Energy Demand Research Articles

Techno-economic analysis of hybrid renewable energy systems for cost reduction and reliability improvement using dwarf mongoose optimization algorithm

The global energy crisis, particularly in isolated and remote regions, has increased interest in renewable energy sources (RESs) to meet growing energy demands. Integrating RESs with energy storage systems offers a promising solution to mitigate fluctuations and intermittency, but concerns about cost and reliability remain. This study explores the optimal design of various microgrid configurations, combining photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS), and diesel generator (DG) systems for Najran city, Saudi Arabia, via real-world meteorological and load demand data. The Dwarf Mongoose Optimization Algorithm (DMOA), alongside the salp swarm algorithm (SSA) and whale optimization algorithm (WOA), was applied to minimize the levelized cost of energy (LCOE) while improving system reliability. The results demonstrate that the PV/BESS configuration, although cost-effective with an LCOE of 0.038 USD/kWh, fail to meet reliability constraints with a loss of power supply probability (LPSP) of 0.679. In contrast, the PV, WT, BESS, and DG configurations achieved an LPSP of 1.9 × 10^--8% with an LCOE of 0.199 USD/kWh, offering a robust and reliable solution for the region's energy needs. This paper presents a novel application of the DMOA for optimizing hybrid renewable energy systems, demonstrating its effectiveness in achieving a balance between cost and reliability. This strategy provides a viable approach for sustainable energy planning in similar regions facing energy challenges.

Decomposition analysis of renewable energy demand and coupling effect between renewable energy and energy demand: evidence from China

Driving up demand for renewable energy is key to China's response to the energy crisis and to controlling carbon emissions. Several studies have been conducted to explore the factors affecting renewable energy demand. However, the literature on the importance of technological innovation in fossil energy production in affecting renewable energy demand is scant. Therefore, it is essential to explore the impact of technological innovations in fossil energy production on renewable energy demand, as well as to reveal the dynamic penetration mechanism of renewable energy in the field of energy demand, to provide policy guidelines for China to promote its energy revolution. This study decomposes renewable energy demand into 10 drivers with the extended IDA-PDA (Index Decomposition Analysis and Production-theory Decomposition Analysis) model. Then the dynamic penetration mechanism of renewable energy on the energy demand side is revealed by combining the coupled model and the decomposition results. The results show that the investment intensity effects and energy restructuring contributed the most to renewable energy demand growth, contributing 60.73% and 79.97% respectively. Conversely, technological advances in fossil energy combustion made the largest negative cumulative contribution to renewable energy demand growth, with a cumulative negative contribution of -39.57%. The drivers of renewable energy demand vary considerably across types, regions and resource endowment levels, which means that regions should develop differentiated development strategies based on their own characteristics. In addition, the coupling effect between renewable energy demand and total energy demand is weakening, with market and policy effects becoming the drivers for augmenting the response of renewable energy to total energy demand. The findings of this study provide clear pathways for expanding renewable energy demand, offering important insights for the government to balance between promoting capital-driven interests and policy-driven development.

Optimal Configuration for Shared Electric-hydrogen Energy Storage for Multiple Integrated Energy Systems With Mobile Hydrogen Transportation

The flexible operation and storage of hydrogen and electric energy provide an effective path for the development of low-carbon energy and transportation systems. This paper introduces a configuration method for electric-hydrogen shared energy storage supporting the multiple energy and capacity demands of integrated energy systems (IESs). A Stackelberg game-based shared energy framework with gaseous hydrogen transportation by transportation network for shared energy storage operators (SESO) and IESs is established. In this framework, the electric power and electric storage sharing are accomplished by power lines, while the shared hydrogen is achieved by the transportation of mobile trucks. The target of the energy storage operator is to maximize its trading benefits with IESs and lower the life-cycle cost of the whole system, while the IESs aim to meet their energy demand and lower the operation cost via energy storage sharing. To solve this planning model, a two-level sine cosine based grey wolf optimizer (GWO-SCA)-bisectional method is provided to realize the system configuration, mobile hydrogen planning, and economic trading of the shared energy operator. A case study with 3 IESs, real-world geographic roads, and environmental conditions is carried out to verify the effectiveness of the method and the life-cycle configuration and operation economy of the shared energy storage. The comparisons show that the proposed energy storage sharing frame can achieve a higher energy utilization ratio of 92% and the proposed method can solve the two-level problem more efficiently, the calculation time is reduced by 80.2%.

