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In this paper, the formulation of the energy market in two wholesale and retail models for different energy networks such as electric, gas and thermal networks in the presence of energy hubs according to the two-layer energy management system is presented. In the first layer of EMS, the coordination of resources and ALs with the operator of EHs is considered, and in the second layer of EMS, the coordination of the operator of EHs with ENOs is considered. In the proposed design, the mentioned networks have participated in the wholesale market as private distribution companies or DisCo and then buy energy from it. These companies have shared the purchased energy in the retail energy market environment between consumers and EHs connected to itself. This design is expressed in the form of two-level optimization, the upper level of which is the minimization of the expected energy cost of ENs in the mentioned markets, and the other level is the minimization of the expected energy losses of ENs in the retail market. In the following, the Karush–Kuhn–Tucker (KKT) method and Pareto optimization technique based on epsilon constraint method were used to derive the single-level and single-objective problem. Then, the unscented transformation (UT) method was used to model the uncertainties of load, energy price, renewable power and EV energy demand. Finally, based on the numerical results, it was observed that the proposed plan achieves the highest profit for EHs in proportion to the time-varying energy price. Also, with the optimal energy management of EHs, it has been able to reduce the energy cost of ENs by about 12% compared to load flow studies.

This paper investigates a multi-objective optimal energy planning strategy for a hub, incorporating renewable and non-renewable resources, like PV, tidal turbine, fuel-cell, CHP, boiler, micro-turbine, reactor, reformer, electrolyzer, and energy storage by utilizing the time of use program (TOU). In this strategy, tidal turbine, fuel-cell, and reformer technologies are considered novel technologies that simultaneously reduce the proposed hub’s cost and pollution. The hub’s total cost and pollution are considered objective functions. To make the results more realistic, characteristics of the tidal turbine are investigated by utilizing the manufactory’s company information. The problem is then modeled as real mixed integer programming (RMIP) and is solved in GAMS software using a CPLEX solver. Epsilon constraints method and fuzzy satisfying approach are used to select the optimal solution based on the proposed model. Finally, a sensitivity analysis is performed to assess the effective parameters that affect the planning’s results. The results show that the overall pollution is reduced by about 9% by assuming the proposed planning, and the total profit is increased by about 30%.

The present study finds the proper location and size of virtual power plants (VPPs) in distribution networks, while taking into account network resilience to severe weather conditions. This is formulated as an optimization problem with two objective functions to minimize both the planning cost of the VPPs in a year and expected energy not-supplied of the network during natural events like flood and earthquake. The problem is subject to some boundaries, including AC power flow relationships, limits on operating and resilience situation, and planning terms of the VPP. There are also uncertain quantities present in the problem, such as those related to the amount of load, renewable power generation, price of energy, and accessibility of equipment and VPP devices. The uncertainties are modeled using the stochastic programming. The two objectives are merged to be a single objective by adopting Pareto optimization based on ɛ-constraint method. Krill herd optimization (KHO) and grey wolf optimization (GWO) are also combined to introduce a new hybrid algorithm so that a dependable optimal solution that shows low dispersion. Finally, a 33-bus distribution network is used to test the validity of the proposed method. According to the result, the optimal size and site of VPPs are found and the network achieves more suitable economic, operating, and resilience indices.

In this paper, using an algorithm based on the combination of data based on neural network virology and bacterial nutrition algorithm, improves the performance of the neural network prediction method. Also, the selection of two types of downstream and upstream filters in the wavelet transformation increases the predictive efficacy of neurological prediction. Based on the results, the optimized clustered neural network method has a more favorable response than the other methods. By selecting the appropriate filter and multichannel processing method, the maximum error percentage has improved by 15%. However, compared to the neural network prediction method, the proposed method has more computational volume due to the use of wavelet transform and also three times the use of neural prediction. Due to the large number of layers and used neurons, the neural network method has a much higher computational volume than the linear prediction method, where the linear prediction method has a higher error than the proposed method depending on the data used for training.

Due to the pattern of growth for electricity consumption, there is a need for developing power networks and transmission lines. The power transmission capacity of lines is limited due to a host of factors. Thus, these lines need series and parallel compensations to reduce losses, increase efficiency, and promote system security. In this paper, flexible alternating current transmission system (FACTS) devices including static VAR compensators (SVC) as parallel compensators, thyristor-controlled series compensation (TCSC) as a series compensator, and high-voltage direct current (HVDC) bonding are modeled. In addition, comprehensive modeling of the simultaneous application of these three devices for load flow is performed, and the effects of these types of compensations are compared. The obtained comprehensive model was implemented on MATLAB software using the Newton–Raphson method on two 9-bus WSCC and 5-bus test system. In this case, the calculation speed and convergence were reduced when compared to applying devices individually due to the increase in equations and the addition of new terms to the load flow equations. Furthermore, more losses were observed in this model, which can probably be improved using an optimal power flow and optimal placement of the devices in the network.

The widespread use of electric vehicles is a suitable solution to deal with the reduction of fossil fuel reserves and the increase in air pollution. Reducing pollution can be realized through optimal planning of charging and discharging of electric vehicles as consumers and storage of electric energy. Also, vehicles connected to the network increase the reliability of the network during power outages. However, the connection of electric vehicles to the grid can also bring limitations. The uncoordinated and unmanaged presence of electric vehicles as an additional load in the network can aggravate problems such as voltage drop, voltage stability and increase in network losses. Consequently, these problems will have economic costs. In this way, the management of charging and discharging of electric vehicles will be of special importance in the future of control and operation of distribution networks. In this article, electric vehicle charging and discharging management has been done in order to minimize the amount of active power loss, bus voltage deviation, increase the stability of the network voltage, and as a result, reduce the costs of the distribution company. For this purpose, a 33-bus distribution network that absorbed its required energy only through the connection point to the network was studied with different scenarios in which the objective function of the problem was different. The proposed algorithm was implemented to calculate the best state of charging and discharging of cars. In choosing these vehicles, it has been tried to examine all charging modes. The simulation results show the effectiveness of the proposed plan for planning the development of electric vehicles in the distribution network.

Microgrids have faced an increasing penetration rate of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP), and storage systems. These elements need to be optimally scheduled so that the optimal operation of the microgrid is obtained. This study employs a novel random structure to optimally manage energy in microgrids which contain proton exchange membrane fuel cell-combined heat and power (PEMFC-CHP), RERs, PHEVs, as well as storage devices. The aim is to take into account the uncertainty of PHEVs and RERs models, in which Monte Carlo Simulation (MCS) is incorporated. The hydrogen storage strategy for PEMFC-CHP units is also used in this study, this strategy is considered by a mixed integer nonlinear programming (MINLP) problem. Moreover, smart charging plans are utilized to charger PHEVs. The objective function aims to maximize the market profit. This paper uses the modified adaptive differential evolution (MADE) technique for analyzing the optimal operation of the microgrid, where the intermittent behavior of uncertainty parameters is investigated. Differential evolution (DE) adopts an iteration-based strategy to enhance a candidate solution using a quality criterion and optimize the problem. Moreover, the algorithm is modified in order to enhance its search capability to be able to search and find local and global points. A conventional test system is implemented for verifying the efficiency of the suggested strategy and various planning durations are considered. A comparison is also made between this method and its counterparts for various situations and conditions.