Dynamic Energy Demand Research Articles

A Comprehensive Multi-Objective Energy Management Approach for Wearable Devices with Dynamic Energy Demands

A Comprehensive Multi-Objective Energy Management Approach for Wearable Devices with Dynamic Energy Demands

Dynamic modeling and energy demand analysis of chain heavy load transfer system on marine mobile platform

ABSTRACT The demand for transferring the heavy load on marine mobile platform is steadily increasing, surpassing the capabilities of the current system. This paper introduces a chain heavy load transfer system and develops its dynamic model. This paper studies the sensitivity laws of the system’s energy demand to unknown parameters and proposes an adaptive mutation particle swarm optimization (AMPSO), whose identification accuracy is verified through comparative analysis. Experimental verification confirms the reliability of the system model while studying the influence of environmental loads on the system’s energy demand. The results demonstrate that the chain transfer system meets the requirements of marine mobile platforms, and the system model incorporates the primary influencing factors. Unknown parameters significantly influence the system’s energy demand, and the displacement error after parameter identification using AMPSO is 0.00056 rad, substantially enhancing the accuracy of model calculations. The model calculations of the system closely align with experimental measurements, with a maximum displacement error not exceeding 0.004 rad. Sway exerts a greater influence on the system’s energy demand compared to tilt. When tilt and sway are superimposed, the friction torque between the load and the track results in increased input torque and power for the vertical arrangement of the system, with a maximum torque of 328.6kN·m and maximum power of 400 kW.

Performance and Techno-Economic Analysis of Optimal Hybrid Renewable Energy Systems for the Mining Industry in South Africa

This paper presents an exploration of the potential of hybrid renewable energy systems (HRESs), combining floating solar photovoltaics (FPV), wind turbines, and vanadium redox flow (VRF) battery energy storage systems (BESSs) to expedite the transition from conventional to renewable energy for the mining sector in South Africa. The feasibility study assesses how to enhance the overall efficiency and minimize greenhouse gas emissions from an economic standpoint by using the Hybrid Optimization of Multiple Energy Resources (HOMER) grid software version 1.11.1 and PVsyst version 7.4. Furthermore, the BESS Covariance Matrix Adaptation Evolution Strategy (CMA-ES) dispatch algorithm is proposed to make the most of the battery storage capacity and capability, aligning it with the dynamic energy demand and supply patterns of an HRES. The proposed HRES includes a highly efficient SFPV with a performance ratio of 0.855 and an annual energy production of 15,835 MWh; a wind turbine (WT) operating for 2977 h annually, achieving a 25% wind penetration rate; and a dynamic VRF-BESS with a 15,439 kWh life throughput and a 3 s dispatch response time. This HRES has a CapEx of R172 million, a 23.5% Internal Rate of Return (IRR), and an investment payback period of 4.9 years. It offers a low Levelized Cost of Energy (LCoE) at 4.27 R/kWh, a competitive Blended Cost of Energy (BCoE) at 1.91 R/kWh, and a positive net present cost (NPC), making it economically advantageous without external subsidies. Moreover, it annually reduces CO2 emissions by 1,715,468 kg, SO2 emissions by 7437 kg, and NOx emissions by 3637 kg, contributing to a significant environmental benefit.

Optimization of building demand flexibility using reinforcement learning and rule-based expert systems

The increasing use of renewable energy in buildings requires optimization of building demand flexibility to reduce energy costs and carbon emissions. Nevertheless, the optimization process is generally challenging that needs to consider the on-site intermittent energy supply, dynamic building energy demand, and proper utilization of energy storage systems. Leveraging the growing availability of operational data in buildings, data-driven strategies such as reinforcement learning (RL) have emerged as effective approaches to optimizing building demand flexibility. However, training a reliable RL agent is practically data-demanding and time-consuming, limiting its practical applicability. This study proposes a new strategy that integrates a rule-based expert system (RBES) and RL agents into the decision-making process to jointly reduce building energy costs, minimize the peak-to-average ratio (PAR) of grid power, and maximize PV self-consumption. In this strategy, the RBES determines system operation directly in less complex decision-making scenarios, while, in more intricate decision-making environments, it provides a reference decision for RL to explore optimal solutions further. This integration empowers RL agents to avoid unnecessary exploration and significantly enhance learning efficiency. The proposed strategy was tested using PV generation data and energy consumption data of a low energy office building. The results demonstrated an 85.7% improvement in RL learning efficiency and this strategy can successfully avoid sub-optimal convergence during policy learning. Compared to relying solely on the RBES, the proposed strategy led to 5.4% and 19.2% reductions in the electricity costs and daily PAR of grid power at peak hours, respectively. The strategy also achieved a satisfying PV self-consumption ratio of 62.4%, which is merely 0.4% lower than the optimal value determined by the RBES strategy that prioritized maximizing PV self-consumption. Additionally, compared with a model predictive control method developed for cost reduction, the strategy achieved similar cost savings while significantly reducing the decision time.

