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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]

Combined heat and power economic dispatch (CHPED) problem is one of the challenging issues in power system control, and operation, that searches for the optimal values of power and heat generation of different units to minimize the total operation cost. The interdependency of heat and power outputs of CHP units and valve point loading effects of power-only units make this problem very nonlinear and non-convex. So far, a wide variety of optimization algorithms based on simulation of various natural phenomena have been proposed and applied to solve this complex problem, claiming that the proposed algorithm is better than the similar optimization algorithms in terms of accuracy and run-time. In this paper, the CHPED problem is investigated in different case studies by using the imperialist competitive Harris hawks optimization (ICHHO) algorithm, as a combination of the imperialist competitive algorithm and Harris hawks optimization. The effectiveness of the proposed algorithm is first evaluated by testing it on the standard cases of 5, and 7 units as small-scale systems and 48-unit as the large-scale test system, considering the electrical losses. Also for the first time, the multi-zone CHPED (MZCHPED) problem is solved by a metaheuristic algorithm. The obtained results show that ICHHO algorithm reduces the operating costs in the range of 0.02–1.1%, compared to the best-reported results, in different single-zone CHPED (SZCHPED) problems. Also, the MZCHPED results, prove the effective performance of the algorithm compared to the similar SZCHPED problem. [ABSTRACT FROM AUTHOR]

The problem of economic dispatch is one of the significant topics in modern power system control, monitoring, and operation studies due to economic and environmental issues. The multi-area economic dispatch problem (MAED) is the extended version of the economic dispatch problem in modern, and interconnected power systems, especially in competitive environments, which leads to the improvement of power networks economically and technically. The main goal of the MAED problem is to find the optimal amounts of generation and power interchange between adjacent areas by minimizing the generation, and transmission costs, satisfying different operational, and physical constraints governing the problem. This study endeavors to present a comprehensive classification of different techniques, and methods applied to the multi-area economic dispatch problem while reviewing the most prominent studies in this field. Also, it covers comprehensive formulations of the problem and some important issues in the field of probabilistic MAED. Furthermore, some concepts, such as used test systems, and hardware specification are addressed. Finally, suggestions and future directions are highlighted. [Display omitted] • Presenting the formulation of MASED, MADED, RCMAED, RCMAEED, MAEED, and MANCEED. • Classify the methods based on problem-solving, and mechanism handling techniques. • Describing the mathematical-based optimization, and meta-heuristic methods. • Addressing the single-objective, and multi-objective proposed algorithms. • Detailing the probabilistic MAED techniques, and some data of the proposed methods. [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]

Examine how women aged 35–50 respond to messages about limiting cancer screening. A national sample of women aged 35–50 (n = 983) were randomly assigned to read one of four media vignettes: three provided information about potential harms of mammograms using evidence, norms, or an anecdote strategy, and one provided no such information. Participants listed thoughts they had about the message, and after coding these themes, we tested for associations between the themes evoked, message exposure, and mammogram history. Thematic categories included emotions (8 %); behavioral intentions (14 %); and cognitions, attitudes, and beliefs (67 %). Pro-screening attitudes, questioning, and cues to get screened were most prevalent. The anecdote message often elicited pro-screening attitudes, while the evidence message often elicited negative emotions and anger, as well as questioning or skeptical responses. Those with a history of mammograms expressed more pro-screening attitudes and disagreed with the message more often. Media messaging about guideline-supported care, especially when it involves reducing a clinical service that is routine and valued by patients, may evoke counterarguing, skepticism, and other negative responses. Clinicians should recognize the role of the media in potentially shaping women's attitudes, beliefs, and intentions when it comes to breast cancer screening. • Respondents expressed emotional, behavioral, and cognitive responses to media vignettes about breast cancer screening. • The media vignettes aroused thoughts, attitudes, and beliefs more often than emotions and behavioral intentions. • Messages describing limiting screening promoted pro-screening thoughts and raised questions. • Women's screening routines may be related to their response to news media messages about screening. [ABSTRACT FROM AUTHOR]

The purpose of this study is to explore existing research on determinants of cervical cancer screening among immigrants and refugees in the U.S. A scoping review was conducted on 77 studies targeting immigrant and/or refugee women in the U.S., investigating factors related to cervical cancer screening. Sixty-three percent of studies were conducted in the past ten years, and included 122,345 women. Studies predominately explored knowledge, beliefs and barriers related to cervical cancer and screening. Common beliefs included fear of cancer, treatment and death. Participants perceived pap smears to be associated with embarrassment, pain and fear. Barriers to screening were reported in three categories: psychosocial (shame and embarrassment), communication (inability to speak in English), and barriers related to access (lack of insurance or primary care provider). Study findings indicate research focused at the individual-level and future research should focus on exploring multilevel influences on cancer screening uptake. [ABSTRACT FROM AUTHOR]

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]

