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Integrating wind power plants into the electricity grid poses challenges due to the intermittent nature of wind energy generation. Energy storage systems (ESSs) have shown promise in mitigating the intermittent variability associated with wind power. This paper presents a distributionally robust optimization (DRO) model for sizing energy storage systems to dispatch wind power plants. The variable wind power is formulated as a moment-based ambiguity set. Dispatchability is described by the expected value of the insufficient power of wind power relative to the dispatch command, which is a sum of nonlinear functions and is taken as the optimal index. A deterministic semi-definite positive model is derived to solve the problem effectively. Numerical studies are conducted to demonstrate the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

To satisfy the large-scale grid-connected demands of distributed energy resources (DERs), a bi-level optimization model is proposed for private DER dispatch managed by a virtual power plant (VPP) in a smart grid environment in this paper. The optimization models of the upper and lower layers serve as the planning layer and the operational layer, respectively, wherein the upper optimization model is mainly aimed at the optimization allocation of DERs under an unchanged network topology, and the lower-level optimization model is mainly responsible for the optimization of the distributed network topology from the optimization results of the upper layer. An improved harmony searching algorithm (HSA) is then applied to resolve the model through continuous iterations and communications between the upper and lower levels until an expected aim is achieved, which can satisfy the aim demand of the upper and the lower levels at the same time. Finally, six optimization scenarios are planned and selected in order to evaluate the performance of the bi-level optimization strategy in the IEEE33 bus distribution system and the 69-bus distribution system, and the optimized results show that the proposed method can effectively improve the grid-connected capacity of DERs and their system performance, and are more flexible in facing a growing ambient smart environment. [ABSTRACT FROM AUTHOR]

The growing presence of EVs in regional microgrids introduces increased variability and uncertainty in the areas' load profiles. This paper presents a novel approach for optimizing energy and reserve minimization in a sustainable integrated microgrid with electric vehicles (EVs) by the use of the dynamic and adjustable Manta Ray Foraging (DAMRF) algorithm. The DAMRF algorithm harnesses the inherent flexibility of EVs as controllable loads and develops a comprehensive dispatch model for a large-scale EV response. The model takes into account the management, operational, and environmental costs associated with load fluctuations in the microgrid. Simulation evaluations conducted based on a practical microgrid environment validate the effectiveness of our wind–solar energy storage and management strategy. The results showcase significant improvements in energy and reserve minimization, highlighting the potential advantages of integrating EVs into sustainable microgrid systems. In addition, the DAMRF algorithm achieves lower environmental pollution control costs (USD 8000) compared to the costs associated with the Genetic Algorithm (GA) (USD 8654.639) and PSO (USD 8579.546), emphasizing its ability to effectively control and minimize environmental pollution. In addition, the DAMRF algorithm offers a more cost-effective solution for managing the power grid, and the shorter solution running time of the DAMRF is almost the same as PSO's quicker decision-making and response times, enhancing the overall responsiveness and adaptability of the power grid management system. [ABSTRACT FROM AUTHOR]

The main purpose of the emergency medical system is a timely and appropriate response that significantly impacts health outcomes. In Thailand, due to lack of staff, emergency medical personnel at the dispatch center have to multitask to interview and dispatch the EMS responses. According to these problems the novel triage mobile application "Triagist" was developed based on Criteria Based Dispatch (CBD) to optimize prehospital care. This study is a pilot study aiming to test the app by evaluating the reliability and rapidity of code dispatching using "Triagist" compared with the usual method; ITEMS, Criteria Based Dispatch Handbook, or emergency medical personnel' experiences only. A randomized-controlled crossover (AB/BA) design was conducted which compared mobile application "Triagist" and conventional methods. Eighteen experienced emergency medical personnel each were randomly assigned to AB or BA arm. The primary outcome measure was the accuracy and rapidity of code dispatching to 6 simulated scenarios among experienced emergency medical personnel. Using triage mobile application by experienced emergency medical personnel had gained a chance of correct dispatch 1.5 times, which was significantly more accurate than the conventional method (95%CI 1.03-2.28) and also significantly more rapid dispatch than the conventional method (82 vs 95 sec., p = 0.03). [ABSTRACT FROM AUTHOR]

Integrating solar PV inverters and storage devices into the modern power grid generates multiple power profiles with varying magnitudes. The intermittent nature of PV necessitates installing storage devices to reduce unit commitment challenges and accommodate reserve power. This paper proposes various operational factors to determine the boundaries for individual and aggregated power profiles within a cluster of PV systems and storage units. The study utilizes dispatchability and operating reserve factors (primary and secondary) during storage discharge mode to extract dispatchable power and primary and secondary reserve power. Similarly, storage arbitrage and non-dispatchability factors extract arbitrage, non-dispatchable, and a portion of supplemental or tertiary reserve power during storage charging mode. An optimization model leveraging mixed-integer linear programming is formulated to minimize operational expenditures considering these hybrid PV-storage power profiles. Extrapolated electricity market rates for hybrid power profiles are projected using the prevailing prices of photovoltaic (PV) systems and storage technologies. This paper utilizes the proposed aggregated operational factors as constraints while extracting these power profiles for a cluster of PV-storages. The efficacy of the proposed optimization framework is tested on a modified IEEE 34-bus system by adding 10 PV-storage devices on 10 buses. The final quantitative values correspond to the percent penetration and arbitrage of these power profiles.

