Optimal power flow (OPF) minimizes the production cost while satisfying the constraints of the system, including real and reactive power balance, equipment capability constraints, and voltage limits. This paper presents a method of including a frequency security constraint within the framework of the OPF problem. Increasing displacement of conventional generation by renewable resources that contribute little or no frequency regulation capability may necessitate the enforcement of such a constraint to meet frequency security requirements. The system frequency is required to be maintained within a safe limit, thus indicating the balance between generation and consumption. Hence, the solution for optimization of power flow should not only present a minimum cost of generation within the operating conditions, but also ensure frequency stability. In order to obtain this solution, the requirement of frequency stability is introduced as a new constraint of the power dispatch problem and is represented by the maximum frequency deviation limit. This new constraint is constructed as a non-linear function of system inertia and the frequency regulation constant, since frequency deviation is highly sensitive to these factors. It is shown that the inclusion of this constraint causes the OPF to preferentially select higher inertia generators as necessary, to satisfy the frequency security requirement. A genetic algorithm is utilized as the optimization tool in this work. The IEEE RTS-79 test system is used to demonstrate efficacy of the proposed method.

The rapid advancement of grid modernization and the proliferation of smart grids have engendered a critical need for cyber-physical security. Recent cyber-attacks targeting grid infrastructure, notably leading to substantial blackouts in Ukraine, underscore the vulnerabilities and potentially catastrophic consequences of such incursions. These attacks, whether stemming from cyber threats such as Denial of Service (DOS), False Data Injection Attacks (FDIA), or complex cyber-physical manipulations, emphasize the imperative of robust cybersecurity protocols in smart grid operations. This research investigates a pivotal approach to fortify and safeguard smart grid systems by integrating blockchain technology with wireless sensor nodes. By leveraging a Proof of Authority (PoA) Ethereum Blockchain framework, the study delves into the transformative capabilities of Blockchain within Supervisory Control and Data Acquisition (SCADA) networks. Specifically, it examines configurations across IEEE 14-bus, 30-bus, and 118-bus topologies. In addition to elucidating the inherent vulnerabilities in traditional SCADA systems, this study meticulously evaluates an array of performance matrices. Statistical analyses encompassing mean, standard deviation, skewness, kurtosis, and confidence levels provide nuanced insights into the efficacy of blockchain mechanisms in enhancing SCADA resilience against contemporary cyber threats. This research endeavors to bridge the gap in modern cybersecurity paradigms by fusing blockchain technology with wireless sensor nodes. By fortifying data integrity, elevating the reliability of data transmission, and augmenting trustworthiness within SCADA infrastructures, this study aims to present robust solutions to the escalating cybersecurity challenges faced by smart grid systems.

Phasor measurement units (PMUs) significantly benefit the operation and control of power systems. They offer precise phasor measurements with a high refresh rate. These measurements are utilized for wide-area measurement system to improve situational awareness and enhance the control infrastructure. In spite of these advantages, the industry has been reluctant to adopt PMU technology, largely due to the high cost of PMUs and the communication infrastructure. However, judicious selection of PMU locations through optimal placement of PMUs (OPP) enables the minimization of installation cost. There have been several approaches to solve the OPP problem most of which assume that the minimum number of PMUs achieves the minimum cost or consider the cost of PMUs without considering the communication infrastructure. This paper presents an OPP approach that considers both the communication infrastructure and the installation cost of PMUs. The proposed approach uses multistage installation where each stage is dependent on the cost set by the utility instead of the number of PMUs. Higher priority buses can be chosen under the proposed approach. An opposition-based elitist binary genetic algorithm is used to solve the OPP problem. The proposed approach is tested on the IEEE reliability test system, and on the IEEE 14-bus, 30-bus, and 118-bus test systems.

Phasor measurement units (PMUs) have offered significant advancements in power system operation and control. Despite these advantages, the industry has been slow in adopting PMU technology. This slow adoption is partially due to the high cost of installing PMUs, which motivates the optimal PMU placement (OPP) problem. The work presented in this paper is motivated by the idea that the value of a high cost asset can be increased by deploying it to improve network fault-tolerance. This strategy of deploying PMUs in the proximity of higher probability contingencies increases the likelihood of more effective remedial actions, both preventive and corrective. The proposed OPP therefore considers both the cost and the extent to which the PMU placements will benefit system observability in the presence of network vulnerabilities. A bi-level framework is used for optimizing the network vulnerability and the installation cost of PMUs. In the proposed framework, the installation cost of PMUs is treated as the primary objective, and the observability, which incorporates the vulnerability analysis, is treated as the secondary objective. The proposed approach can be used as a multi-stage installation process or as a single-stage installation process. This approach uses evolutionary algorithms to optimize and prioritize locations and installation cost of the PMUs. The proposed approach is tested on the IEEE 14-bus, IEEE 30-bus, and IEEE reliability test system.