Reduce Energy Costs Research Articles

Water–Energy Nexus-Based Optimization of the Water Supply Infrastructure in a Dryland Urban Setting

Managing water supply systems is essential for developing countries to face climate variability in dryland settings. This is exacerbated by high energy costs for pumping, water losses due to aging infrastructures, and increasing demand driven by population growth. Therefore, optimizing the available resources using a water–energy nexus approach can increase the reliability of the water distribution network by saving energy for distributing the same water. This study proposes a methodology that optimizes the Water Distribution Network (WDN) and its management that can be replicated elsewhere, as it is developed in a data-scarce area. Indeed, this approach shows the gathering of WDN information and a model to save energy by optimizing pump schedules, which guarantee water distribution at minimal operational costs. The approach integrates a genetic algorithm to create pumping patterns and the EPANET hydraulic simulator to test their reliability. The methodology is applied for a water utility in the dryland urban setting of Lodwar, Turkana County, Kenya. The results indicate a potential reduction in energy costs by 50% to 57% without compromising the supply reliability. The findings highlight the potential of WEN-based solutions in enhancing the efficiency and sustainability of data-scarce water utilities in dryland ecosystems.

Theoretical Study of the Influence of Electroconvection on the Efficiency of Pulsed Electric Field (PEF) Modes in ED Desalination

Pulsed electric field (PEF) modes of electrodialysis (ED) are known for their efficiency in mitigating the fouling of ion-exchange membranes. Many authors have also reported the possibility of increasing the mass transfer/desalination rate and reducing energy costs. In the literature, such possibilities were theoretically studied using 1D modeling, which, however, did not consider the effect of electroconvection. In this paper, the analysis of the ED desalination characteristics of PEF modes is carried out based on a 2D mathematical model including the Nernst–Planck–Poisson and Navier–Stokes equations. Three PEF modes are considered: galvanodynamic (pulses of constant electric current alternate with zero current pauses), potentiodynamic (pulses of constant voltage alternate with zero voltage pauses), and mixed galvanopotentiodynamic (pulses of constant voltage alternate with zero current pauses) modes. It is found that at overlimiting currents, in accordance with previous papers, in the range of relatively low frequencies, the mass transfer rate increases and the energy consumption decreases with increasing frequency. However, in the range of high frequencies, the tendency changes to the opposite. Thus, the best characteristics are obtained at a frequency close to 1 Hz. At higher frequencies, the pulse duration is too short, and electroconvective vortices, enhancing mass transfer, do not have time to develop.

Multi-scale retrofit pathways for improving building performance and energy equity across cities: A UBEM framework

Institutional inequality has created an environment in which our physical surroundings reflect racial and economic biases – causing inequitable thermal comfort and energy consumption across neighborhoods. Just as energy and thermal comfort challenges are unique to each climate and built environment, the solutions to address these issues should be optimized for their context. We introduce a novel integrated data-driven and building energy simulation method that incorporates urban context, satellite imagery, and socioeconomic data to analyze multi-scale retrofit scenarios across disparate communities. We apply this method to case studies in New York City and Los Angeles to analyze energy and thermal comfort inequities across distinct climate zones and urban morphologies. Through parametric analysis, we demonstrate the efficacy of multi-scale retrofits at improving building performance and reducing existing thermal inequities. Our research shows that retrofit effectiveness is influenced by the microclimatic changes induced by the urban context and existing infrastructural inequity. For example, instantaneous building cooling energy demand in a disadvantaged community of Los Angeles is reduced more due to window retrofits (around 30 kWh) than by a greenspace retrofit (around 3 kWh). Greenspace installation dissipates more heat overnight and in the early morning, whereas window retrofits reduce solar heat gain during the day, showing the different heat reduction pathways that retrofits can achieve. Although the window retrofits lead to an order of magnitude higher utility cost savings ($70 savings from windows vs $9 for greenspace and $12 for reduced cars), the district-scale retrofits show promising pathways for reducing the cost burden across all residents. We also analyze pathways for achieving city-specific CO2 reduction targets and find that building-level retrofits are most effective at meeting building performance standards (reducing CO2 by 40%), whereas district-level retrofits should be used to achieve city-scale climate goals because of the sequestered and abated emissions offered by these pathways. This generalizable modeling framework empowers policymakers, urban planners, and building owners around the world to analyze pathways for meeting their climate action plan goals by reducing extreme heat, reducing greenhouse emissions, and reducing energy cost burden while improving environmental justice in their cities.

Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems.

This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms-grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)-were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution.

Multi-Agent Reinforcement Learning for Smart Community Energy Management

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. LSD-MADDPG modifies the conventional MADDPG framework by limiting data sharing during centralized training to only discretized strategic information. During execution, it relies solely on local information, eliminating post-training data exchange. This approach addresses critical challenges commonly faced by EMS solutions serving dynamic, increasing-scale communities, such as communication delays, single-point failures, scalability, and nonstationary environments. By leveraging and sharing only strategic information among agents, LSD-MADDPG optimizes decision-making while enhancing training efficiency and safeguarding data privacy—a critical concern in the community EMS. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening the community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings by ensuring fair alignment between individual buildings’ energy management actions and community-wide goals, highlighting its potential for advancing future smart community energy management.

Optimization of Water Distribution Network Demand Patterns Using Real-Coded Genetic Algorithms

The penetration rate of water supply via water supply facilities in the Republic of Korea has reached 99%, with 94% of the energy for operation consumed by pumps transporting water. Consequently, developing efficient pump operation techniques is crucial for reducing energy costs in water supply systems. This study employs real-coded genetic algorithm techniques to compute optimized demand patterns, considering the utilization of water tanks within networks. Hydraulic appropriateness is verified by evaluating pressure within nodes determined by derived patterns through numerical analysis simulations. Furthermore, after calculating flows supplied to the networks, pump power is determined, and resultant energy costs are estimated to evaluate economic feasibility. Results indicate that pressure distribution in networks with optimal patterns is hydraulically appropriate, meeting hydrodynamic pressure conditions suggested in water supply design standards. Additionally, this study demonstrates a 9% reduction in network energy costs compared to existing patterns. The model presented herein offers a means to efficiently operate water supply systems through water tanks, thereby reducing energy costs.

Assessment of agroclimatic resources in agricultural landscapes of the Turkestan region of the Republic of Kazakhstan Askha

The article presents the scientific results of a study assessing agroclimatic resources in agricultural landscapes located in various natural zones of the Turkestan region of the RK (Republic of Kazakhstan). The research methods included classical and modern methods of mathematical statistics using digital technology and time series graphs to develop a mathematical model for climatic and hydrological indicators. Assessment of changes in indicators of agroclimatic resources in agricultural landscapes for 1941–2020 showed that the sum of air temperature, evaporation from the water surface and radiation balance of the daytime surface, characterising the energy resources of landscapes, increased by 10–15%, which contributes to increase in the total water consumption of agricultural land by 10–12%. Meanwhile, the decreasing tendency of the amount of precipitation by 5–10% in all natural climatic zones of the region has become one of the factors leading to a decrease in the natural moisture supply of the soil and vegetation cover of landscapes by 10–15%, acting as important environment-forming and ecological functions. The combined impact of these environment-forming factors has become the key reason for the increase in the deficit of agricultural land water consumption by 15–20%, the reduction of solar energy costs for the soil-forming process by 10–15% and the increase of the climate aridisation, and has become a signal for the need in the safety of agricultural activities, requiring the development of a set of adaptive measures to mitigate this process in the region.

Acceleration is the key to drag reduction in turbulent flow

A turbulent pipe flow experiment was conducted where the surface of the pipe was oscillated azimuthally over a wide range of frequencies, amplitudes, and Reynolds numbers. The drag was reduced by as much as 35%. Past work has suggested that the drag reduction scales with the velocity amplitude of the motion, its period, and/or the Reynolds number. Here, we find that the key parameter is the acceleration, which greatly simplifies the complexity of the phenomenon. This result is shown to apply to channel flows with spanwise surface oscillation as well. This insight opens potential avenues for reducing fuel consumption by large vehicles and for reducing energy costs in large piping systems.