Looking at the quality through the results of an activity, in this case an economic activity, our paper aims to identify some causal links between the preventing criminality preventing and control mechanism, based on telematics security systems, and the criminality against property status at country level. The analyzed statistical data series are focused on the evolution of criminality in Romania in the last years, and also on the monitoring marketing based on private dispatches. The big picture reveals that in over 43% of the European countries there is an ascending level of burglaries. In this paper we try to identify if the development of the telematics security market based on private monitoring dispatches a decrease of the criminality against property in Romania. Based on this study we will be able to see if the private companies which provide security services can reach an Excellency level (considered Excellency in business), identified through country level results, based on the use of security telematics solutions. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach based on the moth flame optimizer algorithm. Moth flame optimizer simulates the natural fervent navigation technique adopted by moths looking for a source of light. The proposed method is further improved by priority list-based ordering; the unit commitment problem (UCP) is a non-linear, non-convex, and combinatorial complex optimization problem. It contains both continuous and discrete variables. This further increases its complexity. Moth flame optimizer is very good at obtaining a commitment pattern: allocation of power on the committed units obtained by mixed-integer quadratic programming method. Heuristic search strategies are used to adopt for the repair of minimum up and downtime, and spinning reserve constraints. MFO effectiveness is tested on the standard UCP reference IEEE model buses 14 and 30, and 10 and 20 units. The efficiency of the projected algorithms is compared to classical PSO, PSOLR, HPSO, PSOSQP, hybrid MPSO, IBPSO, LCA-PSO, NPSO, PSO-GWO, and various other evolutionary algorithms. The comparison result shows that MFO can lead to all methods reported earlier in literature. [ABSTRACT FROM AUTHOR]

The number of electric vehicles (EVs) is expected to increase significantly in the future to combat air pollution and reduce reliance on fossil fuels. This will impact the power system. However, appropriate charging and discharging of EVs through vehicle-to-grid (V2G) operations could also provide support for the power system and benefits for EV owners. This raises the questions of when and how EV battery storage should be dispatched, taking into account both vehicle users' and power system's requirements and priorities, as well as the constraints of the battery system. This paper proposes a novel decentralized dispatch strategy based on the analytic hierarchy process (AHP), taking into account the relative importance of the different criteria, including cost, battery state of charge (SOC), power system contingency, and load leveling. The proposed AHP-based dispatch strategy was tested on an IEEE Reliability Test System with different EV numbers and capacities to investigate the efficacy of such an approach. The simulation results demonstrate the feasibility and benefits of this dispatch strategy. [ABSTRACT FROM PUBLISHER]

To realize the integrated energy system (IES) low-carbon and economy dispatches and renewable energy utilization, the integrated energy system economic dispatch model introduces the liquid carbon dioxide energy storage (LCES) and carbon capture system (CCS). This paper proposes a low-carbon economic dispatch model for an integrated energy system that considers LCES and carbon capture system. The paper considers the impact of carbon trading mechanisms on systemic carbon emissions, aims to minimize the total operating cost of the system, and comparison of integrated energy system dispatch for two scenarios: integrated energy system equipped with LCES and integrated energy system equipped with battery energy storage. CPLEX simulation software simulates this comprehensive energy system. Analyzing the dispatching results from different perspectives, such as electric energy, thermal energy, and CO2 emissions. These results show that the proposed model effectively reduces carbon emissions, improves energy utilization, and achieves comprehensive low-carbon economic operation of the integrated energy system.

The optimal management of a multi-generation energy system is one of the challenges that an ever-growing energy demand requires to deal with. To tackle this urgent issue, this paper presents a methodology to identify the optimal dispatching strategy for a multi-generation energy system. The so-called time-of-use rate is one of the main time-based demand response programs which allows shifting the critical loads from a time interval to another (e.g., shifting electricity use to lower-priced hours of a day when demand is lower). Thus, the time-of-use rate is adopted in this paper in order to add flexibility to the management of the multi-generation energy system and thus optimize the interaction between energy production and user demand.In this paper, the goal is the minimization of primary energy consumption or operating costs. Whatever the considered objective function, the goal can be achieved by simultaneously acting on two levels, i.e., optimization of the demand response program and identification of the most favorable management strategy of the multi-generation energy system. A mixed-integer linear programming algorithm is employed to identify the optimal strategy. The case study considers an entire year of operation with a time step of one hour, by means of a real-world load profile. The proposed methodology allows both saving primary energy (more than 1%) and reducing operating costs (more than 8%). The proposed methodology demonstrates that the implementation of a demand response program within the optimal strategy for energy dispatch allows both saving primary energy and reducing operating costs with respect to the baseline scenario (i.e., no load shifting). The reduction of both primary energy consumption and operational costs is higher in the scenario with higher load shifting (in this paper, 30% of the daily electrical energy peak).

