Structural Engineering, Robotics and Mechatronics, Sustainable Power Supply

Ensuring access to reliable and sustainable power supply is becoming more and more challenging due to a combination of factors such as more frequent power grid outages caused by extreme weather events, the large-scale introduction of renewable energy resources that increases the complexity of the power system, but also aging infrastructure, supply and demand imbalance and power theft in some areas. Combined, all these factors can cause outages and together they can make electricity supply unreliable. The implications of this are many, ranging from minor inconveniences to major failures of critical infrastructures. A potential solution to ensure power supply during outages is to use local generation in the form of renewable resources to supply energy. This paper proposes a community-based mechanism that demonstrates that when community members can determine for themselves how excess energy generation is distributed, the power supply of specific members can be ensured. Self-determination is achieved by prioritizing and differentiating between community members as well as automatically and continuously redistributing energy, thereby adapting to sudden changes in supply and demand. Simulation results show that the proposed mechanism can be used to empower local communities to decide for themselves how local resources are distributed during events such as outages, ensuring prolonged power supply for differentiated members of affected communities. Harnessing the potential of renewable resources and smart technologies for intelligent coordination through empowerment of consumers to become pro-active participants is a promising solution for the future power systems.

The development of excellently stretchable, highly mobile, and sustainable power supplies is of great importance for self-power wearable electronics. Transpiration-driven hydrovoltaic power generator (HPG) has been demonstrated to be a promising energy harvesting strategy with the advantages of negative heat and zero-carbon emissions. Herein, this work demonstrates a fiber-based stretchable HPG with the advantages of high output, portability, knittability, and sustainable power generation. Based on the functionalized micro-nano water diffusion channels constructed by the discarded mask straps (MSs) and oxidation-treated carbon nanomaterials, the applied water can continuously produce electricity during the spontaneous flow and diffusion. Experimentally, when a tiny 0.1mL of water encounters one end of the proposed HPG, the centimeter-length device can yield a peak voltage of 0.43V, peak current of 29.5µA, and energy density of 5.833mWhcm-3. By efficiently integrating multiple power generation units, sufficient output power can be provided to drive commercial electronic devices even in the stretched state. Furthermore, due to the reversibility of the electrical output during dynamic stretching-releasing, it can passively convert physiological activities and motion behaviors into quantifiable and processable current signals, opening up HPG's application in the field of self-powered wearable sensing.

Extreme events can extensively damage power systems, causing customers to experience long-lasting outages. During such events, an electric vehicle (EV) can be used to directly power a house, i.e., vehicle-to-home (V2H). Specifically, the EV serves as a mobile energy storage system—running errands to “transport” energy from other places. Vehicle-to-grid (V2G) further allows cooperation among houses. It enables EV fleets to take turns running the errands so that sustained power supply is possible. Moreover, autonomous driving technology can also benefit system adequacy because the charging errands of EVs can be scheduled flexibly without being bonded to human activities. An emergency power supply strategy featuring scheduled EV charging errands as introduced above is proposed. It answers the questions whether and to what extent a system can survive an extended period of outage with the use of EVs only. An optimization problem is formulated with the purpose of maximizing the supply adequacy of the isolated system during the outage period. Both V2H and V2G scenarios are considered in the problem formulation, as well as self-driving capability. The complex optimization problems are solved with genetic algorithm. It is significant to find from the case study that the proposed strategy is able to fully restoring an islanded system when V2G and self-driving EVs are implemented.