Reduced order 1 + 3D numerical model for evaluating the performance of solar borehole thermal energy storage systems

This study presents a computationally efficient numerical model for solving the fluid flow and heat transfer phenomena in co-axial boreholes heat exchangers arranged on a rectangular grid (N × N) drill hole pattern. Such systems of boreholes are an integral component of a Solar-Borehole Thermal Energy Storage System for seasonal thermal storage application. The numerical model couples a one-dimensional fluid flow and convective heat transfer model in the heat exchanger with a three-dimensional model of conductive heat transfer in the strata. In addition to solving mass and heat transfer in borehole systems, it integrates a solar thermal collector system and building dynamic thermal load to design a Solar- Borehole Thermal Energy Storage system for a 182-unit residential apartment building. Simulation also considers and evaluates the conductive, convective, and radiative thermal losses of the system. The model is validated using experimental data from a coaxial borehole heat exchanger and compared to the results of a commercial finite volume solver for multiple co-axial boreholes. The computational cost of developed numerical model is reduced by a factor of 194 compared to a commercial finite volume solver. Solar- Borehole Thermal Energy Storage system simulation is performed for a period of five years considering hourly fluctuations in solar irradiance and building dynamic thermal energy demand. Parametric study is performed on depth and spacing of boreholes, and mass flowrate in the solar thermal collectors. It is found that a system of 100 boreholes, each 100 m deep, linked to 674 solar thermal collectors can provide the heating for the building with a total thermal demand of 5600 GJ per year.

A comprehensive molecular profiling approach reveals metabolic alterations that steer bone tissue regeneration

Bone regeneration after fracture is a complex process with high and dynamic energy demands. The impact of metabolism on bone healing progression and outcome, however, is so far understudied. Our comprehensive molecular profiling reveals that central metabolic pathways, such as glycolysis and the citric acid cycle, are differentially activated between rats with successful or compromised bone regeneration (young versus aged female Sprague-Dawley rats) early in the inflammatory phase of bone healing. We also found that the citric acid cycle intermediate succinate mediates individual cellular responses and plays a central role in successful bone healing. Succinate induces IL-1β in macrophages, enhances vessel formation, increases mesenchymal stromal cell migration, and potentiates osteogenic differentiation and matrix formation in vitro. Taken together, metabolites—here particularly succinate—are shown to play central roles as signaling molecules during the onset of healing and in steering bone tissue regeneration.

Deployment optimization of battery swapping stations accounting for taxis’ dynamic energy demand

Current research on battery swapping stations (BSSs) deployment problems overlooks the influence of BSS layouts on the daily performance of battery swapping taxis (BSTs). This study aims to propose a BSS deployment optimization model to guarantee taxi transport capacity. To this end, the dynamic swapping demands of BSTs are predicted by modeling battery swapping behavior, reconstructing trip chain, and simulating BSS operation. Furthermore, BSSs’ deployment is optimized to minimize the swapping loss time. Using the daily trajectory data of 9862 taxis in Tianjin, we set up six scenarios to elucidate the trade-off among the layout and service level, the transport capacity, and the environmental efficiency. The optimal layout ensures 98.1% of the taxi transport capacity and reduces carbon emissions by 44.09%. The employed behavioral model enhances the network equilibrium from 0.33 to 0.067. Finally, the roles of driving range and charging speed in attracting and assigning energy demand are revealed.

