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With the increase in population and the growth of technology, the load demand has increased and major changes in spinning reserve are unavoidable. Short‐term forecasting to hourly predict the required load and spinning reserve is of great importance. All of the power system studies in planning and operation fields are depend on short‐term hourly load forecasting. In this work, the problem of load forecasting and spinning reserve based on deep learning (DL) algorithms and traditional methods is investigated with the help of the proposed information combination system. The proposed method tries to reduce the weaknesses of the stated methods and increase the accuracy of the predicted signal. First, short‐term predicting of load and spinning reserve is performed using a combination of adaptive network‐based fuzzy inference system (ANFIS) and meta‐heuristic algorithms including differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The ANFIS‐PSO is selected as the best ANFIS combination in load and spinning reserve prediction with a lower error criterion than other methods. Also, the long short‐term memory (LSTM) network can provide good accuracy for load and spinning reserve forecasting. Therefore, the combination of ANFIS‐PSO and LSTM is used to reduce the average error and error variance. [ABSTRACT FROM AUTHOR]

The aim of electrical load dispatch (ELD) is to achieve the optimal planning of different power plants to supply the required power at the minimum operation cost. Using the combined heat and power (CHP) units in modern power systems, increases energy efficiency and, produce less environmental pollution than conventional units, by producing electricity and heat, simultaneously. Consequently, the ELD problem in the presence of CHP units becomes a very non-linear and non-convex complex problem called the combined heat and power economic dispatch (CHPED), which supplies both electric and thermal loads at the minimum operational cost. In this work, at first, a brief review of optimization algorithms, in different categories of classical, or conventional, stochastic search-based, and hybrid optimization techniques for solving the CHPED problem is presented. Then the CHPED problem in large-scale power systems is investigated by applying the imperialist competitive Harris hawks optimization (ICHHO), as the combination of imperialist competitive algorithm (ICA), and Harris hawks optimizer (HHO), for the first time, to overcome the shortcomings of using the ICA and HHO in the exploitation, and exploration phases, respectively, to solve this complex optimization problem. The effectiveness of the combined algorithm on four standard case studies, including 24 units as a medium-scale, 48, 84, units as the large-scale, and 96-unit as a very large-scale heat and power system, is detailed. The obtained results are compared to those of different algorithms to demonstrate the performance of the ICHHO algorithm in terms of better solution quality and lower fuel cost. The simulation studies verify that the proposed algorithm decreases the minimum operation costs by at least 0.1870%, 0.342%, 0.05224%, and 0.07875% compared to the best results in the literature. [ABSTRACT FROM AUTHOR]

Combined generation units of heat and power, known as CHP units, are one of the most prominent applications of distributed generations in modern power systems. This concept refers to the simultaneous operation of two or more forms of energy from a simple primary source. Due to the numerous environmental, economic, and technical advantages, the use of this technology in modern power systems is highly emphasized. As a result, various issues of interest in the control, operation, and planning of power networks have experienced significant changes and faced important challenges. In this way, the unit commitment problem (UCP) as one of the fundamental studies in the operation of integrated power, and heat systems have experienced some major conceptual and methodological changes. This work, as a complementary review, details the CHP-based UCP (CHPbUCP) in terms of objective functions, constraints, simulation tools, and applied hardwares. Furthermore, some useful data on case studies are provided for researchers and operators. Finally, the work addresses some challenges and opens new perspectives for future research. [ABSTRACT FROM AUTHOR]

Background Among adults with cancer, malnutrition is associated with decreased treatment completion, more treatment harms and use of health care, and worse short-term survival. To inform the National Institutes of Health Pathways to Prevention workshop, "Nutrition as Prevention for Improved Cancer Health Outcomes," this systematic review examined the evidence for the effectiveness of providing nutrition interventions before or during cancer therapy to improve outcomes of cancer treatment. Methods We identified randomized controlled trials enrolling at least 50 participants published from 2000 through July 2022. We provide a detailed evidence map for included studies and grouped studies by broad intervention and cancer types. We conducted risk of bias (RoB) and qualitative descriptions of outcomes for intervention and cancer types with a larger volume of literature. Results From 9798 unique references, 206 randomized controlled trials from 219 publications met the inclusion criteria. Studies primarily focused on nonvitamin or mineral dietary supplements, nutrition support, and route or timing of inpatient nutrition interventions for gastrointestinal or head and neck cancers. Most studies evaluated changes in body weight or composition, adverse events from cancer treatment, length of hospital stay, or quality of life. Few studies were conducted within the United States. Among intervention and cancer types with a high volume of literature (n = 114), 49% (n = 56) were assessed as high RoB. Higher-quality studies (low or medium RoB) reported mixed results on the effect of nutrition interventions across cancer and treatment-related outcomes. Conclusions Methodological limitations of nutrition intervention studies surrounding cancer treatment impair translation of findings into clinical practice or guidelines. [ABSTRACT FROM AUTHOR]

Microgrids are new concepts in power systems that can upgrade current power systems due to their technical, economic, and environmental advantages. In addition, the increasing penetration of renewable energies and their use in microgrids have increased the complexity of these new grids in terms of planning and operation. Along with numerous research and practical projects built in different countries with multiple applications, countless types of research have also been performed relying on different aspects of MGs. In this paper, based on a review of studies and review articles related to MGs, an attempt has been made to evaluate and report the optimal energy management of MGs, based on what is addressed in the literature. In addition, the most critical surveys on various topics of MGs are introduced as a guide for researchers to draw a road map for future works. [ABSTRACT FROM AUTHOR]

