Use Of Energy Storage Systems Research Articles

Empowering energy management in smart buildings: A comprehensive study on distributed energy storage systems for Sustainable consumption

The increment of photovoltaic generation in smart buildings and energy communities makes the use of energy storage systems desired to increase the self-consumption efficiency. This paper proposes and explores a model for energy storage systems management that considers local renewable generation, local demand, and retailer energy prices. The proposed model was tested at both energy community-level and the smart building level, demonstrating their capabilities of deployment. To validate the proposed model, a case study with two scenarios, including a 251 members energy community, was executed. The results demonstrate significant cost reductions for community members when adopting energy storage systems and the proposed management model. Regarding the smart building application four scenarios were tested, it is demonstrated that the demand for energy from the retailer could be set to zero during periods of time to enable its participation in demand response events. Overall, this paper contributes to the state-of-the-art by identifying and evaluating a model that manages the energy storage systems charge and discharge operation to actively reduce energy costs at the community-level (19.26%) and building level (11.75%) and to demonstrate that part of the loads can be optimized to understand if the building can be energy net-zero.

Metal-Organic Frameworks as Electrode Materials for Lithium-Ion Battery

Over recent decades, Metal-Organic Frameworks (MOFs) have distinguished themselves as a unique class of porous materials due to their adaptable surface and structural properties. This versatility has made MOFs highly relevant across various fields, including drug delivery, gas separation, catalysis, and sensor technology. Additionally, their conductive properties have made them promising candidates for use in energy storage systems like high-energy-density batteries and supercapacitors. MOFs are particularly noted for their role in the development of rechargeable lithium-ion batteries (LIBs) and supercapacitors, where they serve as both anode and cathode materials. The ability to fine-tune MOFs at a molecular level allows for precise control over their structure and chemistry, enhancing their functionality in energy storage applications. This control facilitates superior electronic and ionic transport within MOFs, which is critical during the charging and discharging cycles of LIBs. This review delves into the various synthetic methods used to develop specific MOF structures, focusing on their implementation within LIBs to improve cyclic stability and discharge capacity. Recent advancements in MOF technology as anode and cathode materials are explored, providing insights into how these developments can optimize reaction conditions and design choices within the battery development community and broader electrochemical energy storage sectors. The aim is to highlight how MOFs’ inherent characteristics can be leveraged to enhance the performance and efficiency of energy storage devices.

Analysis of a Grid-Connected Solar PV System with Battery Energy Storage for Irregular Load Profile

In recent decades, Saudi Arabia has experienced a significant surge in energy consumption as a result of population growth and economic expansion. This has presented utility companies with the formidable challenge of upgrading their facilities and expanding their capacity to keep pace with future energy demands. In order to address this issue, there is an urgent need to implement energy-saving solutions such as energy storage systems (ESSs) and renewable energy sources, which can help to reduce demand during peak hours. To ensure optimal use of ESSs, it is crucial to integrate a load forecasting model with the ESS in order to control charging and discharging rates and schedules. The irregular load profile is a particularly significant consumer of energy, consuming approximately 2.5 GWh annually at the cost of USD 3 billion in Saudi Arabia. In light of this, this paper develops a load forecasting model for the irregular load profile with a high degree of accuracy: achieving 95%. One of the key applications of this model is load peak shaving. Given the region’s abundance of solar irradiation, the paper propose an integration of a solar PV system with a battery energy storage system (BESS) and analyzes various scenarios to determine the efficacy of the proposed approach. The results demonstrate significant savings when the proposed forecasting model is integrated with a BESS and PV system, with the potential to reduce monthly imported power by more than 22% during the summer season.

