11
Integration of Renewable Energy Resources in the Smart Grid: Opportunities and Challenges

Mohammad Upal Mahfuz1, Ahmed O. Nasif2 Md Maruf Hossain1 and Md. Abdur Rahman3

1Department of Natural and Applied Sciences (Engineering Technology Program), University of Wisconsin‐Green Bay, 2420 Nicolet Drive, Green Bay, Wisconsin, 54311, USA

2Department of Engineering Technology, University of Wisconsin‐Oshkosh, 800 Algoma Blvd. Oshkosh, Wisconsin, 54901, USA

3Department of Electrical and Electronic Engineering, American International University‐Bangladesh, House 83/B, Road 4, Kemal Ataturk Avenue, Banani, Dhaka, 1213, Bangladesh

11.1 Introduction

Electrical power grids are considered as an enormously important subject in the path of human civilization. An electrical power grid has mainly four components, namely, the generation, transmission, and distribution systems, and the customers. Apart from the customers, who are the users of electricity, the generation, transmission, and distribution systems are responsible for bringing electrical power from the generation sites through a complex network of transmission lines and distribution systems to the customer premises (Kirtley, 2010). It goes without saying that electrical power grids have contributed a lot to our individual lives, society as a whole, and its continuous development through many industrial and manufacturing processes. However, the existing power grids have been facing several issues at the present time for which it is now necessary to modernize them through the use of new power system and information and communication technologies. For instance, it has been known that the power grids faced more voltage sags, blackouts, and overloads in the past decade than in the past 40 years (Gao et al., 2012), where in most of the cases these blackout events are thought to be caused by slowness of the response time when the devices communicate over the grid. As the population is increasing, the electrical power consumption is also increasing. This is further true when, due to the rise in the number of personal devices needing electrical power, there is a high demand of electricity to the existing power grid, which makes the existing power grid more unstable in terms of its performance. Finally, old power grids are a significant source of carbon emissions in the environment, which is not encouraging in terms of the global movement toward sustainability and green electricity. For example, in the United States, the power system alone produces 40% of all nationwide carbon emissions. These factors, when combined, are sufficient to consider a suggested change from the old existing power grid to a modernized power grid (Gao et al., 2012).

In recent years, the concept of the smart grid (SG) has been very popular in order to envision the future of energy‐efficient power grid better equipped with efficient support systems including sensor and actuator networks to sense and actuate individual “smart” devices respectively, demand management systems, and renewable energy options for environment‐friendly power grid operation (Fang et al., 2012; Guizani and Anan, 2014; Shafiullah et al.; 2010, Aroge, 2014; Rehmani et al., 2016). Unlike in a conventional power grid, in the SG, the power generation, transmission, and distribution components as well as a huge number of sensors and actuators can both transfer electrical power between each other and communicate with each other through information flow taking place between them. The SG has faced several challenges from multiple viewpoints ranging from electrical power generation through transmission and distribution networks to consumers and utility management sides (Aroge, 2014). The era of traditional fossil fuel–based electrical power generation systems would perhaps come to an end soon as the depletion of natural resources and increased level of environmental pollution have established a growing threat for the present‐day world (Aroge, 2014). The field of SG is an important hub where researchers from a diverse background can combine their research, expertise, and experience to improve the traditional electrical power grid toward the SG paradigm (Rehmani et al., 2016).

Renewable energy (RE) resources, namely, wind, solar, and hydro, provide alternative sources of electrical power generation and sustainable solutions and controls to our increasing environmental pollution. Technological advancement of RE‐harnessing systems and growing awareness and an environment‐friendly attitude of the human society toward a sustainable future have realized an increased level of RE systems used today. Therefore, in today's world, RE systems have become an inevitable option for electrical power generation support to the grid, which demands that RE systems should be integrated in future power grids and hence the SG.

Since the electrical power generation potential of RE‐based systems entirely depends on variation of RE resources involved, the intermittent nature of the RE systems poses a major challenge in the integration of RE systems in the SG. For the same reason, unlike traditional fossil fuel–based power generation systems, RE systems in general cannot be dispatched easily and are not easily controllable, and so enhanced technical, financial (cost‐benefit), and regulatory research are more likely to take place when integration of RE in the SG is concerned (Aroge, 2014).

In this chapter, the prospects and challenges of integrating RE resources in the SG have been addressed. The chapter is organized as follows: Section 11.2 describes the SG paradigm briefly, followed by Section 11.3 discussing the issue of integrating RE resources in the SG. Prospects and challenges of integrating RE resources have been discussed in Section 11.4. Section 11.5 briefly presents some case studies within the subject matter. Finally, Section 11.6 concludes the chapter.

11.2 The Smart Grid Paradigm

11.2.1 The Smart Grid Concept

The SG is a new power grid paradigm. The SG makes use of the digital technologies in all the system components as well as the latest information and communication technologies in order to save energy, reduce cost, and provide enhanced reliability and ensures quality electrical power to all, anytime, anywhere within the power grid (Thomas and McDonald, 2015). As the SG has several stakeholders, some of which are from energy economics and business, different perspectives of the SG among different stakeholders may be possible. However, the main objectives and the general concept of the SG are to motivate the customers to take a role in electrical power systems and to include them in the consumption‐decision process, resist attack and ensure security, provide quality power to numerous devices connected in home area networks (HAN), accommodate energy storage and generation options, e.g., renewable energy resources, optimize assets, operations, and functionality, ensure reliability, create business, and be able to heal automatically (Thomas and McDonald, 2015). The basic concept of the SG is to incorporate enhanced digital information capabilities, automation, and communication technologies in the existing electrical power grid so that it can work smartly, provide useful solutions, and contribute to sustainability reducing the carbon footprint. A SG would be able to optimize the entire energy management system, which would ensure that customers be benefitted financially by reducing the utility bills without changing their lifestyle (Thomas and McDonald, 2015). As the SG is by default equipped with intelligence through digital technologies, communication systems, and automation, it is anticipated that one of the major benefits that the SG would bring about is the high penetration of renewable energy resources in the power grid ensuring less carbon emissions. In the SG framework, all the units involved can communicate with one another by means of advanced communication systems and networks. In addition, the level of automation and the use of digital technologies are anticipated to be higher in the SG framework in comparison with the same in a traditional grid framework. This could bring a huge potential to better handle the intermittent nature of RE resources in the SG than in a traditional grid.

Figures 11.1a and 11.1b show the conceptual diagrams of a traditional power grid and the SG respectively. As seen from Figure 11.1, both traditional power grid and the SG have four basic units, namely, power generation, transmission, and distribution systems, and customers. In Fig. 11.1a, the power generation unit includes not only fossil fuel–based but also all forms of RE electrical power generation units. While in a traditional power grid, the electrical power flow is unidirectional from the generation side to the customers, in the SG, electrical the power flow is bidirectional, allowing the power consumers to play a role to send electrical power back to the grid (Fang et al., 2012). Another important aspect of the SG concept that makes the SG a unique system is that there is a bidirectional flow of communication information among the components of the SG. This means, the SG units can communicate with each other through a communication network of sensors and actuators so that power flow can be controlled and monitored by information flow among the components. As shown in Figure 11.1b, the SG includes three additional components, namely, the market, operations and management, and service providers, where the four basic units continuously communicate to each order to ensure proper functionality of the system. In Figure 11.1a, the unidirectional arrows denote the unidirectional power flow of the grid. In Figure 11.1b, the bidirectional red and blue arrows denote the bidirectional power flow and communication information flow in the grid, respectively. In addition, in Fig. 11.1b) the black and the red solid lines respectively denote the interaction among RE units and that between an RE unit and the SG, where the RE unit is integrated in the SG.

