Discharge Schedule Research Articles

BLITZ-HF study: a nationwide initiative to assess and improve guidelines recommendations adherence in cardiology centers managing patients with acute and chronic heart failure

Abstract Background Physicians adherence to heart failure (HF) guidelines is generally sub-optimal with consequent negative prognostic implications. Strategies to improve adherence to guideline recommendations are strongly needed. Aims To assess and improve adherence of Italian cardiology sites to guidelines recommendations on performance indicators in patients with acute (AHF) and chronic heart failure (CHF). Methods BLITZ-HF was a prospective study based on a web based recording system used during two enrollment periods (phase 1 and 3), interspersed by face-to-face macro-regional benchmark analysis and educational meetings (phase 2). Both management (creatinine and echocardiographic evaluations or discharge follow-up planning) and treatment (according to ejection fraction categories, focusing on guidelines directed medical treatments - GDMTs) performance indicators were considered for patients in both settings. Results Overall, 7218 patients with acute and chronic HF were enrolled at 106 sites. During the enrollment phases, 3920 and 3298 patients were included respectively, 84% with CHF and 16% with AHF in phase 1, 74% with CHF and 26% with AHF in phase 3. In Figure 1 we report adherence to management and treatment indicators in the two enrollment phases. Among AHF patients improvement was obtained in two of seven indicators. A significant rise in echocardiographic evaluation was observed, while discharge schedule of a cardiology ambulatory evaluation within four weeks was overall poor (less than 50%) and did not improve in the 3 phase. Overall GDMTs prescription rate in HFrEF was good and we observed a nominal increase in betablockers prescription rate in Phase 3. Among CHF patients with HFpEF and HFmrEF we observed a performance increase in two of three indicators: creatinine end echocardiographic evaluations, while oral anticoagulation in atrial fibrillation remained stably high. Performance measures in CHF HFrEF patients improved in six of nine indicators although significantly only in two. Prescription rate of GDMTs was good already in phase 1 and a significant increase in ACE-I/ARB or ARNI prescription was reported, with a nominal increase in the use of one of these three drugs in combination with MRAs and a BB. Conclusions A structured multifaceted educational intervention can improve adherence to HF guidelines on several indicators in a context of an already elevated level of adherence to guideline recommendations. Extension of this approach to other non-cardiology health professional settings, in which patients with HF are generally managed, should be considered. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): The study was funded by Heart Care Foundation with a partial unrestricted support from Abbott, Daiichi Sankyo, Medtronic, Servier, Vifor.

Reinforcement Learning Based Peer-to-Peer Energy Trade Management Using Community Energy Storage in Local Energy Market

Many studies have proposed a peer-to-peer energy market where the prosumers’ actions, including energy consumption, charge and discharge schedule of energy storage systems, and transactions in local energy markets, are controlled by a central operator. In this paper, prosumers’ actions are not controlled by an operator, and the prosumers freely participate in the local energy market to trade energy with other prosumers. We designed and modeled a local energy market with a management algorithm that uses community energy storage for prosumers who competitively participate in trade in the real-time energy market. We propose an energy-trade management algorithm that manages the trades of prosumers in two phases based on bids and offers submitted by prosumers. The first phase is to manage the trade of prosumers who have submitted fair prices to trade with other prosumers in the real-time energy market. The second phase is managing the trade of prosumers that could not trade in the first phase. Community energy storage is employed in the second phase and controlled by a reinforcement learning-based trading algorithm to decide whether to buy, sell, or do nothing with the prosumers. The action of buying and selling means charging and discharging the community energy storage, respectively. Numerical results show that the proposed trading algorithm gains a near-maximum profit. Besides, we verified that community energy storage yields more profit than the battery wear-out cost.

