Distributed generation and reactive power resource allocation will affect the economy and security of distribution networks. Deterministic scenario planning cannot solve the problem of network uncertainties, which are introduced by intermittent renewable generators and a variable demand for electricity. However, stochastic programming becomes a problem of great complexity when there is a large number of scenarios to be analyzed and when the computational burden has an adverse effect on the programming solution. In this paper, statistical machine learning theories are proposed to quickly solve the optimal planning for capacitors. Various technologies are used: Markov chains and copula functions are formulated to capture the variability and correlation of weather; consumption behavior probability is involved in the weather-sensitive load model; nearest neighbor theory and nonnegative matrix decomposition are combined to reduce the dimensions and scenario scale of stochastic variables; the stochastic response surface is used to calculate the probabilistic power flow; and probabilistic inequality theory is introduced to directly estimate the objective and constraint functions of the stochastic programming model. The effectiveness and efficiency of the proposed method are verified by comparing the method with the scenario reduction algorithm and the Monte Carlo method in a 33-bus distribution system.

Statistical Machine Learning Model for Stochastic Optimal Planning of Distribution Networks Considering a Dynamic Correlation and Dimension Reduction

Accurate wind power forecast is very important in order to construct smart electric grids. Nevertheless, this task still constitutes a challenge because wind is a very variable and local phenomenon. It is difficult to downscale information coming from Numerical Weather Prediction (NWP) models down to wind farm level and this is especially true onshore, in complex terrain conditions. Artificial Intelligence often comes at hand, for its power in learning what is hidden inside data: Artificial Neural Networks (ANN) are therefore commonly employed for wind power forecast. In this work, a pure ANN method is compared against a hybrid method, based on the combination of ANN and a numerical method based on physically-consistent assumptions (Computational Fluid Dynamics). Both approaches are validated against the SCADA data of a wind farm sited in Italy in a very complex terrain. It arises that the two methods have overall similar performances on average. However, pure ANN turns out to forecast better at mid-energy levels and during cut-off events at the highest wind speed, whereas the hybrid method forecasts better during low and high wind speed ranges. This makes the two approaches complementary and promising for future applications through an ensemble strategy.

https://www.ijrer.com/index.php/ijrer/article/download/6186/pdf

Evaluation of Two ANN Approaches for the Wind Power Forecast in a Mountainous Site

This paper discusses the effect of multi-objective function in angle stability improvement for a single machine connected to an infinite bus system (SMIB). Minimum damping ratio, ξmin and maximum damping factor, σmax which are commonly used to indicate oscillatory stability condition in power system are merge in certain ratio to produce a multi-objective function, FMO. This new index brings the advantages of the two indices without compromising the weakness of the index involved. In this study, FMO is applied to tune parameters of static var compensator with proportional-integral-derivative controller (SVC-PID) to improve damping efficiency in SMIB. The result is compared with a system connected to power system stabilizer attached with lead lag controller (PSS-LL). The parameters of SVC-PID and PSS-LL are optimized by particle swarm optimization method. Validation based on speed response, phase plane and determination of eigenvalues confirms that the proposed FMO is more effective for solving angle stability problems compared to single objective function.

Swarm Intelligence Approach for Angle Stability Improvement of PSS and SVC-Based SMIB

The invention discloses a method for heuristically calculating random optimal power flows of electric-pneumatic interconnected systems. The method comprises the following steps of: firstly considering a plurality of randomness on the basis of an electric-pneumatic interconnected integrated energy system model, and importing chance constraint to establish a chance constraint planning-based electric-pneumatic interconnected integrated energy system random power flow model; and introducing a cumulant method and interior point method-based heuristic algorithm, converting the chance constraint into certainty constraint through a cumulant method and a probability power flow method of Cornish-Fisher series, and iteratively solving the model. Example calculation results indicate that the cumulant method and interior point method-based heuristic algorithm is capable of efficiently and reliably solving change constraint planning-based electric-pneumatic interconnected integrated energy system random power flow models.

Method for heuristically calculating random optimal power flows of electric-pneumatic interconnected systems

Wind farms can be regarded as complex systems that are, on the one hand, coupled to the nonlinear, stochastic characteristics of weather and, on the other hand, strongly influenced by supervisory control mechanisms. One crucial problem in this context today is the predictability of wind energy as an intermittent renewable resource with additional non-stationary nature. In this context, we analyze the power time series measured in an offshore wind farm for a total period of one year with a time resolution of 10 min. Applying detrended fluctuation analysis, we characterize the autocorrelation of power time series and find a Hurst exponent in the persistent regime with crossover behavior. To enrich the modeling perspective of complex large wind energy systems, we develop a stochastic reduced-form model of power time series. The observed transitions between two dominating power generation phases are reflected by a bistable deterministic component, while correlated stochastic fluctuations account for the identified persistence. The model succeeds to qualitatively reproduce several empirical characteristics such as the autocorrelation function and the bimodal probability density function.