In the path to the future implementation of the smart grid, microgrids are presented as a cornerstone. An efficient and optimal microgrid operation is paramount. In this paper, we present a hierarchical microgrid management system using task sharing and an evolutionary game theory based dispatch strategy as a coordination algorithm to integrate the three main control levels needed for microgrid operation, which can be considered as a distributed intelligent system. The proposed algorithm shares the total load demanded into the distributed generators efficiently, while the microgrid utility is maximized dynamically, increasing the system reliability. Some simulation results are presented to illustrate the effectiveness of the dynamic performance of the proposed approach compared with a traditional hierarchical algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an analytic review of the recent methodologies tackling the problem of dynamic allocation of ambulances was carried out. Considering that state-of-the-art is moving to deal with more extensive and dynamic problems to address in a better way real-life instances, this research looks to identify the evolution and recent applications of this kind of problem once the basic models are explored. This extensive review allowed us to identify the most recent developments in this problem and the most critical gaps to be addressed. In this sense, it is essential to point out that the dynamic location of emergency medical services (EMS) is nowadays a relevant topic considering its impact on the healthcare system outcomes. Issues related to forecasting, simulation, heterogeneous fleets, robustness, and solution speed for real-life problems, stand out in the identified gaps. Applications of machine learning the deployment challenges during epidemic outbreaks such as SARS and COVID-19 were also explored. At the same time, a proposed notation tries to tackle the fact that the word problem in this kind of work refers to a model on many occasions. The proposed notation eases the comparison between the different model proposals found in the literature. [ABSTRACT FROM AUTHOR]

In order to ensure security of supply in a future energy system with a high share of volatile electricity generation, flexibility technologies are needed. Industrial demand-side management ranks as one of the most efficient flexibility options. This paper analyses the effect of the integration of industrial demand-side management through the flexibilisation of aluminium electrolysis and other flexibilities of the electricity system and adjacent sectors. The additional flexibility options include electricity storage, heat storage in district heating networks, controlled charging of electric vehicles, and buffer storage in hydrogen electrolysis. The utilisation of the flexibilities is modelled in different settings with an increasing share of renewable energies, applying a dispatch model. This paper compares which contributions the different flexibilities can make to emission reduction, avoidance of curtailment, and reduction of fuel and CO 2 costs, and which circumstances contribute to a decrease or increase of overall emissions with additional flexibilities. The analysis stresses the rising importance of flexibilities in an energy system based on increasing shares of renewable electricity generation, and shows that flexibilities are generally suited to reduce carbon emissions. It is presented that the relative contribution towards the reduction of curtailment and costs of flexibilisation of aluminium electrolysis are high, whereby the absolute effect is small compared to the other options due to the limited number of available processes. [ABSTRACT FROM AUTHOR]

With the rise and improvement of artificial intelligence technology, the express delivery industry has become more intelligent. At the terminal of modern logistics, each dispatch center has hundreds of express mail deliveries to be dispatched every day, and the number of dispatchers is far less than the number of express mail deliveries. How to assign scientific tasks to each courier dispatch is the main target of this paper. The purpose is to make the number of tasks between the various couriers in the express center roughly the same in each cycle, so that there is a more balanced income between the couriers. In the simulation experiment, the delivery addresses are clustered according to the balanced k-means algorithm. Then, the ant colony algorithm is used to plan the delivery order of the express items in each class. Then, the time cost model is established according to the delivery distance of the express items in each class and the delivery mode of the express items to calculate the delivery time cost. Through a large amount of experimental data, the standard deviation of delivery time cost of each courier gradually decreases and tends to stabilize, which suggests that this method has a good effect on the dispatching task assignment of the express center. It can effectively make the delivery workload between the distributors roughly the same, and improve the delivery efficiency of the courier, save energy, and promote sustainable development. [ABSTRACT FROM AUTHOR]

