Conversion System Research Articles

Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators

This study investigates strategies for enhancing the performance of dual-star induction generators in wind power systems by optimizing the full control algorithm. The control mechanisms involved include the PID (Proportional-Integral-Derivative) controller for speed regulation and the PI (Proportional-Integral) controller for flux, DC-link voltage, and grid connection control. The primary objective is to optimize the entire system by fine-tuning PID and PI controllers through the application of meta-heuristic algorithms, specifically Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). These algorithms play a crucial role in estimating the optimal values of Kp, Ki, and Kd for the PID speed controller, as well as Kp and Ki for the PI controller used in the flux, DC-link voltage, and grid connection for wind energy conversion system based dual-star induction generator. This comprehensive optimization ensures accurate parameter tuning for optimal system performance. A comparative analysis of the optimization results has been conducted, focusing on the outcomes obtained with the GWO algorithm. The findings reveal a notable reduction in steady-state error, signifying improved stability, and an overall enhancement in the wind power system’s performance. This study contributes valuable insights into the effective application of meta-heuristic algorithms for optimizing dual-star induction generators in wind power systems.

Dynamic performance enhancement of DFIG wind turbine using an active disturbance rejection control -based sliding mode control

The present paper proposes a hybrid control law that combines the active disturbance rejection control (ADRC) and the sliding mode control (SMC) to improve the decoupled control of the active and reactive power stator applied to a doubly-fed induction generator (DFIG) integrated into a wind energy conversion system. The stator is directly connected to the electric grid, while the rotor is connected to the electric grid via a back-to-back converter. The Lyapunov theory proves the stability of nonlinear control. The performance of the proposed strategy is compared with that of traditional ADRC and proportional-integral control (PI). The simulation results in MATLAB/SIMULINK environment using a 1.5MW wind system model show the proposed approach. The proposed algorithm shows an excellent dynamic performance that reduces the time response, overshoot, and steady-state error compared to the conventional controllers.Considering parametric variation, applying a novel controller results in fewer oscillations in stator powers, outperforming the PI regulator and classic ADRC.

Optimized hybrid osprey with PSO control for improved VSC-HVDC-wind power integration

Innovative power systems include offshore wind farms (OWFs) to generate electricity from renewable sources. So, reliable control systems are also necessary to make sure these power systems run smoothly. The purpose of this study is to combine the Osprey Optimization with Particle Swarm Optimization (OOPSO) to fine-tune the gains of Proportional-Integral (PI) controllers within the investigated wind energy conversion system (WECS) to enhance its performance. The study confirms the effectiveness of using the Hybrid OOPSO algorithm to optimize PI controllers in a WECS. The WECS includes two voltage source converter (VSC) stations at each end of a high voltage direct current (HVDC) transmission system. The transmission system links OWFs to the grid. The research demonstrates that the Hybrid OOPSO algorithm is superior to the genetic algorithm (GA) and PSO by enhancing setup stability and allowing fast voltage regaining following different fault cases. The introduced method performs more remarkably than traditional methods such as GA and PSO. The results of this study have shown that the proposed OOPSO enhances the overshoot in onshore voltage transient response under a symmetrical fault condition by 26% over PSO, 24% over GA, and 16% over OOA. The study is conducted using MATLAB/Simulink. The study underscores the importance of advanced optimization methods to guarantee offshore wind energy systems' efficient and dependable functioning.

Operational optimization of a rural multi-energy system supported by a joint biomass-solid-waste-energy conversion system and supply chain

Biomass, as one of the most accessible renewable resources, could reduce dependency on fossil fuels and mitigate associated environmental impacts. With economic development and improved living standards, biomass in rural areas is converted into various forms of energy to meet high energy demands. However, existing studies on rural multi-energy system operations assume energy production from biomass is a given input parameter and consider it to be a fixed constant or subject to upper and lower bounds, independent of the biomass supply chain. Indeed, biomass energy production is significantly influenced by the collection, transportation, storage, and other processes within the supply chain. This work proposes a mixed integer linear program (MILP) for the joint optimization of an integrated rural multi-biomass-solid waste energy conversion system with a source-grid-demand-based biomass-solid waste supply chain (IRMBS-BSC system). The IRMBS-BSC system encompasses three sub-systems: the biomass-solid waste source sub-system, the biomass-solid waste grid sub-system, and the biomass-solid waste demand sub-system. Various biomass-solid wastes are classified, and a source model is established to evaluate their availability. Subsequently, a supply chain model is developed within the biomass-solid waste grid sub-system. Based on the available and transported quantity from the biomass-solid waste source sub-system and grid sub-system, a multi-energy system operation model, considering refined biomass conversion processes, is proposed. A case study involving a multi-energy system that integrates multiple rural community biomass-solid waste supplies is conducted. The results demonstrate that the proposed MILP, which considers the biomass-solid waste supply chain, can reduce energy imports during the load peak periods and can also decrease the overall operational costs of the multi-energy system.

