16.2.1 Device-Level Control

Voltage/frequency control enables prevention of reductions in the power factor or efficiency of a motor in a wide range of variable speed operations for changes in the frequency for speed control by outputting a voltage (V/f characteristic) corresponding to the frequency set by a parameter in an inverter. The revolution speed of an induction motor is proportional to the frequency, which can reduce the power factor and efficiency of a motor even with a variable frequency because changes to the frequency cause the internal impedance of a motor to change. Therefore, you must change the voltage corresponding to the frequency. V/f control reduces the torque in low-speed operation with the primary resistance voltage drop even though it attempts to keep the torque stable regardless of the frequency.

Fault protection involves monitoring the microgrid system, identifying when a fault has occurred, pinpointing the type of fault and its location and setting into motion-control schemes for recovery from the fault. The major protection problem in microgrid is a result of a large difference between fault currents in the grid-connected and islanded modes. An optimal smart-grid protection system must be very sensitive to faults and be able to selectively sectionalize the microgrid, especially in the case of renewable-energy resources with power electronic interfaces (low-fault-current levels). A decision must be made by the integrated device control whether to sectionalize/isolate the microgrid or to shut it down entirely. This decision, in the event of a fault, is dependent on the needs of the customers and on cost benefits. Cost benefits in this case imply whether the cost involved in protection and communication could be justified for benefits gained by sectionalizing the microgrid instead of shutting it down entirely. The benefit in sectionalizing the microgrid involves reducing the end-consumer service interruption time.

Droop control enhances the power-quality parameters, like active and reactive power controls both in grid and autonomous operation. Droop-control techniques have been employed in voltage (V) and current (I) to provide power-quality control in the microgrid. Droop control is designed for batteries and generators to coordinate the sources and regulate the system frequency.

16.2.2 Local-Area Control

Peer-to-peer strategy implies that every distributed source maintains the same status in the microgrid. The benefit is that extra sources can be integrated into the microgrid without modifications to the control and protection of units that are already part of the system. This implies that every controller must be able to respond effectually to system changes without requiring data from other sources or locations, resulting in an improvement in the reliability of the smart grid and reducing system cost.

Peer-to-peer strategies can be realized by droop control. There are two control methods based on droop control:

• P-f/Q-V droop control produces reference active power and reactive power by measured system frequency and DG output voltage amplitude.
• P-f/V-Q droop control produces output frequency and voltage amplitude by measured output active power and reactive power. P-f/V-Q control is the more wide applicable peer-to-peer strategy.

Master–slave strategy refers to the presence of a master controller in the smart grid while others are slave controllers that obey the rules of the master controller through a communication link. When in a grid-connected mode, the microgrid requires no frequency regulation because the traditional grid can stabilize the system frequency. However, when islanded from the main grid, the master controller takes up the task of maintaining system frequency, voltage, and power balance separately by V/f control and regulating its own output.

Resynchronization control: While connected to the grid, micro sources can maintain a high level of output and high efficiency. In islanded mode, the master micro source switches from power-quality control to V/f control to provide frequency and voltage support to the microgrid, while other micro sources are still power-quality controlled. The regulation of this control strategy can only involve micro sources’ output power, but not the transfer power between the microgrid and the traditional grid.

Seamless transfer control is an important control technology to affect a smooth transition of the microgrid between islanded and grid-connected modes. This smooth transition is done by controlled triggering of antiparallel thyristor (SCR) considering bulk grid state, microgrid operation mode and direction of power flow.

16.2.3 Supervisory Control Functions

The supervisory control system is the master control of a microgrid. It organizes and schedules the midterm and long-term energy flow in the power system, and aims to achieve an optimal operation of the system as a whole. It manages the dispatching and control of all the active components of a microgrid. Supervisory control may be regarded as a piece of intelligence-embedded software, a computer algorithm, or similar that aims to achieve optimal system operations. These operations depend on a set of goals, rules, and constraints, which are to be defined before the system is developed. According to the International Energy Agency, following supervisory control goals is of primary importance.

Effective supervisory control should consider economics, reliability and service delivery factors discussed below.

