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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]

Zero‑carbon electricity is a pre-requisite for decarbonisation of the wider economy and many scenarios envisage rapid expansion of renewables. IEA estimates a need to double the annual rate of investment to 2030. A rapid transition to low carbon electricity creates a risk to investors because projects being financed now will generate their revenue in a zero‑carbon electricity system for which there is no track record of price formation. Although the cost of renewable power has fallen greatly, future revenues from renewables are still affected by market price risks that are often outside the control of generators. If risks are difficult to quantify, they may be mis-priced, leading to inefficient premiums being added to the cost of capital, which could increase the overall cost of the transition. In this context many countries around the world provide support for low carbon energy, such as the UK Contracts for Difference. In this paper we investigate exposure to price risk caused by uncertainty over the mix of technologies used to achieve decarbonisation, which we term 'transition risk', and how this varies under different technology mixes, and for different policy regimes. We show that exposing investors to transition risk could increase the cost of delivering the renewables needed for a zero‑carbon electricity system by around 25% or £7bn/year compared to policy options that reduce exposure to wholesale market price risk. The paper concludes that as long as transition risks remain high, de-risking policy mechanisms can help to minimise the overall cost of achieving net-zero. • We characterise the current stage of the low-carbon transition as a 'build phase', requiring large volumes of investment • Uncertainty over the final physical state of the decarbonised system causes significant financial risk to investors • 'Contracts for difference' (CfDs) help to manage this 'transition risk' and reduce the cost of capital for projects • Exposing projects fully to transition risk could increase costs by a third, and raise the cost of transition by £7bn per year [ABSTRACT FROM AUTHOR]

Long-distance power support through High-voltage Direct Current (HVDC) has provided feasible solutions for power dispatch and control problems in multi-area power systems under high share of renewable energy. In this paper, an advanced multi-area intra-day dispatch strategy for power systems with high penetration of renewable energy considering power support capacity is established to increase the accommodation of wind power and enhance the stability of the power system. At first, a novel assessment method of regional active power support capacity is carried out in this paper, which can sufficiently quantify the regional power margin (RPM), the DC dispatchable power (DCDP), and the regional power support distance (RPSD) of a power system. Subsequently, a multi-area intra-day dispatch strategy based on power support capacity analysis is proposed, where constraints on conventional units, wind farms, transmission lines, and RPSD are developed. Finally, to enable the development of the dispatch model, a model predictive control (MPC) scheme is used to update the power support capacity indicators dynamically and improve model reliability. The effectiveness and superiority of the proposed strategy are verified by case studies. Numerical results show a 7.02% cost reduction and overload risk reduction by using the proposed strategy compared to conventional global optimization methods. • A novel assessment method of regional active power support capacity is proposed. • A multi-area dispatch strategy considering power support capacity is established. • A model predictive control scheme updates the power support capacity indicators. • Proposed strategy enhances the power system stability and reduces dispatch costs. [ABSTRACT FROM AUTHOR]

Wind power efficiency is an essential factor affecting wind power development, and efficient wind power control methods are the key to improving wind power efficiency. Previous wind power control methods require high internal system expertise in internal systems and struggled to balance the accuracy of the maximum power control and the output stability under high wind speed. Therefore, this paper proposes a reward-adaptive control method for wind power tracking based on Deep Deterministic Policy Gradient (DDPG). The method can use one controller to simultaneously control the generator torque and pitch angle in various operating conditions following the system state and the designed flag of operating conditions, thus enabling efficient tracking of the maximum power generation, and stabilizing the output power under high wind speed. Moreover, this paper proposes an internal and external reward algorithm that integrates the Intrinsic Curiosity Module (ICM) and the Actor–Critic (AC) architecture, which can adaptively calculate the reward of DDPG, thereby effectively resolving the sparse reward problem, accelerating the convergence of neural network, and improving the learning effect. From the simulation, the control method proposed in this paper can effectively improve the power generation efficiency under turbulent wind speed, reduce the pitch angle variation by about 30%, and improve the power tracking accuracy by more than 70%. • A reward-adaptive wind power control method based on DDPG is studied. • The method uses one controller to control the power in different operation. • A reward algorithm integrates the ICM and the Actor–Critic is proposed. • The results show that the method improves the power tracking 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]

