System Cost Research Articles

Thermally integrated pumped thermal energy storage systems based on organic Rankine cycle: Comparative investigation and multi‐objective multiverse optimization

AbstractThis work introduces two new thermally integrated pumped thermal energy storage (TIPTES) systems, including thermally integrated vapor compression heat pump (TIHP) as a charging cycle and dual‐pressure organic Rankine cycle (DPORC) and dual‐loop organic Rankine cycle (DLORC) as discharging cycles to investigate their capability of improving roundtrip efficiency compared with the basic ORC (BORC). The thermodynamic and economic performance of the proposed TIHP‐DPORC and TIHP‐DLORC are analyzed and compared with TIHP‐BORC using various working fluids. The multi‐objective multiverse optimization algorithm is conducted to ascertain the systems' optimum roundtrip efficiency and cost rate. Results indicate that at the same storage temperature, the TIHP‐DLORC gives the highest roundtrip and exergy efficiencies of 219.9% and 43.53% with R1233zd(E), indicating 6.69% and 8.04% improvements compared with the optimum TIHP‐BORC. Moreover, it shows the lowest levelized cost of storage (LCOS) and payback period (PP) of 0.160 $/kWh and 4.5 years, respectively, with a maximum net present value of 1.973 M$. Although the cost rate of TIHP‐DLORC is high, its excellent thermodynamic and economic performance compared with the existing TIPTES systems in the literature indicates that DLORC is a more appropriate candidate to be applied in the TIPTES systems.

The potential role of concentrated solar power for off-grid green hydrogen and ammonia production

This paper investigates the potential role of concentrated solar power (CSP) in off-grid green electrolytic hydrogen and ammonia production using an open-source techno-economic assessment optimization model. The green ammonia plant comprises a flexible Haber-Bosch loop, an electrolyzer park, a desalination plant, storage systems (thermal, electrical, and hydrogen), and a power supply optionally including wind power, solar photovoltaic (PV), and CSP (solar tower or parabolic trough collectors). Various system configurations and cost assumptions are tested to evaluate when CSP technologies are economically viable for green fuel production. Results demonstrate that CSP and thermal energy storage (TES) technologies can play a minor but useful role in providing nighttime power supply to the ammonia plant, in combination with onshore wind power and electrical storage. Solar PV with 1-axis tracking remains the primary power provider, and the optimal system always includes large-scale hydrogen storage. In the cheapest configuration, the LCOA reaches 646 €/tNH3. With an 80% reduction in CSP and TES costs, CSP technology becomes a major player in power production. In scenarios where hydrogen storage is not feasible and the ammonia plant lacks full flexibility, a 20% cost reduction (to 2750 €/kW) is enough for them to become major power providers.

Multi-objective optimization of series-parallel system with mixed subsystems failure dependencies using NSGA-II and MOHH

In complex systems, failure dependencies play a crucial role in determining their overall performance. This paper explores the multi-objective optimization of series-parallel systems with mixed failure dependencies. By optimizing system cost and availability, the study aims to identify the most efficient redundancy and repair strategies. Two optimization algorithms, the non-dominated sorting genetic algorithm II (NSGA-II) and a novel multi-objective algorithm named the multi-objective hoopoe heuristic (MOHH), are utilized alongside constraint handling techniques to produce Pareto fronts. These fronts illustrate the trade-offs between cost and availability. Additionally, a fuzzy decision method is utilized to determine the best compromise solutions from each optimization technique. Comparing the results, NSGA-II consistently outperforms MOHH in providing better compromise solutions across five independent runs. However, MOHH demonstrates a better standard deviation in its performance.

Optimal operation of co‐phase traction power supply system with HESS and PV

AbstractThe co‐phase traction power supply system (TPSS) with hybrid energy storage system (HESS) and photovoltaic (PV) is proposed to eliminate the neutral section and improve the regenerative braking energy (RBE) utilization. Although the integration of HESS and PV facilitates the energy saving and cost reduction of the co‐phase TPSS, the high cost and configuration of HESS should be considered, which is the key to affect the optimal operation strategy of co‐phase TPSS. Here, the optimal operation strategy of co‐phase TPSS with HESS and PV is proposed to design the HESS configuration, recycle RBE and improve power quality. The proposed model aims to minimize the total system cost, including HESS investment cost, electricity cost and operation and maintenance cost. Moreover, the proposed model is formulated as a mixed integer linear programming by employing linearization approaches. Finally, case studies verify that the 29.2% cost reduction rate is achieved and the three‐phase voltage unbalance meets the standard requirements.

