Africa faces challenges with low access to electricity, resulting in many relying heavily on firewood and charcoal to meet their household energy needs. Of note, large quantities of raw crop wastes and by-products (collectively called ‘residues’) are generated daily from agricultural production. A large proportion of these is, however, underutilised and/or improperly managed. This study thus provides, for the first time, a continent-wide assessment of the energy recovery potential of underutilised crop residues across all 54 African countries, based on FAO statistical harvest data for the 2011 to 2021 period for 34 crops and 59 residue types. This assessment covers a significantly broader scope than do previously reported country- or region-specific studies. Estimates of potentially available residues for energy recovery were derived using residue-to-product ratios (RPR), surplus availability factors (SAF), and an assumed average moisture content of 25%, parameters commonly applied in biomass assessments, but that are inherently subject to variability across local contexts. Based on these inputs, the analysis indicates that the quantity of residues potentially available for energy recovery averages ~1.01 billion tons on a dry basis. This includes 0.53 billion tons of surplus primary residues and 0.16 billion tons of surplus secondary residues, which are available after meeting the demand for all existing competing uses. To beneficiate these residues into fuels, densification and pyrolysis, both recognised for their ability to transform raw biomass into high-value energy products are considered. Both conversion routes are reviewed to assess the energy potential of their resulting products and to identify their most suitable operating conditions. In conclusion, this study showed that implementing certain technologies could elevate the technical energy recovery potential for Africa to 8.60–9.61 exajoules per year. Finally, a greenhouse gas (GHG) emission analysis indicated that converting 100% of secondary residues into biofuels could reduce Africa’s GHG emissions by up to 14.57 mega tons of CO2 equivalent, as compared to direct combustion, which is currently the most widely used method for residue management. While these findings highlight significant opportunities, they rely on literature-based assumptions and average values, which represents an inherent limitation and underscores the need for region-specific analyses.
Ethan Chervonski, Ethan Chervonski, Marisa A. Guerrero
et al.
Anthropogenic climate change, while once regarded primarily as an environmental concern, has evolved into a global health crisis. As a victim of escalating climate-related phenomena, New York City (NYC) has positioned itself at the forefront of climate resilience and public health action. Local Law 97 (LL97) is the latest in NYC’s long trajectory of climate action initiatives, setting progressively stringent caps on greenhouse gas (GHG) emissions from large buildings greater than 25,000 square feet. LL97 represents one of the most ambitious—and divisive—climate action policies in the United States and if successful, is poised to make NYC carbon neutral by 2050. At the same time, the potential public health benefits of LL97 are broad, including improved local air quality, decreased cost barriers to residential cooling, and if in principle applied to city buildings worldwide, reduced global GHG emissions capable of stabilizing global warming for posterity. Nevertheless, LL97’s reliance on a carbon-intensive electricity grid, creation of complex financial incentives, and divisive reception by political groups threaten its impact. The following paper reviews the public health consequences of building emissions through the lens of NYC’s built environment. It explores the role of LL97 and other relevant local and state legislation in mitigating the public health impacts of building emissions. Finally, the law’s limitations are critically assessed. By analyzing LL97’s potential successes and obstacles, this paper aims to provide actionable insights for other cities seeking to design effective climate action plans that balance sustainability, public health, and equity.
Unquestionably, hospital patient rooms require a proper lighting design. Dissimilar to cultural and artistic settings, where artistic discourse on light has significant importance, in medical settings, the most crucial conversation refers to standards. Research indicates that light in hospital settings has an impact on a patient’s physical and mental health. Effective lighting in medical settings can enhance the hospital’s positive experience and the speed at which patients recover from their diseases. It can also increase staff attentiveness and productivity. It is also critical to consider reducing electricity consumption in hospital settings that require lighting 24/7. Due to the high cost of lighting, access to natural light in combination with time-of-day controls minimizes energy consumption when daylight is available and impacts the hospital’s bottom line. The effect of light on hospital users was investigated in this article; therefore, it is important to understand both natural and artificial light sources in this regard. Natural light has many benefits for humans, and when it comes to electricity consumption, it is the best method because it is a free source; but, since natural light is not always available and cannot be used throughout the day, there is a need to have an artificial light source that gives the best lighting effect in terms of visual comfort and visual performance for users. Secondly, proper artificial light sources can reduce electricity consumption; hence, these two critical aspects were underlined in this study.