Improved rate capability and energy density of high-mass hybrid supercapacitor realized through long-term cycling stability testing and selective electrode design

To render supercapacitors (SCs) more practical, they must exhibit high cycling stability of at least ten thousand cycles with commercial-level mass loadings, which differentiates them from batteries. Metal-organic framework (MOF)-based electrode materials are promising for use in energy storage systems owing to their excellent electrochemical performance. In this study, we report the electroactivities of bimetallic MOFs (Co, Ni, and Co–Ni) using a single-step, facile, and cost-effective hydro/solvothermal method. To optimize their performances, we develop a general strategy for analyzing various binder-free MOFs. Additionally, the effects of the reaction time, reaction temperature, solvents, and elements (Ni, Co, and H2BDC) on the surface morphology and electrochemical performance are investigated. Based on an analysis of the electrochemical properties of all the synthesized electrode materials, the optimal electrode delivers an ultrahigh areal capacity of 2621 µAh cm−2 (297.1 mAh g−1) at 7 mA cm−2, with a high-rate capability of 82.5 % even at 40 mA cm−2. The optimized Co–Ni MOF electrode is employed as a positive electrode to fabricate an aqueous hybrid SC (HSC) with an activated carbon-coated nickel foam electrode as a negative electrode. The as-fabricated HSC exhibits excellent electrochemical properties with exceptional cycling stability (120k cycles) and improved rate capability (60 %). Additionally, we identify factors that contribute to improved redox reactions in the Co–Ni MOF-based HSC, such as the role of Co–Ni MOFs in the redox reaction and the influences of other structural parameters on charge storage and transfer processes. Our aim is to further understand the underlying mechanisms of the improved redox reactions and thus obtain new insights into the design and optimization of Co–Ni MOF-based HSCs. Finally, the energy storage properties of the HSC are validated by using it to power different electronic devices. The promising outcomes obtained in this work can serve as a basis for the future practical implementation of high-energy-density HSCs with high mass loadings and charging rates. SynopsisTo render the practicability of hybrid supercapacitor (HSC) with enhanced energy storage properties, we investigate the electroactivities of bimetallic metal-organic frameworks (MOFs) (Co, Ni, and Co–Ni) using a single-step, facile, and cost-effective hydro/solvothermal method. To optimize their performances, a general strategy is developed for analyzing various binder-free MOFs. Additionally, the effects of the reaction time, reaction temperature, solvents, and elements (Ni, Co, and H2BDC) on the surface morphology and electrochemical performance are studied. In addition, we validate the energy storage properties of the HSC by using it to power different electronic devices. The promising outcomes obtained in this study can serve as a basis for the future practical implementation of high-energy-density HSCs with high mass loadings and charging rates.

Design and fabrication of reduced graphene oxide (RGO) incorporated NiCo2S4 hybrid composites as electrode materials for high performance supercapacitors

One of the most promising bimetallic sulphides for use in energy-storage systems is NiCo2S4, but further research is required to give it a high reversibility and electrochemical reaction capacity. In this study, we show the rational materials design of an ideal NiCo2S4 nanoparticle as a NiCo2S4/RGO nanocomposite, embedded in a reduced graphene oxide (RGO) matrix. NiCo2S4 nanoparticles, which ranged in size from 20 to 25 nm and were firmly fixed on the surface of RGO sheets, made up the produced composite. As predicted, the produced nanocomposite displayed a high specific surface area, a mesoporous structure, and high conductivity, resulting in a large electroactive area and rapid electron and ion motion. Additionally, we present the advancements in material science, showcasing the NiCo2S4/RGO nanocomposite electrode, which has a long cycle life of 5000 cycles, a high capacitance retention of 85.6 %, and an exceptional specific capacitance of 1505 Fg-1 at 1 Ag-1. A high active-material loading asymmetric supercapacitor serves as an example of the real-world use. The NiCo2S4/RGO nanocomposite exhibits an exceptional 48 Whkg-1 energy density at a power density of 985 Whkg-1, while maintaining a high power density of 7227 Wkg-1 density of 25 Whkg-1. The exceptional stability and electrochemical efficiency of the NiCo2S4/RGO nanocomposite validate that our methodical materials science and technology optimisation in the areas of active substance synthesis, electrode expansion, and device design/fabrication will help advance the development of high-performance supercapacitors in future years.

