Cost Of Power Generation Research Articles

Impact of maximized utility benefit based on customer willingness for economic operation of a grid connected microgrid system

Optimal scheduling of distributed energy resources (DERs) to obtain a minimized generation cost of a low voltage (LV) microgrid (MG) system has always gravitated the power system optimization researchers. This paper proposes a dual level optimization framework which emphasizes on maximizing the DISCOM profit in the first level and later minimizes the generation cost of the MG system. The MG system considered for the study consists of renewable energy sources, fossil fueled generators, micro turbine and fuel cell and the cost components involve depreciation cost, fuel cost, penalized emission cost and operation and maintenance (O&M) cost. An incentive based demand response (IBDR) policy is implemented which utilizes customer’s willingness to curtail their load demands and offers incentives for the same. When the DISCOMs share of profit is maximized using IBDR policy in level one, the modified load demand of the MG system obtained by deducting the curtailed load from the forecasted load is considered to minimize the generation cost of the MG system in the second level. A recently developed swift and facile Arithmetic optimization algorithm (AOA) is used as optimization tool for the study. The maximum DISCOM benefit was found to be 211▪ when the customers curtailed 105 kWh energy as per their willingness. Literatures reported the minimum power generation cost of the MG system as 880▪, 871▪ and 807▪ for base load, load shifting based demand side management (DSM) and customer incentive-based DR policies respectively. The proposed IBDR policy outperformed the reported literatures with a minimal generation cost of 768▪. Measures of central tendencies also corroborate to the robustness and efficiency of AOA.

Experimental and simulation study on coal-fired power generation coupling with fluidized bed biomass gasification

Positive-pressure fluidized bed technology utilizes the pressure generated by biomass gasification to facilitate further combustion within the furnace. This technique addresses the safety risks associated with external pressure in fluidizing medium and high-temperature situations and reduces equipment investment and operating expenses. However, research on the effects of the positive-pressure process in fluidized beds on gasification coupled with coal-fired power generation systems is scarce. To fill this research gap, extensive experimentation and Aspen Plus simulation were conducted to investigate the impact of equivalence ratio (ER), gasification pressure, and gasification temperature on the gasification performance. A 660 MW coal-fired boiler model was established to reveal the coupling effect on flue gas temperature and thermal efficiency by gasification from a positive-pressure fluidized bed on the boiler, and its accuracy was proved by comparing the actual boiler design parameters. The power generation cost of the positive-pressure gasification coupled coal-fired power generation system is calculated with the established model. The results indicate that an increase in gasification pressure from 4000Pa to 8000Pa resulted in no significant change in the operating indicators of the boiler, with the tail gas temperature near 132 °C and a boiler thermal efficiency of 92.68 %. Gas yield, carbon conversion rate, and gasification efficiency were positively correlated with gasification temperature and ER. However, the heat value of combustible gas increased with the gasification temperature and decreased with an increase in ER. With the rise of the biomass gas blending ratio, the power generation cost of the unit gradually decreases. Respectively, the positive -pressure would lower the power generation costs of a 660 MW coal-fired boiler by 5.76 × 105$/year. This study demonstrates that positive-pressure fluidized bed gasification may retain high gas yields and efficiency, significantly lowering equipment investment and operating expenses, as a very efficient energy-saving and carbon-reduction technique.

Enhancing the efficiency of power generation through the utilisation of LNG cold energy by a dual-fluid condensation rankine cycle system

As a clean energy source with high calorific value and low pollution, liquefied natural gas (LNG) has gained much attention and increased fast in the current energy market. It also has considerable cold energy resources that can be used to generate electricity during the regasification process. To fully utilise the cold energy of LNG, a double-Rankine cycle power generation system that incorporates heat exchange between LNG cold energy utilisation and a propane-ethylene cycle working medium is proposed and optimised. The optimisation is based on the Process Integration method, which uses Pinch Analysis to develop a Heat Exchange Network. Upon a specified LNG flow rate of 15.6 kg/s and natural gas delivery pressure of 7.85 MPaG, a retrofit case of the optimised LNG cold energy system generates a power of 1917.21 kW. A 28.6 % increase in power generation efficiency compared with the existing case. The result showed that by employing the Process Integration method, this study maximises the use of LNG cold energy through heat exchange with various working media, effectively addressing power generation efficiency issues. This approach is important in reducing power generation costs, minimising environmental impact, and advancing resource sustainability. Furthermore, it serves as a valuable reference for enhancing power generation efficiency by utilising LNG cold energy.