Image described by caption and surrounding text.

Figure 11.1 Conceptual diagrams of a regular power grid and the smart grid.

11.2.2 System Components of the SG

The SG principally consists of three major systems, namely, the smart infrastructure system, the smart management system, and the smart protection system (Fang et al., 2012). In the following, these systems have been described briefly alongside their importance in the possible integration of RE resources in the SG.

(1) The smart infrastructure system: The smart infrastructure system provides the energy, information, and communication infrastructure of the SG, which is responsible for ensuring reliable two‐way flow of both information and electrical energy. In a traditional power grid, there is no information flow between system components and the power flow is entirely unidirectional, meaning that the power generated by the generation system passes through the transmission and distribution systems and is finally delivered to consumers. Since the power passes through the distribution transformers, if any system fault has taken place, the traditional power grid has no measures to escape from the fault but to suffer power outages in the corresponding units. On the other hand, in the SG, the system components are capable of sending the power back to the grid system and hence the two‐way power flow. Apart from that, the SG system components communicate information through sensor and actuator networks, bringing intelligence to them, which helps the power grid to escape from any faulty part of the grid system, thereby ensuring the uninterrupted power supply to the consumers. Based on the functionality, the smart infrastructure system can be divided into three subsystems, namely, the smart energy subsystem, the smart information subsystem, and the smart communication subsystem, which develop the SG's underlying infrastructure related to energy flow, information flow, and communication flow, respectively (Fang et al., 2012; Guizani and Anan, 2014).

The smart energy subsystem is mainly concerned about the energy aspects of the SG, e.g., advanced techniques for electrical energy generation, transmission, and distribution, as well as efficient ways to consume electricity at the customers' ends. With the integration of RE electrical power units in the SG, this subsystem now needs to incorporate the additional RE supplies in the SG system, handle the intermittent nature of the RE electrical power generations and the associated variability characteristics over daily, weekly, and monthly time frames as well as the transmission, distribution, and consumption of these RE resources at various stages of the SG system.

The advanced information subsystem mainly relies on advanced techniques that are needed to handle information efficiently in the aspects of the SG, which include the advanced metering infrastructure (AMI), information monitoring and management in the customer premises with HANs, and similar technologies (Thomas and McDonald, 2015). With RE resources that are located at the customer premises or a nearby location from the customer premises, by virtue of the bidirectional energy flow possible in the SG, the AMI and HAN systems would be required to keep track of the excess power that would be delivered to the SG system from the customer on a timely manner. In such a scenario, a part of the RE power, which would be available in excess to the power required and stored by the customer, would not be lost and thus be delivered to the power system for use by other customers who need extra power.

The smart communication subsystem mainly focuses on establishing and maintaining reliable communication among all system components in the power system spanning from the generation units through transmission and distribution networks to the customer premises. At different power system components, the communication systems may not be exactly the same but should depend on the type of the devices to communicate. For example, the communication medium from the generator to the transmission and distribution networks may be different from the same at the customer premises where a smart home (Komninos et al., 2014; Pătru et al., 2016) enables all smart appliances to be connected through a reliable and secured HAN. With the inclusion of the RE resources in the SG system, due to the variability of RE resources to be handled, it is anticipated that the smart communication subsystem would need to handle more communication data than before, as the SG would now need to communicate with various subsystems more frequently for these additional RE generation systems.

(2) Smart management system: This system component provides the SG with overall advanced management and control services as well as functionalities that are at the heart of ensuring a reliable experience with the SG. Taking the benefits of modernized capabilities of the existing system and the capabilities of newly deployed systems, it has now become possible to provide advanced management and control services and improved functionalities. The potential services and functionalities that are most commonly achieved with a smart management system are better energy efficiency, reduced operational costs, balance between supply and demand, and maximizing utility usage (Fang et al., 2012). In order to handle the variable nature of the RE resources efficiently, it is anticipated that the smart management system would be required to perform comparatively more management and control services for ensuring a consistently reliable experience with the SG.

(3) Smart protection system: Being a smart system, the SG ensures protection against certain system or grid failures, addresses cyber security issues, and preserves security and privacy of consumer information very strictly. The SG achieves these by making a good use of the smarter infrastructure behind the SG concept (Fang et al., 2012; Guizani and Anan, 2014). The smart protection system needs to be materialized from two points of views, namely, from the SG itself and from the customers. For instance, the subsystem is based upon intelligence that would protect the system from unreliable events and faults and unstable operation at every system component. In the case of failures and faults, the SG has the opportunity to do a post‐analysis of the events and thus make the grid even more reliable for the future. From customers' points of view, since the SG handles customers' data including individual usage patterns through AMI and HAN, security and privacy of customer data is of utmost importance in the SG. With the inclusion of RE resources in the SG, due to the variability of the RE resources, each system component becomes more affected in terms of risks of unstable operation and a higher number of events for post‐analysis, and thus the workload of the smart protection system increases in order to provide an overall reliable system operation in the SG. A smart protection system should take all these into account.

11.3 Renewable Energy Integration in the Smart Grid

11.3.1 Resource Characteristics and Distributed Generation

The demand for electrical energy in the power grid varies over time during the day and also the period of the month and/or year. Daily demand patterns generally peak in the morning or evening and gradually goes down at night. If the electrical energy was to be generated from fossil fuel only, it would have been possible to control the fuel input according to the electrical power demand. Renewable energy resources depend mostly on the environment and weather conditions. Due to the intermittent behavior of RE‐based electrical power generation systems, both the customers' demand and the natural resources of RE vary over time, making management of both sides very challenging when RE‐based systems are integrated in the power grid. Intermittency in RE resources can be broadly categorized into the characteristics that are somewhat periodic and those with random irregularities. It also depends on the particular type of RE resource under consideration. For instance, wind energy can be very variable based on the time of the day, month, terrain type, and any particular weather condition, whereas tidal energy can be quite accurately predictable (Twidell and Weir, 2006). Electrical power output varies with the change in wind speed. Also, solar energy can be very predictable in some regions of the world but somewhat unpredictable in other regions. Solar irradiation profiles also impact the output of solar power generators at any location. For hydropower, reservoir height and flow are the two variables that make the electrical output power intermittent. For tidal power systems, tidal range, contained area, and tidal current are the major variables (Twidell and Weir, 2006). Above all, the intermittent nature of RE resources is evident from resource assessment data in almost every location. Electrical power generation from RE resources does not cost anything from the resource point of view; however, their intermittent nature makes integration to the grid challenging. The abundance of RE‐based electrical power generation opportunities along with their limited power output makes them an ideal candidate for distributed generation systems (Thomas and McDonald, 2015). However, interconnected distributed RE power generation systems could also be effective producing comparable power outputs as in in conventional power generation systems (Guizani and Anan, 2014; Molderink et al., 2010; Thomas and McDonald, 2015).

11.3.2 Why Is Integration Necessary?

In today's world, sustainable engineering design is highly regarded in every aspect of social and economic development. Consumption of electrical energy in an efficient manner saves the fossil fuels that are used in producing electrical power in traditional fossil fuel–based power plants. Technology has progressed much in the field of energy‐efficient systems such that energy‐efficient and smart appliances are more available today than they were in the past. RE‐based electrical power generation is a sustainable way of electrical power generation for the society. Renewable resources are so abundant in nature that, if harnessed properly, they could provide the necessary electrical power for a part of the current world population. Therefore, sustainable use of resources is considered as one of the driving forces for realizing the integration of RE resources in the SG. In fact, it goes without saying that one of the objectives of modernizing the old power grid and developing the new SG is to reduce loss of electrical power consumption by using energy‐efficient and smart appliances. That is how RE and its integration to the SG are tied together through the sense of sustainability, there being the fact that cleaner energy utilization is one of the objectives of RE and its integration to the SG.

