Multi-objective IGDT-based scheduling of low-carbon multi-energy microgrids integrated with hydrogen refueling stations and electric vehicle parking lots

https://doi.org/10.1016/j.scs.2021.103197Get rights and content

Highlight

  • A novel low-carbon CHHP-MG is presented by considering FC-MCHP units, HFs and EVPLs.

  • The P2H, H2P and P2HT technologies are incorporated to minimize the operation cost.

  • A multi-objective IGDT is proposed to model wind and PV power uncertainty simultaneously.

  • The augmented ε-constraint approach is utilized to solve the multi-objective problem.

Abstract

There is little room for doubt that distributed generation systems including renewable energy, microgrids (MGs), combined heat and power (CHP) units and storage systems have been of particular importance in sustaining low-carbon and cost-effective operations due to the tremendous increase in greenhouse gas emissions in recent years. Additionally, hydrogen-based power technologies have earned a great deal of publicity that hydrogen can serve as a zero-emission fuel for electrical power and thermal energy production. In this regard, the current paper proposes an optimal energy management strategy for a combined hydrogen, heat, and power MG (CHHP-MG) with hydrogen fueling stations (HFSs) for hydrogen vehicles (HVs), electric vehicle parking lots (EVPLs) and fuel cell micro-CHP (FC-MCHP) units to meet power and heat requirements. In order to reduce the regular operating expense, the presented CHHP-MG could also communicate with both electricity and hydrogen markets. In addition, to compensate for the associated heat and hydrogen requirements, power-to-X technologies such as the power to heat (P2HT) and power to hydrogen (P2H) are integrated. In order to improve flexibility and build a low carbon MG, multi-energy storage (MES) system along with heat and power demand response (HPDR) programs will be taken into consideration. As the uncertainties associated with the predicted wind and photovoltaic power have a major impact on the energy management of the CHHP-MG, a multi-objective information gap decision theory (IGDT)-based robust approach is applied as an effective non-probabilistic modeling technique for handling such uncertainties. The empirical results show that the proposed model can efficiently handle the uncertainties and reduce the overall operation cost by 76.35%.

Introduction

Global concerns about the upward trend in greenhouse gas emissions have driven researchers to look for low-emission energy sources or even zero-emission. It has been stated by the International Energy Agency (IEA) has reported that various countries are planning to obtain net-zero emissions by 2050 (1). In order to ensure environmentally sustainable power generation, distributed generation (DG) systems, including renewable energy (RE) and energy storage systems (ESS), have been implemented. On the other hand, the development of stable and flexible power markets in the modern power system is an essential criterion in today's world. Recently, the microgrid (MG) concept has emerged to address these challenges. Although an MG can basically encompass a gas turbine, the researchers are attempting to replace it with cleaner power sources, e.g., solar and wind, as well as other emerging technologies, such as storage systems, power-to-X (P2X) and vice versa (X2P) technologies in order to move towards the zero-emission and cost-efficient power system (Yang et al., 2019, Agabalaye-Rahvar et al., 2020).

Hydrogen-based power technologies, i.e., the power-to-hydrogen (P2H) and hydrogen-to-power (H2P), have been of particular interest for the transition to a low-carbon society. In particular, fuel cells (FCs) have often been used as a form of H2P source in multi-energy MGs (MEMGs), in which hydrogen and oxygen reactions produce electricity, heat, and water. Generally, electrodes, namely anode and cathode, and electrolyte are the main parts of an FC, and the chemical reaction that results in electricity production can be described by (Savrun & İnci, 2021, Aygen & İnci, 2020):H2+12O2H2O+Electricity

On the other hand, the processing and storage of hydrogen from electricity is possible using various methods, e.g., by an electrolyzer (Pan et al., 2020). Another source of effective, reliable, and versatile operation of MEMGs is the combined heat and power (CHP). The CHP units are utilized to meet for power and heat demands simultaneously and increase system efficiency (Nasiri et al., 2020, Mirzaei et al., 2020). This technology is an opportunity to efficiently generate heat in the power production process and meet heat demands. Applying FC in a CHP-based MG leads to implementing an idea of the so-called FC-MCHP, which is a highly efficient combination of energy technologies in recent years (Cinti et al., 2019). As the input, an FC-MCHP consumes hydrogen and generates power and heat to meet the intended demands.