Influence of PCHE size and types on thermodynamic and economic performance of supercritical carbon dioxide Brayton cycle for small modular reactors and its optimization

The supercritical CO2 Brayton cycle is a potential technology in small modular reactors (SMRs), and the overall system efficiency can be significantly improved by optimizing the PCHE design parameters. This study develops a thermodynamic-economic analysis model for a supercritical CO2 simple Brayton cycle cooled SMR, with consideration of the detailed parameters of PCHEs. The impact of the length, channel number and channel type for the recuperator and the precooler on system thermal efficiency and levelized cost of electricity (LCOE) are analyzed. A dual-objective optimization study is conducted at the system level to identify the optimal design of size parameters and types for the heat exchangers. The results indicate that the influence of PCHE size parameters variations on system performance varies across different channel types. Additionally, the selection of the recommended recuperator type is influenced by the recuperator inlet Reynolds number. At the optimum design, the recommended channel type for recuperator and precooler are both S-shaped, resulting in a system efficiency of 39.12% and LCOE of 0.0456 $/kWe. The findings are valuable for enhancing energy utilization and reducing the power generation cost of SMRs.

Locational marginal price based scheduling strategy for effective utilization of battery energy storage in PV integrated distribution system

Integration of renewable energy resources (RERs) and complexities associated with varying load patterns urge the demand for energy storage systems (ESS) to ensure real power balancing of distribution system. Among various ESS, battery energy storage system (BESS) guarantees the reliable operation of microgrids in terms of economic and technical functionalities. The scheduling of microgrids becomes essential for the economic and optimal performance of both generating sources and loads. In this work, the load-shifting capability of BESS is utilized for the optimal scheduling of grid-connected microgrids. BESS has a significant role in the effective utilization of power from RERs during grid balancing. This paper discusses the scheduling of grid-connected microgrid with two scheduling strategies, which differ in the charging behaviour of BESS. Scheduling decisions are based on the value of locational marginal price (LMP) of distribution system. In scheduling strategy-I, BESS charges at off-peak hours, either from the PV system or from the grid. Meanwhile, in scheduling strategy- II, BESS charges from the PV system at peak or off-peak hours. These two scheduling methods are also compared with the scheduling of the distribution system without integrating the microgrid. The cost of power generation, power from the main grid, and active power losses of the distribution system are calculated by performing optimal power flow (OPF). The effectiveness of the proposed scheduling strategies are validated on the IEEE-69 bus system for 24 h time duration by using MATPOWER simulation tool. The results obtained after OPF establishes the requirement of effective utilization of BESS in PV integrated distribution system.

A master-slave adaptive linear neuron-based approach for cost-effective use of battery energy storage systems in wind farms

Nowadays, clean and sustainable energy development is of essential concern, which justifies use of renewable energy sources. Wind energy is an important resource to provide such a demand. Due to the high costs of wind power generation compared to other renewable sources, the wind turbines should be designed in such a way that they usually operate at the highest point of their power. In this case, because of the random and alternating wind speed, the output power of the wind turbine generators and thus the windfarms fluctuate. When the capacity of windfarms is increased, the power injected from the windfarm into the grid can have significant negative effects on the power grid stability. In order to prevent these effects, it is necessary to use BESSs in windfarms. To this end, a waveform with acceptable fluctuations is considered for the windfarm output power. This waveform is compared with the output power waveform and then the difference between the two waveforms is compensated by a BESS. Proper tracking with the minimum delay reduces the required BESS capacity and thus initial investment cost for the windfarms is significantly reduced. In this paper, using real windfarm data, the conventional tracking methods for the windfarm output power waveform are analyzed and compared, first. Then, a novel tracking scheme, namely the MSA, is proposed, which is based on a two-fold master-slave adaptive linear neuron. Acquired results show that compared with the conventional methods, using the MSA scheme can reduce the costs of a 99 MW windfarm by 4.8 million dollars.

