Introduction
Risks are unavoidable in business projects, but their impact can be minimized through effective mitigation plans. A risk management methodology that includes systematic steps and tools is useful for identifying and controlling key risks in a project lifecycle to improve the likelihood of success. Most investments are complex and costly, and thus, managing events likely to cause negative effects or prevent the achievement of objectives is a priority. This paper examines the major risk exposures to a project implementing a smart grid system, recommends ways to address them, analyzes performance control via earned value management (EVM), and identifies useful EVM metrics.
Project Summary
The automated power project (APP) aims to pilot a new digital system in a local electrical grid. It involves distribution automation to enhance the flexibility and resiliency of the grid when faced with extreme weather. Given the network architecture and security requirements, this project is exposed to many unknown risks. APP is a partnership between the national energy-generating agency and a local distributor.
The power supplier provides technology and software, while the energy company offers the infrastructure to implement the project. APP has two project managers with different roles and responsibilities. One of them manages distribution and the other oversees the power supply component of the APP. The pilot project has passed through the design and planning stages and is currently in its implementation phase.
Project Risks and Recommendations
Smart-grid operations are subject to specific risks and uncertainties that have various drivers and sources. The APP project’s greatest risks can be determined using the probability-impact (PI) matrix. A major risk that the APP project is exposed to is cyber-attack. Attackers may target advanced metering infrastructure (AMI) installed to ensure efficient meter reads and connect or disconnect clients remotely, causing system failure (Faquir et al. 2021).
The fault would affect the capacity of the smart grid system to support power flow, compromising its stability and reliability. Thus, malicious attacks constitute a risk with a high likelihood and impact. They can occasion a system shutdown (no power output to consumers) or low voltage energy delivery that may degrade user and utility equipment. Cyber-attacks may also compromise the security of customer data. Unsecure smart grids are vulnerable to remote or physical interference that may affect consumer privacy.
Another high-impact risk that APP faces are consumers rejecting the new cost tariffs of smart grids. The demand-side response technology that levies a daily-ahead hourly power commitment to major clients and additional charges for exceeding the agreed consumption values is a critical part of smart grids (Lamba, Šimková, and Rossi 2019). The approach ensures cost-effective demand management that will benefit APP greatly. The new tariff model is likely to face social resistance because it is premised on consumers changing their energy-use behavior. Their willingness to adopt the technology will determine the success of the APP pilot project.
Construction completion risk is another threat with a high likelihood of occurrence and negative impact on the APP’s objectives. The two components of the project (technology and AMI) are managed separately. The different sources of energy may require infrastructure and software modification or replacement, which is costly. As such, cost overruns, structural problems, resource constraints, and design challenges could result in an incomplete project (Faquir et al. 2021). Unpredictable power demand also poses a significant risk to APP. Making accurate forecasts of power needs are critical to designing a smart grid and cost structure for the local project. However, unforeseen surges in electricity demand can affect APP’s financial viability, cause power shortages, and endanger utility and customer equipment.
Depending on the nature of the risk, project managers can choose to avoid, transfer, or reduce its impact or likelihood of occurrence as a risk-management approach. A recommendation for addressing the cyber-attack risk is using standardized components and security standards for smart meters to guarantee data protection and privacy. All software for APP should be sourced from reliable suppliers to transfer this risk. The risk of the low uptake of the tariff technology can be mitigated through customer engagement to increase awareness of its benefits. This strategy is a risk transfer measure for ensuring local support for the project.
The construction completion risk can be insured through the delay in the startup product (Faquir et al. 2021). This solution is available for small projects that need support to achieve objectives. Mitigating the risk of unpredictable power demand can be challenging because of variations in customer composition. Reducing initial generation capacity and increasing it steadily as demand grows are recommended to address this risk.
Project Performance Management
Performance management is required to determine the value of a project to the organization. It helps determine the business impact of an investment, compare costs to outputs, and assess if the initiative is meeting its objectives (Project Management Institute [PMI] 2017). Earned value management (EVM) can help establish APP’s performance: whether it is ahead or behind schedule and over or under the budget. These factors affect the final project cost at its completion.
EMV requires data from the budgeted or scheduled, actual, and earned value of project work done. The approach will be used to evaluate the APP’s performance since it entered the implementation phase. Planned Value (PV) gives the budgeted cost up to a given point in the project schedule (PMI 2017). The cost and timeline for APP will include the physical work planned, such as AMI infrastructure and smart meter installation, and capacity building, and the cumulative amount spent to do these activities. The period for PV calculation will cover the implementation phase, which has taken about six months.
Actual Cost (AC) will comprise the expenditure incurred in implementing planned activities up to date. It will give the sum spent in the six months of piloting the smart grid system. AC can be viewed as a cumulative or a current cost measure. In the first type of AC, the sum of the real cost for project work from the start-up to date is captured. In contrast, the current AC includes the cost incurred within specified days, weeks, or months when the project was being implemented.
The other EVM component is Earned Value (EV), which is the total worth of the work to date expressed as a percentage of the project completed (PMI 2017). It indicates in physical terms the value of the project completed. EV can be expressed as a cumulative and current variable for use in calculating EMV. The first type refers to the amount of the budget spent on activities done from the start of the project to the reporting date. In contrast, the current EV is determined from the total expenditure for activities in a specified period. With data for PV, AC, and EV, different EMV indices can be calculated to determine if the APP project is on budget and schedule.
Key EMV Metrics
EMV will indicate the cost and schedule variance to determine the project’s burn rate. Three EMV metrics will be used to assess APP’s performance up to date. The first one is the schedule performance index (SPI) defined as “a measure of schedule efficiency” of a project (PMI 2021, p. 6). It is expressed as a ratio between EV and PV that estimates the progress made against the baseline plan. An SPI value exceeding one will suggest that more work has been done for APP than scheduled (Project Management Institute 2021). In contrast, a ratio less than one will indicate an unfavorable outcome. If the SPI remains high over the implementation period, APP will be completed ahead of schedule.
The second EMV metric that will be used is the cost performance index (CPI) calculated as a ratio between EV and AC. It is a measure of a project’s cost efficiency (PMI 2017). A CPI value of over one would suggest APP’s expenditures exceeded the budgeted costs. Thus, if CPI is consistently high, the project will overrun the budget. The estimated completion (EAC) is another metric available for project managers. It is computed as the ratio between the total budget and CPI. It helps predict the total project cost at its completion.
Conclusion
The analysis of a pilot project implementing a smart power grid shows that the main risks it is exposed to are cyber-attacks, consumer rejection of new tariffs, construction incompletion, and demand unpredictability. Recommendations for addressing them have been provided to increase the likelihood of project success. Performance management of this project will involve three EMV metrics: SPI, CPI, and EAC. They will help estimate cost and schedule efficiency against those approximated at baseline.
References
Faquir, Dharmesh, Nestoras Chouliaras, Vlachou Sofia, Kalopoulou Olga, and Leandros Maglaras. 2021. “Cybersecurity in Smart Grids, Challenges and Solutions.” AIMS Electronics and Electronic Engineering 5 (1): 24-37.
Lamba, Vikas, Nikola Šimková, and Bruno Rossi. 2019. “Recommendations for Smart Grid Security Risk Management.” Cyber-Physical Systems 5 (2): 92-118.
Project Management Institute. 2017. A Guide to the Project Management Body of Knowledge. 6th ed. Pennsylvania: Project Management Institute.