Synergizing Gas and Electric Systems Using Power-to-Hydrogen: Integrated Solutions for Clean and Sustainable Energy Networks

Highlights

What are the main findings?

• This study compares different operational scenarios of Power-to-Hydrogen (P2H) and renewable energy sources (RESs) integration in coupled power and gas networks using an optimal day-ahead operation approach.
• The proposed models minimize total operational costs by optimizing the interaction between power and gas networks, reducing curtailed RESs power through hydrogen conversion, and addressing seasonal energy demands, filling a gap in existing research that mainly focuses on hydrogen integration within the electrical system alone.

What is the implication of the main finding?

• The findings highlight that co-optimizing gas and power systems through Power-to-Hydrogen (P2H) integration can significantly enhance energy efficiency, reducing total operational costs by up to 20% and curtailing renewable energy wastage by over 50%, thereby improving overall system reliability and resilience.
• By mitigating cascading outage risks and strengthening energy security, the proposed approach supports the transition to clean and sustainable energy systems, aligning with multiple UN Sustainable Development Goals (SDGs) such as affordable and clean energy (SDG 7), resilient infrastructure (SDG 9), and climate action (SDG 13).

Abstract

The rapid growth in natural gas consumption by gas-fired generators and the emergence of power-to-hydrogen (P2H) technology have increased the interdependency of natural gas and power systems, presenting new challenges to energy system operators due to the heterogeneous uncertainties associated with power loads, renewable energy sources (RESs), and gas loads. These uncertainties can easily spread from one infrastructure to another, increasing the risk of cascading outages. Given the erratic nature of RESs, P2H technology provides a valuable solution for large-scale energy storage systems, crucial for the transition to economic, clean, and secure energy systems. This paper proposes a new approach for the co-optimized operation of gas and electric power systems, aiming to reduce combined operating costs by 10–15% without jeopardizing gas and energy supplies to customers. A mixed integer non-linear programming (MINLP) model is developed for the optimal day-ahead operation of these integrated systems, with a case study involving the IEEE 24-bus power system and a 20-node natural gas system. Simulation results demonstrate the model’s effectiveness in minimizing total costs by up to 20% and significantly reducing renewable energy curtailment by over 50%. The proposed approach supports UN Sustainable Development Goals by ensuring sustainable energy (SDG 7), fostering innovation and resilient infrastructure (SDG 9), enhancing energy efficiency for resilient cities (SDG 11), promoting responsible consumption (SDG 12), contributing to climate action (SDG 13), and strengthening partnerships (SDG 17). It promotes clean energy, technological innovation, resilient infrastructure, efficient resource use, and climate action, supporting the transition to sustainable energy systems.

1. Introduction

The term de-carbonization has recently been promoted due to global warming and climate change. In addition, greenhouse gas emissions have increased due to the expansion of the industrial field and the dependence of power generation on fossil fuels. De-carbonization is already progressing in electricity generation due to the promotion of renewable energy sources (RESs). Recently, the penetration of RESs has increased dramatically due to increased concerns about the reduction in fossil fuel and other severe global issues related to greenhouse gases emissions of such as environmental pollution, ozone hole expansion, and gas escalation [1,2,3,4,5,6]. There are multiple renewable energy sources, such as solar energy, bioenergy, wind power, hydropower, tidal power, geothermal energy, and hydrogen gas [7,8,9,10,11,12,13,14]. This type of energy has multiple advantages: it is well-renewable and will never run out; it is clean; and it has a negligible impact on our planet. It is a reliable energy source and is considered cheaper than other forms of energy. This system makes a major contribution to the Sustainable Development Goals (SDGs) [15,16]. Promoting RESs supports SDG 7 by ensuring access to affordable, reliable, sustainable, and modern energy. Advancing decarbonization technologies fosters innovation and builds resilient infrastructure, aligning with SDG 9. Utilizing clean energy reduces environmental pollution, contributing to more sustainable and resilient urban areas, supporting SDG 11. Renewable energy promotes sustainable consumption and production patterns, reducing reliance on fossil fuels, in line with SDG 12. Decarbonization directly addresses climate change by reducing greenhouse gas emissions and mitigating global warming impacts, supporting SDG 13. Finally, the global transition to renewable energy requires cooperation and partnerships across nations and sectors to achieve sustainable development goals, aligning with SDG 17. While this study focuses on large-scale hydrogen integration into energy systems, it is worth noting that decentralized energy systems and microgrids also play a critical role in enhancing resilience, especially in remote or disaster-prone regions. These systems can offer localized, cost-effective alternatives or complements to centralized hydrogen infrastructure by reducing transmission losses and improving energy autonomy. However, increased reliance on hydrogen as a key energy vector may raise new geopolitical and energy security concerns. Nations without sufficient renewable resources or hydrogen production capacity could become dependent on hydrogen imports, leading to new forms of energy interdependence. Ensuring secure and diversified hydrogen supply chains through regional cooperation and investment in domestic production is therefore critical. On the other hand, it has some disadvantages as it relies on the weather conditions (sunshine, wind, rain) and lacks capacity in bad wind conditions, has difficulty generating large quantities, has expensive operational costs, and requires large areas for the plants [17,18].

To account for the RES generation’s variability and uncertainty, a suitable strategy must be in place. Natural gas-fired generation plants were a practical solution to the aforementioned problem because they are more effective at handling pollutant gases than coal-fired plants and can reduce gas emissions by up to 60%. Due to their quick start-up times and high ramp rates, these units can handle variations in RES generation [19,20]. A major remedy for the intermittent nature of RESs is energy storage. Power-to-Hydrogen (P2H) technology is one of the most beneficial and promising solutions that could handle this issue compared to the other technologies, e.g., batteries, especially in large-scale applications [21,22]. Using P2H technology in an electrical system can help to alleviate renewable curtailment by converting and storing hydrogen [23]. Also, it offers unique properties such as low cost, long life, flexibility in energy use, high scalability, seasonal storage, high capacity, and high transmission capability [24,25]. In future energy systems, the P2H technology will play a significant role in which the excess of both renewable energy and conventional energy sources can be stored as a gas using an electrolyzer: this represents a long-term electricity storage system [26]. In addition, recent studies have demonstrated the economic and technical benefits of integrating electric vehicle charging stations, renewable energy sources, and compensators into distribution power grids using modern metaheuristic algorithms such as the equilibrium optimizer and secretary bird optimization algorithm. These findings support the need for advanced planning approaches in integrated energy systems, including hydrogen-based solutions [27]. Recent research has also shown the potential of advanced material engineering—such as multilayer-modified biochar adsorbents—for improving system regeneration and environmental performance in energy applications [28].

