There are various active power curtailment (APC) approaches to mitigate overvoltage. In PV-rich networks, the overvoltage happens to be especially at the end of the distribution feeders. While APC helps maintain voltage within operational limits, it results in varying degrees of renewable curtailment for each prosumer. This curtailment increases as the distance from the transformer grows. Hence, these approaches introduce unfairness among prosumers. This study proposes an equitable APC (EAPC) based on the prosumer's self-consumption rate (SCR). The method calculates each prosumer's SCR, compares it with the precalculated critical SCR, and calculates a fair share of curtailment for each prosumer. Subsequently, leveraging the voltage sensitivity matrix obtained from the inverse of the Jacobian matrix, the new active power injection at the point of common coupling (PCC) is calculated to mitigate the overvoltage. To show the effectiveness of the proposed method, a comparison with three other methods is presented under various PV penetration levels. The proposed EAPC is less sensitive to the prosumer's location and improves fairness among prosumers. In addition, a battery deployment scenario is analysed considering the annual supply and demand balance to suppress the extra curtailment introduced by EAPC without increasing the battery capacity.

In this paper, a novel solution method is proposed for the optimal topology control problem in AC framework with N-1 reliability. The resultant new topology of the proposed method is guaranteed to yield a better objective function value than the global solution of the problem with the original topology. In order to prove our statement, semi-definite programming relaxation is formulated to find a lower bound to the objective function value of the problem. The proposed method is favorable for parallelization to increase the computational efficiency, and the parallel computing flowchart is presented. Computational time of the algorithm for IEEE test cases is reported. An in-house power market simulator is developed to simulate a market environment complete with optimal topology control mechanism. Simulations held by participating human subjects are analyzed under the subtitles of the total operating cost, real power losses, and locational marginal price (LMP) variance. A simple example is illustrated to show that the resultant network topology may increase the real power losses while still providing a lower operating system cost than that of the original topology.

In the electricity market, different pricing models can be applied to increase market competitiveness. Different electricity systems use different market structures. Uniform marginal pricing, zonal marginal pricing, and nodal marginal pricing methods are commonly used market structures. For markets wishing to move from a uniform pricing structure to a more competitive zonal pricing structure, the determination of price zones is critical for achieving a competitive market that generates accurate price signals. Three different pricing zone detection algorithms are analyzed in this paper including the k-means clustering and queen/rook spatially constraint clustering. Finally, the results of a case study for the Turkish electricity system are shared to compare each method.

Regular transmission maintenance is important to keep the infrastructure resilient and reliable. Delays providing on-time maintenance increase the forced outage rate of those assets, causing unexpected changes in the operating conditions and even catastrophic consequences, such as local blackouts. The current process of maintenance schedule is based on the transmission owners’ choice, with the final decision of system operator about the reliability. The requests are examined on a first-come, first-served basis, which means a regular maintenance request may be rejected, delaying the tasks that should be performed. To incorporate optimization knowledge into the transmission maintenance schedule, this study focuses on the co-optimization of maintenance scheduling and the production cost minimization. The mathematical model co-optimizes generation unit commitment and line maintenance scheduling while maintaining N-1 reliability criterion. Three case studies focusing on reliability, renewable energy delivery, and service efficiency are conducted leading up to 4% production cost savings as compared to the business-as-usual approach.

The growing deployment of distributed resources significantly affects the distribution grid performance in most countries. The optimal sizing and placement of these resources have become increasingly crucial to mitigating grid issues and reducing costs. Particle Swarm Optimization (PSO) is widely used to address such problems but faces computational inefficiency due to its numerical convergence behavior. This limits its effectiveness, especially for power system problems, because the numerical distance between two nodes in power systems might be different from the actual electrical distance. In this paper, a scalable bilevel optimization problem with two novel algorithms enhances PSO’s computational efficiency. While the resistivity-driven algorithm strategically targets low-resistivity regions and guides PSO toward areas with lower losses, the connectivity-driven algorithm aligns solution spaces with the grid’s physical topology. It prioritizes actual physical neighbors during the search to prevent local optima traps. The tests of the algorithms on the IEEE 33-bus and the 69-bus and Norwegian networks show significant reductions in power losses (up to 74% for PV, wind, and storage) and improved voltage stability (a 21% reduction in mean voltage deviation index) with respect to the results of classical PSO. The proposed network-oriented PSO outperforms classical PSO by achieving a 2.84% reduction in the average fitness value for the IEEE 69-bus case with PV, wind, and storage deployment. The Norwegian case study affirms the effectiveness of the proposed approach in real-world applications through significant improvements in loss reduction and voltage stability.