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The integration of renewable resources into the existing power distribution system is expanding to reduce gas emissions, treat climate change and satisfy the current global need for clean energy. If the location and size of these renewable generators are determined without considering uncontrollable reactive power compensation caused by their intermittent nature, the resultant power system may suffer from system instability and decreased reliability. Therefore, the issue of optimal location and size of renewable resources attracts great attention. In this paper, a methodology is proposed to optimize the locations and capacities of distributed renewable generators installed in conventional power distribution systems. In particular, uncontrollable reactive power compensation of these renewable resources is considered in this paper and managed through the proposed methodology to ensure power system reliability and stability. As a result, the proposed methodology reminds us of the importance of reactive power compensation by performing better in power losses reduction and the robustness of voltage stability against variable reactive power compensation.

The use of renewable energy sources has increased year-on-year. Thus, there is an increasing rate of small generating units connected directly to distribution networks and micro-grids close to consumers. At the same time, these micro-sources must provide stability and reliability of electrical energy to the power network to which they are connected. In the technical literature, several studies have been done to ensure power systems with traditional generating sources to operate in a stable and reliable way, but there is an issue regarding generation uncertainty when a distribution system has many micro-sources. This is because of the uncertainty of primary sources, for example, wind and radiation intensity, and could result in intermittent generation. In this study, stability and reliability of voltage in a power system with distributed generation is analysed using simulation techniques. In the proposed method in this study voltage security analysis is jointly considered with probability laws. Moreover reliability theory is also considered in the proposed voltage collapse analysis methodology. The responsibility of generator in the voltage collapse process, the probabilistic risk of voltage collapse of each operating point and the probability of enlarging the system load as a function of different operating points are the outcome of the methodology, and it is validated by using the IEEE34 test feeder.

Summary This paper proposes an approach to detect the possibility of long‐term voltage instability, based on online measurement of system bus voltages. An optimization framework is proposed to determine the maximum loading points, with different load increase patterns and different levels of reactive power output. The operating conditions so obtained are used as the training database for an artificial intelligence classifier based on the support vector machines. In an online application, the support vector machine classifier helps in detecting the probability of some generators operating at high reactive power output, which is an important indicator of an impending voltage collapse. The proposed framework is tested with the IEEE 39 bus and the Nordic 32 bus systems. The test results demonstrate that the proposed scheme gives reliable prediction of the power system long‐term voltage stability.

The cumulative number of historical and recent power system outages substantiates the fact that further studies are necessary for an improved solution to the issue of voltage instability on the grid and the subsequent system collapse. Voltage collapse is a serious reliability issue which inhibits the objective of running a reliable and secure power system network. In this study, a new line stability index (NLSI_1) for predicting voltage collapse is presented.  The new index considers a switching logic which is derived from the difference of voltage angle between the two load buses. The index is deployed for performance analysis using the 28-bus, 330-kV Nigeria National Grid (NNG).  The simulation implemented in MATLAB shows that the index gives the same results as Line stability index (Lmn) and Fast Voltage Stability Index (FVSI) indices. The base case and the contingency scenarios were considered during the simulation. The base case analysis using the NNG values of all the three indices FVSI, Lmn, and NLSI_1 for simulation generates a value less than one for the entire lines which implies that the NNG is stable in this mode. The values of the three indices are almost the same, which confirms the accuracy of the novel index developed. The analysis for the contingency case reveals that the load bus 16 (Gombe) which has the lowest, maximum permissible reactive load of 139.5MVAR is the weakest; also power line 16-19 is identified as the critical line. The result of the simulation confirms that the accuracy was improved by using NLSI_1.

This research focuses on the georeferenced location of distributed resources, specifically the injection of active power through distributed generation. A rural-urban marginal feeder of a distribution company in Ecuador with georeferenced information was taken as a case study, which has a three-phase primary link at a medium voltage and several single-phase branches at a medium voltage of great length to supply users who are far away from the local company's network. Consequently, to analyze the behavior of the electrical network, the Cymdist software was used to perform simulations in a steady state without and with the insertion of distributed generation. For the location of the distributed generation, the voltage collapse prediction index was used as a technique for quantifying and identifying problems in the network nodes. Moreover, based on the proposed methodology, the optimal georeferencing of the sites where it is necessary to inject active power to improve the voltage profiles and reduce the voltage collapse prediction index was obtained.

