Total Power Generation Capacity Research Articles

Numerical Study of an Innovative Concept for a Multibody Anti-Pitching Semi-Submersible Floating Wind Turbine

The floating offshore wind turbine provides a feasible solution for the development of renewable ocean energy. However, the sizeable rotor diameter of the wind turbine results in large wind heeling moments and pitch amplitude. It will increase the structural loads and cause safety problems. Additionally, the contradictory nature between the stability and the sea-keeping of the floating structure requires that the more flexible method should be adopted to reduce the motion response of the floating offshore wind turbine. Therefore, an innovative concept of a multibody anti-pitching semi-submersible floating offshore wind turbine, named the MBAPSF, is proposed in this paper. The MBAPSF consists of a 5 MW braceless semi-submersible wind turbine and three wave energy converters. The multibody coupled numerical model is established by using an F2A tool, and the dynamic performance of the MBAPSF is compared with that of the traditional semi-submersible wind turbine named the TSSF. The results show that the innovative concept proposed in this paper can reduce pitch motion up to approximately 27% under different load cases, and the maximum bending moment and shearing force at the tower base are also reduced by more than 10%. Meanwhile, WECs are beneficial for increases in the total power generation capacity.

Using Particle Swarm Optimization with Backpropagation Neural Networks and Analytic Hierarchy Process to Optimize the Power Generation Performance of Enhanced Geothermal System (EGS)

The optimization of the production scheme for enhanced geothermal systems (EGS) in geothermal fields is crucial for enhancing heat production efficiency and prolonging the lifespan of thermal reservoirs. In this study, the 4100–4300 m granite diorite stratum in the Zhacang geothermal field was taken as the target stratum to establish a numerical model of water-heat coupling of three vertical wells. However, relying solely on numerical simulation for optimization is time-consuming and challenging for the determination of the globally optimal production plan. The present study proposes a comprehensive evaluation method for optimizing the performance of EGS power generation based on the integration of particle swarm optimization with backpropagation neural network (PSO-BPNN) and analytic hierarchy process (AHP). Five different PSO-BPNN models were constructed based on the numerical simulation data to predict different EGS power generation performance indexes, including the production temperature, the injection pressure, the total electricity generation, the electric energy efficiency and the levelized cost of electricity. Based on these PSO-BPNN models, the weights of various thermal development evaluation indexes were calculated by AHP to conduct a comprehensive evaluation of the power generation performance of the three vertical wells EGS. The results show that the PSO-BPNN model has good prediction accuracy for EGS prediction of various performance indicators, with a coefficient of determination (R2) exceeding 0.999. The AHP evaluation of all production schemes reveals that the optimal power generation scheme entails a well spacing of 580 m, water injection rate of 56 kg/s, injection temperature of 38 °C and fracture permeability of 2.0 × 10−10 m2. Over a span of 30 years, this scheme can provide a total power generation capacity amounting to 1775 GWh, with an associated LCOE value of 0.03837 USD/kWh. This not only provides a reference for the development and optimization of geothermal systems in the Zhacang geothermal field but also provides a new idea for the optimization design of other geothermal projects.

2D numerical modeling of solute transport Na-K from a geothermal reservoir: The case of Las Tres Vírgenes, BCS

Las Tres Vírgenes geothermal field (LTVGF) is located within the volcanic complex of the same name with temperatures between 250 and 275 °C and a total power generation capacity of 10 MW. For geothermal systems, understanding the thermodynamic-chemical equilibrium of the fluid-rock interaction allows us to study the reactions involved in the precipitation or dissolution of minerals. In this work, we use the hydrothermal alteration mineralogical phases albite, microcline, and zoisite that are in equilibrium with the geothermal fluid as a source of cations (Na+, K+, and Ca2+) to model the chemical composition of the geothermal fluids in the last 20 years. The heat transfer (conductive-convective) and solute transport (advective-diffusive) equations in 2D, were discretized using the Finite Volume Method (FVM) and were coupled to simulate the temperature profile and spatial distribution of the main cations in the LTVGF reservoir. The code evaluates the thermal and chemical evolution of the reservoir from an initial geothermal gradient and an initial chemical composition of the fluid to reach the present concentrations at reservoir conditions. Based on the model results, we consider a reservoir between 1000 and 2000 m in depth and initial concentrations of 100 ppm for sodium to get the average concentrations at reservoir conditions of wells LV-13, LV-11, LV-6, LV-3, LV-4, and LV-1 corresponding to values between 3138 and 3661 ppm and temperatures between 225 and 249 °C. For potassium, the initial concentration was 10 ppm to reproduce the average concentrations between 604 and 663 ppm for wells LV-13, LV-11, LV-4, and LV-5 obtaining temperatures between 223 °C and 257 °C. For calcium, the initial concentration was 2 ppm and reproduced the concentrations of wells LV-5 and LV-6 (251 and 260 ppm) which are at depths of 1737 and 2162 m and temperatures of 225 and 260 °C. This work presents perhaps the first approach to solute transport of the LTVGF reservoir and it was determined that the alteration minerals considered influence the chemical composition of the current discharge fluids of the field. These results promote using Na/K geothermometers based on ionic activities proposed in the literature to estimate the reservoir temperature according to the equilibrium conditions and the chemical composition of the fluids.

