Microgrids Technologies in Future Seaports

Machine learning method for energy consumption prediction of ships in port considering green ports

In this article, five secluded Bi-Direction DC/DC converter topologies are analylized, tested and proposal for the single-mindedness of the Photo-voltaic generation plant and Energy storage System (ESS). The five converters are: secluded Bi-Direction fly-back converter (SBFYC), secluded Bi-Direction forward converter (SBFWC), secluded Bi-Direction push-pull converter (IBPPC), secluded Bi-direction half-bridge converter (SBHBC) and secluded Bi-Direction full-bridge converter (SBFBC). In the renewable energy foundations like PV and wind, wide input voltage abnormality due to the temperature and environmental conditions. That's why; energy production is very less in the Photo-voltaic generation plants. The main control system of the all DC/DC converters are extended phase shift modulation systems (EPS-PSM) for switching of converter to the one side to another side. The control of power flow between the energy storage system and dc-bus-bar has done by the control technique of EPS-PSM of the DC/DC converter. Secluded DC/DC converters are suitable for high voltage range and it's fit for the smart grid voltage level. Moreover, Items such as ideal efficiency, power dissipation of the conductor, converting losses at 0.5MHz, size and sophistication are calculated for them. On the other hand, their output current and voltages are found by the simulation with PSPICE and PSCAD software. A wide-ranging comparison is done amid these converter topologies on the basics of above mentioned items. It is found that secluded Bi-Direction full-bridge DC/DC converter (IBFBC) is best suitable for the Photovoltaic generation plants.

Integrating residential photovoltaic (PV) power generation and energy storage systems into the Smart Grid is an effective way of reducing fossil fuel consumptions. This has become a particularly interesting problem with the introduction of dynamic electricity energy pricing, since consumers can use their PV-based energy generation and controllable energy storage devices for peak shaving on their power demand profile, thereby minimizing their electricity bill. A realistic electricity pricing function is considered with billing period of a month, comprising both an energy price component and a demand price component. Due to the characteristics of electricity price function and energy storage capacity limitation, the residential storage control algorithm should 1)utilize PV power generation and load power consumption predictions and 2)account for various energy loss components during system operation, including energy loss components due to rate capacity effect in the storage system and power dissipation of the power conversion circuitry. A near-optimal storage control algorithm is proposed accounting for these aspects. The near-optimal algorithm, which controls the charging/discharging of the storage system, is effectively implemented by solving a convex optimization problem at the beginning of each day with polynomial time complexity. For further improvement, the reinforcement learning technique is adopted to adaptively determine the residual energy in the storage system at the end of each day in a billing period.

Peer-to-peer (P2P) energy trading has emerged as a novel approach to enhancing the coordination and utilization of distributed energy resources (DERs) within modern power distribution networks. This study presents a techno-economic analysis of different DER characteristics, focusing on the integration of photovoltaic (PV) systems and energy storage systems (ESS) within a community-based P2P energy trading framework in Aswan, Egypt, under a time-of-use (ToU) electricity tariff. Eight distinct cases are evaluated to assess the impact of different DER characteristics on P2P energy trading performance and an unbalanced low-voltage (LV) distribution network by varying the PV capacity, ESS capacity, and ESS charging power. To the best of the authors’ knowledge, this is the first study to comprehensively examine the effects of different DER characteristics on P2P energy trading and the associated impacts on an unbalanced distribution network. The findings demonstrate that integrating PV and ESS can substantially reduce operational costs—by 37.19% to 68.22% across the analyzed cases—while enabling more effective energy exchanges among peers and with the distribution system operator (DSO). Moreover, DER integration reduced grid energy imports by 30.09% to 63.21% and improved self-sufficiency, with 30.10% to 63.21% of energy demand covered by community DERs. However, the analysis also reveals that specific DER characteristics—particularly those with low PV capacity (1.5 kWp) and high ESS charging rates (e.g., ESS 13.5 kWh with 2.5 kW inverter)—can significantly increase transformer and line loading, reaching up to 19.90% and 58.91%, respectively, in Case 2. These setups also lead to voltage quality issues, such as increased voltage unbalance factors (VUFs), peaking at 1.261%, and notable phase voltage deviations, with the minimum Vb dropping to 0.972 pu and maximum Vb reaching 1.083 pu. These findings highlight the importance of optimal DER sizing and characteristics to balance economic benefits with technical constraints in P2P energy trading frameworks.

Extreme events lead to an increasing number of power outages in the distribution network. It is currently the most effective method to restore power supply after distribution network failure to connect distributed photovoltaic to the distribution network in the form of microgrids. However, the randomness of distributed PV output and load is the biggest obstacle limiting its development. Therefore, an optimization method of photovoltaic microgrid energy storage system (ESS) based on price-based demand response (DR) is proposed in this paper. Firstly, based on the influence of the uncertainty of the time of use (TOU) and load on the price-based DR, a price-based DR model is built. Then, taking the minimum difference between photovoltaic output and load demand as the optimization objective, the optimal operation model of price-based DR based on the fuzzy chance constrained program (FCCP) is established, and an optimization model of photovoltaic microgrid ESS based on FCCP is obtained. Finally, the fuzzy chance optimization problem is converted into a deterministic optimization problem through constraint equivalent treatment. The proposed method is verified by numerical simulations.

