Optimal Management Algorithm Research Articles

Improved TLBO algorithm for optimal energy management in a hybrid microgrid with support vector machine-based forecasting of uncertain parameters

The worldwide need for electrical energy is increasing, and integrating renewable energy sources (RES) into the power grid will enhance the efficient use of clean energy to fulfill the growing demand for energy. However, the uncertain nature of power from the RES like solar and wind, utility price, and load demand, necessitates accurate forecasting of the uncertain parameters (UP) to improve the reliability of the hybrid microgrid. In this work, optimal energy management (EM) of a hybrid AC-DC microgrid (HMG) is proposed which comprises of two phases, forecasting and scheduling. In the former phase, the uncertainties like day-ahead utility price, electrical demand, and power from the RES are forecasted using the support vector machine (SVM) algorithm and the results are compared with the artificial neural network (ANN) algorithm. In the second phase, the improved Teaching and Learning-Based Optimization (ITLBO) algorithm isused to reduce the generation costs over a 24-h period in a hybrid microgrid. The forecasted uncertain parameters are used as input in the second phase. Power trading occurs between the utility grid and the hybrid microgrid based on load demand and bidding costs, aiming to minimize generation costs. The proposed framework's viability and performance are assessed using IEEE standard test systems. The generating cost, as well as the optimal power dispatch of the HMG, is obtained using the ITLBO algorithm, and the results are compared with different meta-heuristic techniques such as the teaching and learning-based algorithm (TLBO), Ant Lion Optimization algorithm (ALO) and the artificial bee colony algorithm (ABC). The results obtained demonstrate the superiority of the SVM algorithm in forecasting and the ITLBO algorithm over other methods in minimizing operating costs.

The contemporary approach to chylothorax – Single-center experience with the proposal of a management algorithm

Objectives: Chylothorax is a relatively rare condition of lymphatic fluid accumulation in the thoracic cavity due to a leakage from the thoracic duct or its tributaries. Patients present with dyspnea, malnutrition, and immunosuppression. Treatment can be conservative or surgical, depending on etiology and clinical course. The optimal management algorithm for chylothorax is still controversial. Methods: This is a ten-year period retrospective study of all patients with chylothorax treated at our Department of Thoracic Surgery. Results: A total of 14 patients were identified for the study. Nine patients had chylothorax after lung or esophageal cancer surgery. Four patients had chylothorax in advanced lymphoma. One patient had chylothorax after blunt chest trauma. A conservative approach was initiated in most patients (92%), including pleural drainage, nil per mouth, total parenteral nutrition, and somatostatin 0.1 mg bid subcutaneously. Surgical treatment was indicated in patients with thoracic drain production >800 mL per day beyond the fifth day of treatment and those with blunt thoracic trauma. Two patients had thoracic duct ligation via right-sided thoracotomy, and five patients had video-assisted thoracoscopic thoracic duct ligation with the immediate arrest of chylous leakage. Conclusion: Chylothorax should be treated conservatively initially. Surgical treatment should not be delayed beyond the fifth day in case of failure. In our series of patients, a video-assisted thoracoscopic approach for thoracic duct ligation proved to be minimally invasive, highly efficient, and well tolerated. Therefore, it should be the preferable route of surgical treatment.

Haemorrhagic cholecystitis: a case report—focus on the optimal diagnosis and management algorithm

Haemorrhagic cholecystitis refers to an inflammatory process of the gallbladder, complicated by bleeding into the gallbladder or biliary system (haemobilia). Approximately 10–15% of adults have cholelithiasis, but most patients do not develop symptoms. Acute cholecystitis occurs in about 10% of symptomatic patients. Among the recognized complications of acute cholecystitis, the haemorrhagic form occurs uncommonly. Underlying risk factors include a bleeding diathesis or treatment with anticoagulants, aneurysm rupture of colecystic artery and trauma. It is often associated with cholelithiasis. The presenting feature is right upper quadrant pain, which is a dominant feature and may mimic non-haemorrhagic acute cholecystitis. Haemorrhagic cholecystitis is diagnosed by radiographic imaging. The first-choice exam is ultrasonography. However, ultrasound does not appear to be ideal for differentiating between acute and haemorrhagic cholecystitis. Computed tomography (CT) scan is the gold standard in the diagnosis of haemorrhagic cholecystitis. MRI may show breakdown products of blood and can be used to estimate the age of the haemorrhage. Surgical resection or embolization of a cystic artery pseudoaneurysm should be considered the treatment of choice, due to the high risk of fatality associated with this condition. We describe a case of haemorrhagic cholecystitis in an 80-year-old male with secondary haemophilia and cholelithiasis who presented to our department of radiology with right upper quadrant pain.

