Battery Exchange Research Articles

Long-term efficacy of cardiac resynchronization therapy with triple-site ventricular stimulation in heart failure patients

Abstract Background Cardiac resynchronization therapy (CRT) reduces cardiac electrical dyssyncrhony and improves clinical outcomes in selected symptomatic patients with left ventricular (LV) systolic dysfunction. However, patients after conventional CRT (Bi-V) have residual cardiac electrical dyssynchrony. CRT with multi-site pacing has gained attention to improve short-term outcome refractory to BiV. However, long-term follow up outcome is still uncertain. We evaluated the efficacy of CRT with triple-site ventricular stimulation (Tri-V), involving 2 LV lead in 2 anatomically separated veins and a single RV lead in RV apex for long-term follow up period. Methods Ninety-two consecutive patients with New York Heart Association class II-IV heart failure, LV ejection fraction <0.35, and QRS interval >120ms, who underwent CRT implantation between 2010 and 2016, were enrolled. Acute hemodynamic responses to temporary dual-site and triple-site ventricular pacing were evaluated, and patients were assigned to either the Bi-V or Tri-V group based on the results. The primary endpoint was the HF clinical composite score, which scores patients as improved, unchanged, or worsened. The secondary endpoint included cumulative survival from any cause of death and HF hospitalization. Results Tri-V was successfully implanted in 32 patients. Five patients were excluded during follow-up because of uncaptured pacing in one of the LV leads. One year post-implantation, the clinical composite score significantly improved in 84% of Tri-V patients compared to 57% in the Bi-V group (p=0.026). After a median follow-up of 12.0 years, there was no significant difference about all-cause mortality between Tri-V and Bi-V (p=0.147). However, Tri-V group showed significant lower HF hospitalization (p=0.005) than Bi-V group. The number of battery exchange was not significantly different between Tri-V than Bi-V group (1.4±0.7 vs. 1.2±0.8, p=0.361). Conclusion CRT with Tri-V, characterized by stable lead parameters, appears to offer greater long-term benefits than Bi-V in terms of clinical status without excessive battery exchanges.figure

Research on the Location Selection Problem of Electric Bicycle Battery Exchange Cabinets Based on an Improved Immune Algorithm

The rise of new energy technologies has accelerated progress towards sustainable development, and many companies are beginning to invest in renewable resource-related facilities. Electric bicycles have always been an important mode of green transportation; however, they also have problems such as slow charging, difficult charging, and that burning and short circuiting may occur during charging. Electric bicycle battery exchange cabinets effectively solve these problems by exchanging low batteries with full batteries instead of charging. However, current battery exchange cabinets face the problems of insufficient construction and unreasonable site selection. Therefore, this paper proposes a location selection model for electric bicycle battery exchange cabinets based on point demand theory, aiming to maximize rider satisfaction and the service capacity of exchange cabinets. The immune algorithm is introduced to solve the location model; however, the traditional immune algorithm has some problems such as poor stability and slow convergence. In this paper, the mutation process of the traditional immune algorithm is improved by introducing multi-point mutation, guided mutation, and local search. Finally, based on the data of electric bicycle riders in Shanghai, we verify that the location model based on point demand theory performs well on two objective functions of rider satisfaction and battery exchange cabinet service capability. We also expand the application of point demand theory to location models. Then, by conducting experiments with different parameter groups, through sensitivity analysis and convergence analysis, we verified that the improved immune algorithm performs better than the traditional immune algorithm in its accuracy, search accuracy, stability, and convergence.