Improving the Potential of Fifth-generation District Heating and Cooling Networks through Robust Design and Operational Optimization under Future Energy Market and Demand Uncertainties

Network investments and projected energy prices greatly impact the financial viability and environmental impact of fifth-generation district heating and cooling (5GDHC) networks. Compared to the previous generation, the upfront investment costs in 5GDHC networks are relatively high due to the decentralized energy demand and supply of the participating prosumers. The transition to 5GDHC is also hindered by uncertain energy markets and the fluctuating energy demands of the prosumers. It makes investors hesitant to commit, even for promising business cases. In this work, a framework is presented to assess the economic and environmental performance of a 5GDHC business case, based on a multi-objective robust optimization algorithm (MO-RDO), to identify the optimal trade-offs between environmental and economic designs. The proposed methodology consists of four steps. In the first two steps, a specific business case is modelled, and techno-economic and environmental performances are analyzed against uncertain energy markets. In the third step, the techno-economic and environmental indicator responses are used to develop a data-driven machine-learning model. This model offers better computational efficiency than a high-fidelity nonlinear model and assesses the most influential parameters driving economic and environmental performance via a global sensitivity analysis. This aids in refining the multi-objective robust design optimization (MO-RDO) framework by pinpointing key design variables and objectives amid data uncertainties. Additionally, the formulated MO-RDO provides a mix of environmentally and economically optimal network designs and operational parameters and highlights the trade-offs between them. The designs and trade-offs are evaluated using financial metrics like net present value (NPV), return on investment, and discounted payback period. These metrics are calculated over the project life, considering the initial investment and net savings. For the considered case, the environmental design has a 10% higher investment cost compared to the economically optimal solution but reduces total emissions (TE) by 13% and improves operational cost savings, which is crucial for better financial indicators. The proposed framework aligns with the EU transition plan to incorporate waste energy sources and decarbonization paths, estimating only a 3% increase in NPV as a risk for the environmentally optimal solution on a financial scale.

The role of agriculture for achieving renewable energy-centered sustainable development objectives in rural Africa

Multi-dimensional and overlapping barriers to wellbeing severely affect many areas in rural sub-Saharan Africa. In the region, more than 90% of cropland is rainfed, less than one third of households have electricity, almost 60% of the population reports food insecurity, and more than 35% of the population lives below the international poverty line. Climate change impacts on vulnerable systems with limited adaptive capacity and strong population growth are increasing the magnitude of these challenges, slowing and potentially reversing development. Thus, there is a strong need for multi-sector interventions across multiple levels, from national policies, to regional and river catchment-scale planning, to local planning and investment. To implement such actions, it is key not only to assess technological solutions and their investment needs, but also to appraise their feasibility and implementation potential (from both a policy and a financial point of view). Here, we implement a modelling platform (RE4AFAGRI platform), which soft-links bottom-up process-based water and energy demand and techno-economic infrastructure assessment models (WaterCROP, M-LED, OnSSET) into a multi-node, national Nexus-extended Integrated Assessment Model (MESSAGEix-Nexus) for supply and investment assessment. The results of our analysis shed light on the role of water and energy demand in the agricultural sector for jointly affecting infrastructure and investment requirements for achieving rural sustainable development objectives. We find that scenarios with increased ambition in expanding irrigation and agricultural productivity result in improved diffusion and economic feasibility of infrastructure to provide universal energy access while supporting productive uses of energy. Moreover, we conduct business model analysis to appraise the framework conditions and micro and macro determinants that can ensure feasibility of investment and uptake of small-scale infrastructure, crucial for rural development. Altogether, our research demonstrates how integrated modelling with an explicit focus on Nexus interlinkages can represent the enabling role and the business conditions for renewable energy input in agriculture to become a leverage of rural sustainable development. In turn, important policy and investment-relevant insights can be derived.

How does structured adsorbent channel heterogeneity influence the efficiency of adsorptive CO2 capture?

Adsorbent monoliths are increasingly studied for carbon capture applications. These monoliths often contain a distribution of channels, due to defects in the die or inhomogeneous drying. The effect of such heterogeneities on CO2 capture process performance is not well investigated. In this work, the performance of a fixed bed, an ideal monolith and monoliths containing a wall size distribution are compared in a vacuum swing adsorption (VSA) cycle using a modelling approach. Using a monolith allows for a higher productivity (0.78 mmol/kgs) compared to a fixed bed (0.68 mmol/kgs) and a lower energy demand (190 kWh /tonne CO2 versus 320 kWh/tonne CO2). When the monolith contains a distribution of wall sizes, the process performance decreases. The recovery drops from 81 % to 68 %, the throughput drops from 0.78 mmol/kgs to 0.65 mmol/kgs and the energy demand increases from 190 kWh/tonne CO2 to 251 kWh/tonne CO2 for the widest distribution. Defects can severely impact performance.