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Correlated power time series of individual wind turbines: A data driven model approach

The invention discloses a probabilistic load flow algorithm based on a semi invariant and series expansion method. By the probabilistic load flow algorithm, the uncertainty of a system under access of large-scale new energy sources can be reflected, a Monte Carlo sampling technology is introduced based on the analysis of a traditional semi invariant method to calculate the output of a wind power generator set, complicated mathematical analysis and calculation are prevented, and the probabilistic load flow algorithm can be applicable to a new energy output model with arbitrary random distribution; and relatively high adaptability and favorable convergence can be achieved when photovoltaic or other new energy sources are accessed to the system except wind and power.

Probabilistic load flow algorithm based on semi invariant and series expansion method

Traditional power flow algorithm such as the trend for front-and-spoke networks and backward substitution method and a cyclic structure NR method cannot fully reflect the uncertainties affecting the system, and a lot of new energy sources access to change the original properties of the grid, so that a single flow algorithm has significant limitations, the study calculated adapt to the trend of large-scale new energy future grid connection characteristics is important. A method of probabilistic power flow algorithm based on semi-variable and Gram-Charlier series expansion was proposed in this paper. Wind power output, load fluctuation, fault lines and forced outages of generator and other uncertainties were considered. Based on node voltage and branch power expectations and sensitivity matrix, each order semi-variable of load, conventional generators, wind turbine output and node injection power were calculated. Probability density function and probability distribution function were obtained through Gram-Charlier series expansion. IEEE-30 node test shows that: The algorithm can reflect the uncertainty of large-scale new energy accessing, simplified convolution of calculating probability density function of random variables for algebra of semi-invariant. Greatly reduce the computation time and it has a good convergence.

A probabilistic power flow algorithm based on semi-variable and series expansion

The invention discloses an empirical mode decomposition and Elman neural network combined wind power forecasting method, comprising the following steps: screening samples which are used for forecasting a wind power field, and selecting wind power outputs of forecasting periods within fluctuation months to implement forecasting; implementing empirical mode decomposition on multiple groups of output time sequence sample data of the wind power field, and ensuring that each group can obtain multiple intrinsic mode functions (IMFs) and trend components Res according to decomposition termination conditions; implementing fluctuation degree classification on decomposed IMFs according to a run distinguishing method, and reconstructing the IMFs according to a similar fluctuation frequency principle to obtain total high-frequency components and total low-frequency components; establishing an Elman neural network model, and implementing data normalization on the total high-frequency components, the total low-frequency components and the trend components to obtain training and test data of a neural network; and implementing day-ahead power forecasting for 72h by adopting an Elman improved learning algorithm to obtain a day-ahead forecasting power value of 72h of target wind power outputs. By adopting the empirical mode decomposition and Elman neural network combined wind power forecasting method disclosed by the invention, the number of forecasting components can be reduced, and the forecasting accuracy and forecasting speed can be increased.

Empirical mode decomposition and Elman neural network combined wind power forecasting method

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/joe.2018.8732

Control strategy of UPFC based on power transfer distribution factor

The invention discloses an improved stochastic load flow algorithm involved with new energy power generation prediction errors. Stochastic disturbance is produced to a large-scale new energy access power grid, linear processing is conducted on a nonlinear Monte Carlo method to obtain the disturbance quantity brought by new energy prediction errors, multiple times of iterative computations are avoided, accordingly the calculated quantity can be remarkably decreased, and the computation speed can be improved. The stochastic disturbance brought by new energy power output is added, and the improved stochastic load flow algorithm can adapt to the operational analysis of different types of new energy access power grids and has good convergence. In addition, the problem that the influence brought by the new energy prediction errors on the power grids is not estimated is effectively solved, and the effectiveness and practicability of the stochastic load flow algorithm are improved.

Wei Peng

Papers

Probabilistic load flow algorithm based on semi invariant and series expansion method

A probabilistic power flow algorithm based on semi-variable and series expansion

Empirical mode decomposition and Elman neural network combined wind power forecasting method

Control strategy of UPFC based on power transfer distribution factor

Improved stochastic load flow algorithm involved with new energy power generation prediction errors