Electricity and natural gas utilities in recent decades were operated under separate management systems. Nowadays, natural gas facilities consume a conservable amount of electrical energy due to natural gas nature as the available cheap fuel source and the promotion of the gas–fuel generation units in the electricity production sector. Recently, this interdependency of natural gas and electricity systems has increased. More invented systems enrich this relation on multiple levels for both systems to obtain the maximum benefit of characteristics of both types of power sources [29].

The integration of electricity and gas grid systems has been significantly studied in the latest research and publications in recent years. It focuses mainly on the co-optimization modeling of power and gas in such networks, together with P2H and gas-to-power (G2P) processes. In addition to the penetration of RESs as a source for producing electricity, such studies aim to reduce the total operating costs, increase the system’s flexibility, and move forward into a clean environment with fewer carbon emissions. To satisfy the increased demand for hydrogen, the conversion of electricity to hydrogen would be an innovative idea to study and implement.

To compensate for hydrogen’s seasonal reduction and imbalance, a hydrogen supply chains–electric network (HSC-EN) technology was used [30]. Moreover, the operation of HSC and EN was uniform by applying the uniform hierarchal time discretization method. Furthermore, an industrial and illustrative system in Sichuan Province was chosen to apply an optimal investment model for electrolyzers. Herewith, the results have shown that EN can transfer energy via space. Also, an increase in electrolyzer utilization was noticed, roughly 10% more than usual. In addition, hydrogen’s transportation cost was reduced as electrolyzers and hydrogen storage were located in regions closer to hydrogen demand. The economic studies and observations showed an increase in the net present value (NPV) during ten years with the proposed HSC-EN caused by decreased investment and transportation costs.

In the work by Qiu, Dong, Zhao, Xu, Zheng, Li and Wong [31], the natural gas storage system as a backup option was investigated to store the excessive gas during the off-peak hours and resupply in the network during the peak demand hours. The results show an increase in the total operation cost compared to DC-OPF, but a more accurate model related to the power flow has been provided. Also, the use of natural gas pipes congestion in the scheduling of the generating units led to an increase in the total operational cost. In contrast, the natural gas storage system reduced total operating costs. In the work by Mirzaei, Nazari-Heris, Mohammadi-Ivatloo, Zare, Marzband and Anvari-Moghaddam [32], the integration of the electricity network, natural gas network, and wind energy was studied using information gap decision theory (IGDT) without the need for a probability density function to minimize the operation cost. The gas load uncertainty for residential consumers has an enormous impact on operation costs and the power dispatch of gas-fired plants due to its priority compared to a gas-fired unit’s load. The demand response program and P2G technology were essential to decrease the operation cost and increase the wind power dispatch. The outcomes of the study were as follows: integrating the various technologies decreases the operational costs compared to considering each technology individually. In addition, the emergence of flexible technologies increases the penetration of wind power in power systems. It contributes to high profits by considering the risk against the uncertainty in the gas network at a lower cost.

To improve the overall energy infrastructure, the gas pipelines, gas power plants, and electricity transmission lines must be co-planned to align their conflicting objectives. To reach higher social welfare, various uncertainties in each plant must be considered to achieve the optimum cost, such as fuel cost, demand growth, and other financial constraints. Sequential importance sampling (SIS) was used in the work by Qiu, Dong, Zhao, Xu, Zheng, Li, and Wong [31] to ensure the scenario’s high efficiency, including a test gas system in the integrated IEEE 14-bus. The model helps to identify the weakness in the energy infrastructure to meet the demand for energy in the long term. The study by Zhong, Huang, Hu, Ai, and Fang [33] used a P2H device to apply a real-time optimization operation model over the microgrid. Furthermore, a storage tank was used to store the hydrogen produced by P2H devices. This hydrogen was essential to fuel up the electric vehicles’ fuel cells. The analysis of several case applications aiming to study the P2H device effect and performance has shown that operational cost was reduced by 25% compared to not using a P2H device and operation economy was improved.

Due to the lack of flexibility of combined heat and power (CHP) and local demand, a substantial amount of extra energy is injected into the transmission network from the low and medium distribution networks. Power-to-hydrogen-and-heat (P2HH) is introduced in the work by Li, Lin, Song, Xing, and Fu [34] to address this problem from an electrical and thermal standpoint. The results of the case studies show that the integration of P2HH improves the operational efficiency of the electrolyzer by 15%. At the same time, the system helped improve the flexibility of CHP, eliminating excess energy, reducing the operating cost of the district heating networks (DHNs), and the significant operation cost curtailment. The work by El-Taweel, Khani, and Farag [35] provides a distributed electrolyzer-based hydrogen fueling station model. This suggested model aims at utilizing low electricity prices for hydrogen generation and improving the hydrogen operation in the stations to increase the profitability of the investments. Moreover, participation in the capacity–based demand response (CBDR) program, on the other hand, increases the profit generated. As the new technology matures, hydrogen stations’ capital expenditure (CAPEX) is predicted to decrease, providing more options for the hydrogen fueling station to generate the expected revenue. Each of these studies focuses on a particular uncertainty in the entire system. In our study on the co-optimization of power and gas networks, we will examine a selection of these processes, specifically the combined P2H (Power-to-Hydrogen) and G2P (Gas-to-Power) processes.

This work aims to develop a new co-optimization framework for gas and electricity networks considering resource operation. The proposed work focuses on improving the system’s efficiency, minimizing the overall costs, and reducing the curtailed power and greenhouse gaseous emissions by introducing the concept of P2H. The contributions of this research work can be summarized as follows:

• A comparative study of different operational scenarios of the P2H and RES integration in a coupled power and gas network is introduced and discussed by implementing the optimal day-ahead operation problem.
• The proposed models aim to minimize the total overall operational costs of both electrical and gas systems. Therefore, we propose new approaches for the optimal operation to accomplish this. In this approach, coupled gas and power networks integrate the P2H unit while maintaining both systems’ main components and constraints. The objective function minimizes energy costs while satisfying gas and power loads during summer and winter. It also reduces the curtailed power of the RES by converting it to hydrogen and reusing it at another time.
• To the best of the authors’ knowledge, previous research in this field is limited in scope. They only consider energy transfer in the form of hydrogen in the electrical system. Although P2H offers a promising future to integrate the electrical power and hydrogen system, very few works have investigated the cooperation of these P2H in coupled gas and power systems.