Awareness of environmental pollution control is another significant factor for integrating RE‐based electrical power systems into the SG. Fossil fuel–based traditional power generation systems are a significant source of environmental pollution at the present time. As mentioned earlier, traditional power systems are responsible for a large amount of carbon emissions in the present world and hence the corresponding aftereffects, e.g., global warming and subsequent degradation of the natural environment. An increasing level of environmental awareness among the population has encouraged them to use more RE‐based electrical power generating systems, which, as a result, have facilitated the integration process of many RE‐based power generation systems into the power grid and thus the SG. Unlike the SG, the currently existing power grid is in most cases not capable of handling all these different technologies integrated to it (Guizani and Anan, 2014) while at the same time increasing its energy efficiency and being more environmentally friendly. Therefore, while it is evident that the existing power grid must be modernized and made more environmentally friendly, at the same time it is also necessary to integrate all the RE‐based power systems in the existing power grid and through that to the SG paradigm. Integrating RE resources in the SG is a necessary step toward sustainable development of the human society.

11.4 Opportunities and Challenges

The intermittent nature and the variability of RE resources pose a significant level of challenge in their integration in the power grid, which could possibly be solved with the technologies and concepts of the SG. In this section, the opportunities of integrating RE resources have been identified and the corresponding challenges have been explained. Figure 11.2 presents the key prospects of integrating RE resources in the SG.

Diagram displaying a circle at the center labeled Re-integrated SG Challenge Areas with arrows linking to surrounding boxes labeled Monitoring, Demand Respond, Energy Storage, Distributed Generation, etc.

Figure 11.2 Challenge areas of the RE‐integrated SG system.

11.4.1 Energy Storage (ES)

The intermittent nature (due to meteorological fluctuations) of RE resources causing voltage and frequency variations in electrical power generation makes an accompanying energy storage device a necessity for the RE system (Maharjan et al., 2012; Roberts and Sandberg, 2011). Since the SG allows for intelligence and computing resources including bidirectional information and power flows, there exists an enormous opportunity for energy storage in integrating RE resources in the SG. The intermittency of the RE power generation may be absorbed in the grid itself through proper energy management systems. The SG should have a highly efficient energy management algorithm in place that would not only take care of the variation in the power output of RE systems but also manage it properly so that this variation can be handled efficiently among the system units. The main use of distributed storage systems include peak‐load deduction (peak shaving) at substations, storage of off‐peak wind energy, power smoothing (for large solar arrays, ancillary services), frequency regulation, and transmission, distribution feeder reliability improvement, and customer feeder load management.

11.4.1.1 Key Energy Storage Technologies

Examples of some of the main energy storage options for the RE‐integrated SG system would be battery storage (Sparacino et al., 2012; Maharjan et al., 2012), pumped hydropower (Sparacino et al., 2012; Whittingham, 2012), and flywheel technology (Whittingham, 2012; Daoud et al., 2012), while some additional energy storage options would be supercapacitors, superconducting magnetic energy, and compressed air storage technology (Aroge, 2014). For an ES system to be widely deployable in the SG, it must have high capacity, long discharge times, be cost effective, and not have special site restrictions.

Battery storage (BS): BS systems can be used for renewable integrations, peak shaving, load shifting and levelling, distributed energy storage, power quality management, and temporary/emergency backup. Moreover, a typical BS can be installed at any traditional substation, which consists of three main parts: battery module (stores and dispenses energy), DC‐to‐AC converter (power electronics), and power management unit (to optimize charging and discharging; Sevilla et al., 2016; Pourbeik et al., 2015). Traditional battery storage systems use a multipulse converter with a complicated zig‐zag transformer, whereas modern BS systems use a multilevel converter with an advanced battery technology, such as lithium (Li)‐ion, sodium sulphur (NaS), nickel metal hydride (NiMH), and so on (Maharjan et al., 2012). The multilevel cascade converter is viewed as one of the most promising topologies for BS systems (Rodriguez et al., 2007). For a detailed account on state‐of‐the‐art and future research efforts on battery systems, see Whittingham (2012).

Pumped Hydropower (PHP): PHP operates by storing potential energy during low‐demand hours by pumping water at a higher elevation, which can subsequently be used to increase the generation of power using turbines in peak times. This is, by far, the most common, cost effective, and mature ES system existing at present. However, the problem with PHP is that the energy consumers may be located in zones where the PHP layout is not suitable, and therefore, the SE from PHP may need to be brought to the consumers from far away over long transmission lines (Sparacino et al., 2012; Whittingham, 2012).

Flywheel technology (FW): FW stores energy in kinetic energy form, the amount of stored energy being proportional to the FW rotation speed. Although this type of technology is finding applications in frequency smoothing, it seems to be not very suitable for large scale, long‐term ES (Whittingham, 2012; Daoud et al., 2012).

Compressed air storage technology (CAS): In this type of technology, off‐peak power is used to compress air that is stored in tanks or even the natural gas pipe network, which makes it a difficult choice for distributed ES option. Also, the transformation of energy from electricity to another physical form, namely, the gaseous form, incurs unavoidable conversion losses (Sevilla et al., 2016; Jilek et al., 2015).

Supercapacitors (SC): Supercapacitors (SC) greatly improve energy storage capabilities of traditional capacitors by creating charge separation in two electrodes at small distances apart immersed in electrolytes, as it is in batteries. The advantages of SCs are that they have small inner resistance, large capacity, long life compared to batteries, and are pollution free (ElKady and Kaner, 2015; Whittingham, 2012). Table 11.1 shows a quick overview of ES systems and their performance parameters, namely, power, specific energy, discharge time, efficiency, and capital costs in the aspects of their potential integration in the SG.

Table 11.1 Overview of Storage Systems and Their Parameters (Jilek et al., 2015; Breeze, 2005).

Storage system Power of system Specific energy Discharge time Efficiency Capital costs
Accumulators (lead, NiCd, NiMH, Li‐on) kW–500 MW MWh–100 MWh 1–8 h 88–92% 500–1,000 $/kW
Flywheels 500 kW–1 MW 100 kWh–100 MWh <5 min 90–95% 2,000 $/kW
Pumped storage hydropower plant 100–4,000 MW 500 MWh–15 GWh 4–12 h 75–80% 800–3,500 $/kW
Compressed air energy storage 25–3,000 MW 200 MWh–10 GWh 1–20 h >60% 400 $/kW
NaS 1 MW 1 MWh 1 h about 89% 200 $/kW
Supercapacitors <250 kW 10 kWh <1 min >95% –
Flow batteries 100 kW–10 MW 1–100 MWh 10 h 75–85% 800–1,200 $/kW
Accumulation to hydrogen 10 MW unlimited >5 h <60% 6,000 $/kW
Superconducting induction accumulators 10 kW–10 MW 10 kWh–1 MWh 1–30 min 90% 2,000–3,000 $/kW

11.4.1.2 Key Energy Storage Challenges in SG

Energy storage devices built in the SG system need to provide a reliable and uninterrupted power supply, better power quality, and energy management throughout a period of electrical fault, break during switching, and power outage (Aroge, 2014). Below we mention the main challenges of ES in the SG.

