This paper proposes a sustainable computing-oriented framework for economic energy scheduling in renewable energy hubs equipped with stationary storage systems within coupled electrical and thermal microgrid environments. The proposed formulation aims to simultaneously reduce operational expenditures and energy loss-related costs by defining a unified optimization objective. The framework integrates detailed hub operation models with optimal power flow analysis to ensure coordinated and efficient microgrid operation. The considered energy hubs include wind turbines, photovoltaic units, bio-waste conversion systems, hydrogen storage facilities, and thermal energy storage units, each subject to their respective technical and operational constraints. The bio-waste subsystem is modeled as a combined producer of electrical and thermal outputs. Multiple sources of uncertainty, namely energy market prices, loss cost coefficients, renewable generation variability, and load demand fluctuations, are explicitly addressed through a stochastic optimization approach. This enables robust and reliable hub operation under prediction inaccuracies and uncertain operating conditions. To derive an optimal and computationally efficient solution, the study employs an artificial intelligence (AI)-based hybrid optimization technique that combines the Ant Lion Optimizer with the Artificial Bee Colony algorithm. The hybrid solver demonstrates strong convergence characteristics and achieves high-quality solutions with reduced computational burden. Simulation results validate the proposed strategy's capability to substantially enhance both economic efficiency and operational reliability of renewable-based microgrids through advanced energy management practices. In particular, the sustainable computing-based optimal scheduling of stationary storage assets yields notable improvements, achieving operational performance gains ranging from 21.7% to 47.6% and an economic cost reduction of approximately 48.8% compared with conventional power flow-based analyses.

This paper introduces an advanced single-terminal, current-based protection scheme for series-compensated transmission lines employing an Adaptive Multi-Neighborhood Energy Operator (AMNEO). The proposed operator extends the conventional energy operator framework (e.g., the Teager–Kaiser Energy Operator) by incorporating a multi-neighborhood structure with adaptive weighting and nonlinear transformation. This enhancement increases sensitivity to instantaneous energy variations, mitigates the influence of measurement noise, and effectively handles signals containing multiple frequency components. By directly processing the three-phase current signals measured at a single terminal, the AMNEO extracts instantaneous energy and phase deviation features for accurate fault detection, classification, and localization, eliminating the need for voltage measurements. The scheme is validated through comprehensive MATLAB/Simulink simulations under diverse operating conditions, including varying fault types, fault locations, compensation levels, line impedances, line lengths, and measurement noise levels. The simulation results confirm that the proposed AMNEO-based method achieves fast and precise fault identification within less than one cycle, while maintaining robustness against noise and compensation-induced distortions. Compared with conventional techniques, the proposed approach demonstrates superior reliability and computational efficiency, rendering it highly suitable for real-time distance protection in modern series-compensated transmission systems.

This study focuses on designing Wide-Area Damping Controllers (WADCs) for a Multi-Terminal Direct Current (MTDC) system linked to a wind farm employing Doubly-Fed Induction Generators (DFIGs). These systems are prevalent in modern power grids to manage disturbances. Vital data for WADCs are acquired from geographically dispersed Phasor Measurement Units (PMUs) and channeled through the IEEE C37.118 protocol to Phasor Data Concentrators (PDCs) for time alignment. Malicious Time-Disrupting Synchronization Attacks (TDSAs) can tamper with PMU data, endangering grid stability. The research proposes an advanced TDSA model exploiting vulnerabilities in the PDC’s time-synchronization process to hinder WADC’s monitoring of oscillatory patterns. A strategy for detecting and mitigating TDSAs is introduced, using a Convolutional Neural Network (CNN) to evaluate temporal quality metrics at the PDC level, pinpointing TDSAs while distinguishing them from network disruptions. A new IEEE C37.118 data protocol extension flags TDSA occurrences in PMUs. When activated, the WADC switches to a robust, location-adaptive state observer tailored to the TDSA’s source, designed using Linear Matrix Inequality (LMI) constraints rooted in Lyapunov stability theory. MATLAB simulations across various scenarios confirm the reliability and effectiveness of the proposed methods.

This study explores sustainable energy management approaches for a smart distribution network that combines multiple infrastructures, such as electric vehicle charging stations, hydrogen refueling facilities for fuel cell vehicles, and renewable energy systems integrated with hydrogen storage. These components are managed in a coordinated manner to satisfy both operational requirements and security criteria defined by the distribution system operator. A key feature of the hydrogen storage unit is its dual functionality, as it not only stores electrical energy but also supplies hydrogen to end users. The primary objective is to reduce overall energy losses within the distribution system. To accomplish this, the research considers several important factors, including AC power flow modeling, grid voltage operational and security constraints, system flexibility, environmental restrictions, operational characteristics of electric vehicles charging and hydrogen stations, and performance models of renewable energy systems coupled with hydrogen storage. Furthermore, the proposed framework accounts for uncertainties related to load demand, renewable generation, and variations in the number of electric vehicles by applying a scenario-based stochastic optimization technique. The findings demonstrate significant enhancements in both system performance and security. In particular, the proposed method decreases voltage deviations, power losses, and peak load capacity by approximately 24.4%, 32.8%, and 38.3%, respectively, compared to conventional load flow analyses. Moreover, voltage security within the network is improved by nearly 10.2%, confirming the efficiency of the proposed integrated energy management strategy.

A six-phase, double-circuit transmission system consists of two independent three-phase circuits powered by a common bus. The increase in the number of phases introduces a variety of faults, including both intra-circuit and inter-circuit faults, which can complicate fault detection and classification algorithms. Accurate identification and classification of these faults are essential for interrupting fault propagation and enabling rapid system restoration. This paper presents a hybrid algorithm that employs signal processing techniques based on mathematical transformations to detect faults, identify the faulty circuit, and classify the fault type. The algorithm operates by sampling the currents from a single terminal and includes zero-sequence current analysis to differentiate between ground and non-ground faults. Given that the threshold values for fault classification vary across different scenarios, a simple model is proposed to determine these thresholds dynamically. The proposed model is simulated on a standard double-circuit transmission system within the MATLAB/Simulink software environment, and its performance is evaluated under various fault scenarios. The simulation results demonstrate the model's capability to accurately detect and classify fault types in the test system. Notably, the objectives of fault detection and classification are achieved with 100% accuracy across all fault scenarios.

In modern power distribution networks, ensuring the rapid detection, classification, section identification, and accurate location estimation of faults is essential to minimize downtime and improve system reliability. This paper presents a traveling wave-based protection method tailored for distribution networks, addressing the challenges posed by fault detection in complex configurations. The proposed protection strategy utilizes voltage measurements at key nodes across the network, applying a time-domain signal processing technique using the Hilbert Transform (HT). A novel frequency modification algorithm is introduced to enhance the accuracy of fault detection and classification, compensating for severe coupling between the phases typically encountered in distribution systems. The method demonstrates superior performance by achieving 100% accuracy in fault detection, classification, and section identification, with an average fault location estimation error of approximately 0.06%. Furthermore, the scheme operates with low computational burden and is robust under varying fault conditions, including resistance, inception angle, location, and fault type variations. Simulation results highlight the efficiency of this approach in providing real-time, accurate fault management, making it a promising solution for the protection of distribution networks.

Electronic power converters play an essential role in power grids, aiming to improve the electrical energy quality and also enabling bidirectional energy transfer between DC lines and circuits. They facilitate the achievement of a sinusoidal current waveform and effective power transfer control with a high power factor. This paper introduces a stationary reference frame based control strategy for grid-connected three phase modular multilevel converters (MMC). This strategy employs conventional PI controllers to track the instantaneous power components that include intentional oscillations at double grid frequency. By employing this method, the MMC converter can maintain an output sinusoidal waveform even under unbalanced grid voltage conditions. Also, there is no need for a transformation from a stationary frame to a synchronous frame, eliminating the requirement for a PLL to estimate the grid voltage phase angle. Furthermore, the use of MMC converter over common two-level and three-level VSC converters is proposed since MMC converters offer merits such as low harmonic components, no need for filters at the DC terminals, no need for filters at the AC side, and low losses, despite some drawbacks such as a large number of IGBT switches or a higher amount of stored energy in the sub-module capacitors. Therefore, the voltage THD and consequently active and reactive powers of the converter have been impressively mitigated by using MMC. To confirm the capability and effectiveness of the proposed method, various simulations are performed in MATLAB/Simulink software. Finally, the results are compared with common methods.

This paper addresses the challenge of online estimation of the voltage stability margin (VSM) in power grids. Given the rapid fluctuations in load and varying operational conditions, fast and accurate estimation of the voltage stability margin is critical for preventing instability events. As power systems expand, the dimensionality of the input space increases significantly, necessitating efficient feature selection and dimensionality reduction techniques. To tackle this, a novel and intelligent hybrid approach is proposed, integrating an Adaptive NeuroFuzzy Inference System (ANFIS) trained by the Solifugae-Inspired Optimization Algorithm (SIOA) with Partial Least Squares (PLS) regression for dimensionality reduction and dominant feature selection. The initial feature set consists of system loading characteristics obtained from Phasor Measurement Units (PMUs), which encapsulate essential information about network topology, load levels, generation patterns, and control system behavior. The effectiveness of the proposed ANFIS-SIOA + PLS framework is validated using the IEEE 39-bus (also known as the 10-machine New England power system) and 118-bus test systems. Comparative analysis with existing models in the literature demonstrates the superior performance of the proposed method, particularly in terms of feature reduction and convergence speed. It is noteworthy that the proposed approach achieves a reduction in root mean squared error (RMSE) of 41.53 % and 32.12 % for the IEEE 39-bus and 118-bus test systems, respectively, compared to the best results reported in the existing literature. These improvements substantiate the efficiency and robustness of the proposed method.

This article presents the planning (sizing) of a renewable off-grid system that depends on hydrogen storage. The system manages both electric and hydrogen energy and considers the smart charging of electric vehicles. The system stated utilizes wind, solar, and bio-waste renewable resources to mitigate environmental pollution. Hydrogen storage serves the purpose of storing electrical energy and supplying hydrogen to users. The suggested approach attempts to possibly decrease the installation and maintenance cost of resources, electronic power converters, and hydrogen storage. It is associated with the planning-operation model of the specified elements, as well as the smart charging model of electric vehicles, which involves the simultaneous management of electric and hydrogen energies. Stochastic optimization is employed to represent the uncertainties associated with load, renewable resources, and electric vehicles. The Red Panda Optimization approach is employed to obtain a reliable optimal solution. The proposed plan includes the development of a renewable island system that utilizes electric and hydrogen energy management. It involves modeling a bio-waste unit and hydrogen storage within the system. Additionally, the plan adopts a smart charging model for electric vehicles in the island system. The plan also addresses uncertainties through modeling and employs a specific algorithm for problem-solving. These innovations are key components of the proposed plan. Ultimately, the strategy was executed using the data from Epsoo, Finland. The quantitative findings demonstrate the acceptable performance of the strategy in boosting the economic status of the island system. Smart charging of electric vehicles resulted in a 7.8 % decrease in planning cost compared to the traditional conventional charging method. Hydrogen storages, unlike battery (compressed air storage), have the added benefit of improving the economic conditions of the island system by approximately 7.9 % (2.1 %). Additionally, the presence of a bio-waste unit in the island system leads to a significant 17.8 % reduction in planning costs.

This research explores stability challenges in power systems from integrating offshore wind farms (OWFs) with voltage source converter (VSC)-based multi-terminal direct current (MTDC) networks. A novel two-level integrated control (TLIC) framework is proposed to enhance frequency regulation at grid-side VSC (GSVSC) stations. The first level features adaptive inertial control (AIC) and adaptive droop control (ADC). By dynamically adjusting AIC and ADC parameters, wind units (WUs) in maximum power point tracking (MPPT) mode effectively mitigate secondary frequency fall (SFF). WUs are clustered by rotor speeds, enabling staged frequency support for improved responsiveness. The second level uses a communication-independent allocation (CIA) strategy, relying on local frequency measurements in the onshore power system (OPS) to balance power distribution among GSVSC stations. This bolsters OPS frequency stability and minimises SFF during MPPT operations. A robust H infinity controller, designed via loop-shaping, is applied at the wind farm-side VSC (WSVSC), employing multi-criteria decision-making (MCDM) for voltage optimisation. The MTDC DC voltage employs a Master-Slave (MS) configuration to suppress variations under disturbances. MATLAB simulations across scenarios validate the strategy's robustness in damping oscillations from uncertainties.

Economic scheduling of multi-microgrids containing distributed units and storage devices is expressed in this scheme according to the multi-objective energy management system. Microgrid operator considers the economic, security, flexibility and operation objectives. The present method minimizes the weighted sum of voltage security index, energy loss, and energy cost. Constraints consider the optimal power flow formulation, flexibility and voltage stability limits in microgrids, and mathematical formulation of sources and storages operation. Microgrid includes non-renewable and renewable units, and storage system in network are battery and compressed air storage. Unscented Transformation approach models the uncertainties of the renewables output, price of energy, and demand. Fuzzy decision approach obtains a compromise point between economic, security and operation objectives. Combining grey wolf and red panda optimizers is able to obtain an optimal solution with low value for variance of the final point. Energy management according to various technical and economic indicators in the several renewable multi-bus microgrids considering battery, compressed air storage and non-renewable unit as flexibility sources based on the Unscented Transformation model and hybrid solver are the advantage, goal and innovation of this project. According to simulation results, the energy management of the energy storage and non-renewable sources in the microgrids with renewable sources can be improved the various indicators, such as reducing the energy cost and loss as well as voltage drop about to 30-60%, 46%, 46-50%, and improving voltage security equal to 10.55% compared with power flow studies. Flexibility of 100% is also reached for microgrids thanks to the incorporation of storage equipment and non-renewable power sources.

[This retracts the article DOI: 10.1016/j.heliyon.2023.e16827.].

Faults in power transmission systems pose significant challenges due to the complexity and length of transmission lines. Effective fault detection, classification and location are essential for preventing further damage to the power grid. While travelling wave-based algorithms are commonly used for fault location, they often focus on identifying the fault's location without classifying the fault type. Accurate classification is crucial for enabling efficient and timely responses from protection systems. This paper introduces an integrated model for fault detection, classification and location using voltage signals from a single terminal of a series-compensated transmission line with a static synchronous series compensator (SSSC). The Gabor Transform (GT) is utilised for feature extraction, enabling both fault detection and classification. Travelling wave theory is then applied to identify the faulty segment and estimate the fault location. Additionally, a novel technique adaptively calculates the threshold value during the protection algorithm's execution. The proposed method is validated through a comprehensive analysis of various fault scenarios and sensitivity analysis. Numerical simulations in MATLAB/Simulink show that the model achieves 100% accuracy for fault detection, classification, and faulty segment identification, with 99.7925% accuracy for fault location estimation, demonstrating its effectiveness in fault management.

This plan presents energy scheduling in a distribution grid with multi-microgrid according to estimation of environmental, economic, flexibility, operation, and security indicators in microgrids. Microgrid has a multi-bus structure, which includes renewable solar, wind and bio-waste devices, non-renewable resources, compressed air and hydrogen storage. Study contains the three objectives optimization. The objective functions are the minimization of operation cost of microgrids and resources, the environmental pollution of microgrids and voltage deviation function. The constraints of the problem include the optimal power flow formulation of microgrids based on the flexibility and voltage security limits, the performance model of renewable/non-renewable units, and storage devices. Study has parameters of price of energy, load, and renewable phenomena as uncertainty. For their modeling, the point estimation approach is used to according to low computational time and accurately model flexibility. The epsilon-constraint method is used to extract the single-objective model, and fuzzy decisionmaking technique is used to achieve the compromise solution. This scheme has a non-convex nonlinear formulation. To access a reliable response considering low deviation for last point, a combination of red panda optimization and ant-lion optimization is used. Funding indicate the ability of plan for improve the technical, environmental, and economic conditions of microgrids. Thus, energy scheduling of the aforementioned units and storages can improve operational, economic, environmental, and voltage stability conditions of microgrids by about 59.2 %, 44.2 %, 24.5 %-75 % and 17.3 %-27.4 %, respectively. In these conditions, study achieves 100 % flexibility for microgrids. Solution approach achieves the sustainable computing conditions, such that it has the most optimal solution at low computational time and a standard deviation of 0.97 % in the final response.

This study presents a planning approach that considers the simultaneous expansion of generating and transmission systems, taking into account the location and sizing of generation units, AC transmission lines, and high-voltage direct-current (HVDC) systems. The HVDC system utilizes AC and DC substations equipped with AC/DC and DC/AC power electronic converters, respectively, to effectively regulate and control the reactive power of the transmission network. The problem aims to minimize the combined annual cost of constructing the specified parts and operating the generation units. This is subject to constraints such as the size and investment budget limits, an AC optimum power flow model, and the operational limits of both renewable and non-renewable generation units. The scheme incorporates a non-linear model. The Red Panda Optimization (RPO) is utilized to solve the provided model in order to attain a dependable and optimal solution. This research focuses on several advances, including the planning of the HVDC power system, the regulation of reactive power in HVDC substations, and the resolution of related issues using the RPO algorithm. The numerical findings collected from several case studies demonstrate the effectiveness of the suggested approach in enhancing the economic and technical aspects of the transmission network. Efficiently coordinating the generation units, AC transmission lines, and HVDC system leads to a significant enhancement in the economic performance of the network, resulting in a 10-40% improvement compared to the network power flow studies.

An energy hub is a unit that coordinates and integrates different resources, storage devices, and loads, which can manage several types of energy simultaneously. Its optimal energy management can be improved the environmental and technical factors of various energy networks. Therefore, this study presents the energy scheduling of environmentally friendly energy hubs including renewable wind, solar, and bio-waste resources, and thermal and hydrogen storage devices in electrical and thermal distribution networks. In the proposed system, the hydrogen storage and bio-waste system include a combined heat and power system. Therefore, they also play a role in heating energy production. The proposed design minimizes the sum of the voltage profile function in the electrical network and the temperature profile function in the heating network. Of course, this objective function is subject to the optimal power flow equations and environmental constraints of the aforementioned energy networks, and the resource and storage device exploitation model in the form of an energy hub. In this design, there are uncertainties such as load and renewable phenomena. For modeling of uncertainties, stochastic programming is used. Numerical results demonstrate the effectiveness of this approach in improving both environmental and technical outcomes for thermal and electrical networks through improved energy hub management. Incorporating renewable hubs with advanced storage units has notably enhanced conditions across key indicators: voltage profile (47% − 56% improvement), temperature profile (38%-40%), energy losses (38.10%), and peak load carrying capacity (23% − 40%), compared to traditional load distribution analyses.

In the energy management of a network, it is expected that by extracting the optimal performance for the power sources, storage equipment, and responsive demand, a favorable economic and technology situation is achievable for the network and the mentioned elements. Virtual power plants, as a unit aggregating resources, storage, and responsive loads, can create more favorable conditions in network energy management. So, it is expected that the positive effect of the virtual power plant format on the economic and technical situation of the distribution system is far more than those of managing individual elements mentioned in the network. Consequently, the distribution network operator's economic, environmental, and technical goals are met through the concurrent administration of reactive and active power in the smart distribution network that is equipped with a flexible-sustainable virtual power plant. The system operator is accountable for reducing the weighted sum of the voltage security index, energy loss, and energy cost of the distribution network. This problem is associated with the optimal power flow formulation, which considers the environmental limits and security of voltage in the distribution network, the renewable resource operation model and flexibility in the form of a virtual power plant, and the system's flexibility constraints. Flexibility resources considered in the present study are pricebased demand response and electric vehicle parking lots. Stochastic optimization relying on the Unscented Transform assists in providing a suitable model for uncertain quantities resulting from the amount of load, electric vehicles, renewable power, and price of energy and eventually shortens the computing time and accurately computes the flexibility index. The optimal compromise solution amongst various objective functions can be found through fuzzy decisionmaking. Some innovations of this research include concurrent administration of active and reactive power in virtual power plant, concurrent modeling of economic, operational, environmental, voltage security, and flexibility indicators in the distribution network, utilization of electric vehicles, and demand response as a source of flexibility, use of Unscented transform for modeling the uncertainties corresponding to the exact calculation of flexibility. The suggested method was simulated in the IEEE 69-bus radial smart distribution system. Regarding the numerical report obtained, the optimal performance of each of the renewable generation, demand response, and parking of electric vehicles can significantly impact the economic and technical condition of the distribution network. However, the best condition was obtained when the mentioned elements were placed in the form of a virtual power plant. So, in such a situation, the energy cost is around $1862 for the said network. The lowest value for the worst security index in this network is around 0.933 p.u. Energy loss, maximum voltage drop, and peak load carrying capability are equal to 1.902 MWh, 0.047 p.u., and 5.624 MW, respectively. As a result, and based on numerical findings, the method can attain sustainable social welfare. The optimal power scheduling of sustainable systems can enhance the economic, security of voltage, and operational, conditions of the network by roughly 43 %, 26.9 %, and 47 %-62 %, respectively, compared to power flow studies. Furthermore, the ideal administration of virtual power plants enables the proposed plan to achieve 100 % flexibility. Additionally, it can substantially diminish the degree of contamination within the distribution network.