Due to the environmental concerns and the high penetration of wind power (WP) and electric vehicles (EVs), reducing the generation costs and emissions, coordinating EV charging in the distribution networks, and addressing the WP uncertainty, are among the most challenging issues in power system control and operation. Hence, the multi-objective dynamic economic emission dispatch problem (MDEEDP) integrated with EVs and wind farms has become one of the hottest topics in this research area. In this paper, the MDEEDP in the presence of EVs, the random behavior of EV drivers, and WP uncertainty is solved using a new multi-objective evolutionary algorithm called the enhanced multi-objective exchange market algorithm. In the proposed algorithm, the novel point-to-point distance technique is introduced to find the Pareto front solutions set (PFSS) with a uniform and diverse distribution. Also, smart strategy management for charging and discharging EVs is proposed to smooth the load curve. The effectiveness of the proposed method and performance of the proposed smart strategy are investigated on two single-objective and multi-objective test systems, including EVs and wind farms, considering uncertainty and system constraints such as ramp rate limits. The simulation results show that the PFSS of the MDEEDP is well extracted by the proposed method while maintaining its uniformity and diversity. The presence of EVs and the application of the proposed smart strategy lead to a reduction of 2,926,497 ($) in the operation costs for test system 1, and, 38,694,698.28 ($) and 7,832,876.64 (kg), respectively in the operation costs and emissions for test system 2, assuming the same load curve throughout the year. In addition, the load factor in test systems 1, and 2 is improved by 11 and 19 percent, respectively. [ABSTRACT FROM AUTHOR]

The economic dispatch (ED) is one of the most important short-term problems in power systems, and solving it quickly is essential. However, classical optimization tools are often too computationally demanding to be considered satisfactory. This has motivated the application of metaheuristic methods, which offer a good compromise in terms of solution quality and computation time. However, these methods have been applied in an isolated way and on different problem definitions and case studies, so that there were no clear insights on how they compared to each other. This paper fills this gap by performing an objective comparison of six metaheuristics solving the ED in several case studies under different conditions. Although mixed-integer programming performs best for small case studies, our results confirm that metaheuristics are able to efficiently solve the ED problem. Genetic algorithms emerge as the best performers in terms of solution quality and computation time, followed by PSO and TLBO. [ABSTRACT FROM AUTHOR]

In this paper, correlation between uncertainties related to loads is addressed in transmission network expansion planning (TNEP). The TNEP problem is formulated in a multi-objective form with objective functions of minimum investment cost, minimum congestion cost, and minimum risk cost. Load correlation is modeled using unscented transformation method and compared to a simulation method. Initially, the impact of load correlation on congestion cost and risk cost is shown. Then, using the Pareto solutions of this multi-objective TNEP problem, impact of load correlation on the TNEP solutions is illustrated. The Pareto solutions are obtained using the Non-dominated Sorting Genetic Algorithm II (NSGA II) that is capable in handling non-convex and non-linear problems with mixed integer structures. Test results are provided for the IEEE 24-bus Reliability Test System (RTS) and a representation of the actual Iranian 400 kV transmission network. [ABSTRACT FROM AUTHOR]

Abstract Environmental concerns have caused application of renewable energy sources as a clean appropriated alternative for conventional thermal generators in power system planning. These uncertain and variable sources have challenged the power system scheduling to ensure that it still remains reliable and economical. Also, plug-in electric vehicles may potentially alter load demand in uncontrolled charging mode and impose new challenges to the unit commitment problem. This paper demonstrates the fast heuristic method based on priority list to solve the stochastic unit commitment problem and applied it to a basic 10-unit case study system accompaniment with an electric vehicle parking lot, a wind farm and a solar farm over a 24-h time horizon. As it is reported, scenario generation with Monte Carlo simulation can relatively compensate for the intermittent behavior of renewable energy sources and result in more economical and robust planning. Moreover, the integration of plug-in electric vehicles and their controlled charging/discharging is considered. Thermal units are scheduled by priority list method with their relevant constraints such as minimum up/down time, spinning reserve, load demand and capacity limitation. The simulations results verify that the penetration of the mentioned renewable energy sources can improve the operational cost and computation time, effectively. Highlights • Presenting a fast heuristic priority list-based method to solve the stochastic unit commitment. • Considering the electric vehicles, wind and solar farms in the problem. • Applying the scenario generation from appropriate distribution with Monte Carlo simulation. • Reporting the case study results regarding to a basic 10-unit case study system. [ABSTRACT FROM AUTHOR]

Stability studies in distribution smart grids (SGs) are very important due to the increased penetration of distributed energy resource making bidirectional flow of electric power. This paper presents two novel fast voltage stability indices (VSIs) applicable to the radial distribution SGs based on the real-time measured voltage data only, obtained by the SG infrastructures such as smart metering device. Each VSI depends on the system bus voltages and does not need additional calculations such as power flow solution, converting the actual distribution network into two-bus equivalent system etc. In other words, for a radial system with n-node, the voltage stability analysis is carried out by parallel n-microprocessor units independently. The concentrator units can collect and evaluate the computational results and decide about control actions directly or indirectly depending on the system requirements. The accuracy and efficiency of the proposed indicators are tested on a 41-node radial distribution system. Six load models including constant and composite types have been considered, and also SG in various states of operation and four types of distributed generation are analysed. The comparison results demonstrate that the proposed VSIs are fast and effective to identify the most sensitive node of the radial grids to the voltage collapse based on the smart infrastructures. [ABSTRACT FROM AUTHOR]