Design and performance analysis of a net-zero energy building in Owerri, Nigeria

Building cooling load depends on heat gains from the outside environment. Appropriate orientation and masonry materials play vital roles in the reduction of overall thermal loads buildings. A net-zero energy building performance has been analyzed in order to ascertain the optimum orientation and wall material properties, under the climatic conditions of Owerri, Nigeria. Standard cooling load estimation techniques were employed for the determination of the diurnal interior load variations in a building incorporating renewable energy as the major energy source, and compared with the situation in a conventionally powered building. The results show a 19.28% reduction in the building’s cooling load when brick masonry was used for the wall construction. It was observed that a higher heat gain occurred when the building faced the East-West direction than when it was oriented in the North-South direction. Significant diurnal cooling loads variation as a result of radiation through the windows was also observed, with the east facing windows contributing significantly higher loads during the morning hours while the west facing windows contributed higher amounts in the evening. The economic analysis of the net-zero energy building showed an 11.63% reduction in energy cost compared to the conventional building, with a 7-year payback period for the use of Solar PV systems. Therefore, the concept of net-zero energy building will not only help in energy conservation, but also in cost savings, and the reduction of carbon footprint in the built environment.

Efficiency Assessment of the Production of Alternative Fuels of High Usable Quality within the Circular Economy: An Example from the Cement Sector

This article aims to present the mechanisms regulating the waste management system of one of the European countries that affect the cement industry. This publication analyses the possibility of using selected fractions of municipal and industrial waste as alternative fuels, including an analysis of ecological costs and benefits. The methodology includes the analysis of production data and the calculation of savings resulting from the use of alternative fuels. On this basis, ecological aspects were also indicated that should be taken into account when analyzing the profitability of the investment. Production data from an example Polish cement plant were used to analyze the research problem. Based on the guidelines of environmental standards and technical specifications, the parameters that PASr alternative fuels should meet were calculated in the company laboratory. This fuel type was then calculated in terms of emission intensity and production efficiency. The research results obtained in this paper study emphasize that the change in cement clinker production technology toward the use of waste raw materials and secondary fuels does not lead to an increase in heavy metal emissions to the extent that would justify qualifying cement as a material requiring systematic control of its harmful impacts on humans and the natural environment. The conclusions show that the use of alternative fuels reduces CO2 emissions and production costs, without negatively affecting the efficiency and production volume. The average energy requirement for the production of 1 ton of cement is approximately 3.3 GJ, which corresponds to 120 kg of coal with a calorific value of 27.5 MJ per kg. Energy costs account for 30–40% of the total cement production costs. Replacing alternative fuels with fossil fuels will help reduce energy costs, providing a competitive advantage for cement plants that use it as an energy source. The presented considerations can provide an answer to all interested parties, including representatives of the executive and legislative authorities, on what path the sector should follow to fit into the idea of sustainable building materials and the circular economy.

Energy-efficient heat pump-assisted pre-concentration integrated with sequential [EMIM][BF4] and ethylene glycol-based extractive distillation for enhanced recovery of ethanol and isopropyl alcohol from wastewater

Recovering ethanol and isopropyl alcohol (IPA) from wastewater offers significant economic and environmental advantages. In this work, 1-ethyl-3-methylimidazolium tetrafluoroborate ([EMIM][BF4]) and ethylene glycol (EG) are proposed as double entrainers for separating IPA/ethanol/water mixtures. The novel process sequentially uses [EMIM][BF4] to separate ethanol/water and IPA/water azeotropes, followed by EG for near-boiling mixture of ethanol/IPA. Energy-economic comparisons of extractive distillation process using single and double entrainers are performed. To address the issue of high water content in mixtures, the heat pump-assisted pre-concentration (HPP) integrated with extractive distillation processes are proposed. Subsequently, the effect of water content in the mixtures on process performances are analyzed. The results indicate that compared to using a single entrainer [EMIM][BF4] or EG process, the double entrainers process reduces energy cost and total annual cost (TAC) by 19.9% to 45.3% and 18.6% to 52.7%, respectively. The HPP scheme effectively reduces the mole fraction of water from 60% to 26.05%, further decreasing energy costs by 4.2% to 22.2%. The energy cost and TAC of the double entrainers process decreases by 4.8% to 50%, and 2.8% to 52.1% compared with the single entrainer process under HPP schemes. The double entrainers [EMIM][BF4]-EG process shows promising prospects from energy and economic perspectives.

