Cost Of Power Generation Research Articles

Research on energy interactive operation optimization strategy of multi-energy network with integrated energy based on cooperative game

Abstract Integrated energy includes multi-energy networks such as electricity, gas, cold, and heat. Horizontal multi-energy coordination and vertical source network storage load interaction can effectively support large-scale new energy consumption. In this paper, a multi-energy flow model of the integrated energy multi-energy network is constructed, an optimization model of energy sharing and mutual benefit of the multi-energy network is established based on Nash negotiation, and then the model is solved to obtain the optimal distribution scheme of cooperation benefits. Finally, a numerical example is given to verify the proposed energy sharing and mutual benefit method. The results show that the total power generation cost of each park is reduced by 27%, and the profit distribution of each park is more reasonable.

Coordinative optimization for wind farms considering improved fatigue load index

In the wind farm(WF), there exists a serious uneven distribution of damage equivalent load(DEL) on the wind turbine(WT), which increases the maintenance cost of wind power generation. Previous researches focus on a single objective such as the total or the standard deviation of fatigue loads for optimal control. To address these, this paper proposes a coordinative optimization by considering an improved fatigue load index. The coordinative optimization is achieved on both WF level and WT level. For WF level, a state-space model is built for the active power dispatch by utilizing small signal linearization. Then, an improved sensitivity analysis is performed to evaluate the fatigue load on the component of the tower and shaft. Further, a coordinative multi-objective function is designed, which minimizes the total DEL of the WF, and balances the individual fatigue load distribution of the WT. For WT level, an adaptive internal model control is proposed to suppress both tower vibrations and torque fluctuation of the main shaft. Several experiments are performed to verify the significance of the proposed method with the weight analysis of power and load coefficient. The comparison results reveals the superiority of the coordinative optimization in restraining the total DEL of the WF, balancing WF fatigue distribution, decreasing the DEL of the single WT, and satisfying the power reference tracking simultaneously.

Regional electricity cooperation model for cost-effective electricity management with an emphasis on economic efficiency

Effectively reducing the total electricity cost while ensuring its overall efficiency is vital for the sustainable development of power systems. Herein, we develop a regional cooperation optimization model to enhance the existing power cost model. This is achieved by incorporating power trading scenarios and pollution considerations, leveraging the distinct characteristics of power generation costs from various sources. The developed regional cooperation optimization model simultaneously accounts for production efficiency and overall power costs, ensuring equitable distribution and compensation of benefits among cooperating entities. In addition, the key findings indicate that the interregional cooperation optimization model significantly reduces the total cost of electricity operations in China. Specifically, during the 11th five-year plan period, the total electricity cost in China exhibited a noteworthy decline of USD 50,876 million owing to multi-objective cooperative optimization, constituting 25.99% of the total electricity cost compared with the preoptimization period. This study identifies the eastern and western regions as principal contributors to the cost savings achieved by the interregional cooperative optimization model. Simultaneously, recognizing their significant contributions, it is recommended that the eastern and western regions provide additional cost compensation to other regions to ensure the long-term viability of the cooperative alliance.

Short-Term Optimal Operation Method for Hydro–Wind–Thermal Systems Considering Wind Power Uncertainty

Wind curtailment, caused by wind power uncertainty, has become a prominent issue with the large-scale grid connection of wind power. To fully account for the uncertainty of wind power output, a short-term hydro-wind-thermal operation method based on a wind power confidence interval is proposed. By utilizing the flexible start-stop and efficient ramp-up of cascade hydropower plants to smooth fluctuations in wind power output, a multi-objective optimal scheduling model that minimizes the cost of power generation and maximizes the consumption of clean energy is constructed. To reduce the solution’s complexity, we chunk the model according to the energy type using a hierarchical solution. The overall solution framework, which integrates a nonparametric method, a heuristic algorithm, and an improved particle swarm algorithm, is constructed to solve the model rapidly. The simulation results of a regional power grid show that the proposed method can attain an efficient solution in 83.5 seconds. Furthermore, the proposed method achieves an additional 455,600 kWh of hydropower and a reduction of ¥233,300 in the cost of coal consumption. These findings suggest that the proposed method is a good reference for the short-term operation of a hydro-wind-thermal combination in large-scale wind power access areas.

