, and the result is a percentage. In practice, the sum is limited by the measurement bandwidth and applicable power-quality standard. The formula assumes harmonic components are represented consistently as RMS magnitudes.

For a balanced three-phase system, apparent power is:

where V_LL is line-to-line RMS voltage in volts, I_L is line RMS current in amperes, and S is apparent power in volt-amperes. Real power is:

for a sinusoidal balanced system, where cosφ is the displacement power factor. With significant harmonics, true power factor also reflects waveform distortion, so the simple sinusoidal relationship must be used cautiously.

IoT power-quality devices should record synchronized waveforms or event features, not merely averages. Time synchronization permits correlation of a voltage sag with motor starting, a feeder fault, or a switching operation. Data quality checks must identify missing samples, sensor saturation, incorrect polarity, and time drift.

8. Electric Mobility and Charging Infrastructure

Electric vehicles (EVs) convert transportation demand into an electrical load and, potentially, a flexible energy resource. Charging infrastructure includes residential chargers, workplace chargers, public AC chargers, and high-power DC fast chargers. The installation must consider service capacity, protection, earthing, cable ratings, thermal limits, accessibility, communications, and payment systems.

The ideal charging time for a required battery energy is:

where t is time in hours, E_req is energy delivered to the battery in kWh, and P_ch is average charging power in kW. If 24 kWh must be delivered at 7.2 kW, ideal charging time is 3.33 h. Actual time may be longer because of conversion losses, tapering near full charge, thermal limits, and auxiliary consumption.

If charger efficiency is η_ch, grid energy is approximately:

Charging coordination can reduce coincident peaks. An aggregator may delay flexible charging, allocate available capacity among vehicles, or respond to network constraints. Vehicle-to-grid (V2G) can export energy when vehicle owners permit it, but it introduces battery degradation, additional conversion losses, contractual questions, and mobility constraints. A vehicle must retain sufficient energy for the driver’s expected trip.

A basic charging-management constraint is:

for vehicle i, with an energy requirement such as:

Here, t_a and t_d are arrival and departure times, η_i is charging efficiency, and E_i,req is required battery energy. The model is valid only when the vehicle is connected during the integration interval and when charger and battery limits are respected.

9. Integrated Energy Management Architecture

An integrated energy-management system combines forecasts, measurements, optimization, and control. A practical architecture may include:

1. Field sensors and intelligent meters.
2. Local controllers for inverters, batteries, buildings, and chargers.
3. Edge computing for rapid filtering, protection support, and operation during communication loss.
4. A utility or site platform for historical data, forecasting, optimization, and visualization.
5. Secure interfaces to markets, aggregators, operators, and customer applications.

Forecasts may cover load, solar irradiance, wind, electricity price, EV arrivals, and equipment availability. Forecast errors should be represented explicitly through scenarios, reserves, or conservative operating limits. A controller that schedules a battery using a perfect forecast may perform poorly under clouds, unexpected departures, or equipment outages.

Artificial intelligence can support anomaly detection, load forecasting, image-based inspection, and predictive maintenance. It should supplement—not automatically replace—engineering protection and accountable decisions. Models require representative data, validation, drift monitoring, explainable alarms where practical, and a safe fallback mode. A false negative in a transformer-failure model may be more serious than a false positive; therefore, the consequence of errors must influence thresholds and human review.

10. Illustrative Case: Solar-Battery-EV Microgrid at a University Campus

Background

Consider an illustrative university campus with a daytime peak load of 1.2 MW, a 600 kW photovoltaic plant, a 500 kWh battery rated at 250 kW, and 80 EV charging points. The campus experiences high afternoon demand and has limited distribution capacity.

Problem

Uncoordinated EV charging begins when staff arrive and coincides with building demand. Solar output declines in the late afternoon while charging demand remains high. The resulting peak increases demand charges and may overload a campus transformer during unusual operating conditions.

