Energy Cost Research Articles

ОБОСНОВАНИЕ ПРИМЕНЕНИЯ ДИФФЕРЕНЦИРОВАННОГО СОЦИАЛЬНОГО ТАРИФА НА ЭЛЕКТРИЧЕСКУЮ ЭНЕРГИЮ ПРИ РЕАЛИЗАЦИИ ПРОЕКТА ЭЛЕКТРОСНАБЖЕНИЯ ОТ АВТОНОМНОЙ ГИБРИДНОЙ ЭНЕРГЕТИЧЕСКОЙ УСТАНОВКИ

The issue of uninterrupted power supply to remote settlements is relevant in many countries of the world, but especially in Russia. Modern autonomous hybrid power plants (AHPP), namely solar power plants (SPP), wind power plants (WPP) and other power plants using renewable energy sources (RES), combined with diesel electric power plants (DES), provide not only an increase in the amount of electricity generated, but, most importantly, they ensure uninterrupted power supply to consumers through a combination of renewable energy sources and covering the missing energy from diesel power plants. In the conditions of the northern areas of the Krasnoyarsk Region, this is especially important. ASPPs make it possible to reduce the cost of electrical energy by saving fuel consumption and budgetary subsidies for its supply to remote areas of the region. The purpose of the study was to substantiate the areas of application of autonomous hybrid power plants for the regions of the Far North with the possibility of establishing a differentiated social tariff for household consumers. To achieve the goal, load coverage graphs were constructed for Vanavara settlement; the parameters of the ASPP were studied for the conditions of Vanavara settlement; simulation of the operating modes of the ASPP was carried out; calculation of the main technical and economic indicators of the use of ASPP for power supply to Vanavara settlement was carried out; recommendations were given on rational areas of application of ASPPs with the possibility of applying special tariffs for the electricity they produce. The authors of the paper carried out a technical and economic assessment of ASUE in the Vanavara settlement with a capacity of 10 MW, including a solar power plant with a capacity of 2,5 MW. It was established that the cost of electrical energy in the first option when powered by diesel power plants is 36,75 rubles/kW.h. In the second option, the cost depends on the time of year and is 32,7 rubles/kW.h in winter, 11.6 in spring, 6.05 in summer, and 12,2 rubles/kW.h in autumn, which makes it possible to establish a differential social tariff for household consumers based on a lower cost in the summer in the amount of 7–9 rubles/kW.h.

Concentrated Solar Power Techno-Economic Analysis in Humid Subtropical South America: The Uruguayan Case

Abstract This study assesses the feasibility of installing concentrated solar power plants in subtropical South America, particularly in Uruguay, by numerical simulations. Parabolic trough and solar power tower technologies are examined. A comprehensive literature analysis is conducted in order to evaluate initial investment, operation, and maintenance costs. Simulation models are validated in order to ensure results accuracy. The study is focused on the optimization of solar fields and storage sizes for five locations. The target set is to minimize the levelized cost of energy. In addition, energy losses and efficiencies are compared between parabolic trough and solar power tower technologies. Salto region in Uruguay is identified as the most suitable location for concentrated solar power projects. Optimized plants yield solar multiples of 3 or higher for solar power tower and around 4 for parabolic trough, with storage sizes ranging from 12 to 15 h, depending on the location. In Salto, the levelized cost of energy ranges from 148 to 175 USD/MW h for 110 MW solar power tower and from 169 to 220 USD/MW h for 55 MW parabolic trough plants, considering different investment cost scenarios. Levelized cost of energy is comparable for other locations, with a slight increase of approximately 10% for the least favorable location, Rocha. This work shows that while not yet competitive with photovoltaic or wind technologies, concentrated solar power plants show promise against fossil-fueled power plants and are expected to decrease further in cost.

Weak neuronal glycolysis sustains cognition and organismal fitness.

The energy cost of neuronal activity is mainly sustained by glucose1,2. However, in an apparent paradox, neurons modestly metabolize glucose through glycolysis3-6, a circumstance that can be accounted for by the constant degradation of 6-phosphofructo-2-kinase-fructose-2,6-bisphosphatase-3 (PFKFB3)3,7,8, a key glycolysis-promoting enzyme. To evaluate the in vivo physiological importance of this hypoglycolytic metabolism, here we genetically engineered mice with their neurons transformed into active glycolytic cells through Pfkfb3 expression. In vivo molecular, biochemical and metabolic flux analyses of these neurons revealed an accumulation of anomalous mitochondria, complex I disassembly, bioenergetic deficiency and mitochondrial redox stress. Notably, glycolysis-mediated nicotinamide adenine dinucleotide (NAD+) reduction impaired sirtuin-dependent autophagy. Furthermore, these mice displayed cognitive decline and a metabolic syndrome that was mimicked by confining Pfkfb3 expression to hypothalamic neurons. Neuron-specific genetic ablation of mitochondrial redox stress or brain NAD+ restoration corrected these behavioural alterations. Thus, the weak glycolytic nature of neurons is required to sustain higher-order organismal functions.

Forestry electric vehicle energy routing and mapping GIS tool

ABSTRACT In the United States, new laws and regulations in several states require increasing percentages of vehicles produced in the future to be zero emission to address climate-related issues. Manufacturers are producing more Electric Vehicles (EVs), but potential customers are hesitant to adopt EVs due to range anxiety. Log truck owners are particularly hesitant to invest in electric log trucks (ELTs) due to forest harvest operations remote nature. However, regenerative braking utilizing gravitational potential energy provides the opportunity to increase the range of ELTs. This manuscript presents the workflow for calculating and mapping energy transportation cost on an ELT using a GIS-enabled tool and demonstrates the tool’s applicability through a case study. The study area, the McDonald Dunn Research Forest, included 5,607 ha of forest stands partitioned into 357 harvest polygons averaging 15.7 ha, 326 harvest landings and 295 km of road. Using remotely sensed data from the study site, the tool digitalizes the wood supply chain and optimizes routing based on minimum energy expenditure, providing results such as trip energy cost, distance, and time. The results show the tools forest operations planning potential, applicability as a GIS navigation system, and provides EV buyers with a tool to make informed decisions when investing in technology that could combat climate-related issues. EV adoption will reduce reliance on fossil fuels and potentially ameliorate other environmental problems.

#1561 A tool for the “screening” of the dialysis facilities environmental impact

Abstract Background and Aims The great number of resources needed for hemodialysis treatment can be a limiting factor in the perspective of access to care, especially in developing Countries, and may have a negative impact on the environment. The aim of the study was to develop a questionnaire that is easy and informative of the ecologic impact (energy costs and waste production) of a single dialysis center. The questionnaire's goal is to highlight potentially modifiable factors that are responsible of the ecologic footprint of dialysis. Method A team of 8 people, 3 nephrologists, 3 representatives of dialytic equipment companies and 2 of water treatment systems, developed the questionnaire. The items have been decided after two meetings in person. The completed questionnaire included 49 items divided into three sections, exploring the organization techniques of the dialysis center, its consumption and its water treatment system's characteristics. The questionnaire was then submitted to 11 dialysis centers and made available from 30 September 2022 to 1 February 2023, and included data collected in May 2022. An investigator assisted within filling in the form and collecting feedback on the comprehension of the questions and on any problems that may have surfaced during the compilation of the questionnaire. Results The questionnaires were completed by the center's managers who, unanimously, thought that the form was easily understandable and without significant problems during the compilation. The data collected evidenced that there is almost no real knowledge of the center's consumptions and that also many data about power and water consumption and of waste production are ignored and are therefore missing. No center is fueled by renewable energies. Data refers to a total of 10337 dialysis treatments, with an average 940 treatments per center. Electrical consumption and waste production are in line with the literature, with an average of 13 KwH and 1,9 kg consumption per treatment, respectively. The average water consumption in our sample (550 L per treatment) seems to be higher than expected. Standard dialysate flow was greater than or equal to 500 ml/min in both HD and HDF, except in two centers. Five centers had a water treatment system which is more than eight years old. Average values of each center are reported in the Table 1. Conclusion Our questionnaire appears to be an easily fillable option to carry out consumption “screenings” of each dialysis center. The form could also prove itself useful for a large-scale investigation, to collect an adequate amount of data and to conduct a thorough research to highlight critical issues that could affect the center's consumption. A wider spread of this questionnaire could improve the awareness of nephrologists about their dialysis structure's consumption, taking part in the cultural revolution which is needed to develop a “greener” nephrology.

Multi-objective placement and sizing of energy hubs in energy networks considering generation and consumption uncertainties

This paper presents the placement and sizing of energy hubs (EHs) in electricity, gas, and heating networks. EH is a coordinator framework for various power sources, storage devices, and responsive loads. For simultaneous modeling of economic, operation, reliability, and flexibility indices, the proposed scheme is expressed as a three-objective optimization in the form of Pareto optimization based on the sum of weighted functions. The objective functions of this problem respectively minimize the planning cost of EHs (equal to the total cost of construction of hubs and their expected operating cost), the expected energy loss of the mentioned networks, and the expected energy not-supplied (EENS) of these networks in the case of an N − 1 event. The problem is constrained by power flow equations and operation and reliability constraints of these network together with the EH planning and operation model, and flexibility constraints of the EHs. Then, to achieve unique optimal solution in the shortest possible time, a linear approximation model is extracted for the proposed scheme. Moreover, scenario-based stochastic programming (SBSP) is employed to model uncertainties of load, energy cost, renewable power, and accessibility of the mentioned network equipment. Finally, the obtained numerical results indicate the capability of the proposed scheme in enhancing the economic and flexibility situation of EHs and improving the reliability and operation status of energy networks along with achieving optimal planning and operation for EHs.

Optimal Allocation of Capacitor Banks and Distributed Generation: A Comparison of Recently Developed Metaheuristic Optimization Techniques on the Real Distribution Networks of ALG-AB-Hassi Sida, Algeria

Recent advancements in renewable energy technologies, alongside changes in utility infrastructure and progressive government policies, have bolstered the integration of renewable-based distributed generation units within distribution systems. This paper introduces the Energy Valley Optimizer, a novel tool designed for the strategic placement of distributed generation units and capacitor banks. This placement is crucial not only for optimizing energy loss and enhancing bus voltage stability but also for promoting sustainable energy use and reducing environmental impact over the long term. By minimizing energy loss and voltage fluctuations, the optimizer contributes to a more sustainable and resilient energy system. It achieves this through the optimal allocation of resources across various load patterns within a 24 h period and is tested on the ALG-AB-Hassi-Sida 157-bus distribution network in South Algeria. Comparative analysis with existing algorithms—such as the Liver Cancer Algorithm, Walrus Optimization Algorithm, and Zebra Optimization Algorithm—demonstrates the superior performance of the Energy Valley Optimizer. It not only enhances technical and economic efficiencies but also significantly lowers the total cost of energy over 24 years, thus supporting sustainable development goals in energy management.

Optimization of the Coupling between Water and Energy Consumption in a Smart Integrated Photovoltaic Pumping Station System

Agricultural irrigation requires significant consumption of freshwater resources and energy. The integration of photovoltaic power generation into irrigation systems has been extensively investigated in order to save the cost of energy. However, current research often neglects the coupling relationship between photovoltaic power generation and irrigation schemes. This study presented a novel smart integrated photovoltaic pump station system to effectively address the issue associated with water and energy consumption in irrigation. An optimization model was proposed to synchronize the energy consumption of irrigation pump stations with photovoltaic power generation, accurately meeting the irrigation water demand while maximizing solar energy utilization. The optimization model incorporates power balance, grid-connected power, and total water demand as constraints while considering pump speed as the decision variable and aiming to minimize daily operational costs. Finally, a high-standard farmland was used as a case study to validate the efficacy of the optimization strategy through two photovoltaic grid-connected policies—one allowing for the sale of surplus power and the other prohibiting it. An improved dynamic programming method was employed to solve for optimal energy consumption schemes under different water demand conditions; the results were compared against traditional methods, revealing potential cost savings ranging from 6.2% to 30.5%. The optimization model and method propose a new operational concept for the irrigation system with photovoltaic generation, effectively utilizing the distinctive features of both irrigation and photovoltaics to optimize water and energy resources.

Bi-Objective Battery Electric Truck Dispatching Problem with Backhauls and Time Windows

The battery electric truck (BET) has emerged as a promising solution to reduce greenhouse gas emissions in urban logistics, given the current strict environmental regulations. This research explores the formulation and solution of the bi-objective BET dispatching problem with backhauls and time windows, aiming to simultaneously reduce environmental impacts and enhance the efficiency of urban logistics. From the sustainability perspective, one of the objectives is to minimize total energy costs, which include energy consumption and battery replacement expenses. On the other hand, from an economic perspective, the other objective is the minimization of labor costs. To solve this bi-objective BET dispatching problem, we propose an innovative approach, integrating an adaptive large neighborhood search-based metaheuristics algorithm with a multi-objective optimization strategy. This integration enables the exploration of the trade-off between fleet energy expenses and labor costs, optimizing the dispatching decisions for BETs. To validate the proposed dispatching strategy, extensive experiments were conducted using real-world fleet operations data from a logistics fleet in Southern California. The results demonstrated that the proposed approach yields a set of Pareto solutions, showcasing its effectiveness in finding a balance between energy efficiency and labor costs in urban logistics systems. The findings of this research contribute to advancing sustainable urban logistics practices and provide valuable insights for fleet operators in effectively managing BET fleets to reduce environmental impacts while maintaining economic efficiency.

Rational primary drying temperature for freeze-drying of fermented dairy products

The influence of primary drying temperature on the quality of freeze-dried fermented dairy products was determined experimentally. The objectives of this research were to investigate the freeze-drying characteristics of yogurt, bio-yogurt and curdled milk dried at primary drying temperatures of −40 ± 2 °C; −25 ± 2 °C; −15 ± 2 °C; −10 ± 2 °C and −5 ± 2 °C. The primary drying time and the total drying time of the tested products were determined for each primary drying temperature. The survival rate of lactic acid bacteria, active and titratable acidity, color and sensory quality evolution were also determined. Increasing the temperature of primary drying (p < 0.05) from −40 ± 2 °C to −25 ± 2 °C was observed to lead to the preservation of 75–80% of lactic acid bacteria. A further increase in the primary drying temperature to −15 ± 2 °C and to −10 ± 2 °C leads to a decrease (p < 0.05) of this parameter to 65-70% and 5-35%, respectively. It is noted that an increase in the temperature of primary drying leads to a decrease (p < 0.05) in pH to 3.7-3.8 and an increase (p < 0.05) in the titratable acidity to 93-95°T for yogurt and bio-yogurt and it is up to 85°T for curdled milk. An increase in the primary drying temperature affected the sensory characteristics of the freeze-dried dairy products. The optimal primary drying temperature for yogurt and curdled milk is found to be −25 ± 2 °C, and for bio-yogurt this temperature is −15 ± 2 °C. The application of the optimal primary drying temperature proposed in the industrial practice is expected to lead to significant savings in specific energy costs along with a reduction in the time of drying.

Reconfigurable stochastic neurons based on strain engineered low barrier nanomagnets

Stochastic neurons are efficient hardware accelerators for solving a large variety of combinatorial optimization problems. ‘Binary’ stochastic neurons (BSN) are those whose states fluctuate randomly between two levels +1 and −1, with the probability of being in either level determined by an external bias. ‘Analog’ stochastic neurons (ASNs), in contrast, can assume any state between the two levels randomly (hence ‘analog’) and can perform analog signal processing. They may be leveraged for such tasks as temporal sequence learning, processing and prediction. Both BSNs and ASNs can be used to build efficient and scalable neural networks. Both can be implemented with low (potential energy) barrier nanomagnets (LBMs) whose random magnetization orientations encode the binary or analog state variables. The difference between them is that the potential energy barrier in a BSN LBM, albeit low, is much higher than that in an ASN LBM. As a result, a BSN LBM has a clear double well potential profile, which makes its magnetization orientation assume one of two orientations at any time, resulting in the binary behavior. ASN nanomagnets, on the other hand, hardly have any energy barrier at all and hence lack the double well feature. That makes their magnetizations fluctuate in an analog fashion. Hence, one can reconfigure an ASN to a BSN, and vice-versa, by simply raising and lowering the energy barrier. If the LBM is magnetostrictive, then this can be done with local (electrically generated) strain. Such a reconfiguration capability heralds a powerful field programmable architecture for a p-computer whereby hardware for very different functionalities such as combinatorial optimization and temporal sequence learning can be integrated in the same substrate in the same processing run. This is somewhat reminiscent of heterogeneous integration, except this is integration of functionalities or computational fabrics rather than components. The energy cost of reconfiguration is miniscule. There are also other applications of strain mediated barrier control that do not involve reconfiguring a BSN to an ASN or vice versa, e.g. adaptive annealing in energy minimization computing (Boltzmann or Ising machines), emulating memory hierarchy in a dynamically reconfigurable fashion, and control over belief uncertainty in analog stochastic neurons. Here, we present a study of strain engineered barrier control in unconventional computing.

Key fuel characteristics and techno-economic aspects of torrefied rubberwood biomass pellets produced by incorporating various cassava-based binders at varied doses.

Improving energy content and hydrophobic nature of woody biomass can be pursued through torrefaction. This gives torrefied biomass with a low bulk density, potentially increasing storage and transport costs. To overcome this issue, densifying the torrefied biomass is necessary. However, poor binding of particles makes densification challenging without using a binder. Therefore, the aim of this study was to investigate the physicochemical characteristics and techno-economic aspects of torrefied rubberwood biomass (TRWB) when pelletized using various cassava-based binders at different blending ratios. The selected binders included cassava starch (CS), cassava pulp (CP), and cassava chip (CC). Each binder at 5%, 10%, or 15% (wt.) was mixed with TRWB and water before pelletizing using a flat die machine. The results revealed that pelletizing TRWB with different cassava-based binders at various blending ratios influenced the physicochemical characteristics of the TRWB pellets, particularly dimensions, bulk density, fuel and atomic ratios, and energy content. The TRWB pellets demonstrated energy densities in the range of 7.95-11.39 GJ/m3, and their mechanical durability and fine content fell within acceptable ranges. The TRWB pellets maintained their shape during 120min of water soaking, with water absorption levels varying by binder dose. The pelletizing ability, material, and energy costs of TRWB pellets depend on binder type and dose. CP can be applied as a binder for pelletizing torrefied rubberwood biomass. However, the mechanical durability of the product needs to be above the user requirement or standard.

Foraging mechanisms in excavate flagellates shed light on the functional ecology of early eukaryotes

The phagotrophic flagellates described as "typical excavates" have been hypothesized to be morphologically similar to the Last Eukaryotic Common Ancestor and understanding the functional ecology of excavates may therefore help shed light on the ecology of these early eukaryotes. Typical excavates are characterized by a posterior flagellum equipped with a vane that beats in a ventral groove. Here, we combined flow visualization and observations of prey capture in representatives of the three clades of excavates with computational fluid dynamic modeling, to understand the functional significance of this cell architecture. We record substantial differences amongst species in the orientation of the vane and the beat plane of the posterior flagellum. Clearance rate magnitudes estimated from flow visualization and modeling are both like that of other similarly sized flagellates. The interaction between a vaned flagellum beating in a confinement is modeled to produce a very efficient feeding current at low energy costs, irrespective of the beat plane and vane orientation and of all other morphological variations. Given this predicted uniformity of function, we suggest that the foraging systems of typical excavates studied here may be good proxies to understand those potentially used by our distant ancestors more than 1 billion years ago.

Load management design and techno-economic analysis for an islanded hybrid Pv-Teg microgrid

Integration of various renewable energy sources (RES) is one of the critical factors behind microgrid (MG) capacity development and MG expansion. Distributed energy resources with and without RES, electrical loads, controllers, and storage units constitute the essential components of MGs. On the other hand, with the addition of different power generation sources, load prediction on the user side of the MG becomes an important issue. Demand response solutions and changing end-user behavior are the other sources of uncertainty in the performance of MGs. Therefore, matching supply and demand is a very important issue that must be done immediately at every moment of the operation. A load management system (LMS) in a community MG is essential to ensure the system’s adaptability. Fuzzy logic (FL) controllers and energy management systems will ensure optimum use of energy resources and efficient operation of MG and reduce energy waste. In this study, an 85 kW photovoltaic (PV) and thermoelectric generator (TEG) hybrid MG system (PVTEG-MG), which can operate as an island grid in all regions of the World with hot water resources, has been designed for the first time and its techno-economic analysis has been made. The state of charge (SOC) for the proposed PVTEG-MG remained stable at 40% charge state by adding charge management. The average value of the Levelized Cost of Energy (LCOE) is $0.456/kWh for PV, $0.456/kWh for TEG, and 0.399/kWh over the total energy produced, 219,000 kWh for the PV and 493,650 kWh for the TEG. Overall, this PVTEG-MG system is a promising solution for hot water regions that can be used as an island grid to reduce electricity costs, increase efficiency and reliability, and provide a more sustainable energy source.

Model-based process optimization of black soldier fly egg production.

Black soldier fly (BSF) larvae (Hermetia illucens) serve as a valuable protein source for animal feed. Limiting factors in the industrial rearing of BSF are the reproduction process and egg output. Studies indicate the potential to shorten preoviposition time and increase egg output through better utilization of environmental variables, such as temperature and light, in industrial settings. Excessive stimulation, however, can lead to stress, elevated production costs, and reduced egg numbers, emphasizing the need for a delicate balance. This study addresses these challenges by investigating controlled manipulation of environmental variables to stimulate mating and enhance egg production, thereby developing a comprehensive model encompassing the adult fly life cycle, mating, and egg production. Model parameters were fitted using literature data, and the model's plausibility was tested through simulations. Using the model and optimal control methods, the calculated dynamic trajectories for environmental variables when compared to the standard approach in a constant environment demonstrated higher output and shorter production cycles at reasonable energy costs. Applications for this model-based optimization are demonstrated for various scenarios, highlighting the practical utility and versatility of the developed model. This study contributes valuable insights for improving rearing practices of BSF through environmental stimulation, offering potential advancements in egg production efficiency and overall sustainability.

Microwave assisted pretreatment of urban green waste with more efficiency and less energy cost

Microwave assisted pretreatment of urban green waste with more efficiency and less energy cost

Multi-objective hierarchical strategy for university dorm renovation in severe cold areas

In China, there are many old buildings whose renovation could enhance building environment quality, reduce energy consumption, lower carbon emissions, and create economic benefits. However, there is currently a lack of efficient architectural renovation optimization methods to provide decision-makers and designers with highly feasible renovation plans. This paper proposes an innovative optimization method for renovating old buildings to address this issue. The research employs a hierarchical optimization framework integrating Artificial Neural Networks (ANN), the NSGA-II algorithm, and the entropy-weighted TOPSIS method. The first layer's three-objective optimization using the trained neural network model significantly improves the algorithm's efficiency in searching for optimal solutions. The second layer's entropy-weighted TOPSIS optimization includes eight objective optimizations, such as energy, economy, and lighting, thus broadening the assessment dimensions. Taking an old multi-story university dormitory in the Shenyang area as an example, the study optimizes nine passive design variables across eight objectives. The hierarchical optimization results show that the high-scoring renovation plans can save 1.52 to 3.17 million yuan in energy costs over the building's remaining lifecycle and reduce carbon dioxide emissions by 644–962 kg per square meter. SRRC results indicate that external windows significantly impact economic efficiency and energy consumption, emphasizing the importance of selecting the right type of windows in actual engineering projects. The new method proposed in this study can offers a comprehensive and highly feasible renovation plan for old buildings.