The rest of this paper is organized as follows: Section 2 presents the scheme of P2H. The methodology and problem formulation of the proposed approach are introduced in Section 3. The simulation results are presented and discussed in Section 4. Section 5 concludes the findings of the proposed approach.

2. Scheme of P2H

The P2H is considered a new technology that helps to conform to the variability in renewable energy by producing hydrogen from the excess energy in the electrical power system [32]. Hydrogen ($H_2$) is considered a perfect medium for renewable energy storage. First, the gravimetric energy density of hydrogen has the highest rate compared to other gases, which is equal to 120 MJ/kg. Its volumetric energy density is low (2.7 MJ/L for 350 bar where 1 bar = 105 Pa compressed Hydrogen, 4.7 MJ/L for 700 bar compressed hydrogen, and 2.36 MJ/L for liquid hydrogen) compared with other fuels in liquid forms like gasoline, ethanol, and propane [36]. In addition, Hydrogen can store energy permanently when proper storage procedures are applied, compared with different energy storage such as batteries. Finally, hydrogen is applicable and used in many industries.

In recent years, a wider interest in P2H has developed (especially in Europe), driven by the growing share of wind and solar energy. Many types of research in the field of P2H technology have been conducted in multiple countries; for example, Denmark, Japan, Germany, and Switzerland. The first step in this process is the electrolysis of the water; during this process, two water molecules are split into two hydrogen molecules and one oxygen molecule using electrical energy, as shown in (1). Following Faraday’s Law, the amount of hydrogen produced is directly proportional to the flowing current. Therefore, it is preferred to have high values of current “high current density” to increase hydrogen production [37,38].

$$H_2{O}_{\left(l\right)}+energy\leftrightarrow H_{2\left(g\right)}+{\displaystyle \frac{1}{2}}{O}_2$$

(1)

The hydrogen will be stored in hydrogen tanks to be used later when the supply is low compared to the demand. The process is illustrated in Figure 1.

It is important to note that the environmental benefits of hydrogen depend on the production method. While green hydrogen is emissions-free, grey and blue hydrogen still involve carbon emissions and potential methane leakage. Additionally, water consumption during electrolysis and the energy losses in hydrogen conversion chains pose environmental trade-offs that must be carefully considered in system planning.

2.1. Types of Electrolyzers

The potential applications of hydrogen gas are increasing rapidly with the development of P2H technology. The P2H cells are classified into three main types as follows:

• AEC is known as an alkaline electrolysis cell.
• PEMEC, proton exchange membrane electrolysis cell.
• Finally, SOEC (solid oxide electrolysis cell).

AEC P2H cells are preferable; they have the lowest equipment cost and the most extended lifespan. However, they have some disadvantages, such as poor security, due to the corrosive liquid inside the cells. For PEMEC, the electrode is made of a precious metal type. Because of this, the proton exchange membrane needs to be changed frequently to avoid further problems, making it more costly. However, it has the most substantial dynamic response and adaptability of renewable energy sources. Finally, the SOEC is considered to have the highest efficiency in energy utilization. However, the speed of starting and shutting down is meager. Also, due to the high-temperature working environment, the choice of materials is rare [39,40].

2.2. Hydrogen Tanks

Hydrogen must be stored once it has been produced from the electrolyzer. Because gas has a high mass energy density but a low-volume energy density, it must be compressed or stored in a concentrated condition to produce a high-volume energy density comparable to other fuels. Storing hydrogen under a high-pressure range of 300–700 bar in steel or composite tanks is probably the preferred method. Compressing hydrogen under high temperatures is used for many small-scale storage systems based mainly on renewable energy resources. On the other hand, more advanced hydrogen storage systems are expected to achieve higher energy density. A variety of different strategies for storing hydrogen are being investigated. Some involve hydrogen adsorption on a high-surface area’s materials, while others entail hydrogen intercalation within the material’s structure [41].

The promising technology P2H, one of our main concerns in this research, can be used to play various roles in energy systems, where it can be used as an energy storage backup system and as an electrical and gas network balancing tool. In addition, it facilitates the distribution of energy between various systems because of the possibility of transforming energy from one type to another. Also, it can lead to a reduction in greenhouse emissions because produced gases are clean. Likewise, it contributes to the security of the energy system.

While P2H provides a promising pathway for large-scale energy storage and sector coupling, it is important to recognize the potential risk of technological lock-in. Over-investment in hydrogen infrastructure could limit flexibility or divert resources from emerging alternatives that may offer higher efficiency or lower costs in the future. Thus, a diversified energy strategy that allows for technological evolution is recommended. Moreover, hydrogen leakage presents an environmental concern, as it may indirectly contribute to global warming by interfering with atmospheric chemistry and extending the lifetime of methane. This underscores the importance of tight system design, leak detection technologies, and regulatory oversight in future large-scale hydrogen infrastructures.

It is worth mentioning that P2H systems typically have a round-trip efficiency of 30–40% [42,43], lower than batteries (85–95%) [44,45] and pumped hydro (70–85%) [45,46]. However, P2H is advantageous for long-duration and seasonal storage, as well as sector coupling across electricity, heating, and transportation. Despite its lower efficiency, P2H’s scalability and cross-sector versatility make it a valuable complement to other storage technologies in renewable-rich energy systems.

2.3. Economic Challenges of P2H Integration

The integration of P2H systems into existing gas and electric infrastructures presents several economic challenges. These main economic challenges include the following:

• High Capital Investment: The initial costs for electrolyzers, hydrogen storage, and compression systems are considerably high. Alkaline and PEM electrolyzers, for example, require significant upfront investments, especially at large scales.
• Operational Costs and Efficiency: While hydrogen offers long-term storage and flexibility, its round-trip efficiency remains lower than alternatives like batteries, impacting cost-effectiveness. Moreover, electricity prices significantly influence hydrogen production costs.
• Infrastructure Compatibility: Retrofitting existing gas and electric infrastructures to accommodate hydrogen (e.g., blending hydrogen into natural gas pipelines or developing dedicated hydrogen pipelines) involves additional costs and regulatory approval, which can be time-consuming and capital-intensive.
• Lack of Market Maturity: The hydrogen market is still developing in many regions. Limited demand and lack of established trading mechanisms hinder economies of scale and financial viability for investors.
• Policy and Incentive Gaps: Inconsistent or underdeveloped policy frameworks and subsidies compared to other renewable technologies can delay investment in hydrogen infrastructure.