Multi-criteria performance analysis and optimization of a solar-driven CCHP system based on PEMWE, SOFC, TES, and novel PVT for hotel and office buildings

Solar-powered multi-generation systems contribute to efficiently reducing the consumption of fossil fuels and lowering greenhouse gas emissions. This study proposes a solar-driven CCHP system combined with a novel photovoltaic thermal collector, proton exchange membrane water electrolyzer, solid oxide fuel cell, and thermal energy storage to meet the user's multiple dynamic energy demands. Multi-criteria performance analysis of the CCHP system is conducted from the perspectives of energy efficiency, sustainability, energy savings, environmental benefits, and economy for typical hotel and office buildings. The multi-objective optimization is carried out to optimize the parameter configurations and equipment capacities of the proposed system. The results demonstrate that the PV coverage ratio of the partially covered parabolic trough photovoltaic thermal (PCPTPVT) collector has noteworthy influences on the PCPTPVT outputs, the determination of the equipment capacities of the CCHP system, and further affects the overall comprehensive performance. The sustainability indicators of the hotel and office rise with the increasing PV coverage ratio, and that of the office is gradually larger than that of the hotel. The optimal comprehensive performance and annual total cost of the CCHP system from the multi-objective optimization are 1.004 and 87601 $ for the hotel and 1.049 and 92759 $ for the office, respectively. The PV coverage ratios of the preferred optimal solution for the hotel and office are 0.659 and 0.859, respectively.

Multi‐state reliability assessment for shipboard hybrid turbine‐diesel generation system with redundancy and aging effects

AbstractComplex system structures, dynamic energy demands, and tough operating environment pose a huge challenge to reliability assessment and risk control. Taking multi‐state, redundant structure and aging impact into account, reliability assessment for shipboard hybrid turbine‐diesel generation system is studied. This work adopts a comprehensive method combining the Markov method and universal generating function (UGF): the Markov method is used for reliability modeling at the unit level, and then synthesized and calculated through UGFs to obtain system‐level reliability. Based on the above methodology, this work presents a multi‐state system (MSS) model with different configuration structures and failure rate types, then compares the availability evaluation results of the single power generation system, the hybrid power system with redundancy and aging effects. The results show that (1) redundant design effectively improves system availability to meet power supply requirements; (2) in the case of system aging and imperfect repair, redundant configuration and power distribution can significantly improve the availability of power supply to be almost unaffected by aging, and effectively reduce the risk of performance failure; and (3) when the redundancy is applied, the system power supply availability is not sensitive in the high‐value range of the redundant installed capacity ratio, but sensitive in the high‐value range of effective coefficient about power compensation control.

Thermal sensitivity and potential cooling-related energy saving of masonry walls through the lens of solar heat-rejecting paints at varying orientations

This study aims to analyse the thermal efficiency of wall elements with varying position-allocation-thickness of insulation, in the aspect of the optical properties of their external paint. A special focus has been placed on the role of solar reflectivity in wall coatings while taking into account the impact of the ambient environment at all cardinal points. In this light, the problem of urban environment warming must be addressed, while considering occupant reliance on air-conditioning. In the initial stage, the key research objective is to shed some light on the performance of analysed wall assemblies in terms of thermal sensitivity (decrement factor and time lag). On the other hand, at the targeting stage, our main intention is to demonstrate the eminence of solar heat-rejecting paints on the cooling power demand of wall arrangements. Furthermore, this work is extended to the assessment of the overall heating and cooling demands on an annual basis. A thermal-network model is developed within this framework to determine temperature variations and heat fluxes in the margins of the examined setups. The potential benefits of the suggested model are twofold. Accordingly, the findings of the numerical analyses reveal the configurations and operating conditions proving the optimal dynamic thermal parameters and energy demand. Numerical simulations indicate that an optimal cooling power capacity is noticeable for wall surfaces covered with solar heat-rejecting paints; cooling saving can exceed 90% for highly solar-reflective surfaces. However, when it comes to unveiling the global performability of ultra-white paints the overall improvement of conditions may vary radically; a reflective paint will probably not be sufficient to counterbalance both heating and cooling concerns. In terms of annual heat transmission loads, results exhibit an optimal solar absorptivity of 0.35 for north/east/west facing walls and 0.75 for south-oriented walls. Also, within the confines of our attention, by the increase of insulation level, the energy benefit can reach up to 40% per annum.

