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The medium- and long-term operation strategy of the hybrid power system needs to adapt to the continuous changes in the market. This paper presented a medium-term (monthly) operational simulation model and nested it into a long-term (yearly) optimization model. It was solved by the genetic algorithm (GA) to maximize yearly revenue. The planned delivered-electricity to power process constraints were present to improve the optimization efficiency. Using penalty price factor (α) and threshold of exempted punishment for the supply shortage δ to characterize the punishment for supply shortage risk. This framework was applied for a new hybrid power system so-called LCHES-WP (large-scale cascade hydropower energy storage system, wind power, PV power). It can participate in the electricity trading market with a bundled output pattern. The real cascade hydropower stations in Qinghai Province, China, are selected as the case study. In the baseline scenario, the current time of use (TOU) electricity price and punishment level (α = 0.05, δ = 5%) were considered. It reveals that (1) the revenue (baseline scenario, 6.15351 billion CNY) in the bundled output pattern is higher by 45.6% than in the separate output pattern. (2) the economic and technical indicators are sensitive to α , but not to δ. α increases make the punishment stricter, supply shortage risk was significantly reduced due to the probability of the shortage event, the maximum monthly supply shortage ratio and punishment electricity are reduced by up to 22.5 percentage points, 26.7 percentage points, and 1880.1 GWh, respectively. The yearly revenue is reduced by up to 4.7%. the reason is the yearly curtailment is increased and the penalty amount does not change dramatically with the changes in punishment. (3) The α = 0.6 and α = 1.8 can be regarded as demarcation point and upper boundary point for the case of hybrid power system, respectively. It is proposed that when planning a capacity scheme, stakeholders should consider that the demarcation point is higher than the current α. In general, this paper provides a reference for the medium- and long-term operation optimization of the LCHES-WP hybrid power system in the future. • Electricity settlement rule in medium and long term electricity trading is considered. • The monthly operational simulation model was nested in the yearly optimization model. • Bundled output pattern can rise by 45.6% of revenue than the separate output pattern. • The supply shortage risk is significantly reduced by increasing the punishment level. • The penalty amount does not change obviously with the changes in the punishment level. [ABSTRACT FROM AUTHOR]

Under the goal of global carbon reduction, hydropower-wind-photovoltaic complementary operation (HWPCO) in the clean energy base (CEB) has become the key to achieving a high-quality clean energy supply. However, stochastic wind power and photovoltaic need the help of adjustable hydropower to get onto the grid in the HWPCO process, which makes the conflict between different power generation entities more and more complex. This paper takes Yalong River CEB as the research object and sets up the separate operation scenario and complementary operation scenario of hydropower stations and wind-photovoltaic plants. Meanwhile, the corresponding day-ahead schedule model considering the stability of the power system is established. On this basis, this paper analyzes changes in profit and loss indexes of each power generation entity and clarifies the profit and loss relationship of different power generation entities in the CEB. Further, based on the model group for quantifying contributions and the compensation electricity contribution value, this paper proposes the benefit compensation mechanism between the cascade hydropower station and wind-photovoltaic plants and the benefit compensation mechanism between each hydropower station within the cascade hydropower station. The main results of the research are as follows: (1) after HWPCO, the total revenue of wind-photovoltaic plants increases by 61.48 million RMB (8.61 million dollars), and the total revenue of the cascade hydropower station decreases by 10.77 million RMB (1.51 million dollars), which means wind-photovoltaic plants are the beneficiary and the cascade hydropower station is the loss. (2) after the benefit compensation, the total revenue of wind-photovoltaic plants increases by 20.97 million RMB (2.94 million dollars), and the total revenue of the cascade hydropower station increases by 29.74 million RMB (4.16 million dollars) compared with the hydropower-wind-photovoltaic separate operation (HWPSO). By the benefit compensation mechanism proposed in this paper, the benefit of each power generation entity can reach a relative balance in the Yalong River clean energy base. • Quantify the profit and loss relationship between different power generation entities in the HWPCO process. • Establish the model group for quantifying contributions between different power sources. • The incremental benefit of HWPHS is reasonably distributed. [ABSTRACT FROM AUTHOR]