Loading pattern optimization of a PWR using newly developed Multi-Verse optimization algorithm

In the fuel management of nuclear reactors, which has a direct impact on power generation costs and environmental factors, it is essential to obtain an optimal arrangement of assemblies and core parameters. This complex task relies on both neutronics and thermal hydraulics. This research solves this by employing PARCS and COBRA-EN codes, alongside Multi-Verse Optimization algorithm (MVO) as a novel algorithm. Findings demonstrate the MVO algorithm has reliability and favorable convergence rate. Moreover, integrating the nodal expansion technique with MVO notably decreases the computational time for optimization in the loading pattern optimizations (LPOs). The optimized loading pattern aligns well with the findings in the literature. Also, using multi objective fitness function leads to improving core flatness, cycle length and safety parameters.

Demand side management with wireless channel impact in IoT-enabled smart grid system

Demand Side Management (DSM) is a vital issue in smart grids, given the time-varying user demand for electricity and power generation cost over a day. On the other hand, wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid. The design of any DSM system using a wireless network must consider the wireless link impairments, which is missing in existing literature. In this paper, we propose a DSM system using a Real-Time Pricing (RTP) mechanism and a wireless Neighborhood Area Network (NAN) with data transfer uncertainty. A Zigbee-based Internet of Things (IoT) model is considered for the communication infrastructure of the NAN. A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link. The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users, decision-makers, and energy providers. A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices. Simulation results indicate that the proposed system benefits users and energy providers. Furthermore, experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN, which can substantially impact the performance of the proposed DSM system. Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price, user welfare, and provider welfare.

Research on the application of computer science in wind power generation and the future trends

In the context of the growing global demand for clean energy, wind power, as an important form of renewable energy, is gradually occupying a dominant position. At the same time, the continuous development of computer technology has also brought new breakthroughs in wind power generation. Traditional wind power generation suffers from low power generation efficiency and large fluctuations in power generation. However, through the integration of wind power generation with advanced computer technologies, the efficiency and cost of wind power generation have been greatly optimized. This paper seeks to provide an overview of how computer science is utilized in wind power generation, focusing on the integration of computational modeling, simulation, and machine learning. It highlights future trendsand emphasizes the significance of developing digital twins and integrating machine learning with physical models for efficient wind farm management. Combining findings from various existing studies, this paper delves into the profound implications for the future of energy, highlighting the transformative role of computer science in advancing sustainable power solutions and shaping the landscape of renewable energy sources.

The application effect of the optimized scheduling model of virtual power plant participation in the new electric power system

To build a comprehensive framework for virtual power plant (VPP) development aligned with market dynamics and to devise effective strategies to foster its growth, this study undertakes several key steps. Firstly, it constructs a VPP development framework based on market conditions, to drive the evolution of new power systems and facilitating energy transformation. Secondly, through a blend of theoretical analysis and model construction, the fundamental principles of VPP are systematically elucidated, and a decision model for the VPP development framework, focusing on price demand response, is formulated. Lastly, an optimal scheduling model for the new power system is developed, with its efficacy validated across three distinct scenarios. The findings underscore the critical importance of integrating energy storage technologies, particularly pumped storage hydropower systems, for achieving balance and optimization within new power systems. Model verification reveals that the incorporation of energy storage power stations significantly enhances system stability and efficiency, particularly in addressing the volatility associated with renewable energy sources. Additionally, the analysis indicates that while the adoption of energy storage technologies may marginally increase overall power generation costs, the total power generation cost declines with the integration of battery storage and pumped storage hydropower stations. This suggests that leveraging energy storage technologies not only enhances system operational reliability but also contributes to reducing the overall cost of power production to a certain extent. In summary, this study presents an economic and environmentally sustainable scheduling model for new power systems within the context of market trading environments. By offering both theoretical insights and practical guidance, it aims to support sustainable development and energy transformation initiatives. Ultimately, the study is poised to foster the adoption of clean energy, facilitate the establishment of smart grids, and bolster the sustainable utilization of energy resources, thereby advancing environmental conservation efforts.

Low-carbon economic scheduling of virtual power plant considering carbon emission flow and demand response

To fully explore the potential low-carbon and economic advantages of a virtual power plant (VPP) that aggregates multiple distributed resources, the paper proposes a VPP scheduling model that considers the carbon emission flow (CEF) and demand response (DR), which is characterized by electro-carbon coupling and source-load interaction. First, the electric-carbon characteristics of each distributed resource under VPP are modeled, and the source-load electric-carbon coupling characteristic model is modeled through the CEF theory. On this basis, a load-side multi-type DR model is established to achieve the purpose of source-load synergy to reduce carbon emissions from VPP. To this end, a two-stage scheduling model of VPP considering the source-load electro-carbon coupling relationship is established, and the implementation of the model can reduce power generation costs, carbon emissions and promote clean energy, and the simulation results of the improved IEEE-14 node system verify the effectiveness of the proposed model.