Facilitating high levels of wind penetration in a smart grid through the optimal utilization of battery storage in microgrids: An analysis of the trade-offs between economic performance and wind generation facilitation

The aim of this paper was to investigate the trade-offs that can be achieved between optimizing the electricity costs of a building integrated microgrid, while simultaneously facilitating high levels of wind penetration in a smart grid. This study applied multi-objective optimization to obtain a daily charge and discharge schedule of a battery bank, which was used to store electricity from the microgrid and smart grid and could also provide electricity to the building and smart grid. Multi-objective optimization was employed due to the independent objectives of minimizing building operating cost and maximizing the facilitation of wind energy from the smart grid. The trade-offs between the two objectives were simulated, evaluated and analyzed. A priority weighting factor (α) was applied to each objective. The purpose of α was to vary the importance of each objective relative to the other in an inversely proportional manner. This enabled the algorithm to optimize the battery operating schedule for the economic performance of the microgrid, the facilitation of wind generation from the smart grid or for trade-offs in between. The results present a comprehensive evaluation of 96 scenarios with varying daily weather conditions, building electricity demand, electricity pricing, microgrid output and wind penetration from the smart grid. A multi-objective optimization approach was then applied for each of the 96 scenarios with 11 α values to determine optimal trade-offs in these scenarios. Generally for the 96 scenarios analyzed, when the α value was 20% or higher, the amount of extra wind generation facilitation obtained was negligible while microgrid operating costs continued to increase. The results showed that when changing from an α value of 0% to an α value of 20%, there was a large increase in wind generation facilitation compared to the corresponding increase in cost, with wind generation facilitation increasing from its minimum value to within 89% of its maximum value (10.7% to 14.3% of facilitated wind generation). The corresponding building cost increased from its minimum value to within 13% of its maximum value (€1.14/day to €1.37/day). This produced a cost of approximately €0.06 for every 1% increase in wind generation facilitation. In comparison to this, changing from an α value of 20% to an α value of 100% implied a cost of approximately €3.64 for every 1% increase in wind generation facilitation. These results indicated that smart grids with large percentages of wind penetration may be substantially aided by utilizing the storage capacity of building integrated microgrids for a relatively low monetary cost.

Over-voltage Disconnection of DER Inverters: Assessing Customer Savings

Distributed energy resource (DER) owners experience a loss in economic benefits due to prolonged and/or frequent inverter disconnection. In this paper, we investigate the economic savings that customers accrue when combining rooftop solar photovoltaic (PV) generation with battery storage systems, considering a time-of-use pricing tariff and the steady-state over-voltage disconnection of inverters. In particular, we compare four quadratic program (QP) optimization-based approaches to designing the charge and discharge schedule of residential batteries. The objective of the first optimization-based approach is to increase the economic savings that PV customers with battery storage accrue. The next two approaches additionally modulate the power to and from the grid, reducing the occurrence of inverter-based disconnection for improved economic savings. By contrast, the fourth approach directly manages customer-based power flows to and from the electric grid to smooth distribution load curve peaks and valleys, without explicitly considering energy savings that accrue to customers. By means of a case study, we observe the over-voltage disconnection of residential-scale inverters decreases with the proliferation of behind-the-meter batteries until an integration level of 60% is reached. At battery integration levels beyond 60%, the fourth grid-focused optimization-based approach continues to improve the grid voltage preventing inverter-based disconnections.

메타 휴리스틱 기법을 이용한 최대수요관리의 정량적 효과가 반영된 ESS 최적운영전략

Electricity charges consist of into electrical energy charges and basic charges. Electricity charges are charged according to time of use(TOU), not according to the total daily usage in household sector. Therefore, using ESS for peak management can decrease electricity charges. The optimal charging / discharging schedule of ESS considering the electrical energy charges can be established by linear programming. The basic charge reduction method through minimization of deviation in the previous researches does not reflect the exact basic charges. To cope with this problem, we proposes peak demand method for an optimal ESS charge / discharge schedule that minimizes total charges. The effectiveness of the proposed method is shown through case studies using various heuristic algorithms.

