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Distributed Load Shedding Algorithm for Islanded Microgrid Using Fog Computing Paradigm
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.
... 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. ...
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]. ...
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.
The utilities have the last control action of load shedding to protect the system from blackout. In this direction a prudent planning to determine optimal load shedding is required to avoid voltage collapse of the system. In this paper, under voltage load shedding (UVLS) problem is formulated as a multi objective minimization problem having following objectives (i) power loss (ii) voltage deviation (iii) cost of load shedding. A hybrid optimization method i.e. evolutionary particle swarm optimization (EPSO) is presented to determine optimal load shedding amount. The proposed method for UVLS is implemented on IEEE-33 test network. The results obtained from EPSO are then compared with traditional PSO algorithm. The comparative analysis shows that EPSO is more accurate in finding precise solution than conventional PSO algorithm.
A microgrid is an eco-friendly power system because renewable sources are used as main power sources. In the islanded operation mode of a microgrid, maintaining the balance between power supply and power demand is a very important problem. In the case of surplus supply, decreased generation output and/or charge of distributed storages can be applied to solve the imbalance between power supply and demand. In the case of supply shortages, increased generation output and/or discharge of distributed storages can be applied. Especially in the case of critical supply shortages, load shedding should be applied. In a distributed load-shedding approach, microgrid components need to make decisions autonomously. For autonomous microgrid operation, a multi-agent system has been investigated. In this paper, a distributed load-shedding system for agent-based autonomous operation of a microgrid is designed. The designed system is implemented and tested to show the functionality and feasibility of the proposed system.
This paper presents a new under-frequency load shedding technique based on the combination of random and fixed priority of loads. It has been observed that placing all of the loads in the distribution system with fixed priority results in un-optimum load shedding. On the other hand, designing the load priority with a combination of random and fixed priority provides the technique with some sort of flexibility in achieving the optimal load shedding. The validation of the proposed scheme on different scenarios proves that the proposed technique is capable of achieving the optimal load shedding and recovering frequency to nominal value without any overshoot. Index Terms—Distributed generation (DG), fixed priority loads, optimum load shedding module (OLSM), random priority loads.
Recently there has been a significant proliferation in the use of forecasting techniques, mainly due to the increased availability and power of computation systems and, in particular, to the usage of personal computers. This is also true for power network systems, where energy demand forecasting has been an important field in order to allow generation planning and adaptation. Apart from the quantitative progression, there has also been a change in the type of models proposed and used. In the '70s, the usage of non-linear techniques was generally not popular among scientists and engineers. However, in the last two decades they have become very important techniques in solving complex problems which would be very difficult to tackle otherwise. With the recent emergence of smart grids, new environments have appeared capable of integrating demand, generation, and storage. These employ intelligent and adaptive elements that require more advanced techniques for accurate and precise demand and generation forecasting in order to work optimally. This review discusses the most relevant studies on electric demand prediction over the last 40 years, and presents the different models used as well as the future trends. Additionally, it analyzes the latest studies on demand forecasting in the future environments that emerge from the usage of smart grids.
Underfrequency load shedding is one of the most important protection systems, as in many cases it represents the last chance to prevent a system blackout after a serious disturbance occurs in a power system. In order to improve traditional schemes, many efforts have been concentrated on attempts to use the frequency gradient as an indicator for determining the lack of active power in a system. This paper concentrates on analyzing the factors that influence the gradient. Analyses have shown that the gradient can give misleading information about the active-power deficit, as long as certain factors are ignored or assumed to be constant. A draft of a gradient-based underfrequency load-shedding scheme is presented, which also highlights the problems associated with the use of gradient. On the one hand, a frequency gradient alone does not appear to be sufficient for the active-power deficit estimation. Nevertheless, for the actual load-shedding procedure it is found to be very useful.
This paper proposes the use of a novel algorithm for smart direct load control and load shedding to minimize power outages in sudden grid load changes and reduce the Peak-to-Average Ratio (PAR). The algorithm utilizes forecasting, load shedding, and smart direct load control. It also uses the Internet of Things and stream analytics to provide real-time load control, and generates a daily schedule for customers' equipped with Intelligent Electronic Device (IED)s, based on their demands, thermal comfort, and the forecasted load model. The demand response techniques are utilized for real-time load control and optimization. To test the algorithm, a simulation system was developed, which takes into account one-hundred customers owning randomly selected appliances. The results indicated that load shedding using ARIMA time series prediction model and applying smart direct load control (S-DLC) and the Internet of Things can significantly reduce customers' power outage.
