Peak Demand Research Articles

Exploring External Peak Demands: The Influence of Contextual Factors on Male Basketball Players

Understanding the external load, particularly the peak demands (PDs), experienced by players is essential. Several factors, such as the playing time, team venue, score outcome, and match nature, can influence basketball performance. Accelerometry-based methods allow for the estimation of the load borne by athletes and the peaks they reach. The aim of this study was to analyze the influence of these factors on PDs across different time windows (30 s, 1 min, and 3 min). Eleven national-level athletes were monitored during 29 matches using measurement devices. Inertial movement units were used to measure peak PlayerLoad™ (PL) across any time window. Mixed linear models were used to compare the groups, and effect size was calculated to quantify the differences. Significant differences (p < 0.05, small effects) were found between occasional players (who play fewer minutes) and core players (who play intermediate minutes), with the latter reaching higher PDs, though with small effect sizes. Core players also reached higher PDs than top players (who play the most minutes), but the differences were not significant (p > 0.05, trivial effects). In summary, core players reach higher external PDs during competition compared to occasional players, while other contextual factors did not show a significant influence on external PDs.

Leveraging Walnut Somatic Embryos as a Biomanufacturing Platform for Recombinant Proteins and Metabolites

Biomanufacturing enables novel sources of compounds with constant demand, such as food coloring and preservatives, as well as new compounds with peak demand, such as diagnostics and vaccines. The COVID-19 pandemic has highlighted the need for alternative sources of research materials, thrusting research on diversification of biomanufacturing platforms. Here, we show initial results exploring the walnut somatic embryogenic system expressing the recombinant receptor binding domain (RBD) and ectodomain of the spike protein (Spike) from the SARS-CoV-2 virus. Stably transformed walnut embryo lines were selected and propagated in vitro. Both recombinant proteins were detected at 3–14 µg/g dry weight of tissue culture material. Although higher yields of recombinant protein have been obtained using more conventional biomanufacturing platforms, we also report on the production of the red pigment betanin in somatic embryos, reaching yields of 650 mg/g, even higher than red beet Beta vulgaris. This first iteration shows the potential of biomanufacturing using somatic walnut embryos that can now be further optimized for different applications sourcing specialized proteins and metabolites.

Planning and Optimisation of Renewable Energy Systems for Decarbonising Operations of Oil Refineries

AbstractGiven the urgency to transition to low carbon future, oil refineries need to identify feasible strategies for decarbonisation. One way to address this is by integrating renewable energy systems. However, the high initial costs and intermittency appeared to be the key barriers for the adoption of renewable energy technologies. Hence, a multi-period optimisation model is developed via mixed integer linear programming in this work to determine the optimal renewable energy system in terms of cost and its optimal energy storage technology to enhance its flexibility for oil refinery operations. This model aims to minimise the costs of the renewable energy system while considering its ability to accommodate the varying energy demands across the time periods. An oil refinery case study is used to demonstrate the effectiveness of the developed model. The developed model is expected to propose an optimal renewable energy system that meets the energy demands and, at the same time, achieves the decarbonisation goal. Based on the results, the optimal renewable energy system comprises cost-effective technologies to generate various energy outputs including electricity, hydrogen, high-pressure and medium-pressure steam to meet energy demands. Additionally, the result of the case study shows that the integration of renewable energy systems achieves a reduction of 5,353 tonnes of carbon dioxide. Apart from that, the incorporation of energy-efficient energy storage results in a 10% reduction in the total cost of the optimal renewable energy system. Compressed hydrogen gas storage and battery were used to store excess hydrogen and electricity during periods with low demands and subsequently consumed during peak demand periods. This can, therefore, reduce the technological capacity required. With the aid of storage facilities, the flexibility of the renewable energy system is elevated in meeting varied demands, which otherwise would incur additional expenses.

Synergistic Effects of Energy Storage Systems and Demand-Side Management in Optimizing Zero-Carbon Smart Grid Systems

The urgent need to mitigate climate change and reduce reliance on fossil fuels has driven the global shift towards renewable energy sources (RESs). However, the intermittent nature of RESs poses significant challenges to the widespread adoption of Zero-Carbon Smart Grids (ZCSGs). This study proposes a synergistic framework to address this hurdle. It utilizes energy storage systems (ESSs) by comparing Vanadium redox flow batteries (VRFBs) and Lithium ion batteries (LIBs) to identify the most suitable option for ZCSGs, with precise models enabling robust performance evaluation. Moreover, an accurate demand-side management (DSM) strategy considering power elasticity to manage discrepancies between electricity load, RES generation, and ESS availability is introduced for estimating fair, dynamic tariffs. An advanced load and weather-forecasting strategy is introduced for improving grid planning and management. An advanced optimization algorithm enhances grid stability and efficiency. Simulations demonstrate significant reductions in carbon footprint, peak power demand, and reliance on fossil fuels. The study finds that VRFBs outperform LIBs in cost and security, and dynamic tariffs based on accurate DSM significantly reduce energy costs. This work explores the challenges and opportunities of this integrated approach, offering policy recommendations and future research directions for truly optimized ZCSG implementation.

Electric Vehicles for a Flexible Energy System: Challenges and Opportunities

As the adoption of Electric Vehicles (EVs) accelerates, driven by increasing urbanization and the push for sustainable infrastructure, the need for innovative solutions to support this growth has become more pressing. Vehicle-to-Grid (V2G) technology presents a promising solution by enabling EVs to engage in bidirectional interactions with the electrical grid. Through V2G, EVs can supply energy back to the grid during peak demand periods and draw power during off-peak times, offering a valuable tool for enhancing grid stability, improving energy management, and supporting environmental sustainability. Despite its potential, the large-scale implementation of V2G faces significant challenges, particularly from a technological and regulatory standpoint. The success of V2G requires coordinated efforts among various stakeholders, including vehicle manufacturers, infrastructure providers, grid operators, and policymakers. In addition to the technical barriers, such as battery degradation due to frequent charging cycles and the need for advanced bidirectional charging systems, regulatory frameworks must evolve to accommodate this new energy paradigm. This review aims to provide a comprehensive analysis of V2G technology, focusing on different perspectives—such as those of users, vehicles, infrastructures, and the electricity grid. This study will also explore ex ante, ex post, and ongoing assessment studies, alongside the experiences of pioneer cities in implementing V2G.

Optimal power dispatch in microgrids using mixed-integer linear programming

Abstract As greenhouse gases emissions continue to rise, society is actively seeking methods to reduce them. Microgrids (MGs), which predominantly consist of renewable energy sources, play a significant role in achieving this objective. This paper proposes an optimized methodology for power dispatch in MGs using mixed-integer linear programming (MILP). The MGs include photovoltaic systems, wind turbines, biogas (BG) generators, battery energy storage systems (BESS), electric vehicles (EV), and loads. The model features an objective function focused on cost minimization, power balance, and the necessary limits and constraints for the system’s safe operation. Real-time pricing is employed for energy transactions between the MGs and the main grid. The results demonstrate a cost-efficient operation for the proposed system comprising two MGs and the main grid. During periods of negative power balance, the demand was met by discharging the BESS, EV’s battery, or purchasing energy from the grid. The BESS was charged when energy prices were low and discharged during peak demand periods and high energy prices. The intermittent nature of renewable sources necessitates an efficient management system to ensure reliable operation. Additionally, storage systems help mitigate the variability in generation. The BG generator was another crucial component for power supply due to its flexibility. Integrating these components into the system improved reliability and ensured a secure and balanced operation.

Cross-Docking Layout Optimization in FlexSim Software Based on Cold Chain 4PL Company

The paper highlights the potential of cross-docking to reduce storage time and costs. The study addresses evolving market demands that push logistics providers to adopt new technologies for operational efficiency, emphasizing the often-overlooked importance of optimizing cross-docking layouts. The research, conducted in two phases, first analyzed the current warehouse layout (Variant I) to identify inefficiencies and then designed a new layout (Variant II) that was simulated using FlexSim 2022 software. The results showed significant improvements with the new layout, including a 35% increase in deliveries and a 3.23% reduction in forklift travel distances, leading to lower operational costs. Even minor adjustments in the warehouse design proved to enhance logistics efficiency, particularly during peak demand periods like holidays. The study demonstrates how FlexSim software can be applied in cold chain logistics to optimize warehouse operations, underscoring the benefits of cross-docking for cost-effective logistics management.

Optimum carbon tax rate for emission targets of electricity generation system by life cycle techno-economic-environmental optimization model

Reduction of greenhouse gas emissions from the electricity sector is of special interest for decarbonization of energy systems. Optimum carbon tax as a powerful policy tool is here studied by integrated techno-economic-environmental life cycle analysis in a unit commitment problem. The present model is formulated as a bi-level optimization problem to reach the optimum carbon tax rates for different emission targets. The total life cycle CO2 emissions are estimated to be 20333.90 tons/hr in a real case study of Iran's electricity sector for the peak demand hour. The carbon tax rate for a 2% and a 3% emissions reduction is equal to 15.87 and 24.65 $/ton, respectively. In the business-as-usual scenario, higher carbon taxes will be required each year to meet the same emission target. However, developing the electricity sector with cleaner technologies and strategic plans may decrease the carbon tax rate. The extent of this reduction depends on the type of development program implemented in the green revolution scenario. Therefore, policy makers play a direct role in ensuring the sustainable development of the electricity sector. The present model provides a robust framework to analyze the real carbon tax rates for the whole life cycle of electricity generation dispatch.

GAPS OF INDIAN ELECTRICAL ENERGY SECTOR AND ITS OPTIMAL MITIGATION BY USING OPTIMAL UTILIZATION OF INDIAN RENEWABLE ENERGY POLICY WITH THE HELP OF THE P&O MPPT TECHNIQUE

Developing countries, especially, India, depend importantly on energy sources to perform their country’s industrial and economic activities. Mainly the two factors continuous population growth of the country and the rapid growth rate of industrialization of the country, the gap between electrical energy supply and demand for electrical energy is slowly and consistently increasing. To resolve this unevenness, there is a jointly planned attempt is required to enhance the contribution of renewable energy in the all over mix of energy. Despite these attempts, unexpected surges caused by the sector of domestic energy consumption could lead to important shortfalls between energy requirements at peak demand and available supply in India. As a solution to this challenge, the Union government of India has launched a new policy, named PM SURYA GHAR: MUFT BIJLI YOJANA, targeted to handle this crucial problem head-on. This initiative requires addressing the increasing energy demand by promoting the utilization of solar power in households throughout the length and breadth of the country. With the implementation techniques of Photo voltaic optimization, this analysis explores how such types of strategies can play a significant role in filling the gap between the energy supply-demand in the Indian context. Employing the solar energy potential and optimizing and utilization of solar energy, creates a path to not only meet the enhancing energy requirements but also creates the path for a more environmentally friendly and sustainable energy in India. By integration of innovative technologies and solutions of renewable energy holds the key to ensuring a balanced, sustainable, stable energy supply for the future of India and country’s growth and development.

Incorporating soil‐structure interaction into simplified numerical models for fragility analysis of RC structures

AbstractSimplified building models are a valuable option for seismic assessment at the regional scale. These models often use calibrated springs to model column behaviour, and recent advances have made them suitable for capturing torsional response in Reinforced‐Concrete Moment‐Resisting‐Frames. Nevertheless, their validation is typically achieved using fixed‐base models, which do not include the influence of soil‐structure interaction (SSI). This study introduces a novel approach to quantify the accuracy of a recently developed simplified model while accounting for dynamic SSI, using a newly implemented, refined 3D Finite Element non‐linear soil model in OpenSees. The accuracy of the simplified structural model is assessed by comparing the results of non‐linear dynamic analyses with those of a refined model in terms of (i) a peak structural demand parameter such as the interstorey‐drift ratio and (ii) fragility curves computed from cloud analysis and accounting for collapse cases. The study presents details of the proposed refined approach for 3D soil modelling in OpenSees, focusing on implementing free‐field boundary conditions and structure‐to‐soil connections. Results show that the accuracy of the simplified model is maintained, even in the presence of SSI, and it successfully captures the overall structural response measured at peak demand. For the proposed case study, the difference between the simplified and refined models’ fragility curves’ medians is 4% and 2% for fixed and SSI models, respectively. The simplified structural model, combined with the refined soil model for SSI effects, presents an innovative and conservative, yet computationally efficient, alternative for seismic risk analysis, even in the presence of structural irregularity.

Optimisation of Integrated Heat Pump and Thermal Energy Storage Systems in Active Buildings for Community Heat Decarbonisation

The electrification of residential heating systems, crucial for achieving net-zero emissions, poses significant challenges for low-voltage distribution networks. This study develops a simulation model to explore the integration of heat pumps within active building systems for community heating decarbonisation. The model optimises heat pump operations in conjunction with thermal energy storage units to reduce peak demand on low-voltage networks by using real-time measured electricity demand data and modelled heat demand data for 76 houses. The study employs an algorithm that adjusts thermal storage charging and discharging cycles to align with off-peak periods. Three scenarios were simulated: a baseline with unoptimised heat pumps, a fixed threshold model, and an active building model with daily optimised thresholds. The results demonstrate that the active building model achieves a 21% reduction in peak demand on the low-voltage substation compared to the baseline scenario; it also reduces the total electrical energy consumption by 12% and carbon emissions by 17%. The fixed threshold scenario shows a 16% improvement in peak demand reduction, but it also shows an increase in energy consumption and emissions. These findings highlight the potential of active buildings to enhance the efficiency and sustainability of residential energy systems, marking a significant step toward decarbonising residential heating while maintaining grid stability.

Coordinated Planning for Multiarea Wind-Solar-Energy Storage Systems That Considers Multiple Uncertainties

As the scale of renewable energy sources (RESs) expands, it is essential to optimize the configuration of wind, solar, and storage resources across different areas. Nevertheless, the unavoidable uncertainties associated with both energy supply and demand present significant challenges for planners. This study aims to address the challenge of coordinated planning for multiarea wind-solar-energy storage systems considering multiple uncertainties. First, uncertainties related to future peak demand, thermal generation output boundaries, demand variability, and stochastic unit production are analyzed and modeled on the basis of robust optimization and stochastic programming techniques. Then, a hierarchical coordinated planning model that incorporates both system-wide (SW) and local area (LA) planning models is proposed. The SW planning model is designed to manage the optimal capacity configuration of RESs and energy storage systems (ESSs) within each LA, as well as the operational boundary of LAs. The LA planning models aim to further optimize the capacities of RESs and ESSs and minimize the economic cost within each LA on the basis of local resource characteristics. To achieve the optimal solution, the analytical target cascading (ATC) algorithm is integrated with the column-and-constraint generation (C&CG) algorithm. The simulation results validate the effectiveness and reasonableness of the proposed coordinated planning model, which not only outperforms independent planning approaches but also effectively manages the uncertainties.

Daily peak demand forecasting using Pelican Algorithm optimised Support Vector Machine (POA-SVM)

The knowledge of daily peak load consumption is crucial for energy planning, energy management, and resource allocation, as it is an essential element of supply-side management. This knowledge is obtained from accurate predictions of which traditional methods are falling short due to constantly changing demand. Hence, there is a need for Machine Learning (ML) models that are more efficient in hybridised or enhanced forms. This paper presents the use of a novel Pelican Algorithm optimised Support Vector Machine (POA-SVM), a hybridised ML Algorithm, to predict peak load and its corresponding peak hour for proper planning. It evaluates the performance of four Support Vector Machine (SVM) models: standard SVM, SVM optimised with Bayesian Optimisation (SVMB), SVM optimised with Particle Swarm Optimization (PSO-SVM) and POA-SVM on both raw and normalised datasets. Its analysis includes a comparison of performance metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R²) to determine the models' accuracy and goodness of fit. Furthermore, we experiment with K-fold cross-validation and the hold-out validation method, finding that K-fold cross-validation yields better performance metrics for all models. Notably, the POA-SVM model shows superior performance across all metrics, particularly on the raw dataset, making it a robust choice for forecasting peak demand and its corresponding hour of the day.

Multi-Criteria Decision-Making Approach for Optimal Energy Storage System Selection and Applications in Oman

This research aims to support the goals of Oman Vision 2040 by reducing the dependency on non-renewable energy resources and increasing the utilization of the national natural renewable energy resources. Selecting appropriate energy storage systems (ESSs) will play a key role in achieving this vision by enabling a greater integration of solar and other renewable energy. ESSs allow for solar power generated during daylight hours to be stored for use during peak demand periods. Additionally, the proposed framework provides guidance for large-scale ESS infrastructure planning and investments to support Oman’s renewable energy goals. As the global renewable energy market grows rapidly and Oman implements economic reforms, the ESS market is expected to flourish in Oman. In the near future, ESS is expected to contribute to lower electricity costs and enhance stability compared to traditional energy systems. While ESS technologies have been studied broadly, there is a lack of comprehensive analysis for optimal ESS selection tailored to Oman’s unique geographical, technical, and policy context. The main objective of this study is to provide a comprehensive evaluation of ESS options and identify the type(s) most suitable for integration with Oman’s national grid using a multi-criteria decision-making (MCDM) methodology. This study addresses this gap by applying the Hesitate Fuzzy Analytic Hierarchy Process (HF-AHP) and Hesitate Fuzzy VIKOR methods to assess alternative ESS technologies based on technical, economic, environmental, and social criteria specifically for Oman’s context. The analysis reveals pumped hydro energy storage (PHES) and compressed air energy storage (CAES) as the most appropriate solutions. The tailored selection framework aims to guide policy and infrastructure planning to determine investments for large-scale ESSs and provide a model for comprehensive ESS assessment in energy transition planning for countries with similar challenges.