Efficient Energy-Management System Using A Hybrid Transactive-Model Predictive Control Mechanism for Prosumer-Centric Networked Microgrids

With the development of distributed energy resources (DERs) and advancements in technology, microgrids (MGs) appear primed to become an even more integral part of the future distribution grid. In order to transition to the smart grid of the future, MGs must be properly managed and controlled. This paper proposes a microgrid energy management system (MGEMS) based on a hybrid control algorithm that combines Transactive Control (TC) and Model Predictive Control (MPC) for an efficient management of DERs in prosumer-centric networked MGs. A locally installed home energy management system (HEMS) determines a charge schedule for the battery electric vehicle (BEV) and a charge–discharge schedule for the solar photovoltaic (PV) and battery energy storage system (BESS) to reduce residential customers’ operation cost and to improve their overall savings. The proposed networked MGEMS strategy was implemented in IEEE 33-bus test system and evaluated under different BEV and PV-BESS penetration scenarios to study the potential impact that large amounts of BEV and PV-BESS systems can have on the distribution system and how different pricing mechanisms can mitigate these impacts. Test results indicate that our proposed MGEMS strategy shows potential to reduce peak load and power losses as well as to enhance customers’ savings.

Analysis of the Effect of Organizational Factors on Blast Furnace Operating Indices

The effect is analyzed of organizational factors on blast furnace operating indices under contemporary intense smelting conditions: current banking time, fanning time, and compliance with the discharge schedule. The quantitative effect of these factors on blast furnace performance was established more the thirty years ago and needs adjustment. It is demonstrated that production losses associated with banking depend on duration and number, and production losses associated with fanning depend on duration, amount, and degree of blast pressure reduction. Dependences (equations) are determined for production loss connected with banking and fanning. The effect of banking and fanning time on the change in equivalent coke consumption is determined. Values obtained differ from those used previously. The effect of discharge schedule on furnace performance and specific coke consumption for AO EVRAZ NTMK loses its relevance.

Estimating Dynamic Conditional Spread Densities to Optimise Daily Storage Trading of Electricity

This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast electricity price spreads between different hours of the day. This supports an optimal day ahead storage and discharge schedule, and thereby facilitates a bidding strategy for a merchant arbitrage facility into the day-ahead auctions for wholesale electricity. The four latent moments of the density functions are dynamic and conditional upon exogenous drivers, thereby permitting the mean, variance, skewness and kurtosis of the densities to respond hourly to such factors as weather and demand forecasts. The best specification for each spread is selected based on the Pinball Loss function, following the closed form analytical solutions of the cumulative density functions. Those analytical properties also allow the calculation of risk associated with the spread arbitrages. From these spread densities, the optimal daily operation of a battery storage facility is determined.

Feasibility Analysis of Behind-the-Meter Energy Storage System According to Public Policy on an Electricity Charge Discount Program

Energy storage systems are crucial in dealing with challenges from the high-level penetration of renewable energy, which has inherently intermittent characteristics. For this reason, various incentive schemes improving the economic profitability of energy storage systems are underway in many countries with an aim to expand the participation rate. The electricity charge discount program, which was introduced in 2015 in Korea, is one of the policies meant to support the economic feasibility of demand-side energy storage systems. This paper quantitatively evaluated the impact of the electricity charge discount program on the economic feasibility of behind-the-meter energy storage systems. In this work, we first summarized how electricity customers can benefit from behind-the-meter energy storage systems. In addition, we represented details of the structure that make up the electricity charge discount program, i.e., how the electricity charge is discounted through the discount scheme. An optimization problem that establishes a charge and discharge schedule of an energy storage system to minimize each consumer’s electricity expenditure was defined and formulated as well. The case study results indicated that the electricity charge discount program has improved the profitability of behind-the-meter energy storage systems, and this improved profitability led to investment in behind-the-meter energy storage systems in Korea. As a result of the electricity charge discount program, Korea’s domestic demand side energy storage system market size, which was only 27 billion dollars in 2015 in Korea, has grown to 825 billion dollars in 2018.

