Local Energy Demand Research Articles

Reduced neurovascular coupling of the visual network in migraine patients with aura as revealed with arterial spin labeling MRI: is there a demand-supply mismatch behind the scenes?

BackgroundAlthough neuroimaging investigations have consistently demonstrated that “hyperresponsive” and “hyperconnected” visual cortices may represent the functional substrate of cortical spreading depolarization in patients with migraine with aura, the mechanisms which underpin the brain “tendency” to ignite the cortical spreading depolarization and, consequently, aura phenomenon are still matter of debate. Considering that triggers able to induce aura phenomenon constrain brain to increase global (such as physical activity, stressors and sleep abnormalities) or local (such as bright light visual stimulations) energy demand, a vascular supply unable to satisfy the increased energy requirement could be hypothesized in these patients.MethodsTwenty-three patients with migraine with aura, 25 patients with migraine without aura and 20 healthy controls underwent a 3-Tesla MRI study. Cerebral blood flow and local functional connectivity (regional homogeneity) maps were obtained and registered to the MNI space where 100 cortical regions were derived using a functional local-global normative parcellation. A surrogate estimate of the regional neurovascular coupling for each subject was obtained at each parcel from the correlation coefficient between the z-scored ReHo map and the z-scored cerebral blood flow maps.ResultsA significantly higher regional cerebral blood flow across the visual cortex of both hemispheres (i.e. fusiform and lingual gyri) was detected in migraine with aura patients when compared to patients with migraine without aura (p < 0.05, corrected for multiple comparisons). Concomitantly, a significantly reduced neurovascular coupling (p < 0.05, false discovery rate corrected) in the primary visual cortex parcel (VIS-4) of the large-scale visual network was observed in the left hemisphere of patients with migraine with aura (0.23±0.03), compared to both patients with migraine without aura (0.32±0.05) and healthy controls (0.29±0.05).ConclusionsVisual cortex neurovascular “decoupling” might represent the “link” between the exposure to trigger factors and aura phenomenon ignition. While physiological vascular oversupply may compensate neurovascular demand-supply at rest, it becomes inadequate in case of increased energy demand (e.g. when patients face with trigger factors) paving the way to the aura phenomenon ignition in patients with migraine with aura. Whether preventive treatments may exert their therapeutic activity on migraine with aura restoring the energy demands and cerebral blood flow trade-off within the visual network should be further investigated.

Federated learning assisted distributed energy optimization

AbstractThe increased penetration of distributed energy resources and the adoption of sensing and control technologies are driving the transition from our current centralized electric grid to a distributed system controlled by multiple entities (agents). The transactive energy community serves as an established example of this transition. Distributed energy management approaches can effectively address the evolving grid's scalability, resilience, and privacy requirements. In this context, the accuracy of agents' estimations becomes crucial for the performance of distributed and multi‐agent decision‐making paradigms. This paper specifically focuses on integrating federated learning (FL) with the multi‐agent energy management procedure. FL is utilized to forecast agents' local energy generation and demand, aiming to accelerate the convergence of the distributed decision‐making process. To enhance energy aggregation in transactive energy communities, we propose an FL‐assisted distributed consensus + innovations approach. The results demonstrate that employing FL significantly reduces errors in predicting net power demand. The improved forecast accuracy, in turn, introduces less error in the distributed optimization process, thereby enhancing its convergence behaviour.