REVIEW ON SMART POWER DISTRIBUTION NETWORKS: A SUSTAINABLE DEVELOPMENTAL APPROACH

Smart power grid distribution network is very paramount in building a sustainable Electricity network for smart grid operations. As stipulated and stated in the agenda 2030 sustainable development goals project (agenda goals; 3, 7, 9, 11, and 13), it’s pertinent to create, research and develop an enabling environment within the power grid horoscope with these agendas in view. This helps the design of reliable and quality distribution system to replace the traditional distribution networks which produce carbons and other harmful chemical substances in to the environment. Recently, the distribution system has been burdened with numerous dynamic loads and energy demands due tothe current Covid-19 situation which has brought many to work from home. The Smart distribution network as reviewed in this paper is a critical component and final stage in providing optimum power solutions to customers. Discussed in this paper is the reviews of developmental approaches to smart power distributions, since it networks perform critical functions and bears social responsibility by adopting large and small hydro-dams, solar, wind, and other clean energy sources, for delivering power to its end users safely and effectively.

Thermodynamic analysis of operating strategies for waste heat recovery of combined heating and power systems

The multi-generation system often operates under off-design conditions due to the highly dynamic energy demand. Improving its off-design performance is crucial for energy conservation and emission reductions. To this end, this paper proposed a novel control strategy for the gas turbine cycle applied in combined heating and power (CHP) system, which effectively combines turbine inlet temperature control (TITC) and inlet air throttling control (IATC) methods, to improve system off-design performance and avoid discharging low-temperature gas. The hybrid control strategy can improve system efficiency by 3.70% on average over the traditional TITC method during the entire load range. Moreover, the maximum efficiency difference between these two methods reached 5.15% at the 30%-load factor conditions. To further showcase the effectiveness of the IAT-TITC method, the CHP system is integrated with an electric chiller and thermal refrigeration, respectively, forming four combined cooling, heating, and power (CCHP) system scenarios to supply the energy for a hotel building. Not only did the CCHP system scenarios with the IAT-TITC method show better performance, but also considering an electric chiller as an extra cooling supply machine can reduce primary energy consumption by 30.73%, 28.32%, and 11.56% in summer, transition season, and winter typical days, respectively, compared with separation system.

AsTAR: Sustainable Energy Harvesting for the Internet of Things through Adaptive Task Scheduling

Battery-free Internet-of-Things devices equipped with energy harvesting hold the promise of extended operational lifetime, reduced maintenance costs, and lower environmental impact. Despite this clear potential, it remains complex to develop applications that deliver sustainable operation in the face of variable energy availability and dynamic energy demands. This article aims to reduce this complexity by introducing AsTAR, an energy-aware task scheduler that automatically adapts task execution rates to match available environmental energy. AsTAR enables the developer to prioritize tasks based upon their importance, energy consumption, or a weighted combination thereof. In contrast to prior approaches, AsTAR is autonomous and self-adaptive, requiring no a priori modeling of the environment or hardware platforms. We evaluate AsTAR based on its capability to efficiently deliver sustainable operation for multiple tasks on heterogeneous platforms under dynamic environmental conditions. Our evaluation shows that (1) comparing to conventional approaches, AsTAR guarantees Sustainability by maintaining a user-defined optimum level of charge, and (2) AsTAR reacts quickly to environmental and platform changes, and achieves Efficiency by allocating all the surplus resources following the developer-specified task priorities. (3) Last, the benefits of AsTAR are achieved with minimal performance overhead in terms of memory, computation, and energy.

Comparative environmental and economic evaluation of green roofs under Mediterranean climate conditions – Extensive green roofs a potentially preferable solution

Green roofs constitute a prominent urban green infrastructure solution and are considered to be able to enhance urban sustainability and resilience. Existing literature, however, lacks a holistic evaluation of their life cycle impacts. In this study, a comparative environmental and economic life cycle analysis between green and flat roofs under Mediterranean climate conditions was conducted, examining all green roof types (extensive, semi-intensive, intensive), two types of drainage layer (granular and synthetic) for each one, all green roof layer materials, life cycle stages (production of materials, use and maintenance, disposal) and their quantifiable costs and benefits, aiming to address this research gap. Dynamic whole-building energy demand simulations were also conducted in its context, which additionally investigated the effect of various factors on green roofs' energy saving potential. Thermally insulated green roofs provided a small improvement in the energy consumption for heating and cooling (up to 8.30% and 3.50% respectively) but significant reductions in total life cycle energy consumption (8–31%), CO2 emissions (24–32%) and waste production (15–60%) rendering them a cleaner environmental option compared to flat roofs. However, they also produced a very important increase in total life cycle water consumption (279–835%). The extensive green roof types were the ones to perform better in all these indexes. They were also the only type to constitute a better economic choice than a flat roof for the private owner in a low discount rate (0.25%) scenario, both with or without public benefits. As this study shows, the green roofs' potential is not limited only to the improvement of the buildings’ energy efficiency, as numerous studies in the field of energy efficiency highlight, but compared to flat roofs, expand to reduce the embodied energy, greenhouse gas emissions and material waste. By means of environmental footprint, they are superior to flat roofs when only energy and emissions are considered, while in regions where water availability is a key environmental factor, this superiority is in question, together with their economic feasibility in countries where this technology is not financially supported.