The existing body of research into the environmental and socio-economic benefits of green infrastructure supports the case for it to be positioned as a form of critical infrastructure, particularly in urban settings. It is broadly recognized that extreme weather and climate change pose significant risks to critical infrastructure systems linked to the provision of services, including electricity, water, communications, and transport, and consequently risk assessments and associated adaptation strategies are common practice. However, although green infrastructure is also at risk from extreme weather and climate change, threatening the realization of benefits that it can deliver in urban settings, associated risks to green infrastructure are not widely understood or assessed in practice. This paper discusses the status of existing research on this topic and uses this as a foundation for a Greater Manchester (UK) case study that assesses the risk of low water availability to grassed areas, which represent a key element of the city-region’s green infrastructure. In doing so, the paper demonstrates how risks linked to extreme weather and climate change can be assessed spatially to inform green infrastructure planning. In summary, this paper aims to raise awareness of extreme weather and climate change risk to urban green infrastructure, present an empirical case study and associated methodological approach on this topic, and ultimately support efforts to enhance the resilience of urban green infrastructure to extreme weather and climate change.
Camille Franklin Mbey, Felix Ghislain Yem Souhe, Vinny Junior Foba Kakeu
et al.
With the installation of solar panels around the world and the permanent fluctuation of climatic factors, it is, therefore, important to provide the necessary energy in the electrical network in order to satisfy the electrical demand at all times for smart grid applications. This study first presents a comprehensive and comparative review of existing deep learning methods used for smart grid applications such as solar photovoltaic (PV) generation forecasting and power consumption forecasting. In this work, electrical consumption forecasting is long term and will consider smart meter data and socioeconomic and demographic data. Photovoltaic power generation forecasting is short term by considering climatic data such as solar irradiance, temperature, and humidity. Moreover, we have proposed a novel hybrid deep learning method based on multilayer perceptron (MLP), long short-term memory (LSTM), and genetic algorithm (GA). We then simulated all the deep learning methods on a climate and electricity consumption dataset for the city of Douala. Electrical consumption data are collected from smart meters installed at consumers in Douala. Climate data are collected at the climate management center in the city of Douala. The results obtained show the outperformance of the proposed optimized method based on deep learning in the both electrical consumption and PV power generation forecasting and its superiority compared to basic methods of deep learning such as support vector machine (SVM), MLP, recurrent neural network (RNN), and random forest algorithm (RFA).
Accurate and reliable electricity price forecasting has significant practical implications for grid management, renewable energy integration, power system planning, and price volatility management. This study focuses on enhancing electricity price forecasting in California's grid, addressing challenges from complex generation data and heteroskedasticity. Utilizing principal component analysis (PCA), we analyze CAISO's hourly electricity prices and demand from 2016-2021 to improve day-ahead forecasting accuracy. Initially, we apply traditional outlier analysis with the interquartile range method, followed by robust PCA (RPCA) for more effective outlier elimination. This approach improves data symmetry and reduces skewness. We then construct multiple linear regression models using both raw and PCA-transformed features. The model with transformed features, refined through traditional and SAS Sparse Matrix outlier removal methods, shows superior forecasting performance. The SAS Sparse Matrix method, in particular, significantly enhances model accuracy. Our findings demonstrate that PCA-based methods are key in advancing electricity price forecasting, supporting renewable integration and grid management in day-ahead markets. Keywords: Electricity price forecasting, principal component analysis (PCA), power system planning, heteroskedasticity, renewable energy integration.
Domain specific digital twins, representing a digital replica of various segments of the smart grid, are foreseen as able to model, simulate, and control the respective segments. At the same time, knowledge-based digital twins, coupled with AI, may also empower humans to understand aspects of the system through natural language interaction in view of planning and policy making. This paper is the first to assess and report on the potential of Retrieval Augmented Generation (RAG) question answers related to household electrical energy measurement aspects leveraging a knowledge-based energy digital twin. Relying on the recently published electricity consumption knowledge graph that actually represents a knowledge-based digital twin, we study the capabilities of ChatGPT, Gemini and Llama in answering electricity related questions. Furthermore, we compare the answers with the ones generated through a RAG techniques that leverages an existing electricity knowledge-based digital twin. Our findings illustrate that the RAG approach not only reduces the incidence of incorrect information typically generated by LLMs but also significantly improves the quality of the output by grounding responses in verifiable data. This paper details our methodology, presents a comparative analysis of responses with and without RAG, and discusses the implications of our findings for future applications of AI in specialized sectors like energy data analysis.
Bobby Xiong, Davide Fioriti, Fabian Neumann
et al.
This paper provides the background, methodology and validation for constructing a representation of the European high-voltage grid, including and above 200 kV, based on public data provided by OpenStreetMap. The model-independent grid dataset is published under the Open Data Commons Open Database (ODbL 1.0) licence and can be used for large-scale electricity as well as energy system modelling. The dataset and workflow are provided as part of PyPSA-Eur -- an open-source, sector-coupled optimisation model of the European energy system. By integrating with the codebase for initiatives such as PyPSA-Earth, the value of open and maintainable high-voltage grid data extends to the global context. By accessing the latest data through the the Overpass turbo API, the dataset can be easily reconstructed and updated within minutes. To assess the data quality, this paper further compares the dataset with official statistics and representative model runs using PyPSA-Eur based on different electricity grid representations.