Harnessing Quantum Materials for Enhanced Energy Efficiency and Storage: A Comprehensive Study of Electronic Properties and Performance Optimization

This research explores the exciting potential of various quantum materials to enhance energy efficiency and storage capabilities, filling in a crucial gap in our understanding of their electronic properties and interactions. A major hurdle in this field is the lack of empirical data on key factors such as conductivity, charge mobility, and thermal stability—elements that are vital for optimizing the performance of these materials in energy applications. Our study aims to blend experimental data with computational simulations to create a robust framework for modelling how quantum materials behave under different conditions. This comprehensive approach helps lay a solid foundation for their practical use in energy technologies. We focus on an array of fascinating quantum materials, including topological insulators, transition metal dichalcogenides, and superconductors. To investigate these materials, we employ advanced techniques like scanning tunnelling microscopy and spectroscopy, which allow us to delve deep into their unique properties. While pursuing this research, we do encounter challenges. Variations in sample quality, environmental factors during experimentation, and the complexities of theoretical modelling can affect the consistency of our results and the interpretation of our data. Despite these obstacles, our findings suggest that certain quantum materials hold remarkable potential for enhancing energy storage and conversion systems through improved charge mobility and thermal stability. In fact, some materials have demonstrated conductivity levels that surpass those of traditional options, hinting at transformative applications in energy technologies. These insights highlight the importance of continuing our exploration of quantum materials, positioning them as promising candidates for tackling today’s energy challenges and paving the way for advancements in sustainable energy solutions.

Knowledge-Enhanced Conversational Recommendation via Transformer-Based Sequential Modeling

In conversational recommender systems (CRSs), conversations usually involve a set of items and item-related entities or attributes, e.g., director is a related entity of a movie. These items and item-related entities are often mentioned along the development of a dialog, leading to potential sequential dependencies among them. However, most of existing CRSs neglect these potential sequential dependencies. In this article, we first propose a Transformer-based sequential conversational recommendation method, named TSCR, to model the sequential dependencies in the conversations to improve CRS. In TSCR, we represent conversations by items and the item-related entities, and construct user sequences to discover user preferences by considering both the mentioned items and item-related entities. Based on the constructed sequences, we deploy a Cloze task to predict the recommended items along a sequence. Meanwhile, in certain domains, knowledge graphs formed by the items and their related entities are readily available, which provide various different kinds of associations among them. Given that TSCR does not benefit from such knowledge graphs, we then propose a knowledge graph enhanced version of TSCR, called TSCRKG. In specific, we leverage the knowledge graph to offline initialize our model TSCRKG, and augment the user sequence of conversations (i.e., sequence of the mentioned items and item-related entities in the conversation) with multi-hop paths in the knowledge graph. Experimental results demonstrate that our TSCR model significantly outperforms state-of-the-art baselines, and the enhanced version TSCRKG further improves recommendation performance on top of TSCR.

Nonlinear integral backstepping control based on particle swarm optimization for a grid-connected variable wind energy conversion system during voltage dips

Double-fed induction generator (DFIG) wind turbines connected to the grid are particularly subject to grid problems such as voltage dips. Because of this, it might be challenging to maintain system stability and prevent system disconnections when using a Proportional-Integral (PI) Controller to operate this system. This paper applies the Integral Backstepping Control for the wind power plant system connected to the power grid. The Integral Backstepping is a nonlinear and recursive method that employs the Lyapunov theory to ensure the system's stability. The best selection of gain values for the Lyapunov function guarantees improved system control. These gains are frequently adjusted using the trial-and-error strategy, which is time-consuming and inefficient. This technique becomes more complex when many parameters need to be determined. It also limits the system's performance and restricts this nonlinear approach's advantages. The objective is to apply the particle swarm optimization for computing several constant values of the nonlinear approach by minimizing an integral absolute error criterion index. The weighted sum of errors is employed to solve the multiple objective problems. The proposed controller tracks the maximum power point, maintains the voltage of the DC-Link constant, and controls active and reactive power. The robustness of this method is verified in critical conditions, including system parameter variation and asymmetrical and symmetrical grid faults. The simulation findings highlight the effectiveness and robustness of the combination of Integral Backstepping with Particle Swarm Optimization in terms of reducing response time from 10.6 (ms) to 2 (ms), canceling static error, and improving overshoot compared to the vector control based on PI regulator. Besides, the DC-Link voltage ripples during the asymmetrical grid faults are reduced to ±1 (V) using the suggested controller. The latter can be implemented thanks to advancements in Central Processor Unit technology.