Energy Management System Designed for Residential Grid connected Micro Grid

This paper presents an energy management system supported by PI Controller for a residential grid connected micro grid with renewable hybrid generation (wind and photo voltaic) and battery system. Modeling hybrid system includes non conventional energy sources given at sporadic supply conditions and dynamic energy demand, and to make conceptual energy storage with the help of battery system . Designing an appropriate scheme that dynamically changes modes of renewable integrated system based on the availability of RES power and changes in load. Wind,PV are the primary power supply of the system; battery is going to be act as a substitute.The PI controller is developed and carried out for the aimed hybrid(Wind and PV) energy system to integrate the non conventional energy sources to the serviceability either to grid or to Residential loads.main objective is improvement of transients during switching periods by using an efficient PI controller.maximum power point tracking is also other objective is energy management system designed for the residential grid connected Micro Grid. Simulations are carried out on the proposed Hybrid energy system using MATLAB/ SIMULINK.

Application of Phase Change Materials and Conventional Thermal Mass for Control of Roof-Generated Cooling Loads

Among all of the internal fabric and external enclosure components of buildings, sloped roofs and adjacent attics are often the most dynamic areas. Roofs are exposed to high temperature fluctuations and intense solar radiation that are subject to seasonal changes in climatic conditions. Following the currently rising interests in demand-side management, building energy dynamics, and the thermal response characteristics of building components, this paper contains unpublished results from past studies that focused on innovative roof and attic configurations. The authors share unique design strategies that yield significant reduction of daytime roof peak temperatures, thermal-load shavings, and up to a ten-hour shift of the peak load period. Furthermore, advance configurations of the roofs and attics that are discussed in this paper enable over 90% reductions in roof-generated peak-hour cooling loads and sometimes close to 50% reductions in overall roof-generated cooling loads as compared with traditionally constructed roofs with the same or similar levels of thermal insulation. It is expected that the proposed new roof design schemes could support the effective management of dynamic energy demand in future buildings.

System Design and Optimisation Study on a Novel CCHP System Integrated with a Hybrid Energy Storage System and an ORC

For achieving higher energy transferring efficiency from the resources to the load, the Combined Cooling, Heating, and Power (CCHP) systems have been widely researched and applied as an efficient approach. The key idea of this study is designing a novel structure of a hybrid CCHP system and evaluating its performance. In this research, there is a hybrid energy storage unit enhancing the whole system’s operation flexibility while supplying cooling, heating, and power. An ORC system is integrated into the CCHP system which takes responsibility of absorbing the low-temperature heat source for electricity generation. There are a few research studies focusing on the CCHP systems’ performance with this structure. In order to evaluate the integrated system’s performance, investigation and optimisation work has been conducted with the approaches of experimental studies and modelling simulation. The integrated system’s configuration, the model building process of several key components, the optimisation method, and the case studies are discussed and analysed in this study. The design of the integrated system and the control strategy are displayed in detail. Several sets of dynamic energy demand profiles are selected to evaluate the performance of the integrated system. The simulation study of the system supplying selected scenarios of loads is conducted. A comprehensive evaluation report indicates that the system’s efficiency during each study process differs while supplying different loads. The results include the power supplied by each component, the energy consumed by each type of load, and the efficiency improvements. It is found that the integrated system fully satisfies the selected domestic loads and various selected scenarios of loads with high efficiency. Compared to conventional power plants or CHP systems, the system efficiency enhancement comes from higher amount of recovery waste heat. Especially, the ORC system can absorb the low-temperature heat source for electricity generation. Compared to the original following electrical load (FEL) control strategy, the optimisation process brings overall efficiency improvements. The system’s overall efficiency was increased by from 3%, 3.18%, 2.85%, 17.11%, 8.89%, and 21.7% in the second case studies. Through the whole study, the main challenge lies within the design and the energy management of the integrated system.

