Keywords

1 Introduction

Conventional electrical grids have centralized power plants that provide energy to consumers with limited governance and consumption monitoring [96]. Information and electricity flows in conventional grids are unidirectional and the grids lack self-restoration capabilities after outages [34]. These deficiencies have led to the transformation of conventional power grids to smart grids.

Smart grids incorporate cyber and physical systems in power networks to support the efficient generation, transmission and distribution of electricity [139]. Smart grids employ industrial control systems that leverage information and communications technology to control physical processes [66]. Cyber attacks on industrial control systems have severe consequences because they target the vital physical systems being monitored and controlled [14]. Cyber attacks on smart grids are a grave concern [46]. The most serious example is the December 2015 cyber attack on the Ukrainian power grid, which caused approximately 225,000 consumers to lose power [31].

It is impossible to defend against every cyber attack on a smart grid because new vulnerabilities constantly emerge and attackers continually find new ways to exploit them [6]. However, risk analysis can play an essential role in preventing cyber attacks by identifying potential vulnerabilities and threats, and determining their likelihood and potential impacts [49]. Impact assessments determine the potential consequences of successful cyber attacks in terms of disruption of critical services, financial losses and reputational damage [70], enabling smart grid entities to prioritize their security efforts and allocate resources to address the most critical risks.

This research has attempted to examine the validity of common methods for verifying the consequences of cyber attacks on smart grids. The effort focused on the research literature on consequence verification during risk assessments of smart grids from 2009 to 2023. A total of 839 articles representing the state of the art were reviewed. After filtering duplicate and irrelevant articles, 155 were subjected to in-depth analysis, which eliminated 30 of the articles. The investigation determined that 120 of the remaining 125 articles studied the impacts of assessed risks, with 67 of them also focusing on real consequence verification. The results provide an understanding of the methods for verifying the consequences of cyber attacks on smart grids and the degree to which real consequences can be verified.

2 Smart Grids and Cyber Attacks

This section discusses smart grids, threats and vulnerabilities, and cyber attacks on smart grids and their consequences.

2.1 Smart Grids

In the European context, a smart grid is an electricity grid that intelligently manages the behaviors and activities of all users linked to the grid [141]. This feature enables a smart grid to deliver power more efficiently than a conventional grid while responding to diverse circumstances and events across the grid. The circumstances and events pertain to power generation, transmission, distribution and consumption [34].

The U.S. National Institute of Standards and Technology (NIST) defines a smart grid as an electric power system that uses information, two-way cyber-secure communications technologies, and computational intelligence in an integrated fashion across the spectrum of an energy system from generation to consumption [41]. NIST also specifies a conceptual smart grid model comprising seven domains: power generation, transmission, distribution, consumers, service providers, operations and markets, all of which interact with each other in real time [139].

Fig. 1.
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(adapted from [131]).

Conceptual smart grid model

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Figure 1 shows a conceptual model of a smart grid. The introduction of various domains and enhancements increases the complexity of a smart grid and renders it vulnerable to myriad attacks.

2.2 Threats and Vulnerabilities

A threat is a potential adverse event or action that has the potential to cause harm or damage to an individual or organization. In cyber security, threats are possible malicious actions that compromise the confidentiality, integrity or availability of information systems and the data they process [56].

A vulnerability is a weakness in a system or process that may be exploited by a threat. The weaknesses may exist in software, hardware or organizational processes. For example, a vulnerability in a software application can be leveraged by an adversary to gain unauthorized access to sensitive information [56].

The principal smart grid attacks, threats and vulnerabilities include [6, 42, 47, 65]:

  • Cyber Attacks: Smart grids are highly dependent on computer systems, networks and communications systems, which makes them vulnerable to cyber attacks such as malware, ransomware and denial-of-service attacks.

  • Advanced Persistent Threat: The advanced persistent threat includes targeted, persistent and sophisticated cyber attacks that are designed to steal sensitive information or disrupt grid operations.

