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The increasing penetration of distributed renewable energy sources introduces challenges in maintaining balance within power systems. Civic energy initiatives offer a promising solution by decentralizing balancing responsibilities to local areas, with energy clusters serving as an example of such communities. This article proposes a novel mixed-integer linear programming (MILP) model for optimizing the energy mix within a cluster, addressing both planned balancing (day-ahead) and unplanned real-time adjustments. The proposed approach focuses on mid-term decision-making, including the integration of additional wind energy sources into the cluster and the procurement of new demand-side response (DSR) contracts, that allow for short-term planned and unplanned balancing. While increased wind energy enhances the system’s renewable capacity, it also raises operational stiffness, whereas DSR contracts provide the flexibility necessary for effective system balancing. The model incorporates risk aversion by employing Conditional Value at Risk (CVaR) as a risk measure, enabling a nuanced evaluation of trade-offs between cost and risk. The interactive framework allows decision-makers to tailor solutions by adjusting confidence levels and assigning weights to cost and risk metrics. A representative numerical example, based on a typical energy cluster in Poland, illustrates the model’s applicability. This case study demonstrates that the model responds intuitively to varying decision-maker preferences and can be efficiently solved for practical problem sizes.

In addition to the fact that most renewable energies such as solar and wind energy have become more competitive in the global energy market, thanks to the great development in conversion technologies, it believes that renewable energy can play a crucial role in global environmental issues. However, in Palestine, the situation is different from anywhere else; renewable energy is not only an economic option, but an absolute necessity to get out of the energy crisis that Palestinian cities suffer from long years ago and continue nowadays. The cornerstone of the present research is focusing on the availability of renewable energy resources in Jenin Governorate (JG)—West Bank (WB)—Palestine. Two-year time-series of hourly solar, wind, biomass, and 1-year hourly electrical load data are used in the analysis in this paper. The energy potentials were estimated using System Advisor Model software (SAM), and the optimum combination and sizing of the hybrid renewable energy system were determined using Hybrid Optimization of Multiple Energy Resources (HOMER). The proposed Hybrid Renewable Energy System (HRES) consists of an 80 MW PV solar field, 66 MW wind farm, and 50 MW biomass system with an initial investment of$323 M. The proposed HRES generates 389 GWh/yr and is enough to meet 100% of the electrical demand of JG (372 GWh/yr) with excess in electricity generation of about 4.57% and the unmeet electric load is about 109.6 MWh/yr which is equivalent to less than 2 h off in a year. The estimated Levelized Cost of Energy (LCOE) was found as 0.313 $ /kWh.

The smart grid is an unprecedented opportunity to shift the current energy industry into a new era of a modernized network where the power generation, transmission, and distribution are intelligently, responsively, and cooperatively managed through a bi-directional automation system. Although the domains of smart grid applications and technologies vary in functions and forms, they generally share common potentials such as intelligent energy curtailment, efficient integration of Demand Response, Distributed Renewable Generation, and Energy Storage. This paper presents a comprehensive review categorically on the recent advances and previous research developments of the smart grid paradigm over the last two decades. The main intent of the study is to provide an application-focused survey where every category and sub-category herein are thoroughly and independently investigated. The preamble of the paper highlights the concept and the structure of the smart grids. The work presented intensively and extensively reviews the recent advances on the energy data management in smart grids, pricing modalities in a modernized power grid, and the predominant components of the smart grid. The paper thoroughly enumerates the recent advances in the area of network reliability. On the other hand, the reliance on smart cities on advanced communication infrastructure promotes more concerns regarding data integrity. Therefore, the paper dedicates a sub-section to highlight the challenges and the state-of-the-art of cybersecurity. Furthermore, highlighting the emerging developments in the pricing mechanisms concludes the review.