Coupled transmembrane mechanisms control MCU-mediated mitochondrial Ca2+ uptake

Ca2+ uptake by mitochondria regulates bioenergetics, apoptosis, and Ca2+ signaling. The primary pathway for mitochondrial Ca2+ uptake is the mitochondrial calcium uniporter (MCU), a Ca2+-selective ion channel in the inner mitochondrial membrane. MCU-mediated Ca2+ uptake is driven by the sizable inner-membrane potential generated by the electron-transport chain. Despite the large thermodynamic driving force, mitochondrial Ca2+ uptake is tightly regulated to maintain low matrix [Ca2+] and prevent opening of the permeability transition pore and cell death, while meeting dynamic cellular energy demands. How this is accomplished is controversial. Here we define a regulatory mechanism of MCU-channel activity in which cytoplasmic Ca2+ regulation of intermembrane space-localized MICU1/2 is controlled by Ca2+-regulatory mechanisms localized across the membrane in the mitochondrial matrix. Ca2+ that permeates through the channel pore regulates Ca2+ affinities of coupled inhibitory and activating sensors in the matrix. Ca2+ binding to the inhibitory sensor within the MCU amino terminus closes the channel despite Ca2+ binding to MICU1/2. Conversely, disruption of the interaction of MICU1/2 with the MCU complex disables matrix Ca2+ regulation of channel activity. Our results demonstrate how Ca2+ influx into mitochondria is tuned by coupled Ca2+-regulatory mechanisms on both sides of the inner mitochondrial membrane.

Efficient and sustainable design for demand-supply and deployment of waste heat and cold energy recovery in marine natural gas engines

Considering the impending global energy crisis and increasing environmental pollution, and the rapid growth of the shipping industry, there is an urgent need for efficient, clean, and sustainable technology to increase the energy efficiency and profitability of the shipping industry. In this paper, we propose an integrated waste heat and cold energy recovery system for marine natural gas engines that can conserve energy, reduce pollution, and satisfy the dynamic daily energy demands of a ship. The accuracy of the model is evaluated by comparing it with results of previous studies. The sensitivity of various parameters on each subsystem is analysed, and the relationship between the mass flow and the parallel system is discussed. After determining the optimum operating conditions, the thermodynamic and economic performance of each subsystem and the combined system are analysed. The results indicate that the initial investment cost of the combined system can be recovered in 6.976 years. The fuel savings and reduction in carbon dioxide emissions are 38.55 kg/h and 52.08 kg/h, which proves that the proposed system can achieve excellent economic performance and emission reductions. Compared with the original engine, the net power production and thermal efficiency of the proposed system are 214.5 kW and 5.14% higher. The proposed achieves energy conservation and pollution reduction and satisfies the dynamic energy demands of the ship; it is also economically viable and fills the gap of multiform utilisation of cold energy in waste heat recovery.

Reinforcement learning for building controls: The opportunities and challenges

Building controls are becoming more important and complicated due to the dynamic and stochastic energy demand, on-site intermittent energy supply, as well as energy storage, making it difficult for them to be optimized by conventional control techniques. Reinforcement Learning (RL), as an emerging control technique, has attracted growing research interest and demonstrated its potential to enhance building performance while addressing some limitations of other advanced control techniques, such as model predictive control. This study conducted a comprehensive review of existing studies that applied RL for building controls. It provided a detailed breakdown of the existing RL studies that use a specific variation of each major component of the Reinforcement Learning: algorithm, state, action, reward, and environment. We found RL for building controls is still in the research stage with limited applications (11%) in real buildings. Three significant barriers prevent the adoption of RL controllers in actual building controls: (1) the training process is time consuming and data demanding, (2) the control security and robustness need to be enhanced, and (3) the generalization capabilities of RL controllers need to be improved using approaches such as transfer learning. Future research may focus on developing RL controllers that could be used in real buildings, addressing current RL challenges, such as accelerating training and enhancing control robustness, as well as developing an open-source testbed and dataset for performance benchmarking of RL controllers.