• A two-stage method is proposed for electricity price forecasting. • The method diversifies the data resources improve the forecasting accuracy. • The method is tested using the real-world data of Texas electricity market. • Causal relationship combined with physical models guarantee better results. • Deep neural networks show a great performance in electricity price forecasting. Over the years, the growing penetration of renewable energy into the electricity market has resulted in a significant change in the electricity market price. This change makes the existing forecasting method prone to error, decreasing the economic benefits. Hence, more precise forecasting methods need to be developed. This paper starts with a survey and benchmark of existing machine learning approaches for forecasting the real-time market (RTM) price. While these methods provide sufficient modeling capability via supervised learning, their accuracy is still limited due to the single data source, e.g., historical price information only. In this paper, a novel two-stage supervised learning approach is proposed by diversifying the data sources such as highly correlated power data. This idea is inspired by the recent load forecasting methods that have shown extremely well performances. Specifically, the proposed two-stage method, namely the rerouted method, learns two types of mapping rules. The first one is the mapping between the historical wind power and the historical price. The second is the forecasting rule for wind generation. Based on the two rules, we forecast the price via the forecasted generation and the first learned mapping between power and price. Additionally, we observed that it is not the more training data the better, leading to our validation steps to quantify the best training intervals for different datasets. We conduct comparisons of numerical results between existing methods and the proposed methods based on datasets from the Electric Reliability Council of Texas (ERCOT). For each machine learning step, we examine different learning methods, such as polynomial regression, support vector regression, neural network, and deep neural network. The results show that the proposed method is significantly better than existing approaches when renewables are involved. [ABSTRACT FROM AUTHOR]

• Various models in large scale wind power integration are discussed. • Assessment of wind energy potential, market risk and social benefits were reviewed. • Policy decisions in the areas of wind energy, wind policy and strategy development. This paper reviews the different aspects of modeling wind energy systems namely investment, policies, performance, and social benefits for integration in deregulated power market. The wind energy system models depend on wind resource, electrical response of wind turbine generator and the returns of economic market. The paper focuses on identifying the sub-problems and their modeling approaches. Variability of the local power system and the wind resource are chaotic and are usually difficult to model. Satisfactory and some successful algorithms are discussed in detail. Machine Learning models are presented to predict market return and described by market trend and resource forecast. This paper, proposes the representation of risk from large wind integration in unit commitment and presents regional aggregation. The review is followed by critical costs modeling for wind energy projects and market risk mitigation strategies. Finally, social impact and energy security compliance from large scale wind integration are reviewed. [ABSTRACT FROM AUTHOR]

Highlights • Dynamic gas system model to model H 2 injection into gas systems for better accuracy. • Wobbe Index and Combustion Potential to measure H 2 injection impact on gas quality. • Robust optimisation to manage H 2 mix with other gases to ensure gas quality. • Liquid petroleum gas and Nitrogen for changing Wobbe Index and Combustion Potential. Abstract A large volume of wind power is curtailed worldwide due to the intermittency and limited transportation capacity of electrical power systems. New technologies, Power-to-Gas via electrolysis, can convert excessive wind power into hydrogen to be transported by natural gas systems. However, the injection of H 2 into natural gas pipelines can cause gas quality issues due to changing gas compositions. This paper investigates the impact of injecting H 2 converted from wind power on natural gas quality. Two key indexes used to measure gas quality, Wobbe Index and Combustion Potential, are introduced to examine the impact. Then, in order to bring the two indexes into acceptable statutory ranges, H 2 is mixed with Liquid Petroleum Gas and Nitrogen. A robust optimization model, considering wind power uncertainties, is thereafter developed to manage the gas mixture, maximize H 2 injection. This paper uses the dynamic gas system model to represent real-time pipeline flows, which can better reflect gas flow features over time. The proposed method is demonstrated on a small integrated gas-electricity system. Results illustrate that excessive H 2 injection will reduce Wobbe Index but increase Combustion Potential. The robust optimization approach can effectively manage the mixture while ensuring gas quality with an uncertain wind power supply. The proposed method is beneficial to reducing renewable energy curtailment and maximizing H 2 injection, benefiting electricity system operators with low operation costs and wind power more penetration. [ABSTRACT FROM AUTHOR]

Highlights • We analyze the economic value of participating in multi-stage electricity markets. • The party submitting bids is a wind power producer with generic energy storage system. • The market analyzed as a case analysis has eight sub-markets. We condensate them into one. • We consider uncertainty in power production and prices. We propose a common modeling basis. • We analyze a number of scenarios comprising power and price differences. Abstract This paper provides a methodology to compute the optimal bidding by a wind power producer in a multi-stage market. The methodology is not restricted to the two-stage markets often reported in the literature—a day-ahead bid submission followed by an adjustment in the imbalance market. Instead, it allows studying any number of markets operating on the same dispatch hour. Particularly, this paper analyzes part of the Spanish market, covering the day-ahead, the six intraday, and the imbalance market. They are markets with different schedules, but this paper shows that by simply rearranging the market prices into a single equivalent market and employing the increments of power as bids, the calculations are visibly simplified; despite the different scope and gate closures. The methodology also includes a dynamic programming approach that relies on the equivalent market data to provide an optimal bidding sequence and its economic value when (i) uncertain prices and wind power production are considered, and (ii) energy storage is employed. As an application, the proposed methodology is employed to analyze the revenues derived by a wind power producer using ESS in the Spanish market. [ABSTRACT FROM AUTHOR]