Modeling the co-adoption dynamics of PV and heat pumps in Swiss residential buildings: Implications for policy and sustainability goals

The Swiss energy system is facing a paradigm shift as it strives to achieve net-zero greenhouse gas emissions, as well as phase out nuclear electricity production. This entails significant changes in the country’s energy landscape, including increased adoption of renewable energy technologies in the residential sector. To facilitate informed decision-making and planning by policymakers, this study introduces a system-dynamics model for the long-term adoption of solar photovoltaic (PV) and heat pump (HP) technologies in Swiss residential buildings. Unlike conventional approaches, this framework considers the feedback loops and correlations between PV and HP adoption, while also accounting for building heterogeneity. Through scenario analyses, the model evaluates the impact of regulatory and financial policy interventions on the transition of the residential energy system. The results highlight the significant influence of policy measures on technology deployment rates, energy demand, and greenhouse gas emissions. They demonstrate that slight adjustments in current policy and regulatory framework could allow to safely reach PV deployment targets, but strong modifications are necessary to completely decarbonize the residential sector. This study contributes to advancing our understanding of the complex dynamics shaping residential energy transitions and offers valuable insights to support the formulation and implementation of effective energy strategies.

Potential climate predictability of renewable energy supply and demand for Texas given the ENSO hidden state.

Climate variability influences renewable electricity supply and demand and hence system reliability. Using the hidden states of the sea surface temperature of tropical Pacific Ocean that reflect El Niño-Southern Oscillation (ENSO) dynamics that is objectively identified by a nonhomogeneous hidden Markov model, we provide a first example of the potential predictability of monthly wind and solar energy and heating and cooling energy demand for 1 to 6 months ahead for Texas, United States, a region that has a high penetration of renewable electricity and is susceptible to disruption by climate-driven supply-demand imbalances. We find a statistically significant potential for oversupply or undersupply of energy and anomalous heating/cooling demand depending on the ENSO state and the calendar month. Implications for financial securitization and the potential application of forecasts are discussed.

An analysis of the rebound impact of energy consumption and the factors that influence it in China's agricultural productivity

With China's agricultural sector being a major contributor to both the national economy and energy consumption, the rebound effect—the phenomenon where energy efficiency improvements lead to increased energy use—poses significant challenges to sustainable energy use. The motivation for this study stems from the need to understand the extent of this rebound impact and its underlying drivers, particularly in the context of China's agricultural modernization efforts. This study examines the rebound effect of energy consumption in China's agricultural productivity and the factors influencing it over the period from 1990 to 2023. Using a Generalized Method of Moments (GMM) model, this research analyzes the relationship between energy consumption, agricultural productivity, and several influencing factors, including technological innovation, rural-urban migration, and financial development. The results reveal that (1) energy efficiency improvements have led to a significant rebound effect in China's agricultural sector, limiting potential energy savings, (2) technological advancements have mitigated the rebound effect to some extent, but the effect remains substantial, (3) rural-urban migration has contributed to labor shortages, increasing mechanization and energy demand, and (4) financial development has positively influenced both agricultural productivity and energy use, exacerbating the rebound effect. The study suggests that policymakers should focus on developing stricter energy efficiency standards and promoting technological innovations that reduce energy intensity in agriculture, while also addressing labor migration challenges to curb the rebound impact and achieve more sustainable agricultural growth.

Assessing the energy performance certification effectiveness for the Spanish building stock in response to recent climate change data

This comprehensive study analyses the energy demand for heating and cooling in the building stock across different climatic zones in Spain, incorporating the effects of weather evolution over the past 30 years (from 1992 to 2022). In evaluating 7200 cases across four climatic years and six climatic zones which include six Spanish cities, the analysis examines the construction characteristics of the building stock. The focus is on the thermal envelop properties under two scenarios: the current state and a renovated state that complies with the new Spanish building code (in agreement with the EPBD EU-Directive EU/2024/1275).The study’s findings reveal potentially significant changes in energy demand due to climate. The heating demand decreases significantly due to increasing temperatures ranging from 17% in Madrid up to 37% in Almería. This effect is contrasted by a substantial increase in cooling demand, which has surged 223% in Barcelona, a surge closely linked to higher summer temperatures.The paper concludes that the current energy certification scheme fails to provide a real overview of the energy performance and needs of buildings in Spain. In particular, it underestimates actual sensible cooling demand by 56% in Barcelona and 41% in Almería. Additionally, the failure of existing building regulations to address the issue of indoor overheating is proven.