A REVIEW ON HYBRID ELECTRIC VEHICLES USING VARIOUS ENERGY STORAGE: A MODERN APPLICATION OF RENEWABLE ENERGY SOURCES

Climate change, diminishing reserves of fossil fuels, and energy security demand alternatives to our current course of energy usage and consumption. Energy storage technology has become an enabling technology for renewable energy applications and enhancing power quality in the transmission and distribution power systems. There are various types of energy storage technology are present nowadays, including compressed air energy storage(CAES), flywheel energy storage(FES), pumped hydro energy storage(PHES), battery energy storage(BES), flow battery energy storage(FBES), super conducting magnetic energy storage(SMES), super capacitor energy storage(SCES), hydrogen energy storage, synthetic fuels, and thermal energy storage(TES) and today’s one of the most talked technology using these Energy storage system is Electric Vehicles. Electric vehicles are superior to internal combustion engines (ICE) in efficiency, endurance, durability, acceleration capability and simplicity. Also, they can recover some energy during regenerative braking and they are also friendly with the environment. There are now four types of electric cars: battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), conventional hybrid electric vehicles (HEVs), and fuel-cell electric vehicles (FCEVs). And there are three charging methods for electric vehicles, namely conductive, inductive, and battery exchange. This paper will show a detail study of different energy storage systems’ characteristics with their suitability as a storage unit of Electric vehicle.

Design of a Fertilizing Robot Application with Regard to Energy Consumption

Electrically driven agricultural robots experience faster battery depletion than vehicles moving on asphalt because higher rolling and traction resistance require more energy. The problem arises in areas without access to the electrical grid, where charging electrically driven agricultural robots is not possible, resulting in interruptions to the robot's continuous operation. A combined energy production station powered by biogas, hydrogen, and solar energy with the prototype of autonomous fertilizing robot for blueberry plantations, to perform plant-based precision fertilization on depleted milled peat fields was created by the agrorobotics working group of the Estonian University of Life Sciences [1, 2]. The station includes unique components such as an automatic battery exchange system and an electric generator with a membrane motor. These, together with a solar energy and electric generator control system, as well as a battery charger, are mounted on a mobile platform (Figure 1). The objective of this article was to determine the energy requirements of an autonomous fertilizing robot during movement in the field and for carrying out technological operations. For this purpose, the mechanical power and energy required for the operation of the robot fertilizer spreader were first determined. Based on this, an accumulator with suitable power and capacity for the operation of the robot fertilizer spreader was selected. The travel distance of the robot on a single charge of the chosen accumulator was determined, as well as the traction power efficiency and specific power.

BEET: Blockchain Enabled Energy Trading for E-Mobility Oriented Electric Vehicles

Renewable Energy Sources (RESs) are gaining considerable attention to reduce human dependence on fossil fuels and minimize harmful gases in our surroundings. Existing literature on energy trading focused on providing renewable energy to smart homes, smart buildings, and smart offices to fulfill their daily energy demands obtained from RESs. Besides, Electric Vehicles (EVs) use either power grid energy or a battery exchange mechanism to recharge their low EV batteries. The continuous use of power grids to recharge low EV batteries causes a significant load on power grids. Due to this, power grids are inadequate to fulfill the ever-increasing demands of EVs in the future. In this context, we propose a Blockchain Enabled Energy Trading (BEET) framework oriented EV charging. A system architecture of the BEET framework is presented to describe the functioning of each layer and its associated entities. We formulate an optimization problem that maximizes the revenue in the energy trading process using a knapsack optimization. Smart contracts are designed on the consortium blockchain network to sell and buy renewable energy to aggregators and from producers, respectively. Moreover, an EV charging mechanism is designed to intelligently allocate renewable energy to consumers at a low price. A comparative analysis is performed with state-of-the-art works in terms of charging price, revenue, throughput, and latency. The results indicate that the BEET framework outperforms compared to state-of-the-art works to address the renewable energy demand problem to realize E-mobility. It is clarified that the data considered in the experimental analysis were obtained from statistical simulations in realistic E-Mobility environment settings.

