The purpose of this chapter is to study the application of digital processing and communications to the power grid, as data flow and information management are central to the smart grid. Various capabilities result from the deeply integrated use of digital technology with power grids, and integration of the new grid information flows into utility processes and systems is one of the key issues in the design of smart grids. Electric utilities now find themselves making three classes of transformations: improvement of infrastructure (called the strong grid in China), addition of the digital layer (the essence of the smart grid), and business-process transformation (necessary to capitalize on the investments in smart technology). Much of the work that has been ongoing in electric-grid modernization, especially substation and distribution automation, is now included in the general concept of the smart grid, but additional capabilities are evolving as well. Smart-grid technologies emerged from earlier attempts at using electronic control, metering, and monitoring. In the 1980s, automatic meter reading was used for monitoring loads from large customers, and evolved into the advanced metering infrastructure of the 1990 s, using meters that could record how electricity was used at different times of the day. Smart meters add continuous communications so that monitoring can be done in real time, and can be used as a gateway to demand response-aware devices and “smart sockets” in the home. Early forms of such demand-side management (DSM) technologies were dynamic demand-aware devices that passively sensed the load on the grid by monitoring changes in the power supply frequency. It is expected that the new smart grid will provide: A smart grid utilizes innovative services and products together with intelligent control, monitoring, communications, and self-healing technologies. The comparative analysis between todays Power Grid and Smart Grid is summarized on Table 15.1 TABLE 15.1 Comparison of Current Grid to Smart Grid A working definition of a smart grid should include the following attributes: In this environment, smart control strategies will handle congestion, instability, or reliability problems. The smart grid will be a cyber system that is secure, resilient, and able to manage shock to ensure durability and reliability. Additional features include facilities for the integration of renewable and distribution resources, and obtaining information to and from renewable resources and plug-in hybrid vehicles. New interface technologies will make data-flow patterns and information available to investors and entrepreneurs interested in creating goods and services. Thus, the working definition of a smart grid becomes: The smart grid is an advanced, digital, two-way, power flow, power system capable of self-healing, is adaptive, resilient, and sustainable, with foresight for prediction under different uncertainties. It is equipped for interoperability with present and future standards of components, devices, and systems that are cyber-secured against malicious attack. The reference architecture describes the structure of a system with its elements and their structure, as well as their interaction types, among each other and with the environment. Figure 15.1 shows the architecture of the smart grid. Electric power grids are highly complex dynamical systems vulnerable to several disturbances in day-to-day operations, which make the system challenging. Hence, to handle the complexity and challenges in the system, an electric grid should be smart. The design of the smart grid involves the coupling of tools, technologies, and techniques for three different subsystems: Figure 15.2 illustrates the four advanced optimization and control techniques required to meet the criteria for smart-grid performance. Power plants or power-generating units convert fuel, such as coal, natural gas, and uranium, into electricity. These processes of generation involve several stages, like heating water with coal to create steam that spins turbines to produce electricity. Direct conversion is also possible by using flow of water that spins turbines to produce electricity or wind and solar, such as through wind-rotating generators on towers and solar PV panels. Advances in technology or more efficient fossil-fueled power plants would improve the thermodynamic efficiency of converting fossil fuels (whether coal or gas) into electricity, mechanical efficiencies of carbon capture and storage systems, and efficiencies of auxiliary power loads at plants, such as fans, motors, and pumps. Automation at the generation level involves the use of advanced computation technologies and a new algorithm for dispatch and unit commitments to ensure: Forecasting techniques must be incorporated into real-time operating practices as well as day-to-day operational planning. Consistent and accurate assessment of variable generation availability to serve peak demand is needed in longer-term system planning. High-quality, real-time data must be integrated into existing practices and software. Economic dispatch is a computational process where the total required generation real and reactive power, including renewable-energy resources, is allowed to vary within certain limits, so as to meet a particular load demands with minimum fuel cost. Mathematically, objective function of a load dispatch problem can be formulated as
where, Fc is the total operating cost of the system, NG is the number of generating units, and Fk(Pk) is the fuel cost of the generating unit k for real power Pk. Traditionally, the economic-dispatch problem is formulated as an optimization with cost as the quadratic function of generating units. Mathematically, this formulation can be expressed as:
where, αk, βk, and γk, denote the fuel cost. This is subjected to different constraints as follows: where, PD is total load demand and Ploss represents losses in transmission network. Kron's formula is used to calculate total losses Ploss, calculated using B-coefficients, given by
where, Pi and Pk are the real power injection at ith and kth buses, respectively, and Bki is the loss coefficient, which can be assumed to be constant under normal operating conditions. where, RUk/ RDk are ramp-up/down rate of the kth generating unit and τ is a (Unit Commitment) UC time step. Advanced transmission operations apply advanced digital technologies and power electronics devices to increase the performance of the system, enable interconnection of inaccessible power systems, and increase the size and capacity of existing transmission assets to increase the ability of system operators to control the system. The automation of different functions of the transmission system is important for achieving resilience and sustainability of the system. The following functions are evaluated, and the appropriate intelligent technology proposed: The integration of these functions is shown in Figure 15.3. Congestion minimization of the transmission line can be done utilizing two different approaches. The first approach uses rescheduling of generating units and prioritization and curtailment of loads/transactions. The second approach utilizes operation of transformer taps, phase shifters, or FACTS devices. FACTS devices assume importance in the context of power system restructuring since they can expand the usage potential of transmission systems by controlling power flows in the network. FACTS devices are operated in a manner so as to ensure that the contractual requirements are fulfilled as far as possible by minimizing line congestion. The mathematical formulation can be stated as:
subject to constraints such as:
The illustrated equations are well known as power-balance equations. After Thyristor-Controlled Series Capacitor (TCSC) location, the achieved equations are:
where PFn and QFn are the injected active and reactive powers of TCSC to the bus n. The other constraints are:
where superscript i is the bilateral transaction index; n and m are the bus indices, P and M signify pool transaction and bilateral (or multilateral) transaction, PPgn and PPdn show the pool real power generation and demand at bus n, PMgi, n and PMdi, n are the bilateral injection and extraction of agent i at bus n, QPgn and QPdn are the pool-reactive power generation and load at bus n, Vminn and Vmaxn are the lower and upper limits of voltage at bus n, and Xminc and Xmaxc are the lower and upper limits of capacitive reactance of TCSC, respectively. Advanced distribution operations allow a grid that can self-heal through auto-detection of faults and auto-reconfiguration of circuits, with greater efficiency due to innovative system modeling and analysis and voltage/var control, which is more flexible and situationally aware due to cutting-edge sensing, data acquisition, and automated controls. The distribution system of the future smart grid will permit the incorporation of distributed energy resources and will also operate with novel circuit configurations required for microgrids. The IEEE defines distribution automation (DA) as a system that enables an electric utility to remotely monitor, coordinate and operate distribution companies in real-time mode from remote locations [1]. Strategies such as various DMS applications are utilized to automate and manage electric grid operations. Moreover, DA optimizes a utility's operations and directly improves the reliability of its distribution power system. The goal of DA is real-time adjustment to changing loads, generation, and failure conditions of the distribution system, usually without operator intervention. However, accurate modeling of distribution operations supports optimal decision-making at the control center and in the field. The following functions are evaluated, and the appropriate intelligent technology is proposed: TABLE 15.2 Equipment, Technology, and Control Packages for DA Figure 15.4 shows the distribution automation schemes for distribution systems. Significant progress has been made toward the growth and employment of a smart grid, nevertheless, there are challenges still to be addressed. Various road maps and reports have defined the technical issues and potential methodologies for overcoming them from the industry, state, federal, and even worldwide perspectives. Huge, utility-scale renewable-energy (RE) systems (approximately 50MW or larger) can potentially make a significant influence on future domestic power supplies. A wide assortment of RE sources may be considered for large-scale grid integration, such as wind, geothermal, solar, and hydropower. Even though progress has been made in the deployment of renewable energy technologies, there are still unique challenges associated with the integration of these sources in large-scale systems. Challenges have developed when attempting to integrate power sources that are variable and nontraditional, and sources that are non-base load power onto the grid. The variable nature of the output has to be considered when attempting to optimize the operation of the entire grid. These new sources will require that conventional power plants, along with other system assets, operate differently. While advancement has been made in the accommodation and deployment of DER technologies, there are distinctive challenges related to integrating these