Tutorial on time series prediction using 1D-CNN and BiLSTM: A case example of peak electricity demand and system marginal price prediction

Although research on time series prediction based on deep learning is being actively carried out in various industries, deep learning technology still has a high entry barrier for researchers who have not majored in computer science. This paper presents a tutorial on time series prediction using a deep learning-based model. The entire process of time series data prediction is presented—from data collection to evaluation of prediction results. The details of each step are shown through a case example of predicting peak electricity demand and system marginal price of Jeju Island in Korea using the 1D-CNN and BiLSTM model. In Jeju Island, the proportion of renewable energy in the total power generation capacity is increased to 67% in 2021, requiring more accurate electricity demand forecasts. Therefore, using 808 days of training data from February 2018, electricity demand and SMP for the next 21 days were predicted. To make it easier for readers to follow, the example uses only open public data, and the entire Python source code is shared via a GitHub repository. The prediction error calculated by WRMSSE showed 0.42 in electricity demand and 0.63 in SMP max. A WRMSSE value less than one means that the forecast was relatively good, that is, better than naïve forecasting. This tutorial is not limited to the energy industry but can be utilized for any application requiring time-series data prediction. This article is expected to be of great help to researchers who need to understand the process of time series prediction using deep learning and use it for application in their industry.

Geophysically guided well siting at the Aluto-Langano geothermal reservoir

Volcano-hosted high-temperature geothermal reservoirs are powerful resources for green electricity generation. In regions where such resources are available, geothermal energy often provides a large share of a country’s total power generation capacity. Sustainable geothermal energy utilization depends on the successful siting of geothermal wells, which in turn depends on prior geophysical subsurface imaging and reservoir characterization. Electromagnetic resistivity imaging methods have proven to be a key tool for characterizing magma-driven geothermal systems because resistivity is sensitive to the presence of melt and clays that form through hydrothermal alteration. Special emphasis is often given to the “clay cap,” which forms on top of hydrothermal reservoirs along the flow paths of convecting geothermal fluids. As an example, the Aluto-Langano volcanic geothermal field in Ethiopia is covered with 178 densely spaced magnetotelluric (MT) stations. The 3D electrical conductivity model derived from the MT data images the magma body that acts as a heat source of the geothermal system, controlling geothermal convection and formation of alteration zones (commonly referred to as clay cap) atop the geothermal reservoir. Detailed 3D imaging of the clay cap topography can provide direct insight into hydrothermal flow patterns and help identify potential “upflow” zones. At Aluto all productive geothermal wells are drilled into zones of clay cap thinning and updoming, which is indicative of underlying hydrothermal upflow zones. In contrast, nonproductive wells are drilled into zones of clay cap thickening and lowering, which is an indicator for underlying “outflow” zones and cooling. This observation is linked to fundamental characteristics of volcano-hosted systems and can likely be adapted to other geothermal fields where sufficiently detailed MT surveys are available. Therefore, high-resolution 3D electromagnetic imaging of hydrothermal alteration products (clay caps) can be used to infer the hydrothermal flow patterns in geothermal reservoirs and contribute to derisking geothermal drilling projects.

