A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid

Abstract

The inherent variability of large-scale renewable energy generation leads to significant difficulties in microgrid energy management. Likewise, the effects of human behaviors in response to the changes in electricity tariffs as well as seasons result in changes in electricity consumption. Thus, proper scheduling and planning of power system operations require accurate load demand and renewable energy generation estimation studies, especially for short-term periods (hour-ahead, day-ahead). The time-sequence variation in aggregated electrical load and bulk photovoltaic power output are considered in this study to promote the supply-demand balance in the short-term optimal operational scheduling framework of a reconfigurable microgrid by integrating the forecasting results. A bi-directional long short-term memory units based deep recurrent neural network model, DRNN Bi-LSTM, is designed to provide accurate aggregated electrical load demand and the bulk photovoltaic power generation forecasting results. The real-world data set is utilized to test the proposed forecasting model, and based on the results, the DRNN Bi-LSTM model performs better in comparison with other methods in the surveyed literature. Meanwhile, the optimal operational scheduling framework is studied by simultaneously making a day-ahead optimal reconfiguration plan and optimal dispatching of controllable distributed generation units which are considered as optimal operation solutions. A combined approach of basic and selective particle swarm optimization methods, PSO&SPSO, is utilized for that combinatorial, non-linear, non-deterministic polynomial-time-hard (NP-hard), complex optimization study by aiming minimization of the aggregated real power losses of the microgrid subject to diverse equality and inequality constraints. A reconfigurable microgrid test system that includes photovoltaic power and diesel distributed generators is used for the optimal operational scheduling framework. As a whole, this study contributes to the optimal operational scheduling of reconfigurable microgrid with electrical energy demand and renewable energy forecasting by way of the developed DRNN Bi-LSTM model. The results indicate that optimal operational scheduling of reconfigurable microgrid with deep learning assisted approach could not only reduce real power losses but also improve system in an economic way.

Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

“…MAPE is often utilized in regression and time-series issues to calculate the accuracy of forecasts. The best state to evaluate the results using such metrics is the maximum value for R and the minimum values for predictive error evaluation metrics [4].…”

“…In addition, in [38], the proposed hybrid model is compared with other load forecast methods, such as CNN-LSTM, 2-Dimensional (2D) CNN, GRU, LSTM, ARMIA, k-Nearest Neighbor (kNN) and Neural Network Ensemble (NNE). A Bi-directional LSTM unit-based DRNN model called DRNN Bi-LSTM is proposed in [4] to supply precise aggregated electrical load demand and the forecasting of photovoltaic power production. An enhanced framework for energy management is introduced in [39] to efficiently investigate the uncertainties caused by climate change in an MG.…”

Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

“…Within the scientific article [5], Yaprakdal et al propose a forecasting method based on deep recurrent neural networks (DRNNs), consisting of a BiLSTM ANN, entitled "DRNN Bi-LSTM", in order to obtain an accurate estimation for the day-ahead electricity demand and the total photovoltaic electricity production for a microgrid equipped with both photovoltaic panels and diesel generators. The authors applied their method on a real production dataset.…”

Yenilenebilir Enerji Kaynaklarına Dayalı Bir Sistemde Güç Kalitesi Analizi, Kontrolü ve İzlemesi

“…Akıllı şebekeler geleneksel güç sistemi ile karşılaştırıldığında, çeşitli enerji girişleri, çoklu yük özellikleri ve çeşitli enerji dönüşüm teknolojilerini birleştiren mikro şebeke veya dağıtılmış enerji santrali, kimyasal enerji, termodinamik ve elektrodinamiklerin birbirine bağlanması ile doğrusal olmayan ve karmaşık bir sistem olduğu bilinmektedir. Mikro şebekeler, dağıtılmış enerji üretim sistemleri entegrasyonu ve güç kalitesini güvence altına alma gibi kritik yüklere enerji güvencesi sağlama gereksinimi nedeniyle dikkat çekmektedir [21][22][23]. [18,24], aktif güç filtresi (AGF) [25,26] ve hibrid aktif güç filtresi (HAGF) [27,28] tekniklerini içermektedir.…”