Energy hubs can provide high flexibility in the operation of the integrated systems. This paper presents Time Varying Acceleration Coefficients Particle Swarm Optimization (TVAC-PSO) algorithm to optimize the Multiple Energy Carriers Economic Dispatch (MECED) problem for hybrid electrical and natural gas networks. The simulation results on two case studies are reported. The first one verifies an introduced gas loss formula and another one is devoted to a modified version of IEEE 14-bus test system. The results are compared with PSO, Genetic Algorithm (GA), and Differential Evolution (DE) technique. They show that a hybrid system can operate at a lower cost than independently-operated systems; CHP units can supply more electrical demand than the electrical network while their contribution in supplying the heat demand may be smaller than that of gas furnaces. In a hybrid system, energy efficiency can be reached, producing electrical power and heat locally; CHP units have a great role in reduction of operational cost; In the coupled mode, electrical power losses are decreased, while the gas losses are increased; The simple proposed gas loss formula can provide acceptable results; Cost reductions due to employing CHPs and applying the proposed optimization technique are greater than the other methods. [ABSTRACT FROM AUTHOR]

Cascading line outages may result in minor or major blackouts, which are considered as a main concern in the power system operation. Usually when a line is disconnected from the grid due to any reason, other lines may fail due to the congestion. So, this procedure may cause the cascading line outages. On the other hand, the demand response programs (DRPs) can be applied as an efficient, environmental friendly, and sustainable tool in preventing the cascading line outages. In this paper, a new procedure has been presented by which the direct load control (DLC) is applied intelligently as a common DRP in order to prevent the cascading line outages. In the proposed method, first the critical lines are recognized based on the line failure distribution factors (LODFs) and generalized line outage distribution factors (GLODFs). Then, the optimal busses for implementing DRPs are appointed based on power transfer distribution factors (PTDFs). After that, by applying dynamic DC optimal power flow (DCOPF), not only the cascading line outages are prevented, but also the optimal incentives in DLC are determined simultaneously. In fact, the proposed method has significant benefits both for the customers and utility. To show the effectiveness of our proposed technique, it is applied on the IEEE 118-bus test system. Results indicate the practical benefits of the presented method. [ABSTRACT FROM AUTHOR]

Stability studies in distribution smart grids (SGs) are very important due to the increased penetration of distributed energy resource making bidirectional flow of electric power. This paper presents two novel fast voltage stability indices (VSIs) applicable to the radial distribution SGs based on the real‐time measured voltage data only, obtained by the SG infrastructures such as smart metering device. Each VSI depends on the system bus voltages and does not need additional calculations such as power flow solution, converting the actual distribution network into two‐bus equivalent system etc. In other words, for a radial system with n ‐node, the voltage stability analysis is carried out by parallel n ‐microprocessor units independently. The concentrator units can collect and evaluate the computational results and decide about control actions directly or indirectly depending on the system requirements. The accuracy and efficiency of the proposed indicators are tested on a 41‐node radial distribution system. Six load models including constant and composite types have been considered, and also SG in various states of operation and four types of distributed generation are analysed. The comparison results demonstrate that the proposed VSIs are fast and effective to identify the most sensitive node of the radial grids to the voltage collapse based on the smart infrastructures. [ABSTRACT FROM AUTHOR]

This research work proposes the hybrid meta-heuristic algorithm to solve the multi-area economic dispatch problem to operate the power systems economically. The proposed hybrid algorithm combines the exchange market and grasshopper optimization algorithms. The grasshopper optimization algorithm sometimes suffers from premature convergence. To overcome this drawback, a hybrid algorithm for the better balance between exploration and exploitation and thus finding optimal solutions with high quality and robustness, is proposed in this paper. The main target of this paper is to investigate the application of the hybrid algorithm for optimizing the multi-area economic dispatch problem, satisfying different constraints of the valve point loading effect, multiple fuels, prohibited operation zones, ramp-rate limit, the tie-line constraints, and transmission loses. In this regard, after validation of the proposed hybrid algorithm by applying it on some benchmark functions of diverse nature, the effectiveness and robustness of the proposed algorithm are evaluated in three case studies of the multi-area economic dispatch problem. The comparative results confirmed the effectiveness of the proposed method in terms of robustness and quality solutions. Also, the applied method reduces the fuel cost in the range of 0.0229%–1.1622%. • Introducing a new meta-heuristic algorithm based on the combination of EMA and GOA. • Using the EMA-GOA to optimize some well-known benchmark functions. • Applying the algorithm for the first time to different kinds of multi-area economic dispatch problems. • Investigating the proposed method performance on different types of the mentioned problems. [ABSTRACT FROM AUTHOR]