NCNU dedicates to develop green campus and renewable energy for environmental sustainability

National Chi Nan University (NCNU) is located in Puli in central Taiwan with an altitude of 665 meters, making it the highest university in Taiwan. NCNU has a single campus with a total area of 150 ha and a ratio of green cover of 80% including 39.6% forest and 40.7% planted vegetation. In addition to forest and planted vegetation areas, area for water absorption accounts for 16.3% of campus. NCNU designates around 40% of total budget for sustainability effort each year. Percentage of operation and maintenance activities during Covid-19 pandemic is up to 92% because of good disease prevention measures. NCNU campus is surrounded by dense valleys and forests that provides a habitat for many wild animals such as cattle egrets, pangolins, and butterflies. Recently, NCNU dedicates to construct solar power generation system on campus. The power generation capacity of the system is around 3.0 MW to date, which supplies for 20% of annual power consumption. The solar power generation system is constructing continuously and will reach a total power generation capacity of 8.2 MW in 2023, which accounts for around 90% of annual power consumption.

Application of the MPPT Control Algorithm Based on Hybrid Quantum Particle Swarm Optimization in a Photovoltaic Power Generation System

The Maximum Power Point Tracking method is a mainstream method for improving the operational efficiency of photovoltaic power generation, but it is difficult to adapt to the rapidly changing environment and lacks good steady-state and dynamic performance. To achieve fast and accurate tracking of the Maximum Power Point Tracking, the optimization of the contraction expansion coefficient of the Quantum Particle Swarm Optimization algorithm is studied, and then the Levy flight strategy is introduced to optimize the algorithm’s global convergence ability, thereby constructing the Hybrid Quantum Particle Swarm Optimization algorithm. Finally, the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm is obtained. The research results showed that the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm can always converge to the theoretical minimum value with a probability of more than 94% in the Roserock function and Rastigin function tests. The tracking error of the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm was less than 1% under lighting conditions. The convergence time of the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm in arbitrary shadow occlusion environments can reach a stable state within 0.1 s. In summary, the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm proposed in the study has excellent performance and very wide applicability. To a certain extent, it improves the total power generation capacity of the photovoltaic power generation system and the power generation efficiency of the photovoltaic array.

Photovoltaic generation potential and developmental suitability in southern Hebei

The traditional PV potential calculation based on PV available land only considers a single land use type and lacks a comprehensive assessment of all land types, so the PV potential has not been fully exploited. Considering southern Hebei as the study area, this study quantified the utilisation rate of land that was available for photovoltaic (PV) power by GIS and established a PV potential assessment model that considered various land types. Simultaneously, the regional power consumption was spatialised, and the electricity generation and consumption were compared to categorise areas into four types. The results show that the rooftops in southern Hebei had the largest PV potential, with a theoretical power generation capacity of 64,032 GWh, followed by water bodies, roads, and unused land. These areas had a total power generation capacity of 131,306 GWh. Furthermore, power consumption is spatialised due to its uneven spatial distribution. The spatialisation results of power consumption showed that it was relatively concentrated in cities and their surrounding areas. Therefore, rooftops and roads in urban areas are suitable for priority development. When comparing power consumption and generation, it was found that most of the central urban areas and some economically developed counties belonged to the L–H (Low power generation and high power consumption) category and should be developed first.

Economic profits and carbon reduction potential of photovoltaic power generation for China's high-speed railway infrastructure

China has built the world's largest high-speed railway (HSR) network, which has fueled regional economic growth. Mounting photovoltaics (PV) on the roofs of HSR station houses and platforms can potentially provide electricity for high-speed trains, change the energy mix, and reduce emissions. Therefore, it is crucial to assess the technical potential and economic environmental performance of PV for the HSR infrastructure. In this study, the PV potential of 973 stations of 108 HSR lines in China was studied in conjunction with geographic information system (GIS). The results showed that the PV capacity that can be deployed in China's HSR stations at horizontal and optimum tilt angles was 4.36 GW and 2.81 GW, with a total power generation capacity of 108.55 TWh and 74.88 TWh, respectively, which presented a huge power generation potential. The economic analysis showed that the All-consumption scenario and optimum tilt angle had better economic profits than the All-feed-into-grid scenario and the horizontal angle, respectively. Moreover, the use of PV could reduce carbon emissions by HSR stations by 79,895.73 kilotons and 55,112.53 kilotons at horizontal and optimum tilt angles, respectively. The study revealed that the combination of PV and HSR infrastructure was a good strategy for sustainable transportation and carbon neutrality goals.

