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The economic emission dispatch problem (EEDP) is a nonconvex and nonsmooth multiobjective optimization problem in the power system field. Generally, fuel cost and total emissions of harmful gases are the problem objective functions. The EEDP decision variables are output powers of thermal generating units (TGUs). To make the EEDP problem more practical, valve point loading effects (VPLEs), prohibited operation zones (POZs), and power balance constraints should be included in the problem constraints. In order to solve this complex and constrained EEDP, a new multiobjective optimization technique combining the differential evolution (DE) algorithm and chaos theory is proposed in this study. In this new multiobjective optimization technique, a nondomination sorting principle and a crowding distance calculation are employed to extract an accurate Pareto front. To avoid being trapped in local optima and enhance the conventional DE algorithm, two different chaotic maps are used in its initialization, crossover, and mutation phases instead of random numbers. To overcome difficulties caused by the equality constraint describing the power balance constraint, a slack TGU is defined to compensate for the gap between the total generation and the sum of the system load and total power losses. Then, the optimal power outputs of all thermal units except the slack unit are determined by the suggested optimization technique. To assess the effectiveness and applicability of the proposed method for solving the EEDP, the six-unit and ten-unit systems are used. Moreover, obtained results are compared with other new optimization techniques already developed and tested for the same purpose. The superior performance of the ChMODE is also evaluated by using various metrics such as inverted generational distance (IGD), hyper-volume (HV), spacing metric (SM), and the average satisfactory degree (ASD). [ABSTRACT FROM AUTHOR]

Combined economic emission dispatch (CEED) problems are among the most crucial problems in electrical power systems. The purpose of the CEED is to plan the outputs of all production units available in the electrical power system in such a way that the cost of fuel and polluted emissions are minimized while respecting the equality and inequality constraints of the system and efficiently responding to the power load required. The rapid depletion of these sources causes limitation and increases the price of fuel. It is therefore very important that scientific research in the last few decades has been oriented toward the integration of renewable energy systems (RES) such as wind and PV as an alternative source. Furthermore, the CEED problem including RES is the most important problem with regard to electrical power field optimization. In this study, a classification of optimization techniques that are widely used, such as traditional methods, non-conventional methods, and hybrid methods, is summarized. Many optimization methods have been presented and each of them has its own advantages and disadvantages for solving this complex CEED problem, including renewable energy. A review of different optimization techniques for solving this CEED problem is explored in this present paper. This review will encourage researchers in the future to gain knowledge of the best approaches applicable to solve CEED problems for practical electrical systems. [ABSTRACT FROM AUTHOR]

Nowadays, the growing integration of renewable energy sources poses several challenges to electrical energy systems. The latter need be controlled by grid rules to ensure their stability and maintain the efficiency of renewable energy consumption. In this context, a novel HESS (hybrid energy storage system) control strategy, combining the PV (photovoltaic) generator with FLC (fuzzy logic control), SC (super-capacitor), and lithium-ion battery modules, is advanced. The proposed energy control rests on monitoring of the low-frequency and high-frequency electrical power components of the mismatch between power demand and generation, while applying the error component of the lithium-ion battery current. On accounting for the climatic condition and load variation considerations, the SC undertakes to momentarily absorb the high-frequency power component, while the low-frequency component is diverted to the lithium-ion battery. To improve the storage system's performance, lifetime, and avoid load total disconnection during sudden variations, we consider equipping the envisioned energy control design with controllers of SM and ANN types. The MATLAB/Simulink based simulation results turn out to testify well the investigated HESS control scheme's outstanding performance and efficiency in terms of DC bus voltage rapid regulation, thereby enhancing the battery's lifetime and ensuring the PV system's continuous flow. [ABSTRACT FROM AUTHOR]

