Towards Wind Energy-based Charging Stations: A Review of Optimization Methods

Due to the growing importance of renewable sources in sustainable energy systems, the strategic deployment of robust optimization techniques plays a crucial role in the design of Electric Vehicle Charging Stations (EVCSs). These stations need to smoothly incorporate renewable sources, ensuring optimal energy utilization. This study provides a comprehensive overview of the methodologies and approaches employed in the enhancement of wind energy based EVCSs. The aim is to discern the most efficacious techniques for optimizing charging stations. Researchers engage diverse strategies and methodologies in the realm of sizing and optimization, encompassing a spectrum of algorithmic implementations and software solutions. Evidently, each algorithm or software application bears distinctive merits and demerits. Singular reliance on a solitary algorithm or software for charging utility optimization is discerned to be potentially limiting. The investigation reveals that achieving better results in Electric Vehicle Charging Station (EVCS) optimization is facilitated by the collaborative use of multiple algorithms like GA, PSO, and ACO, among others, or software tools like Homer or RETScreen

Smart home energy management: An analysis of a novel dynamic pricing and demand response aware control algorithm for households with distributed renewable energy generation and storage

Home energy management systems (HEMS) technology can provide a smart and efficient way of optimising energy usage in residential buildings. One of the main goals of the Smart Grid is to achieve Demand Response (DR) by increasing end users’ participation in decision making and increasing the level of awareness that will lead them to manage their energy consumption in an efficient way. This research presents an intelligent HEMS algorithm that manages and controls a range of household appliances with different demand response (DR) limits in an automated way without requiring consumer intervention. In addition, a novel Multiple Users and Load Priority (MULP) scheme is proposed to organise and schedule the list of load priorities in advance for multiple users sharing a house and its appliances. This algorithm focuses on control strategies for controllable loads including air-conditioners, dishwashers, clothes dryers, water heaters, pool pumps and electrical vehicles. Moreover, to investigate the impact on efficiency and reliability of the proposed HEMS algorithm, small-scale renewable energy generation facilities and energy storage systems (ESSs), including batteries and electric vehicles have been incorporated. To achieve this goal, different mathematical optimisation approaches such as linear programming, heuristic methods and genetic algorithms have been applied for optimising the schedule of residential loads using different demand side management and demand response programs as well as optimising the size of a grid connected renewable energy system. Thorough incorporation of a single objective optimisation problem under different system constraints, the proposed algorithm not only reduces the residential energy usage and utility bills, but also determines an optimal scheduling for appliances to minimise any impacts on the level of consumer comfort. To verify the efficiency and robustness of the proposed algorithm a number of simulations were performed under different scenarios. The simulations for load scheduling were carried out over 24 hour periods based on real-time and day ahead electricity prices. The results obtained showed that the proposed MULP scheme resulted in a noticeable decrease in the electricity bill when compared to the other scenarios with no automated scheduling and when a renewable energy system and ESS are not incorporated. Additionally, further simulation results showed that widespread deployment of small scale fixed energy storage and electric vehicle battery storage alongside an intelligent HEMS could enable additional reductions in peak energy usage, and household energy cost. Furthermore, the results also showed that incorporating an optimally designed grid-connected renewable energy system into the proposed HEMS algorithm could significantly reduce household electricity bills, maintain comfort levels, and reduce the environmental footprint. The results of this research are considered to be of great significance as the proposed HEMS approach may help reduce the cost of integrating renewable energy resources into the national grid, which will be reflected in more users adopting these technologies. This in turn will lead to a reduction in the dependence on traditional energy resources that can have negative impacts on the environment. In particular, if a significant proportion of households in a region were to implement the proposed HEMS with the incorporation of small scale storage, then the overall peak demand could be significantly reduced providing great benefits to the grid operator as well as the households

Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques

Wind and solar irradiance are promising renewable alternatives to fossil fuels due to their availability and topological advantages for local power generation. However, their intermittent and unpredictable nature limits their integration into energy markets. Fortunately, these disadvantages can be partially overcome by using them in combination with energy storage and back-up units. However, the increased complexity of such systems relative to single energy systems makes an optimal sizing method and appropriate Power Management Strategy (PMS) research priorities. This thesis contributes to the design and integration of stand-alone hybrid renewable energy systems by proposing methodologies to optimise the sizing and operation of hydrogen-based systems. These include using intelligent techniques such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Neural Networks (NNs). Three design aspects: component sizing, renewables forecasting, and operation coordination, have been investigated. The thesis includes a series of four journal articles. The first article introduced a multi-objective sizing methodology to optimise standalone, hydrogen-based systems using GA. The sizing method was developed to calculate the optimum capacities of system components that underpin appropriate compromise between investment, renewables penetration and environmental footprint. The system reliability was assessed using the Loss of Power Supply Probability (LPSP) for which a novel modification was introduced to account for load losses during transient start-up times for the back-ups. The second article investigated the factors that may influence the accuracy of NNs when applied to forecasting short-term renewable energy. That study involved two NNs: Feedforward, and Radial Basis Function in an investigation of the effect of the type, span and resolution of training data, and the length of training pattern, on shortterm wind speed prediction accuracy. The impact of forecasting error on estimating the available wind power was also evaluated for a commercially available wind turbine. The third article experimentally validated the concept of a NN-based (predictive) PMS. A lab-scale (stand-alone) hybrid energy system, which consisted of: an emulated renewable power source, battery bank, and hydrogen fuel cell coupled with metal hydride storage, satisfied the dynamic load demand. The overall power flow of the constructed system was controlled by a NN-based PMS which was implemented using MATLAB and LabVIEW software. The effects of several control parameters, which are either hardware dependent or affect the predictive algorithm, on system performance was investigated under the predictive PMS, this was benchmarked against a rulebased (non-intelligent) strategy. The fourth article investigated the potential impact of NN-based PMS on the economic and operational characteristics of such hybrid systems. That study benchmarked a rule-based PMS to its (predictive) counterpart. In addition, the effect of real-time fuel cell optimisation using PSO, when applied in the context of predictive PMS was also investigated. The comparative analysis was based on deriving the cost of energy, life cycle emissions, renewables penetration, and duty cycles of fuel cell and electrolyser units. The effects of other parameters such the LPSP level, prediction accuracy were also investigated. The developed techniques outperformed traditional approaches by drawing upon complex artificial intelligence models. The research could underpin cost-effective, reliable power supplies to remote communities as well as reducing the dependence on fossil fuels and the associated environmental footprint

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

A Practical Approach for Coordination of Plugged- In Electric Vehicles To Improve Performance and Power Quality of Smart Grid

This PhD research is undertaken by supplications including 14 peer-reviewed published articles over seven years research at Curtin University. This study focuses on a real-time Plugged-in Electric Vehicle charging coordination with the inclusion of Electric Vehicle battery charger harmonics in Smart Grid and future Microgrids with incorporation of Renewable Energy Resources. This strategy addresses utilities concerns of grid power quality and performance with the application of SSCs dispatching, active power filters or wavelet energy

Optimal Home Energy Management System for Committed Power Exchange Considering Renewable Generations

This thesis addresses the complexity of SH operation and local renewable resources optimum sizing. The effect of different criteria and components of SH on the size of renewable resources and cost of electricity is investigated. Operation of SH with the optimum size of renewable resources is evaluated to study SH annual cost. The effectiveness of SH with committed exchange power functionality is studied for minimizing cost while responding to DR programs

Análisis y gestión óptima de la demanda en sistemas eléctricos conectados a la red y en sistemas aislados basados en fuentes renovables

En esta tesis doctoral se han analizado, de forma detallada, los principales aspectos relacionados con el funcionamiento de los sistemas eléctricos aislados y conectados a la red eléctrica basados en fuentes de energía renovable. En lo referente al análisis de los sistemas aislados de la red eléctrica, se ha analizado el efecto de la eficiencia culómbica y del regulador de carga en la fiabilidad de los sistemas eólicos con baterías. También se ha tratado la estimación de las horas de operación, consumo de combustible y coste neto actualizado de los sistemas que utilizan como respaldo un generador convencional. Por otra lado, se ha desarrollado un modelo probabilístico que permite considerar la incertidumbre existente en la estimación de la vida del banco de baterías, la incertidumbre asociada a los precios del combustible, la producción del generador fotovoltaico, el perfil típico de carga, así como la variabilidad de los recursos eólico y solar. Además, teniendo en cuenta la importancia que tiene el uso racional de la energía eléctrica, en esta tesis se ha desarrollado una novedosa técnica para la gestión de la demanda de sistemas aislados de la red eléctrica que sugiere al usuario del mismo el mejor momento para hacer uso de sus electrodomésticos, reduciendo el consumo de combustible y mejorando el uso de la energía almacenada en el banco de baterías. Finalmente, considerando sistemas conectados a la red eléctrica, se ha desarrollado una estrategia de Adaptación de la Demanda para consumidores residenciales que, haciendo uso de las capacidades de comunicación de la futura Red Eléctrica Inteligente, determina mediante la optimización de la negociación entre el usuario y la empresa de distribución de energía eléctrica, la forma en que el consumidor debe utilizar sus electrodomésticos considerando sus preferencias y su poder adquisitivo. Los resultados obtenidos sugieren importantes mejoras en los modelos que se utilizan habitualmente en la simulación y optimización de sistemas híbridos, específicamente en la consideración del regulador de carga como un importante elemento del sistema, y en la estimación de la vida útil del banco de baterías. Además, las estrategias para la gestión de la demanda, presentadas en este trabajo de investigación, pueden ayudar a que los usuarios de sistemas aislados o conectados a la red eléctrica realicen un uso eficiente de las fuentes de energía locales, y adapten sus patrones de consumo de electricidad a su condición económica actual

Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107