Economic and environmental analysis of EVs' in urban transportation using system dynamics

The higher initial cost of EVs compared to gasoline-powered vehicles can hinder their widespread adoption in various markets. This study aims to evaluate the factors influencing EV adoption or rejection in a developing nation by examining the economic and environmental implications of integrating EVs into Tehran's transportation system. Using a system dynamics approach, the research incorporated a range of elements, including private and public transport, fuel consumption, and environmental costs. Based on this model, causal loop and stock-flow diagrams were developed. Model simulation was conducted, and its validity was carefully controlled. Different scenarios were then generated for incorporating EVs into Tehran's transportation system. The simulations demonstrated that integrating EVs into the taxi fleet is economically advantageous as it offsets the annual purchase and charging station expenses. This scenario led to a 0.2 % decrease in urban costs. Conversely, introducing EVs for private transportation resulted in a 1.9 % increase in overall transportation system costs. Therefore, for cities like Tehran, prioritizing EV adoption in the taxi fleet is a more favorable strategy to balance economic and environmental benefits. This model provides valuable insights for developing countries considering EV adoption, highlighting the economic and environmental ramifications across various sectors.

Optimizing Gaussian process regression (GPR) hyperparameters with three metaheuristic algorithms for viscosity prediction of suspensions containing microencapsulated PCMs

Suspensions containing microencapsulated phase change materials (MPCMs) play a crucial role in thermal energy storage (TES) systems and have applications in building materials, textiles, and cooling systems. This study focuses on accurately predicting the dynamic viscosity, a critical thermophysical property, of suspensions containing MPCMs and MXene particles using Gaussian process regression (GPR). Twelve hyperparameters (HPs) of GPR are analyzed separately and classified into three groups based on their importance. Three metaheuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and marine predators algorithm (MPA), are employed to optimize HPs. Optimizing the four most significant hyperparameters (covariance function, basis function, standardization, and sigma) within the first group using any of the three metaheuristic algorithms resulted in excellent outcomes. All algorithms achieved a reasonable R-value (0.9983), demonstrating their effectiveness in this context. The second group explored the impact of including additional, moderate-significant HPs, such as the fit method, predict method and optimizer. While the resulting models showed some improvement over the first group, the PSO-based model within this group exhibited the most noteworthy enhancement, achieving a higher R-value (0.99834). Finally, the third group was analyzed to examine the potential interactions between all twelve HPs. This comprehensive approach, employing the GA, yielded an optimized GPR model with the highest level of target compliance, reflected by an impressive R-value of 0.999224. The developed models are a cost-effective and efficient solution to reduce laboratory costs for various systems, from TES to thermal management.

Distributed Cooperative Dispatch Method of Distribution Network with District Heat Network and Battery Energy Storage System Considering Flexible Regulation Capability

Flexible resources, including district heat networks (DHN) and battery energy storage systems (BESS), can provide flexible regulation capability for distribution networks (DN), thereby increasing the absorption capacity for renewable energy. In order to improve the operation economy of DN and ensure the information privacy of different operators, a distributed cooperative dispatch method of DN with DHN and BESS considering flexible regulation capability is proposed. First, a distributed cooperative dispatch framework of DN-DHN-BESS is constructed. Then, an optimal dispatch model of DHN under constant flow-variable temperature control strategy is established in order to utilize the heat storage capacity to provide flexible regulation capability for DN. Next, the optimal dispatch models of BESS and DN are established with the objective of minimizing the operation cost, respectively. Finally, a solution method based on the alternating direction multiplier method of distributed cooperative dispatch for DN-DHN-BESS is proposed. Case studies are performed on a system consisting of a 33-node DN and a 44-node DHN, and simulation results demonstrate that the proposed method differs from the centralized dispatch method by only 0.52% in the total system cost, and the proposed method reduces the total system cost by 34.5% compared to that of the independent dispatch method.

Cost burden for healthcare and patients related to the unawareness towards laryngopharyngeal reflux.