  1. Although energy storage apparently encourages RE systems to be integrated in the SG, as more and more RE‐based distributed generations are integrated in the SG, it could appear as a technical challenge to increase the efficiency and the capacity of the energy storage devices and to invent and develop new technologies in this regard (Guizani and Anan, 2014; Boksha et al., 2014; Perez et al., 2015).
  2. Planning for optimal placement, capacity, and type of ES systems remains an open problem (Sardi and Mithulananthan, 2014). Also, the scaling up of more and more distributed energy sources and the associated ES systems to the grid need to be explored (Sardi and Mithulananthan, 2014).
  3. The economic challenges of energy storage devices include deciding on whether the energy storage device would be located profitably at the power generation, transmission, and distribution sides or the consumer side (Guizani and Anan, 2014; Tushar et al., 2016; Perez et al., 2015; Zhu et al., 2014). Community ES has become more common because of the benefit and increased number of RE sources at the customers' end (Sardi and Mithulananthan, 2014). However, it is rather costly to install ES units at residential areas where the customer‐owned RE sources are located. Models of energy management and pricing schemes profitable to both residential users and power utilities need to be proposed. Solutions based on game theory, prospect theory, and auction models, which employ two‐way communications between residential customers and power utilities have been proposed (Tushar et al., 2016; Nguyen et al., 2015; Wang and Saad, 2015; Wang et al., 2014).
  4. There exist technical challenges in developing methods of integrating ES‐coupled RE sources so that overall stability and robustness of the grid can be improved (Hill et al., 2012; Mohd et al., 2008; Rahbar et al., 2015). Some of the key factors that the new integration methods must provide are rapid response, voltage stability, rate variation control, dynamic VAR support and frequency regulation.

11.4.2 Distributed Generation (DG)

One of the main goals of the SG is to have robust contingencies against loss of transmission and generation, as well as load variations over the short and long terms. In fact, according to the Energy Independence and Security Act (EISA07), a major attribute of a SG is the development and integration of distributed energy resources, especially renewable resources, to increase efficiency in generation and utilization (Vaziri et al., 2011). Since RE sources are highly variable and usually not forecastable, it is crucial to have flexible, dispatchable, quick, and distributed energy generation options available. The idea is to have users, whether industrial, commercial, or residential, be supplied with energy on demand from a group of sources varying widely in terms of their location, service times, and energy generation and storage methods.

11.4.2.1 Key DG Sources and Generators

There are four main types of energy sources used in DG, namely, chemical, wind, hydraulic, and solar (Hernández et al., 2012). Chemical energy harvested from biomass, animal waste, natural gas, coal, and diesel, which are transformed into electric power by AC generators using microturbines and internal combustion engines. Windmills harness energy from wind using aerogenerators (wind turbines). In hydraulic turbines, hydraulic potential is exploited to produce energy from water. In solar cells, solar radiation is harvested by means of the photovoltaic effect.

Table 11.2 shows the main types of generators used in harvesting DG sources.

Table 11.2 Types of Generators (Su and Gamal, 2011).

Generator type Response time scale Example
Ramping generators Slow/moderate Base load generators (coal‐fired, hydro, and nuclear power plants), intermediate generators (combined‐cycle combustion turbine), and peaking generators (gas turbines)
Renewable generators Output is intermittent Wind, solar
Fast‐ramping generators Short Gas turbines

11.4.2.2 Key Parts and Functions of a DG System and Its Distribution

The RE sources in SG are distributed by a network of feeders, which will connect customers to the distribution system via smart meters. A smart meter is envisioned to have one‐ or two‐way communication with the electric control, metering, and billing network. In addition, smart meters can communicate with each other to share information for automatic demand response and self‐healing switching (Clarizia, 2016; Hurwitz and Ki, 2011).

Microgrids and feeder networks are important parts to realize distribution in the SG. A microgrid is a company or a community powered, in addition to the main utility grid, by its own collection of RE sources, such as solar panels, fuel cells, stand‐alone generators, wind generators, biofuel generators, etc. Feeder networks connected to multiple substations can automatically detect and isolate fault areas, reroute powers from other sources and microgrids to minimize outages and increase reliability and efficiency.

A key characteristic to achieve this is via distributed automation, which accomplishes monitoring, control, and data communications. Distributed automation involves recognition and integration of distributed resources, such as DG sources, storage systems, and loads, with active and passive interfaces for energy dispatch controlled by distributed algorithms (Huang et al., 2011).

Intelligent fault management schemes also have to be in place, which may employ fault isolation devices for fast response to fault events. To have optimal power flow through the SG, safe and reliable interconnection of DG should be established for load forecasting and control. Data networks will have to be used to implement algorithms to control power flows, distributed automation, DG output levels, demand response, protection systems, and customer feedback and ensure cyber security (Vaziri et al., 2011; Olivares et al., 2014).

11.4.2.3 DG and Dispatch Challenges

Although there exists well‐documented literature on stand‐alone large‐capacity RE systems, DG fits well for RE systems especially because RE resources are very much location‐specific that could contribute to DG of RE. For instance, community PV installation, solar home systems, stand‐alone wind energy systems, community wind farms, and small hydropower generations could be set up to serve the community electricity needs, and the additional power could be contributed to the SG through appropriate SG infrastructure and ES facilities with enhanced energy management algorithms such that, in addition to serving locally, the DGs could also contribute to the SG infrastructure for communities and people in need of electrical power (Zhang et al., 2016; Wang et al., 2016; Tushar et al., 2015).

DG can provide efficient energy management and reliable grid operation. The instability arisen from the distributed RE generations could be handled by means of the SG infrastructure through intelligent algorithms and computing systems existing in the SG. While more and more DGs would be integrated in the SG, providing an intelligent algorithm to manage all these would pose a challenge for the SG (Iqbal et al., 2016; Becerra et al., 2015; Suslov et al., 2015; Biswas et al., 2013; Hubert and Grijalva, 2011; Qiu et al., 2011; Kim et al., 2011; Caron and Kesidis, 2010).

To incorporate renewable DG into SG, several new dispatch challenges will need to be addressed (Cheung et al., 2010; Vaziri et al., 2011). For instance, distributed RE resources that are basically less predictable need to be incorporated in the SG according to a price‐based scheme; the RE resources need to be fulfilling an active and dynamic energy demand; the integration of RE resources in the SG would require the modeling parameters to be adapted; a complicated optimization problem need to be solved in terms of security and meeting the energy demand properly; an efficient and real‐time dispatch mechanism needs to be developed; proper mechanisms need to exist to handle emergency situations; and a post‐event analysis technique needs to be available.

If DG is not implemented properly, it may lead to instability of the grid (Chen et al., 2010; Kroposki et al., 2008; Driesen and Katiraei, 2008). Innovative architectures, e.g., microgrid (Lasseter and Paigi, 2004) and LoCal grid (He et al., 2008), have been proposed to virtualize a local generator as a constant load, source, or zero load to the grid, greatly simplifying its impact on the grid. In addition, realistic modeling and analysis of DGs (Andrew et al., 2011), quality of supply, reliability (Blajszczak et al., 2011), synchronization technology (Abbasi et al., 2012), energy management (Rugthaicharoencheep and Boonthienthong, 2012), information aggregation (Alam et al., 2013), monitoring (Rana and Li, 2015; López, 2013) and opportunity identification (Mutule et al., 2013) are some key factors that have to be leveraged to maximize the benefits of DGs.