Forecast Uncertainties Real-Time Data-Driven Compensation Scheme for Optimal Storage Control

This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts, which are integral to an optimal energy storage control system. By expanding on an existing algorithm, this study resolves issues discovered during implementation and addresses previously overlooked concerns, resulting in significant enhancements in both performance and reliability. The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model, which is utilized for illustrative simulations to highlight its potential efficacy on a real site. Furthermore, a comprehensive comparison with the original formulation was conducted to cover all possible scenarios. This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system. Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management, significant financial losses and penalties, and potential contract violations. The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements. These advancements yield a reliable and efficient real-time correction algorithm for optimal site management, designed as an independent white box that can be integrated with any day-ahead optimization control system.

When the dual-chamber pacing system is the optimal choice for Brugada patients in need of implantable cardiac defibrillator.

Abstract This letter discusses key points from Esposito et al.'s case report on a Brugada Syndrome (BrS) patient treated with S-ICD and later TV-ICD due to recurrent syncope. It highlights the significance of asystole in BrS-related. Additionally, it recommends dual-chamber TV-ICD systems with CLS algorithms for optimal management, citing their efficacy in reducing syncopal recurrences. Finally, it underscores the importance of considering reflex bradyarrhythmias in device selection for BrS patients at increased sudden cardiac death risk.

Utilizing Hybrid Sine Cosine Shuffled Frog Leaping Algorithm for Optimal Energy Management in the Residential building with Renewable Energy Resources and Corresponding Uncertainties

Utilizing Hybrid Sine Cosine Shuffled Frog Leaping Algorithm for Optimal Energy Management in the Residential building with Renewable Energy Resources and Corresponding Uncertainties

Exploring the factors affecting classification and reporting of uncertain prenatal microarray findings, using a "virtual fetus" model-a pilot study.

Significant discrepancy exists between laboratories in classification and reporting of copy number variants (CNVs). Studies exploring factors affecting prenatal CNV management are rare. Our "virtual fetus" pilot study examines these factors. Ten prenatally diagnosed CNVs of uncertain significance (VUS)>1Mb, encompassing OMIM-morbid genes, inherited from healthy parents, were classified by 15 MD geneticists from laboratory, prenatal, and preimplantation genetic testing (PGT) units. Geneticists addressed factors affecting classification, obligation to report, and recommendation for invasive testing or PGT. CNVs were classified likely benign (10.7%), VUS (74.7%), likely pathogenic (8.7%), or pathogenic (6.0%). Classification discrepancy was higher for losses versus gains. Classifying pathogenic/likely pathogenic was more common for losses (adjusted odds ratio [aOR] 10.9, 95% CI 1.55-76.9), and geneticists specializing in gynecology (aOR 4.9, 95% CI 1.03-23.3). 84.0% of respondents would report CNVs, depending on classification and family phenotype. Invasive testing in pregnancies was recommended for 29.3% of CNVs, depending on theclassification and geneticist's specialization. PGT was recommended for 32.4%, depending on classification, experience years, and family's phenotype (38.0% for patients undergoing in vitro fertilization irrespectively, 26.7% otherwise). Factors affecting CNV classification/reporting are mainly dosage, family phenotype, geneticist specialization and experience. Understanding factors from our pilot study may facilitate developing an algorithm for clinical consensus and optimal management.

Design of federated learning-based resource management algorithm in fog computing for zero-touch network

The concept of zero-touch networking involves creating networks that are fully autonomous and require minimal human intervention. This approach is increasingly relevant due to the rapid growth of current cloud architectures, which are beginning to reach their limits due to continuous expansion demands from users and within the network core itself. In response, Fog computing, acting as a smart, localized data center closer to network nodes, emerges as a practical solution to the challenges of expansion and upgrading in existing architectures. Fog computing complements cloud technology. However, the realization of zero-touch networks is still in its early stages, and numerous challenges hinder its implementation. One significant challenge is the NP-hard problem related to resource management. This paper introduces an optimal resource management algorithm based on Federated Learning. The effectiveness of this algorithm is evaluated using the iFogSim simulator within the existing cloud-fog architecture. The results demonstrate that the proposed architecture outperforms the current infrastructure in several key aspects of resource management, including system latency, number of resources processed, energy consumption, and bandwidth utilization.