This article tackles the transmission expansion planning (TEP) problem in a deregulated environment with the objective functions of investment, congestion, and load curtailment costs. The proposed TEP approach is a mixed-integer nonlinear optimization problem with a nonconvex structure and noncommensurable objective function. Until now, the research efforts on this problem have been mostly based on the DC power flow, which has the disadvantage of neglecting reactive load, power losses, and voltage constraints. However, a realistic transmission network operates based on AC power flow conditions. Therefore, the obtained transmission expansion plans based on DC power flow may be unable to satisfy the real conditions of power systems. In this article, the effects of different transmission network models on the objective functions of TEP are analyzed. Furthermore, different uncertainties especially associated with load and wind power generation are considered as the major concerns in TEP studies for each of the previously mentioned models. The point estimation method that enjoys from proper accuracy and low computational efforts is used for modeling the uncertainties. The nondominated sorting genetic algorithm II (NSGA II) is performed to obtain the Pareto optimal solutions of the TEP objectives and the final optimal solution is searched by applying the fuzzy decision-making approach based on decision-maker preferences. The models are simulated on the case studies of IEEE 24-bus reliability test system as well as the Iranian 400 kV transmission grid. The obtained results show that the type of power flow and uncertainties can change the TEP optimal solutions and objectives significantly. [ABSTRACT FROM AUTHOR]

The term smart grid refers to a modernization of the electrical network consisting in the integration of various technologies such as dispersed generation, dispatchable loads, communication systems and storage devices which operates in grid-connected and islanded modes. As a result, traditional optimization techniques in new power systems have been seriously influenced during the last decade. One of the most important technical and economical tools in this regard is the Optimal Power Flow (OPF). As a fundamental optimization tool in the operation and planning fields, OPF has an undeniable role in the power system. This paper reviews and compares the OPF approaches mainly related to smart distribution grids. In this work, the main OPF approaches are compared in terms of their objective functions, constraints, and methodologies. Furthermore, computational performances, case study networks and the publication date of these methods are reported. Finally, some basic challenges arising from the new OPF methodologies in smart grids are addressed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization algorithm, namely hybrid Time Varying Acceleration Coefficients-Gravitational Search Algorithm-Particle Swarm Optimization (hybrid TVAC-GSA-PSO), to solve the large-scale, highly nonlinear, non-convex, non-smooth, non-differential, non-continuous, and complex Combined Heat and Power Economic Dispatch (CHPED) problems. The suggested algorithm is based on the Newtonian laws of gravitation and motion as well as swarm behaviors. The proposed technique combines the best performances of three optimization methods (i.e. GSA, TVAC-GSA, and TVAC-PSO) in terms of their specific strategies in particle movements through a self-adoptive learning strategy. The effectiveness and robustness of the suggested algorithm are tested on a set of five benchmark functions and two large-scale CHPED problems (considering valve-point loading effect and prohibited zones of conventional power units, transmission losses, as well as special characteristic of CHP units). The obtained results by the suggested algorithm in terms of quality solution, computational performance, and convergence characteristic are compared with various algorithms to show the ability of the proposed approach and its robustness in finding a better fuel cost with a less expensive solution. [ABSTRACT FROM AUTHOR]

This paper proposes a new optimization algorithm, namely Self-Adoptive Learning with Time Varying Acceleration Coefficient-Gravitational Search Algorithm (SAL-TVAC-GSA), to solve highly nonlinear, non-convex, non-smooth, non-differential, and high-dimension single- and multi-objective Energy Hub Economic Dispatch (EHED) problems. The presented algorithm is based on GSA considering three fundamental modifications to improve the quality solution and performance of original GSA. Moreover, a new optimization framework for economic dispatch is adapted to a system of energy hubs considering different hub structures, various energy carriers (electricity, gas, heat, cool, and compressed air), valve-point loading effect and prohibited zones of electric-only units, as well as the different equality and inequality constraints. To show the effectiveness of the suggested method, a high-complex energy hub system consisting of 39 hubs with 29 structures and 76 energy (electricity, gas, and heat) production units is proposed. Two individual objectives including energy cost and hub losses are minimized separately as two single-objective EHED problems. These objectives are simultaneously minimized in the multi-objective optimization. Results obtained by SAL-TVAC-GSA in terms of quality solution and computational performance are compared with Enhanced GSA (EGSA), GSA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to demonstrate the ability of the proposed algorithm in finding an operating point with lower objective function. [ABSTRACT FROM AUTHOR]