3. Methodology

The co-optimization of power and gas systems has many advantages for both integrated systems, as we mentioned in the previous sections. However, many constraints must be considered while solving the problem and must not be violated. If one of those constraints is violated, it will affect the power quality and power operation system, which will cause stability issues in the system. In this section, a general framework of the proposed system is introduced. Furthermore, the mathematical model of the system elements is formulated.

3.1. Structure of the Proposed System

The main objective of this paper is to develop a new approach to co-optimize the power and gas grid with the integration of variable energy resources. High penetration levels are considered a paradigm shift toward a clean community with less harmful emissions at most economic points. Also, co-optimization would boost both systems’ efficiencies, improving the power quality and eliminating the risk of power blackouts. In the proposed model, energy storage facilities like P2H technology are utilized to accommodate the variability of RESs. As explained previously, the surplus electricity from the system production is converted to another form of “gas”. A water source is used to run the electrolyzer; this source could be from seawater, any nearest water grid, or water tanks. Gas-fired units are used to build a bridge between electricity and gas networks. Both chain processes will provide a system that can flexibly deal with sudden changes, fluctuations, and outages. On the other hand, gas networks consist of gas pipelines, gas stations, and compressor stations. The compressor stations are located at specific intervals along the network. They are mainly used to reduce the volume of gas and re-pressurize it to flow again at the correct speed through the pipelines. Figure 2 introduces the case study model diagram. In practice, managing the coordination between gas and electric systems requires both technical integration—such as real-time data exchange, co-optimization algorithms, and shared control platforms—and institutional collaboration between system operators, regulators, and market participants. Moving toward joint operational frameworks and cross-sector regulatory policies is essential to fully unlock the benefits of such integrated energy solutions. It is worth mentioning that the effectiveness of the proposed integrated approach may vary across regions due to differences in renewable energy potential (e.g., solar vs. wind dominance), grid operational flexibility, and gas network configurations. Therefore, localized modeling and data inputs are essential for accurately adapting the framework to specific regional conditions.

3.2. Objective Function

The proposed optimization model is presented in (2)–(36) for buses in electrical system $si,jϵI,J=\left\{\mathrm{1,2},3\dots \dots ..,24\right\}$, gas pipe nodes $n,m\in N,M=\left\{\mathrm{1,2},3,\dots ..20\right\}$. The objective function presented in equation (2) minimizes the total operating cost of coupled electricity and natural gas networks by optimizing the decision variables (DVs). The first term in the objective function refers to the cost of thermal units in the electrical network. The second term represents the production cost of natural gas suppliers.

$${min}_{DV}OF=EC+GC$$

(2)

subject to the decision variables:

$$DV=\left\{\begin{array}{c}{\delta }_{i,t},P_{g,t},W_{c,t},W_{w,t}, {PV}_{pv,t},{PV}_{c,t}\\ {Sg}_{n,t},f_{n,m,t},{Pr}_{n,t}\end{array}\right.$$

(1)

Gas Network Model

The gas network model in the work by De Wolf and Smeers [47] is used in this study. The objective function of any gas transmission network is to minimize the total cost of supply at the minimum guaranteed pressure which must meet the demand at the different nodes. In addition, the function must satisfy the linear constraints, which represent the flow conservation at any node of the gas network, and non-linear constraints, which represent the relationship between the decreased pressure and the flow at pipelines. The pipeline “arcs” can be passive or active, and the main difference is that active arcs represent pipelines with compressors and are denoted by $A_a$ unlike the passive arcs which represent simple pipelines and denoted by $A_p$ [48]. Each node at the network is associated with two variables: the pressure at this node which is represented by $P_n$, and the supply at that node which is represented by $S_n$. The supply node can be positive or negative. The positive $S_n$ refers to gas supply where the negative represents gas demand at that node ‘$d_n={-S}_n$’. The gas flowing between nodes “n” and “m” is defined as $f_{n,m}$, as shown in Figure 3.

The compressor stations in the gas network are used in the natural gas transportation process from one area to another. Natural gas must be pressurized at specific intervals while being carried through a gas pipeline. The power required to drive the compressor is expressed in (3).

$$W=\left(0.08531\right) Q {\displaystyle \frac{k}{k-1}} T_1\left[{\left({\displaystyle \frac{P_d}{P_s}}\right)}^{{\displaystyle \frac{\left(z\left(k-1\right)\right)}{k}}}-1\right]$$

(3)

where $k$, $z$, $P_s, P_d, T_1, Q, \mathrm{and}W$ represent the gas at suction pressure, compressibility factor, suction pressure, discharge pressure, suction temperature, the flow rate in terms of MMCFD (million cubic feet per day), and rate of work in horsepower, respectively. The gas at suction pressure is 1.26; the suction temperature is 520 $R^{°}$, and the compressibility factor is 0.88 [49]. The work rate is multiplied by 0.0007457 to be in terms of MW.

For the gas inflow $S_n$ at the supply, the node should remain within certain limitations specified in the gas contract, representing an average quantity that must be taken from the producer by the transmission company, as shown in (4). The transmission company has the possibility to lift the quantity that is bounded between the upper and lower fraction based on the flexibility of the contract [47,48].

$$\underset{\_}{S_n}\le S_n \le \overline{S_{n }}$$

(4)

On the other hand, for the demand node, ${-S}_n$ representing the gas outflows should be greater than or equal to the demand at that node. Another constraint that must be considered is the pressure levels, which should be kept within a specific range for each node. At the entry point, the pressure levels should not exceed the value specified by the supplier, while for the exit point, the demand should be satisfied at the minimum pressure ratings [50], as in (5).

$$\underset{\_}{P_n}\le P_n\le \overline{P_n}$$

(5)

The equation of flow conservation at node n represents the gas balance; refer to (6) and the illustration in Figure 4.

$$\sum _{m\left|\right(n,m)\in A}{f}_{nm}=\sum _{m\left|\right(n,m)\in A}{f}_{nm}+S_n$$

(6)

Equation (7) represents the passive arc equation which describes the relationship between the flow in the arc and the pressure levels. $C_{nm}$ is considered a constant value that mainly depends on the pipe’s absolute rugosity, which is a measure of the pipe’s roughness and the gas composition [51], in addition to the diameter and the length. The flow goes from node m to node n when $f_{nm}<0$.

$$sign\left(f_{nm}\right)f_{nm}^2=C_{nm}^2\left(p_n^2-p_m^2\right),\forall \left(n,m\right)\in A_p$$

(7)

On the other hand, the active arc in (8) shows that, as the pressure increases along the pipeline, the amount of gas flow will become larger.

$$sign\left(f_{nm}\right)f_{nm}^2\ge C_{nm}^2\left(p_n^2-p_m^2\right),\forall \left(n,m\right)\in A_a$$