Adaptive Multi-Agent Reinforcement Learning for Optimizing Dynamic Electric Vehicle Charging Networks in Thailand

The rapid growth of electric vehicles (EVs) necessitates efficient management of dynamic EV charging networks to optimize resource utilization and enhance service reliability. This paper explores the application of adaptive multi-agent reinforcement learning (MARL) to address the complexities of EV charging infrastructure in Thailand. By employing MARL, multiple autonomous agents learn to optimize charging strategies based on real-time data by adapting to fluctuating demand and varying electricity prices. Building upon previous research that applied MARL to static network configurations, this study extends the application to dynamic and real-world scenarios, integrating real-time data to refine agent learning processes and also evaluating the effectiveness of adaptive MARL in maximizing rewards and improving operational efficiency compared to traditional methods. Experimental results indicate that MARL-based strategies increased efficiency by 20% and reduced energy costs by 15% relative to conventional algorithms. Key findings demonstrate the potential of extending MARL in transforming EV charging network management, highlighting its benefits for stakeholders, including EV owners, operators, and utility providers. This research contributes insights into advancing electric mobility and energy management in Thailand through innovative AI-driven approaches. The implications of this study include significant improvements in the reliability and cost-effectiveness of EV charging networks, fostering greater adoption of electric vehicles and supporting sustainable energy initiatives. Future research directions include enhancing MARL adaptability and scalability as well as integrating predictive analytics for proactive network optimization and sustainability. These advancements promise to further refine the efficacy of EV charging networks, ensuring that they meet the growing demands of Thailand’s evolving electric mobility landscape.

Effect of Pre-Treatment, Treatment, and Extraction Technologies on the Bioactive Substances of Coriander

Herbs and spices, with their wealth of bioactive compounds, are widely used in food, medicine, and cosmetics. Among them, coriander (Coriandrum sativum L.) is particularly valued for its medicinal and culinary properties. Growing consumer and industrial interest in natural products has led to the development of modern, environmentally friendly extraction techniques designed to improve the yield and quality of extracts while reducing time, energy, and solvent consumption. These processes make it possible to obtain optimal quantities of active compounds, thereby meeting the growing demand for plant-based products. After showing evidence of coriander’s health benefits, this review summarizes research findings on the impact of some treatments and pretreatments on its phytochemical composition. After that, it summarizes different aspects of the use of conventional and non-conventional extraction techniques for coriander’s bioactive constituents, mainly polyphenols and crude and essential oils (EO). Among these methods, microwave-assisted extraction (MAE/MAHD) emerges as one of the most efficient methods, offering higher yields, better-quality extracts, and a significant reduction in energy costs.

Software for CO2 Storage in Natural Gas Reservoirs

The paper presents a simulation-based approach for optimizing CO2 injection into depleted gas reservoirs, with the goal of enhancing underground CO2 storage. The research employs a two-dimensional dynamic reservoir model, developed using Darcy’s law, to describe gas flow in a pressure-homogeneous porous medium, along with real gas equations. The model integrates the Du Fort–Frenkel and finite-difference methods to accurately simulate the behavior of CO2 during injection and storage. Real data from an operational gas storage facility were used to calibrate the model. CO2sim v1 software, specifically developed for this purpose, simulates CO2 injection cycles and quiescence phases, enabling the optimization of storage capacity and energy efficiency. The reservoir model, based on the engineering of the geological structure, is discretized into approximately 16,000 cells and solved using the finite-difference method, allowing for rapid simulation of CO2 injection and quiescence processes. The average computation time for a 150-day cycle is approximately 5 min. Simulation results indicate that increasing the number of injection wells and carefully controlling the injection rates significantly improves the distribution of CO2 within the reservoir, thereby enhancing storage efficiency. Additionally, appropriate well placement and prolonged quiescence periods lead to better CO2 dispersion, increasing the storage potential while reducing energy costs. The study concludes that further development of the software, along with comprehensive technical and economic assessments, is required to fully optimize CO2 storage on a commercial scale.