This paper introduces a modified version of the DisPATCh (Disaggregation based on Physical And Theoretical scale Change) algorithm to disaggregate an SMAP surface soil moisture (SSM) product at a 20 m spatial resolution, through the use of sharpened Sentinel-3 land surface temperature (LST) data. Using sharpened LST as a high resolution proxy of SSM is a novel approach that needs to be validated and can be employed in a variety of applications that currently lack in a product with a similar high spatio-temporal resolution. The proposed high resolution SSM product was validated against available in situ data for two different fields, and it was also compared with two coarser DisPATCh products produced, disaggregating SMAP through the use of an LST at 1 km from Sentinel-3 and MODIS. From the correlation between in situ data and disaggregated SSM products, a general improvement was found in terms of Pearson's correlation coefficient (R) for the proposed high resolution product with respect to the two products at 1 km. For the first field analyzed, R was equal to 0.47 when considering the 20 m product, an improvement compared to the 0.28 and 0.39 for the 1 km products. The improvement was especially noticeable during the summer season, in which it was only possible to successfully capture field-specific irrigation practices at the 20 m resolution. For the second field, R was 0.31 for the 20 m product, also an improvement compared to the 0.21 and 0.23 for the 1 km product. Additionally, the new product was able to depict SSM spatial variability at a sub-field scale and a validation analysis is also proposed at this scale. The main advantage of the proposed product is its very high spatio-temporal resolution, which opens up new opportunities to apply remotely sensed SSM data in disciplines that require fine spatial scales, such as agriculture and water management. [ABSTRACT FROM AUTHOR]

Integrating wind power plants into the electricity grid poses challenges due to the intermittent nature of wind energy generation. Energy storage systems (ESSs) have shown promise in mitigating the intermittent variability associated with wind power. This paper presents a distributionally robust optimization (DRO) model for sizing energy storage systems to dispatch wind power plants. The variable wind power is formulated as a moment-based ambiguity set. Dispatchability is described by the expected value of the insufficient power of wind power relative to the dispatch command, which is a sum of nonlinear functions and is taken as the optimal index. A deterministic semi-definite positive model is derived to solve the problem effectively. Numerical studies are conducted to demonstrate the effectiveness and advantages of the proposed method.

The Australian electricity sector is expected to undergo major changes to reduce the current sectoral CO2 emission of 190 million tons to 96 million tons by 2050. This paper summarizes two major modeling studies that have been undertaken since 2006 to understand the full set of investment, price and financial impact on existing generators, especially coal-based generators. We have used a long-term capacity and dispatch optimization model to comprehensively analyze both least-cost and market-led expansion of the system to accommodate alternative emission reduction targets from a shallow cut to a deep cut. The modeling results demonstrate the cost of meeting the target can be over $30 billion in NPV terms. A mix of clean coal technologies, gas and nuclear technologies is expected to displace the existing stock of conventional coal which varies significantly depending on the emission reduction target. The analysis also show that mandatory renewable targets or technology standards are inefficient policies in comparison to a market-based policy such as the proposed emissions trading scheme. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66–0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration. [ABSTRACT FROM AUTHOR]

The intraday continuous electricity market (ICM) is a potential target market for the Dispatchable Hybrid Renewable solar–wind–battery energy storage system (BESS) power plant (DHRB). However, the uncertainty of the electricity price jeopardizes economic justification of BESS operation, an essential component of DHRB. Using the duality theory, this paper proposes a unilevel mixed-integer linear programming rolling-approach-based robust optimal scheduling tool for DHRB that keeps BESS operation optimal should the worst price scenario occur. It reflects BESS's degradation as penalty factors and also integrates a BESS degradation model in the scheduling tool for better assessment of the available resources through the BESS's lifetime. This tool aids the DHRB operator to decide the power offer to the ICM in such a way that the BESS's operation remains optimal. A case study is carried out to demonstrate the application of the proposed tool. Both the long-term and short-term losses/benefits of utilizing this tool for scheduling DHRB in the ICM are investigated at various uncertainty levels. It is shown that there will be a risk of loss of income for the DHRB in the short-term due to increased nondispatchable energy. However, by limiting the use of BESS to only those settlement periods that are either certainly profitable or unavoidable, the lifetime of BESS can potentially be extended. Hence, this can result in more income by the DHRB power plant in the long-term. [ABSTRACT FROM AUTHOR]

The substantial increase in the proportion of renewable energy power generation has put forward higher requirements and challenges for the dispatch of the power system. Recently, there is a lack of a complete evaluation system for the dispatching results of power systems with a high proportion of renewable energy, which leads to the lack of feedback and improvement links after the implementation of the dispatching strategy. This paper takes the dispatching results of power systems with a high proportion of renewable energy under four different dispatching modes as the evaluation object, and constructs an evaluation index system including four dimensions of environmental protection, economy, safety and fairness. And a comprehensive evaluation model and evaluation method based on Pythagorean fuzzy set (PFS) and TOPSIS (Technique for Order Preference by Similarity to ideal Solution) are presented, which were verified with the calculating examples.