Enhanced energy management of DC microgrid: Artificial neural networks-driven hybrid energy storage system with integration of bidirectional DC-DC converter

Standalone microgrids using Photovoltaic (PV) systems might be a feasible alternative for powering off-grid populations. However, this form of application necessitates the use of energy storage systems (ESS) to control the intermittent nature of PV production. This paper proposes a novel energy management strategy (EMS) based on Artificial Neural Network (ANN) for controlling a DC microgrid using a hybrid energy storage system (HESS). The HESS connects to the DC Microgrid using a bidirectional converter (BC), that enables energy exchange between the battery and supercapacitor (SC). The effectiveness of the proposed approach in relation to the traditional control method is based on performance metrics such as overshoot and settling time. The proposed ANN control attains 18 % overshoot while step increases in PV generation and attains 21 % overshoot while decreasing PV generation. The results show that the proposed control method ensures continuous power supply from the PV system by optimising load balancing under various operating conditions, maintaining state-of-charge (SoC), and improving the voltage profile.

Energy community with shared photovoltaic and storage systems: influence of power demand in cost optimization

AbstractEnergy management of distributed energy resources has gradually become a complex problem because of the intermittent nature of renewable energy sources, such as photovoltaic power, and the large use of energy storage systems. A way to deal with these issues is to operate within an energy community. However, the efficient management of the community in terms of costs is particularly relevant. Specifically, the minimization of the energy community costs, which consists of properly utilizing shared energy storage and renewable energy sources, becomes an important objective. In this context, a fundamental role is played by demand power characteristics which strongly influence the benefits brought by this energy management scheme. This work investigates the influence of the variability of power demand on the minimization of the operating cost problem of an energy community while determining the optimal capacity of the energy storage system that increases the self‐consumption potential of the photovoltaic source. Two main scenarios are implemented where the effects of considering the community photovoltaic capacity as a variable or a parameter on costs and energy storage system size are investigated. This analysis consists of a multi‐objective optimization coupled with a Monte Carlo framework. The community management is conducted by considering random power demand profiles of each unit belonging to the same community, and different sizes, categories of users and users' aggregations. A comparison is led among different users' categories in terms of costs, photovoltaic unit and energy storage system size. The results provide an overview of how each category benefits from taking part in an energy community both in terms of cost and energy storage and photovoltaic sizes and show how these aspects change within a multi‐category aggregation where each category makes a different contribution to the community. In particular, we find evidence of the “synergy effect” brought by multi‐category aggregations capable of exploiting differences in consumption profiles. Each building category, with its numerosity, has a different effect on the energy community, resulting in a different impact on total costs and cost savings. We also investigate how the energy storage system capacity is affected by both the available photovoltaic capacity and the consumption profiles of the categories within the energy community.

Energy Storage in Urban Areas: The Role of Energy Storage Facilities, a Review

Positive Energy Districts can be defined as connected urban areas, or energy-efficient and flexible buildings, which emit zero greenhouse gases and manage surpluses of renewable energy production. Energy storage is crucial for providing flexibility and supporting renewable energy integration into the energy system. It can balance centralized and distributed energy generation, while contributing to energy security. Energy storage can respond to supplement demand, provide flexible generation, and complement grid development. Photovoltaics and wind turbines together with solar thermal systems and biomass are widely used to generate electricity and heating, respectively, coupled with energy system storage facilities for electricity (i.e., batteries) or heat storage using latent or sensible heat. Energy storage technologies are crucial in modern grids and able to avoid peak charges by ensuring the reliability and efficiency of energy supply, while supporting a growing transition to nondepletable power sources. This work aims to broaden the scientific and practical understanding of energy storage in urban areas in order to explore the flexibility potential in adopting feasible solutions at district scale where exploiting the space and resource-saving systems. The main objective is to present and critically discuss the available options for energy storage that can be used in urban areas to collect and distribute stored energy. The concerns regarding the installation and use of Energy Storage Systems are analyzed by referring to regulations, and technical and environmental requirements, as part of broader distribution systems, or as separate parts. Electricity, heat energy, and hydrogen are the most favorable types of storage. However, most of them need new regulations, technological improvement, and dissemination of knowledge to all people with the aim of better understanding the benefits provided.

