## 1 Introduction  
In recent years, along with the growth of global demand for electricity, concerns over the growing levels of air pollution and greenhouse gases, and, consequently, the causal link to health issues and global warming have increased considerably. Such concerns resulting from international agreements, such as the Paris Climate Accord of 2016, have led to the promotion and rapid growth in the use of renewable energy devices, especially in the electricity sector. This growth has benefited from the improvement of cost-oriented competitiveness of innovative technologies, groundbreaking, committed policies and better access to financing sources. In addition, issues around energy security, environmental concerns, and an increase of energy demand in developing countries and emergingeconomies, as well as the need for access to new energies, have also played a part in growing the sector [1; 2; 3]. To this end, it is vital to design a comprehensive model to reduce the costs regarding consumers and producers, as well as lessening the greenhouse gases emission. In fact, an appropriate demand response (DR) model as the most cost-effective and reliable solution can simultaneously reduce the total costs and air pollution, by changing the customers' power consumption and increasing the participation of renewable resources in H-MGs energy consumption. The DR programs are mainly divided into two distinct categories:  
* Time-based programs are generally utilized to usage over broad blocks of hours. Time-base programs methodologies include Time of Use (TOU), Real-time Pricing (RTP), Critical Peak Pricing (CPP), Critical Peak Rebates (CPR), etc. * Incentive-based programs encourage the consumers to alter their energy consumption by proposing the incentives programs offered by the energy provider. The methods include Direct Load Control (DLC), Interruptible/curtail able service (I/C), Emergency Demand Response Program (EDRP), Ancillary Service Markets (A/S), etc. In the following, a brief overview of literature, and then the flaws of existing solutions and the value proposition of the proposed model are described. H-MGs, based on renewable technologies, include distributed energy resources, renewable sources, and ES system; the latter can work in two interconnected or detached modes [4; 5; 6]. Combining H-MGs together to create a local structure allows each H-MG to form a coalition and thereby share the power demand and supply in a co-ordinated manner [7; 8; 9; 10]. This coalition will increase the productivity of renewable equipment along with load response, reliability and, most importantly, energy independence, all of which ultimately reduce the production of pollutants derived from generation units based on fossil fuels [11]. Although electrical energy management strategies with distributed energy resources and ES system at the H-MG level have been extensively studied, there are few studies about neighboring and the local interactions. In [12; 13; 14; 15], the authors proposed a novel approach regarding local energy interactions for the distribution system in which prosumers are persuaded by different balancing premiums to balance their electricity in a local community. Price-responsive generation and demand of an individual prosumer are affected by his/her attitudes and inherent characteristics. In [16; 17], EMS based on distributed energy resource is considered, however there is no energy exchange between H-MGs, and consumers of any H-MG are not able to access the sources of other H-MGs. In the present study, H-MGs can share their electrical/thermal power surplus/shortage with other H-MGs, thereby allowing the exchange of power. Each of the H-MGs that have surplus power can display the amount of power along with the price it is willing to accept, offering it for sale among other market players in load energy market. Alternatively, each H-MG that is experiencing a lack of power can make the other players in the load energy market aware of the amount of power required along with the price that it is willing to pay.