Wanliang Fang

An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data

Due to the intermittency and randomness of solar photovoltaic (PV) power, it is difficult for system operators to dispatch PV power stations. In order to find a precise expectation for day-ahead PV power generation, conventional models have taken into consideration the temperature, humidity, and wind speed data for forecasting, but these predictions were always not accurate enough under extreme weather conditions. Aerosol index (AI), which indicates the particulate matter in the atmosphere, has been found to have strong linear correlation with solar radiation attenuation, and might have potential influence on the power generated by PV panels. A novel PV power forecasting model is proposed in this paper, considering AI data as an additional input parameter. Based on seasonal weather classification, the back propagation (BP) artificial neural network (ANN) approach is utilized to forecast the next 24-h PV power outputs. The estimated results of the proposed PV power forecasting model coincide well with measurement data, and the proposed model has shown the ability of improving prediction accuracy, compared with conventional methods using ANN.

FACTS Devices Allocation via Sparse Optimization

Although there are vast potential locations to install FACTS devices in a power system, the actual installation number is very limited due to economical consideration. Therefore the allocation strategy exhibits strong sparsity. This paper formulates FACTS device allocation problem as a general sparsity-constrained OPF problem and employs Lq(0 <; q ≤ 1) norms to enforce sparsity on FACTS devices setting values to achieve solutions with desirable device numbers and sites. An algorithm based on alternating direction method of multipliers is proposed to solve the sparsity-constrained OPF problem. The algorithm exploits the separability structure and decomposes the original problem into an NLP subproblem, an Lq regularization subproblem, and a simple dual variable update step. The NLP subproblem is solved by the interior point method. The Lq regularization subproblem has a closed-form solution expressed by shrinkage-threholding operators. The convergence of the proposed method is theoretically analyzed and discussed. The proposed method is successfully tested on allocation of SVC, TCSC, and TCPS on IEEE 30-, 118-, and 300-bus systems. Case studies are presented and discussed for both single-type and multiple-type FACTS devices allocation problems, which demonstrates the effectiveness and efficiency of the proposed formulation and algorithm.

Photovoltaic power forecasting based on artificial neural network and meteorological data

Due to the intermittency and randomness of solar Photovoltaic (PV) power outputs, it is necessary to find a precise method for PV power forecasting. However, conventional methods, using only temperature, humidity and wind speed data, failed to obtain high accuracy when used to predict PV power outputs under extreme weather conditions. Aerosol index which indicates particulate matter in the atmosphere has a strong correlation with PV generated energy. This paper proposes a novel photovoltaic power forecasting model considering aerosol index data as an additional input. Based on weather classification and back propagation artificial neural network approaches, the estimated results of the forecasting model show good coincidence with the measurement data. And the proposed model is able to improve the prediction accuracy of conventional methods using artificial neural network.

Increasing wind power penetration level based on hybrid wind and Photovoltaic generation

Wind power generation have attracted great concentration in the last decade due to its clean and renewable characteristics. In this research, solar Photovoltaic(PV) power is utilized to compensate the active power imbalances of wind farm installed in Western Gansu Province, because of the rich solar and wind energy in that area. Firstly, Weibull and Beta distribution are introduced to simulate the wind speed and solar irradiance fluctuations separately, during a certain period of time. Then wind and PV power generation is calculated on a stochastic basis during that period of time in the same region. The impact of joint stochastic properties of wind and solar power generation on power system voltage is analyzed, based on the probabilistic load flow approach using nodal cumulants. Finally, an increased wind power penetration level is verified through simulations on IEEE-24 bus test system, by comparing the probabilities of bus voltages which violate their operation limits.

Moment-SOS Approach to Interval Power Flow

Intermittent renewable sources and market-driven operation have brought many uncertainties into modern power systems. Power flow analysis tools are expected to be able to incorporate uncertainties into the solution process. Interval power flow (IPF) analysis which aims at obtaining the upper and lower bounds of power flow solutions under interval uncertainties, thereby emerges as a promising framework to meet such expectation. This paper describes a novel optimization-based method to obtain high-accuracy or even exact global solutions to IPF problems. At first, the IPF problems are formulated as polynomial optimization problems probably with rational objective functions. Then Lasserres hierarchy, or moment-SOS approach, is introduced to relax the non-convex problems to convex semidefinite programming (SDP) problems. Correlative sparsity in the polynomial optimization problems is exploited to improve numerical tractability and efficiency. Finally, case studies on IEEE 6-bus, 9-bus and 14-bus systems demonstrate the second-order moment relaxation is capable of obtaining exact global interval solutions on small-scale systems, and numerical results on IEEE 57-bus, 118-bus and 300-bus systems show the proposed method can significantly improve the interval solutions compared with recent Linear Programming (LP) relaxation method on larger systems.