Novel modular multilevel matrix converter topology for efficient high-voltage AC-AC power conversion

Introduction. This paper delves into the practical application of multilevel technology, particularly focusing on the capacitor-clamped converter as a promising solution for medium-to-high voltage power conversion, with specific emphasis on direct AC-AC switching conditions. Problem. The limitations of conventional single-cell matrix converters (MC) in efficiency and performance for medium-to-high voltage power conversion applications are well-recognized. Goal. The primary objective is to investigate the performance of the 3 phase modular multilevel matrix converter (3MC) with three flying capacitors (FCs) modeling. This investigation utilizes the Venturini method for gate pulse generation, aiming to compare the performance of the 3MC with standard converter designs. Methodology. To achieve the research goal, the Venturini method is adopted for generating gate pulses for the 3MC, representing a departure from conventional approaches. Detailed simulations employing MATLAB/Simulink are conducted to comprehensively evaluate the performance of the 3MC in comparison to conventional converter designs. Results. The simulation outcomes reveal a significant reduction of 73 % in total harmonic distortion (THD) achieved by the 3MC. This reduction in THD indicates improved robustness and suitability for medium-to-high voltage power conversion systems necessitating direct AC-AC conversion. These results highlight the efficacy of the 3MC in enhancing power conversion efficiency and overall performance. Originality. This paper contributes novel insights into the practical implementation of multilevel technology, particularly within the realm of capacitor-clamped converters. Furthermore, the utilization of the Venturini method for gate pulse generation in the 3MC represents an original approach to enhancing converter performance. Practical value. The research findings present significant advancements in multilevel transformer technology, offering valuable guidance for optimizing transformer design in various industrial and renewable energy applications. These contributions serve to enhance the development of reliable and efficient power systems, addressing critical needs in the energy sector. References 54, tables 3, figures 4.

Metaheuristic optimizing energy recovery from plastic waste in a gasification-based system for waste conversion and management

In an era where sustainable waste management and clean energy production are paramount, this study presents an innovative integration of plastic waste gasification with solid oxide fuel cell (SOFC) technology, optimized through metaheuristic particle swarm optimization (PSO). The research explores the conversion of polypropylene (PP) waste into syngas via gasification, which is then utilized as a fuel for SOFCs to generate electricity. The optimization process focuses on maximizing power and heating outputs while minimizing carbon dioxide emissions. The study's findings demonstrate the potential of integrating gasification and SOFC technologies to create a sustainable energy system that addresses the challenges of plastic waste. Key findings from the metaheuristic PSO multi-objective optimization reveal that optimal power production, approximately 360 kW, is achieved at temperatures exceeding 1140 K, irrespective of the steam/PP waste ratio. Heating production peaks at 1000 kW with temperatures above 1120 K and utilization factors over 0.765, while the minimum heating output is 700 kW. Emission analysis indicates a significant reduction in carbon dioxide emissions with increased temperatures and utilization factors, achieving a minimum of 700 kg/MWh at temperatures above 1120 K. The study's results demonstrate the effectiveness of the PSO optimization in fine-tuning the operational parameters of the integrated system, leading to improved energy efficiency and reduced environmental impact. Power production of 366.37 kW, a significant heating rate of 995.20 g/s, and emissions of 714.06 kg/MWh are the optimum performances. The research provides valuable insights into the potential of plastic waste-to-energy technologies as sustainable solutions for energy production and waste management.

A hybrid algorithm based on Bayesian optimization and Interior Point OPTimizer for optimal operation of energy conversion systems

Optimization methods are essential to improve the operation of energy conversion systems including energy storage equipment and fluctuating renewable energy. Modern systems consist of many components, operating in a wide range of conditions and governed by nonlinear balance equations. Consequently, identifying their optimal operation (e.g. minimizing operational costs) requires solving challenging optimization problems, with the global optimum often hidden behind many local ones. In this work, we propose a hybrid method that advantageously combines Bayesian optimization (BO) and Interior Point OPTimizer (IPOPT). The BO is a global approach exploiting Gaussian process regression to build a surrogate model of the cost function to be optimized, while IPOPT is a local approach using quasi-Newton updates. The proposed BO-IPOPT combination allows leveraging the parameter space exploration of the BO with the quasi-Newton convergence of IPOPT once solution candidates are in the neighborhood of an optimum. Using a challenging constrained test function, we test BO-IPOPT in accuracy and computational efficiency. Finally, we showcase the proposed method in the optimal operation of a renewable steam generation system. The results show that BO-IPOPT combines high accuracy and computational efficiency, achieving up to 50% better objective function values at the same CPU time than other state-of-the-art methods.

Increasing the Efficiency of Thermoelectric Conversion of Solar Energy

The article considers the latest achievements in the field of thermionic conversion of solar energy. The key aspects of thermionic emission, the problems of reducing the electrode work function and increasing the efficiency of devices are analyzed. It is shown that diamonds doped with phosphorus can be an attractive material for the purposes of thermionic emission. In this case, the presence of donor states can significantly narrow the space charge region, and at the same time reduce the potential barrier in the work function. The studies also found that the most successful and widely used method for overcoming the space charge effect is filling the interelectrode gap with cesium. The efficiency of thermionic conversion devices for solar energy increases by 1.6 times. Particular attention is paid to the use of new materials, such as nanostructures and carbon nanotubes, as well as promising technologies, such as photonic enhancement of thermionic processes. The analysis of the results showed that the photonic enhancement of the electron yield due to illumination is greater in p-type silicon, as predicted by theoretical models. The thermionic current increases by 1.7 times compared to silicon. The distance between the quasi-Fermi level and the Fermi level in n-type silicon is less than this distance in p-type silicon. As a consequence, the number of electrons in the conduction band is smaller. Methods for combating the space charge effect and optimizing the configuration of energy conversion systems are discussed.

Vertically Fluorinated Graphene Encapsulated SiOx Anode for Enhanced Li+ Transport and Interfacial Stability in High-Energy-Density Lithium Batteries.

Achieving high energy density has always been the goal of lithium-ion batteries (LIBs). SiOx has emerged as a compelling candidate for use as a negative electrode material due to its remarkable capacity. However, the huge volume expansion and the unstable electrode interface during (de)lithiation, hinder its further development. Herein, we report a facile strategy for the synthesis of surface fluorinated SiOx (SiOx@vG-F), and investigate their influences on battery performance. Systematic experiments investigations indicate that the reaction between Li+ and fluorine groups promotes the in situ formation of stable LiF-rich solid electrolyte interface (SEI) on the surface of SiOx@vG-F anode, which effectively suppresses the pulverization of microsized SiOx particles during the charge and discharge cycle. As a result, the SiOx@vG-F enabled a higher capacity retention of 86.4 % over 200 cycles at 1.0 C in the SiOx@vG-F||LiNi0.8Co0.1Mn0.1O2 full cell. This approach will provide insights for the advancement of alternative electrode materials in diverse energy conversion and storage systems.

Protection of rotor side converter of doubly fed induction generator based wind energy conversion system under symmetrical grid voltage fluctuations

This paper presents the protection of the rotor side converter in a grid-connected doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) during a symmetrical voltage dip. In order to manage rotor current and regulate DC link voltage, an efficient active crowbar protection circuit is implemented in the rotor side converter (RSC). To improve the low-voltage ride-through capability for grid-connected wind turbine systems, an integrated DFIG-based WECS role is crucial because wind turbines must remain connected to the utility grid during faults to ensure continuity and reliability of power supply. This paper aims to design and implement an efficient active crowbar protection technique to protect the RSC and avoid excessive rotor current during a symmetrical voltage dip. Therefore, the efficient active crowbar protection circuit is designed using MATLAB-Simulink software, and its performance is validated using a real-time (RT) simulator. Finally, the existing methods are compared with the proposed work outcomes, and a conclusion is made.