With the rapid growth of wind power penetration, its inherent stochasticity and uncertainty will seriously affect the stable operation of power systems. How to effectively characterize the uncertainty of wind power is a great challenge for day-ahead power system dispatching, scenario generation is an important method to describe the uncertainty of wind power. Currently, most of the wind power scenarios are generated using a generative adversarial network with two-dimensional convolution as the main structure, which may make it difficult to adequately characterize the temporal features, the day-ahead mode properties, and seasonality of wind power. In this paper, we first establish an auxiliary classification time-series generation adversarial network based on error stratification, construct the numerical characteristic conditional labels that can reflect the fluctuation characteristics of day-ahead wind power and power output level, and design the temporal embedding function that captures the seasonal characteristics of wind power. On this basis, to fully extract the dynamic variation characteristics and global effective information of wind power prediction error sequences, Informer is combined with a time-series generative adversarial network, and a joint loss function incorporating supervised learning and unsupervised learning is constructed. Subsequently, the generated set of prediction error sequences is superimposed with the day-ahead predicted value of wind power to obtain the day-ahead wind power scenario set. Finally, to verify the effectiveness of the proposed method, two datasets from different geographic locations are used to comprehensively evaluate the generated day-ahead wind power scenario set in terms of three aspects: temporal correlation characteristics, fluctuation characteristics, and accuracy. The experimental results indicate that the scenario generation method proposed can improve the quality of the day-ahead wind power scenario set and has an excellent performance in describing wind power uncertainty compared with other methods. • A novel generation model for wind power based on Probspare self-attention and TimeGANs is developed. • A method for generating day-ahead wind power scenarios considering numerical characteristics of prediction errors is proposed. • A joint loss function is designed by combining auxiliary classification supervised training and unsupervised adversarial training. [ABSTRACT FROM AUTHOR]

Supply schemes, specifically delivery times, are essential factors in the resulting final cost of fuels. It is the particular case of hydrogen produced from variable renewable energies. However, the impact of different delivery times on the levelized cost of hydrogen (LCOH) remains unclear. This paper assesses the combined impact of different delivery times, local renewable sources, and onsite generation curtailment on the LCOH. We developed an optimization model of an electrolysis-based hydrogen plant and found key LCOH locational drivers and trends. The model uses hourly solar PV and wind generation data for four locations in Chile and optimizes the capacities of the onsite solar and wind generators, electrolyzers, storage tanks, and compressors. In all the locations examined, we found an almost linear relationship between LCOH and delivery time. LCOH values for a daily delivery range from 2.4 to 3.4 $US/kg (for different locations). Values for a delivery time of 120 days range from 12.2 to 13.6 $US/kg. These are explained by the almost linear increase in storage costs with respect to delivery time, despite the variability of renewable energies at the analyzed locations. Production costs remain almost constant for different delivery times. Furthermore, we obtained that curtailment could decrease LCOHs between 5% and 40%, by increasing the electrolyzer's capacity factor and decreasing the storage size. Moreover, we show that off-grid solar-dominant scenarios are strongly affected by the lack of solar energy in winter, increasing LCOHs considerably. • Linear relationship between LCOH and delivery time. • Low delivery times imply less volume and cost of storage, decreasing the LCOH. • Production costs remain almost constant for different delivery times. • Curtailment reduces LCOHs between 5% and 40% due to increased capacity factors. • Off-grid solar dominant locations present higher LCOHs due to winter's lower solar generation. [ABSTRACT FROM AUTHOR]

Climate change is driving the adoption of sustainable energy, with low-cost solar photovoltaics and wind power at the forefront. However, land-constrained regions and islands have a limited onshore renewable energy potential. Wave power may prove useful for such regions, supported by growing literature in the field. This study delves into wave power's techno-economic potential, addressing a gap in previous assessments focused solely on theoretical or technical prospects. Utilising hourly wave data and a wave energy converter manufacturer's power matrix, global wave electricity yield is estimated. Considering projected costs, levelised cost of electricity is used to gauge economic viability. Although wave power is currently expensive, the results suggest that it could become cost-competitive with offshore wind power in the 2030s, with levelised cost of electricity below 70 €/MWh by 2035 in areas with good wave energy resources. Finally, the paper contributes openly accessible, hourly capacity factor data of global wave power generation, empowering further energy system modelling research. This study paves the way for informed decision-making on wave power's role in a diversified, sustainable energy future. • Global-local wave power potential is estimated. • Economic potential estimation is presented with projection until 2050. • Point absorbers have a wide operating range leading to high yields and low costs. • Wave power is a feasible alternative for power supply in coastal regions and islands. • Energy systems can benefit from the complementarity of wave power and solar PV. [ABSTRACT FROM AUTHOR]

