As part of the smart grid concept, different strategies specialized for power flow control in distribution networks have been developed. One of the possible solutions to optimize the utilization of existing capacities and increase distributed generation penetration is the implementation of Soft Open Points. Soft Open Points are modular devices based on power electronic converters that enable closing loops in the network without negative consequences regarding fault current propagation. Introducing the concept of soft open points in the distribution network enables power flow control in a particular part of the network and voltage control in the soft open point connecting nodes. The control strategy proposed in this paper addresses the main obstacle for appropriate exploitation of soft open points, defining reference values for control variables in the case of large-scale distribution systems. Furthermore, the proposed strategy also deals with data unavailability problems, that is, soft open point control under communication interruption. The proposed control algorithm incorporates centralized optimal power flow calculations and an estimation algorithm based on a multivariate polynomial regression. The optimal power flow is used to calculate the control variables in normal operation modes. The procedure based on multivariate polynomial regression was used to estimate the reference values of the control variables when the optimal power flow results were unavailable. This feature makes the proposed algorithm applicable to communication interruptions when only limited data capture is available. The algorithm proposed in this study was implemented and tested on a test network considering different scenarios. Conclusions and simulation results make this algorithm applicable to an actual soft open point controller.

Transitioning towards carbon-free energy has brought severe difficulties related to reduced inertia in electric power systems. Regarding frequency stability, low-inertia systems are more sensitive to disturbance, and traditional frequency control is becoming insufficient to maintain frequency within acceptable limits. Consequently, there is a necessity for faster frequency support that can be activated before the primary frequency control and that can decelerate further frequency decay. This paper proposes a local control strategy for a multi-stage fast frequency response (FFR) provided as an ancillary service that considers the location of the disturbance and the distribution of system inertia. The novelty of the presented control strategy is the ranking of FFR resources by price, which takes the economic component into consideration. The proposed control is simple, based only on RoCoF measurements that trigger the activation of FFR resources. Its advantage over other methods is the ability to adapt the FFR resource response to the disturbance without complex calculations and the ability to ensure a bigger response closer to the disturbance, as well as in low-inertia parts of the system. In that way, there is a bigger activation of resources in the parts of the system that are more endangered by disturbances, which, as a result, minimizes the propagation of the disturbance’s impact on system stability. The applicability of the presented method is demonstrated in a simple 3-area power system and IEEE 68-bus system implemented in MATLAB/Simulink. The results show that the proposed control enables the largest response closer to the disturbance, thus mitigating the propagation of the disturbance. Furthermore, the results confirm that the proposed control enables lower provision costs and more support in low-inertia areas that are more vulnerable to disturbances.

As part of many national energy transition strategies, solar photovoltaic (PV) capacity is expected to increase significantly because PV deployment represents a key pathway for decarbonizing the energy sector. Alongside utility-scale installations, distributed PV systems connected to near-end users are becoming increasingly common in distribution networks. However, integrating intermittent sources without demand-matching capabilities introduces challenges related to operational network constraints. Consequently, distribution system operators (DSOs) limit PV integration to an acceptable level, referred to as hosting capacity (HC). Moreover, with increasing PV penetration, the curtailment of production becomes necessary during certain periods. Although curtailment has traditionally been avoided owing to the associated loss of clean energy, techno-economic studies show that shifting toward curtailment management can increase both HC and profitability. Curtailment management can be achieved through flexible PV power control, energy-storage integration, demand-side management, and flexible network regulation devices. This study specifically focuses on the techno-economic performance of flexible PV power control and compares it with the conventional approach without curtailment management, energy storage-based solutions, and their combination. The analysis was conducted on a modified IEEE 33-node test network, and key network constraints were incorporated. Using sequential Monte Carlo simulations with correlated stochastic time-series data for PV production, load profiles, and electricity prices over the years, the optimal PV and/or energy storage capacities were determined for various configurations. The results demonstrate that flexible curtailment management can increase both PV HC and expected profit, providing DSOs with boundary HC values while offering investors insight into the profitability of further PV integration.

This paper gives the implementation of a new algorithm in the optimization process for the problem of finding minimal number and locations of phasor measurement units for full power topological observability. The algorithm provides subset of locations for PMU placement which belong to the set of locations with maximal System Observability Redundancy Index. In this way, the number of constraints is significantly reduced which means the problem is solved with less computation time. Next step is to apply some well known optimization methods which will include the reduced system of constraints. In this paper, nonlinear programming method is applied. Modeling of zero injection buses constraints by topology transformations and its implementation in the algorithm has also been proposed. The validity of the proposed method is tested on IEEE 14-bus, IEEE 24-bus, IEEE 30-bus, IEEE 57-bus, IEEE 118-bus, IEEE 300-bus and Polish 2383-bus systems.