Demand response (DR) can be counted as an effective congestion management method in power systems. However, appointing the optimal buses for the demand response programs (DRPs) implementation is one of the main challenges for the power system operators. In this paper, a new procedure has been developed by which the optimal locations and times of DRPs implementation are determined. Optimal buses are identified based on the power transfer distribution factors (PTDFs), available transfer capability, and dynamic dc optimal power flow problem. Applying the developed method results in reducing lines’ congestion, increasing customers, and independent system operator's benefits, improving load curve's characteristics, preventing line outages and black outs, and consequently increasing the network reliability. The proposed method has been applied on the IEEE 39-bus New England test system. Results indicate the effectiveness and practical benefits of the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays, penetration of DG resources into the power systems due to their advantages is rapidly increasing. Islanding performance is one of the most disadvantages of using DGs, which should be considered, because this option may lead to negative effects on the protection, performance, and operation management of distributed systems implementation. An island must be detected in less than 2 sec based on IEEE STD 1547-2003 standard, and DG resources must be disconnected from distribution network immediately. In this paper, a novel hybrid algorithm to identify the islanding has been proposed based on the passive parameters (ROCOV and ROCORP) and capacitor bank connection strategy. In the incompatibility conditions of the large-scale power, by combining two parameters i.e. ROCOV and ROCORP and investigating their changes, the islanding is carefully and quickly identified. This strategy is completely verified in the mismatch conditions of small-scale power by applying the capacitor bank connection strategy. The presented approach is tested on two case studies and the detailed results were completely reported. As it was shown, the proposed approach is able to distinguish between islanding and the other events. [ABSTRACT FROM PUBLISHER]

• Presenting a complete model of an EH contains RES to investigate the OPGF problem. • Modeling the operation limits of CHP, and ramp rate of generators. • Considering demand response programs for both electrical and thermal loads. • Analyzing several EHs, allocated in various buses to minimize the operation costs. • Applying the GAMS software to modified integrated power and gas IEEE14-bus system. Energy hubs (EHs) can help connect different types of energy and create a multi-carrier energy network (MCEN), thereby promoting energy network operation flexibility. Due to the connection of different types of energy in these networks, reduction of operating costs has become a controversial challenge. Herein, the optimal power and gas flow (OPGF) problem in an MCEN was investigated in the presence of wind and solar power sources, electrical and thermal energy storage (EES and TES), and electrical and thermal demand response programs (EDRP and TDRP). In the mentioned EHs, the operating costs of electric and gas networks were minimized, and the operation of EHs was simultaneously investigated. All constraints of the electric and gas networks and different equipment, including combined heat and power (CHP) units, were also considered. The simulation results in GAMS showed that the simultaneous optimization of the operation cost of the case study network, including 14 electric buses and 9 gas buses, during a 24-hour period was reduced up to 4%. Also, due to the lower price of gas, its consumption has increased by about 5%, and this ultimately decreases the total cost of optimal energy distribution of the network compared to the case in which the mentioned strategies and programs were not implemented. [ABSTRACT FROM AUTHOR]

Power quality and power disturbances are the most important increasing factors throughout electrical networks. Ferroresonance as one of these disturbances can cause quality and security problems. This paper analyzes the stochastic behavior of ferroresonance as a nonlinear resonance phenomenon of the erratic nature and difficult prediction. Furthermore, the impact of different parameters on the initiation of ferroresonance is investigated. It is illustrated that a small change in the initial conditions leads to a large difference in the long-term behavior of the system, and this makes the future of the system unpredictable. Based on the stochastic behavior of ferroresonance some papers took different parameters into account and attempted to identify the basins of attraction for different ferroresonance regions. Here, according to real condition it is illustrated that these basins are highly depending on the initial conditions and there are no transparent boundaries for them. Also, by changing different parameters and initial conditions, the probability degree of ferroresonance initiation is calculated and depicted. Moreover, the impact of source angle on the ferroresonance initiation is investigated. [ABSTRACT FROM AUTHOR]

Renewable generation and distributed resources are becoming increasingly relevant due to their various advantages and intense regulatory support. Therefore, there is a need to consider the effects of these new sources in the transmission network. In addition, the accurate assessment of losses is necessary in order to evaluate the impact of distributed generation. In this paper, we present a single-objective optimization method that is applied to different case studies that include an AC and a DC power flow in order to consider losses accurate and efficiently. The proposed algorithm considers uncertainty in wind generation and demand, as well as the costs for investment, repair, maintenance, and losses. This problem is solved using an Imperialist Competitive Algorithm (ICA), a meta-heuristic method that has been proposed recently and has shown promising results compared to the other well-established evolutionary methods such as Genetic Algorithm (GA). The proposed method is investigated on the IEEE 24-bus and IEEE 118-bus test systems. The results are compared to the results reported in the literature. Our results confirm that an accurate evaluation of losses using an ACPF does modify the optimal plan and hence it is important to include an ACPF when performing Transmission Expansion Planning (TEP). In addition, the implemented ICA displays a more efficient performance than a plan GA in the same case study. [ABSTRACT FROM AUTHOR]