To incorporate DG in the SG, optimization plays an important role. Detailed optimization schemes need to be developed to characterize DG sources in terms of their configuration, compatibility, and capacity (Cui and Dai, 2011; Sharma and Kaur, 2016). The objective of the optimization could be to minimize DG operation cost, minimize network loss, and maximize environmental benefits. Some constraints for the optimization may include characteristics of active and reactive power, voltage upper and lower limits, range of power flow changes, impact of uncertainties of RE sources, and effect on surrounding users of generation startup or shutdown (Cui and Dai, 2011). The key challenge is to formulate objective functions and constraints that capture the benefits, risks, and uncertainties of a highly complex SG for both centralized and distributed optimization.

Dynamic pricing can be used as a mechanism to create cooperation, competition, and coalition among different DG sources, which can considerably increase the effectiveness of the SG. Although pricing in the SG has been well studied, most works are done with a consumer‐centric perspective (Chen et al., 2010b; Sotkiewicz and Vignolo, 2006; Wang et al., 2010; Kasbekar and Sarkar, 2011; Wang et al., 2010). Research in this area needs to go beyond the interaction between a single energy source and the customers, to multiple DG sources that are in competition and cooperation with each other in a deregulated energy market. For example, it is possible for microgrids to achieve higher payoffs by rational cooperation among each other, compared to selfishly competing with each other (Huang and Sarkar, 2013).

Novel power electronics devices need to be developed for voltage and frequency control to improve grid stability and maximize power transfer (Yu et al., 2011). DG based on rotating machines need fault current limiters and other advanced protection devices (Hidalgo et al., 2011; Divan et al., 2014; Sirisukprasert, 2014; Kobayashi, 2014; Alain et al., 2015; Cataliotti et al., 2015; Kisacikoglu et al., 2015; Isa et al., 2015; Kinsky et al., 2013).

11.4.3 Resource Forecasting, Modeling, and Scheduling

RE resources forecasting and scheduling are important in order to ensure grid stability in the SG. Utilities need to apply resources forecasting and scheduling in order to take necessary actions or preventive measures to better prepare for any event that take place in the grid system keeping a good time frame. For instance, wind, solar, and sometimes, hydro energy resources are highly dependent on location and weather. Resource forecasting could include collecting resource characteristic data, local geographical information, and weather data. Having performed RE resources forecasting through an effective modeling method and scheduling the resources accordingly, the SG can provide an efficient way for the RE systems to be integrated in the SG, and the consequences that the RE systems could bring during these integrations are taken care of.

11.4.3.1 Resource Modeling and Scheduling

Below we discuss the main issues of resource modeling and scheduling, which include modeling and power flow control of ES systems, information management, scheduling of intermittent RE sources, and the use of computational intelligence in the SG.

  1. Modeling and analysis are critical for gainfully deploying ES systems to reduce RE variability in an environmentally friendly manner. Power flow control algorithms based on proper modeling techniques for both long time scale (days, hours) bulk ES, such as pumped hydroelectric and compressed air energy storage, and short time scale (minutes, seconds) fast‐response ES, such as electric vehicle batteries and flywheels, need to be developed. In Su and Gamal, (2011), the optimal power flow problem is formulated as an infinite horizon average‐cost dynamic program with the cost function taken as a weighted sum of the average fast‐ramping generation and the loss of load probability.
  2. Information management is another important enabler of the SG. It is one of the three main parts of the SG architecture, which comprises an electric power system (includes generation, transmission, distribution, and consumers), a communication system (establishes connectivity among the different systems and devices), and an information system (stores and processes data and information). (IEEE Std., 2030, 2011.) The information management system in the SG is vastly more complex than traditional bulk generation and transmissions systems and uses a supervisory control and data acquisition (SCADA) system.

    Some key challenges for the information system in the SG include the stochastic nature of RE sources, buffering effect of ES systems, behavior patterns of customers in the demand side management, and high mobility of electric vehicle increasing uncertainty in demand estimation. The first step toward an information management system is to develop appropriate stochastic models for the different components in the SG, such as wind power generation, solar power generation, energy demand, vehicle mobility, component outage, and energy storage. Based on these component‐level stochastic models, system‐level stochastic information management can be formed, whose key functions include system planning, system maintenance, unit commitment, economic dispatch, and regulation control and protection, all of which can have widely varying time frames. The system‐level information management consists of mainly four parts: (i) bulk generation and transmission, (ii) distribution system and microgrid, (iii) demand‐side management, and (iv) electric vehicle integration (Liang et al., 2014).

  3. Novel energy scheduling is the hallmark of the SG, which can usually be posed as a constrained optimization problem. Typically, increasing energy production using traditional energy sources leads to increased price. However, in the SG, with RE sources equipped with DG and ES, it is possible to develop device scheduling algorithms that not only take advantage of RE sources during peak hours but also modify consumer demand profile by implementing real‐time pricing. The main challenge becomes the intermittent nature of RE sources, which makes it difficult to create accurate scheduling schemes. Most studies assume that the user demand is known ahead of time and with certainty.

    Uncertainty in energy production and user demand can be modelled as random processes. In this context, the user demands can be categorized as delay tolerant and delay intolerant. The goal is to schedule all energy sources so that the long‐term time‐averaged total cost of the utility company is minimized given that all consumers' load demand is satisfied. In the literature, several numerical optimization algorithms have been proposed, including genetic algorithms (Chen et al., 2011; Logenthiran and Srinivasan, 2009), particle swarm optimization (Alrashidi and El‐Hawary, 2008), simulated annealing (Brown et al., 1996; Jonnavithula and Billinton, 1996), and network‐flow programming (Tang, 1996).

    In Salinas et al. (2013), such a dynamic energy management problem is formulated as a Lyapunov optimization problem, where it is shown that all the delay‐tolerant demand can be met within the user‐specified deadlines, and bounds on the optimal strategy have been found. In Zhao et al. (2014), in the context of microgrids, consisting of a set of DGs, the least‐cost unit commitment is found given that load, environmental, and various system restrictions are met. Unit commitment is the problem of finding the optimal decision on which power generator should be on‐line (or active) over time, which minimizes the cost of operation (Liang et al., 2014).

  4. Holistically speaking, the SG represents a very complex adaptive system under semi‐autonomous distributed control. It is characterized by complexity, non‐convexity, nonlinearity, non‐stationarity, variability, and uncertainties far greater than the existing traditional power grid. Therefore, there is a need to transform data into information, information into knowledge, and knowledge into understanding, which leads to intelligent decision making and action in real time. It is envisioned that computational intelligence, which is a framework that leverages evolving, uncertain, and complex interactions of distributed parts and systems in the SG for learning, sense‐making, decision making, and adaptation, will play an important role (Venayagamoorthy, 2011; Reddy et al., 2011; Chandler and Hughes, 2013; Werbos, 2011; He, 2010; Ma et al., 2009). Some of the main attributes of computational intelligence are identification/prediction of nonlinear dynamics, robust/adaptive/optimal/coordinated nonlinear control, complex, and large‐scale dynamic optimization, scalable, fast, and accurate decision making, self‐healing, immunity, and behavioral modeling (Venayagamoorthy, 2011).

11.4.3.2 Resource Forecasting (RF)

Energy management in the SG is mainly concerned with optimal power scheduling, which requires information about resource forecasting (RF). In a sense, any power dispatch algorithm can only be satisfactory if the forecasting information that is fed to it is accurate. The issue of RF becomes vitally more sensitive for stand‐alone microgrids that are characterized by a limited number of generators with highly variable energy outputs. As a result, RF suitable for SG needs to be able to capture this variability of RE sources. Prediction of solar or wind power is a complex problem because of high spatial and temporal dependencies.