• Most economical operation: This strategy aims to minimize operation and maintenance (O&M) costs. The generating sets will generally operate at best efficiency levels, and energy dumping will be avoided. This strategy may not achieve full use of the input from the solar generator nor minimize the system's greenhouse emissions.
• Highest reliability: Prioritizing system reliability generally involves maintenance of backup contingency generating sets in a multi-generating sets system, and frequent maintenance. This may impact service delivery.
• Lowest carbon footprint (or highest renewable-energy fraction): This strategy optimizes the use of renewable generator input to minimize diesel-generating operations. In some cases, this can lead to increased start-stop cycles of the generating sets.
• Service-delivery optimization: This strategy prioritizes service delivery, and is likely to imply a greater amount of generator run time. In a multi-generator system, a larger amount of spinning reserve may be maintained. This schedule implies greater O&M costs and reduced system lifetimes.
• Component lifecycle optimization: This strategy will configure the system to maximize component lifetime, particularly batteries. Component sizing and generator control will optimize battery cycling for efficient life-cycle operations. This strategy may include service-delivery restrictions.
• Load optimization through demand-side management: This strategy aims to optimize system operation through controlling the load via demand-side management. By deferring loads to periods of solar input where possible, energy-storage demands can be minimized and generators can operate at better efficiencies.
• Best supply quality: In this case, electricity-quality variables such as voltage range and harmonic distortion are prioritized. This may require the use of generators as spinning reserve and/or compensation nodes, resulting in lower system efficiencies, and increased costs.

Operation control strategies generally incorporate multiple objectives in their design.

16.2.4 Evaluation of Control Types Taking Area into Consideration

16.3.1 Physical Power Layer

There are various energy options in powering a smart grid that are consistent with powering any grid, that are part of the Physical Layer on Figure 16.3.

• gas or diesel cogeneration/CHP
• fuel cells and microturbines
• photovoltaic (PV) modules
• wind, biomass, small hydro
• storage capacity

16.3.2 Real-Time Measurement Layer

Real-time measurements, monitoring, and control are the specialty of the smart grid, and involve the latest communication and real-time devices to make control operations faster and facilitate the grid with self-healing and self-restoration qualities.

Core duties are evaluating congestion and grid stability, monitoring equipment health, energy theft prevention, and control strategies support. Technologies include: advanced microprocessor meters (smart meters) and meter-reading equipment, wide-area monitoring systems, dynamic line ratings (typically based on online readings by distributed temperature sensing combined with real-time thermal rating (RTTR) systems), electromagnetic signature measurement/analysis, time-of-use and real-time pricing tools, advanced switches and cables, backscatter radio technology, and digital protective relays. The following are details of some of these real-time measurement layer tools:

(a) Smart Meters

A smart meter, shown in Figure 16.4 [1], is an electronic device that records consumption of power in intermissions of an hour or less and communicates that information at least daily back to the utility for monitoring and billing.

Automated meter reading (AMR)—the replacement of conventional, mechanical meters read once a month with smart meters that can be remotely read every ten minutes or so—is often the starting point for smart grid implementation. AMR, and its two-way enhancement advanced metering infrastructure (AMI), is often the costliest component of a smart grid because of the hundreds of thousands of meter end-points. In some parts of the world, there has been a tendency to consider an AMR/AMI system and its communications requirements as an independent solution rather than as part of the entire smart-grid infrastructure.

(b) Phasor Measurement Units

High-speed sensors called phasor measurement units (PMUs) distributed throughout a network can be used to monitor power quality and in some cases respond automatically. Synchronized PMUs (synchro-phasors) provide real-time measurement of electrical quantities from across a power system. Applications of synchro-phasor measurements include validating system models, determining stability margins, maximizing stable system loading, islanding detection, systemwide disturbance recording, and visualization of dynamic system response. The basic system building blocks are GPS satellite-synchronized clocks, a phasor data concentrator (PDC), and visualization software. Figure 16.5 [2] shows a sample phasor measurement unit.

The accuracy, speed, and reliability of PMUs are critical to fulfilling the promise of the smart grid. However, the current standard that governs PMU performance is quite broad, and testing by the National Institute of Standards and Technology has shown that PMUs from various manufacturers can report dynamic conditions very differently. For example, the primary transducer that converts high line voltages to smaller voltages used by the measuring system can introduce inaccuracies. From the primary transducer, signals pass through an anti-aliasing filter that also generates some level of error. Next, the signal goes through an analog/digital (A/D) converter that can introduce magnitude or gain errors and channel phase shifts. Signal processing is carried out to evaluate the phasor magnitude and phase angle, which may create additional error; the phase is reported with respect to the global time reference so errors in the global positioning system (GPS) source and cable delays may introduce synchronization errors.

The goal of PMU calibration is to ensure that all PMUs have consistent performance across the system, are interoperable regardless of their makes and models, and comply with system application requirements. A general rule of thumb is that a calibration system should have less than one-fourth of the total uncertainties of the system under calibration.