To investigate the impact of physician unawareness towards laryngopharyngeal reflux (LPR) on healthcare costs. Patients with a confirmed LPR diagnosis were consecutively recruited from Belgian Hospitals. Demographics and clinical outcomes (impedance-pH testing features, reflux symptom score, and reflux sign assessment) were extracted. The past consultations and additional examinations dedicated to the investigation of laryngopharyngeal symptoms and findings without suspicion of LPR were collected. The estimated costs of consultations and procedures were those indicated in the National Health Insurance Institute's Charges for 2022. Part was reimbursed by the social security system, and the rest was paid by patients. Seventy-six patients were recruited. Seventeen patients (22.4%) had no previous consultation or additional examination for their LPR-symptoms. The estimated mean (standard deviation) costs related to the unawareness of LPR for the healthcare system and patient, were 310.06 ± 370.49 €, and 54.05 ± 46.28 €, respectively. The highest estimated costs were related to gastroenterology consultations and procedures, which did not lead to a confirmation of LPR diagnosis. The total estimated cost for the Belgian healthcare system and patients (11,590,000 million), could range from 359 359 540 € to 1 078 078 620 €; and 62 643 950 € to 187 931 850 €, respectively. The estimated costs related to gastroenterology practice of patients with severe disease were significantly higher than patients with mild disease. The unawareness of practitioners toward LPR leads to significant costs for healthcare system and patients. The teaching and awareness towards LPR need to be improved in medical schools and clinical practice.

An online cost optimization approach for edge resource provisioning in cloud gaming

Cloud gaming (CG), as an emergent computing paradigm, is revolutionizing the gaming industry. Currently, cloud gaming service providers (CGSPs) begin to integrate edge computing with cloud to provide services, with the aim of maximizing gaming service revenue while considering the costs incurred and the benefits generated. However, it is non-trivial to maximize gaming service revenue, as future requests are not known beforehand, and poor resource provisioning may result in exorbitant costs. In addition, the edge resource provisioning (ERP) problem in CG necessitates a trade-off between cost and inevitable queuing issues in CG systems. To address this issue, we propose ERPOL (ERP Online), a convenient and efficient approach to formulate ERP strategies for CGSPs, without requiring any future information. The performance of ERPOL has been theoretically validated and experimentally evaluated. Experiments driven by real-world traces show that it can achieve significant cost savings. The proposed approach has the potential to transform how CGSPs manage their infrastructure.

Adaptive Top-K Algorithm for Medical Conversational Diagnostic Model.

With advancements in computing technology and the rapid progress of data science, machine learning has been widely applied in various fields, showing great potential, especially in digital healthcare. In recent years, conversational diagnostic systems have been used to predict diseases through symptom checking. Early systems predicted the likelihood of a single disease by minimizing the number of questions asked. However, doctors typically perform differential diagnoses in real medical practice, considering multiple possible diseases to address diagnostic uncertainty. This requires systems to ask more critical questions to improve diagnostic accuracy. Nevertheless, such systems in acute medical situations need to process information quickly and accurately, but the complexity of differential diagnosis increases the system's computational cost. To improve the efficiency and accuracy of telemedicine diagnostic systems, this study developed an optimized algorithm for the Top-K algorithm. This algorithm dynamically adjusts the number of the most likely diseases and symptoms by real-time monitoring of case progress, optimizing the diagnostic process, enhancing accuracy (99.81%), and increasing the exclusion rate of severe pathologies. Additionally, the Top-K algorithm optimizes the diagnostic model through a policy network loss function, effectively reducing the number of symptoms and diseases processed and improving the system's response speed by 1.3-1.9 times compared to the state-of-the-art differential diagnosis systems.

A hierarchical k-out-of-n optimization model for enhancing reliability of fire alarm systems

Fire alarm systems are crucial for mitigating the risks and damages associated with fires in process industries. These systems heavily depend on the performance of fire detection sensors to provide early detection and warnings, facilitating prompt evacuation and intervention measures. In order to improve the reliability of sensor systems typically requires the use of redundancy allocation techniques, such as a k-out-of-n configuration. In k-out-of-n configuration, the signals from all sensors are directed to a voter, which compares them and transmits a final signal once at least k signals are matched. However, challenges arise due to diverse failure modes with distinct impacts on the system. Integrating sensors through a single-layer k-out-of-n configuration, when there is only one voter, fails to fully highlight the potential advantages of redundancy in sensor systems. This paper addresses the limitations of the conventional configurations by introducing a hierarchical k-out-of-n system in which sensors can be integrated across multiple layers. In the k-out-of-n hierarchical system, sensors are organized into distinct groups, each employing its own k-out-of-n sub-system. Subsequently, the outputs of these sensor groups can be integrated with other groups through their respective voters in higher layers of the hierarchy. Ultimately, at the top layer, all the voters from the preceding layers must be connected to a top voter to transmit a final signal. Furthermore, an optimization model is developed to minimize the system cost while maximizing reliability, considering the best selection of sensors and the system configuration. The proposed optimization model takes into account a multi-sensor system with two competing failure modes and incorporates the probability of sensors' failure modes in the system. The hierarchical k-out-of-n system provides an opportunity to validate sensors by comparing them within their designated groups, allowing for the identification of the location of a failure and the planning of appropriate actions accordingly. The proposed optimization model is applied to a case of a fire detection system in storage warehouse to demonstrate its advantages over the conventional model.