This paper presents a new control technique based on uncertain fuzzy models for handling uncertainties in nonlinear dynamic systems. This approach is applied for the stabilization of a multimachine power system subject to disturbances. In this case, a state-feedback controller based on parallel distributed compensation (PDC) is applied for the stabilization of the fuzzy system, where the design of control laws is based on the Lyapunov function method and the stability conditions are solved using a linear matrix inequalities (LMI)-based framework. Due to the high number of system nonlinearities, two steps are followed to reduce the number of fuzzy rules. Firstly, the power network is subdivided into sub-systems using Thevenin's theorem. Actually, each sub-system corresponds to a generator which is in series with the Thevenin equivalent as seen from this generator. This means that the number of sub-systems is equal to the number of system generators. Secondly, the significances of the nonlinearities of the sub-systems are ranked based on their limits and range of variation. Then, nonlinearities with non-significant variations are assumed to be uncertainties. The proposed strategy is tested on the Western systems coordinating council (WSCC) integrated with a wind turbine. The disturbances are assumed to be sudden variations in wind power output. The effectiveness of the suggested fuzzy controller is compared with conventional regulators, such as an automatic voltage regulator (AVR) and power system stabilizers (PSS). [ABSTRACT FROM AUTHOR]

Distributed generation and demand-side participation have been widely deployed for secure, reliable and economic power distribution networks. Microgrids have been merged in power systems to meet this increase in distributed generation and to provide more control on the massive demand expansion. This paper presents an optimization model for scheduling and operating a microgrid considering the participation of the end-user customers in the electricity market. Mixed-integer programming is used in the proposed model to procure the minimum operation cost of the microgrid and to apply load shedding (LS) optimization on the local responsive loads. The objective function is described by piecewise linear functions. The proposed model allows the implementation of high number of constraints related to generation units, power exchange with the main grid, energy balance and LS. The operation decisions are based on binary variables that represent the status of the generation units, grid-connection and the responsive loads. Furthermore, the proposed model demonstrates the relationship between pick-up/drop-off rates of the responsive loads and the hourly operation cost of the microgrid. Example studies have illustrated the performance of the optimization model in finding the minimum operation cost with multiple technical and economic constraints. [ABSTRACT FROM AUTHOR]

Purpose Low-frequency oscillations of 0.1 to 3 Hz are prejudicial to the power system stability. Within this context, this study aims to present an improved artificial bee colony (ABC)-based algorithm for optimal setting of multimachine power system stabilizers (PSSs) under several loading conditions simultaneously.Design/methodology/approach The proposed approach symbolized by GCABC incorporates the grenade explosion technique and the Cauchy operator in the employed bee and onlooker bee phases to avoid random search. The parameters of the grenade explosion method and Cauchy operator based ABC(GCABC)-based PSSs (GCABC-PSSs) are tuned to place all undamped and lightly damped electromechanical modes in a prespecified zone in the s-plan.Findings Simulation results based on eigenvalue analysis and nonlinear time-domain simulation show the potential and the dominance of the proposed controllers GCABC-PSSs in the improvement of the system stability under several disturbances and large set of operating points compared with the classical ABC method and genetic algorithm-based PSSs.Originality/value The novelty of the study is to efficiently implement a new optimization method called GCABC for an optimum design of PSSs. The design problem is formulated as a multi-objective optimization problem. In addition, all PSS parameters have been included in the space research. [ABSTRACT FROM AUTHOR]

This paper proposes a new Teaching–Learning Algorithm ( TLA ) that uses the chaotic map to prevent the conventional TLA from getting stuck on local optima and enhancing the convergence characteristics, due to non-repetitions nature and ergodicity of chaotic functions. Some shortcomings are encountered in the original TLA , for instance, it can be trapped in local optima. This work tries to improve it by substituting the random in the initial algorithm with chaotic sequences. At this level, the initial population is chaotically generated and chaotic values are used in both phases. The global solutions are further enhanced by adding a new third chaotic phase. To demonstrate the effectiveness of the improved Teaching–Learning algorithm ( ITLA ), a fifteen of well-known benchmark functions are used. Experimental results demonstrate that ITLA outperforms significantly the conventional TLA , in terms of the accuracy of the final solution and the speed of convergence. The enhanced optimization algorithm is employed to solve the coordinated design problem of power system stabilizers ( PSS ) and thyristor-controlled series capacitor ( TCSC ), in order to investigate the feasibility and effectiveness of the proposed method in power systems. The performance of the proposed controllers is evaluated on a multi-machine power system under large disturbance and for different operating conditions through a nonlinear time-domain simulation. At the end, the results confirm the robustness of the proposed controllers in comparison to PSS designed by ITLA ( ITLAPSS ) and TCSC designed by ITLA ( ITLATCSC ). [ABSTRACT FROM AUTHOR]