Highlights • Multi-objective model for wind-solar-thermal power generation system is proposed. • Trade-offs between economic benefit and environmental impact is analyzed. • 12 scheduling scenarios to handle natural limits of renewable sources are considered. • Real case in China to demonstrate the model practicality and efficiency is presented. Abstract The integration of renewable sources into traditional power networks through the development of hybrid systems has attracted increasing international attention. However, simultaneously considering the environmental impacts, the economic benefits, and the natural limits of renewable sources in hybrid systems has proven difficult. This paper proposes an optimal scheduling strategy that fully considers the contributions of wind farm, solar parks, and coal thermal power plants to determine economic benefit and environmental impact equilibrium in a hybrid generation system under natural limitations. By factoring in the seasonal fluctuations in wind power, the weather driven characteristics of solar power, and the fuzzy coal thermal plant parameters, the optimal strategy better depicts the system characteristics than current strategies. A case study from Hami, China is presented to demonstrate the practicality and efficiency of the optimization model. The calculation results for twelve scheduling scenarios under different wind speeds and weather conditions found that this multi-objective strategy for hybrid generation systems was a superior method for solving the conflicts between emissions reduction and profits under natural renewable source limitations. Compared with previous studies, the optimal strategy this paper proposed is more applicable for developing countries such as China, and provide system operators with less calculation burdens. Management recommendations including the application of equilibrium scheduling strategy, policy support from the government, and improvements in solar power installed capacity have also been proposed. [ABSTRACT FROM AUTHOR]

Highlights • Exhaustive study of ANNs used in wind energy systems, identifying the most employed techniques. • ANNs classified into: forecasting and predictions; fault detection and diagnosis; optimal control; design optimization. • A statistical analysis of the current state (and future trends) in this field is done. Abstract Wind energy has become one of the most important forms of renewable energy. Wind energy conversion systems are more sophisticated and new approaches are required based on advance analytics. This paper presents an exhaustive review of artificial neural networks used in wind energy systems, identifying the methods most employed for different applications and demonstrating that Artificial Neural Networks can be an alternative to conventional methods in many cases. More than 85% of the 190 references employed in this paper have been published in the last 5 years. The methods are classified and analysed into four groups according to the application: forecasting and predictions; design optimization; fault detection and diagnosis; and optimal control. A statistical analysis of the current state and future trends in this field is carried out. An analysis of each application group about the strengths and weaknesses of each ANN structure is carried out. A quantitative analysis of the main references is carried out showing new statistical results of the current state and future trends of the topic. The paper describes the main challenges and technological gaps concerning the application of ANN to wind turbines, according to the literature review. An overall table is provided to summarize the most important references according to the application groups and case studies. [ABSTRACT FROM AUTHOR]

Highlights • Weather information is a fundamental input for microgrid scheduling. • A wide variety of weather information has been incorporated into prior microgrid studies. • The diversity of procedures to implement weather data in microgrid energy management systems are analyzed. • Discussion and recommendations for future works are conducted. Abstract Meteorological conditions determine the renewable energy generation and, to a lesser extent, the load of microgrids. Weather forecasts are thus necessary to establish optimal plans according to the operational objectives and priorities of each microgid. Weather forecast errors are also responsible for deviations from these plans, thereby being an important source of uncertainty in the scheduling process. Despite this, weather information plays a secondary role in most of microgrid studies. This paper provides a general overview of the use of meteorological data in microgrids, focusing on the implementation of weather forecasts in microgrid energy management systems. Data sources, methodologies, uncertainty approaches and results from a selection of papers with complete information about the forecast context are analysed in detail. Additionally, similarities and differences regarding other energy forecast applications apart from microgrids are discussed. Finally, on the basis of the above, a list of recommendations for future implementations of weather forecasts in microgrid energy management systems is presented. [ABSTRACT FROM AUTHOR]

Highlights • Procurement auctions ignoring grid development may lead to sub-optimal deployment. • Presented auction integrates dynamic transmission planning & renewables procurement. • Full integration of both modelling frameworks reduces investor rent-seeking. • A more efficient deployment path is demonstrated under this policy framework. Abstract Competitive renewable energy procurement auctions are becoming increasingly prevalent. In a pay-as-bid auction, investors bid the price support required and receive that price if successful. Bidding strategy may be influenced by factors external to the auction, such as transmission expansion planning decisions. This may increase costs. In this paper, we show that integrating a pay-as-bid auction with transmission expansion planning may allow for closer total system cost minimisation over many time periods. This paper develops an auction mechanism and associated modelling framework to carry this out. The contributions of this framework are verified using a numerical example. Our results show that ignoring generation costs in transmission expansion planning can have economic consequences, while traditional pay-as-bid auctions can benefit from incorporating features associated with transmission expansion planning, such as multi-period optimisation. Full integration of both modelling frameworks can lead to efficiency improvements, both in terms of reduced investor rent-seeking and a more efficient deployment path. [ABSTRACT FROM AUTHOR]