It may be of great interest to note that population growth in recent decades has led to a huge increase in the emission of pollutants from the transport sector, which is driven by fossil fuels (Tao et al., 2020). In addition, researchers have concentrated on low-carbon, and economic alternatives, especially hydrogen and electricity, so the application of hydrogen vehicles (HVs) and electric vehicles (EVs) have been accelerated (Lekvan et al., 2021). Accordingly, developing hydrogen fueling stations (HFSs) to provide effective infrastructures for refueling the HVs has received significant attention in recent years. On the other side, electric vehicle parking lots (EVPLs) as the units for aggregating and managing EVs have attracted much attention. EVs include several batteries to store electrical energy and can perform in the vehicle-to-grid (V2G) mode as a flexible resource (İnci et al., 2021). In addition, hydrogen performs as the main fuel of HVs without any pollutant emission. Decarbonization is more prominent when the required hydrogen is produced from renewable sources, i.e., green hydrogen (Huang & Liu, 2020). Thus, the utilization of HVs and EVs contributes effectively to the cost-efficient and environment-friendly operation and the reliable and flexible performance of power systems (Zhang et al., 2020, Khalatbarisoltani et al., 2020). Based on the criteria mentioned above, introducing a novel CHHP-MG with HVs and EVs is an important and crucial step towards the low-carbon and economic power system.

The CHHP-MG concept is an innovative notion that is not investigated widely in previous works, and our concise literature review unveils the main research gaps. As stated, the application of MGs has been increased owing to economic and environmental benefits. In (Kumar & Tyagi, 2020), the optimal planning and operation of an MG have been presented, and the stochastic optimization method was used to model the uncertainties associated with solar power and load demand. A multi-objective two-stage stochastic approach has been proposed in (Hemmati et al., 2021) for optimal scheduling of reconfigurable MGs with RE to participate in energy markets. Authors in (Faraji et al., 2020) have proposed a hybrid machine learning approach for weather and load forecasting in the daily scheduling of prosumer MGs. In the mentioned study, the demand response (DR) program and the degradation cost of the battery have been taken into account. Moreover, MEMGs have received a great deal of attention recently. Reference (Xu et al., 2020) investigates the peer-to-peer resource trading scheme for a MEMG with electricity and biogas. Wind power accommodation framework for a MEMG considering electricity, heat and gas has been proposed in (Jiang & Guo, 2019) with power to gas (P2G) facility, in which the numerical results confirmed the model's effectiveness from an economic perspective. In (Yang et al., 2020), the operation optimization problem of a combined cooling, heating and power MG with power-to-gas (P2G) has been presented, taking into account the uncertainties. The downside risk constraints technique was used to examine risk analysis of a MEMG based on environmental and economic parameters in (Faraji et al., 2020). The uncertainties in this analysis were controlled using stochastic optimization, and the DR program has also been used. A multi-objective optimization framework for a MEMG considering the economic and environmental costs and the uncertainty of wind power has been presented in (Tan & Chen, 2019), and the results pointed out the benefits of the presented model.

Due to profit maximization, it is notable that exploiting EVs in renewable-based power systems has got a lot of attention (Sadati et al., 2019). The hybrid IGDT/stochastic method has been utilized in a transmission-constrained unit commitment of power system with EVs, wind power, and DR program in (Ahrabi et al., 2020). The effect of plug-in EVs and DR on flexibility in optimal bidding strategy for a smart grid has been investigated in (Afzali et al., 2020). To deal with volatile variables, the stochastic decision-making model has been considered in the previous work. A mixed-integer linear programming scheme for risk analysis of EVPLs with renewable sources and hydrogen storage system has been presented in (Cao et al., 2020). An optimal bidding strategy for EVPLs with a hydrogen storage system and DR program has been presented in (Liu et al., 2020), in which the uncertainty of power price has been modeled via the IGDT approach. The optimal scheduling of a multi-energy system with EVPL and CHP unit has been investigated in (Zafarani et al., 2020), and the results have illustrated that by implementing the proposed model, the demand power at peak-load periods decreases. The stochastic energy management of a smart MEMG with FC, EV and CHP has been assessed in (Gong et al., 2020). The numerical results have demonstrated that the model positively impacts the system's performance at high-load periods.

