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Optimal power flow (OPF) is a crucial issue to maintain the reliable operation of power systems. However, achieving this objective is not easy, especially when renewable energy sources (RESs) are penetrated into the power system due to their uncertainty nature. This paper provides an optimal solution for the power flow problem including two different types of RESs based on a marine predator algorithm (MPA). The OPF model used in this paper has three different types of energy resources (thermal, wind, and solar). The output power from wind or solar generator has two probabilities either underestimation or overestimation consequently. These two probabilities have been translated into the objective function by two extra costs, penalty cost, and reserve cost, respectively. To check the validity of the proposed algorithm, it is applied to a modified IEEE-30 and IEEE-57 bus systems. The obtained results are compared with some recent optimization methods. The results show the effectiveness of marine predator algorithm in providing the optimal solution for the power flow problem with maintaining the power system constraints inviolate. [ABSTRACT FROM AUTHOR]

Absrtact: Background: Nowadays, numerical relays configured through power swing blocking function are vulnerable to faults with high impedance owing to nonlinearities present in the faulty path. Identification of symmetrical high impedance faults incepting during the power swing scenario is of prime importance for protection engineers to avoid cascade tripping. It is analyzed that the classical approaches for symmetrical fault detection during power swing can mal‐operate owing to high level of uncertainty arise during validation of high impedance fault, symmetrical faults and cross country faults during power swing with varied level of compensation which has been explored in this research. Aims: This paper presents an innovative scheme for series compensated line by utilizing the capability of Relevance Vector Machine (RVM) as a disturbance classifier taking into consideration of cross country faults, CT saturation; power swings prone to symmetrical faults along with high impedance faults in comparison with prevailing methods. Materials & Methods: In order to validate the proposed RVM based disturbance classifier algorithm, different kinds of faults and power swing situations have been applied in IEEE 9 bus test system using PSCAD/EMTDC software followed by validation in MATLAB. The features of sequence components of currents are estimated using Modified Full Cycle Discrete Fourier Transform (MFCDFT). The extracted features are used as control inputs to RVM for discernment among power swing and fault under the influence of wide range of system and fault dynamics. Results: The developed RVM based disturbance classifier scheme generates a tripping signal in the event of high impedance fault during power swing and remains stable during swing conditions. It can be used in conjunction with an adaptive numerical distance relaying scheme for realizing complete protection of the power grid for all zones of protection. A comparative analysis with an existing disturbance classifier algorithm indicates relative superiority with much improved fault classification accuracy. Discussion: In order to validate the proposed algorithm, different kinds of fault and power swing scenario are realized in IEEE 9 bus test system using PSCAD and current samples are acquired at sampling frequency of 4 kHz. The phasor estimation of sampled signals has been performed using MFCDFT which is used to derive feature vector extraction from symmetrical components. The extracted features are applied to RVM based fault classifier algorithm for classification of disturbance. The obtained results are also compared with existing schemes of protections. It has been proved that developed algorithm outperforms with much improved fault classification accuracy. Conclusion: This paper discovers relevance vector machine based adaptive learning approach for dynamic disturbance classification in complex power grid network. The developed algorithm has been validated on IEEE 9 bus test system modelled in PSCAD. The fault and system dynamics are varied over a wider range which includes variation in fault type, fault impedance, fault inception angle, power flow angle, fault location and degree of line compensation level. Moreover, the validation of scripted algorithm has been performed for very complex power grid scenarios like cross‐country faults, high impedance faults with varying degree of compensation, power swing and saturation of instrument transformers etc. The feature vectors are extracted using MFCDFT and applied to supervised RVM based disturbance classifier and outperforms with classification accuracy of 99.74% which indicates it's superiority over existing classifier approaches based on SVM & ANN. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an unsupervised clustering algorithm, called modified Iterative Self‐Organizing Data Analysis Technique Algorithm (m‐ISODATA), to capture representative nonchronological scenarios for representing short‐term uncertainties in power system models. The proposed approach is suitable to automatically obtain the number of scenarios required to fully capture the variability of historical series, avoiding the need of adjusting the number of clusters as in techniques commonly used in the literature. The performance of the m‐ISODATA is discussed and compared with Monte Carlo simulation, the well‐known k‐means, and hierarchical agglomerate clustering algorithms. In addition, the obtained scenarios are applied to a wind‐solar‐thermal power system generation expansion planning and to a probabilistic optimal power flow, considering uncertainties over wind and load demand. Finally, the source codes are provided with the best parameters as default. [ABSTRACT FROM AUTHOR]

This paper presents an alternative approach of Inherent Structural Characteristics Theory (ISCT), for solving loss allocation to network participants as well as generation-to-load matching problems in a deregulated environment. The mathematical formulations of ISCT, based on the fundamental circuit theory laws, are revisited. A Generation-to-Load Allocation Coefficient (GLAC) matrix for solving generation-to-load allocation and network loss allocation to load problems, for efficient transmission pricing, is formulated. The allocation of real and reactive power contributions, by individual generator, required to serve the network demands is also determined on the basis of the GLAC matrix. Total network losses are determined and allocated to individual network loads based on GLAC matrix. The approach is demonstrated using the standard IEEE 30 bus network. The results obtained are compared with that obtained using graph theory approach based on the solved power flow. The comparison of the results shows that the ISCT approach is reasonable and it is a good signal, which could be useful for pricing of electricity by the market regulators. [ABSTRACT FROM AUTHOR]

Summary: Power demand control is required at steelmaking plants and other energy intensive industrial plants where the electrical bills include high demand charges and are subject to demand limit contracts. This paper introduces a novel and simplified algorithm for the implementation of a power demand controller. The presented algorithm does not require a model of the electrical load since it is based on a geometrical approach for the estimation of the disconnection time of the controllable load and only requires the nominal power values and the load factors of both the total (full) load and the noncontrollable (base) load as parameters. The power demand control method presented is verified with a computational simulation, and precise control of the demand limit is achieved under random variations of the base and full loads. The proposed control algorithm is simpler than commercial methods and achieves a competitive performance. Simulation results show a better performance by calculating the disconnection times based on load parameters specified as mean values between the average and nominal powers of the full and base loads. Some considerations to tune‐up the load factor parameters are also discussed. The presented control method has been successfully implemented on industrial controllers in two steelmaking facilities in Mexico, having an electric arc furnace as controllable load in both cases. This novel demand control is adequate for industrial plants with electrical loads that can be disconnected without affecting the quality of the product and with minor impact on productivity. [ABSTRACT FROM AUTHOR]