A review of deep learning for renewable energy forecasting

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

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

The resilience and reliability of modern power systems are threatened by increasingly severe weather events and cyber-physical security events. An effective restoration methodology is desired to optimally integrate emerging smart grid technologies and pave the way for developing self-healing smart grids. In this paper, a sequential service restoration (SSR) framework is proposed to generate restoration solutions for distribution systems and microgrids in the event of large-scale power outages. The restoration solution contains a sequence of control actions that properly coordinate switches, distributed generators, and switchable loads to form multiple isolated microgrids. The SSR can be applied for three-phase unbalanced distribution systems and microgrids and can adapt to various operation conditions. Mathematical models are introduced for three-phase unbalanced power flow, voltage regulators, transformers, and loads. The SSR problem is formulated as a mixed-integer linear programming model, and its effectiveness is evaluated via the modified IEEE 123 node test feeder.

Sequential Service Restoration for Unbalanced Distribution Systems and Microgrids

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.

/pdf/optimal-energy-management-for-industrial-microgrids-with-2060gxgozs.pdf

Optimal energy management for industrial microgrids with high-penetration renewables

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

This paper presents an optimal energy management strategy for the operation of multiple energy storage units (batteries) in grid-connected industrial microgrids with high-penetration renewables in a variable grid-price scenario. The approach is based on a regrouping particle swarm optimization (RegPSO) formulated over a day-ahead scheduling horizon with one hour time interval, considering forecasted renewable energy generations and electric 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 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 reveal that the RegPSO based strategy was able to find a global optimal solution in considerably less computation time than the GA based reference approach.

Grid-Price Dependent Optimal Energy Storage Management Strategy for Grid-Connected Industrial Microgrids

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 day's wind speed by the first stage is applied to the second stage to forecast next day's 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 hybrid PSO-ANN model for short-term wind power prediction

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 day's wind speed by the first stage is applied to the second stage to predict next day's 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-ANFIS approach

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