Low temperature pyrolysis of waste cooking oil using marble waste for bio-jet fuel production

Limestone powder, a by-product of top-down marble processing, exhibited high thermal stability and functionality as catalysts for the low-temperature pyrolysis of waste cooking oil (WCO) into biofuel. The marble waste (MW) contains calcite, dolomite, and quartz composites (CaCO3/MgCO3/SiO2) as non-uniform microcrystallites. Pyrolysis at 380 °C in 1 h increased the hydrocarbon composition of biofuel and reduced carboxylic acid. The biofuel composition contains 80 % hydrocarbon within the jet fuel C8-16 ranges. MW reduced the carboxylic acid concentration from 49 % in catalyst-free pyrolysis to less than 4 %. The optimum condition was obtained at 3 % MW, yielding 43.55 % linear hydrocarbon, 11.1 % cyclic hydrocarbon, and 1.02 % aromatic compounds that satisfied the bio-jet fuel classification. The incorporation of varying percentages of marble waste demonstrates a significant enhancement in the cyclic compounds, elevating the yield from ∼3 % in catalyst-free pyrolysis to an impressive ∼35 %. The hydrocarbon yield is higher when using 3%MW > 1%MW > 6%MW > 9%MW. Dolomite and calcite mixture in MW catalyzed triglyceride conversion to hydrocarbon by adsorbing carbonate in triglycerides to drive C–O bond dissociation. These studies provide a roadmap for the utilization of abundant resources locally available in Indonesia to sustain local energy demand.

Residential segregation and summertime air temperature across 13 northeastern U.S. states: potential implications for energy burden

High ambient summertime temperatures are an increasing health concern with climate change. This is a particular concern for minoritized households in the United States, for which differential energy burden may compromise adaptive capacity to high temperatures. Our research question was: Do minoritized groups experience hotter summers than the area average, and do non-Hispanic white people experience cooler summers? Using a fine-scaled spatiotemporal air temperature model and U.S. census data, we examined local (within-county) differences in warm season cooling degree days (CDDs) by ethnoracial group as a proxy for local energy demand for space cooling across states of the northeast and mid-Atlantic U.S. in 2003–2019. Using state-specific regression models adjusted for year and county, we found that Black and Latino people consistently experienced more CDDs, non-Hispanic white people experienced fewer CDDs, and Asian populations showed mixed results. We also explored a concentration-based measure of residential segregation for each ethnoracial group as one possible pathway towards temperature disparities. We included the segregation measure as a smooth term in a regression model adjusted for county and year. The results were nonlinear, but higher concentrations of white people were associated with lower annual CDDs and higher concentrations of Latino people were associated with higher annual CDDs than the county average. Concentrations for Black and Asian people were nonmonotonic, sometimes with bowed associations. These findings suggest that present-day residential segregation, as modeled by spatially smoothed ethnoracial subgroup concentrations, may contribute to summertime air temperature disparities and influence adaptive capacity. We hope these findings can support place-based interventions, including targeting of energy insecurity relief programs.

Optimal design of greenfield energy hubs in the context of carbon neutral energy supply

This paper proposes a sequential co-optimization (SCO) framework for the design of greenfield energy hubs, aiming to achieve net zero emissions. The SCO framework offers an evolutionary approach to long-term planning and operation, taking into account various evolving factors such as carbon emission policies, technological and economic advancements in energy supply components, changes in land-use, and developments at different stages of the design horizon. The framework ensures that the design of greenfield developments aligns with their evolving characteristics, determining the optimal dispatch flow for firming renewable energy through co-generation and demand flexibility options like smart loads, electric vehicles, and hydrogen-based systems. A sensitivity analysis is conducted to study the impact of emissions, evolving characteristics of greenfield developments, and techno-economic aspects of supply units on the energy hub design. Case studies demonstrate that the proposed design enables large-scale distributed energy systems to meet local energy demand and participate in the electricity market. The SCO framework provides a robust and flexible platform for the correlated optimization of planning and operation of greenfield energy hubs, for advancing towards net zero emissions.

Loading enhances glucose uptake in muscles, bones, and bone marrow of lower extremities in humans.