Analysis

The campus installs smart meters at major buildings, feeder sensors, inverter telemetry, battery monitoring, and networked chargers. Measurements are time-synchronized and sent to an energy-management platform. The platform forecasts building load, solar output, and EV departure requirements. It also receives a maximum import limit from the campus distribution operator.

Suppose 40 vehicles each require an average of 12 kWh before departure. The required battery energy is 480 kWh. If charging efficiency is 90%, grid-side energy would be approximately 533.3 kWh if all energy came from the grid. Solar generation can supply part of this requirement during midday, while the battery can discharge during the late-afternoon peak.

Approach and Solution

The controller first serves critical building loads. It then uses solar generation for coincident load and flexible charging. Vehicles with later departure times are assigned lower immediate priority. The battery charges when solar production exceeds the selected import target and discharges during the peak, while maintaining a reserve for outages or forecast error. Charging power is reduced if the transformer approaches its thermal limit.

Local charger controllers enforce current limits even if the central platform is unavailable. The battery management system prevents operation outside safe SOC and temperature ranges. Operators can override automated schedules, and customers receive information about any change to their expected charging time.

Outcome

In this illustrative scenario, the campus reduces its coincident evening peak, avoids unnecessary transformer loading, increases local solar consumption, and retains an emergency reserve. The outcome depends on customer participation, forecast accuracy, charger availability, battery degradation cost, and the actual tariff. It is not sufficient to report energy savings alone: the project should also evaluate comfort, departure compliance, reliability, cybersecurity incidents, maintenance cost, and equity of service.

Learning Points

The case demonstrates that smart-grid performance comes from coordination rather than from any single device. Metering provides visibility; forecasts support planning; local controllers provide fast and safe action; optimization allocates flexibility; and governance determines who may control assets and how customers are compensated.

11. Cybersecurity, Privacy, Safety, and Regulation

Smart electrical systems enlarge the attack surface because every connected sensor, gateway, charger, and application may become a path to operational systems. Threats include credential theft, malware, denial of service, false data injection, unauthorized switching, ransomware, and supply-chain compromise.

Good practice includes:

• asset inventory and secure device identity;
• strong authentication and least-privilege access;
• encryption in transit and at rest;
• network segmentation between information technology and operational technology;
• signed firmware and controlled updates;
• secure configuration and removal of default passwords;
• logging, intrusion detection, backup, and incident response;
• tested manual and local fallback operation;
• vendor security requirements and vulnerability disclosure processes.

Privacy protection requires purpose limitation, data minimization, aggregation where feasible, access controls, retention schedules, and clear consent or legal authority. Smart-meter data should not be casually repurposed for unrelated surveillance.

Electrical safety remains fundamental. Isolation, earthing, arc-flash controls, overcurrent protection, residual-current protection where required, battery thermal safety, fire detection, and emergency procedures must be designed according to applicable codes and standards. Interoperability standards and regulatory rules differ across jurisdictions; engineers must consult the governing utility requirements, charging standards, cybersecurity frameworks, and power-quality limits rather than assuming that one protocol or standard applies everywhere.

Equity is also a design issue. Dynamic tariffs and automated control can disadvantage customers who cannot shift demand, lack internet access, rent their homes, or depend on medically necessary equipment. Programs should provide opt-out mechanisms, protections for vulnerable customers, transparent billing, and fair compensation.

12. Challenges, Limitations, and Best Practices

The principal challenges are interoperability, data quality, communications reliability, legacy equipment, uncertain business models, cybersecurity, privacy, and the complexity of coordinating many independent assets. More data do not automatically produce better decisions. Poorly calibrated sensors, inconsistent naming, missing timestamps, and incompatible data models can undermine an otherwise sophisticated platform.