(c) Geographical Information Systems

Spatial data systems, including geographical information systems (GISs), have been used successfully by utility companies, government agencies, and many industries for more than three decades. Systems based on spatial technology have had a positive impact on a wide variety of business processes, ranging from marketing to environmental management. Spatial systems bring geography into play within the business processes of a company or organization while enabling data to be visualized more effectively. Smart-grid analytical software such as distribution management systems (DMSs) and outage management systems require high-quality data to provide the right answers. By using the functionality inherent in GIS, smart-grid technology will start with better-quality data and should, in turn, produce better-quality analytics and operational decisions.

(d) Remote Terminal Units

A remote terminal unit (RTU) is a microprocessor-controlled electronic device that interfaces objects in the physical world to a distributed control system or SCADA by transmitting telemetry data to the system, and by using messages from the supervisory system to control connected objects. The primary source of real-time data in a smart grid is the remote terminal unit (RTU). While most utilities have long had RTU technology in almost every transmission substation, these devices have historically been far less common in distribution substations.

(e) Analog to Digital/Digital to Analog

Superior in quality, reliability, and performance, analog devices’ energy-metering ICs combine analog-to-digital converters (ADCs) with fixed-function digital signal processors (DSPs) to perform critical measurements while providing unparalleled functionality and ease of use in energy metering. The need for more efficient utility substations and smart-grid management is growing as worldwide electricity demand increases. Electric utility companies need power-line monitoring systems to monitor and control energy consumption, cost, and quality, as well as to protect expensive equipment from power surges and severe storms. The new simultaneous-sampling ADCs provide the resolution and performance needed for next-generation power-line monitoring system designs that ensure the reliable delivery of electricity to millions of people in all corners of the world.

16.3.3 Communications Layer

The communications layer has premises networks, neighborhood/field-area networks (NAN/FAN), and wide-area networks (WAN). The premises network, such as the home-area network (HAN), building-area network (BAN), and industrial-area network (IAN), provides access to applications in customer premises. The NAN/FAN provides a relatively long-distance communication link between smart meters, field devices, DERs, customer premises’ networks, and WAN. The WAN provides very long-distance communication links between the grid and the utility via a core network and FAN/NAN. However, the coverage area and data-rate requirements for the customer premises’ network, FAN/NAN, and WAN vary for different communication standards and protocols.

16.3.4 Application Layer

Smart-grid concepts encompass a wide range of technologies and applications. We describe a few below that are currently in practice with the caveat that, at this early stage in the development of smart grids, the role of control, especially advanced control, is limited.

1. AMI is a vision for two-way meter/utility communication. Two fundamental elements of AMI have been implemented. First, AMR systems provide an initial step toward lowering the costs of data gathering through use of real-time metering information. They also facilitate remote disconnection/reconnection of consumers, load control, detection of and response to outages, energy-theft responsiveness, and monitoring of power quality and consumption. Second, meter data management (MDM) provides a single point of integration for the full range of meter data. It enables leveraging of that data to automate business processes in real time and sharing of the data with key business and operational applications to improve efficiency and support decision making across the enterprise.
2. DMS software mathematically models the electric distribution network and predicts the impact of outages, transmission, generation, voltage/frequency variation, and more. It helps reduce capital investment by showing how to better utilize existing assets, enabling peak shaving via DR, and improving network reliability. It also facilitates consumer choice by helping identify rate options best suited to each consumer and supports the business case for renewable generation solutions (DG) and for electric vehicles and charging-station management.
3. GIS technology is specifically designed for the utility industry to model, design, and manage critical infrastructure. By integrating utility data and geographical maps, GIS provides a graphical view of the infrastructure that supports cost reduction through simplified planning and analysis and reduced operational response times.
4. Outage management systems (OMSs) speed outage resolution so power is restored more rapidly and outage costs are contained. OMSs eliminate the cost of manual reporting, analyze historical outage data to identify improvements and avoid future outages, and address regulatory and consumer demand for better responsiveness.
5. Intelligent electronics devices (IEDs) are advanced, application-enabled devices installed in the field that process, compute, and transmit pertinent information to a higher level. IEDs can collect data from both network and consumer facilities (behind the meter) and allow network reconfiguration either locally or on command from the control center.
6. Wide-area measurement systems (WAMS) provide accurate, synchronized measurements from across large-scale power grids. They have been implemented in numerous power systems around the world, following initial developments within the Western Electricity Coordinating Council through the early 1990s. WAMS consist of PMUs that provide precise, time-stamped data, together with phasor data concentrators that aggregate the data and perform event recording. WAMS data plays a vital role in post-disturbance analysis, validation of system dynamic models, FACTS control verification, and wide-area protection schemes. Future implementation of wide-area control schemes is expected to build on WAMS.
7. EMSs at customer premises can control consumption, onsite generation and storage, and potentially electric-vehicle charging. EMSs are in use today in large industrial and commercial facilities and will likely be broadly adopted with the rollout of smart grids. Facility energy management can be seen as a large-scale optimization problem: given current and (possibly uncertain) future information on pricing, consumption preferences, DG prospects, and other factors, how should devices and systems be used optimally? Smart-grid implementations are occurring rapidly, with numerous projects under way around the world.

