Renewable Energy Storage Systems Research Articles

Sustainable Combination of Waste with Waste: Utilization of Biomass to Recover Critical Metals from Spent Lithium ion Batteries

The generation of e‐waste from lithium ion batteries (LIBs) is rapidly increasing due to the rising utilization of LIBs in portable electronics, and electric vehicles, with an average life span of 3‐5 years. The disposal of spent LIBs is a major environmental concern due to the presence of high percentages of toxic heavy metals and corrosive electrolytes. Efficient and sustainable recycling of spent LIBs has become immensely important, both environmentally and from a circular economy perspective, as LIBs serve as secondary sources of critical metals needed for renewable energy conversion and storage systems. We provide a concise review of metal recovery from spent LIBs using waste biomass in a green and sustainable approach for resource generation, in addition to the valorization of waste biomass. Biomass is used to recover metals from spent LIBs, acting as lixivient, reductant, and as absorbent. We have also discussed a reverse strategy utilizing ‘black mass’ from spent LIBs as catalyst for biomass conversion to value‐added products. The concept of “circular economy” is highlighted in a “killing two birds with one stone” approach through the utilization of biomass for the recovery of metals from spent LIBs and vice versa.

Optimal dimensioning of renewable energy generation and storage systems

With high energy demand and large available area, agricultural farms offer significant potential for renewable energy investments, like photovoltaic systems and electrical energy storages. However, the profitability of such investments depends strongly on self-consumption, so accurate planning requires computation-intensive simulation and optimization considering local consumption. This study presents a novel methodology for the optimal dimensioning and configuration of photovoltaic systems and electrical energy storages using efficient techniques from continuous non-linear optimization. Combining physical and economic models with measured consumption data, an investment’s net present value over 20 years is maximized. Using gradient-based solver WORHP, the simulation, optimal control, and optimal dimensioning of the local energy system are calculated simultaneously, allowing for efficient computation over an entire year of hourly data to capture both daily and seasonal variations.The approach is demonstrated with simple use cases, including an exemplary day of a dairy farm’s consumption, for which optimal systems with and without storage achieve 77% and 43% of autarky, respectively. Saturation effects of optimal plant size can be observed when sizes are large enough for optimal self-consumption but not expanded further for grid export. With energy storage, this saturation is reached at higher values. Optimizing photovoltaic plants with different orientations to match the specific consumption patterns characteristic of the dairy farm achieves similar autarky as a single plant while reducing investment costs by more than 20%. While thorough validation and comparison against heuristic methods predominantly used in the field is part of ongoing research, the presented use cases demonstrate the flexibility and efficiency of the proposed method and highlight its promise as a planning tool in the agricultural domain and beyond.

AI-Driven Optimization of Renewable Energy Storage Systems in Smart Cities Using Improved Binary Genetic Algorithm

AI-Driven Optimization of Renewable Energy Storage Systems in Smart Cities Using Improved Binary Genetic Algorithm

Frequency stabilization of interconnected diverse power systems with integration of renewable energies and energy storage systems

Recent improvements in renewable energy sources (RESs) and their extensive use in the power industry have created significant issues for their operation, security, and management. Due to the ongoing reduction of power system inertia, maintaining operational frequency at its nominal value and minimizing tie-line power variations constitute essential variables for these improvements. A novel improved frequency stabilization approach based on modified fractional order tilt controller is presented for interconnected diverse power systems with integration of sea wave energy, photovoltaic, wind, energy storage system and biodiesel generator. The fractional order tilt integral double derivative (FOTIDD2) is a novel controller that is developed to address the frequency stabilization problems of a hybrid power structure that is integrated with sources of clean energy and devices for storing energy. A recent optimization algorithm inspired by human behavior called mother optimization algorithm (MOA) is applied to adjust the coefficient of the proposed cascaded controller. The FOTIDD2 controller was compared with other control methods in terms of its transient performance, in order to determine its effectiveness. Further, the mother optimization algorithm results are compared with those of sine cosine algorithm (SCA), fox optimization algorithm (FOA), and grey wolf optimization (GWO). FOTIDD2 controller was found to be more effective than PID controller in respect of reducing overshoot (Osh) by 79.31%, 83.78% and 67.99%, improving time settlement by 33.22%, 29.87%, and 22.45% and reducing undershoot (Ush) by 79.13%, 89.34%, and 86.90% for the (∆F1), (∆F2), and (∆Ptie), respectively.