A novel order characteristic load shifting policy for load factor improvement, peak reduction, and economical operation of distribution systems

Demand side management (DSM) and demand response programs (DRPs) are widely used economic strategies that insist customers shift or curtail loads to economize the generation cost of the distribution system. These policies often involve tedious calculation processes or optimization tools to calculate the apt amount of load to be shredded or shifted to get a new modified load profile. This paper proposes an order characterized load-shifting policy (OCLSP), which is very easy to calculate and yields a considerable decrease in the peak demand, eventually improving the distribution system's load factor, also known as the peak-to-average ratio (PAR). Unlike other DSM and DR strategies, the proposed approach requires no parameters except the forecasted load demand. The proposed OCLSP is implemented on ten distribution systems from the literature which consists of a 3-, 6-, 7-, 10-, 15- units system, one islanded microgrid (MG) system and four grid-connected MG systems. Initially, the load demand of these test systems is restructured using OCLSP, and thereafter, dynamic economic load dispatch (DELD) is performed to minimize the generation cost of the system. A latest circle search algorithm (CSA) is utilized as the optimization tool. Results show that the generation cost of all ten test systems has been decreased by a considerable amount when their load demand is restructured using the proposed OCLSP. The minimum percentage improvement in the PAR of the ten systems was 2 % and maximum improvement was 60 %. Likewise, the percentage decrement in the peak demand was within 2 % to 37.5 % for the ten test systems and the decrement in generation cost rose up to 16 %. All these parameters were in comparison with the base load demand. Numerical results also corroborate to the efficiency and robustness of proposed CSA.

Modified Harris Hawks optimization for the 3E feasibility assessment of a hybrid renewable energy system

The off-grid Hybrid Renewable Energy Systems (HRES) demonstrate great potential to be sustainable and economically feasible options to meet the growing energy needs and counter the depletion of conventional energy sources. Therefore, it is crucial to optimize the size of HRES components to assess system cost and dependability. This paper presents the optimal sizing of HRES to provide a very cost-effective and efficient solution for supplying power to a rural region. This study develops a PV-Wind-Battery-DG system with an objective of 3E analysis which includes Energy, Economic, and Environmental CO2 emissions. Indispensable parameters like technical parameters (Loss of Power Supply Probability, Renewable factor, PV fraction, and Wind fraction) and social factor (Human Developing Index) are evaluated to show the proposed modified Harris Hawks Optimization (mHHO) algorithm’s merits over the existing algorithms. To achieve the objectives, the proposed mHHO algorithm uses nine distinct operators to obtain simultaneous optimization. Furthermore, the performance of mHHO is evaluated by using the CEC 2019 test suite and the most optimal mHHO is chosen for sizing and 3E analysis of HRES. The findings demonstrate that the mHHO has achieved optimized values for Cost of Energy (COE), Net Present Cost (NPC), and Annualized System Cost (ASC) with the lowest values being 0.14130 $/kWh, 1,649,900$, and 1,16,090$/year respectively. The reduction in COE value using the proposed mHHO approach is 0.49% in comparison with most of the other MH-algorithms. Additionally, the system primarily relies on renewable sources, with diesel usage accounting for only 0.03% of power generation. Overall, this study effectively addresses the challenge of performing a 3E analysis with mHHO algorithm which exhibits excellent convergence and is capable of producing high-quality outcomes in the design of HRES. The mHHO algorithm attains optimal economic efficiency while simultaneously minimizing the impact on the environment and maintaining a high human development index.

Chance-constrained programming for drinking water quality management under fuzziness and randomness

ABSTRACT In this paper, an inexact fuzzy random chance-constrained programming (IFRCCP) model was proposed to deal with the randomness and fuzziness in the optimization of booster cost for the water distribution system (WDS). The IFRCCP model was applied to a WDS to analyze the effects of randomness and fuzziness on the booster costs. The booster cost intervals were obtained under various violation levels and confidence levels. The results indicated that the lower and upper booster costs increased with the confidence levels of the lower limits and decreased with the violation levels of the lower limits for both normal and log-normal distribution fuzzy random variables (FRVs). Moreover, the increased magnitude of lower and upper booster costs under normal distribution FRV is a little less than that under log-normal distribution FRV, while the decreased magnitude of lower and upper booster costs under normal distribution FRV is larger than that under log-normal distribution FRV. The same regulation can be found for the FVR with both triangular and trapezoidal distribution FRVs. With the increase of booster number, the increased magnitude of the lower and upper booster costs decreased for both normal and log-normal distribution FRVs.

A Budget Impact Analysis of Adalimumab Biosimilars; The Albanian Context

Adalimumab is used to treat various autoimmune conditions, such as rheumatoid arthritis, ankylosing spondylitis, psoriasis, psoriatic arthritis, Crohn’s disease, and ulcerative colitis. Adalimumab is a tumor necrosis factor (TNF) inhibitor. Adalimumab biosimilars have the potential to reduce costs for the healthcare system. The aim of the study is to develop a drug budget impact analysis to assess the potential financial impact of the availability of the biosimilar adalimumab-aacf in terms of cost savings due to its lower price compared to adalimumab-adaz. Methods: This is a budget impact analysis and cost calculator model in Microsoft Excel based on FDSKSH’s data for 2023. Results: Adalimumab biosimilars accounted for approximately 25% of biologic drug prescriptions in 2023. The reimbursement cost of biologic drugs for autoimmune diseases was ALL 186,523,409 in 2023 (1 ALL = 0.01 USD). Of that cost, 15.21% accounted for treatment with adalimumab biosimilars. Adalimumab-aacf was available at a 15.82% lower price than adalimumab–adaz. The analysis indicated that the replacement of the biosimilar adalimumab-adaz with the biosimilar adalimumab-aacf could have resulted in cost savings of ALL 968,138, and the reimbursement cost of biologic drugs for autoimmune diseases could have been 3% lower. With the potential cost savings, two more patients could have had access to biologic drugs in 2023. Conclusions: The Drug Budget Impact Analysis estimated that the availability of adalimumab-aacf would be more profitable in terms of cost savings compared to adalimumab-adaz. The scenario could result in increased treatment access for patients.

Technological Integration in CRM: A Case Study of Madurai's Hospitality Sector

This study examines the role of technological integration in Customer Relationship Management (CRM) within Madurai's hospitality sector, focusing on how digital tools and software solutions enhance customer engagement and service delivery. As the tourism and hospitality industry in Madurai continues to grow, driven by its cultural significance and heritage, effective CRM practices have become essential for maintaining competitive advantage. This case study investigates how hotels, restaurants, and tour operators in Madurai leverage CRM technologies to streamline operations, personalize guest experiences, and foster long-term relationships. The research analyses the adoption of CRM software, mobile applications, and data analytics in enhancing customer satisfaction, increasing retention rates, and improving operational efficiency. Findings reveal that while the use of technology in CRM has significantly improved service standards and customer interactions, challenges such as system integration, staff training, and cost constraints remain. This study offers insights into the potential of technological CRM solutions to transform the hospitality landscape in Madurai, contributing to both academic literature and practical applications in the tourism and hospitality industries.

Proposing a novel solar adsorption desalination unit using conceptual design and AHP-TOPSIS

In response to the escalating issue of water scarcity, the United Nations has allocated Sustainable Development Goal 6 of ‘Clean Water and Sanitation’ to address the issue by providing clean water and improved sanitation. Solar stills are an attractive solution to water scarcity as they are simple, cost-effective, and convenient for communities with limited resources. However, they have shortcomings, such as limited production and nocturnal ineffectiveness. The present study proposes several alternatives to address these issues using the conceptual design technique. The customer requirements were met using the Quality Function Deployment (QFD) method targeted during the design stage. An integrated AHP-TOPSIS was used to evaluate the design of alternatives considering seven criteria. This proposed method includes many factors, including system efficiency, cost, and ease of operation and maintenance. The three alternatives combine solar stills with adsorption desalination units. Two weighting methods were used, consistency-based ranking index for decision making (CRITIC) and Entropy, to evaluate the results' reliability. The findings showed that the most favorable alternative with CRITIC value of 0.975 and entropy of 0.988, combines a pyramid solar still and an evacuated tube solar collector. The purpose of this investigation is to build on the body of knowledge of solar desalination and support decision-makers in the evaluation process of selecting an appropriate solar still system.