resources. Issues related to economics, regulation, and technology are inhibiting larger integration of DERs. For instance, communication between DER, dispatchers, loads, and utilities is generally inadequate for an optimized smart grid. By and large, new devices and technologies are outstripping the ability to integrate them on the grid at full functionality. Among the technical challenges for the smart grid are: Nontechnical challenges to the smart grid also remain, including: The complexity of smart grid projects adds to the challenges of electric power system modernization as utilities will be required to make significant investments in information communication technology, an area largely outside their core competencies. The smart grid will be as integral to the core systems as enterprise resource planning is to the manufacturing industry, and it will be as geographically diverse as the new telecommunications network. Successful deployment will necessitate robust coordination across customary organizational boundaries, substantial process change, and with rigorous governance. The attributes of a good smart grid as follows: Problem 1 Problem 2 Problem 3 Discuss the existing challenges and benefits to the application of the concept of smart grids. How do we overcome them? Problem 4 Consider a generating station that contains three generating units. The fuel costs of these units are given by
The generation limits of the units are
The total load that these units supply varies between 90MW and 1,250MW. Assuming that all three units are operational all the time, compute the economic operating settings as the load changes. Problem 5 What about cybersecurity? Does the smart grid make us less or more secure? Problem 6 Discuss the following terms that relate to the smart grid: Problem 7 What is the simplest thing about the smart grid? What is the most complex thing about it? Why is it important for states and localities to build a smart-grid infrastructure? Problem 8 Review different test beds for a smart grid design and validate the experimental set-up at CESaC. Develop processes and procedures for achieving an economically viable test bed for studies of different functions of smart-grid concepts outline in the syllabus. In this chapter we discussed the concept of smart grid, its evolution and drivers, the need as well as energy sources and architecture of smart grid. A comparative analysis has been made between smart grid and current power grid. The components forming smart grid are addressed with automation function applications on transmission and distribution of the grid. Design procedures with challenges of integrating DGs are briefly discussed. The materials presented in this chapter are designed to equip the student with fundamental knowledge needed about smart grid functions, controls, and interfaces, which are necessary in smart-grid design.15.1 EVOLUTION, DRIVERS, AND THE NEED FOR SMART GRID
15.2 COMPARISON OF SMART GRID WITH THE CURRENT GRID SYSTEM
Preferred Characteristics
Current Grid
Smart Grid
Active consumer participation
Consumers are uninformed and do not participate
Informed, involved consumers; demand response and distributed energy resources
Accommodation of all generation and storage options
Dominated by central generation; many obstacles exist for distribution energy resources interconnection
Many distributed energy resources with plug-and-play convenience; focus on renewables
New products, services and markets
Limited, poorly integrated wholesale market; limited opportunities for consumers
Mature, well-integrated wholesale market; growth of new electricity markets for consumers
Provision of power quality for the digital economy
Focus on outages; slow response to power-quality issues
Power quality is a priority with a variety of quality/price options; rapid resolution of issues
Optimization of assets and efficient operation
Little integration of operational data with assets management and business processes
Greatly expanded data acquisition of grid parameters; focus on prevention and minimizing impact to consumers
Anticipation of responses to system disturbances (self-healing)
Responds to prevent further damage; focus is on protecting assets; flowing fault
Automatically detects and responds to problems; focus on prevention and minimizing impact to consumers
Resiliency against attacks and natural disasters
Vulnerable to acts of terror and natural disasters
Resilient to attacks and natural disasters with rapid restoration capabilities
15.3 ARCHITECTURE OF A SMART GRID
15.4 DESIGN FOR SMART-GRID FUNCTION FOR BULK POWER SYSTEMS
15.4.1 Generation
Generation-Level Automation
15.4.2 Transmission
Automation of the Smart Grid at Transmission Level
15.4.3 Distribution
Distribution Automation Function and Relationship to the Smart Grid
For distribution automation functions, DSM is classified into three categories:
Equipment
Technology
Control Packages
On Load Tap Changer (OLTC)
Substation/distribution voltage and current sensors
Transformer load tap changers
Distribution capacitor banks
Supervisory control and data acquisition (SCADA) and advanced metering infrastructure (AMI) communication
Capacitors/voltage regulators
Distribution voltage regulators
Smart meters
Distribution management systems and voltage optimization software
15.5 SMART-GRID CHALLENGES
15.5.1 Integration of Utility-Scale Renewable Energy Sources onto the Grid
15.5.2 Integration of Energy Storage and Distributed Generation on the Grid
15.5.3 Technical Challenges
15.5.4 Nontechnical Challenges
15.6 DESIGN STRUCTURE AND PROCEDURE FOR SMART-GRID BEST PRACTICES
Illustrative Problems and Examples
15.7 CHAPTER SUMMARY
BIBLIOGRAPHY