Modified Quasi-Steady State Model of DC System for Transient Stability Simulation under Asymmetric Faults

As using the classical quasi-steady state (QSS) model could not be able to accurately simulate the dynamic characteristics of DC transmission and its controlling systems in electromechanical transient stability simulation, when asymmetric fault occurs in AC system, a modified quasi-steady state model (MQSS) is proposed. The model firstly analyzes the calculation error induced by classical QSS model under asymmetric commutation voltage, which is mainly caused by the commutation voltage zero offset thus making inaccurate calculation of the average DC voltage and the inverter extinction advance angle. The new MQSS model calculates the average DC voltage according to the actual half-cycle voltage waveform on the DC terminal after fault occurrence, and the extinction advance angle is also derived accordingly, so as to avoid the negative effect of the asymmetric commutation voltage. Simulation experiments show that the new MQSS model proposed in this paper has higher simulation precision than the classical QSS model when asymmetric fault occurs in the AC system, by comparing both of them with the results of detailed electromagnetic transient (EMT) model of the DC transmission and its controlling system.

Data-Driven Affinely Adjustable Distributionally Robust Unit Commitment

This paper proposes a data-driven affinely adjustable distributionally robust method for unit commitment considering uncertain load and renewable generation forecasting errors. The proposed formulation minimizes expected total operation costs, including the costs of generation, reserve, wind curtailment and load shedding, while guarantees the system security. Without any presumption about the probability distribution of the uncertainties, the proposed method constructs an ambiguity set of distributions using historical data and immunizes the operation strategies against the worst-case distribution in the ambiguity set. The more historical data is available, the smaller the ambiguity set is and the less conservative the solution is. The formulation is finally cast into a mixed integer linear programming whose scale remains unchanged as the amount of historical data increases. Numerical results and Monte Carlo simulations on the 118- and 1888-bus systems demonstrate the favorable features of the proposed method.

Commutation Failure analysis in single- and multi-infeed HVDC systems

Traditional methods usually use the quasi-steady state model with simplified assumptions for HVDC system analysis, which cannot take voltage distortion into account. The impact of occurrence instant of AC fault on critical commutation voltage is analytically derived and the possibility of commutation failure (CF) is analyzed. However, the complexity and nonlinearity of the single- and multi-infeed HVDC systems limit the applicability of analytical methods to study the CF phenomenon accurately. In this paper, an alternative approach that adopts electromagnetic transient simulation is proposed to assess the susceptibility of HVDC converters to CF. Two simulation-based methodologies are introduced to calculate the Commutation Failure Immunity Index (CFII). Parametric studies on the immunity of converters to commutation failure are investigated on the CIGRE benchmark HVDC test system. Finally, the proposed approach is verified by both local CF and concurrent CF phenomena, and anomalous concurrent CF phenomena are also identified in multi-infeed HVDC systems.

Structure-Exploiting Delay-Dependent Stability Analysis Applied to Power System Load Frequency Control

Linear matrix inequality (LMI) based delay-dependent stability analysis/synthesis methods have been applied to power system load frequency control (LFC) which has communication networks in its loops. However, the computational burden of solving large-scale LMIs poses a great challenge to the application of those methods to real-world power systems. This paper investigates the computational aspect of delay-dependent stability analysis (DDSA) of LFC. The basic idea is to improve the numerical tractability of DDSA by exploiting the chordal sparsity and symmetry of the graph related to LFC loops. The graph-theoretic analysis yields the structure restrictions of weighting matrices needed for the LMIs to inherit the chordal sparsity of the control loops. By enforcing those structure restrictions on weighting matrices, the positive semidefinite constraints in the LMIs can be decomposed into smaller ones, and the number of decision variables can be greatly reduced. Symmetry in LFC control loops is also exploited to reduce the number of decision variables. Numerical studies show the proposed structure-exploiting techniques significantly improves the numerical tractability of DDSA at the cost of the introduction of acceptable minor conservatism.

Research of transformer modeling considering the influence of tap positions on original parameters

Traditional transformer models assume that the tap positions only influence the turns ratio when studying power system load flow, electromagnetic and electromechanical simulations. It is found that the original parameters of transformers, such as the winding resistances and inductances, also vary with different tap positions. An inductance matrix model for transformers is proposed in this paper, based on the law of magnetic circuit. The parameters in the model can be acquired through transformer type test. Case studies on load flow calculation are performed to demonstrate that the deviation of transformer original parameters induced by different tap positions, might cause considerable computation errors.