Integration of renewable energy sources (RES) in a distribution network facilities the establishment of sustainable power systems. Concurrently, the incorporation of energy storage system (ESS) plays a pivotal role to maintain the economical significance as well as mitigates the technical liabilities associated with uncontrollable and fluctuating renewable output power. Nevertheless, ESS technologies require additional investments that imposes a techno-economic challenge of selection, allocation and sizing to ensure a reliable power system that is operationally optimized with reduced cost. In this paper, a deterministic cost-optimization framework is presented based on a multi-input nonlinear programming to optimally solve the sizing and allocation problem. The optimization is performed to obviate the demand-generation mismatch, that is violated with the introduction of variable renewable energy sources. The proposed optimization method is tested and validated on an IEEE 24-bus network integrated with solar and wind energy sources. The deterministic approach is solved using GAMS optimization software considering the system data of one year. Based on the optimization framework, the study also presents various different scenarios of renewable energy mix in combination with advanced ESS technologies to outline an technical as well as economical framework for ESS sizing, allocation, and selection. Based on the optimal results obtained, the optimal sizing and allocation were obtained for lead-acid, lithium-ion, nickel-cadmium and sodium-sulfur (NaS) battery energy storage system. While all these storage technologies mitigated the demand-generation mismatch with optimal size and location. However, the NaS storage technology was found to be the best among the given storage technologies for the given system minimum possible cost. Furthermore, it was observed that the cost of hybrid wind-solar mix system results in the lowest overall cost.

In this study, sustainable mixture designs of three concrete types, including fly ash concrete, silica fume concrete, and ground granulated blast furnace slag concrete, were investigated. To this end, the compressive strength formulas of each concrete type made with supplementary cementitious materials were obtained by introducing a new machine learning algorithm, called coyote optimization programming. The accuracy of this algorithm proved to be greater than that of conventional and recently developed machine learning methods. An optimization problem is modeled, in which the compressive strengths, price, and environmental impact of the sustainable concrete mixture designs were estimated using global warming potential, energy consumption, and material consumption as the sustainability parameters. Results reveal that increasing the compressive strength reduces the sustainability of concrete, and thus, manufacturing concrete with a higher compressive strength than the one obtained from the design process contradicts the concrete’s performance. Moreover, the 30-MPa sustainable fly ash concrete was proven to be the most sustainable mix with a gray relational grade of 1. This optimal mixture designed in this study can decrease the unit cost, global warming potential, energy consumption, and material consumption by 36.6%, 51%, 43%, and 11%, respectively.Research highlightsA novel machine learning method called coyote optimization programming was introduced in this study.Applying optimization techniques to design concrete mixture proportions can reduce the unit cost by 36.6%.The introduced approach can decrease the global warming potential, energy consumption, and material consumption by 51%, 43%, and 11%, respectively.The application of supplementary cementitious materials in the concrete mixtures significantly enhances sustainability.

A properly optimized concrete mix design yields the required workability and strength for the fresh and hardened concrete to sustain desired loads and stresses over time, preventing premature failure. Thus, it is imperative to investigate the behavioural sensitivity of blended cement concrete to mix design variations. The research uses the Box-Behnken design of the response surface method to optimize the slump and compressive strength of blended cement concrete incorporating Shea nutshell ash (SNA). SNA was partially utilized as a Portland limestone cement (PLC) substitute at 5–15 wt% replacement levels using C25, C30, and C40 MPa mix design proportions and tested for compressive strength after 7–90 curing ages. Binder (SNA-to-PLC) ratio, water-to-binder ratio, binder-to-aggregate ratio, and curing age were engaged as continuous (independent) variables to optimize the response (dependent) variables (slump and compressive strength). The slump and compressive strength responses were optimized by the Box-Behnken design. The results exhibited a minimized slump and a maximized compressive strength with approximately 40–63% reduction and 10% increment. The correlations between the optimized and experimental variables were accurate and strong, with 98.89% and 98.44% R 2 for slump and compressive strength. Ultimately, this response model is beneficial in determining the optimum mix design proportions to achieve the desired compressive strength of blended cement concrete incorporating repurposed waste materials.

The pursuit of a cost-effective and low-carbon electricity generation environment is critical to achieving Ghana's economic and industrial ambitions. Ghana's development agenda calls for an average electricity consumption of about 5,000 kWh per capita by 2030. To this end, the effective harnessing of energy resources requires the implementation of robust policies for sustainable electricity generation. This study employs the IAEA MESSAGE analytical tool to conduct a quantitative assessment of electricity generation in Ghana from 2020 to 2048. The findings show that, by 2048, a diversified electricity generation scenario will result in a 32.30% decrease in cost and a 55.27% reduction in CO 2 emissions, compared to an accelerated economic growth (AEG) scenario, which will increase cost and CO 2 emissions by 12.21% and 21.10%, respectively. The results underscore the importance of ensuring that electricity generation policies balance economic, environmental, and social concerns. Achieving a green energy transition agenda in Ghana and other developing nations will require a long-term commitment to a generation mix that is both sustainable and economically viable. The implementation of such a policy will require an informed and dedicated effort from all stakeholders.