Due to the high cost of electricity, interruptions in the power supplied and frequent use of a fossil-powered generator in the College of Engineering, Afe Babalola University Ado-Ekiti (ABUAD), there is an increase in greenhouse gas emissions and this affects the immediate surroundings as well as the environment at large. There is a need to consider alternative means of economic, reliable and sustainable power supply. This can be achieved by modelling and simulating a solar PV system off the rooftops of the College of Engineering ABUAD. Google Earth Tool and Revit software were used to determine the total roof surface area and the useful roof surface area of the rooftops. HOMER Pro was used to model and simulate the solar PV system. The total roof surface area was 7389.161m2, 5210.34m2 was useful for the installation of 300W solar PV panels. The optimal configuration obtained from the solar PV simulation produced 821,080kWh/year which supplied 100% of the 697,150kWh demanded per year and 12% excess energy. It also reduced the cost of electricity by over 45% from N57.14 to N31.26. Utilization of the rooftop spaces was proven to provide access to a cost-saving, clean and sustainable energy supply.

In light of electrical energy’s increasing role in economic systems worldwide, prioritizing investments in sustainable power supplies has become paramount. This study proposes a model based on cloud theory and game theory to evaluate sustainable power supply investment projects. It establishes a foundation for assessing the merits of power supply investments, which are crucial for continuous electricity provision and economic advancement. By integrating an enhanced analytic hierarchy process and the entropy method, the study develops a dual-weighted evaluative index system. This hybrid approach addresses ambiguities and enhances the weight determination accuracy, which, when applied to the Liaojiawan Transformer Substation, verifies the project’s high benefit level, corroborated by empirical data. This innovative methodology offers a strategic framework for future power supply investments.

In respect to the difficulties in power supply for online monitoring equipments at high voltage side, a new design of adjustable and rechargeable induction power supply is presented. A microprocessor is used as the central control unit, which can achieve the intelligent management and control of different modules of the proposed device. The experimental results show that reliable and stable power output can be obtained in the situation of large current variation. Introduction The security and stability of the high voltage transmission lines are very important in the development of smart grid. As a result, different kinds of auxiliary devices are installed on high voltage transmission lines to improve the capability of condition monitoring [1]. However, due to the electrical isolation of safety requirements [2], the electronic monitoring equipments installed on the high voltage side can not be directly powered from the low voltage side. Therefore, the power supply has become one of the key technologies which restrict the development of online monitoring systems. The traditional power supplies, for example, solar energy [3], are unable to provide the monitoring equipments an all-weather, continuous and stable power supply. The induction power supply at high voltage side can work in harsh weather and provide stable and continuous output [4], which makes it an ideal choice for online monitoring equipments to solve their energy supply problem. However, since the current of the transmission lines varies in a wide range, it is crucial how to ensure that the induction power supply device has a stable and reliable output with a wide range of current fluctuation. It is necessary to ensure that the power supply can provide sufficient driving power to the online monitoring equipment when the current of the transmission line is small [5]. It is also needed to ensure that when large current flows in the transmission line, a sustainable and stable power supply still can be obtained. Therefore, a new design of adjustable and rechargeable induction power supply at high voltage side is studied. The proposed induction power supply can produce reliable and stable power supply in the condition of large current variation. The Structure of the Proposed Power Supply Fig. 1. The overall framework of the proposed induction power supply The proposed induction power supply consists of the power capture module, the rectifier module, the over-voltage protection module, the voltage regulator module, the backup power management 4th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2016) © 2016. The authors Published by Atlantis Press 1195 module and the microprocessor control unit (MCU), as shown in Fig. 1. With the MCU, the entire device can be intelligently controlled to produce a stable DC voltage output even with a large current variation in the transmission line [6]. The details of each module of the proposed induction power supply are presented as follows. Power Capture Module. The structure of the power capture module is shown in Fig. 2. The actual current in the high-voltage transmission lines keeps changing. In order to adapt to the dynamic current changes, there are multiple taps at the CT coil in the secondary side. Different taps correspond to coils with different numbers of turns. With the sampling resistor in the rectifier module, the MCU can achieve a real-time control on the number of coil turns. When the current in the transmission line is too large, the current at the secondary side also becomes large. The MCU controls the relay 1 and then the coil 1 will be connected to the circuit in order to reduce the current at the secondary side. When the current in the transmission line is in normal variation, the MCU controls the relay 1 and then the coil 2 is connected. Finally, if the current in the transmission line is too small, then the backup battery may be switched on to power the equipment. Such a design makes the proposed device having efficient power capture capability over a wide range of current variations. Fig. 2. The structure of the power capture module Rectifier Module. Rectifier circuit DB107 is used in this design. RC filter and LC filter are connected in parallel. A transient suppression diode TVS, which plays the role of over-current protection, is inserted between the rectifier circuit and the filter. The selected TVS is SMF45A, which has a breakdown voltage of 75 V and a cut-off voltage of 45 V. Fig. 3. The structure of the rectifier module Voltage Regulator Module. The voltage of the DC output of the rectifier module is 7.5 V – 76 V. The role of the DC-DC voltage regulator module is to convert this wide range of input voltage into a stable but adjustable output voltage. In our design, the step-down voltage converter MAX5035BASA is chosen as the core unit of this module. It has high efficiency and wide input voltage range up to 76 V. Its output voltage is fixed to 5 V. Fig. 4 shows the circuit of this module.