Increased standing time has been associated with improved health, but the underlying mechanism is unclear. We herein investigate if increased weight loading increases energy demand and thereby glucose uptake (GU) locally in bone and/or muscle in the lower extremities. In this single-center clinical trial with randomized crossover design (ClinicalTrials.gov ID, NCT05443620), we enrolled 10 men with body mass index (BMI) between 30 and 35 kg/m2. Participants were treated with both high load (standing with weight vest weighing 11% of body weight) and no load (sitting) on the lower extremities. GU was measured using whole-body quantitative positron emission tomography/computed tomography (PET/CT) imaging. The primary endpoint was the change in GU ratio between loaded bones (i.e. femur and tibia) and non-loaded bones (i.e. humerus). High load increased the GU ratio between lower and upper extremities in cortical diaphyseal bone (e.g. femur/humerus ratio increased by 19%, p = 0.029), muscles (e.g. m. quadriceps femoris/m. triceps brachii ratio increased by 28%, p = 0.014) and in certain bone marrow regions (femur/humerus diaphyseal bone marrow region ratio increased by 17%, p = 0.041). Unexpectedly, we observed the highest GU in the bone marrow region of vertebral bodies, but its GU was not affected by high load. Increased weight-bearing loading enhances GU in muscles, cortical bone, and bone marrow of the exposed lower extremities. This could be interpreted as increased local energy demand in bone and muscle caused by increased loading. The physiological importance of the increased local GU by static loading remains to be determined.

Peer-to-peer multi-period energy market with flexible scheduling on a scalable and cost-effective blockchain

Peer-to-Peer (P2P) electricity trading is an emerging means for allowing communities to share local energy production and demand response through direct trading. It is projected to play an important role in the future energy market by increasing the incentive to own and operate distributed energy resources (DERs) and participate in demand response (DR). A common approach to implementing P2P trading is with blockchain, however, existing solutions feature limitations. Typically, concerns involve either scalability and high transaction costs or projects implementing a permissioned blockchain that does not provide the trust and decentralization desired. In addition, the majority of market designs are simplified and do not capture the distinct complexity of electricity markets. They do not consider the inter-temporal constraints of appliances and DERs that need to run for multiple periods. Complex market designs that address these issues have not been implemented in blockchain based systems due to the computational limitations of such systems. To improve these two aspects, this paper proposes a market formulation that supports products running multiple periods with flexible scheduling, modeled as a mixed integer linear programming problem that optimizes social welfare. The trading platform is then implemented as a side chain of Polkadot, a network of blockchains designed to be secure, scalable, and economically feasible to foster an ecosystem of application-specific sidechains. This design is demonstrated to improve social welfare and achieve scalability by benchmarking the number of transactions that can be included per second, based upon typical assumptions of the market size. The result is a decentralized and performant system that allows for complex market designs to be implemented in a blockchain environment. This approach simplifies the process for participants, allowing them to bid DER or DR devices, directly and easily, even when this requires constraints across periods.

Fluid geochemistry of the Cerro Galán geothermal system (Southern Puna, Argentina): Implications for the geothermal potential of one of the youngest giant calderas in the Andes