Process simulation and techno-economic analysis of CO2 capture by coupling calcium looping with concentrated solar power in coal-fired power plant

The calcium looping (CaL) cycle is an effective high-temperature CO2 capture technology, its primary energy consumption arises from the calcination process, which limits the development of CaL technology. Utilizing concentrated solar power (CSP) instead of combustion for the calcination process in the CaL cycle can significantly reduce the energy penalty associated with CO2 capture of coal-fired power plants. Currently, the application of coupling CaL with concentrated solar power (CaL-CSP) systems in coal-fired power plants is still in its infancy, and there is limited research on its thermal performance and techno-economic analysis. In this study, a process simulation model of a 660 MW supercritical coal-fired power plant is developed as a reference. Subsequently, four different heat transfer routes are integrated into the design of CaL-CSP systems. The results indicate that the coupled power plant achieves a CO2 capture rate of 90 % compared to the reference coal-fired power plant, and reduces annual CO2 emissions by 93 % to only 58.2 g CO2/kWh. The CO2 capture energy penalty is 0.24 MJ/kg CO2. A sensitivity analysis of power generation cost reveals that if the cost of solar concentrator components is halved, the power generation cost will decrease to just 25 % higher than the reference power plant. Given the rapid advancements in CSP technology, this reduction is likely to be realized soon. Moreover, with the increase in carbon taxes and carbon prices, the power generation cost is expected to further decrease.

Joint intelligent optimizing economic dispatch and electric vehicles charging in 5G vehicular networks

In recent years, with the rapid development of 5G networks, the road traffic network composed of vehicles with different energy sources has become more and more complex, and the problems of environmental pollution and road congestion have also become increasingly serious. Electric vehicles are favored by people due to their environmental protection and energy-saving characteristics. However, improper charging dispatching will cause excess energy in charging stations, affecting the power grid and road traffic, such as energy shortages and lower traffic throughput. Therefore, how to design a reasonable charging strategy that can maximize the user’s charging satisfaction and consume the energy of the charging station as much as possible becomes a challenge. Meanwhile, this strategy should consider power economic dispatch to reduce power generation costs and polluting gas emissions. With the support of 5G’s high-bandwidth and low-latency characteristics, this paper designs an intelligent charging model which indirectly reflects the charging satisfaction through the time cost, energy consumption cost, charging cost, and the user’s range anxiety, while consuming the remaining energy of the charging station as much as possible. Due to the uncertainty of wind and photovoltaic power generation, this paper proposes a two-stage economic dispatch model to improve the accuracy of power dispatch and reduce power generation costs and carbon emissions. Due to the highly variable traffic environment and energy demand, we employ proximal policy optimization-based deep reinforcement learning algorithms to realize electric vehicle charging dispatching and charging station power dispatching. Numerical results show the efficiency of our proposed strategy for electric vehicle charging in terms of the convergence speed.

A Surrogate Model Approach to Predict Operations and Maintenance Cost for Combined Cycle Power Plant

Abstract In the transition toward carbon neutrality by 2050, renewable energy sources are gaining prominence. The variability of renewable energy poses challenges for power grid stability. Gas turbine combined cycle (GTCC) power plants play a crucial role in grid stabilization, especially during peak loads. In GTCC power generation, operations and maintenance (O&M) cost is the next most important after fuel and initial investment cost, and it is necessary to predict O&M cost according to changes in the operating environment for efficient and stable operation of GTCC power plant. However, various uncertainties exist when estimating O&M costs because actual design information is limitedly disclosed due to maintenance contracts between operators and manufacturers, and it is difficult to obtain accurate variables according to the operation condition. In this paper, the correlation was analyzed considering the uncertainty of the factors affecting the O&M cost of the GTCC power plant through sensitivity analysis, and the main factors were defined. In addition, a surrogate model was proposed that can quickly calculate fixed and variable O&M cost according to changing operating scenarios such as shortening startup time, enhancing ramp rate, or lower minimum load. Through the case study, short-term and long-term O&M costs can be estimated in the operating environment of the GTCC power plant currently being operated. Additionally, the model facilitates cost projections for new hybrid GTCC facilities (combining gas turbines with energy storage systems), aiding long-term investment planning.