Enhancing economic and performance effectiveness of a geo-solar system using multi aspect artificial neural network

Geo-solar systems, comprising ground-source heat pumps (GSHP) and solar thermal collectors, offer promising solutions for sustainable space heating and hot water provision in residential buildings. However, optimizing these systems for maximum energy efficiency and cost-effectiveness remains a challenge. In this context, this study presents a comprehensive approach to enhancing the performance and cost-effectiveness of geo-solar systems. The proposed methodology integrates artificial neural network (ANN) techniques and multi-aspect optimization to optimize system parameters. The detailed of building mathematical model incorporating energy and exergy equations, thermodynamic, and economic relations for all system components. The neural network model inputs to the include five neurons represents Geothermal temperature, Solar radiation levels, System pressure levels, Ambient temperature, and Operational time, and the model outputs represent Energy efficiency and Exergy destruction rate. Through the training of ANN using simulation data and the application of a multi-objective genetic algorithm, the system is optimized for energy efficiency, cost, and environmental impact simultaneously. The results of analysis show that the exergy efficiency of system is 22.02 % and turbine generates 1963 kW. Changes in P10, ranging from 2000 to 3400 kPa leads to the exergy destruction reduction from 8350 kW to 8000 kW, while the other parameters exhibit an increasing trend.

Optimal sizing of off-grid microgrid building-integrated-photovoltaic system with battery for a net zero energy residential building in different climates of Morocco

An optimal sizing of an off-grid microgrid system composed of photovoltaic (PV)/building integrated photovoltaic (BIPV)/battery energy storage installation is undergone for Net Zero Energy Residential Building blocks across six different climates of Morocco. The Particle Swarm Optimization algorithm is used to find the optimal sizing of the system, by considering the hourly spatiotemporal variations in both solar energy availability and energy demand, with the lowest Total Annualized Cost as the objective function and capacities of BIPV and battery as decision variables. The methodology adopted focuses on main load fulfillment through direct PV and BIPV power supply, backed by battery energy storage technology, to continually guarantee self-sufficiency. A key metric, the load cover factor, is introduced to quantify the ratio by which the load demand is satisfied by the solar PV and BIPV systems. The findings show that the optimal sizing of the BIPV system can help to improve the load cover factor by 0.68–2.58 %. Moreover, integrating BIPV system with PV system and Battery leads to a reduction in the Levelized Cost of Energy with approximately 8.7–20.72 %, as opposed to utilizing only the PV system and battery. Depending on the local climate, the levelized cost of energy ranges from $0.366/kWh in Ouarzazate city up to $0.664/kWh in Ifrane city. Lastly, this holistic approach aims to transform the building from its traditional role as an energy consumer to a carbon-free energy prosumer.

INVESTIGATION OF THE EFFECTIVENESS OF THE ALGORITHM FOR CONTROLLING THE ACCELERATION AND DECELERATION OF A WHEELED VEHICLE BY MEANS OF A STROKE PEDAL

Justification. Since one of the significant costs of operating vehicles is fuel costs, therefore it is very promising to use machines with electric drives, such as battery electric ones, which reduce these costs. The main key property for them is the amount of autonomous mileage on a single charge. To maximize this value, developers are trying to use more advanced, energy-intensive energy sources and reduce energy costs when converting it on the way from the source to the driving wheels. In this circuit, the traction electric drive is the main source of energy loss. Therefore, it is very important not only to use more advanced electric drives, but also to improve control algorithms. To do this, it is necessary to form the setpoints of the requested engine torque using only the stroke pedal, taking into account the speed of movement, other conditions, and vehicle characteristics. The implementation of such a law allows the driver to reduce energy consumption by controlling the car, allowing it to move by inertia (coasting) and maximize the use of regenerative braking with minimal use of the service braking system.The purpose of the work. Investigation of the functioning and efficiency of the algorithm for determining the torque settings of a traction electric drive using only the stroke pedal, using mathematical modeling methods.Materials and methods. The study was performed in the Matlab Simulink software package.Results. The article presents the theoretical foundations of constructing an algorithm for determining the torque setpoint of a traction electric drive, the results of a virtual efficiency study.Conclusion. The practical value of the study lies in the possibility of using the proposed law for determining the setpoint of the traction and regenerative torque of the traction electric drive, the run-out mode for the development of a traction drive control system for transport vehicles in order to increase their energy efficiency.