A major difficulty in integrating large scale wind power generation in an electrical power system is that wind generated power appears to be erratic, intermittent, and volatile. In this paper, we demonstrate the efficacies of a novel ultra short term 1-step ahead wind generated power prediction model, by combining two best of breed machine learning models in their respective areas of applications: a deep convolutional neural network (CNN) model, known to be effective in classification problems, and a bi-directional long short term memory (Bi-LSTM) model, known to be effective in 1-step ahead time series prediction problems, using a cross attention (XA) mechanism on three challenging practical datasets: the East-China dataset, the Yalova (Turkey) dataset, and the 16 MW dataset. There are two alternative cross attention models: (1) using the CNN features as the key–value pair, and the Bi-LSTM features as the query, this we called a XA model, and (2) using the Bi-LSTM features as the key–value pair, and the CNN features as the query, this we called a XA_I model; we found empirically for a properly chosen sliding window length, whichever CNN model or Bi-LSTM model performs well in the 1-step ahead prediction, then its features should be used as the query in the cross attention mechanism, thus, the XA achieves better results than XA_I on the East-China dataset, while XA_I achieves better results than XA on the Yalova dataset, and on the 16 MW dataset. Where comparable results using other state-of-the-art models are available on the Yalova dataset and the 16 MW dataset, we found that our XA_I results are superior. Moreover, it is found empirically, that the cross attention mechanism model produces stable k -step ahead predictions, for k ≤ W 2 , where W is the sliding window length. • A novel prediction model for wind generated power based on cross attention mechanism has been proposed. • The proposed method demonstrated on three practical datasets to have superior performance. • The cross attention mechanism based models give a stable multi-step ahead prediction. [ABSTRACT FROM AUTHOR]

Advancement in smart electronic devices such as smartphones, smart watches, tablets have impacted the requirement of supplying continuous energy for powering and functioning of these devices. There have been several research studies performed on harvesting energy from mechanical vibrations, wind, fluid flow, solar and geothermal etc. using various mechanisms. Piezoelectric mechanism provides high power density of energy conversion from mechanical vibrations or wind into electric voltage or charge, however, due to the lower power output of these devices, many devices must be summoned in order to generate actual usable power for small scale electronic devices. In this paper, we have designed, developed, and extensively tested a multicylinder wind energy harvester that converts rotational motion into linear motion which further converts it into electric voltage using Faraday's law of electromagnetic induction. The multicylinder design and modified crank allow the device to output higher power while operating at lower wind speeds such as 2.4 m/s. Furthermore, this device included a rectifier to convert from AC to DC, a capacitor to clean the output signal, and a 5 V regulator that can be used to charge a USB connected smartphone or other smart devices. Under a regulated low wind speed of 2.4 m/s and across a 305 Ω load, the device had a rotational frequency of 0.76 Hz and a power output of 1.2 mW whereas at the wind speed of 4.9 m/s the rotational frequency was 7.25 Hz and the output 421.9 mW. At a rotational frequency of 6 Hz the device was used to charge a 3.7 V 46 mAh smart watch at 1% charge in 1.4 min. We also used an optimized 305 Ω load in wind speeds reaching 10.1 m/s in real world conditions at which point the harvesters' output peaked at 1.21 W resulting in a power density of 19.98 W/m3. Authors believe that these results are promising for understanding, investigating, and providing continuous power solutions to small scale smart electronic devices. • Novel design uses a modified crank-slider to convert rotational to linear motion. • Low threshold wind speed operation at 2.4 m/s. • Peak output of 1.21 W at 10.1 m/s wind speed in a real world environment. • A modified power management unit compatible with 5 V USB output. • Capable of charging small scale electronics such as a 3.7 V 46 mAh smartwatch. [ABSTRACT FROM AUTHOR]

Accurate ultra-short-term wind power forecasting is a prerequisite for decision making related to the management of power systems. Existing approaches used to forecast wind power ignored the correlation between wind power outputs under similar wind power, which provides important information for wind power forecasting. A spatiotemporal approach based on an indirect strategy and a multi-factor extraction model is proposed in this study to achieve more accurate power prediction. Specifically, wind power and other variables are classified into four categories according to wind direction, and multivariate feature extraction and wind power forecasting are performed separately for each category. In the experimental study, taking two real-world datasets of wind farms in Northeast China as examples, the proposed approach is compared with eight benchmarking approaches, and the results demonstrate the effectiveness and robustness of our approach. In addition, we further conduct the DM test, and the sensitivity analysis to discuss the effect of hyperparameters on forecasting performance. The results of DM verify the superiority of the proposed approach in statistic. This study provides a new high-precision approach and a new forecasting strategy for future wind power forecasting. • An indirect strategy is adopted in the wind power forecasting. • The dependence between data under similar wind direction is considered. • The approach characterizes the spatiotemporal effects of wind power. • This paper uses various information extraction and filtering methods. • Comprehensive experimental studies are conducted. [ABSTRACT FROM AUTHOR]