This paper proposes an efficient optimization algorithm named chaotic teaching–learning algorithm (CTLA), to solve multimachine power system stabilizers design problem. The original teaching learning algorithm as competitive to other optimization algorithms, used two phases to proceed to the global optimal solution: ‘teacher phase’ and ‘learner phase’. However, during the second phase an adequate interaction between the teacher and the learners in entire search space are not guaranteed and the algorithm may be trapped in local optima. Thus, in the proposed CTLA a new phase named “chaotic phase” is added in order to overcome this drawback. The performance of the CTLA is investigated by using a set of benchmark functions. To demonstrate the effectiveness of the proposed algorithm in power systems, the conventional lead-lag power system stabilizers (PSSs) are tuned for: three machines nine bus system (WSCC) and the ten machine thirty-nine bus New England power systems. The performance of the proposed CTLA-based PSS (CTLAPSSs) under different loading conditions and disturbances is investigated through eigen-value analysis, non-linear time domain-simulations and some performance indices. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present the extended version of the conventional DEED to overcome the ramp rate violations when its optimal solutions for one period (normally one day) are implemented repeatedly and periodically over consequent dispatch periods to meet the periodic load demands. This dynamic dispatch problem, which is referred to as EDEED, is a multi-objective optimization problem which simultaneously minimizes both fuel cost and pollutants emission while satisfying a set of constraints. A multi-objective particle swarm optimization (MOPSO) method has been applied in this article for solving EDEED problem. The performance of the proposed method has been evaluated on the 10-unit test system with non-smooth fuel cost and emission level functions in comparison with those methods reported in the literature. [ABSTRACT FROM AUTHOR]

This paper investigates detailed flexible approach for incorporating various flexible alternating current transmission systems (FACTS) controllers in multimachine power system environment such as static var compensator (SVC) and thyristor controlled series capacitor (TCSC) in order to improve power system stability. The incorporation of FACTS devices into multimachine power system requires the development of the mathematical model of the power system with FACTS controllers. The differential algebraic equation (DAE) model of multimachine power system with FACTS controllers such as SVC and TCSC devices has been developed in this paper. Despite the fact that SVC and TCSC devices have been incorporated individually in DAE model of multimachine system, the DAE model with coordination of FACTS controllers has not been developed. So, the methodology of incorporation of series and shunt FACTS controllers in coordinated control manner in the DAE model of power system has been developed in this paper. Thus, the objective of this paper is to develop a general methodology of DAE model of power system with incorporation of series and shunt FACTS controllers in individual and coordinated control manner in order to improve power system stability. The western system coordinating council (WSCC) 3-machine, 9-bus system is used to illustrate the application of all proposed models and techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid algorithm combining chaotic Jaya (CJaya) and sequential quadratic programming (SQP), namely CJaya-SQP, for solving the coordinated design problem of static var compensator (SVC) and power system stabilizers (PSSs). The CJaya serves as a global optimizer and the SQP as a local optimizer for fine-tuning the solution. In the proposed algorithm, chaotic maps are used to generate the initial solutions and control the search process. In order to prove the performance of the CJaya-SQP, a set of benchmark optimization problems is used where the results are compared with those of the basic Jaya and other recognized algorithms. The proposed optimization method is then applied for the optimal tuning of PSSs and SVC controllers in such a way that damping ratios and damping factors of the electromechanical modes are optimally improved. To illustrate the robustness of the CJaya-SQP-based coordinated PSSs and SVC controllers, the four-machine, two-area system is used. Eigenvalue analysis and nonlinear time-domain simulation vividly show that the CJaya-SQP-based coordinated controllers improve greatly the system's dynamic stability with a robust damping of local and inter-area power oscillations. [ABSTRACT FROM AUTHOR]

