A method for predicting IGBT junction temperature under transient condition

In this paper, a method to predict junction temperature of the solid-state switch under transient condition is presented. The method is based on the thermal model of the switch and instantaneous measurement of the energy loss in the device. The method for deriving thermal model parameters from the manufacturers data sheet is derived and verified. A simulation work has been carried out on a single IGBT under different conditions using MATLAB/SIMULINK. The results show that the proposed method is effective to predict the junction temperature of the solid-state device during transient conditions and is applicable to other devices such as diodes and thyristors

Forecasting Global Solar Insolation Using the Ensemble Kalman Filter Based Clearness Index Model

This paper describes a novel approach in developing a model for forecasting of global insolation on a horizontal plane. In the proposed forecasting model, constraints, such as latitude and whole precipitable water content in vertical column of that location, are used. These parameters can be easily measurable with a global positioning system (GPS). The earlier model was developed by using the above datasets generated from different locations in India. The model has been verified by calculating theoretical global insolation for different sites covering east, west, north, south and the central region with the measured values from the same locations. The model has also been validated on a region, from which data was not used during the development of the model. In the model, clearness index coefficients (KT) are updated using the ensemble Kalman filter (EnKF) algorithm. The forecasting efficacies using the KT model and EnKF algorithm have also been verified by comparing two popular algorithms, namely the recursive least square (RLS) and Kalman filter (KF) algorithms. The minimum mean absolute percentage error (MAPE), mean square error (MSE) and correlation coefficient (R) value obtained in global solar insolation estimations using EnKF in one of the locations are 2.4%, 0.0285 and 0.9866 respectively

Multiobjective Optimized Smart Charge Controller for Electric Vehicle Applications

The continuous deployment of distributed energy sources and the increase in the adoption of electric vehicles (EVs) require smart charging algorithms. The existing EV chargers offer limited flexibility and controllability and do not fully consider factors (such as EV user waiting time and the length of next trip) as well as the potential opportunities and financial benefits from using EVs to support the grid, charge from renewable energy, and deal with the negative impacts of intermittent renewable generation. The lack of adequate smart EV charging may result in high battery degradation, violation of grid control statutory limits, high greenhouse emissions, and high charging cost. In this article, a neuro-fuzzy particle swarm optimization (PSO)-based novel and advanced smart charge controller is proposed, which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (photovoltaic) output, and battery state of health. A rule-based fuzzy controller becomes complex as the number of inputs to the controller increases. In addition, it becomes difficult to achieve an optimum operation due to the conflicting nature of control requirements. To optimize the controller response, the PSO technique is proposed to provide a global optimum solution based on a predefined cost function, and to address the implementation complexity, PSO is combined with a neural network. The proposed neuro-fuzzy PSO control algorithm meets EV user requirements, works within technical constraints, and is simple to implement in real time (and requires less processing time). Simulation using MATLAB and experimental results using a dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller

Heuristic Multi-Agent Control for Energy Management of Microgrids with Distributed Energy Sources

The increased integration of distributed Renewable Energy Sources (RESs) and adoption of Electric Vehicles (EVs) require appropriate control and management of energy sources and EV charging. This becomes critical at the distribution system level, especially at a microgrid (MG) level. This control is required not only to mitigate the negative impacts of intermittent generation from RESs but also to make better use of available energy, reduce carbon footprint, maximize the overall profit of microgrid and increase energy autonomy by effective utilization of battery storage. This paper proposes a heuristic multi-agent based decentralized energy management approach for grid-connected MG. The MG comprises of active (controlled) and passive (uncontrolled) electrical loads, a photovoltaic (PV) system, battery energy storage system (BESS) and a charging post for electric vehicles. The proposed approach is aimed at optimizing the use of local energy generation from photovoltaic and smart energy utilization to serve electrical loads and EV as well as maximizing MG profit. The aim of the energy management is to supply local consumption at minimum cost and less dependency on the main grid supply. Utilizing energy available from RESs (PV and BESS), customers satisfaction (fulfilling local demand), considering uncertainty of renewable generation and load consumption and also taking into account technical constraint are the main strengths of the presented framework. Performance of the proposed algorithm is investigated under different operating conditions and its efficacy is verified

Micro market based optimisation framework for decentralised management of distributed flexibility assets