The volatility and uncertainty of wind energy has brought great challenges to its accommodation in electric power systems. To determine a wind power trading strategy in the real-time power market, the accurate assessment of wind energy accommodation capability (WAC) is of great significance. However, most current studies investigate the wind energy accommodation potential from the perspective of dispatch, but rarely provide quantitative assessment results. Additionally, the non-anticipativity of decisions has not been considered in the assessment yet. This paper proposes a novel quantitative assessment method for WAC based on the multi-stage robust optimization (RO), which addresses the anticipativity issues in the traditional two-stage method while maintaining the decision-making process' robustness. In the assessment method, the operational risk is integrated via a tractable scheme to obtain dynamic admissible WAC boundaries. To obtain the optimal solution of the proposed multi-stage RO problem, a fast robust dual dynamic programming (FRDDP) algorithm is employed, where the adaptive techniques are developed to accelerate the computation. Numerical studies on the modified IEEE 14-Bus system and a real-world system in Zhejiang Province of China validate the effectiveness of the proposed assessment model and adaptive acceleration techniques. The simulation results demonstrate the presented method brings a 18.87% reduction in operational cost, and reduces 29.34% curtailment of wind energy. Compared with the original FRDDP, the adaptive technique significantly reduces the computational consumption by 73.34% on the real-world test system. • A novel assessment method for WAC is proposed based on the multi-stage robust optimization. • Tractable risk evaluation method is integrated in the multi-stage framework. • Adaptive techniques are designed for FRDDP method to accelerate the optimal solution. • The proposed method achieves both non-anticipative and robust assessment decisions. [ABSTRACT FROM AUTHOR]

This paper comprehensively investigates the impact of wind turbine layout and hub height on power generation of wind farm. Firstly, an engineering three-dimensional (3-D) wind turbine wake model is improved by the artificial neural network (ANN) technology. The novel 3-D ANN wake model reaches more than 99% accuracy of the original wake model. It can save about 80% of the computational time when predicting the downstream wind speed. Secondly, the influence of wind turbine hub height and position on the equivalent wind speed (EWS) and power is deeply studied. Specially, when reducing the hub height of the downstream wind turbine, both the wake impact from upstream turbines and EWS will decrease, so the overall influence should be assessed according to the specific situation. Finally, the problem of wind farm layout and height optimization is investigated. According to this study, simultaneously optimizing these two factors can obtain a better result than optimizing each factor individually. If economic factor is additionally considered, the optimized hub height and power output results will be quite different. Therefore, considering more factors is important to obtain an appropriate wind farm layout. • A novel 3-D ANN wake model is developed for wind farm layout optimization. • The 3-D ANN wake model is accurate and can save about 80% of computational time. • How the layout and hub heights of wind turbines influence wind farms is discussed. • Decreasing the hub height of downwind wind turbine may have different impacts on itself. • Simultaneously optimizing hub heights and layout can increase the power by 154%. [ABSTRACT FROM AUTHOR]

• A meso-microscale coupling model is applied to a real onshore wind farm. • The atmospheric stability is introduced and refined in the multi-scale coupling process. • The wind flow data is simulated throughout the year with a resolution of 1 h. • Downscaling simulation is performed using mesoscale data from three different heights. The abundance level of wind resources is the first and most important factor to consider in the wind farm design. The accuracy of wind energy resource assessment directly determines the operating income of a wind farm after its construction is completed. Therefore, aiming to meet the requirements of accurate wind resource assessment in areas with a complex terrain or those lacking the observation information, this paper proposes a multi-scale coupling numerical simulation method based on atmospheric stability. First, the atmospheric flow field simulation results at the heights of 200 m, 300 m, and 400 m are extracted from the mesoscale Weather Research and Forecasting (WRF) model and used as inlet boundary conditions of a microscale Computational fluid dynamics (CFD) model. Then, to obtain more accurate coupling data at the height of a wind turbine hub, the atmospheric stability parameter is introduced into the multi-scale coupling process, data correction is conducted at the three heights, and a comparison with the observation data of a wind tower is made. Finally, a numerical analysis is conducted using real data of a wind farm in eastern Inner Mongolia and Jilin. The analysis results show that the proposed method can improve the assessing accuracy of wind resources of wind farms using mesoscale data, which is valuable to a certain extent in real-world applications. [ABSTRACT FROM AUTHOR]