АЛГОРИТМ РАННЕЙ ДИАГНОСТИКИ И ПРОГНОЗИРОВАНИЯ МОЧЕКАМЕННОЙ БОЛЕЗНИ У ДЕТЕЙ С УЧЁТОМ ФАКТОРОВ РИСКА И ИММУНОГЕНЕТИЧЕСКОЙ ПРЕДРАСПОЛОЖЕННОСТИ В АМБУЛАТОРНО-ПОЛИКЛИНИЧЕСКИХ УСЛОВИЯХ

Objective: To optimize the algorithm for early diagnosis of urolithiasis (UL) in children, considering risk factors, clinical and functional characteristics of the disease, and immunogenetic predisposition in an outpatient setting Methods: The literature on managing children with UL outside the hospital was thoroughly analyzed to achieve the goal. Medical records, risk factors, including immunogenetic predisposition, and clinical and laboratory investigation results of 652 hospitalized children with UL were studied. Additionally, a retrospective study of 379 outpatient records (the control group) and a prospective study of 1275 children (the study group) aged 1 to 18 was conducted. Based on the outpatient records analysis, the effectiveness of current and optimized management algorithms for children with UL was compared Results: As a result of the research, an algorithm for early diagnosis and prediction of UL in children, considering risk factors, including immunogenetic predisposition, was developed. The algorithm evaluates the risk of developing UL based on a thirteen-point checklist that assesses benefits and risks to provide a personalized risk score for each child. Based on this risk score, additional therapeutic interventions are determined for each patient Conclusion: The algorithm developed for early diagnosis and prediction of UL in children helps identify the condition at a preclinical stage in an outpatient setting. This algorithm categorizes patients into low-, moderate-, or high-risk groups and guides their management accordingly Keywords: Urolithiasis in children, early diagnosis, risk factors, prediction of urolithiasis, genetic factors, prelithiasis, outpatient service.

Optimal battery scheduling in solar-plus-storage grid-connected microgrid for profit and cost efficiency: A use case on an Israeli microgrid

This paper presents an optimal energy management algorithm for solar-plus-storage grid-connected microgrid simulated on a real full-scale small town microgrid test-case, taking into account the daily solar energy generation as well as the electricity demand to ensure that the battery is charged and discharged at the optimal times to balance energy supply and demand. The optimization is performed using dynamic programming and finding the minimum-cost-path, to minimize the total cash-flow. The formulation is utilized with two variations such that two business models are considered, optimizing cost efficiency in both aspects of minimizing cost and maximizing profits, according to the scenario. The first model maximizes self-consumption, and the second model is the case of retail electricity providers. The results are compared to real data from a solar-plus-storage grid-connected microgrid located in Israel, that is currently managed by a rule-based scheduling procedure. The results show that by using the optimal control schedule, the revenue increases up to 8% during the spring and autumn when the weather conditions are typically moderate. In summer and winter, where the climate conditions are more extreme and the demand for energy is at its peak, the full revenue potential can be realized by using the suggested optimal control algorithm, offering an increased profit of up to 30%. Unlike prior studies which are restricted to small use-cases, this paper leverages a unique and private large-scale data-set from a small town, enabling a thorough exploration of the optimization’s real-world viability and scalability. Through rigorous simulations, the findings underscore the method’s exceptional potential in effectively integrating PV-plus-storage assets and optimizing energy use in power grids.

Adaptively optimal energy management for integrated hydrogen energy systems

AbstractIntegrated hydrogen energy systems (IHESs) have become attractive alternatives to cope with the depletion of fossil fuels and increasingly severe climate change problems. This paper proposes an adaptively optimal energy scheduling method based on deep deterministic policy gradient (DDPG) to improve the operational efficiency of IHES. The optimal scheduling problem is formulated as a Markov decision process problem with action space, environmental states, and action‐value function. The DDPG‐based optimal energy management algorithm with actor‐critic structure is proposed based on policy gradients and neural networks. Through actor‐critic network training and policy iteration, the energy management scheme can be adaptively optimized according to the dynamic responses of IHES. The benefits of the proposed algorithm are analysed through time‐domain simulations, and the scheduling robustness under different uncertain conditions is verified.