There are two types of forecasting approaches: (i) forecast the power generation directly (Kelouwani and Agbossou, 2004; Chatterjee and Keyhani, 2012), (ii) forecast the key variables affecting the power generation and then forecast the power generation using an empirical formula (Kavasseri and Seetharaman, 2009; Deng et al., 2010). For solar power, solar radiation and temperature are predicted, whereas for wind power, wind speed and direction are predicted. However, both of these approaches do not directly model the variability of the prediction.

Some traditional but less accurate forecasting methods include multiple linear regressions and stochastic time series methods, e.g., autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and exponential smoothing; see Zhao and Tang (2016) and the references therein.

Due to the highly nonlinear characteristics of weather prediction, tools of computational intelligence have been applied effectively for developing forecasting algorithms (Xu et al., 2010; Chan et al., 2011; Palma‐Behnke et al., 2011; Bashir and El‐Hawary, 2009; Hinojosa and Hoese, 2010; Amjady et al., 2010). Among these, artificial neural networks have been most widely used (Xu et al., 2010; Chan et al., 2011; Palma‐Behnke et al., 2011). Resource and load forecasting with prediction intervals based on fuzzy modeling to characterize the inherent variability of RE source forecasting have been presented in Sáez et al. (2015) and Hong and Wang (2014). These techniques use weather and performance data to learn the relationship between weather and system performance.

11.4.4 Demand Response

According to the Federal Energy Regulatory Commission, demand response (DR) is defined as “Changes in electric use by demand‐side resources from their normal consumption patterns in response to changes in the price of electricity, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized .” (F. E. R. Commission, 2011). Processed information received as output from the resources forecasting, modeling, and scheduling of the RE resources could be passed to the consumers so that they could initiate efficient demand response. As an example, based on weather forecast, high solar energy can be predicted on a specific day or time of the month, which would be understood from the consumer side as to when the energy storage would be at maximum for planning a high energy usage for that time of the month. In a similar manner, the consumer could also plan on when to depend more on storage device, especially during the cloudy periods of the month (Chen, 2016).

DR is essentially a technology that enables loads to respond to generation, transmission, and distribution. It can be realized by direct load control like reduction of load by curtailing it, as well as complete load interruptions, e.g., residential air conditioners. Some benefits of DR are (Hamidi et al., 2010):

  1. stabilization of pricing;
  2. reduction of the chance of blackouts;
  3. improvement of system security;
  4. reduction of system congestion;
  5. reduction of greenhouse gas emission;
  6. improvement of market efficiency by response from consumers; and
  7. allowing more time for upgrading the SG and network.

DR is implemented by installing DR modules in consumer appliances, which can communicate with the SG via programmable logic controllers (PLCs) or via wireless communication. The module may be smart enough to turn on an appliance after a given period of time or notify the consumer to do so. The charging time of EVs, temperature control on houses and buildings, and washing machine time span are just a few examples. Based on the forecasting of energy cost, the consumer may be given different pricing options to adjust their consumption of energy. For example, in Europe some of the domestic customers are given three color‐coded options, namely, blue, white, and green, representing low, medium, and high prices of energy cost respectively. Such DR has been shown to reduce price and remove fluctuations from the SG. Data centers are seen as good candidates for participating in DR and have the potential to significantly contribute to peak‐load shaving and facilitate the incorporation of RE sources (Wierman, 2014).

In North America, there are 10 independent system operators (ISO) and regional transmission operators (RTO) that control the management of the regional power grids by implementing DR. Rahimi and Ipakchi (2010), give a summary of the policies, programs, and rules of the different ISO/RTO authorities.

Two types of DR models can be considered: price based and incentive based. In price‐based DR, the consumer can reduce the electricity bill by choosing to use appliances during off‐peak hours, instead of peak hours. In incentive‐based DR, the utility company provides some sort of monetary incentive to the consumer to turn off some of its loads during peak demand hours. In both cases, the role of the consumer is passive, that is, they only respond to the utility company by turning off appliances or shifting the time of usage.

As electric vehicles (EVs) become more common, the role of consumers will change from being passive to active. EV users can become active players in the SG, supplying energy to the grid during periods of high demand and make monetary gains or reducing their overall energy cost. Recently, there has been interest in using multi‐agent systems (MASs) to actively take part in DR. MASs are defined as a collection of computational systems that are loosely coupled and can interact with each other for solving a specific problem (Khosla et al., 2004). The MAS is seen as a promising platform to create automatic load management in the smart home/building/community of the future (Li and Nair, 2015). Some key challenges facing DR are:

  1. Robust control structures and advanced forecasting are needed to effectively leverage the highly fluctuating RE sources.
  2. The injection of power from EVs to the grid needs to be facilitated by changing the existing electricity market policies and rules.
  3. New MAS structures need to be developed to realize their full potential in DR. The MAS existing so far simply performs a fixed set of communication and control operations. Therefore, there is a need to devise more sophisticated MAS.

To gain significant benefits from load management, a large number of consumers need to participate in DR. This requires an infrastructure that is capable of handling large volumes of control information throughout the SG.

11.4.5 Demand‐Side Management (DSM)

Demand‐side management (DSM) is the planning and implementation of a set of policies imposed by the utility companies to regulate the energy consumption by the consumers, typically by shifting the electricity consumption from peak to off‐peak demand times. Though appropriate DSM helps to achieve least cost power and maintain a balance between the power generation and power consumption in order to preserve the stability of the power system networks, adopting an efficient DSM is a prevailing challenge in the SG systems.

The main challenge to develop an effective DSM is the uncertain pattern of power generated by the RE sources unlike the conventional power plants. According to the International Energy Agency (IEA), 25% of the world's electricity will be supplied by renewable energy sources by 2035 (Hossain and Ali, 2016). As the penetration of RE sources are gradually growing, to overcome the problem of power generation uncertainty from the RE sources, it is possible to have the RE forecast results available to achieve a better DSM in the grid from the utilities point of view. Through RE resource assessment and modeling, the utilities can estimate the time of day, month, or even year when the RE resources are at a higher or lower profile. Based on the resources data, utilities can adjust their generation system to optimize the overall natural resources, e.g., the consumption of fossil fuel. In addition, it is possible for the utility companies to translate these data to the pricing information per kWh of the energy that consumers pay for during a specific period, which would influence the decision of the consumers on consuming energy during the peak loads and vice versa (Aroge, 2014).

To influence the consumers to take part in the DSM program, the utility companies can implement proper pricing strategies such as TOU (time‐of‐use) pricing, real‐time pricing with lower rates during off‐peak periods, AMI (advanced metering infrastructure), and smart meters (Jalali and Kazemi, 2015). Additionally, there are many forecast methods that can provide acceptable results in price (or load) forecasting such as autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroscedasticity (GARCH) model, empirical mode decomposition (EMD) + extended Kalman filter (EKF) + extreme learning machine with kernel (KELM) + particle swarm optimization (PSO), mutual information (MI) + wavelet transform (WT) + least square support vector machine (LSSVM), MI +WT+ fuzzy neural network (FNN), recursive dynamic factor analysis (RDFA), hybrid method based on modified neural network (NN), WT + extreme learning machine (ELM) + partial least squares regression, WT + grey model, hybrid evolutionary fuzzy, multiple seasonal patterns and modified firefly algorithm, cuckoo search (CS) + optimally pruned extreme learning machine (OP‐ELM), artificial neural networks (ANN), and wavelet packet transform + LSSVM + artificial bee colony (ABC) (Ghasemi et al., 2016). Besides an effective communication infrastructure with the capabilities of handling big data, appropriate intelligent devices are the key components to set up an effective DSM for the smart grid system. Overall, it is possible for the utilities to influence the demand of electricity at some point of time by applying an efficient DSM policy and so achieve a seamless integration of RE resources in the SG system.