Determination of optimal battery utilization to minimize operating costs for a grid-connected building with renewable energy sources

This paper proposes strategies to optimize the daily charge and discharge schedule of a battery bank, in order to minimize the operating cost of a building that uses renewable energy sources. The schedule was optimized using a range of battery charge and discharge rates over a 24 h period. These rates were controlled using a genetic algorithm (GA) and a particle swarm optimization algorithm (PSO), which utilized day-ahead prediction data for electricity consumption and electricity price, as well as electricity output from a photovoltaic system and a wind turbine. The results showed that the building operating costs decreased as the number of available charge and discharge rates was increased. The average daily operating cost was reduced by up to 31% using the GA and by up to 28% using the PSO, compared to the scenario where no battery was used. Furthermore, the reduction in average daily operating costs began to plateau as the number of charge and discharge rates reached 12. It was also shown that the scaling of irradiance, wind speed and electricity price inputs impacted the optimized daily operating cost of the building. A sensitivity analysis was conducted to investigate how this scaling of inputs affected the overall performance of the GA. It was found that the optimized daily operating costs were almost unchanged after numerous scaling percentages were applied to the electricity price, with additional cost reductions of up to 3% compared to the scenario where no scaling percentages were applied. In contrast, scaling percentages applied to weather data were found to have a more significant impact on the optimized operating costs, with additional cost reductions of up to 17% compared to the scenario where no scaling percentages were applied. Moreover, a non-linear relationship was observed between the weather data scaling percentage and optimized daily cost.

Day‐ahead unit commitment model for microgrids

In this study, a heuristics-based optimisation methodology for a day-ahead unit commitment (UC) model in microgrids is proposed. The model aims to schedule the power among the different microgrid units while minimising the operating costs together with the CO2 emissions produced. A storage device is added where the charge and discharge schedule is calculated according to both objectives. In addition, as a part of the demand side participation strategy, a charging schedule was determined for the electric vehicles (EV) in order to increase the system security and further reduce the costs and emissions. A congestion management approach is also introduced, which eliminates congestions by effective unit scheduling according to congestion signals provided by the distribution system operators. The complete day-ahead time horizon is divided in 96 time steps (each with a 15 min time span), which makes the UC problem more complicated. The studied system includes renewable energy resources, a storage unit, two microturbines, a fuel cell and EVs. The results demonstrate that the proposed model is robust and is able to reduce the microgrid operating costs and emissions by optimal scheduling of the microgrid units, and is able to take into account local congestion problems.

The design of remote discharge scenario management system on EAST

The discharge scenarios on EAST plasma control system (PCS), characterized by different waveform parameters and different hardware requirements, will need a systematic discharge scenario management system for remote and local operators, in order to optimize storage structure and rationally manage discharge time. The remote management of discharge scenarios will require extending the functionalities of the present PCS “future shot” and “next shot” modules. Taking advantage of database technique, the operators can acquire detail information of all discharge scenarios directly without PCS user interface and search the specified scenarios by key words. In addition, the system can elect optimal scenarios automatically based on discharge schedule and plasma pulse setting for later artificial selection. To this purpose, a new remote discharge scenario management system (RDSMS) basis for Web is being conceived on EAST. The system contains a database with functions of “user management”, “scenario verification”, “prepared scenario management”, “actual discharge scenario management” and “discharge schedule management”. This paper will present the relevant conceptual design and give an account of the test results for implementation on EAST discharges.

Utilization of Electric Vehicles for Improvement of Daily Load Factor in the Price-Responsive Environment of Smart Grids