ENERGY MANAGEMENT IN HYBRID PV-WIND-BATTERY STORAGE-BASED MICROGRID USING DROOP CONTROL TECHNIQUE

The paper presents an efficient energy management system designed for a small-scale hybrid microgrid incorporating wind, solar, and battery-based energy generation systems using the droop control technique. The heart of the proposed system is the energy management system, which is responsible for maintaining power balance within the microgrid. The EMS continuously monitors variations in renewable energy generation and load demand and adjusts the operation of the energy conversion systems and battery storage to ensure optimal performance and reliability. The primary objective of the energy management system is to maintain power balance within the microgrid, even in the face of fluctuations in renewable energy generation and load demand. This involves dynamically adjusting the operation of the renewable energy sources and battery storage system to match the instantaneous power requirements of the microgrid. Overall, the paper presents a comprehensive approach to designing and implementing an efficient energy management system for a small-scale hybrid wind-solar-battery-based microgrid to extract maximum profit from electricity generation. By integrating renewable energy sources with energy storage and advanced control algorithms, the proposed system aims to enhance the reliability, stability, and sustainability of the microgrid's power supply.

Exploring an economic and highly efficient genetic transformation and genome-editing system for radish through developmental regulators and visible reporter.

Radish (Raphanus sativus L.) is one of the most important root vegetable crops worldwide. However, gene function exploration and germplasm innovation still face tremendous challenges due to its extremely low transformation efficiency. Here, an economic and highly efficient genetic transformation method for radish was explored by Agrobacterium rhizogenes-mediated transformation with the help of combining special developmental regulator (DR) genes and the visual identification reporter. Firstly, the RUBY gene, a betalain biosynthesis system, could result in a visual red-violet color used as a convenient and effective reporter for monitoring transgenic hairy roots screening of radish. However, the hairy roots-to-shoots conversion system of radish still stands as a barrier to the obtainment of whole transgenic plants, although different hormone combinations and various culture conditions were tried. Following, two DR genes including Wuschel2 (Wus2) and isopentenyl transferase (ipt), as well as their combination Wus2-ipt were introduced for the shoot regeneration capacity improvement. The results showed that the transgenic shoots could be directly generated without externally supplying any hormones in the presence of a Wus2-ipt combination. Then, Wus2-ipt along with the RUBY reporter was employed to establish an efficient genetic transformation system of radish. Moreover, this system was applied in generating gene-edited radish plants and the phytoene desaturase (RsPDS) gene was effectively knockout through albino phenotype observation and sequencing analysis. These findings have the potential to be widely applied in genetic transformation and genome-editing genetic improvement of other vegetable species.

Structure-Stability Relation of Single-Atom Catalysts under Operating Conditions of CO2 Reduction.

Single-atom catalysts (SACs) have exhibited exceptional atomic efficiency and catalytic performance in various reactions but suffer poor stability. Understanding the structure-stability relation is the prerequisite for stability optimization but has been rarely explored due to complexity of the degradation process and reaction environments. Herein, we successfully established the structure-stability relation of N-doped carbon-supports SACs (MN4 SACs) under working conditions of CO2 reduction, by using advanced constant-potential density functional theory calculations. Systematic mechanism investigation that considered different factors identifies the key role of initial hydrogen adsorption on the coordination N atom in catalytic stability, where the feasibility of the adsorption eventually determines the leaching of the metal atom. On this basis, a simple descriptor consisting of electron number and electronegativity is constructed, realizing accurate and rapid prediction of the stability of SACs. Furthermore, strategies via modifying the local geometric structure to improve the stability without changing the active centers are proposed accordingly, which are supported by related experiments. These findings fill the current void in understanding SAC stability under practical working conditions, potentially advancing the widespread application of SACs in sustainable energy conversion systems.

Laser-driven synthesis of functional materials for advanced energy applications: A short review

Since the initial exploration of synthesis possibilities at a liquid–solid interface through pulsed laser irradiation of iron in water, the pulsed laser ablation in liquid media (PLAL) process has garnered significant attention for constructing various nanostructures due to its simple, ultrafast, and clean methodology that avoids byproduct formation. Recent advances in functionalized heterostructures via the PLAL process suggest a broad range of applications for energy- conversion systems, including water splitting and energy harvesting. This review surveys recent advancements in PLAL for energy applications, with a specific focus on its applications in oxygen evolution reaction (OER) electrocatalysts and energy harvesting devices. The review discusses the synthesis mechanisms, advantages, challenges, and future research directions of PLAL, aiming to provide a comprehensive understanding of its potential and limitations.