Increasing demand for electricity and the modernization of power systems within competitive markets has induced power systems to operate close to their stability limits. Therefore, the continuous monitoring and control of power systems through voltage stability indices is urgently needed. This is the first-ever effort to examine more than 40 voltage stability indices based on their formulation, application, performance, and assessment measures. These indices are sorted based on a logical and chronological order considering the most recent indices to be applied worldwide. However, the generalizability of these indices in terms of multivariable objectives is limited. Despite its limitation, this study systematically reviews available indices in the literature within the past three decades to compile an integrated knowledge base with an up-to-date exposition. This is followed by a comparative analysis in terms of their similarity, functionality, applicability, formulation, merit, demerit, and overall performance. Also, a broad categorization of voltage stability indices is addressed. This study serves as an exhaustive roadmap of the issue and can be counted as a reference for planning and operation in the context of voltage stability for students, researchers, scholars, and practitioners.

Voltage instability is a serious phenomenon that can occur in a power system because of critical or stressed conditions. To prevent voltage collapse caused by such instability, accurate voltage collapse prediction is necessary for power system planning and operation. This paper proposes a novel collapse prediction index ( NCPI ) to assess the voltage stability conditions of the power system and the critical conditions of lines. The effectiveness and applicability of the proposed index are investigated on the IEEE 30-bus and IEEE 118-bus systems and compared with the well-known existing indices ( L mn , FVSI , LQP , NLSI , and VSLI ) under several power system operations to validate its practicability and versatility. The study also presents the sensitivity assumptions of existing indices and analyzes their impact on voltage collapse prediction. The application results under intensive case studies prove that the proposed index NCPI adapts to several operating power conditions. The results show the superiority of the proposed index in accurately estimating the maximum load-ability and predicting the critical lines, weak buses, and weak areas in medium and large networks during various power load operations and contingencies. A line interruption or generation unit outage in a power system can also lead to voltage collapse, and this is a contingency in the power system. Line and generation unit outage contingencies are examined to identify the lines and generators that significantly impact system stability in the event of an outage. The contingencies are also ranked to identify the most severe outages that significantly cause voltage collapse because of the outage of line or generator.

Voltage stability improvement is a challenging issue in planning and security assessment of power systems. As modern systems are being operated under heavily stressed conditions with reduced stability margins, incorporation of voltage stability criteria in the operation of power systems began receiving great attention. This study presents a novel voltage stability constrained optimal power flow (VSC-OPF) approach based on static line voltage stability indices to simultaneously improve voltage stability and minimise power system losses under stressed and contingency conditions. The proposed methodology uses a voltage collapse proximity indicator (VCPI) to provide important information about the proximity of the system to voltage instability. The VCPI index is incorporated into the optimal power flow (OPF) formulation in two ways; first it can be added as a new voltage stability constraint in the OPF constraints, or used as a voltage stability objective function. The proposed approach has been evaluated on the standard IEEE 30-bus and 57-bus test systems under different cases and compared with two well proved VSC-OPF approaches based on the bus voltage indicator L-index and the minimum singular value. The simulation results are promising and demonstrate the effectiveness of the proposed VSC-OPF based on the line voltage stability index.

A blackout is usually the result of load increasing beyond the transmission capacity of the power system. A collapsing system enters a contingency state before the blackout. This contingency state is characterized by a decline in the bus voltage magnitudes. To avoid blackouts, power systems may start shedding load when a contingency state occurs called under voltage load shedding (UVLS). The success of a UVLS scheme in arresting the contingency state depends on shedding the optimum amount of load at the optimum time and location. This paper proposes a hybrid algorithm based on genetic algorithms (GA) and particle swarm optimization (PSO). The proposed algorithm can be used to find the optimal amount of load shed for systems under stress (overloaded) in smart grids. The proposed algorithm uses the fast voltage stability index (FVSI) to determine the weak buses in the system and then calculates the optimal amount of load shed to recover a collapsing system. The performance analysis shows that the proposed algorithm can improve the voltage profile by 0.022 per units with up to 75% less load shedding and a convergence time that is 53% faster than GA.