Power pooling has emerged as a regional strategy for accelerating generation capacity expansion in West Africa with the aim of leveraging vast domestic energy resources and promoting investment in regional power infrastructure. As part of their climate action pledges, most West African countries have committed to increasing the shares of variable renewable energy (VRE), particularly solar photovoltaic and wind power, in their generation mix. It, however, appears that approaches to utility-scale capacity expansion planning tend to overlook the inherent temporal intermittency and spatial variability of VRE-based generation. Moreover, despite influencing the techno-economic rationale for grid expansion and off-grid electrification, as well as the trade-offs between high renewable supply areas and grid expansion, the spatial distribution of demand has been overlooked in planning approaches, leading to conservative and weak prospects for the contribution of utility-scale VRE. Such inconsistencies with the region-wide potentials and policy ambitions highlight that it is paramount for West Africa to design its power pooling such that spatial and temporal fluctuations of VRE supply are duly considered in capacity expansion planning while taking advantage of complementarities between VRE supply and national electricity demand profiles. To address this, the present paper applies a long-term generation capacity expansion model, OptGen, soft-linked to a least-cost operation module, the SDDP tool, which is enhanced by geospatial electrification analysis using the OpeN Source Spatial Electrification Tool (OnSSET), to a subset of four member countries of the West African Power Pool – Burkina Faso, Côte d'Ivoire, Ghana, and Mali – for the 2023–2040 time horizon. The results highlight that current frameworks lead to missed opportunities for bridging the supply-demand gap in all countries, not only in terms of VRE generation capacity, but also of cross-border power trade. • Long-term capacity expansion planning and dispatch models are soft-linked. • The spatial and hourly distributions of demand and renewables supply are considered. • The 10% limit on renewables share used in the regional master plan is not optimal. • The current system operation is not conducive to optimal cross-border trade. • The relaxed transmission scenario yields higher dispatch factors for renewables. [ABSTRACT FROM AUTHOR]

Wind and solar power are expected to play important roles in many countries to achieve carbon neutrality; however, their inherent instabilities pose significant threats to existing power grids. Because hydropower has been recognized as a viable compensatory resource for solar and wind energy uncertainties, many studies have sought to determine optimal scheduling strategies for hydro-PV, hydro-wind, and hydro-PV-wind systems. However, few studies have simultaneously considered the sizing and scheduling of large-scale hydro-PV-wind hybrid systems connected to long-distance transmission lines. Therefore, this paper develops a mathematical metric to measure the wind and solar output complementarity and incorporates it into a multi-objective sizing and scheduling model for a hybrid hydro-PV-wind system, in which resource complementarity, profit generation, system reliability, and power curtailment trade-offs are concurrently considered. The ɛ -constraint method is employed and decision makers' attitude parameters are introduced to transform the proposed model into its equivalent single objective form. A case study in China reveals that the maximum wind and solar power output complementarity rate can be at least 0.19 for the studied hybrid hydro-PV-wind system. The incorporation of wind enables the hybrid hydro-PV-wind system to provide 4.11% more load than a hydro-PV system. In addition, if 2% of power curtailment is permitted, 1.53% more load can be served and 14.9% higher profits can be earned. To better explore the application of hybrid systems, management recommendations are provided for system operators, the power industry, and management administrations. • Multi-objective sizing and scheduling method for the hybrid renewable system is proposed. • Mathematical metrics to measure wind and solar power complementarity is provided. • A complementarity rate of 0.19 can be reached with 100% reliable service and no curtailment. • Integrating wind power into the hydro-PV system can reliably serve 4.11% more load. • An optimal rather than minimal level of the renewable power curtailment is more efficient. [ABSTRACT FROM AUTHOR]

To make full use of the flexible adjustment capability of DC tie-lines in multiarea AC/DC systems and to coordinate the generation resources and load demand of multiarea AC/DC systems, this paper presents an intraday two-stage hierarchical optimal scheduling model for multiarea AC/DC systems based on analytical target cascading (ATC). To avoid repeated adjustment and overadjustment of DC tie-lines after wind power integration, a two-stage rolling coordinated scheduling model for the area subsystem based on model predictive control (MPC) is proposed. The two-stage rolling coordinated scheduling method takes into consideration the influence of the predicted value in the future finite time domain and the latest measured DC tie-line power on the current scheduling state. Based on the ATC and the area decomposition criterion of the AC/DC grid, an optimal scheduling model for the upper-level system is proposed that takes into consideration the DC tie-line adjustment constraints and the area coupling constraints. The optimal scheduling model for the upper-level system formulates the two-stage DC tie-line plan for the multiarea AC/DC system, and the two-stage rolling coordinated scheduling model of the area subsystem solves the subproblems of the generation plan for each area subsystem in a parallel manner considering the area coupling constraints. The proposed method can achieve intraday two-stage decoupling scheduling of multiarea AC/DC systems and promote the cross-area consumption of large-scale wind power through flexible adjustment of the DC tie line. This approach also reduces the communication burden between the area subsystems and ensures the efficiency of the solution algorithm. • Based on analytical target cascading technology (ATC), we integrate the hierarchical control structure of decomposition-coordination into the model predictive control (MPC) method to achieve decentralized dispatch for multiarea AC/DC systems. • A two-stage rolling coordinated scheduling model for the area subsystem considering the area coupling constraints based on the MPC is proposed. • Based on the ATC and the area decomposition criterion of the AC/DC grid, an optimal scheduling model for the upper-level system is proposed. • Flexible adjustment of the DC tie-line and decoupling scheduling of multiarea AC/DC systems are achieved. • The hierarchical optimal scheduling model reduces the communication burden between the subsystems and ensures the efficiency of the solution algorithm. [ABSTRACT FROM AUTHOR]

