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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.

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.

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%.

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.

With the advent of smart grid theory, distribution networks can include different microgrids (MGs). Therefore, to achieve the desired technical and economic objectives in these networks, there is a need for bilateral coordination between their operators. In the following, by defining an energy management problem for them, it is predicted that the mentioned goals can be achieved. Therefore, this paper presents the hybrid flexible-securable operation (HFSO) of a smart distribution network (SDN) with grid-connected multi-microgrids using a two-layer coordinated energy management strategy. In the first layer, the microgrid (MG) operator is coordinated with sources, storages, and demand response operators. This layer models the HFSO method in the grid-connected MGs, which is based on minimizing the difference between the sum of operating cost of nonrenewable distributed generations and cost of energy received from the SDN, and the sum of flexibility and security benefits. It is constrained to AC optimal power flow, flexibility and voltage security constraints, operation model of sources and storages, and demand response. The second layer concerns coordination between the MG operators and the SDN operator. Its formulation is the same as that of the first layer, except that the HFSO model is used in the SDN according to MGs power daily data obtained from the first layer problem. The strategy converts the mixed-integer nonlinear programming to linear one to obtain the optimal solution with low calculation time and error. Moreover, stochastic programming models the uncertainties of load, energy price, and renewable power. Eventually, numerical results confirm the capability of the scheme to improve technical and economic indices simultaneously. As a result, by expecting the optimal operation for sources, storage, and responsive loads, it succeeded to enhance energy loss, voltage profile, and voltage security of the mentioned networks by up to 30%, 22%, and 5%, respectively, compared to power flow studies. In addition, there was enhancement in economic and flexibility status of the SDN and MGs.

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 paper presents a method for coordinated network expansion planning (CNEP) in which the difference between the total cost and the flexibility benefit is minimized. In the proposed method, the generation expansion planning (GEP) of wind farms is coordinated with the transmission expansion planning (TEP) problem by using energy storage systems (ESSs) to improve network flexibility. To consider the impact of the reactive power in the CNEP problem, the AC power flow model is used. The CNEP constraints include the AC power flow equations, planning constraints of the different equipment, and the system operating limits. Therefore, this model imposes hard nonlinearity onto the problem, which is linearized by the use of first-order Taylor’s series and the big-M method as well as the linearization of the circular plane. The uncertainty of loads, the energy price, and the wind farm generation are modeled by scenario-based stochastic programming (SBSP). To determine the effectiveness of the proposed solution approach, it is tested on the IEEE 6-bus and 24-bus test systems using GAMS software.

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 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.

In order to increase energy efficiency, the energy hub is considered as a form of aggregator and coordinator of various resources and storage. With the optimal performance of resources and storage generators based on a proper energy management system, it is expected that hubs can gain financial benefits from energy markets and ancillary services. So, the paper presents the participation of networked energy hubs in day-ahead (DA) reserve regulation and energy markets, where the hub operator incorporates a coordinated energy management (CEM) strategy to manage power sources and energy storage devices within the hub. Hence, this problem maximizes the total profit of hubs in the DA energy and up and down reserve markets. Also, the problem is constrained by optimal power flow (OPF) constraints in gas, electricity, and thermal networks, reserve limits, and hub constraints, including the model of the combined heat and power (CHP), renewable energy source (RES), electrical/thermal storage, parking lots of electric vehicles (EVs), and boiler. Following that, a linear format is obtained for the nonlinear equation using traditional linearization methods so that an optimal solution is found in less time considering less computational error. Eventually, a standard case system is used to test the strategy, and thus, the capabilities of the approach are investigated. The obtained findings validate the potential of the proposed design in enhancing the economic situation of power sources and storage in hub form, which can enhance operation indices by optimal management of the hub so that the energy management of resources and storage in the form of a hub based on CEM compared to their independent management plan has been able to increase the profit of these elements in energy and up and down reserve markets by about 17%, 28%, and 15%, respectively. Regarding technical indices of energy networks, the proposed scheme by creating low energy losses in the gas network and providing pressure drop, overvoltage, and overtemperature within their permissible limits succeeded in reducing the energy losses in electricity and heat networks by about 83% and 38%, respectively, compared to power flow studies. Also, in these conditions, it has reduced the maximum voltage and temperature drop by 45% and 39%, respectively.