General integration method for system configuration of organic Rankine cycle based on stage-wise concept

Organic Rankine Cycle (ORC) integrated systems with heat sources consist of heat exchange processes and thermodynamic power conversion processes. Fully exploring feasible system configuration while considering fluid selection is crucial for enhancing ORC power generation and balancing economic costs. This study innovatively proposes a theoretical framework that decomposes the Rankine cycle into basic thermodynamic processes. The design of system configuration is regarded as the adjustment of the series-parallel relationships of these basic processes, aiming to achieve simultaneous optimization of the cycle structure and the HEN structure, thereby greatly expanded the search space for feasible ORC system configurations. The optimization problem is solved using a bi-level strategy, The outer layer determines the number of thermal power conversion components, operating parameters, and working fluid selection, then provides the associated stream information to the inner layer for optimizing the heat exchange scheme. Applying this method to two different case studies from the literature validated its effectiveness in optimizing scenarios with single and complex heat sources. The Pareto frontier obtained under the conditions of Case I indicates that the investment cost can be reduced by $33.04 k/yr while maintaining the same net power output. In exchange for an increase of $1.51 k/yr in investment cost, the maximum net power output can be improved by 124.74 kW. Under the conditions of Case II, the maximum net output power can increase by 9.71 %–52.5 %, and the Pareto frontier featuring outputs exceeding 50 kW is provided. By analyzing the relative positions of literature schemes and Pareto front within the solution space, the impact of system configuration improvements on the objective functions is explained. These results confirm the effectiveness of the proposed approach for designing optimal ORC energy recovery systems for heat sources. This contribution advances optimization methods for similar closed cycle configurations, which is significant for advancing the utilization of waste heat and addressing sustainability challenges.

Simulation study of cadmium-free CIGS solar cell for efficiency enhancement by minimizing recombination losses through back surface field mechanism

Thin-film photovoltaic cells provide benefits over conventional first-generation technology, including lighter weight, greater flexibility, and lower power generation costs. Chalcogenide solar cells offer good efficiency and technological maturity among thin-film technology. In this work, a solar cell device structure, Al/FTO/SnS2/CIGS/CuO/Ni, is examined using SCAPS-1D. Further, by incorporating the back surface field (BSF) layer, the conversion efficiency increased from 18.93 % to 29.88 %, followed by VOC of 0.96 V, JSC of 37.07 mA cm−2, and FF of 83.71 %. This increment in device performance is owing to the lowering back surface recombination velocity and the additional hole tunnelling activity offered by the BSF layer by forming a quasi-ohmic contact, i.e. metal-semiconductor contact with negligible junction resistance relative to the total resistance of the device. Throughout this research work, the authors studied several factors such as surface recombination velocity, temperature impact, front and back contact metal work functions, parasitic resistance impact, gallium proportion, and doping density impact on CIGS solar cells. The work also included a calibration with experimental data from published sources to validate the simulation results.This paper presents a new approach for producing high-efficiency, eco-friendly CIGS solar cells with CuO back surface field mechanism.

An innovative bio-inspired Aquila technique for efficient solution of combined power and heat economic dispatch problem

In the field of power systems, the optimization challenge of combined heat and power units economic dispatch (CHPUED) holds immense importance. This study presents an improved Aquila optimization technique (IAQT) that effectively tackles the CHPUED. The primary objective of the enhanced IAQT model is to minimize the overall cost of power generation in CHP systems while satisfying demand and operational constraints. However, to achieve more accurate cost estimations and avoid suboptimal solutions, it is crucial to consider transmission losses in the optimization model. By incorporating transmission losses, the IAQT algorithm can allocate power generation resources more effectively, leading to improved system efficiency and reduced operational costs. The proposed IAQT algorithm addresses the limitations of the standard AQT and introduces novel features to enhance its search capabilities. One key limitation of the standard AQT is its heavy reliance on the best solution found during optimization. To overcome this drawback, the enhanced IAQT model eliminates the dependency on the best solution and enables a more thorough exploration of the search space. Moreover, the algorithm incorporates specific limitations and constraints for each dimension of the newly generated solutions, ensuring their feasibility and validity. The standard AQT and proposed IAQT are tested on CEC 20 benchmark functions. Moreover, the proposed approach is extensively evaluated through experimentation and testing on various scenarios, including 7–48-unit and large 96-unit systems with/without losses. Furthermore, the overall costs for the 7 unit-system are considered including the reserve constraint. The results exhibit the remarkable performance and efficiency of the enhanced IAQT model, outperforming the standard version and several previously reported results. This validation underscores the significant contribution of the study in addressing the CHPUED and highlights its potential for real-world applications.