(8)

Another constraint represents the pressure at the compressor’s exit, where the pressure has an upper bound, as described in (9).

$$p_m\le \overline{p_m} ,\forall \left(n,m\right)\in A_a$$

(9)

The flow direction is fixed in active arcs and represented in (10).

$$f_{nm}\ge 0 ,\forall \left(n,m\right)\in A_a$$

(10)

The objective function of the gas network can be represented in (11), where the aim is to minimize the total cost of supplying the gas, where $c_m$ represents the gas price that is delivered to node m, and $N_s$ shows the supply node [52].

$$\min z=\sum _{m\in N_s}{c}_m{s}_m$$

(11)

(2)

Electrical Network Model

This section will model the electrical network by considering the most important constraints affecting the system. The concept of thermal unit technology is transferring the “Fuel-Based” energy into electricity. Each thermal unit is described by a production cost function as represented in (12).

$$C_i^{th}\left(P_i^{th}\right)=a_i^{th}{{(P}^{th})}_i^2+b_i^{th}{P}_i^{th}+c_i^{th} iϵ{\mathsf{\Omega }}_{th}$$

(12)

The coefficients $a_i^{th}, b_i^{th}, and c_i^{th}$ represent the fuel–cost coefficients of the thermal unit. The total cost function is calculated using this formulation (13).

$$TC=\sum _{iϵ{\mathsf{\Omega }}_{th}}{C}_i^{th}\left(P_i^{th}\right)$$

(13)

Equation (14) represents the system’s objective function; that is, to minimize the total cost.

$${min}_{P_i^{th}}TC=\sum _{iϵ{\mathsf{\Omega }}_{th}}{C}_i^{th}\left(P_i^{th}\right)$$

(14)

Another important point that needs to be considered is the operating limits that indicate the maximum and minimum output limits in which the thermal unit can operate. Refer to (15).

$$P_i^{th,min}\le P_i^{th}\le P_i^{th,max}$$

(15)

Demand–generation balance is represented in (16): output power should satisfy the demand “sum of output power must satisfy demand,” where the hourly total generation should be equal to the hourly demand:

$$\sum _{iϵ{\mathsf{\Omega }}_{th}}{P}_i^{th}\ge L_e$$

(16)

The integration of wind turbines for generating electricity in the power system is formulated as shown in (17). Although for any bus integrating wind turbines, the amount of wind generation mainly relies on the availability of wind power and wind power capacity, it should be noted that $w_t$ is changing over time.

$$0\le P_{i,t}^w\le w_t{∆}_i^w$$

(17)

A portion of the generated power by wind turbines is curtailed and reduced from the system. This is known as wind curtailment and has been represented in (18) [53].

$$P_{i,t}^{wc}=w_t{∆}_i^w-P_{i,t}^w$$

(18)

A linear power source in relation to ambient temperature and irradiation levels is used to model the PV generator [54]. The total output power of the generator at the maximum power point (MPP) is obtained from (19) and (20). The parameters of the PV equation are obtained from the manufacturer data sheets [55].

$$P_{pv}=\left[P_{pv,STC}\times {\displaystyle \frac{G_T}{1000}}\times \left[1-\gamma \times \left(T_j-25\right)\right]\right]\times N_{pvs}\times N_{pvp}$$

(19)

$$T_j=T_{amb}+{\displaystyle \frac{G_T}{800}}\times \left(NOCT-20\right)$$

(20)

where $P_{pv}, P_{pv,STC}, G_T, \gamma , T_j, N_{pvs}, N_{pvp}$ are the output power generated at MPP, the rated PV output power at the STC and MPP, the radiation level at STC, the temperature coefficient of the power at MPP, the cell temperature, and the number of modules connected in series and parallel, respectively [54].

Another software is used to determine the number of modules connected in series and parallel and the other parameters for each of our case studies known as PVsyst [56]. This software is used mainly to design and study PV systems. It includes a large database of meteorological and PV system components and other tools for solar energy. As with the wind turbines, the PV units experience curtailment in their generated power in the power system for many reasons, as we mentioned previously. The PV curtailment is defined in (21).

$$P_{i,t}^{pvc}={PV}_t{∆}_i^{pv}-P_{i,t}^{pv}$$

(21)

The operational principle of the P2H system involves applying a direct current through two electrodes, which are immersed in water, to diffuse the water molecules into hydrogen and oxygen. The steady state equations showing the P2H flow rate are given as follows (Refer (22)–(24)):

$$P_{h,t}^{P2H}={\displaystyle \frac{F_{h,t}^{P2H}}{{\prod }^{P2H}·{\eta }^{P2H}}}$$

(22)

$${\mathsf{\Pi }}^{P2H}={\displaystyle \frac{{\eta }^F}{2·F·{\upsilon }^{P2H}}}$$

(23)

$${\eta }^{P2H}={\displaystyle \frac{F_{h,t}^{P2H}·{LHV}_{H_2}·{\lambda }_{H_2}}{P_{h,t}^{P2H}}}$$

(24)

The P2H operation is limited to its maximum and minimum capacity, as shown in (25). The production of hydrogen in (${\mathrm{m}}^3/\mathrm{h}$) concerning the electrolyzer’s electrical power is presented in (26) [35].

$$F_h^{min}\le F_{h,t}^{P2H}\le F_h^{max}$$

(25)

$$F_{h,t}^{P2H}={\prod }^{P2H}·{\eta }^{P2H}·P_{h,t}^{P2H}$$

(26)

The balance equation of hydrogen storage shows that the total energy stored at any time in the storage reservoir is equal to the produced hydrogen plus the available SOC. The storage of hydrogen is bounded between its maximum and minimum capacity, as shown in (27) and (28) [23].

$${SOC}_{h,t}={SOC}_{h,(t-1)}+F_{h,t}^{P2H}·\mathsf{\Delta }t$$

(27)

$${SOC}_{h,t}^{min}\le {SOC}_{h,t}\le {SOC}_h^{max}$$

(28)

The power of the hydrogen conversion factor ${\prod }^{P2H}$ is set to be 360 m³/MWh, while the efficiency of the P2H unit ${\eta }^{PtH}$ is 60%. It is worth noting that the actual efficiency of P2H systems can vary depending on equipment characteristics, partial load conditions, ambient temperature, and system aging. In this study, a fixed nominal efficiency of 60% was used for consistency across scenarios. However, future work could incorporate efficiency uncertainty or conduct sensitivity analyses to more accurately reflect real-world operational conditions and enhance the robustness of the results.

