Wind DG Research Articles

A MILP model for optimal renewable wind DG allocation in smart distribution systems considering voltage stability and line loss

This paper introduces a new model for optimal wind distributed generation (WDG) allocation based on mixed integer linear programming (MILP) in todays' smart grids considering on-load tap changing (OLTC) and power factor control (PFC) strategies. The objectives of the proposed optimization model are improving voltage stability and energy loss minimization subject to power flow constraints, line thermal limits, maximum number and size of WDG units, penetration limit, discrete OLTC tap position constraints, and PFC constraints. An efficient voltage phasor-information-based probabilistic voltage stability index (VSI) is extracted to measure the improvement of voltage stability with WDGs. Besides energy losses, reduction index is used to measure the energy loss reduction with the integration of renewable WDGs in the distribution system. An efficient linearized power flow (LPF) model as well as piecewise linearized model of the quadratic terms associated to the PFC constraints are represented to convert the original nonconvex problem into the form of a well-known MILP problem, which guarantees optimal solution and computationally is efficient to be solved using powerful commercial solvers. The effectiveness and validity of proposed model is investigated in compared to the state-of-the-art population-based methods and classical models.

Considering the uncertainty of hydrothermal wind and solar-based DG

Lately, researchers have widely centered around renewable energy assets, for example, wind control and solar units to decrease the utilization of fossil fuels as the fundamental wellspring of ecological natural contaminations. One of the fundamental difficulties for the generation of wind and solar energies is that they frequently including the uncertainty because of the stochastic natures of wind speeds and solar radiation. Along these lines, the current uncertainty to assets the wind and solar units and necessary to assess the arranging strategy of distribution systems for having a dependable execution. To display the uncertainty related along with the wind and solar power, the point estimate method (PEM) is utilized. Weibull and Beta distributions are utilized to deal with uncertain information factors. These fundamental goals present work is to minimize the generation cost of the framework is enhanced dependent on the butterfly optimization algorithm (BOA). Four case test frameworks viewed as it is discovered that the proposed strategy gives better arrangement as far as execution time and normal cost viability. The recreation results demonstrate that the entrance of sustainable energy source builds, the generation cost diminishes. The outcomes acquired along with the butterfly optimization algorithm contrasted and another one understood strategy. Also, the precise distribution of generation cost.

A review modeling of optimal location and sizing integrated M–FACTS with wind farm and fuel cell

In recent years, due to the reduction of non-renewable resources and increasing environmental pollution, exploiting of renewable energy sources is a good solution. Wind turbines and fuel cells are appropriate sources of renewable energy. On the other hand, FACTS devices are used to maximize the power injection capability to supply electricity demand and minimizing voltage deviation, power losses and operational cost of power system. Generalized Unified Power Flow Controller (GUPFC) and Interline Power Flow Controller (IPFC) are two kinds of the best FACTS devices. GUPFC can adjust the voltage and the power flow in the connected transmission lines synchronically. IPFC is capable of minimizing voltage drops and reactive power of line, enhancing the system stability against the dynamic disturbance, and load ability of the system. In this paper, with the aim of minimizing the cost function and improving the performance of system wind farm and fuel cell are integrated with power system in the presence of GUPFC and IPFC via power injection model. Then, using a new algorithm integrated with the Newton-Raphson method, the best place for installing IPFC and GUPFC is funded. This algorithm is improved squirrel search which is inspired by nature. On the other hand, the optimum range of parameter (value and angle of injected voltage) is determined with the aim of achieving the best performance of system. Ultimately, the proposed method has been applied in IEEE 57 Bus system in the presence of wind turbine, fuell cell and DG to test the effect of M-FACTS. The result confirms that the new method improved voltage profile, minimized the operational cost and power losses of system. Final, it is concluded that the performance of GUPFC is better than the IPPFC.