11.4.6 Monitoring

Besides the appropriate forecasting tools of the RE sources and customer energy demand, real‐time monitoring is one of the key contributions that the SG makes in developing an environment‐friendly power grid concept. Integrating RE resources in the SG would require a high level of real‐time monitoring. This is because real‐time monitoring would have to be focusing on both the SG for its regular monitoring and the RE system infrastructures for an extended checkup on their operational systems especially due to their variable characteristics. One of the system components of the SG, namely, the smart communication subsystem, provides the appropriate communication infrastructure based on a large number of sensors and actuator networks working on bidirectional information communication. The benefits offered by these technologies hold more accurate and timely broadcasting of the information about the grid, which help the grid operation programs carry out exact and efficient real‐time scheduling to minimize the problems created by the unpredictability of renewable generation and variations in customer demand (Wen and Li, 2014). Additionally, real‐time monitoring of the grid condition provides better reliability and protection in the grid. For example, in the occurrence of a power line fault or failure of the equipment, the grid can sense the problem with the large number of sensors in the system. Next, by applying SG's computational intelligence, the SG can divert the electrical power to a desired destination through an alternative path, providing uninterrupted power to the destination; thus, reliability of the power system is ensured. As an additional challenge, in the SG systems, real‐time monitoring would also cover the RE generation plants for maintenance, planning, and security issues (Aroge, 2014). During system monitoring, as the SG involves bidirectional communication between consumers and other components of the SG system, ensuring information security for the entire network is an important issue for the SG system. In home‐monitoring applications, wireless sensor networks could have huge potential, allowing them to be widely used to enhance cost efficiency through the use of in‐home energy management systems in the SG scenario (Erol‐Kantarci and Mouftah, 2011).

11.4.7 Transmission Techniques

The SG ensures efficient power transmission from generation sites to the consumers. Being more sustainable and environment‐friendly requires the SG to transmit power from the generator to consumers with minimum loss by means of flexible AC transmission systems (FACTS) and high‐voltage DC (HVDC) transmission techniques (Fangxing et al., 2010; Liserre et al., 2010). As the RE systems are very much location specific, the RE resources, e.g., on‐shore and off‐shore wind and solar power generators, are sometimes located in a remote location considerably far away from the load centers. An HVDC transmission scheme can be considered for power transfer for the long distance RE sources due to the following advantages over high‐voltage AC (HVAC) power line (Bresesti et al., 2007): low cable cost, lower voltage drop in the power line with lowest power losses, presence of no resonance between the cables and other AC equipment, faster power control, etc. However, the main drawback of an HVDC system is the high cost substation due to the presence of costly power electronic devices and cooling systems.

On the other hand, during power transfer through the HVAC power line, different types of FACTS devices are used to improve the power systems stability, power quality, and power systems' reliability. The commonly used FACTS devices considered in the power systems are static synchronous compensator (STATCOM), static VAR compensator (SVC), thyristor‐controlled reactor (TCR), thyristor‐switched reactor (TSR), thyristor‐switched capacitor (TSC), superconducting fault current limiter (SFCL), bridge‐type fault current limiter (BFCL), resonant type fault current limiter, series dynamic braking resistor (SDBR), etc. (Hossain and Ali, 2015). Compared to an HVDC transmission system, the major advantage of HVAC is low‐cost substations, as no power electronic devices are required during the power conversion (Bresesti et al., 2007).

11.4.8 System‐Related Challenges

In addition to the specific challenges as outlined above, some system‐related challenges include the following (Shafiullah et al., 2010). First, design of an RE system's generator could be an important factor in its integration in the SG. Second, the interaction between one RE system with another RE system of the same or different type could also affect its integration to the SG. The position of the RE plant with respect to the SG is another important factor in the integration process. Admittedly, the nearer the location of the RE system to the grid, the better would be the electrical power strength due to reduced loss. However, for distributed generation of RE, the location of the RE system should be investigated and chosen carefully. Finally, the energy cost is another important factor that should also be investigated while an RE system is integrated in the SG (Shafiullah et al., 2010).

Furthermore, in the smart grid technology, the growing number of random parameters will complicate the mathematical computations of the control algorithms and reliability indexes of the whole power system (Hossain and Ali, 2015). As the smart grid integrates various generation sources, working with the appropriate performance indexes to evaluate the reliability, stability, and sustainability of the whole system is one of the important issues (Hossain and Ali, 2015) that needs to be considered for the SG system.

In another aspect of the SG, as information and communication networks interact with power system networks, there would be several challenges for information and communication systems and networks as well as data centers, which are out of the traditional power system–related operations. For example, energy efficiency in communication networks in the aspects of the SG could be considered as a potential challenge area in the SG framework (Erol‐Kantarci and Mouftah, 2015), which would definitely need more research focus when RE resources are to be incorporated in the SG.

11.4.9 V2G Challenges

  1. What is V2G?

    V2G refers to the capability of plug‐in EVs (PEVs) to provide energy to the grid. The V2G option can be viewed as another form of distributed ES system in the SG. In V2G, the PEVs are equipped with bidirectional energy delivery capability so that, based on demand, the EV can be drawing energy (charging) from the SG during off‐peak hours, as well as providing energy (discharging) to the SG during peak hours. Although the conventional thinking is that PEVs can be charging at home from evening till morning, we need to consider that most vehicles stay parked 90% of the time. So there is ample opportunity to allow PEVs to interact with the SG for cost minimization and load shaving. However, this process needs to be carefully controlled by using sophisticated communication mechanisms and infrastructure (Kempton and Tomić, 2005; Peterson et al., 2010; Lund and Kempton, 2008; Turton and Moura, 2008; Zhang et al., 2016).

  2. V2G Opportunities and Applications

    Some important opportunities and applications of V2G are outlined below (Hosseini et al., 2012).

    1. Virtual power plant (VPP): A fleet of PEVs can be viewed as a VPP, which can serve as a substitute for intermittent RE sources when needed.
    2. Optimal placement of EVs can be made in the distribution part of the SG to make it more secure and resilient against outages.
    3. The PEVs can reduce the uncertainties associated with RE resources by charging from the SG whenever such resources are available.
    4. V2G can also be used for vehicle‐to‐building (V2B) and vehicle‐to‐house (V2H) applications.
    5. Smart parking lots: Smart parking lots are envisioned to house a fleet of PEVs in a secure environment, which can serve as an interface that provides both charging and discharging services. The PEVs can simply connect to the SG infrastructure to start charging for the duration of the parking, or, get a quick battery replacement by switching uncharged batteries with fully charged batteries available at the lot. The parking lot could also serve as a VPP and aggregate energy from the parked cars to supply energy to the SG.
  3. V2G Challenges

    Below we discuss some of the key challenges in realizing V2G technology.