• Imported green e-H 2 is more expensive than e-H 2 produced in Germany and Finland. • Local supply of e-H 2 found to be more economical across all scenarios and years. • Transportation costs constitute 29–63% of the total imported e-H 2 cost. • Pipeline transmission may be cheaper than shipping for distances of 2000–2500 km. Widely available and low-cost solar photovoltaics and wind power can enable production of renewable electricity-based hydrogen at many locations throughout the world. Hydrogen is expected to emerge as an important energy carrier constituting some of the final energy demand; however, its most important role will be as feedstock for further processing to e-fuels, e-chemicals, and e-steel. Apart from meeting their own hydrogen demand, countries may have opportunities to export hydrogen to countries with area limitations or higher production costs. This paper assesses the feasibility of e-hydrogen imports to Germany and Finland from two case regions with a high availability of low-cost renewable electricity, Chile and Morocco, in comparison to domestic supply. Special attention is paid to the transport infrastructure, which has a crucial impact on the economic viability of imports via two routes, shipping and pipelines. This study has found that despite lower e-hydrogen production costs in Morocco and Chile compared to Germany and Finland, additional transportation costs make imports of e-hydrogen economically unattractive. In early 2020s, imported fuel costs are 39–79% and 34–100% higher than e-hydrogen produced in Germany and Finland, respectively. In 2050, imported e-hydrogen is projected to be 39–70% more expensive than locally produced e-hydrogen in Germany and 43–54% in the case of Finland. e-Hydrogen may become a fuel that is mostly produced domestically and may be feasible for imports only in specific locations. Local e-hydrogen production may also lower dependence on imports, enhance energy security, and add jobs. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to analyze the economic feasibility of offshore wind energy under influences of government financial subsidies, dynamic weather conditions (including typhoon), taxation, expectation of investor, optimal maintenance schedule, generated electricity under real weather conditions, and benefits from reduction of CO 2 emission. Economic criteria are calculated and used to assess efficiency of the government financial subsidies. This paper further proposes more appropriate subsidies for the government in term of satisfying both of investor’s expectation and the government’s target. In the case of Taiwan, government has been currently adopting Feed-in-tariff (FIT) subsidy and offering subsidy on capital cost to promote the development of offshore wind system. However, the currently applied subsidy on capital cost is not attractive enough for the investors and the current FIT subsidy is applied equally for all regions of Taiwan regardless the distinct conditions of each region. This paper proposes a more appropriate subsidy for capital cost, and suggests establishing regional FIT subsidy to subsidize differently for different regions. The results indicate that the proposed subsidy for capital cost and the proposed regional FIT subsidy are more appropriate to promote the expansion of offshore wind system. [ABSTRACT FROM AUTHOR]

Environmental issues and the prospect of an energy crisis inspire humans to exploit wind power. However, with the increase of wind power penetration level, operating power systems securely and reliably is a serious challenge due to the inherent nature of wind power intermittency. Wind power intermittency has been the major barrier for large scale wind power integration. This paper reviews past research on wind power intermittency, including its impacts on power system, how it is measured, and mitigation solutions. It has been found that as wind power integration increase, the system reverses and costs consequently increase, while the system reliability and CO 2 reductions decrease. In order to mitigate wind power intermittency, studies on intermittency measurements and mitigation solutions are necessary. Existing measurements of wind power intermittency are summarized firstly. Considering the limitations of existing methods, new definitions and metrics are proposed based on our study. Then, various wind power intermittency mitigation solutions are comprehensively reviewed, including wind farms, generation-side, demand-side and energy storage. In the final part of this paper, the further work on wind power intermittency is discussed in detail. In summary, wind power intermittency can be effectively mitigated using various technological and managerial approaches based on an in-depth understanding of intermittency. [ABSTRACT FROM AUTHOR]

Given the importance of reliability and availability in wind farms, this paper focuses on the development of fault diagnosis and fault-tolerant control schemes in a cooperative framework (referred to as active fault-tolerant cooperative control) at the wind farm level against the decreasing power generation caused by turbine blade erosion and debris build-up on the blades over time. In more details, the paper presents a novel integrated fault detection and diagnosis and fault-tolerant control approach oriented to the design and development of two active fault-tolerant cooperative control schemes for an offshore wind farm. Each of the schemes employs a fault detection and diagnosis system to provide accurate and timely diagnosis information to be used in an appropriate automatic signal correction algorithm for accommodation of faults in the wind farm. The effectiveness and performance of the proposed schemes are evaluated and compared using different simulations on a high-fidelity offshore wind farm benchmark model in the presence of wind turbulences, measurement noises and realistic fault scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a novel agent-based, stochastic model, which uses game-theoretic principles to simulate Contract for Difference (CfD) auctions. The framework has use cases and implications for policymakers and renewable generators alike, and can be used by developers to prepare bidding strategy and for policymakers to empirically test auction design. The model is demonstrated through replication of the offshore wind CfD Allocation Round 3 (AR3) pot, and utilises high-level cost modelling distribution data to estimate bid prices for the competing projects. The model produces a distribution of most likely results which better categorises uncertainty, and through comparison of AR3 and simulation results, demonstrates how outcomes can be predicted with reasonable confidence by developers. Analysis show that the transmission network and grid connection charges are a significant barrier for projects in some geographical regions to be awarded a CfD contract, potentially hindering renewable deployment in those areas. Moreover, this paper demonstrates how players can use probability theory to select an optimum bidding strategy which maximises expected profit while factoring the uncertainty inherent in CfD auctions. Results show that a 1200 MW wind farm development can increase potential profits by £135 million over the CfD contract length in exchange for a 25 p.p. chance reduction in being awarded a subsidy. • A novel simulation approach is introduced for analysing the UK's CfD auction • We show how players can attempt to optimise their bid based on expected profits • We demonstrate how uncertainty in auctions can be characterised by developers • Transmission charges act as a barrier for offshore wind projects in some regions [ABSTRACT FROM AUTHOR]