Renewable energy cooperatives: Facilitating the energy transition at the Port of Rotterdam

Flexible photo-charging power sources for wearable electronics

Since renewable power is intermittent and uncertain, modern grid systems need to be more elegant to provide a reliable, affordable, and sustainable power supply. This paper introduces a robust optimal planning strategy to find the location and the size of an energy storage system (ESS) and feeders. It aims to accommodate the wind power energy integration to serve the future demand growth under uncertainties. The methodology was tested in the IEEE RTS-96 system and the simulation results demonstrate the effectiveness of the proposed optimal sizing strategy. The findings validate the improvements in the power system reliability and flexibility.

Microgrid control methods toward achieving sustainable energy management: A bibliometric analysis for future directions

This paper presents compared performances of three metaheuristic algorithms in determining the cost of hybrid renewable energy system. Using genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC), the best affordable sizes of solar photovoltaic array, battery bank, and a minimum-rated diesel generator that could be hybridized to meet the demand of a community in Southwest Nigeria were determined. Load profile, solar radiation, and temperature data were employed as required inputs, and the parameters of the algorithms were properly set to ensure the best result. Bonferroni– Holm method was deployed to ascertain the statistical significance among the algorithms. It was found that ABC produced the best configuration comprising 427 numbers of solar photovoltaic panels, 19 battery units, and 163.2 kW-rated diesel generator. With this, a total annualized cost of $167 284 and 0.2443 estimated cost of energy were obtained. These were the lowest when compared with PSO and GA. The t-test between PSO and ABC are both 5.83 × 10−10 < 0.01666667, between ABC and GA are 6.09 × 10−6 0.01666667 and 9.13 × 10−1 > 0.05. PSO/ABC and ABC/GA groups are clarified significant, while GA/PSO group is insignificant; post hoc test reveals that ABC produced the best result. Hence, a reliable and sustainable power supply at a reduced cost is guaranteed for the community. Cite this article as: T.O. Ajewole, O. Oladepo, K.A. Hassan, A.A. Olawuyi, O. Opeyemi, Comparative study of the performances of three metaheuristic algorithms in sizing hybrid-source power system. Turk J Electr Power Energy Syst, 2022; 2(2), 134-146.

Nowadays, the increment of energy demand in the world as well as the development of smart grids and the combination of different types of energy systems have led to the complexity of power systems. On the other hand, ever-expanding energy consumption, development of industry and technology systems, and high penetration of renewable energies have made electricity networks operating in more complex and uncertain conditions. Also, consumers and especially users of sensitive load tend to have access to a reliable and sustainable power supply. Therefore, power producers need a variety of long- and short-term planning methodologies for attaining to sustainable investment, production, and operation. Analysis of traditional power and energy systems requires physical modeling and extensive numerical computation. To analyze the behavior of these systems, advanced metering, and condition monitoring devices and systems are utilized, which generate a huge amount of data. Evaluation of these data is approximately impossible by conventional or numerical methods, and it requires powerful data mining procedures. Regression, classification, and clustering applications of machine learning and deep learning methods are powerful tools to use for dealing with such issues. These procedures can be utilized for load/demand forecasting, renewable energy generation forecasting, demand response evaluation, and power system analysis. Understanding the problem and functioning of each learning methods is, therefore, one of the most important issues in the application of such approaches to solve power system problems. Accordingly, in this chapter, the authors will introduce and discuss selected applications of machine learning and deep learning based on their learning, structure, mode of operation, and application in the load forecasting of power systems. Literature review on machine learning and deep learning applications in load forecasting will be presented in this chapter.

