• Energy Research

Abstract In traditional methods, the process of performance estimation of a photovoltaic (PV) module is achieved through two steps. First, the model parameters are determined under some reference condition, and then, the model parameters are determined under varying operating conditions based on the reference values and the dependence of the model parameters on environmental conditions. This paper presents a novel method for the performance estimation of PV modules under varying operating conditions without setting a reference condition. In the proposed method, the dependence of the model parameters on environmental conditions is modeled and modified without setting a reference condition or using reference values. The model parameters are determined from experimental data using guaranteed convergence particle swarm optimization technology, and are suitable for all operating conditions. Thus, the performance of PV modules can be directly determined under varying operating conditions, which simplifies the process and improves the accuracy. The effectiveness and accuracy of the proposed method are validated by large amounts of experimental data for different types of PV modules at different locations. In comparison with the traditional methods, the proposed method exhibits better accuracy in terms of the I-V and P-V curve and maximum power point estimation under different irradiance and temperature conditions. It can be further used to estimate the output power of PV system under varying operating conditions.

Abstract A robust economic dispatch (ED) considering automatic generation control (AGC) with affine recourse process is proposed in this paper. The approach co-optimizes the base points and participation factors of the AGC units using preemptive goal programming and robust optimization while considering the uncertain nodal power injections and the network constraints. The proposed approach is realized by two steps. The aim of the first step is to maximize the system effective acceptable disturbance range (EADR) while minimize the generation costs and reserve costs with respect to the obtained EADR in the second step. The novelty of the approach is as follows: (a) The security of the power system is optimized by maximizing the system EADR. The approach can obtain a solution which can cover the disturbance as much as possible even when the system does not have enough adjustable capacity to cover it all. The obtained nodal EADR can quantitatively represent the anti-disturbance capability of a node. (b) The economics of the system is significantly improved by minimizing the generation costs and reserve costs while the constraint of EADR requirement is respected. (c) The conservative level of the solution can be tuned according to the user’s requirements. A simplified one-step linear model is also deduced. The effectiveness and validity of the proposed approach are demonstrated by a 6-bus system, the IEEE 118-bus system, and a real 445-bus system.

Abstract The rapid variation of clouds is the main factor that causes the fluctuation of photovoltaic power. 1 The satellite images contain plenty of information about clouds, applicable for photovoltaic power forecast. However, in practice, two main factors obstruct the application of the satellite images: 1) the relatively low update frequency of the satellite images mismatches the photovoltaic power forecasting frequency, and 2) the cloud region that blocks the sunlight changes significantly with time. In this paper, a novel satellite image-based approach for photovoltaic power forecast is proposed to overcome these obstacles and achieve accurate forecasting results. Firstly, concerning the hourly updated satellite images, a nonlinear cloud movement forecasting model, considering the thickness and shape changes of the cloud, is presented to forecast the hourly variation of the images. Secondly, an active cloud region selection rule is derived based on the changing solar position to dynamically select the cloud region that blocks the concerned photovoltaic power station in a satellite image. Thirdly, a sequential cloud region selection algorithm is provided to estimate the intra-hour variation of the cloud to match the photovoltaic power forecasting frequency. Finally, the photovoltaic power is predicted using the XGBoost algorithm concerning the effects of the cloud and other influencing factors. Testing results show that the proposed method can achieve more accurate photovoltaic power forecasts using the low update frequency satellite images. Meanwhile, the superior performance compared with other benchmarks also verifies the effectiveness of considering cloud information obtained by the proposed method for photovoltaic power forecast.

An error criterion is essential in the process of parameter extraction of photovoltaic (PV) modules by fitting I–V curves, which exerts a huge influence on the accuracy of the extracted parameters. This paper proposes a new integrated current–voltage error criterion, named EC-I&V(x), which takes into account the intrinsic I–V properties of the PV module. The deviation in both current and voltage is considered by combining the mean squared error of the current and voltage in different data regions. Four optimization methods are used to validate the proposed error criterion, including guaranteed convergence particle swarm optimization, differential evolution, shuffled complex evolution, and an artificial bee colony algorithm. Different methods with the proposed error criterion are applied to synthetic I–V curves with variable error levels and measured I–V data under different operating conditions. Comparing with the traditional current based error criterion, more accurate results are obtained by using the proposed EC-I&V(x) at different error levels for different optimization methods. The proposed EC-I&V(x) not only improves the accuracy of each extracted parameter but also improves the accuracy of the estimated I–V property near maximum power points.

Recent research has demonstrated that the rotor angle stability can be assessed by identifying the sign of the system maximal Lyapunov exponent (MLE). A positive (negative) MLE implies unstable (stable) rotor angle dynamics. However, because the MLE may fluctuate between positive and negative values for a long time after a severe disturbance, it is difficult to determine the system stability when observing a positive or negative MLE without knowing its further fluctuation trend. In this paper, a new approach for online rotor angle stability assessment is proposed to address this problem. The MLE is estimated by a recursive least square (RLS) based method based on real-time rotor angle measurements, and two critical parameters, the Theiler window and the MLE estimation initial time step, are carefully chosen to make sure the calculated MLE curves present distinct features for different stability conditions. By using the proposed stability assessment criteria, the developed approach can provide timely and reliable assessment of the rotor angle stability. Extensive tests on the New-England 39-bus system and the Northeast Power Coordinating Council 140-bus system verify the effectiveness of the proposed approach.

The estimation of the conditional failure rate (CFR) of an overhead transmission line (OTL) is essential for power system operational reliability assessment. It is hard to predict the CFR precisely, although great efforts have been made to improve the estimation accuracy. One significant difficulty is the lack of available outage samples, due to which the law of large numbers is no longer applicable and no convincing statistical result can be obtained. To address this problem, in this paper a novel imprecise probabilistic approach is proposed to estimate the CFR of an OTL. The imprecise Dirichlet model (IDM) is applied to establish the imprecise probabilistic relation between a single conditional variable and the failure rate of an OTL. Then a credal network is constructed to integrate the IDM estimation results corresponding to different conditional variables and infer the CFR. Instead of providing a single-valued estimation result, the proposed approach predicts the possible interval of the CFR in order to explicitly indicate the uncertainty of the estimation and more objectively represent the available knowledge. The proposed approach is illustrated by estimating the CFRs of two LGJ-300 transmission lines located in the same region; the test results validate its effectiveness. 11 pages, 6 figures

Abstract Power cable is an important transmission component in distribution systems. The full utilization of the cable's transfer capability is of great significance to improve the economic and security performances of distribution systems. In this paper, a calculation method for electrothermal coupling (ETC) power flow for cable-based distribution systems is developed by embedding the solution of the thermal model of the crosslinked polyethylene (XLPE) insulated cable into the forward-backward sweep power flow calculation process of distribution systems. The method can be used to calculate the temperature dynamics of 3-phase cables under anticipated operation scenarios of distribution systems, thus revealing the potential transfer capability exists in the thermal inertia process of cable lines to operators. In case studies, the validity of the proposed ETC power flow calculation method is verified. Besides, the influence of the temperature of cable conductors on power flow is analyzed and the benefits of implementing the ETC power flow for the cable-based distribution systems are also illustrated.