Analysis of graphene as a potential filtration membrane for desalination at varying operating conditions using molecular dynamics simulation and response surface methodology

ABSTRACT Water scarcity is a critical global issue exacerbated by pollution and overuse, necessitating sustainable water management solutions. Desalination using membrane technology presents a promising approach for freshwater production. This study investigated the performance of nanoporous (NPG) membranes for desalination, focusing on the influence of pressure and temperature on water flux and ion rejection. Utilizing molecular dynamics (MD) simulations with the LAMMPS package, this study evaluates NPG membranes under various conditions of pressures and temperatures. The simulations demonstrate that increasing both the pressure and temperature enhances the water flux without compromising ion rejection. The results indicate that at 300 K and 50 MPa, the water flux exceeds 2000 L/m2 h bar, significantly outperforming traditional reverse osmosis membranes, which typically achieve a capacity of approximately 1 L/m2 h bar. These findings were validated experimentally, aligning with previous research and confirming the superior performance of NPG membranes. A statistical model derived from response surface methodology revealed a linear relationship between pressure, temperature, and water flux. The study concludes that NPG membranes offer a high efficiency and scalable solution for desalination, with significant potential for energy savings and cost reduction. This study underscores the viability of NPG membranes in addressing global freshwater shortages and provides a pathway for sustainable water production.

Energy and Exergy Performance Analysis of Solar-Assisted Thermo-Mechanical Vapor Compression Cooling System

Air conditioning is vital for indoor comfort but traditionally relies on vapor compression systems, which raise electricity demand and carbon emissions. This study presents a novel thermo-mechanical vapor compression system that integrates an ejector with a conventional vapor compression cycle, incorporating a thermally driven second-stage compressor powered by solar energy. The goal is to reduce electricity consumption and enhance sustainability by leveraging renewable energy. A MATLAB® model was developed to analyze the energy and exergy performance using R1234yf refrigerant under steady-state conditions. This study compares four solar collectors—evacuated flat plate (EFPC), evacuated tube (ETC), basic flat plate (FPC), and compound parabolic (CPC) collectors—to identify the optimal configuration based on the collector area and costs. The results show a 31% reduction in mechanical compressor energy use and up to a 44% improvement in the coefficient of performance (COP) compared to conventional systems, with a condenser temperature of 65 °C, a thermal compression ratio of 0.8, and a heat source temperature of 150 °C. The evacuated flat plate collectors performed best, requiring 2 m2/kW of cooling capacity with a maximum exergy efficiency of 15% at 170 °C, while compound parabolic collectors offered the lowest initial costs. Overall, the proposed system shows significant potential for reducing energy costs and carbon emissions, particularly in hot climates.

Sinusoidal LED light recipes can improve rocket edible biomass and reduce electricity costs in indoor growth environments.

Accumulation of edible biomass by crop plants relies on maintenance of a high photosynthetic rates across the photoperiod, with assimilation rate (A) generally responding to increasing light intensity in a hyperbolic fashion. In natural environments light fluctuates greatly over the course of the day, however in Controlled Environmental Agricultural (CEA) systems, light intensity can be supplemented or precisely controlled using LEDs to create near optimum conditions. In such indoor growth environments light is often delivered as a square wave and recommendations to horticulturalists are given in the form of Daily Light Integrals (DLI). However, this does not take into account the slow photosynthetic induction at the start of the photoperiod and the decline of A towards the end of the photoperiod, which has been demonstrated by several previous studies. Square wave light regimes therefore potentially cause suboptimal photosynthetic utilization of the applied lighting and waste electricity. Here we have adapted light recipes to gradually increase and decrease in intensity to take account of these findings. We demonstrate that, utilising a sinusoidal light regime capped at 250 μmol m-2 s-1, it is possible to increase edible biomass of rocket (by ca. 20%) compared to square wave delivered at 250 at the same DLI. Additionally, this can be achieved using less electricity (0.6%), therefore reducing energy costs and improving profitability. We suggest that capping maximum light intensity at 250 µmol m-2 s-1 improves the operating efficiency of PSII photochemistry (Fq'/Fm') also known as the photosynthetic efficiency by maintaining A later in the photoperiod. We show that a higher electron transfer rate (ETR) is maintained in these treatments over the photoperiod compared to higher light intensity caps, resulting in a greater Daily Photochemical Integral (DPI). We attribute this to less NPQ due to a greater sink capacity for the end products of electron transport, ATP and NADPH, as A is kept high for longer.