When performing wind integration studies, synthetic wind power forecasts are key elements. Historically, data from operational forecasting systems have been used sparsely, likely due to the high costs involved. Purely statistical methods for simulating wind power forecasts are more common, but have problems mimicking all relevant aspects of actual forecasts. Consequently, a new approach to obtain wind power forecasts for integration studies is proposed, relying on long time series of freely and globally available reforecasts. In order to produce synthetic forecasts with similar properties as operational ditto, some processing (noise addition and error reduction) is necessary. Validations with measurements from Belgium and Sweden show that the method is adequate; and distributions, correlations, autocorrelations and power spectral densities of forecast errors correspond well. Furthermore, abrupt changes when forecasts are updated and the existence of level and phase errors are reproduced. The influence from terrain complexity on error magnitude is promising, but more data is necessary for a proper validation. [ABSTRACT FROM AUTHOR]

The purpose of the Combined Economic Emission Dispatch (CEED) of electric power is to offer the most exceptional schedule for production units, which must run with both low fuel costs and emission levels concurrently, thereby meeting the lack of system equality and inequality constraints. Economic and emissions dispatching has become a primary and significant concern in power system networks. Consequences of using non-renewable fuels as input to exhaust power systems with toxic gas emissions and depleted resources for future generations. The optimal power allocation to generators serves as a solution to this problem. Emission dispatch reduces emissions while ignoring economic considerations. A collective strategy known as Combined Economic and Emission Dispatch is utilized to resolve the above-mentioned problems and investigate the trade-off relationship between fuel cost and emissions. Consequently, this work manages the Substantial Augmented Transformative Algorithm (SATA) to take care of the Combined Economic Emission Dispatch Problem (CEEDP) of warm units while fulfilling imperatives, for example, confines on generator limit, diminish the fuel cost, lessen the emission and decrease the force misfortune. SATA is a stochastic streamlining process that relies upon the development and knowledge of swarms. The goal is to minimize the total fuel cost of fossil-based thermal power generation units that generate and cause environmental pollution. The algorithm searches for solutions in the search space from the smallest to the largest in the case of forwarding search. The simulation of the proposed system is developed using MATLAB Simulink software. Simulation results show the effectiveness and practicability of this method in terms of economic and emission dispatching issues. The performance of the proposed system is compared with existing Artificial Bee Colony-Particle Swarm Optimization (ABC-PSO), Simulated Annealing (SA), and Differential Evolution (DE) methods. The fuel cost and gas emission of the proposed system are 128904 $/hr and 138094.4652$/hr. [ABSTRACT FROM AUTHOR]

Global soil moisture (SM) products are currently available from passive microwave sensors at typically 40 km spatial resolution. Although recent efforts have been made to produce 1 km resolution data from the disaggregation of coarse scale observations, the targeted resolution of available SM data is still far from the requirements of fine-scale hydrological and agricultural studies. To fill the gap, a new disaggregation scheme of Soil Moisture Active and Passive (SMAP) data is proposed at 100 m resolution by using the disaggregation based on physical and theoretical scale change (DISPATCH) algorithm. The main objectives of this paper is (i) to implement DISPATCH algorithm at 100 m resolution using SMAP SM and Landsat land surface temperature and vegetation index data and (ii) to investigate the usefulness of an intermediate spatial resolution (ISR) between the SMAP 36 km resolution and the targeted 100 m resolution. The sequential disaggregation approach from 36 km to ISR (ranging from 1 km to 30 km) and from ISR to 100 m resolution is evaluated over 22 irrigated field crops in central Morocco using in-situ SM measurements collected from January to May 2016. The lowest root mean square difference (RMSD) between the 100 m resolution disaggregated and in-situ SM is obtained when the ISR is around 10 km. Therefore, the two-step disaggregation is more efficient than the direct disaggregation from SMAP to 100 m resolution. Moreover, we propose a moving average window algorithm to increase the accuracy in the 100 m resolution SM as well as to reduce the low-resolution boxy artifacts on disaggregated images. The correlation coefficient between 100 m resolution disaggregated and in situ SM ranges between 0.5–0.9 for four out of the six extensive sampling dates. This methodology relies solely on remote sensing data and can be easily implemented to monitor SM at a high spatial resolution over irrigated regions. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent battery management system (BMS) implementing two large Vanadium Redox Battery (VRB) flow batteries in a master-slave mode to provide grid-level energy storage for a wind-solar hybrid power system. The proposed BMS is formulated to effectively meet a predetermined power dispatch formulated based on forecasted wind and solar data while incorporating features like peak shaving and ramp rate limiting. It is compared to a single battery module operated system to showcase the advantages of the proposed intelligent dual battery module in terms of appreciable reduction in battery size and costs while exhibiting improved lifecycle performance. The battery size is optimized based on heuristic optimization algorithms and modelled in Matlab/Simulink environment. An intelligent fuzzy-based BMS is used to control the dual VRB model to ensure optimized power sharing between batteries. The simulations were carried out and an in-depth economic analysis conducted to analyze the costs and other financial metrics of the hybrid project. Results proved the advantages of the dual battery with the proposed BMS and fortify that the introduction of time-based tariffs and other incentives will further make investments in VRB highly attractive for renewable applications. [ABSTRACT FROM AUTHOR]

This paper investigates the operating regimes of CCS power plants in future generation portfolios with large amounts of variable- output wind generation. An advanced electricity system dispatch model is developed and coupled with a Monte Carlo based energy storage optimization model to simulate the least-cost dispatch of an assumed thermal-energy storage generation portfolio with CCS. A historic high-resolution wind speed reanalysis dataset is employed and the proposed locations of future wind farms are used to produce plausible and internally consistent wind capacity deployment scenarios. The fundamental and structural changes that occur to CCS operating profiles and start-up/shut-down schedules are investigated for increasing levels of wind capacity, which creates seasonal and diurnal variations and potential flexibility implications. Non-linear interactions between flexible CCS power plants and other energy vectors are demonstrated for an illustrative case study example in Great Britain. This temporally explicit analysis of the short-term scheduling decisions of thermal plants with CCS highlights the asymmetric displacement of mid-merit thermal plants and the importance of using time-dependent start-up costs in wind-based unit commitment studies. [ABSTRACT FROM AUTHOR]

Background: Calls for emergency medical assistance at the scene of a motor vehicle crash (MVC) substantially contribute to the demand on ambulance services. Triage by emergency medical dispatch systems is therefore important, to ensure the right care is provided to the right patient, in the right amount of time. A lights and sirens (L&S) response is the highest priority ambulance response, also known as a priority one or hot response. In this context, over triage is defined as dispatching an ambulance with lights and sirens (L&S) to a low acuity MVC and under triage is not dispatching an ambulance with L&S to those who require urgent medical care. We explored the potential for crash characteristics to be used during emergency ambulance calls to identify those MVCs that required a L&S response.Methods: We conducted a retrospective cohort study using ambulance and police data from 2014 to 2016. The predictor variables were crash characteristics (e.g. road surface), and Medical Priority Dispatch System (MPDS) dispatch codes. The outcome variable was the need for a L&S ambulance response. A Chi-square Automatic Interaction Detector technique was used to develop decision trees, with over/under triage rates determined for each tree. The model with an under/over triage rate closest to that prescribed by the American College of Surgeons Committee on Trauma (ACS COT) will be deemed to be the best model (under triage rate of ≤ 5% and over triage rate of between 25-35%.Results: The decision tree with a 2.7% under triage rate was closest to that specified by the ACS COT, had as predictors-MPDS codes, trapped, vulnerable road user, anyone aged 75 + , day of the week, single versus multiple vehicles, airbag deployment, atmosphere, surface, lighting and accident type. This model had an over triage rate of 84.8%.Conclusions: We were able to derive a model with a reasonable under triage rate, however this model also had a high over triage rate. Individual EMS may apply the findings here to their own jurisdictions when dispatching to the scene of a MVC. [ABSTRACT FROM AUTHOR]

Aims Automated external defibrillators (AEDs) are placed in public, but the majority of out-of-hospital cardiac arrests (OHCA) occur at home. Methods and results In residential areas, 785 AEDs were placed and 5735 volunteer responders were recruited. For suspected OHCA, dispatchers activated nearby volunteer responders with text messages, directing two-thirds to an AED first and one-third directly to the patient. We analysed survival (primary outcome) and neurologically favourable survival to discharge, time to first defibrillation shock, and cardiopulmonary resuscitation (CPR) before Emergency Medical Service (EMS) arrival of patients in residences found with ventricular fibrillation (VF), before and after introduction of this text-message alert system. Survival from OHCAs in residences increased from 26% to 39% {adjusted relative risk (RR) 1.5 [95% confidence interval (CI): 1.03–2.0]}. RR for neurologically favourable survival was 1.4 (95% CI: 0.99–2.0). No CPR before ambulance arrival decreased from 22% to 9% (RR: 0.5, 95% CI: 0.3–0.7). Text-message-responders with AED administered shocks to 16% of all patients in VF in residences, while defibrillation by EMS decreased from 73% to 39% in residences (P < 0.001). Defibrillation by first responders in residences increased from 22 to 40% (P < 0.001). Use of public AEDs in residences remained unchanged (6% and 5%) (P = 0.81). Time from emergency call to defibrillation decreased from median 11.7 to 9.3 min; mean difference –2.6 (95% CI: –3.5 to –1.6). Conclusion Introducing volunteer responders directed to AEDs, dispatched by text-message was associated with significantly reduced time to first defibrillation, increased bystander CPR and increased overall survival for OHCA patients in residences found with VF. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a new unit commitment model that captures the sub-hourly variability of wind power. Scenarios are included in the stochastic unit commitment formulation to represent the uncertainty and intermittency of wind power output. A modified Benders decomposition method is used to improve the convergence of the algorithm. The numerical results show that the proposed model based on finer granularity outperforms the conventional model of hourly resolution. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate the representation of wind power forecasting (WPF) uncertainty in the unit commitment (UC) problem. While deterministic approaches use a point forecast of wind power output, WPF uncertainty in the stochastic UC alternative is captured by a number of scenarios that include cross-temporal dependency. A comparison among a diversity of UC strategies (based on a set of realistic experiments) is presented. The results indicate that representing WPF uncertainty with wind power scenarios that rely on stochastic UC has advantages over deterministic approaches that mimic the classical models. Moreover, the stochastic model provides a rational and adaptive way to provide adequate spinning reserves at every hour, as opposed to increasing reserves to predefined, fixed margins that cannot account either for the system’s costs or its assumed risks. [Copyright &y& Elsevier]