With the high penetration of renewable generation systems in the power grid, the accurate simulation of the uncertainty in renewable energy generation is vital to the safe operation of the power system This paper proposes a novel controllable method for renewable scenario generation based on the improved VAEGAN model. The standard VAEGAN model is first improved using spectral normalization technique and the generator of GAN is trained using VAE. Then, the external and internal interpretable features in the latent space are learned as the controllable vector utilizing the principle of mutual information maximization. Finally, the renewable energy scenarios with overall features are generated using the external universal meteorological features, and renewable energy scenarios with specific features are generated by tuning along the internal interpretable feature of the controllable vector in the latent space. The proposed approach is used to produce real-time series data for renewable energy including wind and solar power. Experiments demonstrate that our method has a better performance in terms of controllable generation and enables the generation of preference patterns covering various statistical features. • A novel VAEGAN model with controllable interpretable features is proposed. • External universal meteorological features are used to generate scenarios with unknown patterns. • Internal interpretable latent features are designed to generate scenarios with specific features. • The results show a better performance of the proposed method on controllable scenario generation. [ABSTRACT FROM AUTHOR]

Aiming at a large number of decentralized resources such as rural biomass, rooftop photovoltaic, and decentralized wind power, a novel rural virtual power plant (VPP) has been conceptualized. The VPP is integrated by biomass waste conversion system (BWC), carbon to energy (C2P), demand response aggregator (DRA), and distributed renewable energy (DRE), namely, BDCD-VPP. Then, this paper proposes a data-driven two-stage robust optimization dispatching model for BDCD-VPP considering carbon-green certificates equivalent conversion mechanism. This model aims to address the uncertainty of wind power plants (WPP) and photovoltaic (PV) power generation. Strong duality theorem (SDT) and C&CG algorithm are applied to construct this model. Thirdly, the Aumann-Shapley method (A-S) was enhanced by incorporating risk factor, cost factor, and carbon reduction factor. This refinement results in the development of a synergistic scheduling revenue allocation method for electricity‑carbon-green certificates. Finally, the Lankao rural energy revolution pilot program in China is selected as the case study, the results showed: (1) The BDCD-VPP aggregates distributed energy sources such as the rural WPP and the PV to realize electricity‑carbon-electricity cycle effect. The BDCD-VPP generates a green certificate revenue of 47.17¥/MWh and exhibited carbon emissions of 0.37 t/MWh. The proposed Carbon-Green Certificates Equivalent Conversion Mechanism increases BDCD-VPP benefit ratio by 3.52%. (2) The proposed two-stage robust optimization dispatching model enhances the ability of BDCD-VPP to adapt to uncertainty. Compared to the day-ahead stage, biomass power generation (BPG) and waste power generation (WPG) increase upward peaking power by 24.05% and 9.99% in intra-day stage. Flue gas treatment system (FG-TS), gas-power plant carbon capture (GPPCC) and power to gas (P2G) increase downward peaking power by 65.15%, 70.99% and 25.94%. (3) Utilizing the proposed benefit allocation methodology, BPG and WPG need to concede 38.25¥/MWh and 65.02¥/MWh for carbon emissions associated with electricity. WPP and PV need to concede 64.25¥/MWh and 33.71¥/MWh for power generation uncertainty. GPPCC, P2G, DRA, and small hydropower station (SHS) obtain revenues of 24.48 ¥/MWh, 55.07 ¥/MWh, 70.38 ¥/MWh, and 51.30 ¥/MWh. Compared to the A-S, the proposed benefit allocation methodology exhibits 6.92% improvement in satisfaction. Overall, the proposed data-driven two-stage robust optimization dispatching model and benefit allocation strategy facilitates the optimal aggregation and utilization of rural distributed energy resources, considering the interests of various stakeholders. This is conducive to achieving a clean and low-carbon transformation of the overall energy structure. • Proposed a novel rural virtual power plant considering biomass waste conversion and carbon to power. • Designed carbon-green certificate equivalence conversion mechanism. • Proposed a data-driven scenarios generation and reduction strategy for uncertainty factors. • Construct a two-stage robust optimization dispatching model and strong duality C&CG algorithm. • Construct an improved Aumann-Shapley-based benefit allocation strategy [ABSTRACT FROM AUTHOR]