  • Insider Threat: Smart grid employees and contractors with grid access can introduce malicious software or disrupt operations intentionally or unintentionally.

  • Physical Attacks: Smart grid assets such as power plants and substations are vulnerable to vandalism and sabotage attacks.

  • Supply Chain Threat: Smart grid systems often rely on third-party vendors for software and hardware components that have vulnerabilities that can be exploited.

  • Aging Infrastructure: Smart grid systems and infrastructure are susceptible to malfunctions and failures due to their age.

  • Lack of Security Standards: Smart grid systems are constantly evolving in their technologies, designs, implementations and operations. Security standards and best practices for vendors and operators may not be followed or may not exist.

  • Interoperability: Smart grids are complex systems involving multiple vendors, communications protocols and technologies. Attackers can exploit vulnerabilities that arise from the need to achieve system interoperability.

2.3 Cyber Attack Consequences

The consequences of cyber attacks on a smart grid are severe and wide-ranging. Toftegaard et al. [122] list prominent cyber attacks on European power sector assets over the past eight years. The most serious consequences were caused by the December 2015 cyber attack on the Ukrainian power grid, which cut power to approximately 225,000 consumers [31].

Ding et al. [25] present a review of cyber attacks on smart grids from 2010 through 2022. Their review describes attack targets, methods and consequences. Ding and colleagues note that cyber attacks that exploit smart grid vulnerabilities are responsible for the most serious consequences.

Researchers have shown that large blackouts are often the result of cascading failures [44, 118]. One of the largest blackouts in European history occurred on November 4, 2006. A single incident originating in Northern Germany led to power supply disruptions at more than 15 million European homes [82]. Su et al. [118] posit that a coordinated software-based attack would have greater negative impacts than physical sabotage.

Although power disruptions are the most common consequences of cyber attacks on smart grids, the information-driven processes in smart grids provide myriad attack opportunities with negative consequences. For example, ransomware attacks do not need to disrupt power supply to be successful; instead, they may cripple maintenance and invoicing functions at a utility, resulting in significant economic losses. Smart grids are also susceptible to privacy breaches of customer credit card information, personal information and detailed customer consumption data that may reveal in-home activities.

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) defines consequence as “the effect of a loss of confidentiality, integrity or availability of information or an information system on an organization’s operations, its assets, on individuals, other organizations, or on national interests” [92]. The consequences involve compromises to any or all of the three main security properties, confidentiality, availability and integrity. Because of the great variance in consequences of cyber attacks on smart grids and their potential severity, it is essential to have a deep understanding of the mechanisms that lead to negative consequences.

3 Related Work

Several literature reviews and surveys have focused on cyber security, cyber attacks, threats, impacts and defenses related to smart grids. He and Yan [55] discuss the security challenges facing smart grids. They detail the critical threats, attack schemes and defensive solutions involving protection, detection and mitigation. Attack schemes highlighted include transmission system attacks, distribution system attacks and electricity market attacks. Their work is intended to raise awareness and inspire research efforts focused on developing secure and resilient cyber-physical infrastructures.

Mrabet et al. [87] survey smart grid security challenges and review various attack schemes and defensive strategies. They note that smart grid security efforts tend to focus on confidentiality, integrity and availability, but have yet to consider accountability. They propose a three-step cyber security strategy covering the pre-attack, under-attack and post-attack phases. They review security requirements, describe several severe cyber attacks and classify attacks as focusing on reconnaissance, scanning, exploitation and maintaining access. They also recommend detection techniques and countermeasures, including network security, data security, device security, attack detection and mitigation, and digital forensics.

Ding et al. [25] describe cyber threats that impact the security of smart grid ecosystems. They consider intrinsic system vulnerabilities and external cyber attacks, and analyze the vulnerabilities of smart grid components, including hardware, software, data communications and data management systems. They also present a structured smart grid architecture and a global review of cyber attacks on smart grids between 2010 and 2022.