A Cooperative Multi-Agent Q-Learning Control Framework for Real-Time Energy Management in Energy Communities

Residential and commercial buildings are responsible for 35% of the EU energy-related greenhouse gas (GHG) emissions. Reducing their emissions is crucial for meeting the challenging EU objective of the agenda for becoming a net-zero continent by 2050. The diffusion and integration of distributed renewable energy sources (RESs) and energy storage systems (ESSs), as well as the creation of energy communities (ECs), have proven to be crucial aspects in reducing GHG emissions. In this context, this article proposes a multi-agent AI-based control framework to solve the EC’s energy management problem in the presence of distributed RESs and ESSs as well as considering a shared ESS. The objectives of the proposed control framework are to satisfy the EC members’ load demand to maximize self-consumption and to manage ESSs charging and discharging processes, to enforce cooperative behavior among the EC members by adopting fair and personalized strategies and to maximize EC members’ profits. The proposed control procedure is based on three sequential stages, each solved by a dedicated local RL agent exploiting the Q-Learning algorithm. To reduce the computational complexity of the proposed approach, specifically defined state aggregation criteria were defined to map the RL agents’ continuous state spaces into discrete state spaces of limited dimensions. During the training phase, the EC members’ profiles and the ESSs’ and RESs’ characteristics were randomly changed to allow the RL agents to learn the correct policy to follow in any given scenario. Simulations proved the effectiveness of the proposed approach for different costumers’ load demand profiles and different EC configurations. Indeed, the trained RL agents proved to be able to satisfy the EC members’ load demands to maximize self-consumption, to correctly use the distributed and shared ESSs, to charge them according to respective personalized criteria and to sell the energy surplus, prioritizing sales to the EC. The proposed control framework also proved to be a useful tool for understanding EC performance in different configurations and, thus, for properly dimensioning the EC elements.

Unlocking sustainable lithium: A comparative life cycle assessment of innovative extraction methods from brine

The surging demand for lithium, driven by the widespread adoption of electric vehicles and renewable energy storage systems, underscores the urgent need to develop sustainable lithium extraction methods. This study presents a comprehensive Life Cycle Assessment Using the TRACI method to evaluate and compare the environmental impacts of solvent extraction, adsorption, nanofiltration, and membrane electrolysis as direct lithium extraction methods for recovering lithium from brine to produce lithium carbonate. In terms of global warming, the carbon dioxide emission for each process was determined as follows: solvent extraction emits 52.7 kg CO2eq/kg of lithium carbonate, adsorption emits 47.9 CO2eq/kg of lithium carbonate, nanofiltration emits 17.7 kg CO2eq/kg of lithium carbonate, and membrane electrolysis emits 80.57 kg CO2eq/kg of lithium carbonate. As a result, the nanofiltration process emerges as the most environmentally friendly method, offering a promising solution to the environmental challenges of lithium extraction. In contrast, the membrane electrolysis process has the highest environmental impact.

Self-Assembled Blossom-Shaped NiCo2S4 Nanosheets In Situ Deposited Electrodes: Possessing High Reactivity and Selectivity for Bromine-Based Flow Batteries.

Bromine-based flow batteries (Br-FBs) are emerging rapidly due to their high energy density and wide potential window for renewable energy storage systems. Nevertheless, the sluggish kinetics of the Br2/Br- reaction on the electrode is considered to be the main challenge contributing to the poor performance of Br-FBs. Herein, we report self-assembled blossom-shaped NiCo2S4 nanosheets, enabling in situ growth on graphite felt (GF) via a one-step hydrothermal method. In the prepared NiCo2S4-GF, the gaps formed by the nanosheets restrict bromine diffusion, the sulfuretted blossom-shaped structure provides active sites with bromine adsorption capacity, and the synergistic effect of Ni and Co accelerates the electron transfer rate, which allow the electrode to exhibit excellent electrocatalytic activity compared to commercial GF, CoS-GF, and NiS-GF. Moreover, NiCo2S4-GF demonstrates unique selectivity for enhancing the Br2/Br- redox reaction compared to the bimetallic oxide of NiCo2O4-GF. Consequently, the zinc-bromine flow battery (ZBFB) with NiCo2S4-GF achieves an energy efficiency of 80.16%, which is 16.18% higher than that of the battery with commercialized GF at a current density of 60 mA cm-2, as well as a maximum power density of 260.75 mW cm-2 at 280 mA cm-2. The effective enhancement of the performance of ZBFB suggests that NiCo2S4-GF possesses great application potential in Br-FBs.