The Medical System of Transportation deals with two online real-time decisions: ambulance dispatching and relocation. Dispatching consists of selecting which ambulance to send to an emergency call; relocation consists of determining how to modify the location of available ambulances in response to changes in the system’s state. Although the literature regarding this problem is extensive, only a limited number of online real-time approaches for ambulance management have been proposed, much less one taking into consideration different types of emergencies and vehicles. This paper proposes an online real-time matheuristic algorithm that combines: i) a new preparedness index defined as the availability probability of a multi-server queue model which is used as an optimization objective and as a control variable for relocation strategies, ii) two mathematical models to solve the relocation problem, one oriented to the maximization of coverage and other to the minimization of the maximum relocation time, and iii) two heuristic algorithms oriented to the maximization of the preparedness level, one to solve the dispatch problem and other to solve the location problem of one ambulance. The computational experiments, based on discrete event simulation and historical data of Bogotá, Colombia, have shown their capability to adequately respond to the necessities of real-time operation.

In this article, we propose and experimentally validate a scheduling and control framework for distributed energy resources (DERs) that achieves to track a day-ahead dispatch plan of a distribution network hosting controllable and stochastic heterogeneous resources while respecting the local grid constraints on nodal voltages and lines ampacities. The framework consists of two algorithmic layers. In the first one (day-ahead scheduling), we determine an aggregated dispatch plan. In the second layer (real-time control), a distributed model predictive control (MPC) determines the active and reactive power set-points of the DERs so that their aggregated contribution tracks the dispatch plan while obeying to DERs operational constraints as well as the grid's ones. The proposed framework is experimentally validated on a real-scale microgrid that reproduces the network specifications of the CIGRE microgrid benchmark system. [ABSTRACT FROM AUTHOR]

DISPATCH is a disaggregation algorithm of the low-resolution soil moisture (SM) estimates derived from passive microwave observations. It provides disaggregated SM data at typically 1 km resolution by using the soil evaporative efficiency (SEE) estimated from optical/thermal data collected around solar noon. DISPATCH is based on the relationship between the evapo-transpiration rate and the surface SM under non-energy-limited conditions and hence is well adapted for semi-arid regions with generally low cloud cover and sparse vegetation. The objective of this paper is to extend the spatio-temporal coverage of DISPATCH data by 1) including more densely vegetated areas and 2) assessing the usefulness of thermal data collected earlier in the morning. Especially, we evaluate the performance of the Temperature Vegetation Dryness Index (TVDI) instead of SEE in the DISPATCH algorithm over vegetated areas (called vegetation-extended DISPATCH) and we quantify the increase in coverage using Sentinel-3 (overpass at around 09:30 am) instead of MODIS (overpass at around 10:30 am and 1:30 pm for Terra and Aqua, respectively) data. In this study, DISPATCH is applied to 36 km resolution Soil Moisture Active and Passive SM data over three 50 km by 50 km areas in Spain and France to assess the effectiveness of the approach over temperate and semi-arid regions. The use of TVDI within DISPATCH increases the coverage of disaggregated images by 9 and 14% over the temperate and semi-arid sites, respectively. Moreover, including the vegetated pixels in the validation areas increases the overall correlation between satellite and in situ SM from 0.36 to 0.43 and from 0.41 to 0.79 for the temperate and semi-arid regions, respectively. The use of Sentinel-3 can increase the spatio-temporal coverage by up to 44% over the considered MODIS tile, while the overlapping disaggregated data sets derived from Sentinel-3 and MODIS land surface temperature data are strongly correlated (around 0.7). Additionally, the correlation between satellite and in situ SM is significantly better for DISPATCH (0.39–0.80) than for the Copernicus Sentinel-1-based (−0.03 to 0.69) and SMAP/S1 (0.37–0.74) product over the three studies (temperate and semi-arid) areas, with an increase in yearly valid retrievals for the vegetation-extended DISPATCH algorithm.