A Monte Carlo tree search-based method for decision making of generator serial restoration sequence

Reasonable generator serial restoration sequence is a key issue to the system restoration following blackouts. This paper proposed an optimization method for the decision making of generator serial restoration sequence based on Monte Carlo tree search algorithm. First, the generator serial restoration sequence mechanism during the restoration process is analyzed. Considering the maximization of the total power generation capacity as the objective function, this paper also consider generator’s hot start. Second, the Monte Carlo tree search algorithm (MCTS) is applied to decide the generator serial restoration sequence. In the simulation stage of MCTS, the Dijkstra’s algorithm is utilized to determine the shortest path between the selected generator and the recovered power system. Finally, the IEEE 39 bus system and Hebei power grid system are used to validate the proposed algorithm. Simulation results show that the proposed method is efficiency and it can provide an reasonable generator serial restoration sequence to maximizing power generation during the restoration process.

A Powerful Tool for Power System Monitoring: Distributed Dynamic State Estimation Based on a Full-View Synchronized Measurement System

Modern power systems are witnessing a rapid transition with the increasing penetration of intermittent nonsynchronous renewable generation and distributed energy resources. According to the National Energy Administration, China’s installed wind and solar energy capacity accounted for 26.7% of the country’s total power generation capacity at the end of 2021. In terms of electricity generation, wind and solar energy accounted for 11.8% percent of the total electricity use that year. In the renewable energy development plan in the 14th Five-Year Plan (2021–2025), China aims for renewables to meet 33% of national power consumption and for nonhydro renewables to satisfy 18%. This transition poses challenges for system operators to grasp the actual status of power systems accurately and comprehensively.

Powering Europe with North Sea offshore wind: The impact of hydrogen investments on grid infrastructure and power prices

Hydrogen will be a central cross-sectoral energy carrier in the decarbonization of the European energy system. This paper investigates how a large-scale deployment of green hydrogen production affects the investments in transmission and generation towards 2060, analyzes the North Sea area with the main offshore wind projects, and assesses the development of an offshore energy hub. Results indicate that the hydrogen deployment has a tremendous impact on the grid development in Europe and in the North Sea. Findings indicate that total power generation capacity increases around 50%. The offshore energy hub acts mainly as a power transmission asset, leads to a reduction in total generation capacity, and is central to unlock the offshore wind potential in the North Sea. The effect of hydrogen deployment on power prices is multifaceted. In regions where power prices have typically been lower than elsewhere in Europe, it is observed that hydrogen increases the power price considerably. However, as hydrogen flexibility relieves stress in high-demand periods for the grid, power prices decrease in average for some countries. This suggests that while the deployment of green hydrogen will lead to a significant increase in power demand, power prices will not necessarily experience a large increase.

Techno-economic feasibility and environmental sustainability of waste-to-energy in a circular economy: Sri Lanka case study

Many countries in Asia-Pacific region have programs in place for clean energy transition aligned with their Nationally Determined Contributions. However, in most of these countries waste-to-energy (WtE) has no major role in the current programs. The study presented in the paper has examined WtE in a circular economy as a solution that can have economic, financial, social, and environmental co-benefits through efficient use of natural resources, reduced emissions, and fostering innovation. The case study involving Sri Lanka has shown that similar countries have the potential to implement WtE projects coupled with appropriate circular economy elements with adequate financial and economic returns. The results show the viability of a centralized incineration plant of 500 tons/day capacity and decentralized biogas facilities of 150–200 tons/day capacity for each of the three districts in the country's Western province. The estimated total power generation capacity from incineration is about 20.3 MW and the annual exported electricity is 129.86 GWh. The estimated power generation capacity of the biogas plants is about 7.16 MW, and the annual exported electricity is about 41.4 GWh. The total amount of compost produced is estimated to be about 43,000 tons/year by processing digestate and 125 tons of recyclable waste expect to be recovered daily. The proposed development plan positively impacts the grid emission factor of the country and the estimated avoided annual GHG emission is about 380,000 tons of CO2. This pioneering case study can be used as a basis for immediate action to solve waste management issues within a circular economy helping similar economies in the Asia Pacific region in their efforts to achieve net zero emission target by the middle of this century.