The following are application functions of a smart-grid:

• Power quality (PQ) control is the capability of a power system to operate without causing disturbance or damage to components and loads. PQ is a significant concern because of the sensitivity of modern digital control equipment to PQ distortion and deterioration. A rudimentary requirement for maintaining PQ is balancing demand and supply. Characteristic metrics utilized as indictors of PQ measures include total harmonic distortion, voltage transient impulse, voltage sag, and flicker factor. A number of accepted power-quality standards apply to electrical power distribution systems. The European Norm 50160 standard outlines voltage disturbance standard requirements for acceptable power-quality standards. Studies on PQ considers factors such as:
  • location of power disturbances
  • identification of types and causes of power disturbances
  • quantification of power disturbances and their negative impacts on power systems
  • real-time measurement of parameters of signal components in power disturbances
  • sensitive detection of power disturbances
• Voltage/var control within an identified range of limits and capacitor switching is an effective method of improving voltage profiles, minimizing loss, and delaying construction of new generation within the power-quality and reliability constraints. In electric power systems, var is used to express reactive power in a power system. Reactive power exists in an AC circuit when the voltage and current are not in phase. Voltage/var control considers multiphase, unbalanced power-distribution system operation, DG, and automated control equipment in large electric systems. Power-system distribution automation functions use voltage/var control options to maintain proper parameters throughout the grid, based on the requirements of different customers by employing variable capacitors. The improved control and management of voltage/var facilitates increase performance for customer equipment and allow both the consumer and utility company to realize financial benefit.
• Load frequency control (LFC) is of importance in microgrid system design and operation. The objective of LFC in an interconnected power system is to maintain the frequency of each area within limits and to keep tie-line power flows within some prespecified tolerances by adjusting the megawatt (MW) outputs of the generators so as to accommodate fluctuating load demands. A well-designed and well-operated power system must cope with changes in the load and with system disturbances, and should provide an acceptably high level of power quality while maintaining both voltage and frequency within tolerance limits. As power-load demands vary randomly for an interconnected power system, both area frequency and tie-line power interchange varies also. The primary objective of LFC is to minimize transient deviations in area frequency and tie-line power interchange while ensuring that their steady-state errors are as close to zero as possible. Frequency will not change in an interconnected network if there is a balance between customer demand and power supply resources that can also compensate for electrical losses.

16.3.5 Cost–Benefit Layer

Cost–benefit analysis (CBA) is a systematic approach to estimating the strengths and weaknesses of alternatives and is used to determine options that provide the best approach to achieve benefits while preserving savings. If all the equipment that needs to be installed for communication and control operation are considered, the CBA for the smart grid can be calculated by considering costs for such items as PMUs, smart meters, relative communication infrastructure, relative protection systems, and RTUs, and comparing them to the benefits obtained through installation of these devices. Worst-case analysis should also be carried out to have a better idea of CBA, e.g., consider that after installing everything if grid performance does not change much then benefits obtained will be minimal. Physical benefits can also be converted into societal benefits or utility economic benefits. CBA should be carried out with different contingencies applied. Considering the economic potential of the smart grid and the requisite considerable investments, it is important to estimate the costs and benefits of control and communication of smart grids. There are four major guidelines for performing a comprehensive CBA:

(a) Definition of Assumption and Setting of Critical Parameters

This involves definition of boundary conditions (demand growth forecast, discount rates, local grid characteristics) and of implementation choices (roll-out time, chosen controls, communication functionalities). Furthermore, for CBA studies input information such as critical data related to the project, social discount rate and project value in terms of weight of importance is required.

(b) Steps of Classical CBA

The assessment framework is expressed by the following seven steps based on the approach developed by the Electric Power Research Institute (EPRI). The benefit of charting assets to functionalities and functionalities to benefits is that they assist in thinking of sources of benefits, making a complete set of estimated benefits more likely.

Evaluation of the impact of a project is made easy. Progress measurement, toward attaining characteristics of the comprehensive smart grid control is facilitated. Progress is measured through some previously established standard key performance indicators such as enhanced efficiency and better service in electricity supply and grid operation, and voltage quality performance of electricity grids (e.g., voltage dips, voltage and frequency deviations). The relationship between assets and benefits through functionalities is more than likely not to be easily deduced and requires a good deal of brainstorming. However, execution of these steps adds clarity to a CBA process.