Highly efficient asymmetric supercapacitor based on magnetic CeO2@γ-Fe2O3 composite and reduced graphene oxide electrodes

The CeO2@γ-Fe2O3 nanocomposites with varying CeO2 content (0–50 mol%) were prepared by the sol-gel electrospinning. XRD analysis confirmed the phase transition from α-Fe2O3 to cubic γ-Fe2O3 with addition of Ce ions and dominant phase for 30, and 50% CeO2@γ-Fe2O3 nanocomposites was cubic CeO2. FESEM images revealed that the ribbon-like α-Fe2O3 convert to the CeO2@γ-Fe2O3 nanofibers. The EDS map images and FTIR confirmed the homogenous distribution of elements and chemical bonds, respectively. Interestingly, the induced phase transition resulted in superparamagnetic behavior of 10% CeO2@γ-Fe2O3. The supercapacitive performance of the CeO2@γ-Fe2O3 nanofibers grown on the nickel foam were examined by electrochemical analyzes. The 50% CeO2@γ-Fe2O3 electrode demonstrated the highest specific capacitance of 1169.3 F g−1 at a current density of 1 A g−1 and the capacitance retention of 89.7% after 1000 cycles at 7 A g−1. Additionally, asymmetric supercapacitor (ASC) was assembled using CeO2@γ-Fe2O3 and rGO as cathode and anode electrodes, respectively. The CeO2@γ-Fe2O3//rGO ASC displayed an energy density of 36.8 Wh kg−1 at a power density of 700 W kg−1, demonstrating its potential for use in energy storage systems.

Multipurpose Optimization Method for Energy Storage System Specification Using Measurement Data of DC Traction Substations

The peak demand for railway power occurs when trains operate at full capacity, which calls for the need of facilities that can handle such peaks. These expansive railway power facilities, which cover vast areas, result in increased maintenance and management costs while affecting the power supply to traction substations (TSs). Herein, we investigated the load leveling of TSs using energy storage systems (ESSs). Most of the studies that have been conducted on the use of ESSs in electric railways have focused on high‐speed railways and urban lines. The application of ESSs to local lines is expected to be highly effective in reducing the capacity of power facilities using the load leveling of TSs. However, the introduction of ESSs to such local lines has not yet been discussed in detail. Hence, we focused on the relationship between ESS specifications (battery capacity, kWh) and load peak reduction (kW) and proposed a method to determine the ESS specifications by simultaneous optimization using linear programming. Numerical calculations were performed based on measured data from a real line, and the proposed method was verified in terms of battery capacity, load peak reduction, and ESS operation ratio. The numerical results indicate that the maximum load of the TS can be reduced by 98.7% and 91.1% using 380 kWh and 65.42 kWh ESS, respectively, considering its cooling time. This highlights the potential of Energy Storage Systems (ESSs) to balance TS (Time‐of‐Use) loads and consequently reduce power facility requirements. © 2024 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Development of a simulation model for controlling energy storage systems on electric trains

The article discusses the experience of using electrical energy storage devices on multiple unit rolling stock, both in Russia and abroad. It is noted that the use of energy storage systems in railway transport in Russia lags somewhat behind its foreign colleagues. The main assumptions taken when performing simulation modeling of the operation of energy storage systems on an electric train are presented. Simulations of the operation of energy storage systems were carried out using the MatLab software package. Simulation models of an electric train with an energy storage device, a model of a heater for heating an electric train car, a model of a hybrid energy storage system, a model of a supercapacitor unit, and batteries are presented. The algorithm of operation of the active topology of a hybrid drive is considered. The developed simulation models have been tested. Graphs of the voltage at the pantograph and the current consumed by the electric train were obtained, as well as graphs of the operation of the battery and supercapacitor in charge/discharge modes.