The exploration of novel geothermal systems, particularly those promising for electrical power generation, plays a fundamental role in incorporating new renewable sources into the energy matrix. Geothermal systems associated with volcanic calderas are considered ideal targets for exploration. This study focuses on the geochemical features of fluids from the Cerro Galán hydrothermal system, which is hosted within a major resurgent caldera with >3.5 Myr of magmatic evolution situated on the Southern Puna (Central Volcanic Zone of the Andes, NW Argentina). The main aim is constructing the first geochemical conceptual model and provide information on the geothermal potential of this interesting resource. The main hydrothermal reservoir consists of a NaCl aquifer with estimated temperatures up to 187 °C at depth. This reservoir is likely hosted within the fractured pre-caldera basement rocks, mainly including Miocene-Pliocene volcanic rocks and Proterozoic-Cambrian igneous and metamorphic rocks. The confinement of the deep reservoir is attributed to the deposits of the Toconquis Group and Cueva Negra Ignimbrite, along with the basal section of the Cerro Galán Ignimbrite, which exhibit low permeability due to hydrothermal alteration. The presence of a phreatic explosion crater near one of the hot spring-rich areas is likely indicating past over-pressurization of the hydrothermal aquifer, resulting from efficient sealing. Furthermore, the absence of anomalous soil CO2 flux values on the top of the reservoir, except where the thermal spring discharges are located, can be explained by an effective cap-rock layer. Deep circulation of meteoric water, enriched with atmospheric gases, receives inputs of magmatic fluids (∼11% of primordial helium), leading to the development of the hydrothermal NaCl aquifer. However, this deep fluid contribution might be underestimated due to significant crustal assimilation (up to 50%) involved in the magma genesis of the Cerro Galán Volcanic Complex, a process which modifies the He isotopic signature of the magmatic endmember. The hot springs, characterized by high flow rate (up to 459 m3/h) are positioned at the intersection between the caldera margins and the NNE-SSW oriented tectonic structures, suggesting favorable permeability conditions. The preliminary geothermal gradient for the Cerro Galán area is estimated at around 98–101 °C/km. Such a high gradient can be attributed to the considerable heat flux generated by the transcrustal plumbing system of the Cerro Galán caldera, which includes the shallow crystal mush reservoir (<4 km depth). The preliminary geothermal potential of this giant caldera was performed using the volumetric method along with Monte Carlo simulations. The results indicate a probable power production capacity of 2.09 MWe and 10.85 MWe at 90 and 50% confidence level, respectively. The results presented in this work constitute a foundational knowledge base to promote a more advanced exploration phase for the geothermal resource. Additionally to the local energy demand, lithium and other metal mining operations, which are operating independently from the National Interconnected System, could potentially be interested in power generation through binary cycles.

Reshaping the energy landscape of Crete through renewable energy valleys

Renewable energy valleys (REVs) represent a transformative concept poised to reshape global energy landscapes. These comprehensive ecosystems transition regions from conventional energy sources to sustainable, self-reliant hubs for renewable energy generation, distribution, and consumption. At their core, REVs integrate advanced information and communication technology (ICT), interoperable digital solutions, social innovation processes, and economically viable business models. They offer a vision of decentralized, low-carbon landscapes accessible to all, capable of meeting local energy demands year-round by harnessing multiple renewable energy sources (RES) and leveraging energy storage technologies. This paper provides an overview of the key components and objectives of REVs, including digital integration through advanced ICT technologies and open digital solutions that enable the seamless management of RES within the REV. The social innovation aspect via the REV’s active communities is also examined, encouraging their participation in the co-design, implementation, and benefit-sharing of renewable energy solutions. In addition, business viability through sustainable business models central to the REV framework is proposed, ensuring affordability and accessibility to all stakeholders. The paper presents a case study of Crete, showcasing how the REV idea can work in real life. Crete utilizes various energy sources to become energy-independent, lower carbon emissions, and enhance system resilience. Advanced energy storage technologies are employed to ensure supply and demand balance within the REV. Situated on the picturesque island of Crete, Greece, it is pioneering the establishment of a Renewable Energy Valley ‘Living Lab’ (REV-Lab), integrating Community Energy Labs (CELs) as innovation hubs. This initiative exemplifies the REV model, striving to create a digitalized, distributed, and low-carbon landscape accessible to all residents throughout the year.

Abstract TP27: Transcriptional and Functional Characterization of Microglia in Vascular Dementia