Gunduz and Das [47] present a comprehensive survey of cyber security issues related to Internet-of-Things-based smart grid applications and proposed solutions. They also analyze various types of cyber attacks, network vulnerabilities, attack countermeasures and security requirements.

Smadi et al. [115] discuss the importance of employing smart grid testbeds to analyze power systems. They provide a comprehensive overview of cyber-physical smart grid testbeds, including their architectures, functional analyses, main vulnerabilities and threats, testbed requirements, constraints and applications. They also highlight the use of simulation testbeds, physical testbeds, interconnectivity of testbeds at multiple locations and integration of software-defined networking (SDN) technology.

4 Research Methodology

This research involved a systematic review of the methods used to verify the consequences of cyber attacks on smart grids and their validity. Table 1 summarizes the research methodology, including the research questions, survey period for the research literature, databases containing the research literature, search criteria, search keywords and search inclusion and exclusion criteria.

Table 1. Summary of research methodology.
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Prominent English research article databases, Scopus, Web of Science, IEEE Xplore and ScienceDirect, that cover the technical areas of interest were selected for the research. Literature reviews and surveys in the field were analyzed to produce an appropriate list of keywords for searching article titles and abstracts. Table 1 shows the search criteria and keywords as well as the search inclusion and exclusion criteria.

The articles were restricted to those published from January 1, 2009 to December 31, 2022. This was because the research sought to focus on the European Union (EU) market and the key starting point was the important 2009 EU Electricity Market Directive (2009/72/EC) that established standards and rules for the European electricity market [33]. The articles were also screened to eliminate duplicates culled from the databases. The database search results comprised 839 articles.

The next important step involved reading the article titles and abstracts and eliminating irrelevant articles. Review and survey articles were also excluded. Articles were included in the pool if their abstracts lacked details to make accurate selections. The final survey pool included 155 articles.

Each of the articles in the survey pool was read carefully to answer the following questions:

  • Is a risk assessment performed?

  • Are risk consequences studied?

  • What business areas are impacted by the identified risks?

  • What techniques are used to verify the consequences of cyber attacks on smart grids?

  • To what degree is the consequence verification method capable of revealing real consequences?

The answers for each article were documented in a Microsoft Excel file and categorized by database to structure the data for further investigation. Each answer was recorded as a yes or no, along with relevant keywords and comments. The study took approximately three months, from querying the databases, culling articles, and reading and recording data about all the articles in the survey pool.

The questions related to several articles were answered differently by different readers. For example, one reader assessed an article as verifying consequences whereas another reader assessed it as a theoretical study that did not verify real consequences. These articles were examined by the co-authors of this chapter and their comments were compared to obtain consensus answers to the specific questions.

5 Results

Detailed analysis of the 155 articles in the survey pool revealed that 30 articles were not relevant. Thirteen of the 30 articles investigated the impacts of policy decisions or recommendations, or market reform policy related to smart grids. Three other articles focused on the financial profitability of implementing microgrids or smart grids and two articles analyzed the criticality of cyber-physical infrastructure risks to society. Twelve other articles did not apply specifically to the research. These articles examined risk optimization in the electricity sector, monitoring in smart cities, general cyber risk analyses in other critical infrastructure sectors, intrusion detection systems, microgrid design performance, and conceptual models for representing and tracking compliance based on security standards, among other topics.

Fig. 2.
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Overview of research results.

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Figure 2 presents an overview of the research results. The 125 articles in the survey pool focused on risk analyses of smart grids. Of these articles, 120 (96%) also studied the consequences of the assessed risks.

Real consequence verification of cyber attacks on smart grids is the process of assessing the real physical impacts. This involves evaluating potential equipment damage, power outages, economic impacts, privacy impacts to consumers and overall disruptions in electricity delivery. Real consequence verification is important because it helps identify the most critical grid vulnerabilities and prioritize efforts for mitigating potential cyber attacks and their negative impacts. The analysis indicated that 67 (53.6%) of the 125 articles investigated this important issue to some extent. The fairly large percentage (46.4%) of articles that completely ignored consequence verification demonstrates a key gap in the research literature.