Designing a Fertilizing Robot Application Considering Energy Efficiency

Abstract Electrically driven agricultural robots encounter accelerated battery depletion compared to vehicles operating on asphalt due to heightened rolling and traction resistance necessitating increased energy consumption. This issue becomes pronounced in regions devoid of access to the electrical grid, precluding the possibility of recharging electrically driven agricultural robots and consequently leading to interruptions in their uninterrupted functionality. To address this challenge, the agrorobotics working group at the Estonian University of Life Sciences devised a novel solution: a combined energy production station leveraging biogas, hydrogen, and solar energy. This station was integrated with a prototype autonomous fertilizing robot tailored for blueberry plantations to conduct precision fertilization on depleted milled peat fields. Distinctive features of the station encompass an automated battery exchange system and an electric generator equipped with a membrane motor. These components, in conjunction with a solar energy and electric generator control system, alongside a battery charger, are affixed onto a mobile platform. The primary objective of this study was to ascertain the energy requisites of an autonomous fertilizing robot during field traversal and while executing technological operations. To achieve this aim, the mechanical power and energy necessary for the operation of the robot fertilizer spreader were initially quantified. Subsequently, an accumulator possessing suitable power and capacity for the operation of the robot fertilizer spreader was chosen. The article further delineates the determination of the travel distance achievable by the robot on a single charge of the selected accumulator, in addition to evaluating the traction power efficiency and specific power.

Multi UAV Inspection Optimization for Offshore Wind Farms Considering Battery Exchange Process

Multi UAV Inspection Optimization for Offshore Wind Farms Considering Battery Exchange Process

Poorer outcomes associated with more invasive lead management strategies for Abbott Riata® leads: a large, multicenter experience.

Over 100,000 Abbott Riata® were implanted in the United States before they were recalled in 2010. There are still a significant number of Abbott Riata® leads in use, and it is unclear how these leads should be managed at the time of generator change or lead malfunction. Although data comparing both Sprint Fidelis® and Abbott Riata® leads in this setting is available, there are no multicenter comparative studies of outcomes for various lead management strategies, including lead extraction (LE), lead abandonment/revision (LA), and generator change (GC)only at the time of device at elective replacement interval(ERI) for Abbott Riata® leads. A retrospective, multicenter study was undertaken to compare short-term outcomes (major complications-MC, death, extended or re-hospitalizations within 60days-RH, lead malfunction-LM) and total outcomes (short-term outcomes & lead malfunction during follow-up) of patients with Riata® leads undergoing LE, LA, or GC. 152 patients (65 ± 13years, 68% male) were followed for a mean 33 ± 30months following intervention. Out of 166 procedures,13 patients underwent LE, 16 patients underwent LA, and 137 patients underwent GC. There was 1 major complication in each group, yielding an event rate of 7.7% for LE, 6.3% for LA, and 0.7% for GC cohorts. There were significantly more short-term and total adverse outcomes in the group of patients getting LE and LA versus GC only (38.5% & 31.3% vs 7.3%, P < 0.001). Total Riata® lead dwell time follow-up was 17,067months. A total of 3 Riata® lead malfunctions were noted during long-term follow-up. Inappropriate shocks were similar between LE 7.7% (1/13), LA 6.3% (1/16). and GC 11.0% (4/136); P = 0.57. There were more short-term and total adverse outcomes in more invasive management strategies (LE and LA) versus GC alone. The failure rate of Riata® leads was substantially lower compared to previous reports. Therefore, we recommend only performing battery exchange when a device with an active Riata® lead is at ERI, unless there is malfunction of the Riata® lead noted on testing.