In this paper, an analytic review of the recent methodologies tackling the problem of dynamic allocation of ambulances was carried out. Considering that state-of-the-art is moving to deal with more extensive and dynamic problems to address in a better way real-life instances, this research looks to identify the evolution and recent applications of this kind of problem once the basic models are explored. This extensive review allowed us to identify the most recent developments in this problem and the most critical gaps to be addressed. In this sense, it is essential to point out that the dynamic location of emergency medical services (EMS) is nowadays a relevant topic considering its impact on the healthcare system outcomes. Issues related to forecasting, simulation, heterogeneous fleets, robustness, and solution speed for real-life problems, stand out in the identified gaps. Applications of machine learning the deployment challenges during epidemic outbreaks such as SARS and COVID-19 were also explored. At the same time, a proposed notation tries to tackle the fact that the word problem in this kind of work refers to a model on many occasions. The proposed notation eases the comparison between the different model proposals found in the literature.

Background: Variation in the incidence, survival rate and factors associated with survival after cardiac arrest in Europe is reported. Some studies have tried to fill the knowledge gap regarding the epidemiology of out-of-hospital cardiac arrest in Europe but were unable to identify reasons for the reported differences. Therefore, the purpose of this study was to describe European Emergency Medical Systems, particularly from the perspective of country and ambulance service characteristics, cardiac arrest identification, dispatch, treatment, and monitoring. Methods: An online questionnaire with 51 questions about ambulance and dispatch characteristics, on-scene management of cardiac arrest and the availability and dataset in cardiac arrest registries, was sent to all national coordinators who participated in the European Registry of Cardiac Arrest studies. In addition, individual invitations were sent to the remaining European countries. Results: Participants from 28 European countries responded to the questionnaire. Results were combined with official information on population density. Overall, the number of Emergency Medical Service missions, level of training of personnel, availability of Helicopter Emergency Medical Services and the involvement of first responders varied across and within countries. There were similarities in team training, availability of key resuscitation equipment and permission for ongoing performance of cardiopulmonary resuscitation during transported. The quality of reporting to cardiac arrest registries varied, as well as the data availability in the registries. Conclusions: Throughout Europe there are important differences in Emergency Medical Service systems and the response to out-of-hospital cardiac arrest. Explaining these differences is complicated due to significant variation in how variables are reported to and used in registries. [ABSTRACT FROM AUTHOR]

This paper introduces a modified version of the DisPATCh (Disaggregation based on Physical And Theoretical scale Change) algorithm to disaggregate an SMAP surface soil moisture (SSM) product at a 20 m spatial resolution, through the use of sharpened Sentinel-3 land surface temperature (LST) data. Using sharpened LST as a high resolution proxy of SSM is a novel approach that needs to be validated and can be employed in a variety of applications that currently lack in a product with a similar high spatio-temporal resolution. The proposed high resolution SSM product was validated against available in situ data for two different fields, and it was also compared with two coarser DisPATCh products produced, disaggregating SMAP through the use of an LST at 1 km from Sentinel-3 and MODIS. From the correlation between in situ data and disaggregated SSM products, a general improvement was found in terms of Pearson’s correlation coefficient (R) for the proposed high resolution product with respect to the two products at 1 km. For the first field analyzed, R was equal to 0.47 when considering the 20 m product, an improvement compared to the 0.28 and 0.39 for the 1 km products. The improvement was especially noticeable during the summer season, in which it was only possible to successfully capture field-specific irrigation practices at the 20 m resolution. For the second field, R was 0.31 for the 20 m product, also an improvement compared to the 0.21 and 0.23 for the 1 km product. Additionally, the new product was able to depict SSM spatial variability at a sub-field scale and a validation analysis is also proposed at this scale. The main advantage of the proposed product is its very high spatio-temporal resolution, which opens up new opportunities to apply remotely sensed SSM data in disciplines that require fine spatial scales, such as agriculture and water management.

Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66–0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration.

This letter describes an enhanced multi-period dispatch model for microgrids, in which frequency-aware islanding constraints are established to ensure microgrids with the capability to ride through unplanned islanding events. Rather than specifying the upward/downward primary reserve requirements as fixed amounts, the proposed dispatch scheme determines the primary reserve requirements by explicitly simulating under- or over-frequency dynamics caused by potential islanding events. To remove the nonlinearity, a two-stage solution strategy is proposed. Case studies demonstrate the effectiveness of the proposed microgrid dispatch scheme. [ABSTRACT FROM AUTHOR]