To maintain a dependable and sustainable power supply in a microgrid system, it is crucial to combine renewable energy sources with hybrid energy backup. To achieve maximum output power from solar source, a high gain DC-DC boost converter is managed using the dual Kalman filter based perturb and observe approach. A sliding mode current controller at a three phase inverter with an LC filter is intended to follow an undisturbed reference voltage. To effectively manage the power flow and optimize the utilization of available resources, a robust power management algorithm is required. The novel power management algorithm for a solo operated renewable distribution generation unit with hybrid energy backup in a microgrid is introduced. The algorithm aims to dynamically allocate power among various sources, storage systems, and loads, considering their characteristics and the overall system constraints. The algorithm utilizes sliding mode control techniques to regulate the current flow from the renewable generator and effectively manage the power allocation among different energy sources, storage systems, and loads.

The country, Nigeria is confronted with myriads of challenges hindering its access to reliable and sustainable power supply in both urban and rural areas. The existing energy infrasructures are incapable of translating the so many Renewable Energy Souces (available in almost every part of the country) to a sustainable energy supply system, capable of bridging the wide power supply-demand gap. This paper proposes the use of solar, biomass and small hydropower technologies for a sustainable energy development in rural and urban areas of the country. This work reviewed the present state of energy in the country, recommended the use of decentralised power system and off-grid /on-grid hybrid power system as a sustainable energy development model for the country. Then, the neccesity for aggressive Renewable Energy Sources integration into the national energy mix through sustainable growth involving decentralised renewable energy system and distributed generation applications. This work will assist power sector stakeholders in making informed decisions towards promotion of green economy and growth of hybrid power system technology in Nigeria.

Supercapacitors is an electrochemical energy storage device typically used as sustainable power supply (such as electric vehicles and mobile electronic devices) due to their higher power density than batteries, higher energy density than conventional dielectric capacitors, and long life cycles. [1,2] Graphene, a two-dimensional (2D) one-atom-thick single layer of sp2-bonded carbon, is considered as an ideal electrode material in supercapacitors because of its superior electrical conductivity and exceptionally large specific surface area. Graphene sheets, unless well separated from each other, tend to form irreversible agglomerates through strong π-π stacking and van der Waals interaction.[3] Meanwhile, the electrochemical binders and additives are normally required to make graphene-based electrodes, which would have an adverse effect on the specific capacitance of the resulting electrodes. To tackle these challenges, three dimensional (3D) graphene networks with highly electrical conductivity and large specific surface area, short diffusion pathways for electrolyte ions and fast transport channels for electrons are very promising supercapacitor materials. To these aims, we have developed ways to synthesize a novel macroporous/nanoporous graphene sponge with tunable pore sizes. This 3D porous graphene sponge has i) an interconnected electrolyte-filled macroporous network that enables increase of contact surface between 3D network and electrolytic solution, and rapid ion transport, ii) short ion and electron transport lengths due to nanoporous structures, iii) a large electrode specific surface area and (iv) high electron conductivity in the electrode assembly. The structure characterization and capacitive properties of the 3D macroporous/nanoporous graphene sponge was investigated and discussed, indicating 3D porous graphene sponge as an ideal electrode materials for supercapacitors. Keywords：Graphene, 3D porous materials, supercapacitors, electrochemical energy