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In this brief, a new switched capacitor-based structure is presented for a single-phase multilevel inverter. The proposed structure is capable of boosting the input voltage and producing fifteen voltage levels at the output utilizing two separated DC voltage sources along with a switched capacitor unit. The proposed structure also has self-charging capability and can automatically adjust the capacitor voltage without any sensor. A quantitative comparison with similar structures has been performed to confirm the proposed structure. Furthermore, loss and thermal simulations have been performed for the proposed structure. The functionality of the proposed structure is examined by the simulation results. Finally, a laboratory prototype is implemented to verify the effectiveness and accuracy of the proposed structure and its control method. [ABSTRACT FROM AUTHOR]

Stochastic nature of some input variables dictates the requisite of probabilistic analysis in power systems operation and planning. Wind generation is considered as a main source of intermittency in power systems due to the uncertain nature of wind speed. The proposed probabilistic optimal power-flow (POPF) method investigates spatial correlation among sources to attain more practical output distributions. The method established reduced-discrete point estimate method (RDPEM) along with the Latin hypercube sampling (LHS) in order to attain the stochastic characteristic of optimization’s outputs. Despite needing less computational effort, highly accurate results can be obtained, while there is no prerequisite for probability distribution of the input random variables. In order to more validate the efficiency of the proposed method, the Gram–Charlier (GC) expansion is used to compare the outputs’ cumulative distribution functions (CDFs) that are obtained from Monte Carlo (MC) with RDPEM methods. The performance and precision of the proposed solution are ascertained by comparison with those of Monte Carlo with discrete LHS (MCDLHS) in a hybrid IEEE 14-bus test system. [ABSTRACT FROM AUTHOR]

This paper proposes a secondary voltage, frequency, and active power sharing control for autonomous inverter-based microgrids with event-triggered communications. A proportional-integral consensus-based control scheme is introduced which benefits from need-based (event-triggered) data exchange among distributed generators. The employed event-triggering condition i) ensures the system stability, ii) ensures that the system is Zeno-free and there exists a controllable minimal inter-event time, iii) removes the redundant communications during both transient and steady-state stages, iv) accounts for directed communication network architectures, and v) is fully distributed from both design and implementation standpoints. Effectiveness of the proposed controller for various case studies is verified via MATLAB/Simulink-based simulations. Comparison between different cases and conventional strategies are also included. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) systems are becoming an indispensable source of energy for residential customers and are contributing to increase the sustainability of energy systems. However, practical methods that can aid customers to make informed decision on installing the PV systems considering their integration with the residential energy system have remained scarce. This paper proposes a probabilistic dynamic planning model for optimizing decisions on installing PV systems for a residential energy hub. In addition to the PV system, residential energy hub integrates combined heat and power (CHP) units and gas boilers to supply the heat and electrical demands. The proposed planning model minimizes the investment cost of PV systems along with the energy cost of energy hub that includes possible PV energy curtailment cost over the planning horizon. The proposed problem also integrates a heat storage system and responsiveness of the heat and electrical demands to the respective gas and electricity prices as additional flexibility to increase the benefits of PV systems for the customers. Two-point estimate method is used to model the uncertainty of PV generation. The proposed method is implemented on a test energy hub for a 10-year planning period to demonstrate features of the proposed method. [ABSTRACT FROM AUTHOR]

Enhancing penetration of wind turbines in the modern power grids, the assessment of the transient stability has changed to a more complicated issue. This paper proposes an innovative real-time method based on the corrected kinetic energy, which aims to assess the transient stability of a power system comprising wind turbines by calculating critical clearing time. To enhance the accuracy of the calculations, the proposed method utilizes structure preserving for taking details of the network model into consideration. Moreover, a new indirect calculation method based on Taylor expansion of the rotor angle is proposed, which considers the effects of the wind turbine generator controllers on the corrected kinetic energy calculation. Specifically, the latter indirect calculations take the effects of the potential terms of transient energy function into account for calculating critical corrected kinetic energy. Also, the latter method considers the fault-on trajectory impact on the transient stability assessment and simultaneously avoids heavy computational burden, and as a result, it guarantees accurate real-time assessment of transient stability. The simulation results show that the proposed method calculates the critical clearing time of wind turbine generators with notable precision and very good speed, and consequently, it is sufficient for transient stability assessment of power grids comprising wind turbines. [ABSTRACT FROM AUTHOR]

Natural gas is increasingly becoming the preferred choice of fuel for flexible electricity generation globally resulting in an electricity system whose reliability is progressively dependent on that of the natural gas transportation system. The cascaded relationship between the reliabilities of these system necessitates an integrated approach to planning both systems. This paper presents a chance constrained programing approach to minimize the investment cost of integrating new natural gas-fired generators, natural gas pipeline, compressors, and storage required to ensure desired confidence levels of meeting future stochastic power and natural gas demands. The proposed model also highlights the role of natural gas storage in managing short-time uncertainties in developing a long-term expansion plan for both the electric and natural gas systems. A two-stage chance constrained solution algorithm is employed in solving the mix-integer nonlinear programing optimization problem and illustrated on a standard IEEE 30 bus test system superimposes on the Belgian high-calorific gas network. [ABSTRACT FROM AUTHOR]

This paper presents a new powerful density estimator and the application of the generalized cross-entropy method. A framework for probability density function estimation of the probabilistic optimal power flow problem results is presented. Large-scale probabilistic problems have a lot of uncertain parameters, some of which are correlated. Most of the existing methods for probabilistic power system analysis have a large computational burden or rely on asymptotic approximation to mitigate the computational burden, which reduces the precision level. Others that depend on the uncertainties are not applicable in large-scale problems and do not provide precise results in small cases. Compared to conventional methods, the proposed method has several advantages in probabilistic analysis. Reducing the computational burden while maintaining high level of accuracy is one of them. The proposed method can deal with correlated problems. This method is tested on some valid and conventional networks containing renewable energy resources, nonstationary loads, and their correlations. The results are compared against more accurate results obtained from Monte Carlo simulation, which demonstrate the high level of accuracy according to the error indices. Results confirm the high capacity of the proposed method in accuracy and speed response compared to other methods. [ABSTRACT FROM AUTHOR]

In this paper, a generalized heuristic approach is proposed to solve the optimal power flow problem in multicarrier energy systems. This technique omits the use of any extra variable, such as dispatch factors or dummy variables required for conventional techniques. The unified proposed approach can be utilized with all evolutionary algorithms. Modeling hub devices with constant efficiency may produce a considerable error in finding the actual optimal operating point of the whole network. However, using variable efficiency model adds complexity to the conventional methods while increasing the computation–demand of these techniques, but this target can be simply implemented by the proposed scheme. A multicarrier energy system consists of an electrical, a natural gas, and a district heating network is analyzed by the proposed algorithm using the modified teaching–learning-based optimization method. Results validate the utilized approach and show that it can successfully reach the global optimal solution of the problem. [ABSTRACT FROM PUBLISHER]