Step 1: Review and define project goals.

This step involves summarizing and describing the elements and goals of the project. The goals must be concise and clearly defined. This may involve collation of the following information:

• scale and dimension of the project (e.g., consumers served, energy consumption per year)
• engineering features (e.g., the control technologies adopted and the functionalities of the main components, the local characteristics of the grid control elements)
• relevant stakeholders (e.g., whose costs and benefits count?)
• strong statement of the project's objectives and its expected socioeconomic impacts
• regulatory context and its impacts on the project

Step 2: Define and map assets to functionalities.

Determining which smart-grid functionalities are activated by the control assets proposed by the project is an important early step in a CBA for smart-grid projects. Smart-grid assets provide different types of functionalities that enable benefits. If the assets deployed and/or functionalities enabled by the project are unclear, the analysis is likely to be incomplete.

Step 3: Map functionalities to benefits.

Each functionality should be considered individually and assessed on how it could contribute to any of the benefits. This analysis should be thorough and applied to all applicable functionalities.

Step 4: Define the baseline.

This involves the formal definition of the “control state” that reflects the smart-grid condition that would have occurred had the project not taken place. This is the baseline condition against which all other setups are compared. The CBA is based on the variance between the costs and benefits associated with the “do-nothing” scenario and those associated with the implementation of the project. These two scenarios are:

• Scenario 1, the baseline conditions that depict what the grid condition would have been without the control and communication installations
• Scenario 2, the realized and measured conditions with the smart-grid controls and communication system installed

Step 5: Monetize the benefits.

In this step, data required to quantify the benefits in equivalent monetary forms are gathered. The data may be already analyzed or may be in its raw form. The benefits to be calculated and the baseline scenarios needed to calculate those benefits determine the type of data collected. Each set baseline and established benefit requires compilation of data for computation. Data should be collected both before and after implementation of the control and communication project. Conceptually, the monetary value of a benefit can be calculated as:

For example, the estimate of the benefit reduction in meter-reading costs could be done by comparing the total cost of local meter readings for an established baseline that do not have smart meters installed (i.e., the “do-nothing” scenario) to the cost of local meter readings under project conditions for the fraction of customers who use smart meters (communication scenario).

Next, the benefits quantified are expressed in equivalent economic terms and benefits are allocated to different beneficiaries, like consumers, retailers/aggregators, and society at large. A concluding step would be to carefully assess any uncertainty. This is because it may not be possible to provide a good estimation of some control and communication benefits—for instance those based on environmental or socioeconomic factors—with the same level of precision and clarity as other benefits.

Step 6: Quantify cost.

The costs of a project are incurred in implementing the project, relative to the predefined baseline scenario. This step involves evaluation of the cost-benefits of the project and the advantage is that it facilitates calculation of a project's return on investment (ROI).

Step 7: Compare costs and benefits.

Once costs and benefits have been estimated, there are several methods to compare them to evaluate the cost-effectiveness of the project.

• The net present value (NPV) method consists of estimating the sum of NPVs of individual cash flows of the smart-grid project for the entire study period. The project needs to subtract estimated costs from benefits for each year, discount these annual net benefit amounts, and sum the discounted values. The NPV can be defined as the net benefit “brought back” to the baseline year by applying a discount rate, thereby accounting for the time value of money. The NPV is calculated as follows:
  (16.1)
  where t is the time of the cash flow, I is the discount rate, R_tis the net cash flow (cash inflow minus cash outflow at time t), and n is the total number of periods (typically years) under consideration. A small discount rate sets future values close to present ones, while a larger discount rate incrementally devalues present values of future cash flows, usually indicating the presence of an alternative option of investment in financial markets.
• The benefit-cost ratio is the net present value of project benefits divided by the net present value of project costs. A project is accepted if the benefit-cost ratio is equal to or greater than one. It is used to accept independent projects, but it may give incorrect rankings and often cannot be used for choosing among mutually exclusive alternatives. A project's value can also be represented as a ratio of benefits to costs (either on a present value basis or on an annual basis). This method is a simple way of representing the size of the benefits relative to the costs. If the ratio is greater than one, the project is cost-effective.

(c) Sensitivity Analysis

Sensitivity analysis is performed to determine the range of variables that would result in a positive outcome of a CBA. It requires identifying the value that would have to occur for the NPV of the project to become zero, or more generally for the outcome of the project to fall below the minimum level of acceptability.