Arina Manasikhana Dita, Permanasari Avita Ayu, Puspitasari Poppy
et al.
Photovoltaic thermal (PVT) is a technology capable of converting solar energy into energy in the form of electricity and thermal (heat). Absorption of solar thermal energy can cause PVT to experience a high temperature increase which affects the efficiency of electricity that can be generated by PVT. Nanofluid is a fluid with high thermal conductivity that can be used as a coolant to absorb the high temperature generated by PVT and recover some of the energy lost as heat to increase the efficiency of PVT. The combination of two nanoparticles as a hybrid nanofluid was produced by mixing 1000 ml distilled water with TiO2/Al2O3 hybrid nanoparticles (80:20) of 0.1% with irradiation time for 60 minutes using light intensity of 1200 W/m2. The results showed that TiO2 nanofluid had the best thermal and electrical efficiency compared to hybrid nanofluid, Al2O3 nanofluid, and distilled water. Thermal efficiency decreased due to the long irradiation time with constant intensity causing ineffective cooling over time, while electrical efficiency increased due to heat reduction on the PVT surface, but after 15 minutes there was a decrease in electrical efficiency caused by the PVT surface overheating.
Decarbonizing electric grids is a crucial global endeavor in the pursuit of carbon neutrality. Taking carbon emissions from generation into account when pricing electricity usage is an essential way to achieve this goal. However, such pricing is not trivial due to the requirements of an effective electricity market, such as maintaining budget balance, providing incentives to motivate participants to follow the dispatch schedule, and minimizing the impact on affected parties compared to when they were in the traditional electricity market. Although existing joint electricity-carbon pricing mechanisms have shown promising performance in reducing emissions in power networks, they can hardly meet all the requirements. This paper proposes a novel joint electricity-carbon pricing mechanism based on primal-dual optimality condition-enabled transformation. An algorithm for determining the critical market parameter is developed. The proposed pricing mechanism is proven to possess all the desired properties, including budget balance, individual rationality, dispatch-following incentive compatibility, and truthful-bidding incentive compatibility. These properties ensure the proposed mechanism can incentivize market participants to achieve carbon-aware social optimum in a self-organized and sustainable way. Numerical experiments show the advantages of the proposed pricing mechanism compared to the existing marginal-based and carbon emission flow-based pricing mechanisms.
Erhan Can Ozcan, Emiliano Dall'Anese, Ioannis Ch. Paschalidis
Demand response services at the distribution level are emerging as enabling strategies for improving grid reliability in the presence of intermittent renewable generation and grid congestion. For residential loads, space heating and cooling, water heating, electric vehicle charging, and routine appliances make up the bulk of the electricity consumption. Controlling these loads is essential to effectively partake into grid operations and provide services such as peak shaving and demand response. However, maintaining user comfort is important for ensuring user participation to such a program. This paper formulates a novel mixed integer linear programming problem to control the overall electricity consumption of a residential neighborhood by considering the users' comfort and preferences. To efficiently solve the problem for communities involving a large number of homes, a distributed optimization framework based on the Dantzig-Wolfe decomposition technique is developed. We demonstrate the load shaping capacity and the computational performance of the proposed optimization framework in a simulated environment.
Artificial intelligence (AI) can revolutionize the development industry, primarily electrical and electronics engineering. By automating recurring duties, AI can grow productivity and efficiency in creating. For instance, AI can research constructing designs, discover capability troubles, and generate answers, reducing the effort and time required for manual analysis. AI also can be used to optimize electricity consumption in buildings, which is a critical difficulty in the construction enterprise. Via machines gaining knowledge of algorithms to investigate electricity usage patterns, AI can discover areas wherein power may be stored and offer guidelines for enhancements. This can result in significant value financial savings and reduced carbon emissions. Moreover, AI may be used to improve the protection of creation websites. By studying statistics from sensors and cameras, AI can locate capacity dangers and alert workers to take suitable action. This could help save you from injuries and accidents on production sites, lowering the chance for workers and enhancing overall safety in the enterprise. The impact of AI on electric and electronics engineering productivity inside the creation industry is enormous. AI can transform how we layout, build, and function buildings by automating ordinary duties, optimising electricity intake, and enhancing safety. However, ensuring that AI is used ethically and responsibly and that the advantages are shared fairly throughout the enterprise is essential.