An optimal battery allocation model for battery swapping station of electric vehicles

This paper studies battery of battery charging station (BSS) orderly swapping, efficient battery management and reasonable battery allocation. Firstly, based on a user-centered perspective, this paper first establishes the user adaptive response model according to the battery state of health (SOH) and state of charge (SOC) after battery allocation to realize the user autonomous decision-making. Secondly, the battery exchange priority function is established for the ordered exchange of BSS and EV batteries during the battery exchange process. Thirdly, the batteries in the BSS are divided into three battery libraries to be managed according to the difference of battery SOH so that the batteries are accurately and efficiently allocated to the power system and EV services. Finally, BSS transfers excess electricity to the power system through battery to grid (B2G) technology as regulating energy, increasing the flexibility of the power system. Through the analysis of the optimization model, the results show that compared with the random distribution of batteries, the model not only increases the benefits of BSS, but also ensures the distribution of each battery is rapid, accurate and reasonable, realizing the rational use of batteries.

CARD1: Human Factors Evaluation of HeartMate 3 LVAD Peripherals: Insights gained from an Eye Tracking based Simulation Study

Background: Although left ventricular assist devices (LVADs) have demonstrated excellent clinical outcomes and design improvements over the past decade, human factors issues including limited usability and unintuitive handling continue to challenge this therapy. The aim of this study was to evaluate user experience of former LVAD patients and laypersons (LP) with the HeartMate 3 (HM3) LVAD peripherals in simulated everyday and emergency scenarios. Methods: This cross sectional, single center cohort study included former non-HM3 LVAD patients post heart transplantation (HTX) and LP without any previous LVAD peripherals handling experience. Seven predefined everyday and emergency scenarios, including battery exchanges (without alarm, in response to alarm, in dim light, within carry bag), power supply change to AC power, driveline dis- and reconnection and controller exchange were simulated. Subjects’ gaze behavior was recorded using eye tracking (ET) technology (Tobii Pro Glasses 2, Tobii Pro Lab), whereas areas of interest (AOIs) were defined for LVAD components used. Completion rate, duration to success (DTS), pump-off time, and percental fixation duration per AOI were defined as outcome measures to identify differences between initially successful and unsuccessful subjects and to correlate subject performances with demographics. Results: Thirty gender and age matched, untrained subjects (15 HTX vs. 15 LP, median age: 64 (IQR=11) vs. 63 (10) yrs., p=0.84), 20 vs. 20% female, p=1.00) completed 210 scenarios, whereas 82.9 vs. 81.9% (p=1.00) could be solved at first attempt. Changing power supply from batteries to AC power revealed highest complexity for both groups (DTS=251±93s, p=0.76): only 26.7% of subjects succeeded at first attempt (33.3% vs. 20.0%, p=0.68) and 56.7% at second attempt (66.7% vs. 46.7%, p=0.04) with significantly more LP failing. The first attempt resulted in 10 unnecessary hazards from disconnecting the driveline (5 HTX vs. 5 LP, pump-off time 2-118 s, p=0.25) and significant differences in percental fixation durations for five AOIs in participants with vs. without initial success (p<0.029, see Figure). Significantly decreased DTS during battery exchange (no alarm 32.5 (18.8)s vs. alarm 26.5 (20.8)s and dim light 21.0 (19.0)s, p<0.001, without group differences, p>0.34) indicate a high level of learnability and routine establishment. Exchanging batteries in the bag was more time consuming (75.0 (45.0) s, p=0.09), especially in elderly patients (r=0.61, p<0.001). Conclusion: This ET based human factors study provides insights into user experiences of untrained former non-HM3 LVAD patients and LP in handling HM3 peripherals in simulated everyday and emergency scenarios. It highlights unintuitive and potentially hazardous handling characteristics and provides guidance for future user centered design of LVAD wearables.

Human Factors Evaluation of HeartMate 3 Left Ventricular Assist Device Peripherals: An Eye Tracking Supported Simulation Study

BackgroundDespite recent design improvements, human factors issues continue to challenge left ventricular assist device (LVAD) therapy. The aim of this study was to evaluate user experience of former non-HeartMate 3 (HM3) LVAD patients post heart transplantation (HTX) and laypersons (LP) with HM3 LVAD peripherals in simulated everyday and emergency scenarios.MethodsThis single center cohort study included untrained HTX and LP. Seven scenarios, including battery exchanges (without alarm, advisory alarm, dim light, consolidated bag), change of power supply, driveline dis-/reconnection and controller exchange were simulated. Subjects’ gaze behavior was recorded using eye tracking technology. Success rate, pump-off-time, duration to success (DTS), percental fixation duration per areas of interest and post-scenario-survey results were defined as outcome measures.ResultsThirty subjects completed 210 scenarios, initially solving 82.4% (HTX vs. LP, p = 1.00). Changing power supply revealed highest complexity (DTS = 251 ± 93s, p = 0.76): 26.7% succeeded at first attempt (p = 0.68), 56.7% at second attempt, with significantly more LP failing (p = 0.04), resulting in 10 hazards from driveline disconnections (pump-off-time 2-118s, p = 0.25). Comparison on initial success showed differences in fixation durations for seven areas of interest (p < 0.037). Decreasing DTS during battery exchanges (p < 0.001) indicate high learnability. Exchanging batteries within the bag took longer (median DTS = 75.0 (IQR = 45.0)s, p = 0.09), especially in elderly subjects (r = 0.61, p < 0.001). Subjects with less initial success were more afraid of making mistakes (p = 0.048).ConclusionThis eye tracking based human factors study provided insights into user experiences in handling HM3 peripherals. It highlights unintuitive and hazardous characteristics, providing guidance for future user-centered design of LVAD wearables.

PO-03-058 S-ICD GENERATOR EXCHANGE AND PERFORMANCE: A SINGLE TERTIARY CARE EXPERIENCE

Limited data exist on outcomes of subcutaneous implantable cardiac defibrillators (S-ICDs) generator exchange. Few studies have found that shock impedance increases after S-ICD Gen exchange which correlates with increase in DFT and possibly reduction in S-ICD effectiveness. We describe the performance of S-ICDs after generator exchange in a cohort of patients implanted and followed in a single tertiary-care referral center. We reviewed the device clinic interrogation reports of 525 patients who received S-ICDs between 2012 and 2022 and compared device parameters before and after generator exchanges specifically high-voltage impedance and shock efficacy during follow-up using the paired sample t-test with a 95% CI and p-value of 0.05. A total of 525 S-ICDs were implanted at our facility with an average age at implant of 51±16 years, predominantly males (n=348, 66.3%). The majority of S-ICDs were implanted for primary prevention (413, 79%), and ischemic cardiomyopathy (CM) was the most common type of CM (127, 24%). The average ejection fraction (EF) was 30±12%, and comorbidities were prevalent: 56% hypertension, 29% diabetes, 38% with history of coronary artery disease (CAD), and 33% end-stage renal disease (ESRD). Of those, 76 (15%) underwent generator battery exchange at a median time of 4.5± 2 years. Before generator exchange, a total of 114 shocks (22%) were delivered with an impedance of 68± 23Ω. Of those, 89 (78%) were appropriate shocks. Efficacy of the appropriate shocks was 95%. [F1] [IR2] After generator exchange (n=76), 10 appropriate shocks (13%) were administered with an impedance of 82± 29 Ω, and a shock efficacy of 100%. High voltage impedance and shock efficacy were not statistically different before and after Gen exchange (p=0.178 and p=0.158 respectively). The S-ICD generator exchange demonstrates high safety profile with relatively no change in device impedance or shock efficacy pre- and post- Gen exchange.

A Mission-Oriented Flight Path and Charging Mechanism for Internet of Drones.