Introduction: Proper function of the brain relies on sufficient blood supply to meet local energy demand. Age-related pathologies like cerebral small vessel disease (cSVD) compromise the ability of blood vessels to meet local energy demands and lead, over time, to the development of vascular dementia (VaD). Microglia are essential for maintaining homeostasis and have been shown to exacerbate inflammation and disease progression in neurodegenerative diseases. Activated microglia are present in the white matter lesions of human post-mortem VaD brains. However, their role in VaD progression is poorly understood. Hypothesis: Our main objective is to identify microglial pathways that contribute to VaD. We hypothesize microglial dysregulation interferes with brain homeostasis and wound healing, ultimately contributing to disease progression. Methods: Post-mortem samples of periventricular white matter from male and female elderly individuals exhibiting brain pathology consistent with VaD (n=4), along with age- and sex-matched controls without VaD (n=4) were acquired from the NIH NeuroBioBank. We performed single-nuclei RNA sequencing followed by differential gene expression analysis using Seurat and downstream analyses using Ingenuity Pathway Analysis. Results: Over 30,000 nuclei were analyzed. Transcriptomic results reveal profound differences in the microglial transcriptome in VaD, with 690 differentially regulated genes in microglia between cases and controls. Genes related to pathways such as complement production, phagocytosis, macrophage activation and overall protein production are downregulated in VaD while IL-10 production is upregulated (Figure 1B). Conclusions: Our data suggests microglia in VaD adopt a unique transcriptional signature, distinct from that observed in disease-associated microglia in other neurodegenerative conditions, which may impact their ability to maintain homeostasis in VaD.

Site Suitability Assessment for Petroleum hubs and Oil retail assets in the Jomoro District: A Hybrid Approach using Fuzzy AHP and VIKOR Method

Petroleum Infrastructure is indispensable considering the current local and global energy demands, however explosions from such establishments often cause loss of lives and properties within the surrounding communities thereby posing great concern to government and the citizenry. This condition calls for research that would provide meaningful solutions to mitigate this menace. Unlawful siting of oil refineries, petrochemical plants, berthing terminals, pipelines, storage terminals, oil and gas retail assets results from non-consideration of environmental impact on growing human population, competition for customers and lack of enforcement of energy standards. The study aims to employ a multifaceted approach comprising of suitability, proximity and spatial statistical analysis in assessing viable areas for developing petroleum hubs in the district. This study further investigated the efficiency of the method and level of compliance to standards set by the Ministry of Energy, Environmental Protection Agency (EPA) and the Town and Country Planning Department through validation using the newly acquired land for petroleum hub and existing filling stations in the study area. Primary and secondary data were used for the study. The primary data consist of locations of oil and gas filling stations picked with the Garmin handheld GPS and surveyed boundary of the land. The secondary data was obtained from the Survey and Mapping division of the land commission of Ghana. It comprises of topographic data, geology and soil maps from which soil types, lithology, roads networks, water bodies, terrain slope and land use features of the area were extracted and used. The dataset was reclassified and weighted using Fuzzy AHP and VIKOR. Spatial analyses were carried out using ArcGIS software to show areas suitable or otherwise for siting petroleum hubs in the study area. Results shows 67.44% of the area are highly suitable for establishment of petroleum hubs, 32.33% of the area falls within moderate suitability zones whereas the least suitability zones occupied 0.23% of the total area. The newly acquired government land for the petroleum hub project fell within the highly suitable zone confirming the validity of the studies in comparison with studies from field experts via environmental impact assessment. The proposed petroleum hub covered areas dominated by very high and high area suitability for its establishment constituting 75.9 km2 (90.3%) of its entire area whereas the moderate suitability zones constituted 8.2 km2 (9.7%) of the remaining areas. Towns situated in very high areas includes; Bakakole Nkwanta, Ahobre, Nawule, Allowule, Tikobo No.1, Edu, Damofu, Ave lenu and Ebonloa, Mpatabo. High areas comprises of Kengen Kpokezo, Alenda wharf, Tekyinta. Anwonakrom, Nkwamta, Elubo and Agege are among the moderate and low area zones for hub and oil retail assets establishment 75% of the oil retail assets complied with the required set standards whiles 25% defaulted. The integration of GIS techniques with multi-criteria decision analyses has proved to be an effective tool for selecting suitable sites for petroleum hubs, oil retail assets and very useful in determining places of high fire risk for proper planning of the area. It is recommended that the authorities and stakeholders put measures in place to educate, perform site suitability assessment and enforce set standards in establishment of petroleum infrastructure.