The aviation industry contributes to more than 2% of global human-induced CO2-emissions, and it is expected to increase to 3% by 2050 as demand for aviation grows. As the industry is still dependent on conventional jet fuel, an essential component for a carbon-neutral growth is low-carbon, sustainable aviation fuels, for example alternative drop-in fuels with biobased components. An optimization model was developed for the case of Sweden to examine the impacts of carbon price, blending mandates and penalty fee (for not reaching the blending mandate) on the production of renewable jet fuel (RJF). The model included biomass gasification-based Fischer–Tropsch (FT) jet fuel, Power-to-Liquid (PTL) jet fuel through the FT route and Hydrothermal liquefaction (HTL)-based jet fuel. Thus, this study aims at answering how combining different policies for the aviation sector can support the production of RJF in Sweden while reducing greenhouse gas (GHG) emissions. The results demonstrate the importance of implementing policy instruments to promote the production of RJF in Sweden. The blending mandate is an effective policy to both promote RJF production while reducing emissions. The current level of the penalty fee is not sufficient to support the fuel switch to RJF. A higher blending mandate and carbon price will accelerate the transition towards renewable and sustainable fuels for the aviation industry.

To facilitate the high-value utilization of reclaimed asphalt pavement (RAP), this study investigated the efficacy of fine separation technology as a pre-treatment method. This technology significantly reduced the variability of RAP, controlling the coefficients of variation for asphalt content and aggregate gradation within 5% and 10%, respectively, and minimized false particle content (agglomerates of fines and aged asphalt). Response Surface Methodology (RSM) was employed to optimize the mix design for ultra-high-RAP- content mixtures (50–70%). A predictive regression model was developed to determine the Optimal Binder Content (OBC) based on RAP and rejuvenator dosage. The road performance of the resulting mixtures was comprehensively evaluated. Results showed that the technology markedly enhanced the overall performance of recycled asphalt mixtures. While high-temperature rutting resistance improved with increasing RAP content, low-temperature performance declined. The mixture with 70% RAP failed to meet low-temperature cracking requirements. Consequently, an optimal RAP content of 60% is recommended. Furthermore, the generalized sigmoidal model effectively constructed dynamic modulus master curves, accurately predicting the viscoelastic behavior of these ultra-high-RAP mixtures. This study demonstrates that fine separation is a critical pre-processing step for reliably producing high-quality, sustainable asphalt mixtures with RAP content far exceeding conventional limits.

Abstract Transitioning from a fossil-fuel-dependent economy to one based on renewable energy requires significant investment and technological advancement. While wind and solar technologies provide lower cost electricity, enhanced energy storage and transmission infrastructure come at a cost for managing renewable intermittency. Energy storage systems vary in characteristics and costs, and future grids will incorporate multiple technologies, yet the optimal combination of storage technologies and the role of interconnectors in alleviating storage needs are not widely explored. This study focuses on optimal generation-storage capacity requirements to elucidate associated investments. We propose a multitimescale storage solution consisting of three storage categories and an interconnector between Australia’s eastern and western grids. Subsequently, through an extensive sensitivity analysis, we investigate the impact of specific storage technologies and cost variations. Our findings demonstrate that the proposed interconnector offers a cost-effective solution, reducing generation and storage power capacity needs by 6 and 14%, respectively, resulting in 4% savings on overall investment costs. Moreover, the study’s sensitivity analysis reveals that wind generation provides 50–70% of the energy demand for the least-cost solution. Despite storage inefficiencies, long-duration storage would need to be deployed to support power capacity for 2–4 days, representing 15–40% of peak demand, depending on future technology costs. Subsequently, achieving a fully renewable electricity sector in Australia requires a significant expansion of generation and storage infrastructure, with a 13-fold increase in storage power capacity and a 40-fold increase in storage energy capacity compared to existing levels.