Security-constrained economic dispatch of power systems with line outage using firefly algorithm

The Security-constrained Economic Dispatch (SCED) problem is the problem of finding the optimal generation dispatch for a power system that minimizes the operating cost while ensuring the security of the system. The security of the system is defined as the ability of the system to withstand disturbances without violating any operational constraints. Line outage is a common disturbance that can affect the security of a power system. The Firefly Algorithm, inspired by the flashing behavior of fireflies in nature, is a metaheuristic optimization technique known for its effectiveness in solving complex and dynamic optimization problems. In this study, the FA is adapted to the SCED problem with a specific focus on enhancing grid resilience during line outage scenarios. The proposed method aims to simultaneously optimize the economic cost of power generation and the system's ability to withstand line outages. To demonstrate the effectiveness of the proposed approach, extensive simulations are conducted on standard IEEE test systems with varying degrees of complexity and network sizes. The results showcase the superior performance of the SCEDL-Firefly Algorithm compared to traditional optimization methods, as it provides a resilient dispatch solution that can effectively adapt to unexpected line outages while maintaining economic efficiency. Overall, this research contributes to the advancement of secure economic dispatch techniques and power system resilience by leveraging the Firefly Algorithm's capabilities. The findings offer valuable insights for power system operators and planners seeking to enhance grid reliability in the presence of line outages, ultimately promoting a more sustainable and resilient energy infrastructure.

Performance analysis of a novel hybrid device with floating breakwater and wave energy converter integrated

As a kind of clean energy, wave energy has attracted increasing attention in recent years. However, due to its high cost and low efficiency, wave energy has not been widely commercialized worldwide. According to previous research works, the combination of wave energy devices and floating breakwaters can reduce the cost of wave power generation devices efficiently. In this paper, a hybrid device with wave energy converter (WEC) and flexible porous floating breakwater integrated is proposed. The hydrodynamic performance of this device is studied by numerical simulation based on Reynolds-Averaged Navier–Stokes (RANS) method which has been validated through experimental results. Based on this, a series of numerical simulations are performed under different power take-off stiffness (KPTO) and different power take-off damping coefficient (CPTO) to calculate the motion and transmission coefficient of the device. By comparing the numerical results, the influence of KPTO and CPTO of the hybrid device on its wave attenuation performance and energy conversion efficiency are analyzed. The results indicate that under the incident waves of period 0.56 s and 0.89 s, the recommended value of KPTO is 3.0 N/m and 0.5 N/m respectively. While for incident waves of period 0.56s, in order to achieve better wave attenuation performance and power generation efficiency, the recommended value of CPTO should be between 10.55 Ns/m and 14.23 Ns/m. On this basis, the influence of the size of breakwater on the wave attenuation performance and power generation efficiency of the hybrid device is also discussed. Moreover, through a cost analysis, it can be inferred that the utilization of the baseline model is more economically viable.

Real-time pricing considering carbon constraints for smart grid with multiple energy sources

With the escalation of fossil fuel pollution and the challenges of climate change, the smart grid is forced to address the carbon emissions associated with fossil fuel use. Real-time pricing of smart grid is an effective means to regulate the balance of supply and demand in the grid as well as to achieve energy conservation and environmental protection. This paper takes traditional fossil energy and clean energy power supply as the background, takes social welfare maximization as the goal, combines carbon emission cost with traditional power generation cost, and constructs a real-time pricing model that takes into account carbon emission cost, so that thermal power enterprises participate in the power market and carbon market at the same time. By using duality methods, we transformed the original optimization problem into an equivalent one. Furthermore, a distributed iterative algorithm based on the subgradient projection method is employed to solve the dual problem. The simulation results verify the feasibility and effectiveness of the proposed real-time pricing strategy.

