Minimize electricity generation cost for large scale wind- thermal systems considering prohibited operating zone and power reserve constraints

Wind power plants (WPs) play a very important role in the power systems because thermal power plants (TPs) suffers from shortcomings of expensive cost and limited fossil fuels. As compared to other renewable energies, WPs are more effective because it can produce electricity all a day from the morning to the evening. Consequently, this paper integrates the optimal power generation of TPs and WPs to absolutely exploit the energy from WPs and reduce the total electricity generation cost of TPs. The target can be reached by employing a proposed method, called one evaluation-based cuckoo search algorithm (OEB-CSA), which is developed from cuckoo search algorithm (CSA). In addition, conventional particle swarm optimization (PSO) is also implemented for comparison. Two test systems with thirty TPs considering prohibited working zone and power reserve constraints are employed. The first system has one wind power plant (WP) while the second one has two WPs. The result comparisons indicate that OEB-CSA can be the best method for the combined systems with WPs and TPs

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

Enhancement of Metaheuristic Algorithm for Scheduling Workflows in Multi-fog Environments

Whether in computer science, engineering, or economics, optimization lies at the heart of any challenge involving decision-making. Choosing between several options is part of the decision- making process. Our desire to make the "better" decision drives our decision. An objective function or performance index describes the assessment of the alternative's goodness. The theory and methods of optimization are concerned with picking the best option. There are two types of optimization methods: deterministic and stochastic. The first is a traditional approach, which works well for small and linear problems. However, they struggle to address most of the real-world problems, which have a highly dimensional, nonlinear, and complex nature. As an alternative, stochastic optimization algorithms are specifically designed to tackle these types of challenges and are more common nowadays. This study proposed two stochastic, robust swarm-based metaheuristic optimization methods. They are both hybrid algorithms, which are formulated by combining Particle Swarm Optimization and Salp Swarm Optimization algorithms. Further, these algorithms are then applied to an important and thought-provoking problem. The problem is scientific workflow scheduling in multiple fog environments. Many computer environments, such as fog computing, are plagued by security attacks that must be handled. DDoS attacks are effectively harmful to fog computing environments as they occupy the fog's resources and make them busy. Thus, the fog environments would generally have fewer resources available during these types of attacks, and then the scheduling of submitted Internet of Things (IoT) workflows would be affected. Nevertheless, the current systems disregard the impact of DDoS attacks occurring in their scheduling process, causing the amount of workflows that miss deadlines as well as increasing the amount of tasks that are offloaded to the cloud. Hence, this study proposed a hybrid optimization algorithm as a solution for dealing with the workflow scheduling issue in various fog computing locations. The proposed algorithm comprises Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO). In dealing with the effects of DDoS attacks on fog computing locations, two Markov-chain schemes of discrete time types were used, whereby one calculates the average network bandwidth existing in each fog while the other determines the number of virtual machines existing in every fog on average. DDoS attacks are addressed at various levels. The approach predicts the DDoS attack’s influences on fog environments. Based on the simulation results, the proposed method can significantly lessen the amount of offloaded tasks that are transferred to the cloud data centers. It could also decrease the amount of workflows with missed deadlines. Moreover, the significance of green fog computing is growing in fog computing environments, in which the consumption of energy plays an essential role in determining maintenance expenses and carbon dioxide emissions. The implementation of efficient scheduling methods has the potential to mitigate the usage of energy by allocating tasks to the most appropriate resources, considering the energy efficiency of each individual resource. In order to mitigate these challenges, the proposed algorithm integrates the Dynamic Voltage and Frequency Scaling (DVFS) technique, which is commonly employed to enhance the energy efficiency of processors. The experimental findings demonstrate that the utilization of the proposed method, combined with the Dynamic Voltage and Frequency Scaling (DVFS) technique, yields improved outcomes. These benefits encompass a minimization in energy consumption. Consequently, this approach emerges as a more environmentally friendly and sustainable solution for fog computing environments

Probabilistic Optimization Techniques in Smart Power System

Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy. It is further divided into stochastic, robust, distributionally robust, and chance-constrained optimizations. The topics of probabilistic optimization in smart power systems are covered in this review paper. In order to account for uncertainty in optimization processes, stochastic optimization is essential. Robust optimization is the most advanced approach to optimize a system under uncertainty, in which a deterministic, set-based uncertainty model is used instead of a stochastic one. The computational complexity of stochastic programming and the conservativeness of robust optimization are both reduced by distributionally robust optimization.Chance constrained algorithms help in solving the constraints optimization problems, where finite probability get violated. This review paper discusses microgrid and home energy management, demand-side management, unit commitment, microgrid integration, and economic dispatch as examples of applications of these techniques in smart power systems. Probabilistic mathematical models of different scenarios, for which deterministic approaches have been used in the literature, are also presented. Future research directions in a variety of smart power system domains are also presented.publishedVersio