Design and optimal energy management of a stand-alone PV/WT/Diesel/battery system with EV charging facility for Thar desert camp: a case study

ABSTRACT Due to different financial restrictions, extending the existing power grid to remote locations like desert camps is not practically possible, forcing the camp owner to utilize expensive and ecologically hazardous diesel generators (DiG). In this regard, renewable sources based hybrid microgrid could be a viable approach toward reliable and sustainable electrification of these desert camps. However, optimum designing and proper energy management of such a system can be a challenging task. In these terms, this study presents a novel model based on the multi-objective PSO (MOPSO) algorithm for optimal design and energy management of a hybrid microgrid employing solar photovoltaic (PV) and wind turbine (WT), battery, and DiG for electrification of Thar desert camp in Jaisalmer, India. To address techno-eco-environmental aspects, objectives such as Dump Energy (DE), Installation and Operation Cost (IOC), and Reduction of Pollutant Emission (RPE) are considered. The optimal configuration of PV, WT, battery, and DiG are determined based on the maximization of RPE and minimization of both DE and IOC. The proposed model is formulated taking into account the seasonal load variation of a typical camp and the stochastic behavior of renewable energy sources. Moreover, electric vehicles (EVs) charging facility for the tourists staying in these camps is also included while modeling the microgrid system. Furthermore, three distinct system configurations are carefully analyzed over a 10-year period based on technical, environmental and economic indicators. The optimum configuration obtained is the hybrid PV/WT/DiG/battery system with 62 kW PV, 76 kW WT, 350 kWh battery and a 117 kW DiG. According to simulation findings, this system has an operational cost of 323.7 × 104 $ and a pollutant emission of 2034.3 tons, which is 33.67% and 63.32% less than that of the DiG-only configuration, respectively. Moreover, as compared to PV/WT/DiG system, PV/WT/DiG/battery system can reduce dump energy by 81.40%, highlighting the necessity of battery for fully utilizing renewable energy. Overall, this analysis suggests that the utilization of renewable energy sources along with the battery is the optimal planning solution for the camp owner to maximize their potential benefits. Moreover, the proposed technique can be effectively used to optimally design hybrid renewable energy system for other remote locations.

SDPS-11 IMPACT OF MOLECULAR MARKERS USING FISH TECHNIQUE ON GOS/SURVIVAL IN HIGH GRADE GLIOMAS IN INDIAN SCENARIO

Abstract Aims Molecular markers (MGMT, IDH and 1p/19q, MIB-1 index, TP53) are used for present day diagnosis of high-grade glial tumours. This study evaluates outcome parameters [e.g., GOS, progression free (PFS) and overall survival(OS)] in treated high grade gliomas in Indian context using FISH technique for molecular characterisation. MATERIALS AND METHODS This study used FISH techniques for IDH, MGMT and other IHC markers prospectively on 10 patients with high-grade gliomas, after safe maximal resection. Outcomes were noted for PFS and OS. A GOS of 1-3 (considered unfavourable); 4-5 (favourable), was documented at every 3-monthly follow-up for 2 years. The standardised detection of IDH (by FISH) and methylation profiles of tumours is the strength of the study. RESULTS Most (80%) patients had a GOS > 13 post operatively, and none were less than 9. There was no statistically significant effect of gender, extent of resection or tumour location. IDH-1 positivity was recorded in 10%, MGMT methylation in 40%, intermediate to low MIB-1 index in 83.3%. TP53 mutations were noted in 30% cohort who had adverse outcome. The median PFS was 8.60 months. Mean survival was 12.70 months, with no molecular marker having statistically significant effects on PFS or OS. GOS of 13-15 conferred the best OS in contrast to GOS of 9-12 (16.2 months versus 7.5 months); (p = 0.001). CONCLUSION GOS is an easy-to-use, validated scoring system with broad applicability and high reproducibility as a quantitative benchmark for clinical assessment. Since IHC lacks standardisation, FISH, despite being costlier, was used as a diagnostic benchmark. Further research on the impact of genomic profiling, extent of resection, especially in geriatric age groups is needed, to formulate an algorithm for optimal management.

Diagnosis and treatment of Watershed strokes: a narrative review.

