Extension of zigzag search algorithms for power system multi-objective optimization

The work presented in this thesis focuses on the application and extension the zigzag search algorithms in power systems. The zigzag search method is a multi-objective algorithm which has recently been applied in multiple engineering fields, such as oil well replacement, with fast computational time and accurate results.Multi-objective optimization algorithms in power systems have been investigated for years. Most of the literatures focus on evolutionary algorithms (EA) such as a non-dominated sorting genetic algorithm (NSGA) or multi-objective particle swarm optimization (MOPSO) for their simplicity and ease of implementation. However, there have been several issues regarding the evolutionary algorithm (EA). For example, the computational time of EA is significant and the parameter configurations are complicated. Other approaches mainly reply on the weight sum method by lumping together different objective functions to form a new single objective function; however, the priority is hard to determine and the characteristic between different objectives may be lost.In order to improve the performance of power system multi-objective optimization problems, this thesis will first introduce the zigzag search algorithm. Second, by modifying the classic zigzag search algorithm, the zigzag interior point method and zigzag genetic algorithm method will both be proposed to broaden the applications of the classic zigzag search method. Also, in order to provide a systematic method for step-size configuration, a zigzag search method with adaptive step-size will be proposed. Thirdly, all algorithms will be applied to several practical power system multi-objective problems to demonstrate their practicability and effectiveness.The case study will be carried out on a modified IEEE 30-bus system and the IEEE 118-bus system. A comparison will be made with classic multiobjective algorithms which have been widely applied in power systems to demonstrate the effectiveness and efficiency of the proposed zigzag search methods

An effective algorithm for maed problems with a new reliability model at the microgrid

This paper proposes a new framework for multi-area economic dispatch (MAED) in which the cost associated with the reliability consideration is taken into account together with the common operational and emission costs using expected energy not supplied (EENS) index. To improve the reliability level, the spinning reserve capacity is considered in the model as well. Furthermore, the MAED optimization problem and non-smooth cost functions are taken into account as well as other technical limitations such as tie-line capacity restriction, ramp rate limits, and prohibited operating zones at the microgrid. Considering all the above practical issues increases the complexity in terms of optimization, which, in turn, necessitates the use of a powerful optimization tool. A new successful algorithm inspired by phasor theory in mathematics, called phasor particle swarm optimization (PPSO), is used in this paper to address this problem. In PPSO, the particles’ update rules are driven by phase angles to essentially ensure a spread of variants across the population so that exploitation and exploration can be balanced. The optimal results obtained via simulations confirmed the capability of the proposed PPSO algorithm to find suitable optimal solutions for the proposed model

Review of Metaheuristic Optimization Algorithms for Power Systems Problems

Metaheuristic optimization algorithms are tools based on mathematical concepts that are used to solve complicated optimization issues. These algorithms are intended to locate or develop a sufficiently good solution to an optimization issue, particularly when information is sparse or inaccurate or computer capability is restricted. Power systems play a crucial role in promoting environmental sustainability by reducing greenhouse gas emissions and supporting renewable energy sources. Using metaheuristics to optimize the performance of modern power systems is an attractive topic. This research paper investigates the applicability of several metaheuristic optimization algorithms to power system challenges. Firstly, this paper reviews the fundamental concepts of metaheuristic optimization algorithms. Then, six problems regarding the power systems are presented and discussed. These problems are optimizing the power flow in transmission and distribution networks, optimizing the reactive power dispatching, optimizing the combined economic and emission dispatching, optimal Volt/Var controlling in the distribution power systems, and optimizing the size and placement of DGs. A list of several used metaheuristic optimization algorithms is presented and discussed. The relevant results approved the ability of the metaheuristic optimization algorithm to solve the power system problems effectively. This, in particular, explains their wide deployment in this field

Forecasting Automobile Gasoline Demand in Australia Using Machine Learning-based Regression

We use a variant of machine learning (ML) to forecast Australia’s automobile gasoline demand within an autoregressive and structural model. By comparing the outputs of the various model specifications, we find that training set selection plays an important role in forecasting accuracy. More specifically, however, the performance of training sets starting within identified systematic patterns is relatively worse, and the impact on forecast errors is substantial. Instead of treating these patterns as noise, we explain these systematic variations in machine learning performance, and explore the intuition behind the ‘black-box’ with the support of economic theory. An important finding is that these time points coincide with structural changes in Australia’s economy. By examining the out-of-sample forecasts, the model’s external validity can be demonstrated under normal situations; however, its forecasting performance is somewhat unsatisfactory under event-driven uncertainty, which calls on future research to develop alternative models to depict the characteristics of rare and extreme events in an ex-ante manner

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

Distributed Power Generation Scheduling, Modelling and Expansion Planning

Distributed generation is becoming more important in electrical power systems due to the decentralization of energy production. Within this new paradigm, new approaches for the operation and planning of distributed power generation are yet to be explored. This book deals with distributed energy resources, such as renewable-based distributed generators and energy storage units, among others, considering their operation, scheduling, and planning. Moreover, other interesting aspects such as demand response, electric vehicles, aggregators, and microgrid are also analyzed. All these aspects constitute a new paradigm that is explored in this Special Issue

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems