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.

The study outlines the planning methodology for an islanded hybrid system incorporating electric vehicle parking, focusing on the optimal capacity determination of controllable and uncontrollable renewable resources, alongside batteries, while addressing both economic and environmental criteria. The goal function minimizes the construction and maintenance costs associated with battery storage systems, power electronic converters, and renewable energy units, as well as mitigating the expenses tied to battery degradation, electric vehicles depreciation, and environmental pollution from bio-waste systems. Within this framework, minimizing the total environmental pollution by the bio-waste renewable source minus the environmental cleanliness from waste by this source is considered. These objectives are governed by operation-planning constraints including resource and storage management, and converter functionality. The design also accounts for uncertainties linked to electric vehicles, fluctuating renewable energy output, and load demands. To address these uncertainties, stochastic optimization is implemented using the Unscented Transformation method. Additionally, the problem-solving approach employs a hybrid evolutionary algorithm combining the Ant-Lion Optimizer and the Red Panda Optimizer to achieve enhanced computational efficiency. Numerical simulations demonstrated that the proposed solution algorithm achieves optimal results with rapid convergence, showcasing a final response standard deviation between 0.91% and 0.92%. Notably, the inclusion of stationary energy storage devices, such as batteries, in the islanded system substantially reduces reliance on hydro and bio-waste energy generation, leading to fewer installed elements and a decrease in system planning costs compared to solely using renewable energy resources. The impact of electric vehicles was analyzed under two scenarios: when vehicles function solely as consumers, the planning cost rises by approximately 23.5%; however, when they serve as mobile storage units, the cost reduces by 1.8% in comparison to scenarios without their incorporation. This finding underscores the strategic advantage of integrating electric vehicles and battery bank as flexible storage assets in hybrid energy systems.

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.

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.

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.

The ever-increasing growth of electric energy consumption and consequently the increasing growth of investment in the distribution sector as well as the existence of a major part of the losses of the entire system in the distribution networks have made optimal planning in the field of distribution networks very important. With the change in the structure of power systems, the increase of renewable energy sources, and the smartness of networks, power systems have faced challenges including uncertainty in energy sources. The right solution to deal with these challenges is to use energy storage systems. Therefore, determining the size, installation location, and choosing the type of energy storage systems, to maximize the benefits. They are important. In this paper, distribution network scheduling considering electric vehicles, distributed generation, and energy storage is presented. The proposed problem is applied to the IEEE standard 33-bus radial distribution network through GAMS optimization software and then, the capabilities of the proposed scheme are evaluated.

In the optimal operation of the distribution network, various technical and economic indicators must be in optimal conditions. Distribution system operator use energy management and reconfiguration programs to access this issue. One of the ways to improve the technical and economic status of the network is energy management in the demand response program. With the management of the demand side, it is expected that the demand of energy by the distribution system from the upstream network will decrease during peak hours, power losses and voltage drops of the lines will decrease. Even in the condition that N - 1 fault occurs in the network, it is expected that it will be able to prevent high blackout rate of consumers. Of course, for the implementation of the demand response program, appropriate economic incentives should be considered by the distribution system operator. Therefore, this research provides a reliability-constrained network reconfiguration program and real-time pricing-based energy management of a smart distribution network with demand response. The method is called triobjective optimization. Utility function is taken into consideration in accordance with demand response goals, and the first objective function is to reduce the difference between the overall power bill and unhappiness costs. Other two functions take into account the minimization of the expected energy not supplied and the overall expenses of load fluctuation and operation, respectively. Scheme limits by demand response model, optimum power flow, reliability constraints, and network reconfiguration. Stochastic optimization analyzes the unpredictability of network equipment availability and load. In order to continue fuzzy decision making, epsilon-constraintbased Pareto optimization yields a single-objective formulation and a compromise solution. Dual variable of the active power balancing constraint is the price of energy. The penalty function of the specified constraint is added to the objective function in order to compute it and the problem variables at the same time. Combining of crow search algorithm and krill herd optimization solves the problem. In comparison to power flow studies, numerical findings finally validate scheme's potential to enhance technical and economic circumstances of network and consumers using demand response. So that the used demand response program is able to peak shaving about 30%-31% for the different load profiles. Also, the management of the demand side along with the network reorganization program have been able to improve the economic, operation and reliability status of the distribution network by 29%-32%, 28%-43%, and 77%-80%, respectively, compared to the load flow studies. Therefore, the advantages and innovations of this article include the economic and operation model of the demand response program, the simultaneous improvement of the economic, operation and reliability situations of the distribution network, the evaluation of the effect of demand side management on the reliability of the network, and the use of the hybrid solution algorithm for the proposed scheme.

This study discusses energy management in thermal and electrical microgrids while taking heat pumps, renewable sources, thermal and hydrogen storages into account. The weighted total of the operating cost, grid emissions level, voltage and temperature deviation function, and other factors makes up the objective function of the suggested method. The restrictions include the operation-flexibility model of resources and storages, micro-grid flexibility limits, and optimum power flow equations. Point Estimation Method is used in this work to simulate load, energy price, and renewable phenomenon uncertainty. A fuzzy decision-making methodology is used to arrive at a compromise solution that satisfies network operators' operational, environmental, and financial goals. The innovations of this paper include energy management of various smart microgrids, simultaneous modeling of several indicators especially flexibility, investigation of optimal performance of resources and storage devices, and modeling of uncertainty considering low computational time and an accurate flexibility model. Numerical findings indicate that the fuzzy decision-making approach has the capability to reach a compromise point in which the objective functions approach their minimum values. The integration of the proposed uncertainty modeling with precise flexibility modeling results in a reduction in computational time when compared to stochastic optimization based on scenarios. For the compromise point and uncertainty modeling with PEM, by efficiently managing resources and thermal and hydrogen storages, scheme is capable of attaining high flexibility conditions. Compared to load flow studies, the approach can enhance the operational, environmental, and economic conditions of smart microgrids by approximately 33-57%, 68%, and 33-68%, respectively, under these circumstances.

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.

The research focuses on managing power within renewable flexible integrated energy systems in intelligent distribution systems, considering factors such as harmonic compensation, voltage stability, and environmental indices. The proposed system is based on a deterministic model that aims to optimize four distinct objectives. This objective function collectively minimizes the network's operating costs, emissions, total voltage harmonics, and the symmetrical value of the voltage stability index. Key constraints involve the operational and flexible models of the renewable integrated energy system, along with the linearized AC harmonic optimal power flow model and voltage stability limits. The study acknowledges inherent uncertainties related to the power output from renewable units, electric vehicles energy, price of energy, and load. To address these uncertainties, adaptive robust optimization is employed to ensure resilient solutions. Results indicate that despite these uncertainties, the operation of SDNs remains robust even with a prediction error margin of up to 45%. Moreover, the proposed system reduces voltage drop by 57.7%, emissions by 49.3%, operational cost by 55.2%, energy loss by 45.4%, and harmonic index by 48.6% under 45% uncertainty. In this condition, voltage stability increases 15%.

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.

ABSTRACT High‐voltage DC (HVDC) systems are taken into consideration while simultaneous generation and transmission expansion planning in this paper. It is based on the placement and sizing of generating units, AC transmission cables, and HVDC systems. Within HVDC system, reactive power of transmission network may be managed by AC and DC substations equipped with AC/DC and DC/AC power electronic converters, respectively. Plan takes the form of a bi‐stage optimization, where the upper level aims to minimize yearly cost of constructing the items stated, while taking into account constraints related to size and investment budget. Minimization of yearly planning costs of generating units and the cost of energy losses are taken into consideration in the lower‐level problem. Linearized AC power flow model and the operating parameters of both non‐renewable and renewable generating units bind the goal function. To simulate the uncertainty of demand and renewable electricity, stochastic optimization is used. Utilizing the Benders decomposition approach, problem is solved and the best solution is extracted. Numerical outcomes derived from several cases demonstrate plan's potential to enhance transmission network's technical and economic features. In comparison to network power flow studies, the economic (operating) status of the network is improved by around 10% (10–40%).

This paper presents the formulation of the simultaneous planning of distributed generations (DGs) and automatic distribution according to the goals of reliability, operation, and economy of the distribution system. The objective function aims at minimizing the total costs of construction, maintenance, and operation of distribution automation resources and devices, plus the cost of voltage deviations and expected energy not supplied in this paper. This scheme limits to AC optimal power flow, the planning constraints of DGs and distribution automation devices, and reliability equations. The mentioned scheme has an integer nonlinear optimization format. In the following, a linear approximation model is extracted for it to reach the unique response. Finally, by applying the proposed problem to the standard distribution grid by GAMS optimization software, the numerical results highlight the capability of the proposed scheme in improving the technical and economic conditions of the distribution network with optimal DGs and distribution automation planning.

Due to increased energy consumption in upcoming years, the power system needs to be expanded to meet suitable technical conditions. The primary requirement is to gain accurate information about consumption growth in the planning horizon, which can be obtained via forecast studies. Since renewable sources can grow beside the demand, the accurate prediction should consider simultaneous changes in supply and demand in the future. In this paper, a reliability-constrained transmission expansion planning (RCTEP) is proposed. It simultaneously is based on the load forecasting and renewable sources production, named the net power demand forecasting technique (NPDFT). NPDFT consists of a time series-based logistic method, which forecasts loads at planning years. RES generation forecasting forecasts the following year’s generation by an estimated coefficient. RCTEP minimizes the summation of the planning, operation, and reliability cost so that it is limited to the AC optimal power flow equations, planning constraints, and reliability limitations for N – 1 contingency. Then, the stochastic programming based on the Monte Carlo Simulation and the simultaneous backward approach models the uncertainties of the load, RES power, and availability of network equipment. This problem is solved by the hybrid algorithm of grey wolf optimization and training and learning optimization algorithm to achieve the securable optimal solution with a low standard deviation. Generally, this paper contributes to predicting the net power demand, simultaneous modeling of operation, reliability, and economic indices, besides using hybrid algorithms to solve the defined problem. Finally, this strategy is implemented on the 3-bus, 30-bus, and 118-bus transmission networks in MATLAB software. The numerical results confirm the capabilities of the proposed method in improving network operation and reliability indices. Higher reliability can be found for the network by defining a desirable penalty price. Also, operation indices, such as voltage profile and power loss, increase more than 10

This paper presents the energy management of smart distribution network including integrated system of hydrogen storage and renewable sources. Objective is to assess economic, operation, flexibility, and reliability goals of the distribution system operator. Objective function minimizes costs of operation, reliability, energy losses, and network flexibility. Scheme is constrained by AC optimal power flow equations, network reliability limitation, and integrated system model. Scheme utilizes scenario-based stochastic optimization to model of uncertainties, such as load, parameters of renewable resources, energy prices, and the availability of network equipment. Novelty is modeling and performance evaluation of the proposed integrated system as a type of flexibility resource in the operation of distribution systems proportional to the economic, operation, flexibility, and reliability objectives of the network operator. Numerical results demonstrate energy management capabilities of the discussed integrated energy system, which contribute to enhancing economic and technical conditions of distribution network. The inclusion of hydrogen storage in the renewable integrated energy system has been found to enhance the economic viability, operational efficiency, and reliability of the distribution network by around 46.8%, 41%–53%, and 95%, respectively, when compared to network power flow. Aforementioned integrated system demonstrates 100% flexibility through the utilization of hydrogen storage.

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.

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.

In this article, the robust scheduling of the distribution network is presented considering electric vehicles, distributed generation, and energy storage, in which the energy management of the mentioned elements is considered, and also only one scenario is needed. The proposed deterministic problem is an optimization problem whose objective function is equal to minimizing energy cost. Also, the limitations of the problem are equal to the power flow equations of the network, the limitations of the technical indicators of the network such as the voltage of the buses and the passing power of the lines, the operation equations of electric vehicles, energy storages, and distributed generation. It is worth mentioning that the mentioned problem is non-linear. In the following, to achieve the global optimal point with a high solution speed, the linear model of the mentioned problem is presented with a very low calculation error. In this research, the uncertainty parameters of the problem are equal to active and reactive loads, energy prices, parameters of electric vehicles, and renewable productions. Finally, to simplify the decision-making of the distribution network operator, a robust model of the mentioned problem was presented. Finally, the proposed problem is applied to the IEEE standard 33-bus radial distribution network using GAMS optimization software, and then the capabilities of the proposed design are evaluated.