Dehua Zheng

A double-stage hierarchical hybrid PSO-ANN model for short-term wind power prediction

Power output of wind generators is always associated with some uncertainties due to wind speed and other weather parameters alteration, and precise short-term forecasts are essential for their efficient operation. This can efficiently support transmission and distribution system operators and schedulers to improve the power network control and management. In this paper, we propose a double stage hierarchical particle swarm optimization trained artificial neural network (double-stage hybrid PSO-ANN) model for short-term wind power prediction of a microgrid wind farm in Beijing, China. The model has two hierarchical stages. The first PSO-ANN stage employs numerical weather prediction (NWP) meteorological parameters to forecast wind speed at the wind farm exact site and turbine hub height. The second stage models the actual wind speed and power relationships. Then, the predicted next days wind speed by the first stage is applied to the second stage to forecast next days wind power. The proposed approach has attained significant prediction accuracy improvements. The performance of the proposed model is compared with other two prediction approaches and showed best accuracy improvement than both methods.

A double-stage hierarchical ANFIS model for short-term wind power prediction

Output power determination of wind generators is always associated with some uncertainties due to wind speed and other weather parameters alteration, and precise short-term predictions are essential for their efficient operation. This can efficiently support transmission and distribution system operators and schedulers to improve the power network control and management. In this paper, we propose a double stage hierarchical adaptive neuro-fuzzy inference system (double-stage hybrid ANFIS) model for short-term wind power prediction of a microgrid wind farm in Beijing, China. The model has two hierarchical stages. The first ANFIS stage employs numerical weather prediction (NWP) meteorological parameters to forecast wind speed at the wind farm exact site and turbine hub height. The second stage models the actual wind speed and power relationships. Then, the predicted next days wind speed by the first stage is applied to the second stage to forecast next days wind power. The proposed approach has attained significant prediction accuracy improvements. The performance of the proposed model is compared with three other prediction approaches and showed the best accuracy improvement of all.

Optimal energy management strategy for an isolated industrial microgrid using a Modified Particle Swarm Optimization

In this research paper, a 24-hour ahead optimal energy management system (EMS) for an isolated industrial microgrid containing wind, PV solar, diesel generator, microturbine and energy storage is developed and analyzed. The main goal of the microgrid EMS optimization model is to minimize the cost of energy production, maximize the economical benefit of the energy storage and ensure the renewable energy utilization to the maximum possible extent. The Modified Particle Swarm Optimization (MPSO) technique is proposed to solve the optimization model. The model takes into account the fluctuations of renewable energy resources and load demands within the microgrid and uses appropriate forecasting to overcome these fluctuations. The proposed MPSO-based EMS has been tested on a real microgrid in stand-alone mode (Goldwind Smart Microgrid System, Beijing, China). Simulation results have revealed that the proposed MPSO-based EMS can solve the day-ahead optimization model in acceptable fast computation time efficiently. To validate the performances of the proposed strategy, simulation results were also obtained using Genetic Algorithm (GA). Comparison of simulation results show the robustness of the proposed MPSO-based EMS in achieving a possible reduced total energy production cost within a reasonably short computation time.

Short-term wind power forecasting using a double-stage hierarchical hybrid GA-ANFIS approach

Wind generation power output estimation is always associated with some uncertainties as a result of wind speed and other weather parameters intermittency, and accurate short-term predictions are important for their efficient operation. This can greatly help transmission and distribution system operators and schedulers to improve the power network control and management. In this paper, a double stage hierarchical genetic algorithm based adaptive neuro-fuzzy inference system (double-stage hybrid GA-ANFIS) approach is proposed for short-term wind power forecast of a microgrid wind farm in Beijing, China. The approach has two hierarchical stages. The first GA-ANFIS stage utilizes numerical weather prediction (NWP) meteorological parameters to predict wind speed at the wind farm exact site and turbine hub height. The second stage maps the actual wind speed and power relationships. Then, the forecasted next days wind speed by the first stage is applied to the second stage to predict next days wind power. The presented approach has achieved considerable prediction accuracy enhancement. The accuracy of the proposed model is compared with other four forecasting methods and resulted in the best accuracy improvement of all.

Short-term wind power forecasting using a double-stage hierarchical hybrid GA-ANN approach

Power generation from wind generators is always associated with some intermittency due to wind speed and other weather parameters variation, and accurate short-term forecasts are essential for their efficient and effective operation. This can well support transmission and distribution system operators and schedulers to enhance the power network control and management in the smart grid context. This paper presents a double stage hierarchical genetic algorithm trained artificial neural network (double-stage hybrid GA-ANN) for short-term wind power forecast of a microgrid wind farm in Beijing, China. The approach has two hierarchical stages. The first GA-ANN stage employs numerical weather prediction (NWP) meteorological parameters to forecast wind speed at the wind farm exact site and turbine hub height. The second stage models the actual wind speed and power relationships. Then, the predicted next days wind speed by the first stage is applied to the second stage to forecast next days wind power. The presented approach has achieved considerable prediction accuracy improvements. The prediction performance of the proposed approach was also compared with another double-stage Back Propagation (BP) trained ANN prediction model and showed a better accuracy improvement.