Optimal integration of photovoltaic sources in distribution networks for daily energy losses minimization using the vortex search algorithm

This paper deals with the optimal siting and sizing problem of photovoltaic (PV) generators in electrical distribution networks considering daily load and generation profiles. It proposes the discrete-continuous version of the vortex search algorithm (DCVSA) to locate and size the PV sources where the discrete part of the codification defines the nodes. Renewable generators are installed in these nodes, and the continuous section determines their optimal sizes. In addition, through the successive approximation power flow method, the objective function of the optimization model is obtained. This objective function is related to the minimization of the daily energy losses. This method allows determining the power losses in each period for each renewable generation input provided by the DCVSA (i.e., location and sizing of the PV sources). Numerical validations in the IEEE 33- and IEEE 69-bus systems demonstrate that: (i) the proposed DCVSA finds the optimal global solution for both test feeders when the location and size of the PV generators are explored, considering the peak load scenario. (ii) In the case of the daily operative scenario, the total reduction of energy losses for both test feeders are 23.3643% and 24.3863%, respectively; and (iii) the DCVSA presents a better numerical performance regarding the objective function value when compared with the BONMIN solver in the GAMS software, which demonstrates the effectiveness and robustness of the proposed master-slave optimization algorithm

Optimal integration of photovoltaic sources in distribution networks for daily energy losses minimization using the vortex search Algorithm

Este artículo trata sobre el problema de ubicación y dimensionamiento óptimo de generadores fotovoltaicos (FV) en redes de distribución eléctrica considerando los perfiles diarios de carga y generación. Propone la versión discreta-continua del algoritmo de búsqueda por vórtices (DCVSA) para localizar y dimensionar las fuentes FV donde la parte discreta de la codificación define los nodos. En estos nodos se instalan generadores renovables y la sección continua determina sus tamaños óptimos. Además, mediante el método de flujo de potencia de aproximaciones sucesivas, se obtiene la función objetivo del modelo de optimización. Esta función objetivo está relacionada con la minimización de las pérdidas energéticas diarias. Este método permite determinar las pérdidas de energía en cada período para cada entrada de generación renovable proporcionada por el DCVSA (es decir, la ubicación y el tamaño de las fuentes fotovoltaicas). Las validaciones numéricas en los sistemas IEEE 33 e IEEE 69 nodos demuestran que: (i) el DCVSA propuesto encuentra la solución global óptima para ambos alimentadores de prueba cuando se exploran la ubicación y el tamaño de los generadores fotovoltaicos, considerando el escenario de carga máxima. (ii) En el caso del escenario diario operativo, la reducción total de pérdidas de energía para ambos alimentadores de prueba es de 23,3643% y 24,3863%, respectivamente; y (iii) el DCVSA presenta un mejor desempeño numérico con respecto al valor de la función objetivo cuando se compara con el solucionador BONMIN en el software GAMS, lo que demuestra la efectividad y robustez del algoritmo de optimización maestro-esclavo propuesto.This paper deals with the optimal siting and sizing problem of photovoltaic (PV) generators in electrical distribution networks considering daily load and generation profiles. It proposes the discrete-continuous version of the vortex search algorithm (DCVSA) to locate and size the PV sources where the discrete part of the codification defines the nodes. Renewable generators are installed in these nodes, and the continuous section determines their optimal sizes. In addition, through the successive approximation power flow method, the objective function of the optimization model is obtained. This objective function is related to the minimization of the daily energy losses. This method allows determining the power losses in each period for each renewable generation input provided by the DCVSA (i.e., location and sizing of the PV sources). Numerical validations in the IEEE 33- and IEEE 69-bus systems demonstrate that: (i) the proposed DCVSA finds the optimal global solution for both test feeders when the location and size of the PV generators are explored, considering the peak load scenario. (ii) In the case of the daily operative scenario, the total reduction of energy losses for both test feeders are 23.3643% and 24.3863%, respectively; and (iii) the DCVSA presents a better numerical performance regarding the objective function value when compared with the BONMIN solver in the GAMS software, which demonstrates the effectiveness and robustness of the proposed master-slave optimization algorithm

RAMi: a new Real-time internet of medical things Architecture for elderly patient Monitoring

peer reviewedThe aging of the world's population, the willingness of elderly to remain independent, and the recent COVID-19 pandemic have demonstrated the urgent need for home-based diagnostic and patient monitoring systems to reduce the financial and organizational burdens that impact healthcare organizations and professionals. The Internet of Medical Things (IoMT) i.e., all medical devices and applications that connect to health information systems through online computer networks. The IoMT} is one of domains of IoT where the real-time processing of data and reliability are crucial. In this paper, we propose RAMi, which is a Real-Time Architecture for the Monitoring of elderly patients thanks to the Internet of Medical Things. This new architecture associating a Things layer where data is retrieved from sensors or smartphone, a Fog layer built on a smart gateway, Mobile Edge Computing (MEC), a cloud component, blockchain, and Artificial Intelligence (AI) to addresses concerns of IoMT. Data is processed at Fog level, MEC or cloud in function of the workload, resource requirements, and the level of confidentiality. A local blockchain allows workload orchestration between Fog, MEC, and Cloud while a global blockchain secures exchanges and data sharing using smart contracts. Our architecture allows us to follow elderly people and patient during and after their hospitalization. In addition, our architecture allows the use of federated learning to train AI algorithms while respecting privacy and data confidentiality. AI is also used to detect patterns of intrusion.9. Industry, innovation and infrastructur