PV-fed multi-output buck converter-based renewable energy storage system with extended current control for lifetime extension of Li-ion batteries

Recently, there has been a visible intensification of research on increasing the cycle life of energy storage devices used in Photovoltaic (PV)-fed energy storage systems (ESS). Compared to existing battery technologies, Lithium-ion (Li-ion) batteries have advantages such as high energy density and high cycle life. However, many existing Hybrid energy storage system (HESS) suffers from the fact that Li-ion batteries have limited cycle life, calendar and cycle aging. Therefore, attention has shifted towards the charging techniques necessary to improve the service life of Li-ion batteries by increasing their cycle life and reducing capacity losses. One such approach is to charge the battery by gradually increasing it from a certain level, rather than starting the charging process directly with the maximum charging current. This study proposes Extended Current Control (ECC) to reduce battery capacity losses and extend service life in PV-fed HESSs. The maximum power point (MPP) of the PV module is provided by the Perturb and observe (P&O) algorithm via the supercapacitor (SC) converter, while ECC is performed via the battery converter. This approach requires less hardware, unlike optimization, rule, or filtering based techniques. Controlling the battery current over a large area protects the battery packs from high currents at the start of charge and reduces capacity losses by increasing cycle life. Compared to PV-fed ESS containing only battery packs, the proposed technique provides a 40 % improvement in battery charging current, an 8 % improvement in the converter duty ratio in reaching the MPP point, and a 31.69 % improvement in battery capacity fades.

Emerging and Conventional Water Desalination Technologies Powered by Renewable Energy and Energy Storage Systems toward Zero Liquid Discharge

The depletion of fossil fuels has become a significant global issue, prompting scientists to explore and refine methods for harnessing alternative energy sources. This study provides a comprehensive review of advancements and emerging technologies in the desalination industry, focusing on technological improvements and economic considerations. The analysis highlights the potential synergies of integrating multiple renewable energy systems to enhance desalination efficiency and minimise environmental consequences. The main areas of focus include aligning developing technologies like membrane distillation, pervaporation and forward osmosis with renewable energy and implementing hybrid renewable energy systems to improve the scalability and economic viability of desalination enterprises. The study also analyses obstacles related to desalination driven by renewable energy, including energy storage, fluctuations in energy supply, and deployment costs. By resolving these obstacles and investigating novel methodologies, the study enhances the understanding of how renewable energy can be used to construct more efficient, sustainable, and economical desalination systems. Thermal desalination technologies require more energy than membrane-based systems due to the significant energy requirements associated with water vaporisation. The photovoltaic-powered reverse osmosis (RO) system had the most economically favourable production cost, while MED powered via a concentrated solar power (CSP) system had the highest production cost. The study aims to guide future research and development efforts, ultimately promoting the worldwide use of renewable energy-powered desalination systems.

The Low-Carbon Path of Active Distribution Networks: A Two-Stage Model from Day-Ahead Reconfiguration to Real-Time Optimization

The integration of renewable energy sources and distributed energy storage systems increasingly complicates the operation of distribution networks, while stringent carbon reduction targets demand low-carbon operational strategies. To address these complexities, this paper introduces a two-stage model for reconfiguring distribution networks and ensuring low-carbon dispatch. Initially, second-order cone programming is employed to minimize losses in the network. Subsequently, the outputs of renewable energy and energy storage systems are optimized using the mantis search algorithm (MSA) to achieve low-carbon dispatch, with the network’s carbon potential as the evaluation metric. The proposed model demonstrates a significant reduction in average active power loss by 34.85%, a decrease in daily carbon emissions by 509.97 kg, and a reduction in carbon emission costs by 17.24%, thereby markedly enhancing the economic and social benefits of grid operations.

Integrated energy management for enhanced grid flexibility: Optimizing renewable resources and energy storage systems across transmission and distribution networks

This study explores the enhancement of electric grid flexibility and the realization of smart grid objectives through the integration of renewable energy (RE) resources and energy storage systems (ESS). While prior research has mainly concentrated on optimizing ESS operations either within the transmission network as a versatile power source or within the distribution network using small-scale batteries, our approach offers a more holistic perspective. We introduce a bi-level stochastic model for integrated energy management that encompasses renewable energy, demand side management (DSM), transmission, and distribution networks as interconnected entities. To tackle the binary variables inherent in the model, we employ a precise method based on reformulation and decomposition techniques to ensure globally optimal solutions. We evaluate the efficacy of our proposed model and the influence of ESS on the networks using various integrated transmission and distribution network systems. Our findings demonstrate the model's efficiency and underscore the cost-saving benefits of integrating energy storage systems. Specifically, incorporating ESS into the distribution grid results in a 13 % reduction in distribution network costs, while deploying large batteries in the transmission grid leads to an impressive 83 % cost reduction.