    1. V2G systems face two major challenges, namely, load shaving and frequency regulation. Load shaving refers to meeting the peak time load by providing energy to the grid during peak demand. For the consumer with PEV, it is advantageous to discharge during peak hours and charge during off‐peak hours so that the overall operation cost of the PEV user is minimized. Frequency regulation refers to fine‐tuning of the frequency of the grid at a smaller time scale. There is also environmental gain in using V2G, since it reduces the need for fossil fuel resulting in less carbon emissions. However, there are several factors that limit the amount of power that a PEV can provide to the grid, such as battery size, amount of charge in the PEV, capacity of the plug circuit, battery life span, and battery charging/discharging capacity (Hosseini et al., 2012).
    2. Energy cost minimization in V2G is a key problem. In V2G, factors like bidirectional energy flow between the vehicle and the grid, vehicle's mobility pattern, battery model and its feasibility of use, and time‐of‐use‐based pricing of consumed electrical energy can be considered as influencing factors in the overall energy cost minimization and therefore considered as open issues in this field (Liang et al., 2012).
    3. Fast PEV chargers: Depending on its size and structure, a PEV can consume 4–16 kWh per day. So there is a need for developing fast chargers that are comparable to refueling time (Kisacikoglu et al., 2015). Also, induction and conduction‐based methods are available for charging. Induction‐based chargers do not require physical contact and therefore are more popular.
    4. The energy market policies and regulations are likely to be transformed by the V2G technology. However, to make V2G a significant player in practice, there is a need for an aggregator entity (Xing et al., 2016). This entity will satisfy the demand of the SG by monitoring, controlling, and managing fleets of PEVs.
    5. The PEV impacts the voltage regulation and phase balance of the grid, which determines the quality of power. Therefore, careful requirements need to be developed for determining how PEVs are integrated into the SG (Dharmakeerthi et al., 2011).
    6. PEV also introduces harmonics into the grid for both low and high penetration levels. Proper charger technology to network filtering needs to be employed to mitigate this (Dharmakeerthi et al., 2011).
    7. PEV leads to more power flow in the SG, which leads to more power losses. Charging during off‐peaks, proper phase balancing, and using DGs in nearby locations can be used to counter this issue (Dharmakeerthi et al., 2011).
    8. Cyber security is an important concern. Confidentiality of transmission information needs to be assured, and tampering of data needs to be prevented at all costs. Security mechanisms to counter suspicious traffic patterns and distributed denial‐of‐service attacks must be ensured as well. In Zhang et al. (2016), a framework based on software defined radio architecture to implement secured communication and control for V2G has been proposed.

11.4.10 Security Challenges in the High Penetration of RE Resources

Increased power generation from the highly penetrated RE sources offers a greener and more sustainable future. At the same time this also introduces new risks and challenges for the transmission system, e.g., limited control on power generation from the high penetration of RE resources with additional challenges in scheduling and dispatching controllable resources to follow the net load and control the power balance in the system (Navid and Rosenwald, 2012), which needs to be mitigated to maintain the power system stability, power quality, and system reliability. In addition, it has been noticed that RE sources deteriorate the operations of the protective relays during the network faults. RE sources can introduce additional faults on adjacent feeders even after opening the utility protection devices such as protective relay, circuit breaker, and fuse (Baran et al., 2012). This scenario can be more vulnerable to the power system network due to the presence of highly penetrated RE sources. However, this problem can be minimized with the help of proper coordination and optimal operation of the power system protective devices and with the presence of other compensating devices such as FACTS devices, energy storage systems, etc. In addition, with the centralized energy management system of the SG, decentralized control of the high penetration of RE resources and secured communication without delay will facilitate the SG system to ensure the power system protection and stability, power quality, and overall power system reliability.

11.5 Case Studies

In this section, some case study examples have been shown where RE‐based power generation systems have been integrated into the SG. Singapore is a city‐state with 712.4 square kilometers and 5.183 million people that has undertaken a project to build a microgrid in one of its islands named Pulau Ubin with an area of 10 square kilometers located north‐east of the mainland where 100 villagers live (Koh et al., 2012). The electrical consumption of Pulau Ubin island is estimated to be 1.7 MW with approximately 2,500 MWh annual energy consumption in 2009 (Koh et al., 2012). The Energy Market Authority (EMA) of Singapore, a statutory board whose main goal is to provide reliable electrical power to residents, has planned for a RE‐based power system. To that plan, they carried out a survey to collect data on load point, electricity consumption, and daily load pattern and applied industry methods to forecast the growth of electrical power consumption till 2020. In the absence of any electrical power grid in the island, the residents use diesel generators to fulfil their electricity demand. Even though diesel generators are expensive, unreliable, somewhat inconvenient, building transmission network from Singapore to Pulau Ubin proved to be economically infeasible due to modest load demand in the island. Thus the authority anticipates that the project would be a “living laboratory” relying on RE in a sustainable manner (Koh et al., 2012). In that context, the main intention is to invite companies to build a microgrid infrastructure where the interested companies would “design, build, own, and operate” their microgrid in a competitive and cost‐effective manner.

Another example of integrated RE resources in the SG is the power grid network of India. As of August 2011, India has an installed capacity of 21,000 MW of RE generation with a total estimated potential of 188,700 MW (Mukhopadhyay et al., 2012). In India, wind energy is the top of all RE resources with approximately 15,000 MW out of the 21,000 MW installed capacity. Near future plans of RE projects in India also include creating 20,000 MW solar power grid including 20 million solar lights and 20 million square meters of area for solar thermal energy purposes by 2022 (Mukhopadhyay et al., 2012). The project hopes to be complete in three phases, with phase 1 aiming at setting 1,100 MW of capacity of grid‐connected solar power systems, 100 MW of capacity of small, rooftop solar plants, and 200 MW equivalent of off‐grid solar power systems (Mukhopadhyay et al., 2012). Energy storage systems, namely, pumped hydro and battery storage, were used in conjunction with RE systems to properly take care of their intermittent nature along with the customer participation, which would necessitate a smart power grid with advanced ICT to optimize RE generation and storage systems as well as their interconnectivity and integration to the SG. In India, initiatives have been taken to meet these goals regarding the integration of RE resources in the SG (Mukhopadhyay et al., 2012).

11.6 Conclusion

Integrating RE resources in the SG is a promising yet challenging task. In this chapter, prospects and the corresponding challenges of integrating RE resources in the SG have been presented. Having provided a quick overview of the SG framework, integration of RE resources has been presented. The opportunities and the corresponding challenges of integrating RE resources in the SG have been categorized and presented separately with supporting references for interested readers in the crossroads of RE and the SG fields. On the search for sustainable energy solutions of emerging societies, it is inevitable that RE resources be integrated with the SG system. To that end, this chapter could certainly serve as a starting point for the students and practicing engineers to start research and development in the interdisciplinary field of RE and the SG. The chapter also provides some case studies on this topic. The RE‐integrated SG infrastructure can contribute to the progress toward a sustainable power grid and its efficient management. In particular, a huge amount of opportunity exists for the RE‐integrated SG infrastructure for developing countries where system loss in the power grid can be a concern. Although a RE‐integrated SG system may seem costly in its installation, in the long run it should become a feasible and environment‐friendly solution.

A detailed analysis on modeling RE integration in the SG and its performance evaluation are among the future directions of this field of research. To this end, modeling the effects of variability of RE resources on the performance and stability of the SG system would be worth investigating. It should be noted that the inclusion of RE resources would bring along the most important challenge, namely, the intermittent nature of RE resources themselves, which would require the support systems of the SG to be capable of handling the variability of the RE resources efficiently. Thus, it would be worth investigating the performance of the electrical machines and power electronic circuits necessary in the SG system when RE resources would be integrated. Another possible direction could be to investigate how the business model of the SG system would be affected with the inclusion of RE resources, especially from both utilities' and consumers' points of view. Certainly, the research and development of the RE integration in the SG will be continued to establish this technology intensely to meet up the world electricity demand efficiently.

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