Using electric vehicles, in addition to decreasing the environmental concerns, can play an important role in decreasing the peak and filling the off-peaks of the daily load characteristics. In other words, in smart grids' infrastructure, the load characteristics can be improved by scheduling the charge and discharge process of electric vehicles. In smart grids, the customers are instantaneously informed about the load and its price and are able to react to the prices. This reaction pattern results in a wide range of changes in the load curve of the network. In this paper, a multi-stage model based on neural networks and the neuro-fuzzy network is presented for forecasting the daily electric load in the price-responsive environment of smart grids. Then, in order to determine the load and generation models of the set of electric vehicles based on the forecasted load for the next day, a complete probabilistic model of these vehicles in the range of parking lots is presented by considering three utilization strategies. These utilization strategies are: Uncontrolled Charging Mode (UCM), Controlled Charging Mode (CCM) and Smart Charge/Discharge Mode (SCDM). Finally, the proposed model is applied on the data of a target day in 2015 in NSW region of Australia's National Electricity Market and the charge and discharge schedule of electric vehicles are determined based on the forecasted load for the next day. The results indicate the most improvement in the daily load factor when the SCDM utilization strategy is employed.

Development of a three-phase battery energy storage scheduling and operation system for low voltage distribution networks

Abstract Three phase battery energy storage (BES) installed in the residential low voltage (LV) distribution network can provide functions such as peak shaving and valley filling (i.e. charge when demand is low and discharge when demand is high), load balancing (i.e. charge more from phases with lower loads and discharge more to phases with higher loads) and management of distributed renewable energy generation (i.e. charge when rooftop solar photovoltaics are generating). To accrue and enable these functions an intelligent scheduling system was developed. The scheduling system can reliably schedule the charge and discharge cycles and operate the BES in real time. The scheduling system is composed of three integrated modules: (1) a load forecast system to generate next-day load profile forecasts; (2) a scheduler to derive an initial charge and discharge schedule based on load profile forecasts; and (3) an online control algorithm to mitigate forecast error through continuous schedule adjustments. The scheduling system was applied to an LV distribution network servicing 128 residential customers located in an urban region of South East Queensland, Australia.

PROGRAMA DE ALTA HOSPITALARIA PROGRAMADA PARA PREPARAR EL EGRESO DEL PACIENTE Y FAMILIA

Este artículo describe la implementación y desarrollo del Programa del Alta Hospitalaria Programada (PAH), específicamente en el área de cirugía del Hospital Rafael Angel Calderón Guardia, con la finalidad de mostrar un modelo a implementar para otras instituciones hospitalarias nacionales e internacionales, ya que proporciona herramientas a los pacientes y a sus familiares para llevar a cabo el autocuidado y su posterior reincorporación a la comunidad de la cual forman parte, partiendo de las necesidades mostradas durante su hospitalización y al momento del alta hospitalaria. Se trata de un estudio cuantitativo y descriptivo, con una población conformada por el total de egresos registrados, durante el año 2010, en el alta programada del área de Cirugías, del Hospital Rafael Ángel Calderón Guardia, seleccionando una muestra de 1224 pacientes, quienes requirieron, por parte del profesional de Enfermería responsable , valoración e intervención en la preparación del egreso. En este programa se realiza una valoración de las necesidades del paciente, tanto al momento del ingreso como la condición de egreso. La información recopilada se registra en el Instrumento de Valoración de Necesidades del paciente, el cual constituye la base para elaborar el plan de intervención para el usuario y su familia durante el internamiento y el día del egreso, orientado hacia la preparación del alta por parte del profesional en enfermería. Dicha intervención queda documentada en el Instrumento de registro de la condición de egreso de los usuarios de cirugía y en la nota de enfermería. El paciente y la familia logran, de acuerdo a la intervención del profesional de enfermería encargado del programa y con la colaboración de otros miembros del equipo de salud, adquirir conocimientos y técnicas de acuerdo al grado de dependencia o condición de salud para lograr el autocuidado al egresar del centro hospitalario. Conclusión. Los beneficios que se evidencian en la implementación del PHA giran en torno a: una preparación oportuna, según sea el grado de dependencia y de acuerdo a la condición de salud, para el autocuidado del paciente por parte de la familia al momento de egresar del centro hospitalario, una coordinación con la red externa de salud y la reubicación oportuna de los usuarios con problemas sociales, así como una agilización de los trámites administrativos.