Reference Framework Based on a Two-Stage Strategy for Sizing and Operational Management in Electrical Microgrid Planning

The challenges of today’s energy landscape, marked by the search for sustainable development, the expansion of coverage, and the diversification of the energy matrix, allow for electricity systems focusing on renewable energy resources. Microgrids are considered an efficient paradigm for managing distributed renewable energy generation and providing reliable access to electricity in remote areas where the grid has not been extended. However, their planning is a complex task that requires a thorough understanding of various multi-dimensional aspects and decision-making scenarios to define feasible and sustainable alternatives. In this context, this study presents a new planning framework based on a two-stage strategy. The strategy seeks to optimize the capacity of generation resources, considering the microgrid’s operational knowledge in various scenarios and aspects related to its sustainability. The framework was evaluated through a case of planning a microgrid for a remote community in Vaupés, Colombia, considering the local energy potential and demand requirements. Twenty optimized alternatives were identified based on the best compromise levels achieved for a set of performance criteria in the technical, economic, environmental, and social dimensions.

Business Model Selection for Community Energy Storage: A Multi Criteria Decision Making Approach

This paper explores business models for community energy storage (CES) and examines their potential and feasibility at the local level. By leveraging Multi Criteria Decision Making (MCDM) approaches and real-world case studies in Europe and India, it presents insights into CES deployment opportunities, challenges, and best practices. Different business models, including community energy cooperatives, utility–community partnerships, demand response, energy services, and market mechanisms, are analyzed. The proposed method combines the MCDM method PROMETHEE II with the fuzzy set theory to obtain a complete CES business model ranking, addressing project uncertainties. The analysis emphasizes CES’s role in balancing local renewable energy supply and demand, facilitating energy sharing, and achieving energy independence. Findings prioritize models like Community Cooperative, Energy Arbitrage, and Energy Arbitrage Peak Shaving for CES with renewables. Environmental benefits include reduced diesel use and greenhouse gas emissions. Efficient cooperatives are advocated to recover costs and enable competitive energy prices. The paper highlights the need for novel value propositions to boost the energy transition in local communities. This research contributes to the discourse on CES business models, fostering knowledge exchange and promoting effective strategies for sustainable energy systems.

Optimal operation of smart energy hub considering high-temperature heat and power storage

Due to the impacts of high intermittent renewable resources, increasing their penetration in the generation sector of energy systems is still challenging. In this regard, the integration of different energy networks can play a key role in dealing with this challenge. This paper focuses on the analysis and optimization of multi-generation energy storage (MGES) system’s performance and investigates the role of this type of energy storage in the operation of future smart energy systems by considering 100% green energy goals. Hence, this paper first introduces high-temperature heat and power storage (HTHPS) system, as a novel MGES unit, for a local integrated energy system (IES) with different energy carriers. Then, a novel optimal energy scheduling scheme for this system is presented by considering the energy hub concept. The proposed IES is introduced as a Smart Energy Hub (SEH) which supplies local energy demands through local renewable resources generation, as well as upstream energy networks, by considering a competitive energy market. Finally, the performance of the proposed SEH is investigated by simulating different case studies and its effectiveness is proved in increasing the renewable generation penetration.

Bringing to light a new energy path: the case of the state of Minas Gerais, Brazil

In Brazil, more than 55% of the energy comes from hydroelectricity, making the system highly vulnerable in the context of global climate change, with precipitation and temperature shifts over the years. Characterised by its multiple opportunities in sources and conversion technologies for energy, biomass has a high potential to become responsible for a relevant share of the renewable energy supply. Previous studies on biomass energy production in Brazil confirm promising results. This paper highlights possibilities for biomass power generation in Minas Gerais State. To estimate energy productivity, a Sustainable Technical Coefficient was adopted: a conservative index that considers the portion of residues that could be used to maintain the integrity of the soil. This index was applied with the data on silviculture and selected crop yields. The local energy demand was also calculated and compared to the potential energy production. Results show that 78% of the municipalities could have their basic energy needs and 18% of the demand for productive uses met by crop residues and silviculture production. For the state of Minas Gerais, with its tradition of agriculture, biomass residual energy is viable and should be considered by policymakers.

