Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.

This article proposes a novel platform for microgrid (MG) prosumers that can actively trade energy with the power grid and each other directly. This platform provides Peer-to-Peer (P2P) energy trading among MG prosumers to achieve a win–win outcome in the presence of emerging energy resources, such as electric vehicles (EVs), energy storage systems (ESSs), and demand response programs (DRPs). P2P energy trading, from the power system perspective, can facilitate energy balance locally and self-sufficiency. Despite the conventional noncooperative game that players act individually, prosumers can cooperate in exchanging energy and reaping economic benefits in the proposed model. To this end, a bargaining cooperative game is adopted due to MGs autonomy and self-interest. Besides, to avoid prosumers’ private data launching, the fast-alternating direction method of multipliers is introduced to solve MGs' energy management problem in a decentralized manner. A robust optimization method is also applied to manage electricity market uncertainty, allowing MGs operators to decide how much risk they want to consider by adjusting the uncertainty budget parameter. The numerical results show that MGs can actively trade with each other and achieve economic benefits. Moreover, many of the utilized technologies such as ESS, DRP, and EVs assist MGs in sharing more energy with each other.

Despite the significant merits of plugin electric vehicles (PEVs) and fuel cell electric vehicles (FCEVs), their enormous rise has posed serious challenges in terms of energy supply. This research proposes a sustainable energy platform to integrate PEVs and FCEVs under the concept of microgrid (MG) with power and green hydrogen as energy carriers, so-called integrated power, and hydrogen MG (IPHMG). In addition, a decentralized market clearing model is developed to enable the nearby IPHMG to interact in a local energy market (LEM). The LEM is run by a central manager for both power and hydrogen. To do so, the mobile edge computing (MEC) system is proposed to move the computation from a central cloud server to decentralized edge servers, causing to reducing the computational burden and increasing data security and privacy. Moreover, the fast alternating direction method of multipliers (fast-ADMM) is employed to decompose the problem. Further, this work advances the state-of-the-art in uncertainty management by introducing a tri-level uncertainty controlling approach to fully consider the uncertain parameters. According to the obtained results, the community of IPHMGs could reduce 5.86% of total cost by trading energy in the LEM and the presented hybrid uncertainty management model could protect the community against uncertainties by reducing the total cost by 8.08%.

Autonomous electric vehicles (A-EVs), regarded as one of the innovations to accelerate transportation electrification, have sparked a flurry of interest in trajectory planning and charging scheduling. In this regard, this work employs mobile edge computing (MEC) to design a decentralized hierarchical algorithm for finding an optimal path to the nearby A-EV parking lots (PLs), selecting the best PL, and executing an optimal charging scheduling. The proposed model makes use of unmanned aerial vehicles (UAVs) to assist edge servers in trajectory planning by surveying road traffic flow in real time. Furthermore, the target PLs are selected using a user-driven multiobjective problem to minimize the cost and waiting time of A-EVs. To tackle the complexity of the optimization problem, a greedy-based algorithm has been developed. Finally, charging/discharging power is scheduled using a local optimizer based on the PLs’ real-time loads, which minimizes the deviation of the charging/discharging power from the average load. The obtained results show that the proposed model can handle charging/discharging requests of on-move A-EVs and bring fiscal and nonfiscal benefits for A-EVs and the power grid, respectively. Moreover, it observed that user satisfaction in terms of traveling time and traveling distance is increased by using the edge-UAV model.

The energy hub (EH) concept is an efficient way to integrate various energy carriers. In addition, demand response programmes (DRPs) are complementary to improving an EH's efficiency and increase energy system flexibility. The hydrogen storage system, as a green energy carrier, has an essential role in balancing supply and demand precisely, similar to other storage systems. A hybrid robust‐stochastic approach is applied herein to address fluctuations in wind power generation, multiple demands, and electricity market price in a hydrogen‐based smart micro‐energy hub (SMEH) with multi‐energy storage systems. The proposed hybrid approach enables the operator to manage the existing uncertainties with more flexibility. Also, flexible electrical and thermal demands under an integrated demand response programme (IDRP) are implemented in the proposed SMEH. The optimal scheduling of the hydrogen‐based SMEH problem considering wind power generation and electricity market price fluctuations, as well as IDRP, is modelled via a mixed‐integer linear programming problem. Finally, the validity and applicability of the proposed model are verified through simulation and numerical results.