Hybrid photovoltaic/solar chimney power plant combined with agriculture: The transformation of a decommissioned coal-fired power plant

Due to fossil fuel shortage and high carbon emissions, more and more inefficient coal-fired power plants are being decommissioned. Many redundant resources like chimneys and electric equipment are thus left behind, which can be combined with renewable energy for transformation. Therefore, a hybrid photovoltaic/solar chimney (PV/SC) power plant combined with agriculture is proposed to transform a decommissioned thermal power plant in Ningxia, China. The collector canopy is partially covered with PV modules and simultaneously serves as an agricultural greenhouse for planting activities. Meanwhile, the hot air flow under the canopy can be integrated with air source heat pumps (ASHPs) for domestic heating. Detailed mathematical models are developed to investigate power generation and heat collection performance. The economic and environmental benefits are quantitatively evaluated. The results show that the average daily power generation capacity of PV and SC can reach 334.2 MWh and 9.3 MWh, respectively. The total power generation capacity increases by 5.98% compared with an equivalent single PV power plant. The annual CO2 emission reduction can be up to 1.27 × 106 tons, and the total annual revenue is more than $ 5.47 million. The transformed plant has an impressive comprehensive solar energy conversion efficiency of 14.2% considering power generation, agricultural production and heating, which is 36.9% higher than that of an equivalent conventional PV power plant. The proposed plant has a power generation capacity of 343.5 MWh/d and can bring about notable economic and environmental benefits, which are of great significance to the transition of renewable energy and achieving lower carbon emissions. This work can provide a feasible reference for the upgrading of industries with high pollution and low efficiency.

DEVELOPMENT OF PRİORİTİES AND PROSPECTS OF RENEWABLE ENERGY SOURCES IN AZERBAIJAN

DEVELOPMENT OF PRİORİTİES AND PROSPECTS OF RENEWABLE ENERGY SOURCES IN AZERBAIJAN

Thermoeconomic Analysis of Solar Chimney and Wind Turbine Application to Help Generate Electricity in a Trigeneration Cycle

The main purpose of this study is to evaluate the thermodynamic and economic performance of using a solar chimney and wind turbine to help generate electricity in a multigeneration system. The proposed system is designed to generate power, heating, cooling, hot water, and steam. Parametric studies were conducted to evaluate the effects of various parameters such as Brayton cycle turbine inlet pressure, organic Rankine cycle turbine inlet temperature, solar radiation, wind speed, and absorption refrigeration cycle evaporator temperature on the system efficiency. The effects of these parameters on the energy, exergy, and economic efficiencies of the whole system were investigated. The results showed that the highest energy efficiency and total exergy of the multigeneration system were 22.12% and 11.4%, respectively. Also, the total power generation capacity of the studied system was calculated to be 2103 kW. The results also depicted that the highest rate of exergy destruction for the main components of the system is found in the parabolic dish solar collector. Increasing the turbine inlet pressure, the average wind velocity of the wind turbine and, evaporator temperature increasing of absorption refrigeration cycle has a positive effect on the efficiency of the proposed system.

Commercial Pressure Retarded Osmosis Systems for Seawater Desalination Plants.

The development of renewable energy technologies is of global importance. To realize a sustainable society, fossil-resource-independent technologies, such as solar- and wind-power generation, should be widely adopted. Pressure retarded osmosis (PRO) is one such potential renewable energy technology. PRO requires salt water and fresh water, both of which can be found at seawater desalination plants. The total power generation capacity of PRO, using concentrated seawater and fresh water, is 3 GW. A large amount of energy is required for seawater desalination; therefore, the introduction of renewable energy should be prioritized. Kyowakiden Industry Co., Ltd., has been working on introducing PRO to seawater desalination plants since 2001 and is attracting attention for its ongoing PRO pilot plant with a scale of 460 m3/d, using concentrated seawater and treated sewage water. In this study, we evaluated the feasibility of introducing PRO in existing desalination plants. The feasibility was examined based on technology, operation, and economy. Based on the number of seawater desalination plants in each country and the electricity charges, it was determined whether the introduction of PRO would be viable.