Highly thermally conductive and shape-stabilized phase change materials with desirable solar/electric-to-thermal conversion performance based on high-modulus graphite/PVA foam

The widespread utilization of phase change materials (PCMs) has been impeded by challenges such as leakage, low thermal/electrical conductivity, and inadequate light absorption. Although constructing interconnected three-dimensional (3D) carbon networks within PCMs can mitigate these issues, their complex and energy-intensive fabrication processes, coupled with insufficient mechanical strength causing significant leakage and collapse of liquefied PCMs under external loads or impacts, hinder their practical application. Herein, we present a novel approach for the preparation of 3D graphite/polyvinyl alcohol foam (GPF) through a simple vacuum foaming combined with air-drying method. The obtained GPF-5 exhibits impressive compression strength (4.46 MPa) and compression modulus (93.1 MPa) at low density of 0.30 g/cm3. Upon impregnation with polyethylene glycol (PEG), GPF-5/PEG exhibits notable mechanical strength (7.62 MPa), contributing to its thermal shape stability and resistance to leakage. Even when subjected to temperatures above the melting point of PEG under external loads, GPF-5/PEG maintains its original shape without any liquid leakage. Due to the efficient construction of 3D thermal conductivity pathways within GPF, GPF-5/PEG demonstrates a high thermal conductivity of up to 3.48 W/m K−1 with graphite contents of 21.1 wt%, representing an increase of 136 % and 1640 % compared to graphite/PEG (with equivalent graphite content) and pure PEG, respectively. Additionally, GPF-5/PEG displays high latent heat of melting (136.8 J/g) and superior thermal cycling stability. Moreover, GPF-5/PEG demonstrates high solar-to-thermal and electric-to-thermal energy conversion capabilities, highlighting their potential for diverse applications. The work offers a rational strategy for fabricating high-modulus carbon foam, which can be used for PCMs-based multi energy conversion and storage systems with high thermo-mechanical properties.

User Experience and the Role of Personalization in Critiquing-Based Conversational Recommendation

Critiquing—where users propose directional preferences to attribute values—has historically been a highly popular method for conversational recommendation. However, with the growing size of catalogs and item attributes, it becomes increasingly difficult and time-consuming to express all of one’s constraints and preferences in the form of critiquing. It is found to be even more confusing in case of critiquing failures: when the system returns no matching items in response to user critiques. To this end, it would seem important to combine a critiquing-based conversational system with a personalized recommendation component to capture implicit user preferences and thus reduce the user’s burden of providing explicit critiques. To examine the impact of such personalization on critiquing, this article reports on a user study with 228 participants to understand user critiquing behavior for two different recommendation algorithms: (i) non-personalized , that recommends any item consistent with the user critiques; and (ii) personalized , which leverages a user’s past preferences on top of user critiques. In the study, we ask users to find a restaurant that they think is the most suitable to a given scenario by critiquing the recommended restaurants at each round of the conversation on the dimensions of price, cuisine, category, and distance. We observe that the non-personalized recommender leads to more critiquing interactions, more severe critiquing failures, overall more time for users to express their preferences, and longer dialogs to find their item of interest. We also observe that non-personalized users were less satisfied with the system’s performance. They find its recommendations less relevant, more unexpected, and somewhat equally diverse and surprising than those of personalized ones. The results of our user study highlight an imperative for further research on the integration of the two complementary components of personalization and critiquing to achieve the best overall user experience in future critiquing-based conversational recommender systems.

Caching Historical Embeddings in Conversational Search

Rapid response, namely, low latency, is fundamental in search applications; it is particularly so in interactive search sessions, such as those encountered in conversational settings. An observation with a potential to reduce latency asserts that conversational queries exhibit a temporal locality in the lists of documents retrieved. Motivated by this observation, we propose and evaluate a client-side document embedding cache, improving the responsiveness of conversational search systems. By leveraging state-of-the-art dense retrieval models to abstract document and query semantics, we cache the embeddings of documents retrieved for a topic introduced in the conversation, as they are likely relevant to successive queries. Our document embedding cache implements an efficient metric index, answering nearest-neighbor similarity queries by estimating the approximate result sets returned. We demonstrate the efficiency achieved using our cache via reproducible experiments based on Text Retrieval Conference Conversational Assistant Track datasets, achieving a hit rate of up to 75% without degrading answer quality. Our achieved high cache hit rates significantly improve the responsiveness of conversational systems while likewise reducing the number of queries managed on the search back-end.