Since the hydrogen-based facilities contribute satisfactorily to zero-emission society, several research studies to generate hydrogen from renewable sources have been conducted in recent years (Wu et al., 2019, Bornapour et al., 2017). Authors in (Wu et al., 2019) have proposed optimal scheduling for MGs with HFSs considering several uncertainties, e.g., renewable generation, electrical and hydrogen demands. Besides, in (Bornapour et al., 2017), the stochastic scheduling of a MEMG with wind and PV power as well as FC and hydrogen storage has been presented in a market environment. To manage the uncertainty of wind power and electricity price, a novel hybrid robust/stochastic cost optimization framework for a MEMG that interacts with power, gas, heat and hydrogen markets considering MES and HPDR program has been presented in (Mansour-Saatloo et al., 2020). Optimal robust scheduling of a multi-energy hub including power, hydrogen and heat demands has been investigated in (Mansour-Saatloo et al., 2020), in which the MES and HPDR have been taken into account, and the simulation results revealed the impact of the proposed model on decrementing the operation cost and improving robustness against electricity market uncertainty. Optimal scheduling of interconnected MGs with hybrid ESS and FC has been presented in (Garcia-Torres et al., 2018), in which the degradation causes of ESS have been taken into account. In (Elgamal et al., 2020), a day-ahead energy management scheme for a MEMG with RE, ESS, and FC has been presented considering RE and load uncertainty. The impact of HVs on the operating costs of an islanded MG has been examined in (Alavi et al., 2019), and the numerical findings are indicative of cost minimization. The results of an experimental project with building-integrated PV and hybrid hydrogen-electric vehicles (HHEV) in a residential MEMG have been reported in (Robledo et al., 2018). In (Li et al., 2020), a cost optimization framework for HHEVs has been proposed considering the degradation costs of FC and battery and the cost of hydrogen. Reference (Tao et al., 2020) presents an energy-sharing framework for electricity and hydrogen to maximize social welfare considering HHEVs. Authors of (Xiong et al., 2019) have researched on an energy matching method for HVs and EVs energy systems and the results validated the effect of the proposed model on improving the energy matching and energy efficiency of such systems. Daily scheduling of an MG that communicates with external entities in the local energy market with HVs and EVs has been investigated in (Garcia-Torres et al., 2018). In (Xu et al., 2020), the optimal operation of a hybrid hydrogen/electricity refueling station, which is supplied only by PV power, is presented with HVs and EVs. In the forenamed study, the fluctuations associated with PV output, electricity price, electricity, and hydrogen demands have been handled via the stochastic optimization approach.

Hydrogen-based innovation has an undeniable effect on the transition to low-carbon energy systems. While several research studies for incorporating hydrogen-based technologies into the power systems and MGs have been presented, there are several flaws and research gaps that need to be addressed in order to obtain efficient models.

  • In some studies that consider the optimal scheduling of hydrogen, heat and power, the exploitation of the FC-MCHP unit has been ignored (Kumar & Tyagi, 2020), (Yang et al., 2020), (Cao et al., 2020), (Liu et al., 2020), (Zafarani et al., 2020), (Gong et al., 2020), (Wu et al., 2019).

  • The impact of HPDR and MES on the cost and pollutant emission of MGs have not been investigated extensively in many of the above-reviewed works (Yang et al., 2020), (Zafarani et al., 2020), (Robledo et al., 2018), (Xu et al., 2020), (Tang et al., 2020).