Watershed strokes have been described previously as ischemic strokes located in vulnerable border zones between brain tissue supplied by the anterior, posterior, and middle cerebral arteries in the distal junction between two non-anastomotic arterial territories. Ischemic strokes in border zones are well-recognized entities and well-described in terms of imaging features, but the pathophysiological mechanism of brain injury production is not fully defined. Border zone ischemia is caused by cerebral hypoperfusion through decreased cerebral blood flow and arterial embolism in unstable atheroma plaque. It is often difficult to say which mechanisms are fully responsible for producing cerebral ischemic lesions. This review aimed to highlight the imaging aspect of watershed strokes and to correlate the clinical characteristics of this type of stroke with the diagnostic algorithm for optimal therapeutic management. Neurologists should promptly recognize this type of stroke and investigate its etiology in the shortest possible time.

Stochastic optimisation and economic analysis of combined high temperature superconducting magnet and hydrogen energy storage system for smart grid applications

High Temperature Superconducting (HTS) Magnetic Energy Storage (SMES) devices are promising high-power storage devices, although their widespread use is limited by their high capital and operating costs. This work investigates their inclusion in smart grids when used in tandem with hydrogen fuel cells and other energy storage devices using a novel two-stage model. The first stage presents a stochastic allocation algorithm to optimally size the smart grid assets with the maximum expected profit. The next stage models the time system evolution with these optimal capacities using a dynamic Monte Carlo model to investigate the system reliability, capacity credit and loss of load expectation. A novel energy management algorithm is also presented that maximises the time with which a fleet of energy storage devices can fulfil a stochastically unknown power request. The analysis shows that from a purely economic standpoint, HTS SMES and hydrogen energy do not achieve high penetration levels (up to 10 %) due to their high costs, although the penetration levels can be improved with higher power imbalance penalties in day ahead markets. The novel capacity credit and reliability investigation shows that they can improve the reliability and capacity credit of wind turbines by approximately 30 %, thereby improving potential wind penetration levels. The optimal energy management algorithm improves the fleet lifespan and reliability by up to 9 % depending on the connected capacity.

Optimal Energy Management for Virtual Power Plant Considering Operation and Degradation Costs of Energy Storage System and Generators

Even though generating electricity from Renewable Energy (RE) and electrification of transportation with Electric Vehicles (EVs) can reduce climate change impacts, uncertainties of the RE and charged demand of EVs are significant challenges for energy management in power systems. To deal with this problem, this paper proposes an optimal energy management method using the Virtual Power Plant (VPP) concept for the power system considering solar PhotoVoltaics (PVs) and Electric Vehicle Charging Stations (EVCS). The Differential Evolution (DE) algorithm is applied to manage energy in the power system to minimize the operation cost of generators and degradation costs in Energy Storage Systems (ESS) and generators. The effectiveness of the proposed approach is examined and tested on the IEEE 24 bus Reliability Test System (RTS 24) using the MATPOWER tool on the MATLAB program for calculating Optimal Power Flow (OPF). In this study, two situations before and after applying the proposed method are considered. The simulation results demonstrate that a branch constraint violation occurs before using optimal energy management using the VPP concept. In order to solve this issue, the DE algorithm for optimal energy management using the VPP concept is applied by dividing the studied case into two subcases as follows. For the first subcase, two objectives consisting of the minimization of the generator operation cost and the minimization of the battery degradation cost in ESS are considered. In the second case, three objectives comprising the two mentioned objectives with the minimization of generator degradation cost are considered. The results demonstrate that branch constraint violations can be avoided by applying optimal energy management using the VPP concept. This study also suggests considering the generator degradation cost in the objective function, which can minimize the total costs by 7.06% per day compared with the total cost of the first case.

Information Retrieval and Optimization in Distribution and Logistics Management Using Deep Reinforcement Learning

Resource balance is one of the most critical concerns in the existing logistic domain within dynamic transport networks. Modern solutions are used to maximize demand and supply prediction in collaboration with these problems. However, the great difficulty of transportation networks, profound uncertainties of potential demand and availability, and non-convex market limits make conventional resource management main paths. Hence, this paper proposes an integrated deep reinforcement learning-based logistics management model (DELLMM) to increase and optimize the logistic distribution. An optimization approach can be used in inventors and price control applications. This research methodology gives the fundamentals of information retrieval and the scope of blockchain integration. The conceptual framework of use cases for an efficient logistic management system with blockchain has been discussed. This research designs the deep reinforcement learning system that can boost optimization and other business operations due to impressive improvements in generic self-learning algorithms for optimal management. Thus, the experimental results show that DELLMM improves logistics management and optimized distribution compared to other methods with the highest operability of 94.35%, latency reduction of 97.12%, efficiency of 98.01%, trust enhancement of 96.37%, and sustainability of 97.80%.