A Comparative Smart Computation Algorithm for Economic And Emission Dispatch Optimization of Tanjung Jati Power Plant

This research delves into optimization strategies for enhancing power generation efficiency and reducing costs in PLTU Tanjung Jati. The study explores three computational algorithms for optimization: Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), focusing on optimizing power generation costs while considering penalty costs and emissions. Through extensive simulations and evaluations, the results indicate that the GWO algorithm achieves the most optimal outcomes in terms of cost optimization, demonstrating fast convergence and lesser simulation time compared to the other algorithms. The research provides valuable insights into selecting optimal algorithms for complex optimization problems like Combined Economic Emission Dispatch (CEED) in power generation systems, ultimately contributing to more efficient and cost-effective power generation.

An Effective Method of Equivalent Load-Based Time of Use Electricity Pricing to Promote Renewable Energy Consumption

The variability and intermittency inherent in renewable energy sources poses significant challenges to balancing power supply and demand, often leading to wind and solar energy curtailment. To address these challenges, this paper focuses on enhancing Time of Use (TOU) electricity pricing strategies. We propose a novel method based on equivalent load, which leverages typical power grid load and incorporates a responsibility weight for renewable energy consumption. The responsibility weight acts as an equivalent coefficient that accurately reflects renewable energy output, which facilitates the division of time periods and the development of a demand response model. Subsequently, we formulate an optimized TOU electricity pricing model to increase the utilization rate of renewable energy and reduce the peak–valley load difference of the power grid. To solve the TOU pricing optimization model, we employ the Social Network Search (SNS) algorithm, a metaheuristic algorithm simulating users’ social network interactions to gain popularity. By incorporating the users’ mood when expressing opinions, this algorithm efficiently identifies optimal pricing solutions. Our results demonstrate that the equivalent load-based method not only encourages renewable energy consumption but also reduces power generation costs, stabilizes the power grid load, and benefits power generators, suppliers, and consumers without increasing end users’ electricity charges.

A novel design and analysis of hybrid fuzzy logic MPPT controller for solar PV system under partial shading conditions

Renewable energy resources are more useful when associated with the thermal power generation network because of their high accessibility in the environment, good system response, easy manufacturing, plus high scalable. So, the present research is going on solar power to reduce consumer grid dependency. The running of the PV network is quite easier, plus less human sources are involved. However, the solar modules’ power generation is nonlinear fashion. So, the collection of peak power from the sunlight-dependent systems is a highly challenging task. In this article, a Modified Differential Step Grey Wolf Optimization with Adaptive Fuzzy Logic Controller (MDSGWO with FLC) is developed for collecting the maximum power from renewable energy resources under diverse Partial Shading Conditions (PSCs). The introduced method comprehensive analysis has been done along with the other recently existing MPPT methods in terms of convergence speed, MPP tracking accuracy, operating efficiency of the introduced method, functioning duty value of the DC–DC boost power converter, dependence of MPPT on sunlight system, total number of sensing devices are needed, plus peak power extraction from the proposed system. Here, the sunlight power generation cost is more to limit this issue, a power converter is selected in the second objective to develop the voltage source capability of the PV network. The overall PV-interfaced power converter network is examined by utilizing the MATLAB environment.

Techno-economics of multi-stage reverse electrodialysis for blue energy harvesting

Multi-stage reverse electrodialysis (MSRED) offers a promising way for efficient salinity gradient energy harvesting. Here, an improved model of the MSRED system under serial control strategy is proposed. The technical–economic analysis is conducted with considering discount, depreciation and different regional tax and electricity price levels under the maximum net power output conditions. Results reveal that net power output and energy efficiency both increase first with increasing stage numbers, reach their maximum values, and then decrease. For 5 M/0.05 M solutions, the optimal net power output of 4.98 kW is obtained at the stage number n = 12. The optimal stage number corresponding to the maximum net power increases with increasing feed solution concentrations. Due to the compromise between net power generation and capital cost, there exist optimal stage numbers leading to the lowest LCOE and largest NPV, respectively. Higher feed solution concentration can significantly decrease the system LCOE and increase the NPV. The optimal stage number corresponding to the maximum NPV increases with increasing feed solution concentrations. In Germany, for 5 M/0.05 M solutions, the lowest LCOE of 0.061 €·kWh−1 is achieved at n = 3 while the highest NPV over the system lifecycle of 52,005 € is obtained at n = 8. Lower tax, higher electricity price, appropriate membrane price and stage numbers, and high salinity gradient sources can significantly accelerate the commercial completeness of the MSRED systems.