Figure 3 shows the 23 significant business areas impacted by the risks identified in the research articles. The most impacted business area in terms of article coverage is advanced metering infrastructures/smart meters with 21 articles, followed by grid distribution, microgrids and grid communications with 18, 15 and 13 articles, respectively.

Table 2 lists the 23 business areas along with the specific articles in the survey pool that cover their risks.

Figure 4 shows the six principal methods for verifying the real consequences of the identified risks to smart grids. The most widely researched method in the literature involves the use of IEEE test systems (22 articles). These test systems, which are commonly employed in power system analysis research [99], simulate power system behavior under conditions such as power flow, voltage stability and transient stability. They enable researchers to analyze and understand the behavior of power systems and their components in a variety of scenarios. They also offer simplified representations of real power systems for testing and validating power system analysis techniques and algorithms.

Fig. 3.
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Overview of impacted business areas.

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Fig. 4.
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Methods for verifying real consequences.

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Table 2. Business areas impacted by risks.
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The IEEE test systems employ 9-bus, 14-bus, 30-bus and 33-bus models, among others. The IEEE 9-bus system comprises three synchronous generators, nine buses, six transmission lines, three transformers and three real/reactive power loads. The IEEE 14-bus bus system is a simple approximation of the American electric power system with 14 buses, five generators and 11 loads. The IEEE 30-bus system, with 30 buses, 41 branches and six generators, is extensively used for power system analysis. The IEEE 33-bus system, which is used as a benchmark test case for power system analysis, has 33 buses, 38 branches and six generators.

Thirteen articles used simulation or emulation methods to verify the real consequences on smart grids. These include hybrid simulation-emulation, Mininet emulation and the use of simulation/emulation tools such as OMNeT++ [94], GridLAB-D [95] and Simulink [84]. Also covered in the research literature were scenario-based methods (three articles), probabilistic methods (two articles), Markov modeling methods (two articles) and electric vehicle charging network based methods (one article).

Table 3. Methods for verifying real consequences.
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Table 3 lists the six types of methods used to verify real consequences on smart grids along with the specific articles in the survey pool that cover the methods.

The research reveals that, although several methods demonstrate the real consequences as a result of risk analysis, more research needs to be done to ascertain the degrees of the actual consequences. One reason is that most verification mechanisms focus on specific areas such as advanced metering infrastructures/smart meters, grid distribution and supervisory control and data acquisition (SCADA) systems or specific cyber attacks such as denial-of-service and false data injection.

However, no articles have as yet employed simulation to compare the results of the most serious consequences of cyber attacks on smart grid assets. Simulations may be based on the perceived consequences of cyber attacks on smart-grid-enabled business cases as described in [122]. By basing simulations on business cases with the highest consequence levels, there is a better chance of identifying, through accurate verification, the business cases that are most crucial to smart grid operation.

6 Discussion

The primary consequences of cyber attacks on smart grids include equipment damage, power outage, economic impact, consumer privacy impact and overall electricity delivery disruption. These consequences may be evaluated as follows:

  • Equipment Damage: This is evaluated by assessing the vulnerabilities of equipment to cyber attacks and conducting simulations that demonstrate how the equipment would perform under attack conditions.

  • Power Outage: This is evaluated by analyzing historical data on power outages, conducting simulations of different scenarios and assessing the impacts of outages on consumers.

  • Economic Impact: This is evaluated by analyzing the costs of power outages, impacts on revenue and business continuity, and recovery costs.

  • Consumer Privacy Impact: This is evaluated by analyzing the types of information at risk, potential consequences of breaches and identifying the best practices for protecting customer data.

  • Overall Electricity Delivery Disruption: This is evaluated by assessing the general resilience and robustness of smart grids and identifying potential vulnerabilities and areas for improvement.