  • There are some papers that have not considered uncertainties in their scheduling models, while it has a great impact on the decision-making process (Garcia-Torres et al., 2018), (Robledo et al., 2018). Also, there are plenty of works which have used probabilistic and scenario-based tools for uncertainty modeling; however, such methods need probability distribution factors (Kumar & Tyagi, 2020), (Cao et al., 2020), (Xu et al., 2020), (Tang et al., 2020).

This paper proposes an optimal self-scheduling model for a novel CHHP-MG with an FC-MCHP unit, the P2HT technology, HVs, and EVs, as well as HFSs and EVPLs. Additionally, the P2H technology is utilized to provide the required hydrogen for HFSs from wind and PV sources. In particular, integrating HFS into a low-carbon CHHP-MG is one of the main contributions of this work that has not been studied extensively in previous studies. An MES includes the power, heat, and hydrogen storage system also considered. Since the DR programs positively impact the economic and cleaner operation of power systems, an HPDR program is implemented to modify the heat and power load profile and peak-load sharing. Furthermore, since the output power of wind and PV sources time, a high-performance uncertainty modeling technique is required. In line with this issue, the IGDT method is described as a non-probabilistic approach with a robust strategy for managing the uncertainties associated with both wind and PV power. The main contributions of this paper are as follows:

  • I

    A novel low-carbon CHHP-MG with FC-MCHP units is presented, considering the HFSs for HVs and EVPLs for EVs. The FC-MCHP unit consumes hydrogen as a zero-emission fuel and produces power and heat to compensate for the associated demands. As a result, this technology increases the potential of using P2H technology and leads to introducing a modern low-carbon CHHP-MG.

  • II

    The P2H, H2P and P2HT technologies are incorporated to compensate for the associated electrical, hydrogen and heat demands with various flexibility resources, i.e. MES and HPDR programs. In other words, the opportunity for converting and storing multiple energy carriers in low-price periods to utilize during the high-price periods is provided in a comprehensive manner. Moreover, exploiting HPDR has a strong effect on decrementing the cost and emission of the CHHP-MG by shifting the non-obligatory loads from on-peak to off-peak periods.

  • III

    A multi-objective IGDT-based robust optimization framework is presented to simultaneously model the uncertainties related to wind and PV power. The proposed model has a light computational burden since it does not require the probability distribution function or scenario generation. The augmented ε-constraint approach is utilized to solve the multi-objective problem, and the best compromise answer is selected based on fuzzy decision-making and the min-max method.

The remainder of the paper includes the problem description and formulation in section II, results and discussions in section III, and finally, the conclusion in section IV.

Section snippets

Problem description

According to Fig. 1, the introduced CHHP-MG interacts with electricity and hydrogen markets to compensate for the variety of demands at the lowest possible operating cost. The proposed CHHP-MG mainly consists of a wind farm and a photovoltaic facility, as the aim is to build a low-carbon power system. The P2H technology, which is an electrolyzer, transforms the power produced by these units into green hydrogen. Available hydrogen acts as the main source for HFSs. Based on the electricity and

Results and discussions

The proposed model is verified on a CHHP-MG composed of FC-MCHP units; P2HT technology; P2H and H2P technologies; electrical, heat, and hydrogen storage systems; electric and hydrogen vehicles; wind turbine and photovoltaic panels as depicted in Fig. 1. The CHHP-MG operator can contact operators of the power and hydrogen markets to guarantee the energy balance. In addition, the operator of the CHHP-MG has the authority to shift electrical and thermal loads up to 5% to handle the peak periods

Conclusion

This paper proposed a low-carbon energy management model for a combined hydrogen, heat, and power microgrid (CHHP-MG) with hydrogen refueling stations (HFS) and electric vehicles (EVs) parking lots to satisfy the hydrogen and electric demands of vehicles simultaneously. A fuel cell micro combined heat and power (FC-MCHP) unit was also considered as a zero-carbon novel technology that extends the ability to use power-to-hydrogen (P2H) technology to provide both power and heat at the same time.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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