In the deregulated electricity market, as covered in Reference [19], knowing the rival GenCos is helping to gain maximum benefit, which is performed by a grey wolf optimizer (GWO) algorithm. Another meta-heuristic solution called whale optimization algorithm (WOA) was proposed in Reference [15], to obtain the maximum profit by determining the optimal bidding strategy. Moreover, the authors of Reference [20] have taken into account the IGDT procedure to distinguish the risk-averse or risk-taker of bidding strategy for price-taker GenCos in the uncertain conditions day-ahead energy and reserve electricity markets. The authors of Reference [21] presented a robust framework for micro EH (mEH) combined with gas-fired GenCos with analyzing the impacts of integrated demand-response program (IDR) and hydrogen storage system (HSS) technologies. In Reference [22], a renewable-based power generation multi-objective robust scheduling methodology was presented, to lessen the effects of uncertainties on the proposed system's stable operation. A bi-level hierarchical decision-making was published in Reference [23], to specify the character of DRA and GenCos bidding strategy on the adaptability of loads. An upper level of the proposed problem in Reference [23] is minimizing generation costs, as well as the cost of demand curtailment. Meanwhile, the lower level aims at determining the optimal demand response (DR) quantity and prices of demand curtailment from various aggregators. The second category of analyzed works is related to the effects of P2G conversion technology on the integrated energy systems. In Reference [2], a best-coordinated optimal scheduling tool between integrated power and natural gas networks (IPGNs) equipped with a P2G facility was represented. Moreover, a market equilibrium-based game theory model was presented in Reference [2], to study these effects on the optimal dispatch of IPGNs. The linearized constraints of both electricity and natural gas systems were constructed in Reference [3], in which the impacts of deploying P2G facilities on daily economic scheduling were investigated. Moreover, Reference [24] focused on the risk-averse approach, which was modeled as an improved conditional value at risk (CVaR) to handle the uncertainty associated with wind and solar power units. A two-stage robust scheduling method was introduced in Reference [25] for the IPGNs equipped with P2G and hydrogen compressed natural gas (HCNG) technologies, to decrease operational risk and increase the system's stability in the worst cases. In Reference [26], a two-stage multi-objective stochastic unit-commitment approach was proposed for IPGNs, in which flexible energy devices as P2G facility and DR are embedded to lessen the environmental gas emissions and operating costs. The optimal scheduling structure of an EH was reported in Reference [27]; it was equipped with GenCos and multi-carrier energy storage systems, i.e., a P2G system, in order to demonstrate the effectiveness of the P2G system on the operation costs. In Reference [15], the valuable outcomes of the P2G facility and compressed air energy storage (CAES) system on reducing the RESs' intermittency and operation costs of the proposed EH were indicated. In addition to the RESs' uncertainty, the fluctuations of the electricity prices and various demands were considered in Reference [15], where CVaR was utilized to analyze the risk of the introduced strategy. A probabilistic optimal scheduling framework of a viable P2G-based IPGNs was presented in Reference [28], in which load shifting-based DR programs were taken into account. In Reference [29], a probabilistic power flow methodology was established for IPGNs coupled with the P2G system and wind power, and it indicated that the P2G system decreases the impacts of wind power on the security of the power system. In Reference [14], a hybrid bi-level IGDT-stochastic co-optimization framework for IPGNs was proposed, in which gas demand, electrical demand, and wind-power generation were considered as uncertain parameters.