Assessment of heating degree-days factor (HDDF) for building-oriented climate zoning in Türkiye

ABSTRACT In this study, a novel heating degree-days factor (HDDF) is proposed to determine the optimum facade design for buildings in Türkiye as a function of both cooling degree-days (CDD) and heating degree-days (HDD) data. For this purpose, the HDDF (HDD/(HDD+CDD)) is defined as the ratio of the heating degree-days of a region to the total heating and cooling degree-days. Firstly, the HDDF values of all provinces in Türkiye are calculated by using meteorological data from 81 meteorological stations covering the period 1991–2022, and HDDF performance is evaluated. Then, the building-oriented climate zoning is realized based on the HDDF values. Moreover, historical trends of monthly and yearly average HDD, CDD, and HDDF values are revealed and yearly HDD, CDD, and HDDF maps were created using the ArcGIS program. According to the results, HDDF values in Türkiye varied between 0.4519 and 0.9998, and HDDF values between 0.9 ≤ HDDF ≤ 1 cover 46 out of 81 cities in the country and span a broad area across the country. The findings suggest that if global warming continues at the current rate, cooling energy demand in the residential sector will dramatically increase in the future, while heating energy demand will fall in Türkiye. This indicates that domestic energy consumption regulations would require modification. The building-oriented climate zoning resulting from this study is unique to Türkiye and can aid engineers, architects, and policymakers in building energy efficiency programs, studies, regulations, and standards.

The impact of urban morphology on energy demand of a residential building in a mediterranean climate

This paper uniquely and systematically explores the relationship between energy performance of buildings and urban morphology by studying an existing urban fabric in a Mediterranean climate. The study took place in the city of Hebron which is geographically confined in area with continuous urbanization. Using (GIS), the K-means clustering technique was chosen to perform the statistical analysis. The coefficient variables of Floor Space Index (FSI), Ground Space Index (GSI), Open Space Ratio(OSR), and buildings’ height (L), were identified at the urban level to classify the urban tissue of the city. Accordingly, six urban patterns with equal area of 33,000 m2 were selected as representative samples of the different urban patterns of the city’s urban fabric. Then, a prototype building was placed in each of the selected patterns by the mean centre in the GIS based on the horizontal-vertical built density. Building energy simulation was performed using Design Builder software to assess the prototype’s performance. The results showed that the urban form plays a significant role in determining performance quality and energy demand. The results indicate that the cooling demand decreases when the ratio of (FSI) and (GSI) increases and when the (OSR) decreases, and vice versa in relation to the heating demand. Notably, Pattern 4, with (FSI) of 1.2, (GSI) of 0.6, (OSR) of 0.6, and (L) of 1.7, exhibited the highest heating demand at 40.3 kWh/m2/year, whereas Pattern 3, with (FSI) of 0.7, (GSI) of 0.3, (OSR) of 2.5, and (L) of 2.0, recorded the lowest at 18.6 kWh/m2/year. Conversely, Pattern 3 had the highest cooling demand, while Pattern 2, with (FSI) of 1.0, (GSI) of 0.3, (OSR) of 1.6, and (L) of 3.0, had the lowest cooling demand. To enhance energy efficiency, urban planners should consider increasing the (FSI) and (GSI) while managing the (OSR) to balance heating and cooling demands. Patterns with moderate (FSI) and (GSI) and higher (OSR) generally result in lower cooling demands and more efficient energy use. Enhancing the energy efficiency of cities should be tackled not only on the single building level but also on the urban scale. This should be addressed in urban regeneration, master plan,s and building regulations. Incorporating tools that consider the impact of all urban parameters can contribute to energy savings.

Does the income elasticity of energy demand vary with the stages of economic development?

This paper revisits the empirical evidence on the income elasticity of energy demand by investigating whether it varies with the level of economic development. We estimate the elasticity using mean-group type estimators for a sample of 111 countries spanning three decades. Our analysis reveals that the elasticity is not constant and tends to decline as countries become wealthier. As economies become more developed, their energy intensity falls, eventually reaching zero elasticity, indicating a decoupling between economic growth and energy demand. This finding underscores the importance of considering countries’ stages of economic development when making energy outlook projections.

