Microgrids energy management systems: A critical review on methods, solutions, and prospects

A review of deep learning for renewable energy forecasting

Application of support vector machine models for forecasting solar and wind energy resources: A review

Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information

Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network

This paper presents a day-ahead optimal energy management strategy for economic operation of industrial microgrids with high-penetration renewables under both isolated and grid-connected operation modes. The approach is based on a regrouping particle swarm optimization (RegPSO) formulated over a day-ahead scheduling horizon with one hour time step, taking into account forecasted renewable energy generations and electrical load demands. Besides satisfying its local energy demands, the microgrid considered in this paper (a real industrial microgrid, “Goldwind Smart Microgrid System” in Beijing, China), participates in energy trading with the main grid; it can either sell power to the main grid or buy from the main grid. Performance objectives include minimization of fuel cost, operation and maintenance costs and energy purchasing expenses from the main grid, and maximization of financial profit from energy selling revenues to the main grid. Simulation results demonstrate the effectiveness of various aspects of the proposed strategy in different scenarios. To validate the performance of the proposed strategy, obtained results are compared to a genetic algorithm (GA) based reference energy management approach and confirmed that the RegPSO based strategy was able to find a global optimal solution in considerably less computation time than the GA based reference approach.

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Optimal energy management for industrial microgrids with high-penetration renewables

Determination of the output power of wind generators is always associated with some uncertainties due to wind speed and other weather parameters variation, and accurate 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 adaptive neuro-fuzzy inference system (double-stage hybrid ANFIS) for short-term wind power prediction of a microgrid wind farm in Beijing, China. The approach 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 day’s wind speed by the first stage is applied to the second stage to forecast next day’s wind power. The influence of input data dependency on prediction accuracy has also been analyzed by dividing the input data into five subsets. The presented approach has resulted in considerable forecasting accuracy enhancements. The accuracy of the proposed approach is compared with other three forecasting approaches and achieved the best accuracy enhancement than all.

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Short-term wind power forecasting using a double-stage hierarchical ANFIS approach for energy management in microgrids

Short-term wind power forecasting using adouble-stage hierarchical ANFIS approachfor energy management in microgrids

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.

Dehua Zheng

Papers

Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information

Optimal energy management for industrial microgrids with high-penetration renewables

Short-term wind power forecasting using a double-stage hierarchical ANFIS approach for energy management in microgrids

Short-term wind power forecasting using adouble-stage hierarchical ANFIS approachfor energy management in microgrids

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