This paper describes a novel modified optimization algorithm based on a new heuristic method, namely Time Varying Acceleration Coefficient Gravitational Search Algorithm (TVAC-GSA), to solve both single- and multi-objective Optimal Power Flow (OPF) problems in hybrid systems especially focusing on electricity-gas network. The suggested method is based on the Newtonian laws of gravitation and motion. Sum of the complexity of both electrical and gas-based networks in terms of the valve-point loading effect of generator units, energy hub structure, energy flow equations, and different related equality and inequality constraints make the optimization problem highly nonlinear, non-convex, non-smooth, non-differential, and high-dimensional. The effectiveness of the proposed algorithm to solve such a complex problem is verified on a new introduced hybrid system based on a modified version of IEEE 14-bus network. Comparison of results obtained by the presented method with those obtained by GSA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) shows the better accuracy and fast convergence of the new method in finding an operating point with lower objective function value. [ABSTRACT FROM AUTHOR]

DED (Dynamic economic dispatch) problem schedules generation units during the whole dispatch period in order to minimize the fuel costs. On the other hand DRPs (Demand Response Programs) focus on increasing customers' benefit and improving network reliability. If these two problems are optimally integrated considering their interactions at each side, they will be implemented more effectively. One of the main concerns in the TOU (Time of Use) DRP is the optimal pricing during different periods. In this paper, TOU which focuses on the demand side has been intelligently integrated with the DED problem which focuses on the supply side. In the combined problem namely DEDTOU, a new procedure for the optimal pricing will be presented so that the fuel costs in the DED problem are minimized and the optimal prices during different periods i.e. valley, off-peak, and peak periods in TOU are determined simultaneously. By the way, not only the network reliability and customers' benefit are increased but also fuel costs are decreased and generation units are optimally scheduled. Actually, DEDTOU is a win-win game both for the demand and supply sides. DEDTOU is applied on a ten units test system and results indicate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

• Proposing a new MES model for IOPGF problem, including different electrical and heat equipment. • Applying an improved crow search algorithm (ICSA) to solve the proposed model. • Reporting the obtained results of simulation on the modified IEEE-30 bus network, and some benchmark functions. This paper solves the integrated optimal power and gas flow (IOPGF) problem in a multicarrier energy system (MES), to find the operating variables of electrical and natural gas networks satisfying some practical constraints. The proposed problem models optimal power flow (OPF), optimal gas flow (OGF), and economic heat dispatch (EHD) equations which make it a very complex optimization problem that cannot be easily solved by the classical optimization techniques. In addition, a population-based evolutionary algorithm (PEA), namely the improved crow search algorithm (ICSA) is introduced and applied to solve the OPF and the IOPGF problems. The results are compared with other well-known PEAs to ensure the higher performance of the ICSA. Also, the superior performance of the ICSA is confirmed by applying it to solve five well-known benchmark functions (BFs). [ABSTRACT FROM AUTHOR]

• Improving the voltage in a wind farm by a self-corrective static VAR compensator. • Considering the steady-state and transient modes under harsh faults in analysis. • Enhancing the system performance by employing an optimized Battery Energy Storage. • Reporting the results in different cases, with or without the mentioned equipment. Maintaining synchronism and voltage stability, especially in the presence of wind farms, has a crucial role in confirming the reliability requirements of the power grid, as the natural fluctuations of wind speed result in intermittency and uncontrollability of power generation. To this end, an optimized Battery Energy Storage System (BESS) was suggested to make an equilibrium between the generation and consumption in wind farms under harsh conditions. Additionally, the instability of wind farms is very likely after transmission line faults occur. To overcome this problem, in this paper, the impact of a constant capacitor bank, connected in parallel with the wind generators, is investigated. Also, a novel self-corrective Static Volt-ampere reactive Compensator (SVC) is proposed to overcome the variations of voltage and damp the reactive power oscillations after occurring the faults. The proposed SVC compares the network voltage profile as the reference voltage with the voltage deviations in a control block diagram. For this purpose, three similar wind farms of Permanent Magnet Synchronous Generator (PMSG) type are considered in a wind park. The simulation case studies are performed in Matlab/Simulink under different conditions of transient and steady-state voltage stabilities in the presence of proposed BESS including, only capacitor bank (without self-corrective SVC), and with self-corrective SVC. The results authenticate the ability of the proposed SVC strategy to correct the voltage deviations, and voltage stability improvement of the under study system. [ABSTRACT FROM AUTHOR]

This paper presents a novel Long-Term Load Forecasting (LTLF) technique based on the new heuristic method, namely Gravitational Search Algorithm (GSA). The objective of the suggested approach is establishing a more accurate LTLF model to minimize the average error of modeling. In order to estimate different fitting functions based on the proposed algorithm, two different case studies include Egyptian and Kuwaiti grids are selected. Also, the results are compared with a conventional approach, namely Least Squares (LS) method, and Particle Swarm Optimization (PSO) as a heuristic algorithm, to select the best LF model. Finally, based on the average and maximum errors arise from the estimations as a decision condition; the best function is selected for the LTLF problem. [ABSTRACT FROM AUTHOR]