How to choose mobile energy storage or fixed energy storage in high proportion renewable energy scenarios: Evidence in China

With the large-scale integration of renewable energy and changes in load characteristics, the power system is facing challenges of volatility and instability. Therefore, enhancing the safe and stable operation capability of the power system is an urgent problem that needs to be solved. Mobile energy storage can improve system flexibility, stability, and regional connectivity, and has the potential to serve as a supplement or even substitute for fixed energy storage in the future. However, there are few studies that comprehensively evaluate the operational performance and economy of fixed and mobile energy storage systems. To comprehensively evaluate the economic benefits of large-scale mobile energy storage systems, this paper constructs an overall horizontal cost model for energy storage systems that considers the power system and train transportation system. This model not only introduces the traditional concept of lifecycle cost of energy storage systems, but also considers the costs of traditional transmission lines and battery transportation. Secondly, to achieve simulation of large-scale mobile energy storage system planning and operation, this paper establishes a multi-region power planning and operation simulation (MPO) model and a battery transportation and logistics (BTL) model to accurately reflect the operation mode of fixed energy storage and mobile energy storage in the power system. Finally, taking the actual power grids and railway networks in Northeast and North China as case studies, this article provides an in-depth analysis of the technical, economic, and environmental performance of large fixed and mobile energy storage systems, and conducts a detailed comparison. The research results indicate that under high grid connection ratios (using 75% and 66% as examples), the overall cost of mobile energy storage systems continues to decrease to 1.42 CNY/kWh and 0.98 CNY/kWh. Compared to fixed energy storage at 5.45 CNY/kWh and 4.79 CNY/kWh, it has an absolute economic advantage and shows significant carbon reduction capabilities, reaching 241,800 to 1,394,600 tons. This discovery fully confirms the enormous potential and application value of mobile energy storage in high proportion renewable energy scenarios, providing strong technical support and economic analysis basis for the sustainable development of the power system.

Voltage Stacking: A First-Order Modelization of an m × n Asynchronous Array for Chip and Architectural Design Exploration

Voltage stacking is a technique in which multiple integrated circuits are stacked in series between the supply voltage instead of in parallel, thus improving the energy efficiency of the power distribution network. Unfortunately, voltage stacking presents stability challenges for integrated circuits within the stack. A first-order model to quantify variability, stability, and power metrics for an array of voltage-stacked asynchronous integrated circuits is presented. Voltage variability and power consumption are accounted for and discussed. Limitations of the model are identified outside of the nominal behavior. The number of columns in the architecture, chip leakage, and supply voltage are shown to be the key contributors to the stability, performance, and energy efficiency of a system of voltage-stacked asynchronous processors. A higher leakage to active power ratio, though usually avoided by chip designers, is shown to improve stability and be key in designing stacks without external balancing. Outputs of the model enable system and chip designers to evaluate first-order trade-offs in energy efficiency, performance, and system cost. These fundamental data allow designers to make informed design and optimization trade-offs between asynchronous voltage-stacked architectures and the integrated circuits used therein. Analysis of this model shows that various voltage-stacked configurations, such as one with a 48 V supply using 100 rows and 11 columns, can be designed with less than 10% voltage variation per chip, mitigating the need for external voltage balancing.

Aftercare following second trimester abortion procedures.

The purpose of this review is to examine patient experiences following second-trimester abortion care to identify clinical interventions to improve aftercare. Early validation of a five-question Reproductive Grief Screen suggests that this instrument may be useful for clinicians to identify patients with maladaptive grief responses following a second-trimester abortion. Patients want to talk with their healthcare providers about grief following abortion and desire frequent mental health check-ins. Most people will have breast symptoms following a second-trimester abortion; a one-time dose of cabergoline at time of abortion appears to be an effective intervention to prevent bothersome breast symptoms. As patient desires vary regarding disposition of fetal remains, clinicians should be prepared to discuss donation for research or cremation/ burial as alternatives to routine disposal. With significant variation in availability and cost of ceremonial disposition, clinicians and healthcare systems should identify local resources to facilitate this aspect of aftercare. Clinical practices and healthcare systems should prioritize the aftercare experience including the management of breast symptoms, bereavement, disposition of fetal remains, and follow-up care.