Recommended engineering practices are:

1. Define operational objectives and measurable success criteria before selecting devices.
2. Use a layered architecture with local safety and control functions independent of cloud availability.
3. Establish time synchronization, data-quality flags, calibration procedures, and a common asset vocabulary.
4. Validate calculations using physical constraints and independent measurements.
5. Test communications loss, corrupted data, extreme weather, cyber incidents, and equipment failure.
6. Begin with a pilot, measure both benefits and unintended effects, and scale only after review.
7. Include operators, customers, maintenance staff, cybersecurity specialists, and regulators in design.
8. Treat battery degradation, customer inconvenience, and maintenance effort as real costs.
9. Apply human oversight to high-consequence automated decisions.
10. Document assumptions, version control, interfaces, alarms, and responsibilities.

Contemporary Developments

Current development is moving toward grid-edge intelligence, distributed flexibility markets, interoperable energy data, digital twins, grid-forming inverters, solid-state power conversion, second-life batteries, and coordinated fleets of EVs. Advanced distribution-management systems increasingly combine outage management, DER management, and volt/VAR optimization. Digital twins can compare measured behavior with a network model, but their value depends on accurate topology, parameters, and asset records.

The long-term direction is not complete automation without people. It is a more observable, adaptable, and participatory power system in which automation performs repeatable tasks, engineers handle exceptions and accountability, and customers can obtain value from flexibility without surrendering safety or privacy.

Chapter Summary

Smart grids combine electrical infrastructure with sensing, communications, computation, and control. AMI provides interval measurements and operational visibility; demand response changes consumption timing or level; DERs and microgrids coordinate local generation, storage, and loads; and distribution automation improves fault isolation and restoration. Renewable forecasting and battery state estimation are essential because generation and storage are constrained and uncertain. EV charging creates substantial new demand but can also provide flexibility through coordinated charging and, where appropriate, bidirectional operation.

IoT applications must be engineered around power-system realities: voltage, frequency, thermal limits, protection, power quality, energy balance, and equipment safety. Cybersecurity, privacy, interoperability, data quality, resilience, and equity are not optional additions. Successful smart-energy projects combine local protective controls, reliable communications, validated analytics, transparent governance, and measurable operational objectives.

Key Terms

Smart grid: An electricity network integrating power infrastructure with sensing, communications, computation, and control.

Advanced metering infrastructure: Smart meters, communications, data systems, and applications supporting two-way metering services.

Demand response: Intentional change in electricity demand in response to prices, incentives, or reliability signals.

Distributed energy resource: A generation, storage, or controllable-load resource connected near customers or within a distribution network.

Microgrid: A coordinated group of loads and DERs that can operate connected to a utility and, when designed for it, in islanded mode.

State of charge: The available energy in a battery expressed relative to its rated or usable capacity.

Battery management system: Electronics and software supervising battery voltage, current, temperature, balancing, and protection.

Distribution automation: Sensors, communications, and controls used to monitor and reconfigure distribution networks.

FLISR: Fault location, isolation, and service restoration.

Power quality: The degree to which voltage, current, and frequency characteristics support proper equipment operation.

Total harmonic distortion: A measure of harmonic content relative to the fundamental component.

Electric vehicle supply equipment: Equipment that safely supplies electrical energy to an electric vehicle.

Vehicle-to-grid: Transfer of electrical power from an EV battery back to the grid under an authorized control arrangement.

Energy-management system: Software and control infrastructure coordinating energy resources and loads.

Edge computing: Processing performed near field devices to reduce latency and dependence on a remote cloud.

Digital twin: A data-connected model representing an asset, process, or network for monitoring and analysis.

Demand baseline: An estimate of the demand that would have occurred without a demand-response action.

Grid-forming inverter: An inverter capable of establishing voltage and frequency references.

Interoperability: The ability of different devices and systems to exchange and correctly use information.

Review Questions

Short Answer Questions

1. What distinguishes a smart grid from a conventional electricity distribution network?
2. What functions are provided by advanced metering infrastructure?
3. Define demand response and distinguish it from energy efficiency.
4. Why can high photovoltaic penetration create voltage-management problems?
5. What is the purpose of a battery management system?