Optimization of a multi-energy microgrid in the presence of energy storage and conversion devices by using an improved gray wolf algorithm

The growth of electricity consumption and demand for higher quality of electricity has directed the electricity industry towards using new technologies. In this paper, a novel model is proposed for the optimal scheduling of multi-energy microgrid (MEM) systems that rely on renewable energy sources. The model incorporates various emerging high-efficient technologies, including electric vehicle (EV) parking lots, power-to-gas (P2G) facilities, and demand response programs. The MEM system described in this paper comprises several components, such as wind energy generation, multi-carrier energy storage technologies, a boiler, a combined heat and power unit, power-to-gas (P2G) capability, electric vehicles (EVs), and demand response programs. The main objective of the system is to minimize the total operational cost. Moreover, the system operator has the opportunity to actively participate in the electricity, heat, and gas markets, enabling them to fulfill local energy demands and maximize profits through energy exchanges. The proposed system is exposed to uncertainties caused by wind power, demand, electric vehicles and power price. The optimization process in this study utilizes the Gray Wolf Algorithm, which has been enhanced by incorporating the Local Escaping Operator method. This improved algorithm is employed to optimize the scheduling and operation of the multi-energy microgrid (MEM) system. The goal of the Gray Wolf Algorithm, in combination with the Local Escaping Operator method, is to find optimal solutions that minimize the total operational costs of the MEM system while considering factors such as renewable energy generation, energy storage, demand response programs, and the integration of emerging technologies. The results obtained from the optimization process demonstrate a reduction in the total cost of operations when various technologies are integrated into the grid. By applying the proposed optimization model, which incorporates the Gray Wolf Algorithm improved via the Local Escaping Operator method, the overall operational costs of the MEM system are decreased. This reduction in costs is achieved by efficiently managing the integration of renewable energy sources, energy storage technologies, demand response programs, and other emerging technologies within the grid. Simulations are conducted for different scenarios, and the results show that integrated energy scheduling, along with the incorporation of emerging technologies and vehicle-to-grid capability, can reduce the total operating cost by 15%. Furthermore, the integration of multi-carrier energy storage systems with electricity-to-gas technology can reduce daily performance costs by up to 10%.

Forecast Optimization of Wind Speed in the North Coast of the Yucatan Peninsula, Using the Single and Double Exponential Method

Installation of new wind farms in areas such as the north coast of the Yucatan peninsula is of vital importance to face the local energy demand. For the proper functioning of these facilities it is important to perform wind data analysis, the data having been collected by anemometers, and to consider the particular characteristics of the studied area. However, despite the great development of anemometers, forecasting methods are necessary for the optimal harvesting of wind energy. For this reason, this study focuses on developing an enhanced wind forecasting method that can be applied to wind data from the north coast of the Yucatan peninsula (in general, any type of data). Thus, strategies can be established to generate a greater amount of energy from the wind farms, which supports the local economy of this area. Four variants have been developed based on the traditional double and single exponential methods. Furthermore, these methods were compared to the experimental data to obtain the optimal forecasting method for the Yucatan area. The forecasting method with the highest performance has obtained an average relative error of 7.9510% and an average mean error of 0.3860 m/s.