Probabilistic generation model for grid connected wind DG

The uncertainty associated with renewable energy sources, particularly wind, makes them an unpredictable means of power generation. To guarantee continuous and reliable power supply, wind speed variability modeling is considered a vital step for meeting the planning and operational challenges of an electric power grid. In this paper, a novel probabilistic generation model is developed to estimate and generate time-coupled wind speed patterns. A 1 h time step is considered to construct the time-coupled probabilistic wind speed patterns based on a two-parameter Weibull distribution. These parameters of the Weibull distribution are found by considering the variations of reference wind speed patterns at two successive time steps. The probabilistic model is then used to create a number of aggregate wind speed generation scenarios. The validity of our proposed approach is evaluated with the help of goodness-of-fit test indicators such as average mean absolute percentage error and the Kolmogorov–Smirnov test error. The results of goodness of fit tests and comparison of output power determined through the proposed model with the existing model in the literature suggest that the proposed model is appropriate for wind speed uncertainty modeling and can be applied in power system planning studies.

An analytical and probabilistic method to determine wind distributed generators penetration for distribution networks based on time-dependent loads

This paper proposes an analytical approach for optimal allocation of wind DG units in radial distribution networks. Using this analytical approach, size and location of wind DG units are determined at different optimal power factors of a wind DG (OPFws), while different types of time-dependent load models and probabilistic wind DG generation are considered. The proposed approach is based on the partial derivative of a multi-objective index (IMO) including four impact indices of real power loss (ILP), reactive power loss (ILQ), voltage profile (IVD) and voltage stability (IVS). IVS methods based on analytical expression are usually assumed separately (not in an IMO), to find only the optimum location. In this study, the IVS is defined for a new application as an index in the IMO so as to determine both optimal size and location of the wind DG units. Also, the impact of the wind DG penetration on system voltage stability is evaluated. The 33 and 69 bus systems are examined to show the validity of the proposed approach. From the results, the load models have a significant effect on the wind DG penetration in the network, and the match rate between wind DG output and demand curves for each load model is different.

Back-Propagation Algorithm-Based Controller for Autonomous Wind–DG Microgrid

This paper deals with a wind–diesel generator hybrid configuration of the microgrid using a voltage source converter (VSC) as a voltage and frequency (VF) controller. The wind power generated by permanent magnet brushless dc generator, and the maximum power is captured by a maximum power point technique using a boost converter with an incremental conductance approach. This power is supplied to the consumer loads and surplus power is stored into battery system (BS). BS is attached at dc link of VSC, which provides load leveling during less or no wind conditions. With such combination of energy resources, a reduced rating, diesel engine-driven squirrel cage induction generator feeds loads, and VSC at point of common coupling (PCC) supports the system when the wind generation is unable to meet out the load demand. Back-propagation feed-forward control algorithm is used for VF control of VSC. This controller provides harmonics elimination, load leveling, and reactive power compensation, and also regulates the voltage at PCC. This microgrid is modeled in MATLAB Sim power tools, and simulation results are produced to verify the appropriate working of both the converters and the overall system. A prototype of the microgrid is also developed to validate the design and model through test results. A comparative study of simulated and experimental work is given in detail.

Using Copulas for analysis of large datasets in renewable distributed generation: PV and wind power integration in Iran

Renewable distributed generation introduced as an environmental friendly alternative energy supply while it provided the power system with ever-growing technical benefits such as loss reduction and feeder voltage improvement. The evaluation of the effects of small residential photovoltaic and wind DG systems on various system operating indices and the system net load is complicated by both the probabilistic nature of their output and the variety of their spatial allocations. The increasing penetration of renewable distributed generation in power systems necessitates the modeling of this stochastic structure in operation and planning studies. An advanced stochastic modeling of the system requires multivariate uncertainty analysis involving non-normal correlated random variables. Such an analysis is to epitomize the aggregate uncertainty corresponding to spatially spread stochastic variables. In this paper, an integration study of photovoltaics and wind turbines, distributed in a distribution network, is investigated based on the stochastic modeling using Archimedean copulas as a new efficient tool. The basic theory concerning the use of copulas for dependence modeling is presented and focus is given on an Archimedean algorithm. A comprehensive case study for Davarzan area in Iran is presented after reviewing Iran's renewable energy status. This study shows an application of the presented technique when large datasets, assuming 10-min interval between data points of PV, wind and load profiles, are involved where a deterministic study is not trivial.