In addition to traditional battery exchange services and stationary charging stations, researchers have proposed wireless charging technology, such as decentralized laser charging or drone-to-drone charging in flight, to provide power to drones with insufficient battery electricity. However, the charging methods presented in the literature will inevitably cause drones to wait in line for charging during peak hours and disrupt their scheduled trips when the number of drones grows rapidly in the future. To the best of our knowledge, there have been no integrated solutions for drone flight path and charging planning to alleviate charging congestion, taking into account the different mission characteristics of drones and the charging cost considerations of drone operators. Accordingly, this paper provides adaptive charging options to help drone operators to solve the above-mentioned problems. Drones on ordinary missions can use conventional battery swap services, wired charging stations, or electromagnetic wireless charging stations to recharge their batteries as usual, whereas drones on time-critical missions can choose drone-to-drone wireless charging or decentralized laser charging deployed along the fight paths to charge the batteries of drones in flight. Notably, since fixed-wing drones have larger wing areas to install solar panels, they can also use solar energy to charge during flight if the weather is clear. The simulation results exhibited that the proposed work reduced the power load of the power grid during peak hours, met the charging needs of each individual drone during flight, and cut down the charging costs of drone operators. As a result, an all-win situation for drone operators, drone customers, and power grid operators was achieved.

Design Procedure of a Low-Cost System for Energy Replenishment in a Quadrotor UAV through a Battery Exchange Mechanism

This paper describes the design and construction of an energy replenishment service station for a quadrotor. The prototype includes a small number of actuators, making it a low-cost solution. The system consists of three batteries: two charged and one discharged (within the quadrotor). Once the quadrotor lands, the battery with the highest charge is selected, which is then exchanged for the discharged battery. In order to perform this action, position control is used, in which the desired value depends on the location of the sensor that detects the highest voltage. In addition, the system has a mechanical design that facilitates the coupling of the unmanned aerial vehicle (UAV) with the structure for battery exchange, ensuring that the discharged battery is always in the same position. Furthermore, the design of a mechanism to release and hold the battery placed in the quadrotor is presented, which works by means of voltage and force sensors that identify the instant that the battery is discharged and when the UAV has landed on the exchange platform, thus initiating the exchange process. Likewise, the criteria for selecting the elements used, acquiring and processing signals, and routines for changing batteries are detailed.

Automated Drone Battery Management System—Droneport: Technical Overview

The popularity of using vertical take-off and landing unmanned aerial systems continues to rise. Although the use of these devices seems to be almost limitless, the main drawback is still the battery capacity and the need to replace or recharge it several times per hour. This article provides a technical overview of the development of an experimental mechatronic system for automatic drone battery management called Droneport. It was developed as a system with a landing platform, automatic battery exchange and recharging outside the drone, allowing a quick return to the mission. The first part presents its mechanical design, installed instrumentation and software environment. The next part is devoted to the description of the individual hardware components, highlighting the specific problems that had to be solved to optimize size, weight and robustness requirements. The final section summarizes our observations regarding the contribution of this tool to the autonomy of drones or UAVs in general.

Evolutionary algorithm for vehicle routing for shared e-bicycle battery replacement and recycling

Shared electric bicycles (e-bicycles) are becoming an increasingly popular model of sharing economy that provides the public with a convenient and labor-saving transport alternative. Efficient battery replacement is critical to maintain the operational capability of e-bicycles, but is quite difficult as there are often a large number of e-bicycles that are widely dispersed. In this paper, we study a problem of scheduling multiple vehicles to deliver fully-charged batteries to and take back low-power batteries from shared e-bicycles scattered in numerous parking spots, while the low-power batteries are recharged in battery depots for potential re-utilization. The aim of this problem is to minimize the completion time of all battery replacement tasks so as to keep a high operational efficiency of the system. The problem differs from existing vehicle routing problems in two main special features: (1) equivalent exchange of fully-charged batteries and low-power batteries; (2) recycling of batteries. To efficiently solve this special problem, we propose an evolutionary algorithm using variable local-search-based mutation to balance global exploration and local exploitation and employing enhanced local search around each newly found best known solution to improve accuracy. Experiments on test instances and real-world instances demonstrate the competitive performance of the proposed method.