Towards Positive Energy Districts: Energy Renovation of a Mediterranean District and Activation of Energy Flexibility

The paper presents the analysis of energy retrofitting, integration of renewable energy and activation of energy flexibility in a cluster of buildings in the surroundings of a port on the Mediterranean Sea in Southern Italy, with the aim of checking the potential for it to achieve the status of positive energy district (PED). The objective of this study is to improve the contemporaneity between local energy generation and energy demand and reduce CO2eq emissions by considering signals that reflect the environmental variability of the electricity grid, through flexibility solutions applied to the HVAC system. The proposed scenarios are based on the dynamic simulation of the district and analyze the effect of actions that activate the energy flexibility of buildings through advanced control strategies of the air conditioning system. The results show that the joint action of energy efficiency strategies, integration of solar energy and energy flexibility improves the environmental sustainability of the district and the balance of energy flows. Specifically, the activation of energy flexibility contributes to a 10% reduction in operational CO2eq emissions and increases in self-consumption of energy per year. The operational emissions of the district vary from the base value of 33.37 tons CO2eq/y to 19.52 tons CO2eq/y in the scenario based on the integration of solar energy systems and energy efficiency measures, and to 17.39 tons CO2eq/y when also the demand-side energy flexibility is activated.

Operational optimization for the grid-connected residential photovoltaic-battery system using model-based reinforcement learning

The development of distributed photovoltaic and energy storage devices has created challenges for energy management systems due to uncertainty and mismatch between local generation and residents' energy demand. Reinforcement learning is gaining attention as a control algorithm, but traditional model-free RL has data quality and quantity limitations for energy management applications. Therefore, this study proposed a model-based deep RL method to optimize the operation control of the energy storage system by taking the measured dataset of an actual existing building in Japan as the research object. With an optimization goal of reducing the microgrid's energy cost and ensuring the PV self-consumption ratio, we designed a new reward function for these goals. We took the benchmark strategy currently used by the target building's energy management system as the baseline model in the experiment. We applied four advanced RL algorithms (PPO, DQN, DDPG, and TD3) to optimize the baseline model. The results show that the proposed RL design can better achieve the two optimization objectives of minimizing energy cost and maximizing the PV self-consumption ratio. Among them, the TD3 algorithm presented the best performance. Compared with the baseline model, its annual energy cost can be reduced by 17.82%, and the photovoltaic self-consumption ratio can be increased by 0.86%. In addition, the model-based RL method proposed in this paper can provide a better energy management strategy with the training set of only one and a half years of measured data, which proves that it has a high potential for practical application.

Pelletization Temperature and Pressure Effects on the Mechanical Properties of Khaya senegalensis Biomass Energy Pellets

Biomass pellets are one of the most crucial feedstocks for bioenergy production on a global scale due to their numerous advantages over raw biomass resources. Pellets provide improved energy density, bulk density, moisture content, and homogeneity thereby reducing storage, handling, and transportation costs. To produce high-quality solid fuel, it is necessary to comprehend the properties of wood fuel. This study explored the potential of Khaya senegalensis (khaya) as a dedicated energy crop (DEC) for the production of green energy. It thrives in less-than-ideal conditions and grows rapidly. The low durability of energy pellets raises the risk of dust and fire during handling and storage. In addition, the potential for fines and dust formation is strongly correlated with the mechanical strength of materials. Due to this necessity, the current study examines the effects of pelletization factors, including temperature and pressure, on pellet properties, particularly on its mechanical properties. The durability and compressive strength of pellets were determined using a sieve shaker and a universal testing machine, respectively. The highest mechanical durability was observed at 3 tons of pressure and 75 degrees Celsius, each with a value of 99.6%. The maximum axial compressive strength was measured at 57.53 MPa under 5 tons of pressure. When pelletized at 125 °C, the axial compressive strength increased by 13.8037% to 66.06 MPa compared to the strength obtained at 5 tons of pressure. Pelletizing Khaya feedstocks at 4 tons of pressure, on the other hand, produced a slightly lower diametral compressive strength of 7.08 MPa compared to 7.59 MPa at 125 °C. The experimental results revealed that the aforementioned factors significantly affect the mechanical properties of pellets. The elucidation of wood biomass, solid fuel qualities and pelletization parameters of this potential energy crop may facilitate the production of high-quality pellets from Khaya senegalensis wood to meet the increasing local and worldwide energy demands.