(d) Evaluation of Merit and Social Impact

A qualitative-impact analysis is performed to evaluate the social impacts, typically representing a significant portion of the project externalities. These include:

• job impact
• safety
• environmental impact
• social acceptance
• time lost/saved by consumers
• enabling new services and applications and
• market entry to third parties
• reduction of the gap in skills and personnel
• privacy and security

16.4.1 Need for Command, Control, and Communication

Smart grid is the term generally used to describe integration of the elements connected to the electrical grid with an information infrastructure to offer numerous benefits for both the providers and consumers of electricity. It is an intelligent future electricity system that connects all supply, grid, and demand elements through an intelligent communication system. The backbone of a successful smart-grid operation is a reliable, resilient, secure, and manageable standards-based, open communication infrastructure that provides for intelligent linkages between the elements of the grid while participating in the decision-making that delivers value to the utility and supply-and-demand entities connected to it. The general purpose of a smart grid is to monitor electricity consumption and to manage its delivery to avoid blackouts or brownouts. RER integration and other advanced real-time communication devices have added complexity into the system and posed many questions, such as:

• How can we retrofit and engineer a stable, secure, robust, and resilient grid with large numbers of such unpredictable power sources?
• What roles will asset optimization, novel control algorithms, increased efficiency, control/coordination of energy storage, advanced power electronics, power quality, electrification of transportation, cybersecurity, policies, and technologies play in the smart grid of the future?
• How can robust controls and observers be developed that use secure sensing to identify and build realistic models and appropriate responses?
• Will renewable energy sources be able to adapt, control, and mitigate disturbances to achieve the goals for reliability and stability of overall system?

The above questions must be answered by researchers in related fields, industry experts, and standards organizations before advancing the grid. In addition, certain technical areas concentrating in controls should be evaluated, such as:

• optimization of DR under variable pricing
• integration of DG and storage in automation and control strategies
• distributed state estimation of power systems and elements, with limited sensing and communication
• cybersecurity in smart-grid control systems
• new power market designs that exploit feedback and information integration
• stability issues, especially given latency and uncertainty in communication networks
• self-monitoring and self-healing in intelligent transmission systems
• modeling and analysis of multiscale, complex grids

16.4.2 Application of Control and Communication to the Smart Grid in Real Time

Power systems are fundamentally dependent on control, communications, and computation for ensuring stable, reliable, efficient operations. Generators rely on governors and automatic voltage regulators (AVRs) to counter the effects of disturbances that continually buffet power systems, and many would quickly lose synchronism without the damping provided by power system stabilizers (PSSs). FACTS devices, such as static var compensators (SVCs) and HVDC schemes rely on feedback control to enhance system stability. At a higher level, EMSs use SCADA to collect data from expansive power systems and sophisticated analysis tools to establish secure, economic operating conditions. Automatic generation control (AGC) is a distributed, closed-loop control scheme of continental proportions that optimally reschedules generator power set points to maintain frequency and tie-line flows at their specified values.

The use of time-synchronized, high-data-rate sensor technology is widely viewed as a critical enabler for increasing the reliability of the power grid while allowing the integration of many more stochastically variable renewable-energy sources such as solar radiation and wind.

PMUs are capable of sampling frequency, voltage, and current thousands of times per second and outputting accurate, time-stamped measurements 30–120 or more times per second. It is difficult, however, for utilities to take full advantage of these devices due to a lack of tools for designing and evaluating the control systems that exploit them. Furthermore, the behavior of such control systems also depends on the performance of the wide-area communications systems that connect the sensors, control logic, and actuators, and whose design and specifications are themselves still evolving.

The smart grid will consist of intelligent devices that use advanced communication methods, which in turn will improve situational awareness and enable networked control. Networked control will increase efficiency, reliability, and safety; enable plug-and-play asset integration, such as in the case of DG and alternative energy sources; support market dynamics; and reduce peak prices and stabilize costs when supply is limited.

Networked smart devices currently include smart meters at the customer site and PMUs (including synchro-phasors) in the transmission and distribution grids. In the future, smart appliances will adjust their functions depending on instantaneous electricity costs, and a host of communicating devices will enable the feedback-loop networked control of the smart grid.