To explain the upper and lower operating limits in our formulated constraints, the following assumptions need to be considered:

-

The operating power should be less than the unit’s maximum capacity (29).

$${\overline{P}}_{i,t}\le P_i^{max}$$

(29)

-

If the unit was operating in the previous hour (t − 1) and continues to be ON for the next hour (t), then the generated power cannot be increased more than the Ramp up rate; this is shown in (30).

$${\overline{P}}_{i,t}\le P_{i,t-1}+{RU}_i{u}_{i,t-1}$$

(30)

-

The same concept complies with the Ramp down rates; if the unit was ON in the previous hour (t − 1) and continues to be ON for the coming hours (t, t + 1), then the power generated at the time (t) must be greater. Equation (31) shows the relation.

$$\overline{P_{i,t}}\ge P_{i,t-1}-{RD}_i{u}_{i,t}$$

(31)

-

Also, the system is subject to the following voltage and power flow constraints, as shown in (32)–(34).

$$V_i^{min}\le V_{i,t}\le V_i^{max}$$

(32)

$$P_{i,t}=\left|V_{i,t}\right|\sum _{j\in N}\left[\left|V_{j,t}\right|·\left|Y_{i,j}\right|·\cos \left({\delta }_i-{\delta }_j-{\theta }_{i,j}\right)\right]$$

(33)

$$Q_{i,t}=\left|V_{i,t}\right|\sum _{j\in N}\left[\left|V_{j,t}\right|·\left|Y_{i,j}\right|·\sin \left({\delta }_i-{\delta }_j-{\theta }_{i,j}\right)\right]$$

(34)

Equations (35)–(37) show the active and reactive power balance equations.

$$∆P_{i,t}=P_{i,t}^G+P_{i,t}^w+P_{i,t}^{pv}+P_{i,t}^{net}-P_{i,t}^D-P_{i,t}^{COM1}-P_{i,t}^{COM2}-\left|V_{i,t}\right|\sum _{j\in N}\left[\left|V_{j,t}\right|·\left|Y_{i,j}\right|·\cos \left({\delta }_i-{\delta }_j-{\theta }_{i,j}\right)\right]$$

(35)

$$∆Q_{i,t}=Q_{i,t}^G+Q_{i,t}^w+Q_{i,t}^{pv}+Q_{i,t}^{net}-Q_{i,t}^D-\left|V_{i,t}\right|\sum _{j\in N}\left[\left|V_{j,t}\right|·\left|Y_{i,j}\right|·\sin \left({\delta }_i-{\delta }_j-{\theta }_{i,j}\right)\right]$$

(36)

$$P_{i,t}^{net}=\eta \times P_{i,t}^{H2P}-{\displaystyle \frac{P_{i,t}^{P2H}}{\eta }}$$

(37)

The above-explained models for gas and electricity grids were translated into a code using GAMS software. The proposed system interconnects multiple models with power and gas networks. The P2H storage facility, VRES, gas and electricity load, hydrogen storage, and water source all represent the system’s elements. The model is designed to balance system operation economics and environmental benefits; to efficiently supply the electrical and gas load demands with high penetration of RESs. In addition, the constraints related to generation and demand loads and the network uncertainties are going to be considered in the system.

In system planning, balancing the constraints related to the whole system, potentially conflicting with the stated objectives, can be formulated into a multi-objective optimization problem. The objective functions of the described model will focus on minimizing the distribution grid’s operation cost, maximizing the utilization rate of renewable energy, and minimizing the total emissions from the system. Suitable historical data models are used for the framework to know the ambiguity and inconsistency of these energy resources, as the data models can produce artificial time series that represent probable comprehensions of future VRES power output. Then, it will be capable of determining the best and most ideal method to solve the multi-objective problem and the algorithm to use. As the proposed model utilizes historical data to emulate the expected patterns of renewable output and load behavior, it adopts a deterministic formulation. This approach may not fully account for the wide range of uncertainties in real-time operation. Future work will explore stochastic optimization, scenario-based simulation, or robust techniques to capture the probabilistic behavior of RES generation, load fluctuations, and gas consumption, thus improving the model’s adaptability and reliability in practical applications.

Furthermore, the proposed P2H integration offers promising benefits, the limited availability of hydrogen infrastructure, such as dedicated pipelines, large-scale storage, and refueling stations, but it remains a key constraint for short-term deployment. These infrastructure gaps may delay practical implementation and require coordinated investment and policy support to accelerate adoption.

4. Results and Discussions

This section presents the simulation results of the optimal operation optimization problem by studying and analyzing different case studies to show the effectiveness of the co-optimization of power and gas grids with high RES and energy storage systems penetrations. The model is defined as a mixed integer nonlinear programming (MINLP), implemented in GAMS 33, and solved using the Knitro solver [57,58]. This study focuses on the technical and operational feasibility of integrating gas and electric networks under static pricing assumptions. However, incorporating dynamic energy market mechanisms, such as fluctuations in electricity and gas prices, carbon pricing schemes, and government subsidies, is important. These factors can significantly impact investment decisions, operational strategies, and the overall economic viability of real-world systems. As such, this work can be extended to include market-based parameters, enhancing both its practical applicability and relevance to policymaking.

4.1. System Model

The electrical network implemented in the model is the IEEE RTS 24-bus network, which is mainly a transmission network with an apparent base power (S_base) level of 100 MVA, and voltage levels of 138 kV and 230 kV. This system comprises 12 generating units in which each of these units has its own characteristics. Bus 13 of this network is considered the slack bus. The line, bus, and generation data of this system are available in [59]. In addition to that, wind turbines are used and indicated with green color in the constructed model.

The gas network data are taken from the work by De Wolf and Smeers [47]. The electrical network is connected to the gas network with four gas nodes, namely Loenhout, Voeren, Sinsin, and Petange gas nodes connected to the generating units 12, 6, 3, and 4, respectively. The gas network consists of multiple supply and demand points. The gas is injected into the system through supply points and flows outside the system through demand points and other intermediate points where the gas is rerouted. Pipelines connecting the nodes are called arcs [47,60].

4.2. Case Studies

Many studies have paid much attention to the integration of P2H in power systems, intending to minimize excess energy curtailment. Therefore, a comparative study of different operational scenarios of the P2H and RES integration in coupled power and gas networks are conducted in this section.

Table 1. Parameters of co-optimization problem.