Design of sustainable integrated energy systems for green ports – selection and sizing

Abstract Design of sustainable and integrated energy systems for marine port is a complex problem, involving multiple conflicting objectives like energy efficiency and minimum losses, cost-effectiveness, and avoiding excessive cycling and degradation. This paper addresses the sizing of integrated and sustainable port energy systems with renewable energy sources and energy storage systems based on two different methods. Both developed methods start by selecting design sets representing the gradual expansion of renewable sources and storage to the maximum size, limited by various constraints. Each design variant is evaluated based on the energy demands of the port, historical prices, and site energy production capacity. The first method, developed by ITK Engineering GmbH, uses a conventional deterministic forecasting approach with detailed annual data. The latter, developed by NTNU, employs several 24-hour data sets to capture annual energy dynamics. Key performance indicators for both methods are defined based on the total cost, with the benchmark cost obtained using the first (deterministic) method with 100% grid-supplied energy. Two virtual ports of different sizes (Port A and B) are established in two electric markets (1 and 2) for verification and benchmarking. The paper then discusses the influence of uncertainty in 24-hour data sets, used by the second (stochastic) method, for all cases.

Performance analysis of hybrid metaheuristic assisted collateral FO controller for HRES system integrated UPQC

Due to the fluctuating characteristics of the environment, it is advisable to connect a minimum of two renewable energy sources (RES) to the grid to mitigate unreliable power supply. The integration of Hybrid Renewable Energy Storage (HRES) with nonlinear loads introduces harmonics and other power quality issues, which are significant concerns for both utility providers and customers. Power quality challenges, such as real power fluctuations, voltage interruptions, and reactive power imbalances, can be mitigated through the use of a Unified Power Quality Conditioner (UPQC). For optimal performance, the series and shunt compensators within the UPQC rely on precise PWM signals to control the converters' outputs. These signals are correctly tuned by controllers using updated algorithms. The paper develops an optimized Hybrid Metaheuristic assisted Collateral Controller for the improvement of the 3-phase HRES system-based Distribution Grid incorporated with UPQC. It consists of a Fractional order Proportional Integral derivative (FOPID) controller combined with a proportional integral (PI) controller. As a consequence, the DC link voltage regulation and controller optimization are achieved with the optimal tuning of gain parameters by using a hybrid Metaheuristic Algorithm named Amplified Slime Mould with WildeBeest Herd Optimization (ASM-WHO) Algorithm. The optimization of the weights (W1 and W2) and parameters of the Collateral Fractional Order Controller is achieved using an innovative hybrid metaheuristic method that combines the Wildebeest Herd Optimization (WHO) and Slime Mould Algorithm (SMA). The proposed system is implemented in MATLAB Simulink to analyze the compensation efficiency during voltage sag/swell. The proposed controller showcases robust performance, emphasizing its potential for enhancing power quality and distribution stability in Three-Phase Hybrid Renewable Energy Storage systems integrated with UPQC, outperforming or matching the performance metrics of conventional methods such as PI, FOPID controller, FOPID-PI with various optimization algorithms (CSO,SOA, WHO, SMA, AQO, HBA). The settling minimum and maximum of 170.5668 and 189.4736 for the proposed FOPID-PI controller with ASM – WHO is notably superior to that of the controller without optimization, the proposed collateral controller method (using SOA or CSO), WHO optimization, and the PI controller, improving by 1.7 %, 13.78 %, 1.338 %, and 13.66 %, respectively. The ASM-WHO algorithm shows robust performance and improved convergence, as validated by benchmark function tests, and surpasses competing algorithms.