Smart-grid communication should protect users’ privacy and security. Networked control requires adequate, real-time performance and reliability. Communication should use existing infrastructure whenever possible, due to:

• high cost of deploying a new communications infrastructure
• substantial investments made in the past (e.g., dark fiber) or currently under way in hardware (e.g., universal broadband) and software (e.g., secure real-time middleware)
• potential for widespread technology adoption, which will result in increased grid responsiveness and larger economic benefits

Smart-grid devices can communicate over a variety of substrates with different properties. Furthermore, different media may be appropriate in different circumstances. For example, smart appliances in the home can exploit existing Ethernet home LANs, 802.1I home wireless LANs, or power line home networks. At the other end, if a synchro-phasor is deployed in the transmission grid, it may be better served by wide-area wireless, such as a mesh network or a power-line wide-area network. Since the smart grid will exploit different types of data from a variety of devices communicating over heterogeneous networks, communication should be possible across various media, which in turn requires a convergence layer, such as the IP.

BIBLIOGRAPHY

1. Edison Foundation, Utility-Scale Smart Meter Development, Plans, and Proposal, IEE Report, May 2012, www.edisonfoundation.net/iee/documents/iee_smartmeterrollouts_0512.pdf.
2. A. Allen, S. Santoso, and E. Muljadi, Algorithm for Screening Phasor Measurement Unit Data for Power Systems Events and Categories and Common Characteristics for Events Seen in Phasor Measurement Units Relative Phase-Angle Differences and Frequency Signals, National Renewable Energy Laboratory Technical Report, August 2013.
3. US Department of Energy, What the Smart Grid Means to Americans, www.doe.gov/sites/prod/files/oeprod/DocumentsandMedia/ConsumerAdvocates.pdf.
4. Demand, Response and Advanced Metering Coalition, Overview of Advanced Metering Technologies and Costs, April 2004, www.drsgcoalition.org/resources/whitepapers/DRAM White Paper 2004-04.pdf.
5. G. Heydt, “Electric Power Quality: A Tutorial Introduction,” IEEE Comput. Appl. Power 11, 1 (January 1998).
6. S. Darby, The Effectiveness of Feedback on Energy Consumption: A Review for DEFRA of the Literature on Metering, Billing and Direct Displays, April 2006.
7. A. Schatz, “Cellphone Users to Get Billing Alerts under New Voluntary Standards,” Wall Street Journal, October 17, 2011, http://online.wsj.com/article/SB10001424052970203658804576635053172551850.html.
8. National Energy Technology Laboratory, Advanced Metering Infrastructure, February 2008, www.netl.doe.gov/smartgrid/refshelf.html.
9. Federal Energy Regulatory Commission, Assessment of Demand Response and Advanced Metering, Staff Report, February 2011, www.ferc.gov/legal/staff-reports/2017/DR-AM-Report2017.pdf.
10. B. Heile, “Surveying the International AMI Landscape,” Electric Light & Power 15, 7 (July 1, 2010), http://www.elp.com/articles/powergrid_international/print/volume-15/issue-7/Features/surveying-the-international-ami-landscape.html.
11. S. Renner, M. Albu, H. van Elburg, et al., European Smart Metering Landscape Report, Feburary 2011, www.sintef.no/globalassets/project/smartregions/d2.1_european-smart-metering-landscape-report_final.pdf.
12. M. Bauer, W. Plappert, C. Wang, et al., “Packet-Oriented Communication Protocols for Smart Grid Services over Low-Speed PLC,” IEEE International Symposium on Power Line Communications and Its Applications, March 2009.
13. T. Tran, A. P. Auriol, and T. Tran-Qouc, “Distribution Network Modeling for Power Line Communication Applications,” IEEE International Symposium on Power Line Communications and Its Applications, April 2005.
14. S. E. Collier, “Delivering Broadband Internet over Power Lines: What You Should Know,” Rural Electric Power Conference, May 2004.
15. T. Khalifa, K. Naik, and A. Nayak, “A Survey of Communication Protocols for Automatic Meter Reading Applications,” IEEE Commun. Surv. Tut. 13, 2, (2011).
16. G. N. Ericsson, “Communication Requirements: Basis for Investment in a Utility Wide-Area Network,” IEEE T. Power Deliver. 19, 1 (January 2002).
17. H. Tan, C. Lee, and V. Mok, “Automatic Power Meter Reading System using GSM Network,” IEEE Power Engineering Conference, December 2007.
