Embedded Applications of MS-PSO-BP on Wind/Storage Power Forecasting

Higher proportion wind power penetration has great impact on grid operation and dispatching, intelligent hybrid algorithm is proposed to cope with inaccurate schedule forecast. Firstly, hybrid algorithm of MS-PSO-BP (Mathematical Statistics, Particle Swarm Optimization, Back Propagation neural network) is proposed to improve the wind power system prediction accuracy. MS is used to optimize artificial neural network training sample, PSO-BP (particle swarm combined with back propagation neural network) is employed on prediction error dynamic revision. From the angle of root mean square error (RMSE), the mean absolute error (MAE) and convergence rate, analysis and comparison of several intelligent algorithms (BP, RBP, PSO-BP, MS-BP, MS-RBP, MS-PSO-BP) are done to verify the availability of the proposed prediction method. Further, due to the physical function of energy storage in improving accuracy of schedule pre-fabrication, a mathematical statistical method is proposed to determine the optimal capacity of the storage batteries in power forecasting based on the historical statistical data of wind farm. Algorithm feasibility is validated by application of experiment simulation and comparative analysis

Optimal Battery Energy Storage Placement for Transient Voltage Stability Enhancement

A placement problem for multiple Battery Energy Storage System (BESS) units is formulated towards power system transient voltage stability enhancement in this paper. The problem is solved by the Cross-Entropy (CE) optimization method. A simulation-based approach is adopted to incorporate higher-order dynamics and nonlinearities of generators and loads. The objective is to maximize the voltage stability index, which is set up based on certain grid-codes. Formulations of the optimization problem are then discussed. Finally, the proposed approach is implemented in MATLAB/DIgSILENT and tested on the New England 39-Bus system. Results indicate that installing BESS units at the optimized location can alleviate transient voltage instability issue compared with the original system with no BESS. The CE placement algorithm is also compared with the classic PSO (Particle Swarm Optimization) method, and its superiority is demonstrated in terms of fewer iterations for convergence with better solution qualities.Comment: This paper has been accepted by the 2019 IEEE PES General Meeting at Atlanta, GA in August 201

THE STABILITY ANALYSIS FOR WIND TURBINES WITH DOUBLY FED INDUCTION GENERATORS

The quickly increasing, widespread use of wind generation around the world reduces carbon emissions, decreases the effects of global warming, and lowers dependence on fossil fuels. However, the growing penetration of wind power requires more effort to maintain power systems stability. This dissertation focuses on developing a novel algorithm which dynamically optimizes the proportional-integral (PI) controllers of a doubly fed induction generator (DFIG) driven by a wind turbine to increase the transient performance based on small signal stability analysis. Firstly, the impact of wind generation is introduced. The stability of power systems with wind generation is described, including the different wind generator technologies, and the challenges in high wind penetration conditions. Secondly, the small signal stability analysis model of wind turbines with DFIG is developed, including detailed rotor/grid side converter models, and the interface with the power grid. Thirdly, Particle swarm optimization (PSO) is selected to off-line calculate the optimal parameters of DFIG PI gains to maximize the damping ratios of system eigenvalues in different wind speeds. Based on the historical data, the artificial neural networks (ANNs) are designed, trained, and have the ability to quickly forecast the optimal parameters. The ANN controllers are designed to dynamically adjust PI gains online. Finally, system studies have been provided for a single machine connected to an infinite bus system (SMIB), a single machine connected to a weak grid (SMWG), and a multi machine system (MMS), respectively. A detailed analysis for MMS with different wind penetration levels has been shown according to grid code. Moreover, voltage stability improvement and grid loss reduction in IEEE 34-bus distribution system, including WT-DFIG under unbalanced heavy loading conditions, are investigated. The simulation results show the algorithm can greatly reduce low frequency oscillations and improve transient performance of DFIGs system. It realizes off-line optimization of MMS, online forecasts the optimal PI gains, and adaptively adjusts PI gains. The results also provide some useful conclusions and explorations for wind generation design, operations, and connection to the power grid. Advisors: Sohrab Asgarpoor and Wei Qia

THE STABILITY ANALYSIS FOR WIND TURBINES WITH DOUBLY FED INDUCTION GENERATORS

The quickly increasing, widespread use of wind generation around the world reduces carbon emissions, decreases the effects of global warming, and lowers dependence on fossil fuels. However, the growing penetration of wind power requires more effort to maintain power systems stability. This dissertation focuses on developing a novel algorithm which dynamically optimizes the proportional-integral (PI) controllers of a doubly fed induction generator (DFIG) driven by a wind turbine to increase the transient performance based on small signal stability analysis. Firstly, the impact of wind generation is introduced. The stability of power systems with wind generation is described, including the different wind generator technologies, and the challenges in high wind penetration conditions. Secondly, the small signal stability analysis model of wind turbines with DFIG is developed, including detailed rotor/grid side converter models, and the interface with the power grid. Thirdly, Particle swarm optimization (PSO) is selected to off-line calculate the optimal parameters of DFIG PI gains to maximize the damping ratios of system eigenvalues in different wind speeds. Based on the historical data, the artificial neural networks (ANNs) are designed, trained, and have the ability to quickly forecast the optimal parameters. The ANN controllers are designed to dynamically adjust PI gains online. Finally, system studies have been provided for a single machine connected to an infinite bus system (SMIB), a single machine connected to a weak grid (SMWG), and a multi machine system (MMS), respectively. A detailed analysis for MMS with different wind penetration levels has been shown according to grid code. Moreover, voltage stability improvement and grid loss reduction in IEEE 34-bus distribution system, including WT-DFIG under unbalanced heavy loading conditions, are investigated. The simulation results show the algorithm can greatly reduce low frequency oscillations and improve transient performance of DFIGs system. It realizes off-line optimization of MMS, online forecasts the optimal PI gains, and adaptively adjusts PI gains. The results also provide some useful conclusions and explorations for wind generation design, operations, and connection to the power grid. Advisors: Sohrab Asgarpoor and Wei Qia