This paper formulates the offering problem for a cluster of wind-storage systems in the day-ahead energy market using a risk-constrained stochastic programming approach that anticipates different operating conditions in the real-time energy market. Wind-storage systems can be jointly operated as a cluster so as to achieve higher profitability. However, a meaningful positive correlation among the production provided by wind farms located in the cluster results in a higher level of uncertainty that imposes additional risk. A key issue is how this correlation influences the operation of the cluster in the energy markets. In order to study this subject, this paper presents the uncertainties involved by means of a number of correlated scenarios including: (i) the correlated prices in the day-ahead and the real-time markets, and (ii) the correlated wind power production of multiple wind farms jointly generated using an innovative scenario generation methodology. The comparative statistical analysis validates the good accuracy of the method proposed in order to capture the spatio-temporal correlation among the wind farms. The results of a realistic case study are, moreover, compared with those obtained by considering that the scenarios are generated individually for each wind farm. Upon considering the latter, the variability of wind power production is underestimated, which has a negligible impact on the expected profit; however, the profit risk modeled using the conditional value-at-risk is significantly overestimated. The overestimation error particularly concerns a less risk-averse operator of the cluster in the case of low wind power production. • Correlated scenarios for wind production are jointly generated. • Individual uncertainty model of wind production cannot fully capture correlation. • Offering of wind-storage systems is compared using joint and individual models. • Conditional value-at-risk of profit is overestimated by individual uncertainty model. • If wind production is low, the higher-risk offering intensifies overestimation error. [ABSTRACT FROM AUTHOR]

• BRICS countries exhibit expansionary strategies in solar and wind power. • DE countries exhibit conservative strategies in solar and wind power. • The solar and wind power markets in BRICS countries are capital-oriented. • BRICS countries should prevent the new energy technology dilemma. Carbon neutrality is proposed by many countries for responding to climate change and energy transformation. Developing new energy is crucial to achieving carbon neutrality goals. In the context of carbon neutrality, this paper analyzes the factor structure and market orientation of the new energy market in BRICS countries (including the Federative Republic of Brazil, Russian Federation, the Republic of India, People's Republic of China, and the Republic of South Africa) and developed European (DE) countries in recent years. The primary conclusions are as follows: (1) In wind and solar energy markets, the BRICS countries exhibit an elastic substitution effect in production, indicating they have chosen the expansionary strategy with unbalanced factors. Through the contribution analysis, we find the development of wind and solar energy in BRICS countries is capital-oriented. However, the DE countries have opted for a conservative strategy with balanced factors, in which the wind and solar energy markets need promotional measures for capital investment and technological progress. (2) In the biomass energy market, both the BRICS countries and DE countries adopt the expansionary strategy. Besides, the development of the biomass energy market is technology-oriented except for India. (3) For the nuclear energy market, the BRICS countries and DE countries choose the conservative strategy and expansionary strategy, respectively. However, due to safety concerns, the expansion of nuclear energy in all countries has slowed. (4) Although a capital-oriented strategy can quickly promote the development of the new energy market, this practice compromises the local welfare and the benefits of the new energy technology progress to varying extents. The core insight of this paper is that BRICS countries should avoid the new energy capital trap and prevent the new energy technology dilemma. For the BRICS countries, policymakers should further evaluate the balance between capital-oriented interest and technology-oriented development. Also, achieving carbon neutrality is more difficult for the BRICS countries than DE countries. Hence, the BRICS countries should further refine their energy strategies for the long term. [ABSTRACT FROM AUTHOR]

With many countries planning to significantly increase grid renewable energy penetration levels, we consider the role of wave energy in supply–demand matching. We investigate how incorporating wave power into an offshore wind farm affects farm power predictability, smoothness, required energy storage capacity, and cost. In this paper, we do a first-order cost analysis of an offshore farm comprised of floating wind turbines and wave energy converters that are both standalone and combined and onshore compressed air energy storage. Then, we do a parameter sweep investigation of an isolated power network supplied by varied grid renewable energy penetration levels supplemented by natural gas, varied distribution of renewable energy between wind and wave power, and varied power capacity of a compressed air energy storage system supplying power to a shoreline community. For each parameter set, we consider the historical hourly electricity demand and wind-sea data of a coastal California community over a year, and optimize the energy storage schedule to reduce curtailed power, stored energy, and base gas plant operational cost. We show that a co-located wind-wave farm has smoother power supply, less energy curtailment, and higher farm-to-grid efficiency than a solely wind farm. That is, a 50%–50% wind-wave farm has a 15% smaller coefficient of variation in the power supply, 6% less curtailed power, and 2% higher farm-grid efficiency than a 100% wind farm when the grid is 100% renewable energy. These benefits of wave power potentially decrease the need for interconnecting regional transmission lines to match power supply with demand. The intent of this paper is to provide baseline system technical results to help future researchers and policy makers make decisions about offshore hybrid wind-wave-storage farms. • We do a joint parameter sweep and energy storage schedule optimization on the farm. • A co-located wind-wave farm has 15% smoother power than a wind farm. • A co-located wind-wave farm has 6% less energy curtailment than a wind farm. • A co-located wind-wave farm has 2% higher power efficiency than a wind farm. • Attaching a wave energy converter (WEC) to a wind turbine reduces WEC cost by 43%. [ABSTRACT FROM AUTHOR]