Several materials have the ability to absorb, accumulate and transmit solar radiation to their surrounding environment. The thermal island effect and micro-climate of urban areas are subject to notable influence from the composition of materials within the city's crust. The present investigation was carried out with the aim of assessing the impact of roofing and pavement materials' properties on solar reflection and their potential to mitigate urban heat islands (UHIs). A study of an experimental nature was carried out on a total of 20 conventional materials that are commonly used in the construction of roofs, sidewalks, streets and rooftops in Ha'il, Saudi Arabia. The objective of the research was to investigate the impact of different urban surface materials on the rise of ambient air temperature in their vicinity. The present investigation aimed to assess the thermal performance of the specimens, with the purpose of ascertaining the impact of the thermophysical and radiative characteristics of the substances. The findings of the experiments indicate that the Solar Reflectance and temperature of the surrounding environment are primarily influenced by the color and substance of surface materials. Conversely, the texture's condition has a comparatively minor effect. Both effective factors play an equally significant role in the overall mechanism of increasing the ambient air temperature. The results suggest that a significant proportion of the samples exhibit a maximum surface temperature exceeding 59.4°C, while concurrently displaying a Solar Reflectance Index and albedo <49%. Meanwhile, the implementation of green roofs composed of various types of vegetation, including grass, can effectively alleviate the UHI phenomenon. [ABSTRACT FROM AUTHOR]

Information-centric networking (ICN) has been developed as a potential candidate for future networks. In this model, users are provided with content rather than communication channels between the different hosts. The ICN network has several problems such as scalability issues and bandwidth consumption. However, software-defined networking (SDN) has been proposed to improve the networking architectures. The goal of our paper is to propose a new approach to named-data networking (NDN) based on the paradigm of SDN. Our work introduces various research studies carried out in the SDN and ICN contexts. We first present the SDN architecture. Then, we focus on work that combines ICN and SDN architectures. Finally, we show the effects of using the SDN architecture on the named-data network (NDN). Our experimental results show that the use of the SDN architecture has a positive effect on NDN network performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method for drawing a cryptographically strong substitution box using the Lorenz system and quantum genetic algorithm techniques. We used the chaotic function to generate an initial random sequence of bits and the quantum crossover to provide a new and improved substitution box with increased non-linearity. The aim of the proposed algorithm was to generate a strong and secure substitution box that can be utilized in symmetric cryptosystems. The use of chaotic Boolean functions, genetic algorithm techniques, and the quantum crossover helped achieve this goal, and statistical tests further confirmed the randomness and efficiency of the generated substitution box. The results of the statistical test suite showed that the substitution box produced by the proposed algorithm is a "pass" in terms of randomness and has strong cryptographic properties. The tests include a frequency (monobit) test, a frequency test within a block, a linear complexity test, an approximate entropy test, and a cumulative sums test among others. The p-values obtained in the tests indicate that the randomness of the generated substitution box meets the requirements of a cryptographically secure substitution box. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT), a lot of constrained devices are interconnected. The data collected from those devices can be the target of cyberattacks. In this paper, a lightweight cryptosystem that can be efficiently implemented in highly constrained IOT devices is proposed. The algorithm is mainly based on Advanced Encryption Standard (AES) and a new chaotic S-box. Since its adoption by the IEEE 802.15.4 protocol, AES in embedded platforms have been increasingly used. The main cryptographic properties of the generated S-box have been validated. The randomness of the generated S-box has been confirmed by the NIST tests. Experimental results and security analysis demonstrated that the cryptosystem can, on the one hand, reach good encryption results and respects the limitation of the sensor's resources, on the other hand. So the proposed solution could be reliably applied in image encryption and secure communication between networked smart objects. [ABSTRACT FROM AUTHOR]

This work aims to improve the stability of a system that consists of one generator using a dual-input stabilizer of a power system. For this purpose, a new design method based on a chaotic JAYA algorithm is proposed. This algorithm hybridizes the original JAYA algorithm with chaos to improve its exploration and exploitation capabilities. Indeed, the disordered numbers generated by the chaotic map are exploited to control the standard JAYA algorithm's search equations. The issue of design is stated as an optimization problem. The proposed technique is involved in the selection of optimal values of the dual-input power system stabilizer (PSS) parameters. The efficiency of the proposed controller is assessed under various loading conditions and compared with the original JAYA, cuckoo search and particle swarm optimization (PSO) algorithms. The target function consists of the integration of absolute error multiplied by the time, including the speed deviation is considered for the controller design method. The controller tuned by a chaotic JAYA algorithm is tested using a single machine infinite bus (SMIB) power system. The findings of the simulation results demonstrate the robustness and effectiveness of the suggested method in damping oscillations over an extended range of loading conditions. [ABSTRACT FROM AUTHOR]