A review on recent developments in control and optimization of micro grids

Load forecasting is a vital part of smart grids for predicting the required electrical power using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the smart grid using the artificial neural network (ANN). Generally, computing the deep learning in the smart grid requires massive data aggregation or centralization and significant computational time. This paper presents a survey of deep learning-based load forecasting techniques from 2015 to 2020. This survey discusses the studies based on their deep learning techniques, Distributed Deep Learning (DDL) techniques, Back Propagation (BP) based works, and non-BP based works in the load forecasting process. Consequent to the survey, it was determined that data aggregation dependency would be beneficial for reducing computational time in load forecasting. Therefore, a conceptual model of DDL for smart grids has been presented, where the HSIC (Hilbert-Schmidt Independence Criterion) Bottleneck technique has been incorporated to provide higher accuracy.

/pdf/hsic-bottleneck-based-distributed-deep-learning-model-for-2dc43gdryl.pdf

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

Sustainable energy planning for cost minimization of autonomous hybrid microgrid using combined multi-objective optimization algorithm

Electrical load forecasting plays a key role in power system planning and operation procedures. So far, a variety of techniques have been employed for electrical load forecasting. Meanwhile, neural-network-based methods led to fewer prediction errors due to their ability to adapt properly to the consuming load's hidden characteristic. Therefore, these methods were widely accepted by the researchers. As the parameters of the neural network have a significant impact on its performance, in this paper, a short-term electrical load forecasting method using neural network and particle swarm optimization (PSO) algorithm is proposed, in which some neural network parameters including learning rate and number of hidden layers are determined in order to forecast electrical load using the PSO algorithm precisely. Then, the neural network with these optimized parameters is used to predict the short-term electrical load. In this method, a three-layer feedforward neural network trained by backpropagation algorithm is used beside an improved gbest PSO algorithm. Also, the neural network prediction error is defined as the PSO algorithm cost function. The proposed approach has been tested on the Iranian power grid using MATLAB software. The average of three indices beside graphical results has been considered to evaluate the performance of the proposed method. The simulation results reflect the capabilities of the proposed method in accurately predicting the electrical load.

/pdf/short-term-load-forecasting-using-neural-network-and-1ps1hv6ikk.pdf

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Short-term load forecasting method with variational mode decomposition and stacking model fusion

Multiobjective optimization (MOO) dispatch for microgrids (MGs) can achieve many benefits, such as minimized operation cost, greenhouse gas emission reduction, and enhanced reliability of service. In this paper, a MG with the PV-battery-diesel system is introduced to establish its characteristic and economic models. Based on the models and three objectives, the constrained MOO problem is formulated. Then, an advanced multiobjective particle swarm optimization (MOPSO) algorithm is proposed to obtain Pareto optimization dispatch for MGs. The combination of archive maintenance and Pareto selection enables the MOPSO algorithm to maintain enough nondominated solutions and seek Pareto frontiers. The final trade-off solutions are decided based on the fuzzy set. The benchmark function tests and simulation results demonstrate that the proposed MOPSO algorithm has better searching ability than nondominated sorting genetic algorithm-II (NSGA-II), which is widely used in generation dispatch for MGs. The proposed method can efficiently offer more Pareto solutions and find a trade-off one to simultaneously achieve three benefits: minimized operation cost, reduced environmental cost, and maximized reliability of service.

https://downloads.hindawi.com/journals/mpe/2020/5695917.pdf

Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch

In this paper, we focus on the accuracy improvement of short-term load forecasting, which is useful in the reasonable planning and stable operation of the system in advance. For this purpose, a short-term load forecasting model based on frequency domain decomposition and deep learning is proposed. The original load data are decomposed into four parts as the daily and weekly periodic components and the low- and high-frequency components. Long short-term memory (LSTM) neural network is applied in the forecasting for the daily periodic, weekly periodic, and low-frequency components. The combination of isolation forest (iForest) and Mallat with the LSTM method is constructed in forecasting the high-frequency part. Finally, the four parts of the forecasting results are added together. The actual load data of a Chinese city are researched. Compared with the forecasting results of empirical mode decomposition- (EMD-) LSTM, LSTM, and recurrent neural network (RNN) methods, the proposed method can effectively improve the accuracy and reduce the degree of dispersion of forecasting and actual values.

/pdf/short-term-load-forecasting-based-on-frequency-domain-3mos4i6vsd.pdf

Short-Term Load Forecasting Based on Frequency Domain Decomposition and Deep Learning

This paper focuses on the environmental and economic optimal operation of microgrid (MG) in various scenarios. An AC/DC hybrid MG including photovoltaic, diesel generator, lithium battery, electric vehicle charging points is analyzed. Considering the operation constrains of MG, a constrained multi-objective optimization problem (CMOP) is built. The optimization objectives of the proposed CMOP are the fuel cost, depreciation expense and emission cost of distributed generators. The fuzzy comprehensive evaluation is introduced for conversion from the multi-objective problem to a single-objective one. Then the comprehensive learning particle swarm optimization (CLPSO) is applied for the solutions of distributed generators' outputs. Optimization results in grid-connected and islanded modes demonstrate the effectiveness of the proposed models, methods, and algorithm.

Optimal operation of PV-diesel-battery MG based on fuzzy comprehensive evaluation

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Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch

Short-Term Load Forecasting Based on Frequency Domain Decomposition and Deep Learning

Optimal operation of PV-diesel-battery MG based on fuzzy comprehensive evaluation