Control Algorithm of Energy Storage System Based on Virtual Synchronous Generator

Frequency control is one of the most important tasks in electric power systems. However, as a result of the ongoing introduction into modern power systems of inertia-free generating facilities based on power converters, mainly renewable energy sources, this task is becoming more complex. This specificity is especially severe in microgrids, which are characterized by significant frequency changes. An effective way to solve such a problem is the use of energy storage systems (ESS) with grid-forming power converters. For this purpose, the paper develops a new structure of the control algorithm based on a current-controlled virtual synchronous generator (CC-VSG), in which the damping of oscillations is implemented via a feedforward controller. Also, a proportional-integral control loop is added to the structure of the CC-VSG to control the state of charge of the ESS. A methodology based on the separation of bandwidths of different control loops is proposed for tuning the CC-VSG. To confirm the obtained results, time-domain simulation of the microgrid with ESS using the PSCAD software was performed.

ОПТИМИЗАЦИЯ УМНЫХ СЕТЕЙ (SMART GRIDS) С ИНТЕГРАЦИЕЙ ВОЗОБНОВЛЯЕМЫХ ИСТОЧНИКОВ ЭНЕРГИИ

The article considers the issues of smart grid optimization with renewable energy sources (RES) integration. Particular attention is paid to solving problems associated with the instability of RES generation and the need to ensure the reliability of the power system. Various approaches to demand management, the use of energy storage systems and distributed energy resources are analyzed. The challenges of cybersecurity and grid sustainability in the context of RES integration are considered. The article also provides case studies and simulation results demonstrating the effectiveness of the proposed solutions. The economic and environmental aspects of smart grid implementation are assessed.

Uncertain lithium-ion cathode kinetic decomposition modeling via Bayesian chemical reaction neural networks

Lithium-ion batteries are the focus of significant recent research interest due to their use in energy storage systems, electric vehicles, and other green technologies. Under various abuse conditions, these batteries undergo thermal runaway, which can lead to rapid temperature rise, flammable gas release, and fires. Current simulation approaches for thermal runaway at the full-cell scale involve utilizing kinetic models fit to experimental data from individual battery components. Despite substantial experimental uncertainty in the literature for these component experiments, prevalent models have not accounted for such uncertainty or noise. Here, we introduce uncertainty quantification for lithium-ion battery cathode thermal decomposition modeling via a novel Bayesian inference methodology. This approach leverages Chemical Reaction Neural Networks and particle-based uncertainty quantification to infer uncertain kinetic parameters while eliminating traditionally used simplifications, allowing for improvements to model accuracy and broader consideration of correlated parameters. We validated this new framework by learning an uncertain decomposition model for NCM333 (nickel-cobalt-manganese) cathode materials using differential scanning calorimetry (DSC) measurements with added synthetic noise. Then, we quantified the uncertainty in NCM811 cathode thermal runaway chemistry using experimental DSC measurements from various sources in the literature. Our methodology’s ability to account for correlated Arrhenius parameters led to much broader uncertain parameter ranges and thus more generalizability at higher temperatures. We additionally found that the NCM811 model distribution learned directly from experimental data in the literature has 4σ onset temperature ranges up to 20 °C wide and specific reaction enthalpy ranges accounting for upwards of 80% of the mean value, carrying significant implications for downstream applications. Our work bridges the gap between noisy or uncertain experimental data and practical cell-scale simulations, thereby facilitating more realistic and robust thermal runaway models that support enhanced battery safety and performance optimization.

