M.H. Amini

Determination of the minimum-variance unbiased estimator for DC power-flow estimation

One of the most important features of the Smart Grid (SG) is real-time self-assessment which may threat that target power system stability. In order to improve robustness of power systems against such attacks, accurate estimation of the power system operation is required and conventional power flow methods should be upgraded. In this paper, we derive minimum variance unbiased estimators (MVUEs) for active power based on the voltage phase at each node of the power system. The state variables are the voltage phases and the received measurement signals are active power measurements. The proposed method is implemented on a four-bus test system. Three scenarios are defined to investigate the effect of covariance matrix topology on the estimation accuracy. The results shows that lower correlation between the noise vector elements leads to a more accurate estimation of power system operation.

DC power flow estimation utilizing bayesian-based LMMSE estimator

In recent years, Smart Grid was introduced to achieve an environmentally-friendly, adequate, secure and fossil fuel-independent power system. The large scale smart grid studies require accurate state estimation to obtain an acceptable adequacy level. There exist some challenges regarding anomalous power flow studies which motivate grid operators to utilize robust and accurate estimation methods. Therefore, power system state estimators play a pivotal role in real-time grid management. In this paper, a sequential linear minimum mean square error (LMMSE) estimator is utilized to solve the DC power flow problem. First, we introduce the classic linear estimator model which assumes that to-be-estimated parameter values are unknown but deterministic. The LMMSE estimator will be discussed which treats the to-be-estimated parameter as a random variable with a known prior probability density function (pdf). We evaluate the accuracy of the LMMSE estimator by comparing it with maximum likelihood estimator (MLE). Finally, the effect of covariance matrix topology will be studied by defining three scenarios with different noise covariance matrices.

Sparsity-based error detection in DC power flow state estimation

This paper presents a new approach for identifying the measurement error in the DC power flow state estimation problem. The proposed algorithm exploits the singularity of the impedance matrix and the sparsity of the error vector by posing the DC power flow problem as a sparse vector recovery problem that leverages the structure of the power system and uses l1-norm minimization for state estimation. This approach can provably compute the measurement errors exactly, and its performance is robust to the arbitrary magnitudes of the measurement errors. Hence, the proposed approach can detect the noisy elements if the measurements are contaminated with additive white Gaussian noise plus sparse noise with large magnitude, which could be caused by data injection attacks. The effectiveness of the proposed sparsity-based decomposition-DC power flow approach is demonstrated on the IEEE 118-bus and 300-bus test systems.

Forecasting the PEV owner reaction to the electricity price based on the customer acceptance index

Concerns about the environment and green energy brings a huge development opportunity to plug-in electric vehicles (PEVs). PEVs have an outlook to enhance the functionalities of the power grid. Using PEVs, lets us feed power from the vehicles battery packs back to the grid or to pull power from the grid to recharge the batteries. It means that we have a bidirectional flow of power between the vehicle and the grid. PEVs improves the social welfare by reducing the customer costs. The most important point in using a vehicle to grid (V2G) is to evaluate the behavior of the plug-in hybrid electric vehicle (PEV) owners and estimate their acceptance facing the demand side managements programs (DSM). In this paper, a mathematical model is presented for evaluating the reaction of PEV owners in response to electricity price. This method allows the utilities to have a short term scheduling in order to balance the available power and demand utilizing the PEVs.