Carbon-subsidized inter-regional electric power system planning under cost-risk tradeoff and uncertainty: A case study of Inner Mongolia, China

Inter-regional electricity transmission inevitably brings about unbalanced carbon emission and air pollution among different regions. It is vital to compensate for the economic and environmental loss of power output region by taking the carbon subsidy strategy into account. In this study, a risk explicit interval multistage stochastic programming model considering inter-regional carbon subsidy is developed to support an optimal energy system planning in Inner Mongolia, China. This model can not only deal with uncertainties embedded in the complex energy system described as interval values and random variables but also reflect dynamic linkage between multiple stages over a time series. It advanced the existing optimization methods by introducing the cost-risk tradeoff information based on the risk preferences of decision-makers. The obtained crisp solutions are more feasible, effective and optimum for the practical policymaking process. Different scenarios associated with carbon subsidy policy implementation, renewable energy share adjustment and carbon emission reduction strategy are employed to project the optimal power generation scheme, net system cost, installed electricity capacity, carbon intensity and pollution emission in the future. The results indicate that the amount of exported electricity will keep increasing, and the carbon subsidy strategy will prominently promote the economic growth of the electric power sector by 3.05%. The further power supply pattern is going to transform from fossil fuel to renewable energy, by which wind and solar power will occupy around 44% of total power generation capacity in West Inner Mongolia. Raising the share of renewable energy will be the most effective approach to adjust the power generation scheme; meanwhile, reducing carbon intensity will significantly contribute to enlarge green energy capacity, enhance low-carbon emission and mitigate air pollution. These findings will help decision-makers gain insights into a more scientific energy system planning under various uncertainties.

A planning perspective on Hydropower Development in the Indian Himalayan Region

The Indian Power Sector(IPS) is under gradual transition from over-reliant fossil fuel (62%) to Sustainable Energy Source(SES), primarily to achieve targets of SDGs and the Paris Agreement to base 40% of the total power generation capacity on non-fossil fuel resources by 2030. In this context, the solar power generation is on the fast-track whereas, hydropower development is lagging behind due to various reasons causing time and cost escalation, hence the sustainability of IPS in terms of flexibility and reliability in integration with other Renewable Energy Source will remain a challenge. With this concern, the focus of this study is to (i) analyze and prioritize the hydropower potential (HPP) in the Indian Himalayan Region, (ii) identify the prime constraints in the way of hydropower development and (iii) discuss the way-forward for sustainable planning of hydropower development whilst appropriately managing time & cost over-runs including socio-environmental concerns. The methodology involves literature review and analysis of secondary data about IPS, hydropower resources and project-specific risks prevalent in ongoing HEPs in India. The result shows that the Indian Himalayan Region has enough (73%) balance HPP in 12 different States; sustainable harnessing of which requiresproper addressing of the prime constraints viz., multiple public consultations in clearance process, litigations, high investment, socio-political and contractual issues, mainly through procedural reforms by the State Governments which have constitutional right over land and water in the federal structure of India. The finding of study will be useful for planning process of entrepreneurs, investors and policy makers in the direction to achieve the target of SES beyond India’s Nationally Determined Contribution.

Integration of Hydrogen into Multi-Energy Systems Optimisation

Hydrogen presents an attractive option to decarbonise the present energy system. Hydrogen can extend the usage of the existing gas infrastructure with low-cost energy storability and flexibility. Excess electricity generated by renewables can be converted into hydrogen. In this paper, a novel multi-energy systems optimisation model was proposed to maximise investment and operating synergy in the electricity, heating, and transport sectors, considering the integration of a hydrogen system to minimise the overall costs. The model considers two hydrogen production processes: (i) gas-to-gas (G2G) with carbon capture and storage (CCS), and (ii) power-to-gas (P2G). The proposed model was applied in a future Great Britain (GB) system. Through a comparison with the system without hydrogen, the results showed that the G2G process could reduce £3.9 bn/year, and that the P2G process could bring £2.1 bn/year in cost-savings under a 30 Mt carbon target. The results also demonstrate the system implications of the two hydrogen production processes on the investment and operation of other energy sectors. The G2G process can reduce the total power generation capacity from 71 GW to 53 GW, and the P2G process can promote the integration of wind power from 83 GW to 130 GW under a 30 Mt carbon target. The results also demonstrate the changes in the heating strategies driven by the different hydrogen production processes.