Developing Hybrid Demand Response Technique for Energy Management in Microgrid Based on Pelican Optimization Algorithm

• A new application for newly developed Pelican Optimization Algorithm in solving energy management problem for Microgrid. • New technique for integration the demand response in Microgrid energy management. • The Microgrid energy management strategy is designed. • The optimization method considers the demand response. • Two case studies are presented. This paper proposes a new application of Pelican Optimization Algorithm (POA) for optimal Energy Management (EM) in Microgrid (MG) considering Demand Response program (DRP). To maximize the MG operator (MGO) benefit and to reduce the overall operating cost, including the cost of conventional generator fuel and power transaction cost, multi-objective optimization is formulated. To achieve the optimal operation of the MG, a Hybrid DRP is proposed, based on Incentive-based Demand response (IDR) to reduce the peak load and to ensure MG reliability. Reliability is achieved by applying the Hybrid technique to encourage customers to reduce their consumption during peak hours. Applying the conventional IDR (CIDR) for optimal operation leads to customers curtailments to be in off-peak hours. Also instead of using predetermined or specifying fixed hours as peak hours, the proposed hybrid DR technique is a dynamic technique based on the load profile and the average load value. Furthermore, Peak reduction percentage (PRP) was employed to show MG's reliability enhancement. Two distinct MG test systems are examined; the effectiveness of the proposed hybrid dynamic demand response (HDDR) with the proposed POA is demonstrated by comparing its simulation results to those of well-known metaheuristics and newly developed algorithms. According to HDDR with POA technique results, a total reduction in peak hours’ load is about 14.6% in the first test system and 7.6% in the second test system. The results indicate that the POA has superiority in solving the EM problem.

Harnessing the Power of Metaheuristic Algorithms for Optimal Logistics Management in Epidemic Response

This paper investigates the use of metaheuristics as computational tools for managing epidemic logistics. Epidemics pose severe risks to world health, necessitating coordinated, effective, and prompt response plans. Effective logistics management is a critical component of this, requiring, among other things, timely distribution of vaccinations and the efficient deployment of workers and resources. Such logistical difficulties are frequently dynamic and complex, demanding more sophisticated computational techniques. The complex logistic optimization problems are addressed by metaheuristics, which offer higher-level problem-solving techniques. The Multi-Depot Vehicle Routing Problem (MD- VRP), a common metaheuristic, and its solution in the context of epidemic logistics are the specific topic of this paper. The objective of MDVRP, which is categorized as an NP-Hard issue, is to efficiently distribute supplies from many depots to numerous demand nodes (hospitals, clinics). Due to this problem’s complexity, time-sensitivity, scalability concerns, and dynamic and uncertain situations, traditional methods frequently fail to solve it effectively. However, the genetic algorithm can potentially improve the MDVRP inside epidemic logis- tics, delivering effective and adaptable solutions in a fair amount of time. This work advances knowledge of the function of metaheuristics in improving epidemic response logistics through a thorough literature analysis, potential applications discussion, and case study illustration. We acknowledge the necessity for additional study in customizing these algorithms considering the many uncertainties and dynamic aspects in the real-world application as we come to a close.

Novel Architecture for Transactive Energy Management Systems with Various Market Clearing Strategies

The recent advancements in demand‐side management techniques add significant benefits to the distribution systems. One such technique is transactive energy management systems (TEMS) which motivate the energy end‐users to take part in local energy trading. The end‐users can effectively increase the monetary benefits by trading the surplus generation/demand within the local energy market (LEM). The LEM operator frames a viable market clearing strategy to fix the market clearing price to enhance the monetary benefits of all the market players. In this study, LEM architecture with different market clearing strategies is proposed for TEMS to ensure profitable power transactions between the neighboring end‐users. An optimal energy management algorithm is also proposed for time scheduling the operation of flexible loads and batteries, considering dynamics in end‐users’ behavior, variations in utility parameters, and the intermittent nature of renewable power generation. Further, an optimal load scheduling algorithm is developed at the end‐users’ premises to improve the profits in the LEM. Correspondingly, the trading strategies are extended to increase market reliability by penalizing participants for their abnormal activities in energy trading. The proposed framework is validated with different case studies considering ten residential participants in a locality.