Long Answer Questions

1. Explain the layered architecture of an IoT-enabled smart grid and discuss the role of each layer.
2. Describe the operation of a microgrid in grid-connected and islanded modes, including the importance of power balance and protection.
3. Discuss how coordinated EV charging can reduce network stress while preserving customer mobility requirements.
4. Explain IoT-based fault detection and asset monitoring, including the limitations of analytics and machine learning.
5. Evaluate the cybersecurity, privacy, safety, and equity issues associated with smart-meter and demand-response programs.

Analytical / Application Questions

1. A 75 kW photovoltaic system receives 5 equivalent full-sun hours per day and has a performance ratio of 0.78. Estimate its daily energy production and state the assumptions in your calculation.
2. A 60 kWh battery is at 30% SOC. It charges at 12 kW for 2 h with 92% charging efficiency. Calculate the final SOC, assuming no operating limits are violated.
3. Design a control strategy for a campus with solar generation, battery storage, a transformer import limit, and workplace EV chargers. Identify measurements, control decisions, constraints, fallback modes, and performance metrics.

Multiple Choice Questions with Answers

Question 1

Which function is most directly associated with advanced metering infrastructure?

A. Remote interval measurement and communication of customer energy data

B. Mechanical conversion of coal into electricity

C. Replacement of all distribution protection devices

D. Elimination of the need for electrical safety procedures

Correct Answer: A. Remote interval measurement and communication of customer energy data

Question 2

A constant 4 kW load operates for 2.5 h. How much energy does it consume?

A. 1.6 kWh

B. 6.5 kWh

C. 10 kWh

D. 16 kWh

Correct Answer: C. 10 kWh

Question 3

What is a defining capability of a microgrid designed for islanded operation?

A. It must always import power from the utility

B. It can coordinate local resources to maintain operation after separation

C. It operates without any protection system

D. It cannot contain energy storage

Correct Answer: B. It can coordinate local resources to maintain operation after separation

Question 4

A 40 kWh battery begins at 50% SOC and receives 8 kWh with 90% charging efficiency. What is its approximate final SOC?

A. 32%

B. 50%

C. 68%

D. 90%

Correct Answer: C. 68%

Question 5

Which action is an example of demand response?

A. Replacing an inefficient motor with a higher-efficiency motor permanently

B. Reducing industrial load during a peak-price event in exchange for compensation

C. Increasing electricity use during every system peak

D. Removing all customer energy meters

Correct Answer: B. Reducing industrial load during a peak-price event in exchange for compensation

Question 6

Why can uncoordinated electric-vehicle charging create a distribution-system problem?

A. It always reduces feeder current to zero

B. It can create coincident demand and transformer or feeder overload

C. It prevents any energy from being stored in vehicle batteries

D. It eliminates voltage variation

Correct Answer: B. It can create coincident demand and transformer or feeder overload

Question 7

In the three-phase expression P = √3 V_LL I_L cosφ, what does cosφ represent for a sinusoidal balanced system?

A. Frequency

B. Displacement power factor

C. Battery state of charge

D. Harmonic order

Correct Answer: B. Displacement power factor

Question 8

What is the main purpose of FLISR?

A. To forecast solar irradiance only

B. To locate a fault, isolate the affected section, and restore healthy sections where possible

C. To calculate customer billing without meters

D. To increase harmonics deliberately

Correct Answer: B. To locate a fault, isolate the affected section, and restore healthy sections where possible

Question 9

Which practice best reduces the risk of unauthorized access to smart-grid devices?

A. Using shared default passwords

B. Disabling all event logs

C. Applying strong device identity, authentication, and least-privilege access

D. Connecting every device to one unsegmented network

Correct Answer: C. Applying strong device identity, authentication, and least-privilege access

Question 10

A charger delivers 7.2 kW to a vehicle that requires 24 kWh of battery energy. Ignoring tapering and losses, what is the charging time?

A. 0.30 h

B. 2.4 h

C. 3.33 h

D. 31.2 h

Correct Answer: C. 3.33 h