Parameters	Value
$i$	[1–24]
$∆t$	1
$\kappa$	1.26
$z$	0.88
$T_1$	$520 R^o$
${\eta }^{p2h}$	60%
$\mathrm{Conversion} \mathrm{factor} \left(fac\right)$	$360 {\mathrm{m}}^3/\mathrm{M}\mathrm{W}\mathrm{h}$
${\eta }^{ch}, {\eta }^{dch}$	95%, 90%
$VOLW$	50
$LossC$	30 cents per kwh
$G$	0.001
$Sbase$	100 MVA
$a$	$L1-L24$

defines the simulation parameters used in this study.

Four case studies are contemplated as follows:

• Case I: Effect of RES integration percentage in the power system considering one type of source.
• Case II: Effects of using multiple types of RESs in the power system.
• Case III: Effect of P2H unit on the system.
• Case IV: Effect of the seasons.

The proposed model aims at the following:

-

Minimizing the total overall costs of gas and power systems, including the cost of gas supplies and thermal units.

-

Reducing the curtailed power of the RES by integrating ESS in the coupled system.

Table 2. Different scenarios.

Case	RES Mix		RES Percentage			Season		P2H
	Wind	PV	16%	32.61%	50.725%	W	S	With	Without
A	100%	0	A.1	A.2	A.3
B	50%	50%		B.2	B.3	√	√	√	√
	Wind	PV	50.725%			W	S	With	Without
C	100%	0	C.1(L)	C.2(M)	C.2(H)		√	√

W: Hourly load variation in winter, S: Hourly load variation in summer, L: low P2H capacity, M: Medium P2H capacity, H: High P2H capacity.

represents the different scenarios conducted in our study for the operation problem. Case A uses one type of RES, considered wind turbines with different integration percentages from the total load as follows 16%, 32.16%, and 50.725%, represented by A.1, A.2, and A.3, respectively. The same is applied for case B but by mixing two different types of RESs: Wind turbines and PV, and represented by B.2 and B.3 with two different integration percentages, 32.61% and 50.725%, respectively. Both scenarios consider the effect of seasons and the integration of P2H. Case C studies the P2H unit by analyzing the effect of the capacity of this unit in the coupled systems and is defined by three cases as low, medium, and high capacities depending on the RES integration. In this case, the season effect is fixed and assumed to be in summer. Three wind turbines are allocated on buses 8, 19, and 21. The proposed model considers two PV units allocated at buses 15 and 22 with different capacities based on the RES integration percentages. The details of the units are shown and declared in

Table 3. PV and WT details.

RES%	WT Capacities	PV Capacities
16%	200,150,100 MW	-
32.61%	350,300,250 MW	195,250 MW
50.725%	550,450,400 MW	400,450 MW

.

(1)

Scenario A (Considering only WT with different RES integrations)

In the first scenario, the proposed model will be tested in winter and summer seasons with different LPU (Load as per fraction of Peak), as shown in Figure 5. Then, the simulation results will be compared with the same model but with P2H integrated. The P2H unit is fixed on bus 12 with a capacity of 300 ${\mathrm{m}}^3$, where each 1 kg of hydrogen is stored in 11.2 ${\mathrm{m}}^3$. Therefore, for 300 ${\mathrm{m}}^3$ it can store around 26.78 kg of $H_2$.

The results are presented in Figure 6a–c. As the integration of WT is increased in the power system, the generated wind power will increase, which results in massive wind curtailment in the system, as shown in Figure 6b,c. It is good to highlight that wind curtailment is higher in winter than summer. As for the total cost, we can see a reduction as we increase the integration of RESs in the system. Another reduction is experienced in the cost and the wind curtailment when installing the P2H unit in the system. The wind curtailment decreases by 52% for the first case of the scenario, 34% for the second case, and 20% for the last case during summer days.

In the next step, the focus will be placed on case A.1 to analyze the effect of P2H integration in detail. Figure 7a,b illustrates the power generation of the thermal units and the wind turbines in the system before and after integrating the P2H unit.

As shown in Figure 7a, the wind curtailment appears mostly between t = 14 and t = 18, and this is because of the low demand during this period. Therefore, the grid operators and the utilities will curtail the wind-generated power from the wind turbines when the fossil-fuel power plant’s generation levels reach their minimum value; because it can be substantially more expensive to stop and restart the thermal units within a few hours. This condition can arise at night when there is a lot of wind but not a lot of loads, and it can be worsened in small balancing zones. Adopting a P2H unit (power-to-hydrogen) in the coupled system can mitigate the renewable energy curtailment by converting the excess energy to hydrogen and storing it, as shown in Figure 7b. The P2H unit starts charging when we have excess energy, as illustrated in Figure 8. It fully discharges at different periods when the generation cannot supply all the load in the system. This will result in decreasing the amount of energy curtailment.

(2)

Scenario B (Considering PV&WT with different RES integrations)

In this scenario, a different type of RES, PV, will be used besides the WT. The study will be conducted in the summer and winter seasons. Therefore, the solar radiation will be changed based on the season we are conducting the study, as shown in Figure 9. During the summer season, the available sun peak hours are much longer than in the winter season. As a result, the solar radiation levels are higher in summer.

The results of scenario “B” are illustrated in Figure 10a,b. Using different types of renewables in the system can help reduce the amount of fossil fuel used in thermal generators for power generation. However, the amount of energy curtailments will increase dramatically compared to scenario “A”. The same conclusion is proved here; the integration of the P2H unit will reduce the amount of curtailments from both wind and PV in the system. Also, the cost is reduced, which is the objective of our system.

(1)

Scenario C (Study the performance of P2H)

In this scenario, different cases will be applied to study the performance of the P2H. The system’s other parameters will be fixed, including the season effect and RES integration percentages. In the summer season, case 3 from scenario A is used to conduct our study with a P2H of 300 ${\mathrm{m}}^3$ capacity.

• Case I (L): Increasing the capacity of P2H to 450 ${\mathrm{m}}^3$.
• Case II (M): Increasing number of P2H units (2 units of 300 ${\mathrm{m}}^3$) allocated at busses 6 and 12.
• Case III (H): Increasing the number of P2H units and their capacities in the system to (2 units of 450,350 ${\mathrm{m}}^3$) allocated at busses 6 and 12. The results are presented in

  Table 4. Scenario C results.

  	Without P2H	With P2H
  Case	Base	O	L	M	H
  WC(MW)	2359.88	1951.67	1503	1352	692.8
  Elec cost	391,723.8683	391,683.1646	391,652.9499	391,625.7114	391,592.3981
  Gas cost	563,297.1923	563,297.0351	563,297.0554	563,297.0048	563,296.9414

  .