It is important to note that as smart grid complexity and interdependencies increase, the impacts should be evaluated using a holistic approach and advanced modeling and simulation tools. The research results indicate that 23 significant business areas are impacted by the cyber attack risks identified by the 120 of the 125 research articles in the survey pool (Table 2). Additionally, 67 of the 120 articles (53.6%) focus on the verification of the real consequences of cyber attacks. Table 3 lists the methods used to evaluate the real consequences of cyber attacks on smart grids. However, detailed analysis of the 67 articles revealed that, although the degrees of cyber attack consequences can be confirmed, their focus is limited to specific portions of smart grids and/or particular types of cyber attacks.

For example, Soykan and Bagriyanik [117] employed the Gridlab-D open-source power system simulation and analysis tool on the IEEE European Low Voltage Feeder test system. Their simulations demonstrated that cyber attack consequences include regional outages that could lead to large-scale blackouts due to cascading effects on the power system. However, they only studied a phishing attack launched via a text message to capture the credentials needed to access a demand response program. Manipulation of demand response program operations enabled the theft of sensitive information as well as electricity supply disruption.

Likewise, Teixeira et al. [121] evaluated the consequences of false data injection attacks on power transmission networks using an IEEE 14-bus benchmark test system, but they only considered attacks on sensor data.

Yan et al. [136] employed the IEEE 39-bus system model, but only to monitor voltage stability during and after cyber intrusions. AlMajal et al. [5] also used the IEEE 39-bus test system to evaluate the consequences of manipulating circuit breakers and the effects of integrating photovoltaic systems on smart grid stability under circuit breaker manipulation scenarios.

Akhtar et al. [2] analyzed the reliability of integrating solar and wind energy resources in a smart grid, but without considering cyber threats. Dogaru and Dumitrache [27] used the IEEE-9 bus benchmark system to simulate the effects of false data injection and message replay attacks on power grid operations. Alrowaili et al. [7] employed a 12-bus power system model using a PowerWorld simulator and launched cyber attacks on critical assets such as circuit breakers and evaluated their impacts on the physical system.

Lanzrath et al. [74] applied scenario-based methods using real devices. However, they only evaluated electromagnetic interference. Yayilgan et al. [137] employed a Mininet emulator with an IEC 61850 library to simulate cyber attacks on digital substations and demonstrate their impacts. This research needs to be moved to real devices via simulation and extended beyond digital substations to verify the real-world consequences of cyber attacks on smart grids.

7 Validity Evaluation

Construct validity [108] reflects the extent to which the contents of the articles analyzed in this research actually represent what was intended and what was assessed according to the research questions. The key point is how closely the consequence verification concepts are understood by the authors of the analyzed articles and the researchers involved in this survey study. Clear criteria and human evaluations were applied to analyze the strength of the consequence evaluation in each article in the survey pool. However, variances in the details of the analyzed research articles rendered the consequence evaluation strengths difficult to assess. Therefore, the interpretations may be characterized as posing threats to the construct validity. Similarly, the need for human interpretation poses a threat to the reliability of the research results.

8 Conclusions

This research has conducted a thorough analysis of the research literature on smart grid cyber risk assessment and consequence verification from 2009 to 2023. A systematic search of prominent research databases covering the technical areas of interest yielded 839 articles. Preliminary culling of articles followed by deep analyses of article content yielded a pool of 125 articles that focused on smart grid risk analysis. A total of 120 (96%) of the articles in the pool also studied the consequences of the assessed risks to some extent. However, the fairly large percentage (46.4%) of smart grid risk assessment articles that ignored real consequence verification demonstrates a key gap in the research literature.

Two key results of this research are the identification of 23 smart-grid-enabled business areas impacted by cyber risks and six methods for verifying the real consequences of cyber attacks on smart grids. Future work will apply real consequence verification techniques to rank the smart-grid-enabled business areas as well as individual business cases based on their potential disruptive impacts. Real consequence verification is important because it helps prioritize security investments for mitigating potential cyber attacks and their negative impacts.