Energy Efficiency of Glasshouses and Plant Factories for Sustainable Urban Farming in the Desert Southwest of the United States of America

The extreme heat and water scarcity of the desert southwest in the United States of America present significant challenges for growing food crops. However, controlled-environment agriculture offers a promising solution for plant production in these harsh conditions. Glasshouses and plant factories represent advanced but energy-intensive production methods among controlled-environment agriculture techniques. This review aims to comprehensively assess how controlled-environment agriculture can thrive and be sustained in the desert southwest by evaluating the energy efficiency of controlled glasshouses and building-integrated plant factories. The analysis focuses on the efficiency of these systems’ energy and water consumption, mainly using artificial lighting, heating, cooling, ventilation, and water management through various hydroponic techniques. Approximately 50% of operational energy costs in controlled glasshouses are dedicated to cooling, whereas 25–30% of energy expenses in building-integrated plant factories are allocated to artificial lighting. Building-integrated plant factories with aeroponic systems have demonstrated superior water use and energy efficiency compared to controlled glasshouses in desert environments. Integrating photovoltaic solar energy and glass rooftops in building-integrated plant factories can significantly reduce energy costs for urban farming in the desert southwest.

Net-zero carbon emission oriented Bi-level optimal capacity planning of integrated energy system considering carbon capture and hydrogen facilities

The integrated energy system (IES) is considered an efficient energy provision paradigm to improve the utilization efficiency of renewable generation (RG), reduce energy cost and improve energy supply reliability. However, the multi-dimensional uncertainties and the hydrogen utilization have posed enormous challenges to the optimal planning of the IES. This paper proposes a net-zero carbon emission oriented bi-level optimal planning model for the IES with full consideration of the multiple operational uncertainties in the IES (RG, multiple demands). The upper level is an optimal capacity configuration model, and the economic costs, energy selling profits, the penalty for power abandonment and energy supply shortage are settled as the objectives. The lower level is an optimal energy dispatch model with the objective function of minimizing the total operating cost of the IES on typical days. An integrated energy system framework consisting of multiple energy flows is formulated in the presence of carbon capture system (CCS) and hydrogen-to-power (H2P) facilities. To fully characterize the uncertainties of RG and multiple demands, a data-driven scenario generation method is adopted to expand the operational scenarios, in which probability distribution assumptions and prior knowledge are not necessary. Finally, the effectiveness of the proposed solution is extensively demonstrated through numerical simulation, and a sensitivity analysis is carried out to assess the impact of changes in electricity and gas prices on the IES planning results.

A hierarchical framework for aggregating grid-interactive buildings with thermal and battery energy storage

The behind-the-meter (BTM) thermal and battery energy storage can help improve energy efficiency, reduce energy costs, and enhance energy resilience, particularly in rural areas and for disadvantaged communities. Aggregating numerous BTM energy storage systems can act as a price influencer with a significant source of load shifting and peak demand reduction. An integrated and scalable control mechanism is required to effectively utilize energy storage systems and flexible building loads to maximize the economic benefits, considering various distribution system constraints. This paper presents an innovative hierarchical coordination framework for energy storage and flexible load in buildings, considering various factors such as electricity prices, thermal comfort, and distribution system modeling and constraints. At the upper level, a distribution system operator optimizes the power flow to minimize its power procurement costs from the electricity wholesale market, while at the lower level, aggregators determine the optimal dispatch of battery and thermal energy storage systems in multiple buildings on behalf of end-users to minimize operating costs according to the power prices. These problems are solved using a game-theoretic approach through negotiations between the distribution system operator and aggregators as a bi-level decision model. Simulation case studies have been performed for a test distribution network with a number of building end-users using energy storage systems to quantify the performance of aggregators. The results demonstrate that the proposed strategy can reduce peak load for a reliable electricity distribution network while saving electricity bills for customers.