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In the pursuit of sustainable development, accurate renewable energy demand forecasting holds great significance for climate change mitigation and promoting sustainability. However, renewable energy forecasting has been consistently challenged by seasonality and nonlinearity. Identifying the periodic and nonlinear characteristics concealed within renewable energy sources accurately is still an unexplored problem. Consequently, an innovative nonlinear discrete seasonal grey model is proposed for renewable energy forecasting, which incorporates seasonal dummy variables and a power exponent term for handling the seasonality and nonlinear patterns in time series. Furthermore, an intelligent algorithm matching framework is proposed to augment the flexibility of the newly developed model. For practical purposes, the new methodology is contrasted against a range of benchmarks encompassing statistical, machine-learning, and traditional grey models in forecasting the quarterly total renewable energy consumption in the United States. The proposed model exhibits over 27% improvement rates over its counterparts, achieving the most superior predictive accuracies of 1.45%, 39.27, and 0.79 in MAPEP , RMSEP , and MASEP metrics, respectively. Furthermore, the probability density and sample size analyses are conducted to validate the robustness of the new model, confirming its adaptability and stability towards algorithm randomness and historical information volume. Consequently, the novel model is employed to forecast the short-to-long-terms renewable energy consumption in the U.S., showcasing an upward trend and seasonal fluctuations of the consumption for the forthcoming 24 quarters from 2023Q4 to 2029Q3. These insights can offer valuable implications to the stakeholders such as energy suppliers, utility managers, and policy advocates, highlighting actionable strategies for optimizing renewable energy consumption forecasting and aiding sustainable development initiatives. • An innovative nonlinear discrete seasonal grey model is proposed for renewable energy forecasting. • The optimum algorithm matching the novel model is determined from five alternatives. • The Monte Carlo Simulation and parameter sensitivity analysis advance the robustness. • PSO with the optimal parameter group has been selected for searching coefficients. • The new model possesses higher accuracy and robustness than various competitors. [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]

Direct air capture (DAC) technology plays a crucial role in mitigating the effects of global warming by capturing carbon dioxide (CO 2) directly from the atmosphere. A significant technical challenge for DAC is its high thermal energy consumption, which results in an unacceptable overall cost (600 $ t CO 2 – 1). The combination of a high-temperature heat pump (HTHP) and DAC system offers a substantial reduction in thermal energy consumption, decreased reliance on renewable energy sources, and enhanced DAC system flexibility. This study presents three distinct thermal integration strategies that combine an HTHP with solid adsorbent-based DAC and verifies their effectiveness in reducing energy consumption and CO 2 emissions compared to a traditional DAC system. Among them, the deeply integrated DAC-HTHP system (I-DAC) exploits low-grade adsorption heat and waste heat, providing direct heating and cooling energy to the DAC system via refrigerant condensation and evaporation processes. Simulation results indicate that the I-DAC system achieves an extremely low operating energy consumption of 2.77 GJ t CO 2 – 1 , representing a 69.5% reduction than traditional DAC under the same conditions. Consequently, the I-DAC offers a cost-effective (32.0–46.2 $ t CO 2 – 1) negative emission solution. We also demonstrate that the I-DAC is promising for wide application in cities to recycle waste heat and remove CO 2 from the air. The comprehensive implementation of the I-DAC in 105 cities could yield a net annual productivity of 980 MtCO 2. • Proposed a deeply thermal integrated DAC system (I-DAC) with waste heat recovery heat pump. • I-DAC achieved a 69.5% reduction in specific energy consumption compared to base-DAC. • I-DAC offers a cost-effective and scalable negative emission solution for climate change mitigation. • Successful implementation of I-DAC in 105 cities could yield a net annual capture capacity of 980 MtCO 2 by 2050. • Highlights the potential of I-DAC to recycle waste heat and remove CO 2 from the air, with policy implications [ABSTRACT FROM AUTHOR]