Few-Shot Short-Term Wind Power Forecasting (FS-STWPF) is designed to develop accurate short-term wind power forecasting models with limited training data, reducing the losses suffered by wind farms and power systems due to the data scarcity. Based on the idea of extracting valuable knowledge from the source wind farms and then applying it to the target wind farm, a novel Meta-Learning approach (WG-Reptile) has been proposed in this paper. Building on the existing Reptile algorithm, two specific designs have been made in WG-Reptile for FS-STWPF: (1) Within-Task Samples Assignment method based on Operational Scenario (WTSAOS) has been proposed to improve the adaptability of the models to changing conditions. (2) Gradients Conflict Attenuation method based on Cosine Similarity (GCACS) has been proposed to enhance the effect of knowledge fusion from different source wind farms. Two open wind power forecasting datasets and three deep learning models have been used to implement 24-h-ahead FS-STWPF experiments with different amounts of training data. The results illustrate that the proposed WG-Reptile is able to outperform the other few-shot learning approaches. Intuitively, with only 30-day training data, the accuracy of the proposed WG-Reptile can be equivalent to the conventional supervised learning approaches trained on 6-month. • A novel Meta-Learning approach, WG-Reptile, has been proposed to perform FS-STWPF, which is able to obtain accurate STWPF models with a limited amount of training data. • WTSAOS (Within-Task Samples Assignment method based on Operational Scenario) has been devised to reasonably assign within-task training samples to different updating steps in Inner Loop , which is able to improve the applicability of the models to the changing conditions. • GCACS (Gradients Conflict Attenuation method based on Cosine Similarity) has been devised to alleviate gradient conflicts by scaling the outer learning rates for different layers of the models, which is able to filter valuable gradients to enhance the effectiveness of the knowledge extraction. [ABSTRACT FROM AUTHOR]

The Danish government plans two energy islands to collect offshore wind power for power distribution and green fuel production. Wind power is often criticized for lacking stability, which challenges downstream fuel synthesis processes. Solid oxide electrolysis cells (SOEC) are promising for green hydrogen production on a commercial scale, but the impact of fluctuating power on SOEC remains uncertain. This paper explores the feasibility of a Wind-SOEC coupled system by conducting a 2104-h durability test with the state-of-the-art Topsoe TSP-1 stack. Three periods of steady operation and two periods of dynamic operation were conducted. Wind power fluctuation was simulated during the dynamic period, and two control strategies were used to handle it. The constant flow (CF) and constant conversion (CC) strategies maintain the feedstock flow rate and conversion ratio of steam-to‑hydrogen, respectively. Compared to steady operation, the stack shows no signs of additional degradation in dynamic operation. Thus, the TSP-1 stack has been proven robust and flexible enough to handle fluctuating wind power supplies under both operation strategies. Further, stack performance during dynamic periods was compared and analyzed by removing degradation effects. Accordingly, SOEC stacks with CC control will consume less external heat than CF to maintain a heat balance. Nevertheless, SOEC systems with CF and CC control strategies may have different efficiency or hydrogen production costs. Tech-economic analyses will be needed to investigate control strategies at the system level. • A 2104 h SOEC stack test with simulated fluctuating wind power input was conducted. • Deployed constant flow and constant conversion strategies in the dynamic period. • The Topsoe stack is robust and flexible for harsh and varying working environments. • Fluctuations didn't introduce extra degradation in the current test. [ABSTRACT FROM AUTHOR]

Estimating the total wind power output from the meteorological information at a province level (called Provincial Regional Wind Power Conversion Model, PRWPCM) plays vital and fundamental roles in energy modeling community and regional wind power forecasting. How to construct a reliable PRWPCM is a real challenge, since PRWPCM involves a large number of widely distributed wind turbines, massive meteorological data across the whole province, and complex nonlinear correlations. This paper proposes a lightweight PRWPCM by integrating Geographic Information System (GIS) analysis technology and Convolutional Neural Network (CNN). First, we conduct the land suitability analysis for wind turbine sites through the multi-criteria GIS layer overlay method to make the provincial wind turbine land suitability map (WTLSM) with scored divisions from the least suitable to the most suitable areas. On this basis, a new fusion mechanism for geographic and meteorological information is proposed, through which the raw meteorological data matrix can be reconstructed to filter and amplify the meteorological information that is more relevant to the total wind power output of the province, and avoid the time-consuming and labor-intensive data collection and processing, large-size model construction and validation. Second, a CNN-based regression architecture is designed to further capture the mapping relationship between the reconstructed meteorological data and total wind power output of the province; each type of meteorological factor is considered as an input channel and the attention modules are introduced to adaptively enhance useful channels and suppress less useful ones. Numerical experiments based on the wind power operation data of Yunnan Province, China, are conducted to validate the superiority of the proposed PRWPCM via benchmarking against 13 classical methods. • Mapping relationship between wind power output and meteorological information at a province level is recognized by integrating GIS and CNN. • A new fusion mechanism for geographic and meteorological information is proposed to achieve lightweight modeling. • Comparative experimental studies are conducted in Yunnan province, China. [ABSTRACT FROM AUTHOR]

