State estimation operation depends on the measurement numbers, types and locations. Therefore, the adoption of an efficient placement method for selecting the required numbers of meters and their location is an essential process for power systems monitoring. In fact, the Phasor measurement Unit (PMU) is, currently, one of the most efficient devices for measuring real-time electrical quantities. Consequently, this paper aims to present the main categories for PMUs placement strategies according to the objectives of PMUs employment. The PMUs placement strategy should take into consideration main factors that required for enhancing the performance of a state estimation process such as reliability, accuracy and the special requirements of the modern distribution grid. A review of literatures that address PMUs placement methods is presented in this paper in addition to suggestions for developing the PMUs placement methods to be more feasible for modern power networks.

This paper proposes a new algorithm for solving the problem of PMU placement for improving the numerical stability of the State Estimation (SE) solution. The proposed method is designed for power systems monitored using conventional measurements, but with an inaccurate SE solution. An efficient and adaptable approach for determining the optimal locations of the Phasor Measurement Units (PMUs) to improve the SE performance is proposed. Accordingly, the PMUs number is restricted to the locations that require accurate measurements using the PMUs. Simulation tests on IEEE 14-and 30-bus systems have been implemented to verify the performance of the proposed algorithm.

The performance of the power system state estimation (SE) is influenced by the configuration of the meters and measurement redundancy. Therefore, the measurement set needs to be updated by installing new SCADA meters and phasor measurement units for improving the quality of the SE solution. However, the potential inconsistency between the existing meters and the new meters should be addressed. Otherwise, the additional meters may lead to numerical problems such as collinearity (linear dependence due to duplicated measurements) and the existence of high leverage points (HLPs) (influential measurements). Hence, this paper proposes an incremental meter placement method. The proposed method utilizes the HLPs and aims to improve the numerical performance of the SE and facilitate the elimination of bad data. The cuckoo search optimization is used for selecting the optimal locations and the numbers of the new meters. The performance of the proposed algorithm is tested on UK 18-bus, the IEEE 30-bus, and 118-bus systems and simulation results show improvements in the quality of the SE solution.

To support increasing penetration of small PV sources within existing distribution networks, there is an increasing interest in providing supporting, monitoring and controlling infrastructure to transform from passive to Active Distribution Networks (ADN). In order to operate ADN in an optimal way, the Hosting Capacity (HC) measure related to the maximum feasible capacity of PV sources should be estimated on an hourly basis. In this paper, HC has been estimated by conducting stochastic simulations in which the uncertainties of the solar irradiance and load variation are considered. The spatial correlations among PVs and loads have been taken into account by utilizing the N ataf transformation. Two performance indices based on technical violations (overvoltage and overloading) are established in order to depict the deterioration in the operational performance as the number of PV connections increase. Both the number and severity of a technical violation for each hour are considered. The relative frequency distribution is used to compute the probabilistic expectation and percentiles for each violation. The method is tested on large distribution network with several feeders and sub-feeders located in South Australia.