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Severe droughts, water-use conflicts, and minimum outflow requirements have caused infeasibilities in modeling large-scale complex hydrothermal systems. This paper presents and discusses a new formulation of objective function and constraints to better represent multiple water uses and deal with infeasibilities in the HIDROTERM model, a nonlinear programming (NLP) model developed to optimize the operation of the Brazilian interconnected hydrothermal system. The system is one of the largest in the world. In the last 10 years, approximately 72% of electricity consumed in Brazil has been supplied by hydropower plants, but with increasing shares from thermal, wind, and, more recently, solar power generation. Because of hydrological changes in recent years and the additional operational constraints imposed by environmental agencies, the use of models for decision making, such as HIDROTERM, frequently encounters the problem of infeasibility. To avoid this problem, a new objective function is proposed in HIDROTERM that incorporates economic penalties when the preset requirements are not met. Based on economic considerations, some hard constraints are removed from the constraint set and added to the objective function as penalty terms. These constraints are tightly linked with multiple water uses, including environmental protection, flood control, consumptive uses, and navigation purposes. The proposed change in the objective function and constraints avoids the problem of infeasibilities and makes it possible to apply the model to extreme conditions. Additionally, this formulation helps identify and minimize the risk, duration, and intensity of unavoidable non-compliance occurrences related to multiple water uses. The presented new version of the HIDROTERM model can be used to study and support decision making in the management and operation of large-scale hydrothermal systems. These systems can be formed by a set of hydraulically connected individual hydropower plants and reservoirs, located within a set of electrically connected subsystems. Each subsystem contains demands and other power plants, including thermal, wind, nuclear, and solar plants, and exchanges. The model has been developed with continued improvements since 2008 and applied to large systems such as the Brazilian Interconnected Power System (BIPS) with over 150 reservoirs. A graphical user interface is provided. The objective function is to minimize the total costs of additional thermal dispatch and exchanges, as well as avoid or minimize deficits in energy supply in subsystems, minimum releases, consumptive uses, and minimum storages in reservoirs. The decision variables are the power and nonpower releases for each hydropower plant and the thermal dispatches and exchanges for each subsystem. Some typical applications include (1) monthly planning operation of the system, considering a planning horizon from 3 to 5 years; (2) diagnostic studies about reliability and resilience of existing systems, processing the same system configuration to a set of different hydrological or energy demand scenarios; (3) impact analysis of changes in operational rules and constraints related to multiple uses of water; and (4) planning and evaluation of expansion alternatives. The model can be applied to a complete hydrothermal system or subsets of it, including subsystems, basins, cascades, or a single hydropower plant. [ABSTRACT FROM AUTHOR]

This paper propounds a new metaheuristic optimization algorithm to obtain the optimum site and size of distributed generation (DG) in radial distribution system (RDS). This problem is concomitant with reducing the active power loss, the total voltage deviation and the voltage stability index of the RDS considering different types of load models (such as constant power (CP), industrial (IL), residential (RES) as well as commercial (COM)). To solve this weighting factor-based multi-objective DG allocation problem, a novel metaheuristic optimization algorithm, student psychology-based optimization (SPBO), is suggested in this article. A multi-criteria approach (such as the analytic hierarchy process) is employed to optimize the weighting factors involved. To the best of the authors' knowledge, this is the first time that this novel SPBO algorithm is being used for optimal siting and sizing of DGs in the RDS for different types of load models (like CP, IL, RES and COM) for IEEE 33-bus, IEEE 69-bus and practical Brazil 136-bus RDS. The simulation results attained by the proposed SPBO algorithm are compared with the results provided by recently surfaced Harris hawks optimization (HHO) algorithm and other state-of-the-art algorithms. The outcomes prove that the suggested SPBO algorithm is more efficient to solve the optimal multiple DG allocation problem with minimum real power loss, less computational time and prominent convergence rate. [ABSTRACT FROM AUTHOR]

Renewable cooling via absorption chillers being supplied by various green heat technologies such as solar collectors has been widely studied in the literature, but it is still challenging to get positive economic outcomes from such systems due to the large expenses of solar thermal systems. This study offers the use of a new generation of solar collectors, so-called eccentric reflective solar collectors, for driving single-effect absorption chillers and thereby reducing the levelized cost of cooling. This article develops the most optimal design of this system (based on several different scenarios) using multi-objective optimization techniques and employs them for a case study in Brazil to assess its proficiency compared to conventional solar-driven cooling methods. For making the benchmarking analyses fair, the conventional system is also rigorously optimized in terms of design and operation features. The results show that the eccentric solar collector would enhance the cost-effectiveness by 29%. In addition, using optimally sized storage units would be necessary to get acceptable economic performance from the system, no matter which collector type is used. For the case study, at the optimal sizing and operating conditions, the levelized cost of cooling will be 124 USD/MWh and an emission level of 18.97 kgCO2/MWh. [ABSTRACT FROM AUTHOR]