Hydropower, an excellent power source for peak shaving, can respond quickly to load changes, which helps to overcome the anti-peak characteristics of wind and solar power. However, complementary operation with wind and solar power changes the operating boundaries of hydropower stations, making the original operating rules no longer applicable. Considering daily peak-shaving demands, this paper proposes a methodology to develop operating rules for a hydro–wind–solar hybrid system (HWSHS). First, a multi-objective and multi-timescale operation model is developed to ensure energy generation and peak-shaving performance simultaneously. Second, the feasible and reasonable region of available energy-based operating rules is analyzed, and three rule forms are employed to explore the optimal operating rules. Last, NSGA-II is utilized to optimize the rule parameters. A decoupling method is proposed to handle the shape constraints of operating rules, and the multi-timescale simulation method for a HWSHS is developed as the fitness function. In the simulation method, a load reconstruction-based load shedding (LRLS) method is introduced to determine the hourly output of each hydropower station. A forebay elevation check method is performed to obtain the hourly forebay elevation and correct the hourly output. The Beipan HWSHS in Guizhou Province, China, is selected as a case study, and the optimal operating rules are determined. The results also show that (1) the standard operating rules with hedging and packing increase peak-shaving performance by an average of 4.31% and 2.03% compared to the standard operating rules and linear operating rules, respectively; (2) the peak-shaving performance can be significantly increased (6.70%) with a slight decrease (0.37%) in the energy generation through changing the seasonal distribution of water and energy of the cascade hydropower stations; (3) the LRLS method, which makes the interday energy distribution more uniform, increases the peak-shaving performance by an average of 9.17% compared to the traditional load shedding method; (4) the energy generation and peak-shaving performance of the HWSHS are affected by the transmission capacity, and it is necessary to increase the transmission capacity by 25% to 35% of the installed capacity of wind and solar power stations. • A methodology is proposed to develop operating rules considering peak-shaving demands. • A multi-objective and multi-timescale operation model is developed. • The feasible and reasonable region of available energy-based operating rules is analyzed. • A multi-timescale simulation method is designed to simulate the operation of a HWSHS. • The methodology significantly improves the peak-shaving performance. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present options to make low-carbon footprint large-scale desalination a reality on arid islands with weak electrical grids. Through these options, the goal is to reconfigure on-grid wind energy/desalination systems for large- and medium-scale water production. In this context, it is proposed to use lithium-ion batteries for stationary energy storage together with management strategies aimed at avoiding the wind energy/desalination systems having to consume energy from the conventional grid they are connected to. The control strategy is based on ensuring that the power provided by the wind farm and batteries remains in synchrony with the power demand of the desalination plant throughout the system's useful life. The interannual variation of wind energy is considered when sizing the renewable energy system and processes for its estimation are proposed. The case study is centred on the Canary Archipelago, a region that is especially vulnerable to the impacts of climate change, but which enjoys exceptional characteristics for the exploitation of wind energy. The results obtained show the optimal wind farm and energy storage system capacities of the analysed configurations. The approach presented allows a low-carbon operational footprint. If the control strategy were to be put into practice today, the current grid restrictions and a life cycle assessment of the system carried out in a societal context that continues to be fossil fuel dependent indicate a potential reduction of 77.4% of the footprint. However, the remaining 22.6% could be eliminated in the future when the manufacturing processes of wind turbines, batteries and desalination plants receive the benefits of carbon-neutral societies. [Display omitted] • A method for achieving large-scale, low-carbon desalination is presented. • Optimal wind farm and storage capacities for the system are determined. • The method offers substantial CO2 emission reduction potential. • Using climate ERA5 reanalysis data, enhances robustness by addressing wind variability. • Proposed adjustments enhance adaptability to technological advances and future scenarios. [ABSTRACT FROM AUTHOR]

As the building stock is projected to double before the end of the half-century and the power grid is transitions to low-carbon resources, planning new construction hand in hand with the grid and its capacity is essential. This paper presents a method that combines urban building energy modeling and local planning of renewable energy sources (RES) using an optimization framework. The objective of this model is to minimize the investment and operational cost of meeting the energy needs of a group of buildings. The framework considers two urban-scale RES technologies, photovoltaic (PV) panels and small-scale wind turbines, alongside energy storage system (ESS) units that complement building demand in case of RES unavailability. The urban buildings are modeled abstractly as "shoeboxes" using the Urban Modeling Interface (umi) software. We tested the proposed framework on a real case study in a neighborhood in Chicago, Illinois, USA. The results include estimated building energy consumption, optimal capacity of the installed power supply resources, hourly operations, and corresponding energy costs for 2030. We also imposed different levels of CO2 emissions cuts. The results demonstrate that solar PV has the most prominent role in supplying local renewables to the neighborhood, with wind power making only a small contribution. Moreover, as we imposed different CO2 emissions caps, we found that ESS plays an increasingly important role at lower CO2 emissions levels. We can achieve a significant reduction in CO2 emissions with a limited increase in cost (75% emissions reduction at a 15% increase in overall energy costs). Overall, the results highlight the importance of modeling the interactions between building energy use and electricity system capacity expansion planning. • Optimization framework combines urban building energy modeling and local planning of renewables. • Urban neighborhood buildings are modeled in an abstracted manner as "shoeboxes". • Considering local wind and solar resources along with energy storage and grid supply. • PV has the most prominent role in supplying local renewables to the neighborhood. • Substantial reduction in CO2 emissions (75%) can be achieved with limited increase in energy costs (15%). [ABSTRACT FROM AUTHOR]