Continuously changing electricity demand and intermittent renewable energy sources pose challenges to the operation of power systems. An alternative to reinforcing the grid infrastructure is to deploy and manage distributed energy storage systems. In this work, a micro-energy market is proposed for smart domestic energy trading in the low-voltage distribution systems in the context of high penetration of photovoltaic systems and battery energy storage systems. In addition, a micro-balancing market is proposed to address the congestions due to unforeseen energy imbalance. Centralised and decentralised management strategies are simulated in real time, based on generation and demand forecasts. In addition, electric vehicles are also simulated as potential storage solutions to improve grid operation. A techno-economic evaluation informs key stakeholders, in particular grid operators on strategies for a sustainable implementation of the proposed strategies. The results show that the micro-energy market reduces the energy cost for all grid users by 4.1–20.2%, depending on their configuration. In addition, voltage deviation, peak electricity demand and reverse power flow have been reduced by 12.8%, 7.7% and 85.6% respectively, with the proposed management strategies. The micro-balancing market has been demonstrated to keep the voltage profile and thermal characteristic within the set limit in case of contingency

On Beneficial Vehicle-to-Grid (V2G) Services

A number of studies have investigated the possibility of extending Electric Vehicle (EV) Lithium-ion battery life by deliberately choosing to store the battery at a low to moderate state of charge. Recently, there has been considerable interest shown in the scheme of a deliberate discharge and subsequent recharge of a battery to yield an overall reduction in battery degradation whilst carrying out Vehicle-to-Grid (V2G) services (so-called `beneficial V2G'). This paper presents an investigation of the conditions permitting successful operation of this method by examining incremental time variation of the relevant parameters for two types of cells from results of the same physical size and chemistry, and similar capacity. These two types of cells are found in this present analysis to offer differing degrees of suitability for beneficial V2G

Online Sensorless Solar Power Forecasting for Microgrid Control and Automation

Meteorological conditions such as air density, temperature, solar radiation etc. strongly affect the power generation from solar, and thus, the prediction and estimation process should consider weather conditions as critical inputs. The nature of weather forecast is highly unpredictable, so many applications use meteorological data from in-place on-site sensors to add to the forecast and some use complex networks with complicated mapping. The in-situ sensor approach and dense mapping methods, however, present several drawbacks. First, the use of sensors give rise to extra operational, installation and maintenance cost. Second, it requires significant amount of time to capture and accumulate data for various occasions and scenarios, and in addition, sensor itself can be the cause of error measurements. The complex methods are computational inefficient and may present suboptimal convergence. This paper presents a sensorless solar output power forecasting based on historical weather (publicly available from met office) and PV data. The algorithm uses simple to implement neural networks with few neurons and hidden layers for its training and allows for day a head forecast. The proposed methodology presents a guideline on how to select the relevant data from weather and how it affects the accuracy and training time of neural network. The benefit of developed method is an improvement on the energy management, utilization and reliability of the microgrid

Autonomous energy management system with self-healing capabilities for green buildings (microgrids)

Nowadays, distributed energy resources are widely used to supply demand in micro grids specially in green buildings. These resources are usually connected by using power electronic converters, which act as actuators, to the system and make it possible to inject desired active and reactive power, as determined by smart controllers. The overall performance of a converter in such system depends on the stability and robustness of the control techniques. This paper presents a smart control and energy management of a DC microgrid that split the demand among several generators. In this research, an energy management system ( EMS) based on multi-agent system ( MAS) controllers is developed to manage energy, control the voltage and create balance between supply and demand in the system with the aim of supporting the reliability characteristic. In the proposed approach, a reconfigurated hierarchical algorithm is implemented to control interaction of agents, where a CAN bus is used to provide communication among them. This framework has ability to control system, even if a failure appears into decision unit. Theoretical analysis and simulation results for a practical model demonstrate that the proposed technique provides a robust and stable control of a microgrid

Research priorities in prehabilitation for patients undergoing cancer surgery: an international Delphi study

Background Recently, the number of prehabilitation trials has increased significantly. The identification of key research priorities is vital in guiding future research directions. Thus, the aim of this collaborative study was to define key research priorities in prehabilitation for patients undergoing cancer surgery. Methods The Delphi methodology was implemented over three rounds of surveys distributed to prehabilitation experts from across multiple specialties, tumour streams and countries via a secure online platform. In the first round, participants were asked to provide baseline demographics and to identify five top prehabilitation research priorities. In successive rounds, participants were asked to rank research priorities on a 5-point Likert scale. Consensus was considered if > 70% of participants indicated agreement on each research priority. Results A total of 165 prehabilitation experts participated, including medical doctors, physiotherapists, dieticians, nurses, and academics across four continents. The first round identified 446 research priorities, collated within 75 unique research questions. Over two successive rounds, a list of 10 research priorities reached international consensus of importance. These included the efficacy of prehabilitation on varied postoperative outcomes, benefit to specific patient groups, ideal programme composition, cost efficacy, enhancing compliance and adherence, effect during neoadjuvant therapies, and modes of delivery. Conclusions This collaborative international study identified the top 10 research priorities in prehabilitation for patients undergoing cancer surgery. The identified priorities inform research strategies, provide future directions for prehabilitation research, support resource allocation and enhance the prehabilitation evidence base in cancer patients undergoing surgery