The current study concentrates on the planning (sitting and sizing) of a renewable integrated energy system that incorporates power-to-hydrogen (P2H) and hydrogen-to-power (H2P) technologies within an active distribution network. This is expressed in the form of an optimization model, in which the objective function is to reduce the annual costs of construction and maintenance of integrated energy systems. The model takes into account the planning and operation model of wind, solar, and bio-waste resources, as well as hydrogen storage (a combination of P2H, H2P, and hydrogen tank), and the optimal power flow constraints of the distribution network. Electrical and hydrogen energy are administered in an integrated energy system. The modeling of the uncertainties regarding the quantity of load and renewable resources is achieved through stochastic optimization using the Unscented Transformation method. The novelties of the scheme include the sizing and placement of a combined hydrogen and power-based renewable integrated energy system, the consideration of the impacts of bio-waste units, P2H, and H2P systems on the planning of the integrated energy system and the operation of the active distribution network, and the modeling of uncertainties using the Unscented Transformation method to reduce the calculation time. The study’s results demonstrate the scheme’s ability to improve the technical conditions of the distribution network by considering the optimal planning of integrated energy systems. In comparison to the network power flow, the operation status of the network has been improved by approximately 23-45% through the optimal siting, sizing, and energy management of hydrogen storage equipment, as well as renewable resources in the form of integrated energy systems. In other words, optimal energy management and planning of the integrated energy systems in the distribution network has been able to reduce energy losses and voltage drop by 44.5% and 42.4% compared to the load flow studies. In this situation, peak load carrying capability has increased by about 23.7%. In addition, compared to the case of the network with renewable resources, the overvoltage has decreased by about 43.5%. Also, Unscented Transformation method has a lower calculation time than scenario-based stochastic optimization.

Identifying, classifying, and locating various faults in HVDC networks can significantly reduce maintenance costs in these systems. For this reason, intelligent methods for detecting and locating faults with high accuracy and speed have recently been the focus of system planners and operators. This paper proposes an intelligent fault detection, classification, and location scheme based on the adaptive neuro-fuzzy inference system (ANFIS), whose training and testing data are generated using the Hilbert-Huang (HH) transform-based feature extraction method. DC link current is measured as a fault signal, and its statistical characteristics are extracted employing HH transform. Furthermore, for training and testing ANFIS, a meta-heuristic algorithm with dynamic search capability and high convergence speed called the Modified Cuckoo Search (MCS) Algorithm is utilized. In addition, two conventional training methods, i.e., least square estimation (LSE) and gradient descent (GD), are applied to train ANFIS, and its performance is compared with the MCS algorithm. The numerical analysis results validate the accuracy of 97.37% in classification and 98.39% in location using the proposed framework. Additionally, the comparative study confirms a 12.18% reduction in mean squared error (MSE) value compared with the best result obtained in the literature.

This paper discusses the simultaneous management of active and reactive power of a flexible renewable energy-based virtual power plant placed in a smart distribution system, based on the economic, operational, and voltage security objectives of the distribution system operator. The formulated problem aims to specify the minimum weighted sum of energy cost, energy loss, and voltage security index, considering the optimal power flow model, voltage security formulation, and the operating model of the virtual power plant. The virtual unit includes renewable sources, like wind systems, photovoltaic, and bio-waste units. Flexibility resources include electric vehicle parking lot and price-based demand response. In the mentioned scheme, parameters of load, renewable sources, electric vehicles, and energy prices are uncertain. This paper utilizes the Unscented Transformation method for modeling uncertainties. Fuzzy decision-making is utilized to extract a compromised solution. The suggested approach innovatively considers the simultaneous management of active and reactive power of a virtual unit with electric vehicles and price-based demand response. This is performed to promote economic, operational, and network security objectives. According to numerical results, the approach with optimal power management of renewable virtual units is capable of boosting the economic, operation, and voltage security status of the network by approximately 43%, 47-62%, and 26.9%, respectively, to power flow studies. Only price-based demand response can improve the voltage security, operation, and economic states of the network by about 19.5%, 35-47%, and 44%, respectively, compared to the power flow model.