Wind curtailment reduces as we increase the number and capacities of the P2H units in the power system. As for the high capacity of P2H, the WC reduces to 692.8 MW compared to the base case (without P2H), where the WC is 2359.88 MW and the original case (with P2H); where it was 1951.67 MW. As a result, the electrical cost decreases slightly to become USD 391,592.3981 compared to the base case (without P2H), which is USD 391,723.8683.

(1)

Scenario D (Different objective functions)

In this scenario, case A.1 is selected to apply different objective functions besides the main objective function of our study, as stated below, and analyze the impact on our coupled electricity and gas system. The results are shown in

Table 5. Scenario D results.

OF	Case I (Base)	Case II	Case III
Elec Cost	574,167.5806	731,126.9348	752,829.4195
Gas Cost	564,132.3391	564,183.1801	564,192.8098
OF	1,138,299.92	1,295,310.11	1,317,022.229
WC	223.7	499.65	434.11
Power loss	834.614	488.04	488.8

.

• Case I: Minimize total cost of thermal units.
• Case II: Minimize power loss and cost of thermal units.
• Case III: Minimize wind curtailment cost, power loss, and cost of thermal units.

In case II, where the objective function is set to minimize the power loss and the cost of thermal units, we can see that the electrical cost and wind curtailment increased in the integrated system. In contrast, the power loss decreased to 488.04 MW, representing a 50% reduction. As a scientific explanation for this, one of the ways to reduce power loss in transmission lines is by increasing the voltage to allow less current to pass through the lines to produce the same power. This will increase the generation of the system to accommodate the demand, which will increase the cost of the electrical system. While in case III, when the cost of wind curtailment is added to the objective function, a slight reduction in the amount of wind curtailment is experienced compared to case II. The cost of the electrical system is increasing to reach USD 752,829.4195.

The detailed results for scenarios A, B, and C in terms of wind curtailment, PV curtailment, power generation of different units in the system (Thermal units, PV, WT), electrical cost, and gas cost are shown in

Table 6. Detailed results of scenarios A, B, and C.

Scenario A
No P2H								P2H
	G (MW)	WT (MW)	PV (MW)	WC (MW)	PVC (MW)	Ploss	Tot cost (USD)	G (MW)	WT (MW)	PV (MW)	WC (MW)	PVC (MW)	Ploss	Tot cost (USD)
A.1.S	45,649.33	6017.34		459.65		599.04	1,138,690.74	45,648.93	6253.31		223.70		834.61	1,138,299.92
A.2.S	40,043.78	11,790.75		1164.27		766.90	1,065,529.53	40,040.82	12,188.28		775.70		1161.47	1,065,475.85
A.3.S	34,225.74	17,790.80		2359.88		948.91	955,021.06	34,233.18	18,198.77		1951.67		1364.32	954,980.20
A.1.W	27,781.45	6442.13		54.87		366.08	868,413.57	27782.08	6452.53		24.47		397.11	868,410.74
A.2.W	22,418.35	12,326.95		627.05		907.80	829,336.50	22,417.22	12,708.72		245.26		1288.44	829,325.61
A.3.W	19,730.35	16,065.14		4085.51		1957.99	812,004.46	19,643.88	16,927.28		3225.39		2733.66	760,496.30
Scenario B
No P2H								P2H
	G (MW)	WT (MW)	PV (MW)	WC (MW)	PVC (MW)	Ploss	Tot cost (USD)	G (MW)	WT (MW)	PV (MW)	WC (MW)	PVC (MW)	Ploss	Tot cost (USD)
B.2.S	45,536.30	5998.88	129.15	478.13	1405.66	596.70	1,136,460.61	45,207.08	6001.69	130.15	475.80	1294.74	271.29	1,136,251.15
B.3.S	39,691.51	11,705.31	450.67	1248.68	2484.91	779.86	1,056,871.14	39,369.19	11,939.68	450.67	994.57	2420.31	691.91	998,096.14
B.2.W	28,333.06	5619.68	61.58	118.68	1470.76	302.41	873,867.83	27,720.91	6357.42	61.58	80.22	1398.19	176.82	867,843.57
B.3.W	22,278.16	12,381.97	197.27	1270.17	2729.62	1019.90	828,598.75	21,888.25	12,205.47	197.27	582.37	2703.46	453.49	820,488.27
Scenario C”
No P2H								P2H
	G (MW)	WT (MW)	PV (MW)	WC (MW)	PVC (MW)	Tot cost (USD)		G (MW)	WT (MW)	PV (MW)	WC (MW)	PVC (MW)	Tot cost (USD)
C.1.S	34225.74	17,790.80		2359.88		EC = 391,683.1646		34,222.07	18,647.54		1503.15		954,950.01
C.2.S						GC = 563,297.0351		34,220.71	18,799.07		1351.62		954,922.71
C.3.S						TC = 954,980.1997		34,219.00	19,562.11		692.79		954,889.34

.

5. Conclusions

This study presents an innovative approach to integrating gas and power networks with the goal of optimizing total operational costs while adhering to system constraints. The incorporation of P2H technology plays a crucial role in reducing energy curtailments from RESs, thereby enhancing system flexibility. Formulated as an MINLP model, the optimization problem targets the minimization of daily operational costs for both gas supplies and thermal units. Comparative studies across various operational scenarios for P2H and RES integration during summer and winter demonstrate significant cost reductions and decreased energy curtailments with increased RES penetration. Specifically, the results indicate a 52% reduction in wind curtailment in certain scenarios with P2H installation, and in other cases, wind curtailment decreased by 34% and 20% during summer days. When the P2H capacity is increased to 450 m³, wind curtailment drops from 2359.88 MW to 692.8 MW, highlighting the scalability and effectiveness of the P2H solution. The total operational costs also see a notable decrease, with electrical costs reducing from USD 391,723.87 to USD 391,592.40 as P2H capacity is enhanced. This research contributes to multiple United Nations Sustainable Development Goals (SDGs), including Affordable and Clean Energy (SDG 7), by reducing energy costs and curtailments; Industry, Innovation, and Infrastructure (SDG 9), through promoting advanced technological integration; Sustainable Cities and Communities (SDG 11), by improving energy system resilience and efficiency; Responsible Consumption and Production (SDG 12), by minimizing waste and optimizing resource use; Climate Action (SDG 13), through significant reductions in greenhouse gas emissions; and Partnerships for the Goals (SDG 17), by fostering collaboration across sectors and nations. Future work will focus on detailed planning approaches and long-term studies involving various battery charging and discharging patterns.