Multi-objective-based economic dispatch and loss reduction in the presence of electric vehicles considering different optimization techniques

Currently, electric vehicles (EVs) are the most liked mode for green transportation. However, the vehicle-to-grid (V2G) technology can reduce the peak demand on the power grid, which is an efficient way to encourage the integration of EVs. This paper proposes a multi-objective-based economic dispatch management including EVs to minimize the generator cost and active power loss. The entire system is retained for keeping in mind the economic operation of the whole system. Then, EVs are introduced to the system, taking into account vehicle requirements and load demands and considering EV constraints. The target of the proposed work is to demonstrate how effectively large-scale EVs can participate in valley filling and peak load shaving along with multi-objective-based cost and loss reduction. The proposed optimization problem is employed in an IEEE 30-bus system. The multi-objective grasshopper optimization algorithm and the ant-lion optimization are compared to observe the minimum cost and total loss of the system. The results show that the total generation cost and power loss of the system decrease due to the V2G mode of operation. In addition, EVs provide an alternative method for dealing with peak load, while filling the off-peak hours effectively. The total generation cost and power loss for 24 h using MOGOA without implementation of EVs are 8,757.128 $/hr and 65.28509 MW, respectively, and with EVs, the total generation cost and power loss for 24 h are 8,617.077 $/hr and 55.65349 MW, respectively. Thus, with the implementation of EVs, the total generation cost reduced by 1.59% and the total power loss reduced by 14.75%, and with MOALO, the total generation cost and power loss for 24 h without EVs are 8,977.077 $/hr and 44.20877 MW, respectively, and with EVs, the total generation cost and power loss for 24 h are 8,923.529 $/hr and 41.69524 MW, respectively. Thus, with the implementation of EVs, the total generation cost reduced by 0.59% and the total power loss reduced by 5.68%. The analysis of the results demonstrates how effectively EVs in the V2G mode can reduce the dependency over the grid power during the time of peak load demand.

Implementation and effectiveness of the cashless banking system

This thesis analyses the implementation and effectiveness of the cashless banking system at Ecobank, focusing on various dimensions such as usability, stakeholder satisfaction, and customer perspectives. A mixed-methods approach was employed, incorporating quantitative and qualitative data from 25 participants to evaluate the system's performance. The analysis reveals that while the cashless banking system is generally well-received, particularly in ease of task completion and overall workflow improvements, there are notable challenges in usability, particularly with interface intuitiveness and performance during peak hours. Stakeholders and customers reported moderate satisfaction, with varying experiences regarding ease of use and reliability. The study concludes that although the cashless banking system has enhanced Ecobank’s service delivery and met broad expectations, further user experience, system performance, and continuous evaluation mechanisms are necessary to optimise its effectiveness and customer satisfaction. Recommendations include focusing on user-centred design, improving scalability for peak demand periods, and establishing robust feedback loops for ongoing system refinement.

Long-term climate-based sizing and economic assessment of air-water heat pumps for residential heating

This study presents a novel methodology for sizing air–water heat pumps (ASHP) for space heating and hot water supply in buildings. Utilizing long-term local climatic data, the developed tool determines the coefficient of performance (COP) and feasibility of ASHP. The sizing algorithm incorporates peak heat demand, unitary final energy demand, and seasonal heat demand across various detached house sizes. Annual local heating system operation time and key economic indicators are derived for each case across 3 European countries. A new approach is precise investigation of 10-year outdoor temperatures and apply it to heat pump (HP) modeling, which enabled a distinction between so-called warm and cold years. The results underscore substantial variations in net present value (NPV) between warm and cold years, illustrating the critical influence of precise climate data on the performance of HPs. The study revealed that for a 160 m2 house in Poland, annual electricity consumption by HPs ranged from 3736 kWh during warm years to 12,908 kWh during cold years, showing a significant variation in performance based on climate conditions. The comprehensive tool allows for precise adjustments of heating systems to actual local climate conditions, improving both economic and environmental outcomes. This comparative assessment aids in the optimal selection of HPs, ensuring energy efficiency and sustainability in residential heating systems. Building on studies from various climatic regions, this work addresses the knowledge gap in performance and explores the feasibility of HP in a changing climate using precise modeling techniques.