Electrochemical investigations of PVdF-HFP-NaTFSI-[BMPYr][TFSI] polymer-salt-IL electrolyte for Na-rechargeable battery

Sodium-ion batteries (SIBs) are being aggressively pursued for potential use in energy storage systems due to the natural abundance and low cost of sodium. In the present investigation, structural, thermal and electrochemical properties of a ionic liquid based Na-ion conducting polymer-salt-IL electrolytes (IL-NCPEs) formed using copolymer poly(vinylidene fluoride-co-hexafluoropropylene) (PVdF-HFP), sodium bis(trifluoromethanesulfonyl)imide (NaTFSI) salt along with IL; 1-butyl-1-methypyrrolidinium bis(trifluoromethanesulfonyl)imide ([BMPYr] [TFSI]) is being reported. The samples were characterized using ionic conductivity, transference number measurement, thermal & electrochemical studies as a function of wt% IL. Incorporated [BMPYr][TFSI]-IL in the NCPE (i.e., PVdF-HFP + 10 wt% NaTFSI) modifies the different physicochemical properties such as complex formation, thermal, transport and electrochemical properties. The ionic conductivity of prepared IL-NCPEs has been found to increase with increasing the content of [BMPYr][TFSI]-IL and reaches to a maximum value of ∼0.85 mS/cm (at 30 °C). Also, the temperature dependent ionic conductivity obeys Arrhenius type thermally activated behaviour. For the optimized [BMPYr][TFSI]-IL, compatibility of IL-NCPEs with Na-metal electrode, high total ionic transference number (tionic ∼ 0.98), cationic transference number (tNa+ ∼ 0.31) and wide electrochemical stability (ESW ∼ 4.0 V) have been obtained. The above obtained properties of the prepared IL-NCPEs are promising for application in Na-rechargeable battery.

Organisation of the Structure and Functioning of Self-Sufficient Distributed Power Generation

During the operation of solar and wind power plants, it is necessary to solve issues related to the guaranteed capacity of these plants, as well as the frequency stabilisation in the power system where they operate, and maintain an operating mode of self-sufficiency conditions. One of the solutions to these problems is the use of energy storage systems. This article proposes a mathematical model for the study of frequency and power regulation processes in power systems with distributed generation, which includes renewable energy resources and energy storage systems. The novelty of the model lies in the possibility of determining energy cost indicators based on instantaneous energy power data. The model allows us to estimate the conditions under which distributed generation becomes self-sufficient. The results of the model calculations of two variants of power system operation, which includes wind generators with a capacity of 1500 MW, demonstrate the ability of the proposed model to accurately reproduce the dynamics of the frequency stabilisation process. The calculation results of the energy-economic indicators of a real power system combined with a powerful subsystem of wind generation and a battery-type energy storage system prove the competitiveness of self-sufficient renewable energy power plants.

Construction of FeSe2 nanoparticles encapsulated in TiN/N-doped carbon nanofibers intertwined with in-situ grown CNTs for pseudocapacitive Na+ storage

Metal selenides are recognized as potential anodes for sodium-ion batteries because of their high theoretical capacity. However, they face the hurdles of low electronic conductivity and significant volume change. In this study, a composite material consisting of FeSe2 nanoparticles enclosed within TiN/N-doped carbon nanofibers intertwined with in-situ grown carbon nanotubes (FeSe2@TNCF/CNTs) is successfully prepared by electrospinning, carbonization, and selenization approach. The TiN/N-doped carbon nanofibers enhance electronic conductivity, active sites for sodium-ion storage, and structural stability. Moreover, carbon nanotubes (CNTs) that in-situ grown on the carbon nanofibers not only enhance conductivity but also facilitate electrolyte penetration and buffer volume expansion. Benefiting from the synergistic effects of FeSe2, TiN, N-doped carbon nanofibers and CNTs, the hybrid FeSe2@TNCF/CNTs anode demonstrates long-term cycling stability and ultrafast pseudocapacitive sodium storage capability, with a specific capacity of 388.5 mAh/g delivered at 0.2 A/g after 1000 cycles. This work could provide inspiration for further research into the design and manufacture of high-performance materials for use in energy storage systems.