The Influence of Battery Exchange Electric Vehicle and Transmission Line Age and Loading Related Failures on Power System Reliability

The Influence of Battery Exchange Electric Vehicle and Transmission Line Age and Loading Related Failures on Power System Reliability

Adopting Electric Motorcycles for Ride-Hailing Services: Influential Factors from Driver’s Perspective

Recently, the Indonesian government has been increasingly encouraging the population to utilize electric vehicles. In the public transportation sector, including ride-hailing services, the use of electric vehicles, especially motorcycles, is promoted. This study aims to explore the willingness to adopt electric motorcycles among ride-hailing drivers by specifying the preference for buying or renting in adopting electric motorcycles. Using a stated preference method, an interview survey was conducted on 416 ride-hailing drivers in Yogyakarta, Indonesia. Nine variables were tested to understand the determinants of electric motorcycle adoption, including purchase or rental costs, cost for motorcycle title transfer, fuel price, maximum coverage distance, maximum speed, the distance between battery exchange stations, annual tax, and credit payment. By using the ordered logit model, the results revealed two variables affecting the purchase of electric motorcycles: purchase price and maximum coverage distance. Meanwhile, four variables significantly influenced the determination of electric motorcycle rental: coverage distance, fuel price, rental fees, and the distance between battery exchange stations. Sociodemographic variables also significantly affect the decision to rent but not purchase electric motorcycles. Meanwhile, full-time drivers and students who work part-time as drivers are more likely to rent electric motorcycles. This study also found that renting electric motorcycles had a better likelihood of adoption than owning them. Finally, several policies were proposed to boost the adoption of electric motorcycles among ride-hailing drivers, including subsidy provisions, technology improvement, and priority provisions for drivers who use electric motorcycles.

Day-ahead robust optimal dispatch of integrated energy station considering battery exchange service

In order to improve the energy utilization efficiency and decrease the environmental pollution, multiple energy production, conversion and storage devices are integrated as a whole, called an integrated energy station (IES), which can meet various energy demands. In this paper, a day-ahead robust optimal scheduling strategy of IES for electricity, cold energy and heat energy supply is proposed, where the battery exchange service is incorporated by introducing the electric vehicle exchange station (EVES). Distinguished from the prevalent detailed modeling of each battery, an integrated model of EVES based on the state of charge (SOC) interval of batteries is established to reduce the number of variables, by which the charging priority of batteries is embedded into the optimal dispatch of IES. Considering the uncertainties of renewable energy resources and load demands, a two-stage robust optimization model of day-ahead dispatch of IES is formulated to minimize the total operation cost under the worst case, which can be solved efficiently by the column-and-constraint generation (C&CG) algorithm. The effectiveness of the proposed method is demonstrated in the case study. The results show that the total operation cost of IES decreases by 9.2%, indicating the role played by the flexibility of EVES. Moreover, the worst-case cost is reduced compared with the deterministic approach.

Uncertainty Costs Optimization of Residential Solar Generators Considering Intraday Markets

The uncertainty of solar generation and the bull market are unavoidable in energy dispatch. The purpose of this research is to validate an uncertainty cost function of residential photovoltaic energy in a real microgrid by varying the number of auctions in intraday markets. Therefore, the following procedure is proposed. First, the variability of photovoltaic generation is quantified through Monte Carlo simulations. Second, a statistical function calculates the variability costs of photovoltaic generation. Third, the uncertainty costs are estimated by varying intraday auction markets. Other complementary services are added to the network, such as battery exchange stations for electric vehicles, demand response loads, market power restrictions, and energy storage systems, which are estimated as total costs in an index ranking. The total costs are optimized in a benchmark microgrid and take complimentary services as a black box. Only the uncertainty costs of residential solar generators are discriminated. The main findings were that (1) the uncertainty costs have an error of less than 0.0168% compared to the Monte Carlo simulations and that (2) the uncertainty costs of solar generation are reduced with a decreasing trend to a more significant number of auction markets in intraday markets.