Transforming Wastewater-Derived Algae Biodiesel with Ammonium Hydroxide Emulsion for Enhanced Energy Efficiency and Emission Reduction

The growing demand for sustainable energy has prompted a search for alternative fuels, with microalgae-based biodiesel emerging as a promising option. However, improving its energy efficiency and minimizing environmental impacts remain key challenges. This study presents a novel approach by integrating wastewater-derived algae cultivation with ammonium hydroxide emulsion to enhance biodiesel performance. Chlorella vulgaris microalgae were grown in a medium of diluted human urine, where nutrient removal efficiency was evaluated, and biooil was extracted via solvent extraction. The resulting biodiesel was analyzed for elemental, chemical, and physicochemical properties. A 30% blend of chlorella vulgaris algae biodiesel (CVAB) with normal diesel fuel (NDF) was prepared. To further boost energy efficiency and emissions performance, ammonium hydroxide emulsion was added at concentrations of 5% and 10%. Results confirmed that wastewater-sourced algae cultivation is a viable method for sustainable biodiesel production. While CVAB's combustion characteristics were similar to NDF's, it exhibited a 7% decrease in brake thermal efficiency and a 5% increase in nitrogen oxide emissions. The addition of ammonium hydroxide emulsion notably improved both energy efficiency and emission profiles. This study highlights a breakthrough in using ammonium hydroxide emulsion to enhance algae biodiesel, making it a more competitive alternative to fossil fuels. Economically, this approach offers a dual advantage: utilizing low-cost wastewater for biodiesel production while improving fuel performance and reducing emissions.

Performance analysis of a novel dual exhaust mixed refrigerant heat pump with large temperature lift

The substantial demand for heat energy, ranging from 70 − 100°C in industrial and commercial sectors, presents a significant challenge when utilizing traditional air source heat pumps. The conventional single-stage air source heat pumps often struggle with low system efficiency and poor operating conditions when tasked with heating with large temperature lift. To address these issues, a novel dual exhaust mixed refrigerant heat pump cycle was proposed. By incorporating an intermediate pressure compression stage in parallel, the proposed system can achieve an optimal temperature match in the recuperator, and lead to a significant enhancement in the coefficient of performance (COP). Compared to a single stage mixed refrigerant heat pump cycle, the novel system improves the COP from 5.063 to 5.756, representing an increase of 13.68 %. Concurrently, the exergy loss proportion of the recuperator decreases from 18.8 % to 13.7 %. The proposed system consistently demonstrates superior COP and exergy efficiency regardless of whether the ambient temperature is within the range of 0 °C to 20 °C or the outlet water temperatures between 80 °C to 100 °C are present. These findings provide theoretical guidance for enhancing the performance of high-temperature heat pumps with large temperature lift.

Biofuel production from palm oil deoxygenation using nickel-molybdenum on zirconia catalyst using glycerol as a hydrogen donor

The growing demand for renewable energy has generated interest in biofuels as alternatives to fossil fuels. Second-generation biofuels, derived from deoxygenating fats and oils, have garnered a higher level of interest from industry and academia due to their potential for direct replacement of diesel and jet fuels. Palm oil, mostly cultivated in Thailand and composed of C16 and C18 fatty acids, is a primary feedstock sought for biofuel production. Palm oil deoxygenation contains several pathways that may or may not require hydrogen gas. This study aimed to produce biofuels in different fuel ranges, such as gasoline, jet fuel, and diesel, through palm oil deoxygenation using glycerol as a hydrogen source. Glycerol, a low-value byproduct, was used as a hydrogen donor, whereas nickel-molybdenum-supported catalysts were chosen for their high efficiency in deoxygenation and cost-effectiveness. The study investigated the impact of reaction time, temperature, and catalyst activation method on palm oil deoxygenation. Catalyst characterization methods, including XRD, SEM, TEM, XPS, FTIR, TGA, and N2-sorption, were employed to understand the role of catalysts’ activity during palm oil upgrading. Findings indicated that alkane hydrocarbons are the major components in liquid products. The presence of excess H2 in post reaction gaseous phase proves the hydrogen donation capability of glycerol. Increased reaction time and temperature facilitated the removal of oxygen from palm oil. Nickel-molybdenum on zirconia activated by sulfidation demonstrated higher stability than by reduction activation.