Wind power serves as a clean and sustainable form of energy. However, its generation is fraught with variability and uncertainty, owing to the stochastic and dynamic characteristics of wind. Accurate forecasting of wind power is indispensable for the efficient planning, operation, and grid integration of wind energy systems. In this paper, we introduce a novel forecasting method termed Temporal Collaborative Attention (TCOAT). This data-driven approach is designed to capture both temporal and spatial dependencies in wind power generation data, as well as discern long-term and short-term patterns. Utilizing attention mechanisms, TCOAT dynamically adjusts the weights of each input variable and time step based on their contextual relevance for forecasting. Furthermore, the method employs collaborative attention units to assimilate directional and global information from the input data. It also explicitly models the interactions and correlations among different variables or time steps through the use of self-attention and cross-attention mechanisms. To integrate long-term and short-term information effectively, TCOAT incorporates a temporal fusion layer that employs concatenation and mapping operations, along with hierarchical feature extraction and aggregation. We validate the efficacy of TCOAT through extensive experiments on a real-world wind power generation dataset from Greece and compare its performance against twenty-two state-of-the-art methods. Experimental results demonstrate that TCOAT outperforms existing methods in terms of both accuracy and robustness in wind power forecasting. Moreover, we conduct a generality study on an additional real-world dataset from a different climate condition and wind power characteristics. The results show that TCOAT can achieve comparable or better performance than the state-of-the-art methods, confirming the generalization ability of TCOAT. • Novel deep learning model for wind power forecasting • Temporal collaborative attention and fusion for wind data • Superior performance on real-world wind power dataset • Attention maps for interpretability and end-to-end training [ABSTRACT FROM AUTHOR]

In this paper, two variants of the magnetless dual rotor wound field excited flux switching generator (DRWFEFSG) are comparatively analyzed for single rotor wind turbine (termed as machine I (MI)) and counter rotating wind turbine applications (termed as machine II (MII)) for household direct current (DC) microgrid system. First, design concept and integration to wind turbine system are discussed. Secondly, comprehensively performance analysis i.e., static analysis, dynamic load/speed analysis and electromagnetic performance analysis are executed. Finite element analysis (FEA) based static analysis evident that MI offers 7.6% higher torque in inner rotor whereas MII exhibits 18% higher torque in outer rotor. Dynamic respond unveils that in comparison to MII, MI suffers from the issue of voltage regulation. Additionally, electromagnetic analysis reveals that both MI and MII exhibits excellent response under variable field Magneto-motive force (MMF). Comparative analysis unveils that MII requires lower field MMF to achieve same response to counterpart MI. Quantitative analysis divulge efficiency of 88.49% and power factor of 0.93 in MI whereas MII at rated operating point demonstrates 85.9% efficiency and 0.96 power factor. Finally, effectiveness of the study and validity of DRWFEFSG for wind turbine applications are tested with hardware prototypes of both MI and MII that fairly matched with FEA results. • Generator design and comparative analysis for household DC microgrid system. • Generator design for co-rotating and counter rotating wind turbine application. • Generator for rooftop wind turbine integrated with wind turbine. • Improve power wind turbines generators. • Analysis and comparative study of generator design for wind power generation. [ABSTRACT FROM AUTHOR]

Frequency security is one of the most critical factors for ensuring the safe and stable operation of power systems. In wind-photovoltaic-hydro-thermal multi-power systems (WPHTMPS), the increase in new energy share and the water hammer effect weaken the frequency modulation (FM) capability, threatening the frequency security of the power system. To address this issue and ensure the frequency security of WPHTMPS under the trend of large-scale development of wind and photovoltaic (PV) power, this paper proposes a novel expansion planning method for wind and PV power in WPHTMPS that considers FM capability in the planning stage. The method realizes the coordinated optimization of long-term planning and short-term operation while adhering to the frequency security constraints. The constraints are derived from the multi-power system frequency response model (MPSFR) which integrates virtual inertia, synchronous inertia, and the water hammer effect. However, due to the high-order characteristic of the MPSFR model, the frequency security constraints cannot be expressed explicitly to be embedded in the planning model by current study. Therefore, the boundary equivalence method is proposed to deal with this problem, which is effective to convert the frequency security constraints into inertia constraints and embed them in the planning model according to their relationship with installed capacity and unit commitment. Finally, simulation for a power system in Southwest China validates the proposed method, with results indicating that around 13% more capacity of new energy is needed to provide adequate FM reserve and power supply. Furthermore, it is demonstrated that the FM capability of new energy can improve the installed share of new energy from 46.5% to 70% while effectively ensuring frequency security. • The frequency modulation capability is necessary in generation expansion planning. • An integrated system frequency response model with multiple power sources is built. • A two-layer model optimizing long-term planning and short-term operation is built. • The frequency security constraint is linearized by the boundary equivalence method. [ABSTRACT FROM AUTHOR]

