Load management, energy economics, and environmental protection nexus considering PV-based EV charging stations

Energy vulnerability is gaining attention in light of global economic, social, and political conflicts. The digital economy has the potential to become a vital tool in addressing current issues. This paper explores the relationship, mechanisms, and heterogeneity between the digital economy and energy vulnerability for 110 economies by using a generalized method of moments (GMM) model. Our key findings are as follows: (1) The digital economy effectively reduces energy vulnerability, with digital infrastructure and social impact as the main contributors to this effect. (2) The digital economy helps upgrade the industrial structure and the level of financial development, thereby reducing energy vulnerability. (3) Heterogeneity results show that the negative impact of the digital economy on energy vulnerability is more significant in regions with higher income levels, particularly Europe and North America. (4) The level of economic development acts as a practical threshold variable, with the digital economy having a more pronounced negative impact on energy vulnerability at higher threshold levels. These findings provide new development ideas for improving global energy security and energy efficiency, and reducing energy vulnerability. 

Is the digital economy an effective tool for decreasing energy vulnerability? A global case

Citizen Energy Communities (CECs) have emerged as a promising solution with numerous benefits for society. CECs have contributed to a higher adoption rate of Distributed Energy Resources (DERs). However, the variable power generation of DERs is a critical challenge, resulting in an increase in the ramp rate of the system's net load. To address this challenge, this paper introduces a unique robust optimization model that utilizes the flexibility constraint to limit the ramp rate of power trading CECs with the upstream grid and assesses the impact of flexibility limits on energy costs. Moreover, two primary goals of CECs are to ensure the prosumer's comfort, which includes thermal comfort, and to achieve self-sufficiency in their energy supply. In this context, this paper uses the Predicted Mean Vote (PMV) index to calculate the thermal comfort of prosumers. Additionally, a sensitivity analysis has been conducted on CECs self-sufficiency. Moreover, a robust optimization approach is utilized to consider the uncertainty in upstream energy prices. The results indicate that the Community Energy Storage (CES) leads to a 34.6% reduction in operation cost. Additionally, the inclusion of CES in CECs allows the Energy Community Manager (ECM) to reduce curtailment power to prosumers by 602.98 kW during their engagement in the self-sufficiency program. 

Energy management in citizen energy communities: A flexibility-constrained robust optimization approach considering prosumers comfort

Field-oriented control based on parallel proportional–integral controllers of induction motor drive

In this paper, a new design and flexible energy management strategy are presented for microgrids. The proposed intelligent energy management system (IEMS) achieves effective integration between the resilient microcontroller, chosen for its rapid response speed and its capability to perform multiple operations simultaneously, and the optimization techniques to enhance the power quality. The IEMS is designed using the FPGA board, chosen for its flexibility and capability to handle multiple and complex operations simultaneously. The experimental testing of the IEMS demonstrates a significant level of effectiveness in managing energy. To enhance system performance and ensure cost-effective reliability, advanced optimization techniques are employed. This study deals with a complex multi-objective optimization problem involving the limitations of energy generation, load demand, and a hydrogen-battery hybrid energy storage system. The moth-flame optimization (MFO) algorithm is chosen to solve this optimization problem due to its rapid convergence rate and accuracy. The effectiveness of the MFO algorithm is assessed by comparing it with several new algorithms. The obtained results show the robust performance of the IEMS and its high responsiveness to dynamic operational scenarios. It can observe, gather, and analyze data in real-time. It achieves a remarkable 1.287 % reduction in operating costs within a short timeframe. 

A resilient and intelligent multi-objective energy management for a hydrogen-battery hybrid energy storage system based on MFO technique

A new optimized demand management system for smart grid-based residential buildings adopting renewable and storage energies

Agriculture activities are completely dependent upon energy production worldwide. This research presents sensorless speed control of a three-phase induction motor aided with an extended Kalman filter (EKF). Although a proportional integral (PI) controller can ensure tracking of the rotor speed, a considerable magnitude of ripples is present in the torque generated by a motor. Adding a simple derivative to have a proportional integral derivative (PID) action can cause a further increase in ripple magnitude, as it allows the addition of high-frequency noise in the system. Therefore, a fractional-order-based PID control is presented. The proposed control scheme is applied in a closed loop with the system, and simulation results are compared with the PID controller. It is evident from the results that the fractional order control not only ensures 20 times faster tracking, but ripple magnitude in torque was also reduced by a factor of 50% compared to that while using PID and ensures the effectiveness of the proposed strategy.

https://www.mdpi.com/1996-1073/16/4/1901/pdf?version=1676454850

A Fractional Order Controller for Sensorless Speed Control of an Induction Motor

Classical model-free predictive current control (MFPCC) is a robust control technique for a two-level inverter-fed induction-motor drive, with advantages that consist of a simple concept, rapid response, simple implementation, and excellent performance. However, the classic finite-control-set MFPCC still exhibits a significant current ripple. This article presents a method to enhance performance using a combination of model-free predictive current control (MFPCC) and discrete-space vector modulation (DSVM). The MFPCC employs an ultralocal model with an extended-state observer (ESO) that does not consider motor parameters, therefore improving the control system’s reliability by eliminating the parameter dependency. The proposed method integrates DSVM, which divides a single sample period into N equal intervals and generates virtual vectors to reduce stator current ripple. It achieves the minimum cost-function value across the entire operating range of the induction-motor (IM) drive by selecting the optimal vector from a limited set of permissible voltage vectors. Using DSVM effectively reduces the total harmonic distortion (THD) without any detrimental effects during transients or steady states. Experimental studies validate the effectiveness and superiority of the suggested technique over the Finite-Control-Set (FCS) MFPCC, which only considers real voltage vectors in its computations. 

DSVM-Based Model-Free Predictive Current Control of an Induction Motor

The problem of determination of reliability indicators is relevant due to the lack of data on the current values of reliability indicators of electrical equipment of power systems., in particular., the values of reliability indicators of electric energy storage systems installed in electrical networks of voltage class 0.,4 kV. The paper presents and analyzes statistical data on the number of emergency failures of electric energy storage systems in the branches of the electric grid organization of P JSC «Rosseti Center and Privolzhye». Statistical data on the number of defects in electric energy storage systems that occurred during their operation from 2019 to 2022 are also presented and analyzed. The types of inverters used in electric energy storage systems are considered and their failures during operation are analyzed. In the course of the study., the values of the failure flow were determined both for each individual system of electric energy storage., and in general for all electrical installations of the electric grid organization of PJSC «Rosseti Center and Privolzhye». It was found that the average value of failure rate for electric energy storage system was 25 year−1/100 pes, The highest value of the failure rate with a value of 167 year−1/l00 pcs was revealed for the electric energy storage system installed in the Belgorod city electric networks of the branch of P JSC «Rosseti Center»-«Belgorodenergo».

Determination of Reliability Indicators for Electric Energy Storage Systems

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A new optimized demand management system for smart grid-based residential buildings adopting renewable and storage energies

A Fractional Order Controller for Sensorless Speed Control of an Induction Motor

DSVM-Based Model-Free Predictive Current Control of an Induction Motor

Determination of Reliability Indicators for Electric Energy Storage Systems