Capacity design of distributed battery storage for controlling power variation with large‐scale photovoltaic sources in distribution network

AbstractWe developed a method for the design of distributed battery storage capacity for evaluating battery storage with demand–supply imbalance control in distribution systems to which large‐scale home photovoltaic power generators are connected. The proposed method is based on a linear storage capacity minimization model with design‐basis demand load and photovoltaic output time series subject to battery management constraints.We experimentally applied the design method to a sample distribution system with substation storage and terminal area storage. The developed method successfully reveals the charge–discharge schedule and stored power variation for design‐basis demand load and photovoltaic output, satisfies the peak‐cut requirement, and pinpoints the minimum distributed storage capacity. © 2012 Wiley Periodicals, Inc. Electr Eng Jpn, 181(4): 29–37, 2012; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.22324

Development of the supervisory discharge operation monitoring system for JT-60

For safety and efficient operation of a fusion device, appropriate information should be provided to the personnel participating in the experiment. In a continuing effort to meet this requirement, we have recently developed highlight displays showing the plasma movie, the status of the discharge sequence, the discharge schedule, the status of present troubles, etc. These highlight displays are presented on the large (65 in.) TV monitor in front of the central consoles. These displays are automatically changed according to the timing signals of the JT-60 discharge sequence and/or at the JT-60 session leader's request. We call this system “the supervisory discharge operation monitoring system.” This report describes the technical details of both this system and the discharge information management system (DIMS).

Adverse events and readmissions after day-case urological surgery

The literature lacks of studies on postoperative outcomes after urological ambulatory surgery. Our study aims to identify parameters associated with postoperative complications within 30 days after ambulatory urological surgery. Adjusted and unadjusted comparisons between clinical features and postoperative outcome (complicated and uncomplicated). Postoperative course was complicated in 5% of the patients. Discharge schedule was not completed in 1.1% while unplanned visits resulted in admission in 0.5%. Multivariate analyses could only confirm the independent effect of type of anesthesia and diagnosis-related group (DRG) relative weight. Ambulatory urological surgery can be safe in terms of postoperative complications. In the present study surgery under general anesthesia, or a higher DRG relative weight procedure, increased the risk of complications compared to surgery under regional or local anesthesia or lower DRG relative weight operations. Patients scheduled for general anesthesia or undergoing complex urological procedures should be warned about an increased risk of postoperative incidents and/or readmission.

The ASDEX Upgrade discharge schedule

ASDEX Upgrade's recently commissioned discharge control system (DCS) marks the transition from a traditional programmed system to a highly flexible “data driven” one. The allocation of application processes (APs) to controllers, the interconnection of APs through uniquely named signals, and AP control parameter values are all defined as data, and can easily be adapted to the requirements of a particular discharge. The data is laid down in a set of XML documents which APs request via HTTP from a configuration server before a discharge. The use of XML allows for easy parsing, and structural validation through (XSD) schemas. The central input to the configuration process is the discharge schedule (DS), which embodies the dynamic behaviour of a planned discharge as reference trajectories grouped in segments, concatenated through transition conditions. Editing, generation and validation tools, and version control through CVS allow for efficient management of DSs.

Treatment of brewery slurry in thermophilic anaerobic sequencing batch reactor

Treatment of brewery slurry in a thermophilic anaerobic sequencing batch reactor (ASBR) was studied using conventional fully mixed semi-continuous digestion as a control. The process phases were adapted to fit the brewery slurry discharge schedule. ASBR experiments were conducted under different organic loading rates (OLR) from 3.23 to 8.57 kg of COD/m 3 day of reactor and control was conducted with OLR of 3.0 kg of COD/m 3 day. The ASBR COD degradation efficiency was from 79.6% to 88.9%, control experiment efficiency was 65%. ASBR VSS removal efficiency was from 78.5% to 90.5%, control experiment efficiency was 54%. The ASBR methane production yield was from 371 to 418 L/kg COD inserted, control experiment methane yield was 248 L/kg COD inserted. The ASBR process was superior to conventional fully mixed digestion, and is fully adaptable to brewery slurry discharge, needs no additional collection and settling pools and experiences no solids settling problems.