Experimental study on thermal economy of 330 MW subcritical thermal generating unit

Abstract To achieve carbon peak and neutrality targets, improve energy efficiency and reduce power generation costs, it is particularly important to study the thermal economy of thermal generating units under variable operating conditions. Taking the 330 MW subcritical thermal generator unit as the research object, based on the deep mining of SIS operation data, combined with the testing of key parameters, the thermal characteristics of the boiler, pipeline and turbine are deeply analyzed, and the key factors of heat loss of the unit are studied. The heat input, loss and output are calculated and counted in detail, the heat balance data relationship of the unit thermal system is established, and the heat utilization and loss of the unit are clarified. The main parameters of the thermal system were calculated and analyzed to study the main reasons affecting coal consumption and corresponding optimization measures. Through the research and analysis of thermal characteristics, heat utilization, and economic indicators of coal consumption, the targeted measures of energy saving and consumption reduction can provide a scientific basis for the daily scientific management and operation of thermal generating units and equipment transformation.

Reversible solid oxide cells-based hydrogen energy storage system for renewable solar power plants

The power-H2-power system based on reversible solid oxide cell is a promising pathway for large-scale renewable energy storage but not well understood due to the absence of comprehensive system analyses. In this study, a reversible solid oxide cell-based H2 energy storage system for a 100 % renewable solar power plant is proposed and analyzed through detailed modeling approach and optimization framework. The detailed parametric analyses and economic evaluations are performed in electrolysis (12:00 pm, sufficient solar energy) and power generation (6:30 am, insufficient solar energy) conditions. Notably, it is found that larger stack capacity (Ncell) and higher operating temperature (TReSOC) of the cell enhances system net H2 production and system economics despite additional investment cost. Afterwards, the optimal system performance is obtained (VH2,produce = 498.40 Nm3·h−1, Z=263.59 $·h−1 for electrolysis, and VH2,consume = 380.33 Nm3·h−1 for power generation) through multi-objective optimization by fully considering cell internal operating characteristics and system energy-exergy-economic factors. Besides, it is also found that the performance of power-H2-power system is still limited by remarkable fuel (H2) costs for nighttime power generation (daytime H2 production is smaller than nighttime H2 consumption when Wtot = 1 MW). Therefore, the critical power capacity (Pcritical) is obtained, offering a convenient approach to determine the maximum output capacity (Pcritical = 880 kW) to make the power-H2-power system profitable at specific solar energy input. This study provides valuable insights between system capacity, economics, and cell operating features in solar power plants, which are useful for the design and optimization of practical power-H2-power systems for renewable energy storage.

A Study on Hybrid Power Plant (Solar & Biomass)

We have designed an on-grid hybrid power plant that combines solar and biomass energy sources. The solar plant has a capacity of 6 MW, and the biomass plant has a capacity of 2 MW. The hybrid solar-biomass power plant is still a recent renewable technology. It is an ongoing investigation that not only proposes dispatchable power generation but also benefits from having a flexible input source, including solar and bioenergy. We aimed to successfully design a hybrid project using proper analysis, to reduce the cost of power generation through a grid-connected hybrid system, while also reducing greenhouse gas emissions. We used PVsyst software to simulate the solar plant and created the PV mounting structure and Single Line Diagram (SLD) using AutoCAD software. We have used Microsoft Excel software to carry out the cost analysis. We also calculated how much biomass waste and gas the biomass plant will need. We have been able to successfully design our hybrid power plant and, at the same time, reduce the power generation cost comparatively. The hybrid system design has made it possible to ensure the most convenient arrangement of electricity.

Dynamic Evaluation Method of Electric Power New Energy and River Basin Ecological Restoration