Grid-integrated solutions for sustainable EV charging: a comparative study of renewable energy and battery storage systems

IntroductionThe integration of electric vehicles (EVs) into the power network challenges the 1) grid capacity, 2) stability, and 3) management. This is due to the 1) increased peak demand, 2) infrastructure strain, and 3) intermittent charging patterns. Previous studies lack comprehensive integration of renewable energy and battery storage with EV charging.MethodsTo address these challenges, this study explores the effectiveness of incorporating renewable energy resources (RERs) and battery energy storage systems (BESS) alongside the traditional grid. The proposed study utilizes the HOMER Grid® and conducted a comprehensive analysis.ResultsThe proposed study compares two grid integrated scenarios: 1) Case-1 (grid and photovoltaic (PV) systems), and 2) Case-2 (grid, PV systems, and BESS). Both these scenarios are compared against a Base case relying solely on grid power. The evaluation employed techno-economic analysis while focusing on 1) net present cost (NPC), 2) cost of energy, and 3) annualized savings. Additionally, the proposed study analyzed 4) seasonal variations in EV charging demand, 5) grid interactions, 6) PV production, and 7) the operation of BESS in both summer and winter. The comparative analysis reveals that the Base case incurs a net present cost (NPC) of $546,977 and a cost of energy (COE) of $0.354 per kWh. In contrast, Case-1, which integrates a 100 kW PV system, shows a significantly lower NPC of -$122,962 and a reduced COE of -$0.043 per kWh, with annualized savings of $61,492. Case-2, incorporating both the 100 kW PV system and a BESS with a capacity of 9.8 kWh, has a higher NPC of $309,667 but a COE of $0.112 per kWh and provides annual savings of $51,233 compared to the Base case.DiscussionSeasonal analysis highlights that Case-2 achieves the lowest carbon emissions in summer, ranging from 2.0 to 2.5 tons, while Case-1 shows the lowest emissions in winter, ranging from 3.2 to 3.4 tons. This model 1) reduces operational costs, 2) minimizes carbon emissions, while 3) making it compelling for future energy systems in increasing EV adoption.

Comparative Analysis and Performance Evaluation of Dual Active Bridge Converter Using Different Modulation Techniques

The development in the renewable energy systems and the necessity of the enhanced grid that includes the conventional energy sources and the renewable energy sources and storage systems have realized the importance of power electronics conversion systems. These interfaces are crucial for enhancing the efficiency as well as the control in bi-directional power flow. The use of HEVs as a means of preserving the future supply of fossil fuels, as well as the need for improving the efficiency of the power electronics interfaces required for efficient power management between the two energy sources of the vehicle, is discussed. Furthermore, the improvements in the Uninterruptible Power Supply (UPS) systems and regenerative power systems also require sophisticated power electronic conversion systems. To address these issues of modern technologies, the Dual Active Bridge (DAB) converter comes in as a potential solution. In this paper, several modulation techniques employed in DAB converters are discussed and compared in detail in order to find the best approach to control the converter’s performance in the whole range of its operation. The paper also presents an enhanced modulation strategy applied to DAB converters and built with FPGAs and MPC to minimize the power loss. This proposed optimization technique is more general and easier than the current ones. The performance of the converter is assessed in terms of efficiency, current stress, and the power of backflow, in addition to the proposed optimization and modulation techniques. The article also looks at the closed-loop performance under step load fluctuations and circuit performance as well as efficiency.

Optimal control strategies for energy storage systems for HUB substation considering multiple distribution networks

With the global consensus to achieve carbon neutral goals, power systems are experiencing a rapid increase in renewable energy sources and energy storage systems (ESS). Especially, recent development of hub substations (HS/S) equipped with ESS, applicable for resolving site constraints if implemented as mobile transformers, is expanding the development of ESS-equipped facilities. However, these units require centralized control strategies considering variability within integrated networks. While studies on electric vehicle charging considering the variability of renewable energy or load are widely studied, ESS management scheme for individual substations requires further optimization, especially considering the state of distributed sources at lower levels and transmission system operators. Thus, in this study, an optimal control approach for ESS located at the connection point of transmission and distribution systems, including further consideration of the loss in distribution lines and the constraints of renewable energy sources is presented. This study attempts to derive proactive control strategies for ESS in HS/S to operate with various distribution networks. By establishing control priorities for each source through optimal operation strategy, a suitable capacity of ESS and its economic benefits for distribution network management can be examined. Validation of the current analysis results is performed by utilizing MATPOWER. By adapting the operational range of design scenarios, diverse distribution systems can be tested against multiple configurations of connected devices.