Novel PID Controller on Battery Energy Storage Systems for Frequency Dynamics Enhancement

Frequency dynamics is one of the important aspects of power system stability. From the frequency dynamics, the operator could plan how is the reliability of the electricity. The frequency can be maintained by controlling the balance between load demand and generation. To maintain the balance of the generation, the governor is playing an important role to increase the speed of the turbine and enhance the generating capacity of the generator (ramp-up). However, as the speed of the governor is slower than the increasing load demand, in the sub-transient area, the frequency may experience higher overshoot. Hence, it is important to add additional devices such as battery energy storage systems to enhance the frequency dynamics response in the sub-transient area. One of the important parts of storage is the controller. The controller must make sure the storage charges and discharge energy are in the sub-transient area. Hence PID controller can be the solution to make the storage operate optimally This paper proposed a novel PID controller on battery energy storage systems (BESS) to enhance the dynamics performance of frequencies. The five-area power system is used as the test system to investigate the efficacy of the proposed novel idea. Time domain simulation is investigated to see the improvement of the frequency dynamics response. From the simulation results, it is found that adding a PID controller on BESS could enhance the BESS response and result in frequency dynamics response improvement

Optimal operation of an integrated hybrid renewable energy system with demand-side management in a rural context

A significant portion of the Indian population lives in villages, some of which are located in grid-disconnected remote areas. The supply of electricity to these villages is not feasible or cost-effective, but an autonomous integrated hybrid renewable energy system (IHRES) could be a viable alternative. Hence, this study proposed using available renewable energy resources in the study area to provide electricity and freshwater access for five un-electrified grid-disconnected villages in the Odisha state of India. This study concentrated on three different kinds of battery technologies such as lithium-ion (Li-Ion), nickel-iron (Ni-Fe), and lead-acid (LA) along with a diesel generator to maintain an uninterrupted power supply. Six different configurations with two dispatch strategies such as load following (LF) and cycle charging (CC) were modelled using nine metaheuristic algorithms to achieve an optimally configured IHRES in the MATLAB (c) environment. Initially, these six configurations with LF and CC strategies were evaluated with the load demands of a low-efficiency appliance usage-based scenario, i.e., without demand-side management (DSM). Later, the optimal configuration obtained from the low-efficiency appliance usage-based scenario was further evaluated with LF and CC strategies using the load demands of medium and high-efficiency appliance usage-based scenarios, i.e., with DSM. The results showed that the Ni-Fe battery-based IHRES with LF strategy using the high-efficiency appliance usage-based scenario had a lower life cycle cost of USD 522,945 as compared to other battery-based IHRESs with LF and CC strategies, as well as other efficiency-based scenarios. As compared to the other algorithms used in the study, the suggested Salp Swarm Algorithm demonstrated its fast convergence and robustness effectiveness in determining the global best optimum values. Finally, the sensitivity analysis was performed for the proposed configuration using variable input parameters such as biomass collection rate, interest rate, and diesel prices. The interest rate fluctuations were found to have a substantial impact on the system's performance.Web of Science1514art. no. 517

Load frequency controllers considering renewable energy integration in power system

Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency

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

Optimal Design of Photovoltaic, Biomass, Fuel Cell, Hydrogen Tank units and Electrolyzer hybrid system for a remote area in Egypt

In this paper, a new isolated hybrid system is simulated and analyzed to obtain the optimal sizing and meet the electricity demand with cost improvement for servicing a small remote area with a peak load of 420 kW. The major configuration of this hybrid system is Photovoltaic (PV) modules, Biomass gasifier (BG), Electrolyzer units, Hydrogen Tank units (HT), and Fuel Cell (FC) system. A recent optimization algorithm, namely Mayfly Optimization Algorithm (MOA) is utilized to ensure that all load demand is met at the lowest energy cost (EC) and minimize the greenhouse gas (GHG) emissions of the proposed system. The MOA is selected as it collects the main merits of swarm intelligence and evolutionary algorithms; hence it has good convergence characteristics. To ensure the superiority of the selected MOA, the obtained results are compared with other well-known optimization algorithms, namely Sooty Tern Optimization Algorithm (STOA), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA). The results reveal that the suggested MOA achieves the best system design, achieving a stable convergence characteristic after 44 iterations. MOA yielded the best EC with 0.2106533 $/kWh, the net present cost (NPC) with 6,170,134$, the loss of power supply probability (LPSP) with 0.05993%, and GHG with 792.534 t/y