The wide application of renewable energy can reduce the carbon emissions to the environment and the consumption of petrochemical resources, improve the improvement rate of the natural environment, and at the same time, ecological restoration can improve the ecology of the river basin and produce sustainable improvement of the environment together with renewable energy. In this paper, renewable energy is taken as the research object, and the ecological restoration method of the river basin is jointly evaluated to comprehensively evaluate the natural environment. First, the carbon neutrality data of ecological restoration and renewable energy in the river basin were collected, classified, collated and integrated, and the analysis indicators included CO2 emissions, plant coverage, soil and water conservation, and the cost of power generation. Then, the spatial regression model was used for data processing and calculation, and the ecological coefficient and carbon emission coefficient were determined in an iterative manner. Finally, the actual case is used for dynamic evaluation and analysis. The results show that the new power and watershed ecological restoration methods can improve the natural environment, improve the plant coverage rate and soil and water conservation rate, with an increase rate of 25~30%, reduce carbon dioxide emissions and power generation costs, and reduce the reduction rate of 10~30%, and at the same time, a good ecological environment will also react to new energy and expand the development space of new energy, with an expansion rate of 10%. Therefore, the method of electric power new energy and river basin ecological restoration can improve the natural environment, promote the development of electric power new energy, and optimize the overall structure of new energy.

Proportional‐integral‐differential‐inspired acceleration in distributed optimal control strategy for direct current microgrids

AbstractA PID‐inspired accelerated distributed optimal control algorithm is proposed for the economic dispatch problem of a multi‐bus DC microgrid, which contains both conventional generators (CGs) and renewable generators (RGs). Firstly, a constrained optimization problem with the aim of minimizing the power generation cost of the DC microgrid is established. To solve the optimization problem, an accelerated distributed optimal control algorithm in the discrete‐time domain is proposed. The convergence speed of the proposed algorithm is significantly improved compared to the existing distributed optimization algorithms without acceleration terms. More importantly, the communication cost is greatly reduced. The proposed algorithm is in a fully distributed manner, which means each controller only relies on the limited information from neighbouring controllers to achieve optimal cooperative control and bus voltage regulation across multiple buses. Finally, the effectiveness of the proposed algorithm is validated through numerical simulations.

Two-step preparation of methylammonium lead triiodide perovskite film via electrospray deposition of methylammonium iodide solution for solar cell applications

Solution-processable perovskite solar cells (PSCs) have the potential to revolutionize solar cell technology by enabling low power generation costs via low-cost device fabrication. However, most existing research regarding PSCs relies on the spin-coating method, which is not conducive to large-area film deposition. Therefore, the development of an alternative deposition method for perovskite films has become increasingly important for commercialization, for which electrospray deposition is a promising technique. This study investigates the two-step preparation of methylammonium lead triiodide (MAPbI3) perovskite films via the electrospray deposition of a methylammonium iodide (MAI) solution on a spin-coated PbI2 film. The gradual conversion of PbI2 to MAPbI3 with increasing MAI deposition time was revealed, accompanied by an increase in the size of the perovskite crystals. In addition, PSCs were successfully fabricated by electrospraying MAI, achieving a considerable power conversion efficiency of 7.86 % at the optimal MAI deposition time.

Study on the Optimal Configuration of Battery Energy Storage System in Distribution Networks Considering Carbon Capture Units

Background: The implementation of Battery Energy Storage Systems (BESSs) and carbon capture units can effectively reduce the total carbon emissions of distribution networks. However, their widespread adoption has been hindered by the high investment costs associated with the BESSs and power generation costs of carbon capture units. Objective: The objective of this paper is to optimize the location and sizing of BESSs in distribution networks that comprise renewable power plants and coal-fired power units with carbon capture systems. The optimization process aims to minimize the grid’s impact from the configuration while maximizing economic cost savings and the benefits of reducing carbon emissions. Methods: A bi-layer optimization model is proposed to determine the configuration of BESSs. The upper layer of the model optimizes the size and operation strategy of the BESSs to minimize the configuration and power generation costs, using YALMIP and CPLEX optimization tools. Carbon emission reduction benefits are considered through deep peak-shaving and carbon tax. The lower layer of the model aims to optimizes the placement of the BESSs to minimize voltage fluctuation and network loss in the power grid. To achieve this, we improved the efficiency of the Nondominated Sorting Genetic Algorithm II (NSGA-II) to update the BESS’s placement. Results: The IEEE33-bus and IEEE118-bus systems were utilized for simulation and comparison in various scenarios. The findings demonstrate that the proposed configuration method can decrease the cost of investment and power generation. Furthermore, it reduces the degree of node voltage fluctuation and network loss in the distribution network. Conclusion: The study reveals that determining the optimal scale of BESSs can mitigate high energy consumption in carbon capture systems and improve the overall performance of power systems that integrate carbon capture technology and renewable power plants.