Optimizing and Exploring Untapped Micro-Hydro Hybrid Systems: a Multi-Objective Approach for Crystal Lake as a Large-Scale Energy Storage Solution

Increasing electricity demand and concerns about climate change and fossil fuel consumption have highlighted the importance of renewable energy resources and storage systems. This paper proposes a method for exploring untapped pumped hydro storage potentials to accommodate intermittent renewable energy generation profiles. Hourly measured data from 2022 in Benzie County, Michigan, United States, were gathered for system sizing and a thorough, realistic analysis. By employing the multi-objective grey wolf optimization algorithm, we formulated optimal sizing and energy-management strategies for three different scenarios. Unlike similar studies, the 3rd with triple objective functions (OFs) scenario aims to maximize both reliability and ecological OFs while minimizing the cost OF. It has shown promising results with multiple solutions, considering economic, environmental, and reliability factors. A case study conducted in Crystal Lake, Michigan, revealed that although Crystal Lake would function only as a micro-hydro power facility, it is a promising and huge storage unit with a substantial storage capacity of around 14.9734GWh. The system investigated is significant in the USA due to its rapid deployment capabilities, minimal construction requirements, and ease of integration with the distribution grid. The fuzzy logic method was employed to identify the best non-dominant solution among the other solutions. These outcomes include a notably low levelized cost of energy at 0.046147$/kWh, a robust index of reliability of 99.705%, and a significant reduction in CO2 emissions amounting to 7.9142×103 tons/year, when considering the triple OFs. The paper’s methodology provides valuable insights for regions aiming to utilize renewable energy from untapped storage sources.

Automated Identification of Cylindrical Cells for Enhanced State of Health Assessment in Lithium-Ion Battery Reuse

Lithium-ion batteries are pervasive in contemporary life, providing power for a vast array of devices, including smartphones and electric vehicles. With the projected sale of millions of electric vehicles globally by 2022 and over a million electric vehicles in Europe alone in the first quarter of 2023, the necessity of securing a sustainable supply of lithium-ion batteries has reached a critical point. As the demand for electric vehicles and renewable energy storage (ESS) systems increases, so too does the necessity to address the shortage of lithium batteries and implement effective recycling and recovery practices. A considerable number of electric vehicle batteries will reach the end of their useful life in the near future, resulting in a significant increase in the number of used batteries. It is of paramount importance to accurately identify the manufacturer and model of cylindrical batteries to ascertain their State of Health (SoH) and guarantee their efficient reuse. This study focuses on the automation of the identification of cylindrical cells through optical character recognition (OCR) and the analysis of the external color of the cell and the anode morphology based on computer vision techniques. This is a novel work in the current limited literature, which aims to bridge the gap between industrialized lithium-ion cell recovery processes and an automated SoH calculation. Accurate battery identification optimizes battery reuse, reduces manufacturing costs and mitigates environmental impact. The results of the work are promising, achieving 90% accuracy in the identification of 18,650 cylindrical cells.

Policy and regulatory framework supporting renewable energy microgrids and energy storage systems

Policy and regulatory framework supporting renewable energy microgrids and energy storage systems

Assessing residential sustainable energy autonomous buildings for hot climate applications

This paper introduces an innovative solution for clean energy autonomous building (AB) sustainability, offering a 5Z concept—zero-carbon, zero-energy, zero grid connections, zero energy bills, and zero emission mobility. This paper focuses on fundamental research to design sustainable, energy-ABs striving for self-sufficiency in arid climates, using Kuwait as a case study. The study highlights the importance of stringent engineering AB modeling, renewable technology integration, and clean energy storage. The technical approaches include characterizing non-thermal and thermal demands, achieving net-zero energy generation, and custom sizing renewable energy and energy storage systems (ESS) for electric vehicle (EV) charging points. The research methodology involves local construction with yearly weather and energy profile data to simulate the actual system using ESP-r building model. The study's findings reveal the need for a large-scale energy system, energy demand reduction (EDR) inclusive of heating, cooling, appliances, and EV, with the corresponding changes in local energy production system size, battery capacity, and the number of photovoltaics (PVs). The results show varying EDR levels ranging from base case to 10% and to 50% leading to proportional changes in energy system size, size of battery from 3415 kWh to 1710 kWh and the number of PVs from 362 to 181, which means EDR not only optimizes space but also proves cost-effective. The significant implication of this study, not only bridging the knowledge gap and the lack of how-know, but also making a significant advancement and forward thinking in sustainable green electric home modernization and environmentally friendly transportation. This approach transforms energy management practices for more sustainable cities and societies, reducing emissions in both urban infrastructure and transportation.