KEYWORDS:
Smart Grids; Smart Load control; Internet of Things; IoT; Power outages; Energy; Cost Of Energy; Rate of Return; Load Shedding; Load forecasting; load selection; Energy Saving; utility consumers; load classification; Customer Service; Power plants
This paper proposes a Multi-Agent System (MAS) modeling and control architecture for a solar driven DC microgrid. The microgrid consists of solar system as a source of power, energy storage system, critical and non-critical houses (loads) with their own solar and storage as well. The proposed MAS aims at increasing power production in the microgrid whereby each load can have the ability to sell extra power to the main solar source. The main solar source can generate power and provide to the community when needed. The MAS also controls and monitors an automatic load shedding challenge to disconnect non critical loads when there is a deficiency of power supply to the system. These objectives are achieved by agent based system which runs under REPAST simulation tool. The results are validated with simulations run with three loads: hospital and two houses.
The common practice to distribute the electricity generated by a grid-tied renewable generation system results in two drawbacks: double conversion loss because of DC-AC-DC conversion and the challenge to maintain the stability and reliability of AC grid because of the intermittent generation. This paper reports a solution to both of them by using a DC microgrid. The reported DC microgrid integrates multi-renewable generations in the system. Instead of converting all the generated DC power into AC, the DC microgrid consumes the renewable energy in DC mode to reduce the double conversion loss. Meanwhile, the energy storage of the system can reduce the impact of the intermittent generation to AC grid. Furthermore, the intelligent control algorithm of the system can wisely use the stored energy to maximize the system benefit and lower the demand to the AC grid in peak hours. The modular design of the major components makes the system scalable easily. The intelligence of the control algorithm also allows the system to function as a smart endpoint in the future smart grid.
The underfrequency load shedding scheme is an important protective scheme, responsible for maintaining frequency of power systems within permissible limits in case of severe power deficits and as such, its proper performance is of great interest. In microgirds, disconnecting/connecting power generation sources and loads result in variations in the system parameters. Moreover, the output power of some distributed energy resources such as photovoltaic/wind turbine generators, might change during load shedding process and consequently affect the performance. Hence, an underfrequency load shedding scheme is proposed here which is not only independent of the microgrid parameters, but also considers power generation variations during the process. A microgrid power system with several distributed energy resources is adopted to demonstrate the effectiveness of the proposed method.
The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.
Under frequency load shedding (UFLS) and under voltage load shedding (UVLS) are attracting more attention, as large disturbances occur more frequently than in the past. Usually, these two schemes work independently from each other, and are not designed in an integrated way to exploit their combined effect on load shedding. Besides, reactive power is seldom considered in the load shedding process. To fill this gap, we propose in this paper a new centralized, adaptive load shedding algorithm, which uses both voltage and frequency information provided by phasor measurement units (PMUs). The main contribution of the new method is the consideration of reactive power together with active power in the load shedding strategy. Therefore, this method addresses the combined voltage and frequency stability issues better than the independent approaches. The new method is tested on the IEEE 39-Bus system, in order to compare it with other methods. Simulation results show that, after large disturbance, this method can bring the system back to a new stable steady state that is better from the point of view of frequency and voltage stability, and loadability.
Under-Frequency Load Shedding (UFLS) is a common technique to maintain power system stability by removing the overload in some part of the system. The existing UFLS schemes operate successfully in the interconnected grid system. A particular approach of UFLS scheme is required for an islanded system which is known to be not as strong as the grid. This paper presents a new approach of UFLS scheme for the implementation in an islanded distribution system. The scheme is based on a combination of adaptive and intelligent techniques. The disturbance magnitude for response based and event based is treated differently: (1) event based is based on the power imbalance and (2) response based is based on the swing equation which relies on the frequency and rate of change of frequency measurement. Dynamic simulation on an intentional islanding operation is performed on an existing Malaysian network interconnected with a mini hydro generation. The objective is to investigate the effectiveness of the proposed scheme. The simulation results demonstrate that the proposed scheme is capable of improving the frequency response of the islanded system. A significant finding is that the scheme can successfully reduce the effect of power deficiency following the grid connected to islanding operation transition.