This paper presents the application of a novel optimization algorithm, namely gravitational search algorithm (GSA) to solve the non-convex combined heat and power economic dispatch (CHPED) problems. The proposed approach is based on the gravitational law and the law of particles motion. The effectiveness of the suggested algorithm is tested on study-cases which include modeling of valve-point loading effect and transmission losses. Results of GSA-based CHPED problem in terms of quality solution and computational performance are compared with various algorithms to show the ability of the introduced algorithm in finding an operating point with lower fuel cost. [ABSTRACT FROM AUTHOR]

In this work; we suggest a new intelligent optimization method to solve the optimal energy management issue in the renewable micro-grids (MGs). The proposed method is constructed based on the harmony search (HS) algorithm as well as a novel adaptive modification method to solve the problem effectively. The proposed modification method consists of three sub-modifications to provide the algorithm with powerful tool to escape from the local optima of the problem with fast convergence. The feasibility and satisfying performance of the proposed method is examined on a typical MG including different types of renewable energy sources such as photovoltaic, fuel-cell, micro-turbine, wind turbine and battery as the storage device. Comparative studies demonstrate the consistent superiority of the proposed method over the other well-known algorithms in the area. [ABSTRACT FROM AUTHOR]

Transmission expansion planning (TEP), the determination of new transmission lines to be added to an existing power network, is a key element in power system planning. Using classical optimization to define the most suitable reinforcements is the most desirable alternative. However, the extent of the under-study problems is growing, because of the uncertainties introduced by renewable generation or electric vehicles (EVs) and the larger sizes under consideration given the trends for higher renewable shares and stronger market integration. This means that classical optimization, even using efficient techniques, such as stochastic decomposition, can have issues when solving large-sized problems. This is compounded by the fact that, in many cases, it is necessary to solve a large number of instances of a problem in order to incorporate further considerations. Thus, it can be interesting to resort to metaheuristics, which can offer quick solutions at the expense of an optimality guarantee. Metaheuristics can even be combined with classical optimization to try to extract the best of both worlds. There is a vast literature that tests individual metaheuristics on specific case studies, but wide comparisons are missing. In this paper, a genetic algorithm (GA), orthogonal crossover based differential evolution (OXDE), grey wolf optimizer (GWO), moth–flame optimization (MFO), exchange market algorithm (EMA), sine cosine algorithm (SCA) optimization and imperialistic competitive algorithm (ICA) are tested and compared. The algorithms are applied to the standard test systems of IEEE 24, and 118 buses. Results indicate that, although all metaheuristics are effective, they have diverging profiles in terms of computational time and finding optimal plans for TEP. [ABSTRACT FROM AUTHOR]

The unit commitment problem is one of the most significant and basic issues in the monitor, control, and operation of modern power systems, which has always been a subject of great concern to researchers and operators as the most extensive human-made system. Before restructuring, one of the main objectives of unit commitment problem was the minimization of the total operation cost of power plants subject to various constraints, including unit and network ones. As the privatization and restructuring process became more serious, the primary purpose of the unit commitment problem has been changed to maximizing the total profit, which led to the emergence of a new concept known as profit-based unit commitment problem. Accordingly, the maximization of the profit for generation companies, all over the studied period, is a top-priority direction. This paper presents a comprehensive overview of the profit-based unit commitment problem in restructured power systems by investigating the most important studies on this topic and providing a complete classification. It also outlines the challenges facing researchers in this field, offers new insights, and suggests future directions. • Describing a comprehensive formulation for profit-based unit commitment problem. • Presenting a complete and comprehensive overview of profit-based unit commitment problem. • Reporting the used case study systems, simulation tools, hardware, and run-time. • Briefly reviewing the fitness functions, parameter setting, convergence criterion, and the obtained profit values. • Addressing the main challenging issues regarding the mentioned problem. [ABSTRACT FROM AUTHOR]