In recent years electricity sectors worldwide have undergone major transformations, referred to as the "energy transition". This has required energy planning to quickly adapt to provide useful inputs to the regulation activity so that a cost-effective electricity market emerges to facilitate the integration of renewables. This paper analyzes the role of system planning and regulations on two specific elements in the energy market design: the concept of firm capacity and the presence of distributed energy resources, both of which can be influenced by regulation. We assess the total cost of different regulatory mechanisms in the Brazilian and Mexican systems using optimization tools to determine optimal long-term expansion for a given regulatory framework. In particular, we quantitatively analyze the role of the current regulation in the total cost of these two electricity systems when compared to a reference "efficient" energy planning scenario that adopts standard cost-minimization principles and that is well suited to the most relevant features of the new energy transformation scenario. We show that two very common features of regulatory designs that can lead to distortions are: (i) renewables commonly having a lower "perceived cost" under the current regulations, either due to direct incentives such as tax breaks or due to indirect access to more attractive contracts or financing conditions; and (ii) requirements for reliability are often defined more conservatively than they should be, overstating the hardships imposed by renewable generation on the existing system and underestimating their potential to form portfolios. [ABSTRACT FROM AUTHOR]

This article proffers a novel quasi-oppositional chaotic Harris hawk's optimization (HHO) (QOCHHO) algorithm for interpreting global optimization problems. In the proposed QOCHHO algorithm, quasi-opposition based learning (QOBL) and chaotic local search (CLS) approaches are integrated with the basic HHO for better quality of solution and faster convergence. The idea of QOBL assists to explore new regions of the search space and offers superior exploration. Again, CLS guides the search process nearby the most favorable regions of the search space yielding superior exploitation. Thus, a superior balance between the exploration and the exploitation holds in the case of QOCHHO making this newly projected algorithm more robust as correlated to the HHO algorithm. To demonstrate and validate effectiveness of the suggested QOCHHO algorithm, twenty-nine benchmark test functions of various categories, varied complexities (i.e., unimodal, multimodal, fixed dimension and composite functions) and different dimensions (i.e., 30 and 100) are used for simulation experiments. The simulation results attained by the projected QOCHHO algorithm are compared with the results obtained by recently surfaced HHO and other state-of-the-art algorithms (i.e., particle swarm optimization, moth-flame optimization algorithm, grey wolf optimizer, sine cosine algorithm, salp swarm algorithm, whale optimization algorithm and multi verse optimization algorithm). The outcomes of the benchmark test functions evidence that the anticipated QOCHHO algorithm is able to offers better outcomes in terms of improved exploration, local optima circumvention and faster convergence characteristics. The proposed QOCHHO algorithm is further employed to decipher real world engineering optimization problem (i.e., optimal siting and sizing of distributed generation (DG) in IEEE 33-bus and practical Brazil 136-bus radial distribution system (RDS) considering different types of load models at three load levels) and proffers a real application of the suggested algorithm in the field of electrical engineering. The simulation outcomes evidence that the obtained location and size of DGs in the RDS may be feasible one and the suggested QOCHHO algorithm may be a promising optimization algorithm for the chosen engineering optimization application. [ABSTRACT FROM AUTHOR]

The Zika virus presents an extraordinary public health hazard after spreading from Brazil to the Americas. In the absence of credible forecasts of the outbreak's geographic scope and infection frequency, international public health agencies were unable to plan and allocate surveillance resources efficiently. An RNA test will be done on the subjects if they are found to be infected with Zika virus. By training the specified characteristics, the suggested Hybrid Optimization Algorithm such as multilayer perceptron with probabilistic optimization strategy gives forth a greater accuracy rate. The MATLAB program incorporates numerous machine learning algorithms and artificial intelligence methodologies. It reduces forecast time while retaining excellent accuracy. The projected classes are encrypted and sent to patients. The Advanced Encryption Standard (AES) and TRIPLE Data Encryption Standard (TEDS) are combined to make this possible (DES). The experimental outcomes improve the accuracy of patient results communication. Cryptosystem processing acquires minimal timing of 0.15 s with 91.25 percent accuracy. [ABSTRACT FROM AUTHOR]