A Review on Optimal Operation of Distributed Network Embedded to Wind-Battery Storage System

Energy is a vital requirement for today's socio-economic welfare and development. But due to the continuous increase of the demand the conventional energy resources are depleting day by day and on the verge of extinction. Hence more renewable generation units are emphasised to integrate to the power network to supply the required demand. This incorporation of the distributed generation into the distributed network has the advantages of controllability, flexibility and tremendous potential if it can be exploited properly. However, due to their intermittent and unpredictable nature, there is a need for energy storages to ensure continuous operations, i.e., to meet the load all the time. There are many possible options for energy storage, but the most popular and technologically sound option is battery storage. Along with this battery storage system (BSS), a power conditioning system (PCS) has to be connected for generation of both active and reactive power from the BSS which in turn increases the overall installation cost of BSS. Moreover, the energy storage cost is a function of the storage device power, energy capacities and their specific costs depending on the chosen technology of optimization. Thus, profit from those renewable energy producers have to be maximized, and losses are to be minimized by using dynamic optimization techniques. But along with the advantages there comes the complexities due to the inclusion of distributed generation and the additional energy storages in the power system network. Moreover, it is highly critical to operate the vast system optimally. Hence there are lots of research had been done or are in process for finding the proper approach of optimization of the system. This paper presents a review of the current state of the optimization methods applied to renewable and sustainable energy source embedded with the Energy storage for maximization of the revenue obtained from the power trading to the network

Comparative analysis of optimal power flow in renewable energy sources based microgrids

Adaptation of renewable energy is inevitable. The idea of microgrid offers integration of renewable energy sources with conventional power generation sources. In this research, an operative approach was proposed for microgrids comprising of four different power generation sources. The microgrid is a way that mixes energy locally and empowers the end-users to add useful power to the network. IEEE-14 bus system-based microgrid was developed in MATLAB/Simulink to demonstrate the optimal power flow. Two cases of battery charging and discharging were also simulated to evaluate its realization. The solution of power flow analysis was obtained from the Newton–Raphson method and particle swarm optimization method. A comparison was drawn between these methods for the proposed model of the microgrid on the basis of transmission line losses and voltage profile. Transmission line losses are reduced to about 17% in the case of battery charging and 19 to 20% in the case of battery discharging when system was analyzed with the particle swarm optimization. Particle swarm optimization was found more promising for the deliverance of optimal power flow in the renewable energy sources-based microgrid

Particle swarm optimization-based superconducting magnetic energy storage for low-voltage ride-through capability enhancement in wind energy conversion system

This article presents a novel application of the particle swarm optimization technique to optimally design all the proportional-integral controllers required to control both the real and reactive powers of the superconducting magnetic energy storage unit for enhancing the low-voltage ride-through capability of a grid-connected wind farm. The control strategy of the superconducting magnetic energy storage system is based on a sinusoidal pulse-width modulation voltage source converter and proportional-integral-controlled DC-DC converter. Control of the voltage source converter depends on the cascaded proportional-integral control scheme. All proportional-integral controllers in the superconducting magnetic energy storage system are optimally designed by the particle swarm optimization technique. The statistical response surface methodology is used to build the mathematical model of the voltage responses at the point of common coupling in terms of the proportional-integral controller parameters. The effectiveness of the proportional-integral-controlled superconducting magnetic energy storage optimized by the proposed particle swarm optimization technique is then compared to that optimized by a genetic algorithm technique, taking into consideration symmetrical and unsymmetrical fault conditions. A two-mass drive train model is used for the wind turbine generator system because of its large influence on the fault analyses. The systemic design approach is demonstrated in determining the controller parameters of the superconducting magnetic energy storage unit, and its effectiveness is validated in augmenting the low-voltage ride-through of a grid-connected wind farm

A Review on Optimal Operation of Distributed Network Embedded to Wind-Battery Storage System

Energy is a vital requirement for today’s socio-economic welfare and development. But due to the continuous increase of the demand the conventional energy resources are depleting day by day and on the verge of extinction. Hence more renewable generation units are emphasised to integrate to the power network to supply the required demand. This incorporation of the distributed generation into the distributed network has the advantages of controllability, flexibility and tremendous potential if it can be exploited properly. However, due to their intermittent and unpredictable nature, there is a need for energy storages to ensure continuous operations, i.e., to meet the load all the time. There are many possible options for energy storage, but the most popular and technologically sound option is battery storage. Along with this battery storage system (BSS), a power conditioning system (PCS) has to be connected for generation of both active and reactive power from the BSS which in turn increases the overall installation cost of BSS. Moreover, the energy storage cost is a function of the storage device power, energy capacities and their specific costs depending on the chosen technology of optimization. Thus, profit from those renewable energy producers have to be maximized, and losses are to be minimized by using dynamic optimization techniques. But along with the advantages there comes the complexities due to the inclusion of distributed generation and the additional energy storages in the power system network. Moreover, it is highly critical to operate the vast system optimally. Hence there are lots of research had been done or are in process for finding the proper approach of optimization of the system. This paper presents a review of the current state of the optimization methods applied to renewable and sustainable energy source embedded with the Energy storage for maximization of the revenue obtained from the power trading to the network