BIM-based parametric energy analysis of green building components for the roofs and facades

Buildings are distinguished by their high energy demand over their lifespan. Iimproving energy efficiency in buildings necessitates mitigating the operation energy performance by evaluating more sustainable building components such as green infrastructures. The novelty herein is to conduct a BIM-based parametric energy analysis of green building components for the roofs and facades, taking into consideration the climate classification and orientation of the building. The proposed framework of this study uses Autodesk Revit software and Autodesk Green Building Studio as BIM tools, and Minitab software as a parametric analysis tool to estimate the energy performance of several alternative components based on Energy Use Intensity (EUI) and Annual Peak Demand (APD). A case study of a residential building is examined in four cities within different climate zones in Brazil; Rio de Janeiro (climate zone 08), Macaé (climate zone 05), Petrópolis (climate zone 03), and Nova Friburgo (climate zone 02). The base model design of components of the building includes a reinforced concrete roof and ceramic masonry façade. The utilized alternatives are roof components (i.e. green roofs and expanded clay roofs), and green façade. This work insight using green façade for all elevations and green roofs to enhance energy efficiency in terms of EUI by (10,17 %) in Rio de Janeiro, (6,67 %) in Macaé, (11,43 %) in Pertópolis, and (16,43 %) in Nova Friburgo. While the efficiency in terms of APD would be about (28,07 %) in Rio de Janeiro, (22,79 %) in Macaé, (21,67 %) in Pertópolis, and (12,58 %) in Nova Friburgo. When the construction project is designed to install a green façade to the main elevation only, this work recommends using green roofs rather than the expanded clay roof or the base model design roofs to increase energy efficiency in such types of buildings. This work contributes to integrating green building components into building designs to improve energy efficiency and empower decision-making and sustainability in buildings.

Techno-economic analysis of solar, wind and biomass hybrid renewable energy systems in Bhorha village, India

The present study investigates the potential use of Hybrid Renewable Energy Systems (Solar photovoltaic, wind, biomass, and diesel), both with and without the inclusion of battery/supercapacitor storage in the Bhorha village, Bihar, India. A comprehensive assessment of different possible system configurations is conducted using hybrid optimization model for electric renewable (HOMER) software to determine the most economically viable and efficient system for the designated place. The current analysis is focused on six distinct cases in the village community, in view of sufficing the daily energy requirement of 615.625 kWh and a peak demand of 86.47 kW, pertaining to major factors viz. system efficiency, financial viability, and ecological consequences. The primary aim of the research is to elucidate the comparative analysis of energy generation for different proposed designs of hybrid renewable energy systems. Detailed techno-commercial assessments are also carried out to examine the energy production, consumption, and financial impacts of each HRES configuration. The research outcome of this study obtained from HOMER software reveals that the optimized hybrid system comprises 86.7 kW solar photovoltaic, 30 kW wind turbine, 5 kW biogas generator, a 50 kW diesel generator, 280 kWh battery bank with nominal capacity, and 38.8 kW converter to sustain the for energy needs of the nominated place. This system has a minimum cost of energy of 0.309 $/kWh with a net present cost of $854894 along with operating cost 51847 $/year and net carbon dioxide emission of 56728 kg/yr. The research offers useful insights for designers, scholars, and policymakers on the existing design constraints and policies of biomass-based hybrid systems for a safe, sustainable and independent green future for the generations to come.

Thermal request optimization of a smart district heating system

This paper proposes a solution approach to manage the heating plans of tenants served by a district heating plant located in Sweden. To do that, the daily temperature request of each household in the associated pilot region is obtained, and the daily temperature profile of each household is optimized with the help of the proposed decision support system and smart valves. The hot water inflow rates of radiators are remotely controlled via smart valves at each flat to minimize the total energy consumption, carbon emission and cost associated with the energy consumption of the district heating plant. We aim to shave the peak demands while fully satisfying the temperature requests of households without violating their thermal comfort. Peak demand shaving is achieved by generating preheating schedules via mathematical optimization and using the thermal storage potential of the insulated flats. The resulting mathematical optimization model presents significant computational challenges that cannot be efficiently solved using optimization solvers within a reasonable time limit. To this end, we develop three genetic algorithm approaches that are computationally scalable for realistically-sized instances and verified to yield near-optimal solutions for the test instances. Extensive numerical analyses show the effectiveness of the proposed approach and the genetic algorithm since we yield significant carbon emission reduction and cost savings compared with the method that the experts of the utility company propose.