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Abstract

Demand Side Management, DSM, is a program supported by the smart-grid which aims at matching the energy consumption and production. Several techniques for demand-side management have been proposed, including load-shedding, time of use pricing, real-time pricing, and critical peak pricing. In this work, we propose a distributed load-shedding algorithm using the multi-agent system. The agents in residential areas collaborate to reduce the energy demands using various forecasting techniques. The computational distributed framework is provided via fog computing to minimize power consumption, costs, and latency when designed using LoRaWAN protocol.

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... The classifications and comparisons among these protocols have been discussed in [61]. The LoRa communication protocol is a very attractive solution for building private, secure, and low-power communication infrastructure for microgrids [62,63]. LoRa uses spread spectrum technology which employs a low-complicity receiver, making it a suitable choice for machine-to-machine (M2M) communication, IoT, wireless sensor networks, and many other applications. ...
... In many applications, communication latency is a crucial element in any actuator-type or control-type application. The latency requirements for implementing the DR program have been studied in research and industrial trends [62][63][64]. However, in the context of smart grids, there have been limited efforts devoted to using them in isolated-mode microgrids. ...
Article
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This study describes the design and control algorithms of an IoT-connected photovoltaic microgrid operating in a partially grid-connected mode. The proposed architecture and control design aim to connect or disconnect non-critical loads between the microgrid and utility grid. Different components of the microgrid, such as photovoltaic arrays, energy storage elements, inverters, solid-state transfer switches, smart-meters, and communication networks were modeled and simulated. The communication between smart meters and the microgrid controller is designed using LoRa communication protocol for the control and monitoring of loads in residential buildings. An IoT-enabled smart meter has been designed using ZigBee communication protocol to evaluate data transmission requirements in the microgrid. The loads were managed by a proposed under-voltage load-shedding algorithm that selects suitable loads to be disconnected from the microgrid and transferred to the utility grid. The simulation results showed that the duty cycle of LoRa and its bit rate can handle the communication requirements in the proposed PV microgrid architecture.
... Active research areas in TIERS include multi-robot coordination [1], [2], [3], [4], [5], swarm design [6], [7], [8], [9], UWB-based localization [10], [11], [12], [13], [14], [15], localization and navigation in unstructured environments [16], [17], [18], lightweight AI at the edge [19], [20], [21], [22], [23], distributed ledger technologies at the edge [24], [25], [26], [27], [28], [29], edge architectures [30], [31], [32], [33], [34], [35], offloading for mobile robots [36], [37], [38], [39], [40], [41], [42], LPWAN networks [43], [44], [45], [46], sensor fusion algorithms [47], [48], [49], and reinforcement and federated learning for multi-robot systems [50], [51], [52], [53]. ...
Thesis
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Smart Grid (SG) is a modernized grid that provides efficient, reliable and economic energy to the consumers. Energy is the most important resource in the world and almost everything relies on it. As smart devices are increasing dramatically with the rapid increase in population, there is a need for an efficient energy distribution mechanism. Furthermore, the forecasting of electricity consumption is supposed to be a major constituent to enhance the performance of the SG. Various learning algorithms have been proposed in the literature for efficient load and price forecasting. However, there exist some issues in the proposed work like increased computational complexity. The sole purpose of the work done in this thesis is to efficiently predict electricity load and price using different techniques with minimum computational complexity. Chapter 1 provides an introduction of various concepts present in the power grids. Afterwards, the unified system model, different sub-problems and the contributions made in the thesis are also presented. Chapter 2 discusses the existing work done by different researchers for performing electricity load and price forecasting. In Chapter 3, Enhanced Logistic Regression (ELR) and Enhanced Recurrent Extreme Learning Machine (ERELM) are proposed for performing short-term load and price forecasting. The former is an enhanced form of Logistic Regression (LR); whereas, the weights and biases of the latter are optimized using Grey Wolf Optimizer (GWO). Classification And Regression Tree (CART), Relief-F and Recursive Feature Elimination (RFE) are used for feature selection and extraction. On the basis of selected features, classification is performed using ELR. Moreover, cross validation is done using Monte Carlo and K-Fold methods. In order to ensure optimal and secure functionality of Micro Grid (MG), Chapter 4 focuses on coordinated energy management of traditional and Renewable Energy Sources (RES). Users and MG with storage capacity are taken into account to perform efficient energy management. A two stage Stackelberg game is formulated. Every player in the game tries to increase its payoff, and ensure user comfort and system reliability. Furthermore, two forecasting techniques are proposed in order to forecast Photo-Voltaic Cell (PVC) generation for announcing optimal prices. Both the existence and uniqueness of Nash Equilibrium (NE) for the energy management algorithm are also considered. In Chapter 5, a novel forecasting model, termed as ELS-net, is proposed. It is a combination of an Ensemble Empirical Mode Decomposition (EEMD) method, multi-model Ensemble Bi Long Short Term Memory (EBiLSTM) forecasting technique and Support Vector Machine (SVM). In the proposed model, EEMD is used to distinguish between linear and non-linear Intrinsic Mode Functions (IMFs). EBiLSTM is used to forecast the non-linear IMFs and SVM is employed to forecast the linear IMFs. The usage of separate forecasting techniques for linear and non-linear IMFs decreases the computational complexity of the model. In Chapter 6, a novel deep learning model, termed as Gated-FCN, is introduced for short-term load forecasting. The key idea is to introduce automated feature selection and a deep learning model for forecasting, which includes an eight layered FCN (FCN-8). It ensures that hand crafted feature selection is avoided as it requires expert domain knowledge. Furthermore, Gated-FCN also helps in reducing noise as it learns internal dependencies as well as the correlation of the time-series. Enhanced Bidirectional Gated Recurrent Unit (EBiGRU) model is dovetailed with FCN-8 in order to learn temporal long-term dependencies of the time-series. Furthermore, weight averaging mechanism of multiple snapshot models is adapted in order to take optimized weights of BiGRU. At the end of FCN-8 and BiGRU, a fully connected dense layer is used that gives final prediction results. The simulations are performed and the results are provided at the end of each chapter. In Chapter 3, the simulations are performed using UMass electric and UCI datasets. ELR shows better performance with the former dataset; whereas, ERELM has better accuracy with the latter. The proposed techniques are then compared with different benchmark schemes. The comparison is done to verify the adaptivity of the proposed techniques. The simulation results show that the proposed techniques outperform the benchmark schemes and increase the prediction accuracy of electricity load and price. Similarly, in Chapter 4, simulations are performed using Elia, Belgium dataset. The results clearly show that the proposed game theoretic approach along with storage capacity optimization and forecasting techniques give benefits to both users and MG. In Chapter 5, simulations are performed to examine the effectiveness of the proposed model using two different datasets: New South Wales (NSW) and Victoria (VIC). From the simulation results, it is obvious that the proposed ELS-net model outperforms the benchmark techniques: EMD-BILSTM-SVM, EMD-PSO-GA-SVR, BiLSTM, MLP and SVM in terms of forecasting accuracy and minimum execution time. Similarly, the simulation results of Chapter 6 depict that Gated-FCN gives maximum forecasting accuracy as compared to the benchmark techniques. For performance evaluation of the proposed work, different performance metrics are used: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Square Error (RMSE). The overall results prove that the work done in this thesis outperforms the existing work in terms of electricity load and price forecasting, and computational complexity.
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