15.1 EVOLUTION, DRIVERS, AND THE NEED FOR SMART GRID

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:

• greater penetration of renewable resources
• effective and extensive communication overlay from power generation customers
• utilization of high-speed control and cutting-edge sensors to make the grid more robust
• superior operating efficiency
• superior resiliency against natural disasters and attacks
• rapid service restoration after storms
• effective automated metering
• real-time as well as time-of-use pricing of electrical power by users
• increased customer participation in selling and generation of electrical energy generated by through renewable resources

A smart grid utilizes innovative services and products together with intelligent control, monitoring, communications, and self-healing technologies.

15.2 COMPARISON OF SMART GRID WITH THE CURRENT GRID SYSTEM

The comparative analysis between todays Power Grid and Smart Grid is summarized on Table 15.1

A working definition of a smart grid should include the following attributes:

• assess grid health in real time
• predict behavior, anticipate requirements
• adapt to new environments like distributed resources and renewable-energy resources
• handle stochastic demand and respond to smart appliances
• provide self-correction, reconfiguration, and restoration
• handle randomness of loads and market participants in real time
• create more complex interactive behavior with intelligent devices, communication protocols, and standard and smart algorithms to improve smart communication and transportation systems

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.

15.3 ARCHITECTURE OF A SMART GRID

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.

15.4 DESIGN FOR SMART-GRID FUNCTION FOR BULK POWER SYSTEMS

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:

• generation
• transmission
• distribution

Figure 15.2 illustrates the four advanced optimization and control techniques required to meet the criteria for smart-grid performance.

15.4.1 Generation

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.

Generation-Level Automation

Automation at the generation level involves the use of advanced computation technologies and a new algorithm for dispatch and unit commitments to ensure:

• economic dispatch
• unit commitment under different uncertainties and constraints
• reliability
• stability
• security analysis
• distributed generation control

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

(15.1)

where, F_c is the total operating cost of the system, N_G is the number of generating units, and F_k(P_k) is the fuel cost of the generating unit k for real power P_k.

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:

(15.2)

where, α_k, β_k, and γ_k, denote the fuel cost.

This is subjected to different constraints as follows:

• Power balance constraints: Total power output from generating units should exactly satisfy load demand for that hour and corresponding network losses.
  (15.3)

where, P_D is total load demand and P_loss represents losses in transmission network. Kron's formula is used to calculate total losses P_loss, calculated using B-coefficients, given by

(15.4)

where, P_i and P_k are the real power injection at i^th and k^th buses, respectively, and B_ki is the loss coefficient, which can be assumed to be constant under normal operating conditions.

• Thermal constraints
  (15.5)
  (15.6)
  (15.7)
  (15.8)

where, RU_k/ RD_k are ramp-up/down rate of the k^th generating unit and τ is a (Unit Commitment) UC time step.

15.4.2 Transmission

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.

Automation of the Smart Grid at Transmission Level

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:

• congestion minimization of the transmission line
• voltage stability, collapse detection, and prevention using intelligent database
• monitoring and controlling reactive power using intelligent controls
• based on intelligent-switching operations performing fault analysis and reconfigurations
• load balance and power generation via intelligent switching operations and minimizing demand interruption
• DG and DSM via DR strategy

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:

(15.9)

subject to constraints such as:

(15.10)

(15.11)

The illustrated equations are well known as power-balance equations. After Thyristor-Controlled Series Capacitor (TCSC) location, the achieved equations are:

(15.12)

(15.13)

where P^F_n and Q^F_n are the injected active and reactive powers of TCSC to the bus n. The other constraints are:

(15.14)

(15.15)

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, P^P_gn  and P^P_dn show the pool real power generation and demand at bus n, P^M_{gi, n} and P^M_{di, n} are the bilateral injection and extraction of agent i at bus n, Q^P_gn and Q^P_dn are the pool-reactive power generation and load at bus n, V^min_n and V^max_n  are the lower and upper limits of voltage at bus n, and X^min_c and X^max_c are the lower and upper limits of capacitive reactance of TCSC, respectively.

15.5 SMART-GRID CHALLENGES

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.

15.6 DESIGN STRUCTURE AND PROCEDURE FOR SMART-GRID BEST PRACTICES

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:

• unconditional reliability of power supply
• optimal utilization of bulk electric power storage and generation in tandem with distributed energy resources and dispatchable/controllable consumer loads to guarantee lowest cost
• minimal environmental impact of electricity production and delivery
• reduction in the generation of electricity and an increase in the efficiency of the power delivery system and effectiveness of end use
• resiliency of supply and delivery from physical and cyberattacks and major natural disasters
• assuring optimal power quality for all consumers who require it
• monitoring of critical power system components to enable automated maintenance and prevention of outages

15.7 CHAPTER SUMMARY

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.

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