Energy management of micro-grid using cooperative game theory

Micro-grid (MG) has been introduced as a low voltage and a very small power system connected to a distribution grid through the point of common coupling. It consists of distributed energy resources (DERs) such as solar Photovoltaic (PV), wind turbine, fuel cell, etc.), interconnected load and energy storage sources. It can operate in grid-connected (i.e. when connected to the main grid) or islanded (i.e. when not connected to the main grid) mode. It has an advantage of utilizing low carbon sources and the possibility of its use in the remote local environment, which means that the transmission infrastructures and their associated costs may be deferred. Although there has been a proliferation of optimization methods of energy management in the MG, most of these methods consider self-interest of the players in profit distribution. Moreover, only a few of them consider a fair profit distribution using Nash bargaining solution (NBS) (i.e. when utility function is linear) leading to even profit distribution and high degree of dissatisfaction. For the MG to achieve better economic outcomes, a novel method based on weighted fair energy management among the participants (i.e. building of different types, such as residential buildings, schools, and shops) is proposed. The novelty of the proposed method lies in the new profit sharing method to favour certain participant by assigning a weight to each participant with cooperative game theory (CGT) approach using generalized Nash bargaining solution (GNBS). The proposed approach achieves a fair (reasonable or just) profit allocation with negotiating power indicator. In this work, a case study of six different participant sites is proposed using the CGT method of energy management. The proposed method is able to cope with the drawbacks of the existing independent method, which negotiate directly with other participants for selfish profit distribution. It is demonstrated that the independent method results in (1) a reduction in the profit of each participant of MG when compared with CGT approach and (2) the variation of transfer prices in some participants having profit below the specified lower bound profit since the method does not take into consideration the lower profit bounds. The use of CGT method (i.e. when participants form a coalition) to finding multi-partner profit level subject to specified lower bounds is demonstrated. This results in (1) increase in the profit of the MG participants (2) maintaining the profit level of all the participants above status-quo profit (lower specified profit bounds) with variation in transfer prices and (3) allowing certain participant to be favoured by assigning higher negotiating power to such participant. To achieve the optimal solution in the proposed method, a teaching-learning-based optimization (TLBO) algorithm is presented to efficiently solve the problem. For TLBO algorithm, no specific control parameters are needed except the number of generations and population size. This is in contrast with other heuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) that require other control parameters (i.e. GA requires selection and crossover operation, while PSO makes use of social parameters and cognitive weight). To demonstrate the effectiveness of the proposed TLBO method, the profit allocations are tested in the grid-connected and the islanded mode using both the CGT and the independent method. In this work, the proposed TLBO method is compared with one traditional method, i.e. Lambda iteration method and two heuristic methods, i.e. PSO and GA. Thus, by using TLBO a considerable amount of computation time is saved. Using the same parameter setting for all the heuristic algorithms used, 20 trials are performed to be able to compare the quality of solution and convergence characteristics. The investigation reveals that TLBO gives the highest quality solutions and better convergence characteristics compared to PSO and GA

Optimal Bandwidth Allocation and Control for Networked Control Systems with Disturbance and Noise

A networked control system (NCS) is an interconnected control system in which sensors, actuators, and controllers communicate with each other through a shared network. Although NCSs are beneficial thanks to easy maintenance, architectural flexibility, decreased wiring weights, and tele-operating possibilities, NCSs also have some challenges such as disturbance, noise, bandwidth limitation, delay, and packet dropout. The popularization of smartphones and the drastically increasing number of internet of things (IoT) devices require not only a high-speed internet such as 5G, but also a wise strategy for optimal bandwidth allocation. In this dissertation, optimal bandwidth allocations for NCSs with disturbance and noise are proposed based on performance index function (PIF), artificial neural network (ANN), and Q-learning algorithms. A ball magnetic-levitation (maglev) system, four DC motor speed-control systems, and a wireless autonomous robotic wheelchair are implemented as test beds. The relationship between system performance, sampling frequency, and the standard deviation of white Gaussian disturbance are approximated using a 6 th-degree polynomial. The PIF and ANN methods can estimate the standard deviation of disturbance when current a sampling frequency and an error variance are provided. Dynamic bandwidth allocation using PIF, ANN, and Q-learning is proposed and verified by experimental results for a single-server and single-client DC motor system. The proposed methods show integral absolute errors (IAE) of 166 615, 16 773, and 16 945 and bandwidth utilizations (BU) of each method are 13.15%, 13.38%, and 13.98%, respectively, after 15 000 iterations, when various standard deviations of disturbance are injected. These results present a better performance and a reasonable average BU compared to fixed sampling frequencies. When information of the estimated standard deviation of disturbance, BU margin of safety, weight of each system, and total time delay is given, the optimal sampling frequency for a multi-server and multi-client system can be determined based on the PIF, ANN, and Q-learning, respectively. They are validated by experiments in two cases. The first case is conducted with a ±0.8-V disturbance, 10% safety margin of BU, 1.25-ms total time delay, and various weights for four DC motor systems. The second has the conditions of a ±0.8-V disturbance, 10% safety margin of BU, 1.25-ms total time delay, various weights for four DC motor systems with a maglev and a wheelchair robot system, of which BUs are 44% and 1% respectively. Experimental results prove that all three methods can be used to find the optimal sampling frequencies for each system when an NCS has limited bandwidth as well as sufficient bandwidth

Output Feedback Control and Optimal Bandwidth Allocation of Networked Control Systems

A networked control system (NCS) is a control system where sensors, actuators, and controllers are interconnected over a communication network. This dissertation presents a framework for modeling, stability analysis, optimal control, and bandwidth allocation of the NCS. A ball magnetic-levitation (maglev) system, four DC motor speed-control systems, and a wireless autonomous robotic wheelchair are employed as test beds to illustrate and verify the theoretical results of this dissertation. This dissertation first proposes an output feedback method to stabilize and control the NCSs. The random time delays in the controller-to-actuator and sensor-to-controller links are modeled with two time-homogeneous Markov chains while the packet losses are treated with Dirac delta functions. An asymptotic mean-square stability criterion is established to compensate for the network-induced random time delays and packet losses in the NCS. Then, an algorithm to implement the asymptotic mean-square stability criterion is presented. Experimental results illustrate effectiveness of the proposed output feedback method compared to conventional controllers. The proposed output feedback controller could reduce the errors of the NCS by 13% and 30–40% for the cases without and with data packet losses, respectively. The optimal bandwidth allocation and scheduling of the NCS with nonlinear-programming techniques is also presented in the dissertation. The bandwidth utilization (BU) of each client is defined in terms of its sampling frequency. Two nonlinear approximations, exponential and quadratic approximations, are formulated to describe the system performance governed by discrete-time integral absolute error (DIAE) versus sampling frequency. The optimal sampling frequencies are obtained by solving the approximations with Karush-Kuhn-Tucker (KKT) conditions. Simulation and experimental results are given to verify the effectiveness of the proposed approximations and the bandwidth allocation and scheduling algorithms. In simulations and experiments, the two approximations could maximize the total BU of the NCS up to about 98% of the total available network bandwidth

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Recent Development of Hybrid Renewable Energy Systems

Abstract: The use of renewable energies continues to increase. However, the energy obtained from renewable resources is variable over time. The amount of energy produced from the renewable energy sources (RES) over time depends on the meteorological conditions of the region chosen, the season, the relief, etc. So, variable power and nonguaranteed energy produced by renewable sources implies intermittence of the grid. The key lies in supply sources integrated to a hybrid system (HS)