• Proposing a new passive islanding detection scheme for synchronous-type DG based on the combination of (dδ/dt) and (dP m /dt) parameters. • Including the load angle parameter in the proposed algorithm to make it more reliable. • Addressing the sensitivity analysis in the presence of three DG units with different inertia constants, voltages, and power levels. • Removing the swing feature from the studied signal and reducing the monitoring time much more than what was needed before (900 msec). This paper offers a novel passive islanding detection scheme for synchronous distributed generations (SDGs) based on the combination of (dδ/dt) and (dP m /dt). By the occurrence of islanding and grid-connected events, the load angle (δ) and mechanical power (P m) of SDGs will change. These signals will oscillate during non-islanding events, while they will reach a stable point without any oscillation during islanding events. However, during some grid-connected disturbances such as high impedance faults and small capacitor switching, the oscillations of δ and P m may not be significant. Therefore, the swing feature cannot be a reliable index and can lead to an inappropriate operation. Hence, to select the best parameters, a combination of four parameters, including dP m /dt, dδ/dt, dP m /dδ, and dδ/dP m is employed. The sensitivity analysis to discriminate islanding from non-islanding events has been reported in the presence of SDGs with various inertia constants, voltage levels, and loads. Based on the results, the combination of dδ/dt and dP m /dt has high accuracy and speed in clustering the occurred events. The efficiency of the proposed approach is tested on the IEEE 33-bus system. It is proved that the proposed approach decreases the non-detection zone to 0.01 MW and 0.01 MVAr of the powers nonconformity. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive model is proposed for long-term planning of various combined heat and power units in an integrated heat and electricity network. The proposed model takes into account the uncertain electric loads, and market price applying the information gap decision theory. Furthermore, the security of the power grid from the voltage stability viewpoint utilizing the L-index approach is considered. The model is based on the risk-averse multi-objective combined heat and power planning methodology, which maximizes the profit of combined heat and power owners and minimizes the system operator costs over the planning horizon in the presence of the environmental emissions cost. The best compromise solution is achieved via a fuzzy logic-based min-max method. The risk-averse strategy of Information gap decision theory is applied to the obtained solution, which demonstrates the impact of data uncertainty. The proposed mixed-integer non-linear programming model is solved using the general algebraic modeling system package and tested on the IEEE 14-bus standard system. The results indicate that the risk-averse strategy improves the robustness of the network against the uncertainty. Also solving the model using the multi-objective framework gives comprehensive results, and shows that the voltage stability constraints affect the planning decisions. • Presenting a comprehensive model for long-term planning of various combined heat and power units. • Investigating the uncertainty of electric loads, and market price using information gap decision theory. • Modeling the long-term security of the power grid from the voltage stability viewpoint. • Introducing a risk-averse multi-objective planning model. • Considering the environmental objectives in the combined heat and power investment. [ABSTRACT FROM AUTHOR]

• Applying the GA-PLB strategy to limit the vast search space of the UCP by focusing on only some feasible solutions. • Initial checking conditions of MDT, MUT, and SR which reduce the computing time effectively. • Arranging the unit combinations and output power of thermal power plants, stored (injected) power in (by) ESSs applying GA. • Applying TOAT to overcome the hardness of uncertainty modeling in UUCP. The main purpose of unit commitment problem (UCP) is to find the optimal operation cost (OOC) by considering power system constraints. In this paper, an uncertain UCP (UUCP) is proposed in the presence of energy storage systems (ESSs) via applying genetic algorithm-priority list-based (GA-PLB) strategy and considering operational constraints such as power balance, minimum down time (MDT), minimum up time (MUT), and spinning reserve (SR). To find the robust optimal scheduling due to modeling the uncertainty of renewable energy sources (RESs), the Taguchi orthogonal arrays technique (TOAT) is applied. Furthermore, to consider the environmental concerns, the emission produced by thermal power plants is modeled using the emission cost function as a term of the objective function. The proposed model is tested on the standard cases of IEEE 10, and 38 units and the results are reported. The obtained results confirm that the proposed approach can effectively reduce the OOC. One of the main advantages of the proposed method is the reduction in computational burden due to the modeled uncertainties in solving the UUCP compared to the other proposed methods based on iterations while improving the final 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.

• The proposed control method eliminates the reactive power sharing errors. • The proposed control method causes just small transient active power disturbances. • The main advantages are simplicity and non-dependence on MG configuration. In this paper, a control strategy has been presented to improve the reactive power sharing among distributed generation units in the islanded microgrids. The proposed control method is based on exerting a partial transient coupling between active and reactive powers. This partial coupling is performed by adding a small reactive power droop term in the conventional frequency droop controller during the compensation time. This coupling term causes a small active power disturbance that used to generate a compensation voltage signal. The suggested compensation scheme is enabled by the synchronized flag signal through a low-bandwidth communication link. To assess the performance and effectiveness of the suggested control approach, a MG with three DGs is simulated. The simulation results demonstrate that the reactive power is shared among DGs accurately. Simplicity, no need for additional communication links, non-dependence on MG configuration and insignificant transient disturbances are the main advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a control strategy to improve adaptive virtual impedance performance. The proposed method compensates voltage drop across the output impedances of distributed generation (DG) units, intensified due to the use of virtual impedance, and regulates the voltage of busses feeding microgrid‐connected loads in a desired level. In addition, it preserves the advantages of adaptive virtual impedance strategy, including proper active and reactive power sharing and decreased circulating current among parallel DGs. To compensate voltage drop proportional to load variation, the reference voltage used in the droop control strategy is adaptively regulated. To evaluate the performance and efficacy of the proposed control strategy, it was implemented on an islanded microgrid with two DGs. Simulation results show that the proposed strategy makes a compromise between control objectives, including appropriate power sharing, compensation of voltage drop, and reduction of circulating current. [ABSTRACT FROM AUTHOR]