This paper proposes a hierarchical look-ahead framework to conduct conservation voltage reduction (CVR) when distributed energy resources such as solar photovoltaic (PV) systems and energy storage systems (ESSs), and demand response programs are integrated into distribution systems. With the increasing deployment of PV systems in distribution systems, their frequently varying power output due to cloud movements could have a detrimental impact on the consumer’s voltage quality, consequently leading to degraded CVR performance. A two-level CVR framework for the coordination of an on-load tap changer (OLTC), capacitor banks (CBs), and the smart inverters of PV systems/ESSs is presented, in which these elements operate to reduce the voltage profile along the distribution feeder at different temporal scales. At the global level, the operations of the OLTC and the CBs are scheduled every hour to achieve the best CVR performance in an optimization problem using mixed-integer linear programming. When voltage violations occur rapidly, the smart inverters of PV systems and ESSs help to maintain a lower voltage profile every second based on the proposed piecewise droop control functions at the local level. A simulation study is carried out in an IEEE 33-bus distribution system with an OLTC, CBs, PV systems, and ESSs, and our results demonstrate the advantages of the proposed approach in terms of voltage level and energy savings. Furthermore, the impact of demand reduction on the proposed approach is quantified, and we verify that a higher demand reduction yields more energy savings in the proposed framework.

With the development of photovoltaic power generation and energy storage technology, scale of photovoltaic power station and energy storage power station increasing. After a large area of power outages, photovoltaic power station based on the energy storage can be considered as a black-start power. In this paper, the sustained and effective output of photovoltaic power plant is analyzed and evaluated according to power forecasting data of photovoltaic power station as a criterion for photovoltaic power station used as black-start power. A system black-start scheme optimization model is proposed with target of maximizing the total power provided by photovoltaic power station and energy storage combined system in the start-up time of the conventional thermal power units to be started. Also, photovoltaic power station based on energy storage assisting power grid black start process is presented. Numerical example is used to demonstrate the feasibility of using photovoltaic power station and energy storage system as system auxiliary black-start power and validity of system black-start optimization model.

The objective of this article is to propose a photovoltaic (PV) power and energy storage system with bidirectional power flow control and hybrid charging strategies. In order to optimize the battery charging performance, five charging strategies, including the constant-current charging, the pulse-ripple-current charging, the sinusoidal-ripple-current charging, the bidirectional pulse-ripple-current, and the bidirectional sinusoidal-ripple-current charging, are adopted. Traditionally, in order to realize these charging strategies, the PV charger should abandon the maximum power point tracking function to maintain the power flow balance. As a result, the output power of the PV array will be decreased. Therefore, bidirectional power flow control strategies are proposed to achieve the maximum PV power utilization as well as to realize the hybrid charging methods. In addition, with the proposed strategies, the bidirectional charging/discharging capability of the battery is able to achieve the maximum PV power utilization. All the proposed strategies can be realized by the digital signal processor without adding any additional circuit, component, and communication mechanism. Theoretical analysis and control principles will also be revealed. Finally, a 5-kW prototype circuit with both simulation and experimental results demonstrate the performance and feasibility of the proposed strategies.

Accelerated development of eco-friendly technologies, such as renewable energy (RE), smart grids, and electric transportation will shape the future of electric power generation and supply. The power consumption characteristics of modern power systems are designed to be more flexible and easily controllable, which will also affect the sizing of power generation system. This paper presents a methodology for the joint capacity optimization of a typical residential standalone microgrid (MG) employing RE sources, i.e., solar photovoltaic (PV), wind turbines (WTs), diesel generators (DGs), and battery energy storage system (BESS). The MG supplies a residential community load comprising of typical residential load plus electric vehicles (EVs) charging load. The realistic mathematical models of PV, WT, diesel generation system, BESS, and EV load are formulated to improve the capacity optimization methodology, which involves various realistic constraints associated with the RE sources, diesel generation system, BESS, and EV load. The labyrinthine optimization problem is formulated and solved innovatively to 1) minimize the cost; 2) reduce greenhouse gases (GHG) emissions; and 3) curtail dump energy. All three objectives have special significance in designing a standalone MG, for example, cost is related to the economics, GHG emissions deal with global warming, and dump energy is related to the stability and economics of the system. The optimization problem is solved for different possible combinations of PV, WT, DG, and BESS to determine the best possible combination to serve the load effectively and economically. In addition, the impact of load shifting on the sizes of distributed generators and BESS in terms of per-unit cost and GHG emissions is analyzed using the concept of controllable loads. This study could be assumed as a powerful roadmap for decision makers, analysts, and policy makers.