Accurate forecasting of wind power is of significance for the operation and scheduling of the power grid in which wind power is integrated. However, distribution shifts caused by time-varying data would damage the stable generalization of forecasting models in future testing. In this paper, a temporal domain generalization approach incorporating adversarial relationship-based training and hybrid model is proposed to address wind power forecasting from the distribution perspective. Two joint modules, the temporal domain split and domain-adversarial hybrid model, are employed to function as a forecasting framework. The former splits training data into temporal domains with the maximum distribution differences, while the latter aims to learn shared knowledge from the above domains. The shared knowledge is independent of distribution shifts, which can be generalized to future testing well. Specifically, the domain-adversarial hybrid model, which is jointly optimized by minimizing the loss of power prediction and maximizing the loss of temporal domain classification, can be divided into a base model incorporating temporal convolutional network and gated recurrent unit, and a domain classifier. The data from actual wind turbines are performed to validate the proposed approach. The experimental results show that the proposed approach has superior performance to benchmark models and the significant improvement in accuracy reached 39.48% using adversarial relationship-based training. • A temporal domain generalization approach is proposed to improve wind power forecasting. • The training data is split into temporal domains by referring to distance metrics. • Adversarial training is to overcome distribution shifts. • The proposed approach is validated using data from actual wind turbines. [ABSTRACT FROM AUTHOR]

This paper proposes a technique for the probabilistic wind power forecasting (WPF) of a newly built wind farm (NWF) using a limited amount of historical data. First, the state-of-the-art Transformer network is employed to capture the power generation pattern of different wind farms (WFs) based on abundant historical training samples. Then, the Bayesian averaging regression method is applied to transfer the learned power generation pattern to the NWF by assigning proper weights to the WPF results of different WFs. This enables the proposed method to yield accurate NWF power predictions utilizing a limited amount of historical data. The Bayesian characteristics further enable the quantification of multiple uncertainties in forecasting results that may be essential for the NWF operator when the input is uncertain. Comprehensive tests were also performed by employing other deterministic and probabilistic WPF methods using field data. By comparing the results, the proposed method is demonstrated to produce accurate forecasting results with sparse historical data. Moreover, the uncertainties of outcomes are quantified, and acceptable performance is achieved. • A Bayesian averaging regression-based transfer learning for newly built wind farms with limited data is developed. • A cross-attention mechanism is designed to establish correlations among different types of available features. • Bayesian characteristics enable the proposed method to quantify the forecasting uncertainties of a newly built wind farm. • Wind forecasting data assist the learning process, further reducing the data requirement for newly built wind farms. [ABSTRACT FROM AUTHOR]

Wind turbines with taller hubs and longer blades are more feasible and cost-effective than their predecessors since they reduce the unit cost of energy production. However, the larger size of these structures makes them more visible and dominant features in the landscape. Higher visibility of wind turbines generally raises economic and environmental concerns in communities. The increasing size of wind turbines, therefore, poses a dilemma between lower costs of wind energy and higher visibility impacts. Most MCDM applications highlight the importance of wind turbine visibility and conceptualize this factor as the distance from settlements, coastal areas, etc. They assume that the higher distance from a potential turbine site lowers the impact of visibility or vice versa. This assumption helps introduce visibility impacts into the MCDM to some degree. However, calculating visibility can provide more reliable and realistic geospatial information to be used as a decision variable. This paper conceptualizes turbine visibility as quantitative visibility scores derived from multiple viewshed calculations. Geospatial information on visibility, capacity factor, land cover, topographic ruggedness, slope, and eight other distance criteria (e.g., distance from settlements) were processed in a GIS environment. Fuzzy standardization and the analytic hierarchy process (AHP) were used to standardize the maps and calculate criteria weights. A suitability map was produced by combining the weighted maps. Policy recommendations were made for wind power deployment. [Display omitted] • Suitability for wind power deployment was analyzed using a multi-criteria assessment. • The criterion of visibility was integrated with twelve other criteria. • Standardized and weighted criteria maps were combined to derive suitability scores. • Suitability is high in remote mountainous areas and low in the coastal zone. • Low suitability in the coastal zone is an advantage for coastal tourism planning. [ABSTRACT FROM AUTHOR]