A novel generative approach to the parametric design and multi-objective optimization of horizontal axis tidal turbines

As the demand for ocean energy continues to grow, the development of efficient design and optimization methods for tidal current turbines is crucial. Traditional approaches, often based on parameterized models, face challenges in fully capturing the intricate geometric features of turbine blades, limiting the optimization space and affecting convergence efficiency. In response, this study introduces a novel design methodology for horizontal axis tidal turbines (HATTs) using a variational autoencoder generative adversarial network (VAEGAN) model. This approach uses unsupervised learning from a custom dataset to generate new HATT designs, with the VAEGAN model encoding distinct geometric features of turbine blades within a compact latent space, enabling more efficient design space exploration and facilitating the discovery of innovative shapes. Furthermore, in the multi-objective optimization process targeting both hydrodynamic performance and structural strength, the reduced dimensionality of the design variables accelerates convergence while maintaining a broad and meaningful design space. The proposed methodology demonstrates the VAEGAN model's ability to generate diverse and effective turbine blade designs, highlighting its potential as a powerful tool in advancing HATT technology.

Synergy between biphenyl mesomorphic structure and organic crystal phenazine for improving the intrinsic thermal conductivity of epoxy

The inherent low intrinsic thermal conductivity of epoxy resin severely restricts its potential application, making it difficult to meet the growing demand of energy, electrical and electronic technologies. The liquid crystal epoxy resin, which is wildly employed to improve the intrinsic thermal conductivity of epoxy resin, is constrained by its complex molecular structure design and synthesis procedures. In this work, the rigid group of 4,4’-dihydroxybiphenyl was introduced into the main chain of epoxy resin by ring-opening polymerization (ROP) to change the molecular structure of epoxy resin. The introduction of biphenyl groups enhances the orderliness of molecular structure and forms hydrogen bonds between molecules, which further enhances the transport of intramolecular phonons. The thermal conductivity of the graft modified epoxy resin (GMR) after DDM curing is 0.30 W·m-1·K-1. After adding of organic crystal phenazine (PNE) to the GMR matrix, the inherent strong crystallization behaviors of PNE can accelerate the regularity arrangement of rigid groups in GMR, and promote the nucleation and crystallization process of GMR/PNE. The thermal conductivity of GMR/PNE after DDM curing can reach 0.33 W·m-1·K-1. It is 165% of the thermal conductivity of traditional epoxy resin, which is 0.2 W·m-1·K-1. This study will provide a new method to improve the intrinsic thermal conductivity of polymer materials.

Incorporating perovskites in photovoltaic-powered electrochemical cells for a sustainable future: An inclusive review

Due to the escalating demand for energy across all sectors, traditional energy sources are proving insufficient to meet our requirements. Moreover, their extensive use is exacerbating environmental issues. Consequently, solar energy has garnered significant attention due to its affordability, cleanliness, and abundance. This review presents an analysis of various solar cell systems, comparing their efficiency, cost, and stability based on literature spanning the past decade. While perovskite-based solar cells have demonstrated promising performance in laboratory settings, challenges with stability hinder their industrial-scale implementation. On the other hand, perovskites exhibit favorable catalytic properties, making them suitable for the electrochemical reduction of CO2. To address stability concerns, this review proposes structural engineering approaches aimed at maximizing electricity generation from solar energy to power electrochemical cells for CO2 reduction in the environment.

Organizational and Economic Mechanisms for Promoting Residential Battery Energy Storage Systems in Ukraine

Energy storage technologies are one of the ways to decrease the intermittency of renewable energy such as sun or wind. The article aims to consider the organizational and economic mechanisms of promoting residential battery energy storage systems (R-BESS) in Ukraine, as households have ensured the significant growth of installed capacities of residential PV installations, reaching 1.4 GW by 2024. The energy system of Ukraine faces balancing challenges, and partial covering of the demand for energy by households with the help of R-BESS should be beneficial for the energy system. Using methods of levelized cost of electricity and cost of storing electricity, and based on the projected electricity demand and the number of R-BESS, measures were suggested to stimulate the R-BESS installation. Significant investments are required to employ R-BESS, reaching EUR2024 2.9-3.6 billion by 2050. If the interest rates for loans are subsidized, the expenditures on subsidies will reach only EUR2024 35-44 million, and the households themselves must allocate the remainder of the funds.