An ultimate peak load shaving control algorithm for optimal use of energy storage systems

Peak load shaving is one of the applications of energy storage systems (ESS) that will play a key role in the future of smart grid. Peak shaving is done to prevent the increase of network capacity to the amount of peak demand and also increase its reliability. Although the development of diverse ESS with high round-trip efficiency is very important and is considered a necessary condition for optimal peak load shaving, its sufficient condition is to follow an appropriate control algorithm. In this study, a new control algorithm called ultimate peak load shaving (UPLS) is developed for the optimal use of ESS for the peak shaving and valley filling purposes. The UPLS control algorithm is not only able to guarantee peak load shaving in any condition considering any load profile but also can ingeniously control ESS charging and discharging rates. This feature distinguishes it from other algorithms developed in the field.In this study, first, the UPLS control algorithm was explained in detail. Then, by comparing it with two other common control algorithms, its four unique features were investigated. After that, by applying these three control algorithms on different hypothetical load demand profiles A, B, C, and D, these features were demonstrated. Through sensitivity analysis and changing the allowed range for ESS charging and discharging rates (other parameters are assumed constant), how to design the grid power, Pg(t) by the UPLS control algorithm is described. It was found that the mentioned parameters are completely under the control of this algorithm. The optimization results show that peak demand can be reduced by 28.12 % and 27.82 % for load profiles B and C, respectively and the ESS discharging rate is significantly reduced to 55.28 % and 77.93 %, respectively compared to their corresponding values in basic design.

Multi-scenario design of ammonia-based energy storage systems for use as non-wires alternatives

Energy storage can be used by power distribution system operators as a non-wires alternative to defer infrastructure upgrades and improve feeder reliability. One emerging energy storage technology is energy storage via the synthesis and subsequent consumption of chemicals in internal combustion engines or fuel cells (i.e., ”chemical energy storage”). Some chemicals, such as hydrogen and ammonia, can be synthesized from renewable, carbon-free feedstocks using excess renewable generation. In this work, we develop an optimization model for investing in and using mobile ammonia-powered generators, ammonia storage equipment, and mobile batteries in tandem to provide non-wires alternatives to distribution networks efficiently. Specifically, we develop a mixed-integer quadratically constrained program to optimize the design and operation of distribution systems with ammonia and battery energy storage devices under multiple operational scenarios. This formulation is applied in a case study on a 15-bus test system. Ultimately, we find that ammonia-powered generators enable flexible operation and are best suited for long-term, high-total-energy use cases, whereas batteries are best suited for short-term use cases. Additionally, we find that a combination of chemical and electrochemical energy storage is more cost effective at responding to a variety of operational conditions than either form of energy storage alone.

ДВОНАПРАВЛЕНИЙ КАСКАДНИЙ ПЕРЕТВОРЮВАЧ ПОСТІЙНОЇ НАПРУГИ ДЛЯ ПОТУЖНОЇ СИСТЕМИ ЕНЕРГОНАКОПИЧЕННЯ В МЕРЕЖАХ ЕЛЕКТРОПОСТАЧАННЯ SMART GRID

Electromagnetic processes in a bidirectional cascade DC converter with the possibility of increasing and decreasing the output voltage levels in both directions of energy transfer for electric power storage systems are considered. Using the method of averaging in the state space, a mathematical model of the pulse converter was obtained. Analytical ex-pressions for calculating the ripple characteristics of a bidirectional cascade converter have been determined, with the help of which you can calculate the main parameters of the constant voltage converter, which provide permissible volt-age ripples on the DC buses and power choke current. To illustrate the developed calculation methodology, the ripple characteristics and parameters of a 30 kW converter for various load modes with 400V and 700V DC busbars for use in energy storage systems are determined. The bidirectional converter was calculated, analyzed and simulated in Math-cad/PSim packages at idle speed and at different load levels in order to confirm the effectiveness of the conducted re-search. Ref. 7, fig. 2. Keywords: method of averaging in state space, electric power storage systems, bidirectional constant voltage converter, non-isolated four-quadrant bidirectional converter.