This work tackles a relatively new issue in power system operation, known as the Environmental/Economic Dispatch problem. For this purpose, the combination of two powerful heuristic algorithms, namely, the Exchange Market Algorithm (EMA) and Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO), was employed. Additionally, the Multiple Constraint Ranking (MCR) technique was used to address the system constraints such as prohibited operating zones and ramp rate limits. Furthermore, the mutation operator was used to improve the performance of the global search mechanism. The main purpose of combining these two algorithms was utilizing the EMA's high performance to explore the global optimum and local exploitation ability of AIWPSO. The algorithm performance was evaluated on six standard benchmark functions and was scrutinized on several different test systems, including 6–40 units. By using the proposed method, the minimum values of the reduction in annual costs, with equal or less emissions, compared to other methods, were USD 17,520, 8760 and 10,801,080, respectively, for the 6-unit, 10-unit, and 40-unit test systems (assuming the same load profile throughout the year). Similarly, in the 14-unit test system for 1750, 2150, and 2650 (MW) load demands, these values were USD 229,879, 148,438, and 4483, respectively. [ABSTRACT FROM AUTHOR]

Power system optimization, especially in the field of operation, has experienced essential changes in recent years regarding the introduction of the concept of the hybrid energy system (HES). As a result, the optimal power flow (OPF) problem, as the cornerstone of power system operation studies, has faced new challenges regarding the modeling of these complicated systems. In this article, the OPF in a HES is proposed using the hybrid particle swarm optimization-genetic algorithm (HPSO-GA) approach considering the fuel cost as the main objective and the emission cost as the primary objective function. As the mentioned objectives are counterintuitive, to obtain the optimal point, they should be considered simultaneously. To overcome this problem, the Pareto optimal front (POF) is applied and the multi-criteria technique for order performance by similarity to ideal solution (TOPSIS) is used simultaneously. The results of the simulation case study on an improved IEEE 14-bus standard system confirmed the performance of the proposed method against the mathematical method by applying MATLAB optimization. [ABSTRACT FROM AUTHOR]

Conventional transmission expansion planning (TEP) has evolved with the increasing influence of distributed generations (DGs) and electric vehicles (EVs). This increases uncertainty in power systems. On the other hand, reactive power plays a key role in voltage stability, so modeling reactive power planning in TEP is very important. The amount of reactive power is a determining factor regarding the stability of the network voltage and in case of shortage, occurrence of some events may endanger the voltage stability. This research aims at proposing a hybrid TEP and reactive power planning for the first time. Furthermore, the impact of wind, PV power plants, and EVs, as the common sources of uncertainties, are included. The ultimate goal of HTEP‐RPP is to find the transmission line candidates that meet the reactive power requirements. The simulation case studies on IEEE 24‐ and 118‐ buses confirm that the TEP based on DCPF may lead to some unreliable operating conditions due to violating the voltage constraints. In this case, the HTEP‐RPP results, by applying the imperialist competitive algorithm (ICA), are more comprehensive and effective than the results obtained by gravitational search algorithm (GSA), for the studied problem or even the popular TEP solutions. [ABSTRACT FROM AUTHOR]

An editorial is presented in which the authors discuss the core functions of public health, also referred to as the 10 essential public health services (EPHSs) and the need to develop policies pertaining to the elimination of white supremacy.

This article presents a novel linear programming (LP) based two-stage stochastic approach for microgrids (MGs) under uncertainties. In this regard, the day-ahead programming of dispatchable resources in MG was modeled, considering the uncertainties in demand loads, and upstream network electricity price. Moreover, the inherent stochastic nature of wind and solar resources as well as the environmental aspects were modeled to find a realistic solution. Also, real-time pricing (RTP), demand response (DR) program considering the energy storage system (ESS) as a DR option was implemented on an MG as a smart customer. The extensive form of the two-stage stochastic recourse model was properly implemented for dispatchable and non-dispatchable resources. Furthermore, scenario generation and reduction procedures were realized with autoregressive and moving average (ARMA) model-based time-series (TS), and backward reduction (BR) method by the Kantorovich distance (KD), respectively. The simulations on a grid-connected MG, including micro-turbine (MT), fuel-cell (FC), wind-turbine (WT), photovoltaic module (PV), and ESS were reported in operational cases based on one month of real recorded data for wind speed, solar irradiance, demand load, and upstream network electricity price. The results for the next day in real-time confirmed the accuracy of the developed optimization methodology. [ABSTRACT FROM AUTHOR]

Load modeling is one of the most important features in power system operation and stability studies. So far, various methods have been applied based on measuring the relevant load data, and obtaining the load models through the mathematical models. This paper presents a method for load modeling based on processing the recorded data by the measurement equipment with PSO algorithm, which is located in substations. In the proposed method, for improving the accuracy of the load model, some constraints are applied for eliminating the undesirable measured data. These constraints are applicable for any time periods, such as; hourly, daily, seasonally, and annually. Furthermore, a combination of the static and dynamic models is utilized. The performance of the proposed method is tested on two industrial power grids from the west regional electric company (WREC) network, and the results are thoroughly analyzed. The results have shown that the proposed method has an acceptable accuracy for load modeling in comparison with real model of the case study systems. [ABSTRACT FROM AUTHOR]