The purpose of this study is to track the emergence of research related to green ports over time and determine the knowledge map and trends of green port research topics in the future using thematic map bibliometric analysis. This study also provides an overview of the researchers and contributors of research countries in green port research globally. This research provides a comprehensive bibliometric analysis of historical developments and current trends in green port research, by examining 462 articles published from 1991-2023 on the Scopus database. This research uses R studio Cloud bibliometric analysis and biblioshiny package. It was found that there are three phases in green port research, namely the initial stagnation phase (1991-2006), infant growth (2007-2015), and expanding phase (2016-2022), the number of articles increased quite rapidly in the third phase until now. This research also highlights related to authors, contributor countries and it was found that the country of China contributed the most contributor authors compared to other countries, this is because China is a country that places many container ports as the top 10 largest container ports in the world, with a total market share reaching 70% of the global. From this analysis, it is found that the knowledge map and the direction of future topics related to green ports are “sustainable development”, “port development”, “port operation”, “carbon emissions” and “renewable energy resources”, besides that it is also found that there is a direction of the current green port and “smart port” research trends. So, it can be recommended that in the future these topics will be very relevant to develop, the themes of research themes needed include alternative strategies in net zero emission (NZE) in ports in the operational and development aspects of the port.

Due to the existence of wind turbines in virtual power plants (VPPs), to eliminate the fluctuation characteristics of the wind turbine in the VPP and quantify the impact of the capacity of the energy storage system (ESS) on VPPs’ profitability, a multi-objective optimal schedule model, including the operating cost of the VPP, operation cost of the ESS and the electricity cost between the VPP and the grid, was proposed, and was solved by a quantum-inspired evolutionary algorithm; the output of conventional distribution generation and the state of charge were analysed on two different load days; the impact of different sub-object weights on the ESS capacity and the ESS cost and VPP profit under different rated capacities were also investigated. The results showed that the wind turbines' output error can be eliminated by the ESS on peak load day and valley load day; different weights of the VPP operating cost and the trading power cost would result in different capacities of the ESS; and the VPP's profit would rise with the increase of the ESS capacity when the capacity of the ESS was less than 8 MW.

Major faults arise in power converters mainly due to faulty MOSFET switches, diode and electrolyte capacitors. Converter with fault tolerant circuit improves the overall reliability of DC‐DC converter. We develop a self‐test configuration that identifies the faults in analog to digital converter (ADC), DC to DC converter and battery. IEEE 21451 standard‐based interface is implemented to develop a knowledge‐based smart controller. The centralized controller is performed to execute the maximum power point tracking (MPPT). Photovoltaic (PV) energy storage system is constructing to prove how the converter’s sensing, processing, and actuation capabilities in actual time may allow effective fault identification. The simulation result of the proposed method is executed in the MATLAB/Simulink working platform. The experimental results are implemented with field‐programmable gate array of Spartan‐6 Xilinx. The simulation as well as experimental result shows different fault identification in PV with energy storage system. Battery is provided through 12 V/4.5 Ah battery. The simulation and experimental results make ensure that the proposed method reveals its proficiency. It shows that the received results may fault identification at less than the period of one switching usually by 100 ms with suggested method.

The use of renewable energy sources through Distributed Generation in residences has been consolidating in recent years due to net metering. The intermittent generation characteristics of these sources, together with the implementation of variable electric energy tariffs throughout the day, bring the perspective of the use of Energy Storage System in residences, since it allows the strategic use of the energy produced by distributed generation in a way to balance generation x electricity consumption of the residence. In this context the distributed generation, energy storage system and loads form the Residential Energy Resources that must be managed to obtain its best use. Therefore, this paper proposes a Home Energy Management System to realize the residential energy resources optimization, in order to minimize energy costs in homes using hybrid systems, consisting of photovoltaic systems and battery energy storage systems, considering as parameters the values of variable tariffs over time and the behavior of generation, storage and loads. As a result one has the operating regime of the battery to obtain the balance generation x demand and the reduction of the daily cost in electricity bills. This paper is preliminary results of the Strategic Project between the COPEL Distribution and the Federal University of Santa Maria which deals with technical and commercial arrangements for the insertion of energy storage systems in the Brazilian electrical system.

This paper proposes a high-efficiency 3-kW hybrid ESS with emergency power supply. The proposed system enables efficient use of power from photovoltaic (PV) cells and energy storage system (ESS). The proposed system can operate as an uninterruptible power supply (UPS) when grid fault occurs, providing seamless transfer from grid-connected mode to stand-alone mode. The LLC converter for PV achieves ZVS turn-on of switches and ZCS turn-off of diodes, and the isolated bidirectional DC-DC converter for ESS achieves ZCS turn-off regardless of load condition, resulting in high efficiency. The efficiency and performance of the proposed hybrid ESS has been verified by a 3-kW prototype.

Harnessing demand-side management benefit towards achieving a 100% renewable energy microgrid