A flexible and deep peak shaving scheme for combined heat and power plant under full operating conditions

Improving the flexible and deep peak shaving capacity of combined heat and power (CHP) plant under full operating conditions to facilitate renewable energy consumption is the main choice of novel power system. Accordingly, a dry/wet state automatic conversion control scheme fuses the precise mechanism structure, reinforcement learning and multi-objective model predictive control (MPC) algorithms is designed to promote the deep peak shaving ability of CHP plant in this paper. Firstly, the detailed mechanism models of once-through boiler at dry and wet state are presented by analyzing the dynamic characteristics of steam-water flow. Secondly, the large-scale unknown parameters in mechanism models are identified via the Takagi-Sugeno fuzzy modeling, reinforcement learning algorithms and actual dry/wet state conversion data. Thanks to the proposed hybrid modeling strategy, the high-precision boiler-steam unit models at dry and wet state are rapid obtained. Then, to meet different peak shaving demands, the multi-objective MPC algorithm is respectively constructed under dry and wet state with the comprehensive consideration of operational constraints, load tracking error, algorithm stability, power generation cost, CO2 and NOx emission costs. Aiming at maximizing the peak shaving efficiency and flexible operation ability of plant, a dry/wet automatic control scheme combined the designed multi-objective MPC algorithms and identified dry/wet state models is proposed. Finally, the load variation rate of 5 % and 2 % RCM/min is respectively achieved in the dry/wet state conversion tests on a 350 MW CHP plant based on the proposed control scheme. Thus, the rapid and thorough dry/wet state conversion performance of once-through boiler has successfully improved the operational flexibility of CHP plant under full operating conditions.

Multi-Objective Dynamic Economic and Emission Generation Scheduling of Coordinated Power System Considering Renewable Energy Sources and Pumped Storage Hydro Plants via Fuzzy Surrogate-Assisted Coronavirus Herd Immunity Optimization

Globally, the power generation from renewable energy sources (RES) has received considerable attention to reduce pollutant emissions and the total operating costs of power generation. In this context, this study aims to extend the multi-objective dynamic economic and emission dispatch (MODEED) problem by incorporating RES and pumped storage hydro plants. The complex constrained MODEED problem is solved by a fuzzy surrogate-assisted coronavirus herd immunity optimization (FSACHIO) algorithm in which a self-adaptive speed factor and Lévy flight mechanism are utilized to ensure populace diversity, prevent premature convergence and achieve an ideal stability among the exploration and exploitation of the algorithm. Surrogate worth tradeoff methodology is used to discover a solution that provides the optimal balance between the objectives such as operating expenses and emission pollutants. The performance of FSACHIO algorithm is validated on a small-scale and a large-scale test systems, involving 10-unit, and 40-unit test systems, and compared with that of the coronavirus herd immunity optimization, red fox optimizer, Remora optimization algorithm, and other erstwhile approaches. Besides, a fuzzy constraint handling method based on a dominance relationship is integrated to fulfill the restrictions of the MODEED problem. The simulation results reveal that: (i) the operating costs and emissions are reduced by 173785.54 $/day and 2992.1848 lb/day in the small-scale test system and by 6383.9903 $/day and 216631.4877 lb/day in the large-scale test system, respectively for the MODEED problem with the presence of RES and pumped storage hydro plants; (ii) the suggested approach may offer a well-distributed Pareto-optimal frontier and superior tradeoffs among the operating expenses and pollution objectives in comparison to the other approaches.