As many countries increase their installed offshore wind capacity, floating offshore wind turbines (FOWTs) are critical to gaining access to deep-water resources where fixed-bottom turbines would not be economically feasible to install. The levelized cost of energy (LCOE) of FOWTs must be minimized to ensure competitiveness with traditional power sources. Innovative substructure designs and advanced control methods can help decrease the LCOE of FOWTs by increasing annual energy production (AEP) and the turbine's lifetime. LCOE can also be reduced by minimizing capital expenditures and operation and maintenance costs. The novel SpiderFLOAT substructure described in this paper decreases the LCOE through its light-weight and easy-to-manufacture design while providing unique opportunities for platform actuation. Specifically, this paper details the design and testing of a buoyancy can ballast controller to achieve a desired platform pitch angle for increasing AEP while also reducing tower-base fatigue loads and maintaining safe operation. The proposed buoyancy can ballast control is shown to increase AEP by 1.1%, reduce tower-base bending damage equivalent loads (DELs) by up to nearly 9%, reduce maximum platform pitch angles, and not interfere with the baseline controller. The robustness of the proposed controller is tested for misaligned wind and wave direction cases and similarly yields promising performance in the metrics of interest. • Floating offshore wind turbine platform control to augment wind turbine controller for a lightweight semi-submersible. • Low-bandwidth buoyancy can ballast control for power increase and load reduction. • Includes robustness check for misaligned wind and wave directions. [ABSTRACT FROM AUTHOR]

• The convolution and pooling operations are used to extract the wind power feature. • A double Gaussian activation function is proposed for Radial Basis Function network. • Coefficients of determination (r-square) of the results exceed 0.88 in 4 seasons. Wind power generation is always associated with uncertainties as a result of fluctuations of wind speed. Accurate predictions of wind power generation are important for the efficient operation of power systems. This paper presents a hybrid deep learning neural network for 24 h-ahead wind power generation forecasting. This novel method is based on a Convolutional Neural Network (CNN) that is cascaded with a Radial Basis Function Neural Network (RBFNN) with a double Gaussian function (DGF) as its activation function. The CNN is utilized to extract wind power characteristics by convolution, kernel and pooling operations. The supervised RBFNN, incorporating a DGF, deals with uncertain characteristics. Realistic wind power generations, measured on a wind farm, were used in simulations. The proposed method is implemented using TensorFlow and Keras Library. Comparative studies of different approaches are shown. Simulation results reveal that the proposed method is more accurate than traditional methods for 24 h-ahead wind power forecasting. [ABSTRACT FROM AUTHOR]

• FuturES framework optimises electricity scenarios with high renewables penetration. • Reservoir and concentrating solar power modelled based on storage value. • Feasible scenarios created with 81–100% renewables by 2050. • Long-term energy storage helps to achieve low electricity costs. Although electricity supply is still dominated by fossil fuels, it is expected that renewable sources will have a much larger contribution in the future due to the need to mitigate climate change. Therefore, this paper presents a new framework for developing Future Electricity Scenarios (FuturES) with high penetration of renewables. A multi-period linear programming model has been created for power-system expansion planning. This has been coupled with an economic dispatch model, PowerGAMA, to evaluate the technical and economic feasibility of the developed scenarios while matching supply and demand. Application of FuturES is demonstrated through the case of Chile which has ambitious plans to supply electricity using only renewable sources. Four cost-optimal scenarios have been developed for the year 2050 using FuturES: two Business as usual (BAU) and two Renewable electricity (RE) scenarios. The BAU scenarios are unconstrained in terms of the technology type and can include all 11 options considered. The RE scenarios aim to have only renewables in the mix, including storage. The results show that both BAU scenarios have a levelised cost of electricity (LCOE) lower than, or equal to, today's costs ($72.7–77.3 vs $77.6/MWh) and include 81–90% of renewables. The RE scenarios are slightly more expensive than today's costs ($81–87/MWh). The cumulative investment for the BAU scenarios is $123-$145 bn, compared to $147-$157 bn for the RE. The annual investment across the scenarios is estimated at $4.0 ± 0.4 bn. Both RE scenarios show sufficient flexibility in matching supply and demand, despite solar photovoltaics and wind power contributing around half of the total supply. Therefore, the FuturES framework is a powerful tool for aiding the design of cost-efficient power systems with high penetration of renewables. [ABSTRACT FROM AUTHOR]