18. A. Abdollahi, M. Dehghani, and N. Zamanzadeh, “SMS-Based Reconfigurable Automatic Meter Reading System,” IEEE International Conference on Control Applications, October 2007.
19. B. Brown, “802.11: The Security Differences between B and I,” IEEE Potentials 22, 4 (Oct.–Nov. 2003).
20. P. Kulkarni, S. Gormus, F. Zhong, et al. “A Self-Organising Mesh Networking Solution Based on Enhanced RPL for Smart Metering Communications,” IEEE International Symposium on a World of Wireless, Mobile and Multimedia, June 2011.
21. D. Bernaudo, et al., SmartGrid/AEIC AMI Interoperability Standard Guidelines for ANSI C12.19 End Device Communications and Supporting Enterprise Devices, Networks and Related Accessories, Association of Edison Illuminating Companies, Meter and Service Technical Committee Report, version 2, May 2010, www.smartgrid.gov/files/AEIC_Guidelines_v20_SmartGridAEIC_AMI_Interoperability_Stdar.pdf.
22. E. Queen, A Discussion of Smart Meters and RF Exposure Issues, An EEI-AEIC-UTC White Paper, March 2011, www.occeweb.com/pu/SMARTGRID/smart%20meters%20and%20rf%20exposure_031511.pdf.
23. V. Aravinthan, V. Namboodiri, S. Sunku, et al., “Wireless AMI Application and Security for Controlled Home Area Networks,” IEEE Power and Energy Society General Meeting, October 2011.
24. C. Bennett and D. Highfill, “Networking AMI Smart Meters,” IEEE Energy 2030 Conference, November 2008.
25. Open SG Users Group, “AMI-SEC Task Force Overview,” http://osgug.ucaiug.org/utilisec/amisec/default.aspx.
26. DLMS User Association, www.dlms.com.
27. L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A Survey,” Comput. Netw. 54, 15 (October 2010).
28. J. Northcote-Green, and R. Wilson, Control and Automation of Electric Power Distribution Systems, CRC Press, Boca Raton, FL, September 2006.
29. K. Fehrenbacher, “AEP Calls for Dedicated Wireless Spectrum for Smart Grid,” Gigaom, August 25, 2009, https://gigaom.com/2009/08/25/aep-calls-for-dedicated-wireless-spectrum-for-smart-grid.
30. J. G. Kassakian, R. Schmalensee, W. W. Hogan, et al., The Future of the Electric Grid: An Interdisciplinary MIT Study, 2011, http://energy.mit.edu/wp-content/uploads/2011/12/MITEI-The-Future-of-the-Electric-Grid.pdf.
31. Y. Hu, A. Kuh, and A. Kavcic, “A Belief Propagation Based Power Distribution System State Estimator,” IEEE Comput. Intell. M. 6, 3 (2011).
32. S. Gong, H. Li, L. Lai, et al., “Decoding the ‘Nature Encoded’ Messages for Distributed Energy Generation Control in Microgrid,” IEEE International Conference on Communications, June 2011.
33. P. Chiradeja, “Benefit of Distributed Generation: A Line Loss Reduction Analysis,” IEEE PES Transmission and Distribution Conference and Exhibition, August 2005.
34. C. H. Lo and N. Ansari, “Decentralized Controls and Communications for Autonomous Distribution Networks in Smart Grid,” IEEE T. Smart Grid 4, 1 (2013).
35. Y. Wang, P. Yemula, and A. Boss, “Decentralized Communication and Control Systems for Power System Operation,” IEEE T. Smart Grid 6, 2 (2014).
36. R. Ma, H. H. Chen, Y. R. Huang, et al., “Smart Grid Communication: Its Challenges and Opportunities,” IEEE T. Smart Grid 4, 1 (2013).
37. C. H. Lo and N. Ansari, “The Progressive Smart Grid System from Both Power and Communications Aspects,” IEEE Commun. Surv. Tut. 14, 3 (2012).
38. V. Giordano, I. Onyeji, G. Fulli, et al., Guidelines for Conducting a Cost-Benefit Analysis of Smart Grid Projects, Reference Report by the Joint Research Centre of the European Commission, 2012, pp 10–72.
39. J. Reilly, “Microgrid Controllers: Standards for Specifications and Testing,” Advanced Grid Technologies Workshop Series, National Renewable Energy Laboratory, July 9, 2015, www.nrel.gov/esif/assets/pdfs/agct_day3_reilly.pdf.
40. C. Natesan, S. K. Ajithan, P. Palani, et al., “Survey on Microgrid: Power Quality Improvement Techniques,” ISRN Renewable Energy 2014 (2014).
41. M. Vandenbergh, C. Garza, and A. Notholt-Vergara, “Overview of Supervisory Control Strategies including a MATLAB Simulink Simulation Tool,” International Energy Agency Photovoltaic Power Systems Programme, August 2012.
42. Software Engineering Institute, Cyber-Physical Systems, www.sei.cmu.edu/cyber-physical.
43. G. R. Kunkolienkar and J. G. Priolkar, “Phasor Measurement Units for Power Systems,” Electrical India, November 5, 2015, www.electricalindia.in/blog/post/id/8522/phasor-measurement-units-for-power-systems.