Economic Dispatch of Multi Regional Power Systems Based on CMOPSO Algorithm

With the progress of society and the aggravation of environmental pollution, the economic dispatch of the power system is developing towards multiple environmental and economic goals. To improve energy utilization efficiency, this study innovatively proposes a multi-objective particle swarm optimization algorithm based on competitive learning, and uses this algorithm to solve multi regional environmental and economic scheduling problems. In addition, the study solves static and dynamic economic (S-DE) scheduling problems in multiple regions through improved competitive group optimization algorithms. The research results show that under different testing systems, the average distribution uniformity indicators of the research algorithm built on competitive learning are 0.8058 and 0.8457, and the average anti generation distance is 67.6316 and 1664.0978. The improved competitive group optimization algorithm solves the maximum, minimum, and average fuel costs for static economic scheduling in multiple regions, which are 656.2243 $/h, 655.8592 $/h, and 655.9866 $/h, respectively. Thus, the designed algorithm can effectively solve economic scheduling problems, which is of great significance for resource integration, saving power generation costs, and reducing pollution emissions.

Technoeconomic Analysis of a Wind Power Generation System and Wind Resource Mapping Using GIS Tools: The Case of Twelve Locations in the Commune of Evodoula, Cameroon

This paper performs a technoeconomic analysis of the wind power potential and evaluates the cost of wind power generation in Evodoula, comparing two methods, the conventional method and the uncertainty method based on a comparative spatiotemporal approach using the geographic information system (GIS) software tool. This study is based on satellite wind data measured at 10 m above ground level (AGL) over a 40-year period (1980-2019), by the meteorological service NASA (National Aeronautics and Space Administration)/Goddard Space Flight Center (GSFC). The main objectives are to obtain an appropriate design for a proven optimal location and to assess the viability of wind power at Evodoula. For this type of study, there is little literature available. The optimization of an onshore wind farm and deployment in the wind energy development interest area (ZIDEE) is carried out to obtain a minimum and parsimonious discounted cost production unitary (CPU) of the electricity produced. The results showed that in Okok, a location with a large energy deficit, an onshore wind farm with an electricity generation capacity of 12.5 MW with 5 NORDEX N100-2.5MW wind turbines would have a total energy production (TEP) of about 64.0254825 TWh and a selling price of electricity that would be 0.0034 CAD$/kWh, which is very low compared to the utility price (about 98% cheaper). The total cost of the wind farm would be about $11 million, with a net present cost of about $218 million. The annual profit generated by the wind farm would be over $6 billion. The return on investment (ROI) of the project is estimated at 2880.882%. The constructed onshore wind farm would avoid CO2 emission at over 11 MtCO2,eq/year as the energy generated is from the atmosphere. The wind farm would realize an average annual cash flow estimated at nearly $30 million after 20 years of operation. These savings would allow the installation of CO2 capture systems in conventional power plants. In addition, the analysis of uncertainties and risks was identified and quantified to estimate the confidence levels of the project development results. The risks have been assessed, and we recommend that the total uncertainty of the project is around 15%. The energy values in P75 and P90 are 10.08% and 19.22%, respectively, lower than the energy value in P50 of 11.60 GWh/An. Finally, the main policy recommendations for an inclusive design process are highlighted. The contribution of this study is to assist policy makers in making appropriate decisions in the development and implementation of energy and environmental policy in Cameroon and in many continental areas.

LEAP model-based analysis to low-carbon transformation path in the power sector: a case study of Guangdong–Hong Kong–Macao Greater Bay Area

As a major carbon emitter, the power sector plays a crucial role in realizing the goal of carbon peaking and carbon neutrality. This study constructed a low-carbon power system based on the LEAP model (LEAP-GBA) with 2020 as a statistic base aiming of exploring the low-carbon transformation pathway of the power sector in the Guangdong–Hong Kong, and Macao Greater Bay Area (GBA). Five scenarios are set up to simulate the demand, power generation structure, carbon emissions, and power generation costs in the power sector under different scenarios. The results indicate that total electricity demand will peak after 2050, with 80% of it coming from industry, buildings and residential use. To achieve net-zero emissions from the power sector in the GBA, a future power generation mix dominated by nuclear and renewable energy generation and supplemented by fossil energy generation equipped with CCUS technologies. BECCS technology and nuclear power are the key to realize zero carbon emissions from the power sector in the GBA, so it should be the first to promote BECCS technology testing and commercial application, improve the deployment of nuclear power sites, and push forward the construction of nuclear power and technology improvement in the next 40 years.