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Manufacturers are facing pressure to reduce electricity costs. Onsite renewable energy generation may be a solution, but its high capital cost and intermittent power generation limit its use. Grid-responsive smart manufacturing could effectively incorporate renewables in industrial processes. This study integrates grid-responsive smart manufacturing with renewables on an industrial plant scale and demonstrates both a favourable economic and environmental outcome. A user-friendly decision-aid model for energy management is provided to manufacturers. A case study shows how solar panels, industrial batteries, smart pumping strategies, and various combinations of those elements can save on electricity costs. Dynamic simulation results demonstrate that grid-responsive smart manufacturing can effectively lower peak demand. The economic results show that grid-responsive smart manufacturing and renewables synergistically optimise cost reductions. The solar coupled with smart pumping scenario shows annual cost savings of $755,200, accounting for 4.6% of the total electricity cost. Smart pumping alone saves $371,900 annually with a 0.7-year payback period, demonstrating how the manufacturing sector can utilise its own processes in load shifting. This study supports that incorporating grid-responsive smart manufacturing with renewables can effectively reduce electricity costs and emissions for industry. Abbreviations: e: Equivalent; GHG: Greenhouse gas; PBP: Payback period; PV: Photovoltaics; SP: Setpoint; VFD: Variable speed drives [ABSTRACT FROM AUTHOR]

Building and microgrid designs with highly-distributed electrical storage have potential advantages over today's conventional topologies with centralized storage. This paper studies the capital cost benefits of several residential behind-the-meter distributed-storage topologies, including AC and DC versions of systems with load-packaged batteries and resilient sub-networks. The study begins by defining the block configuration of each topology. This work then develops a model for the cost of the power electronics necessary to interface with the storage elements. Finally, the analysis develops a model for the total cost of each storage topology, incorporating the installation and soft costs. The results suggest that while the cost of power electronics is lower in centralized topologies, the total cost is lower for distributed storage due to the avoided costs of installation and permitting. This paper also explores the benefits of load-packaged batteries for savings in electrical infrastructure.

Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. However, the PV installation should provide financial benefits for the utilities. Considering that the utility companies often incur costs for both energy and peak demand, PV installations should aim to reduce both energy and peak demand charges. Although PV systems can reduce energy needs during the day, their effectiveness in reducing peak demand, particularly in the early morning and late evening, is limited, as PV generation is zero or negligible at those times. To address this limitation, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV and the accompanying battery remains a challenge. While valuable optimization models have been developed to determine the optimal size of PV–battery systems, a certain gap remains where peak demand reduction has not been sufficiently addressed in the optimization process. Recognizing this gap, this study proposes a novel statistical model to optimize PV–battery system size for peak demand reduction. The model aims to flatten 95% of daily peak demands up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model’s effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV–battery system successfully flattens 95% of daily peak demand with a selected threshold of 2000 kW, yielding a financial benefit of USD 812,648 over 20 years.

Nearly zero-emission buildings (nZEBs) are increasingly being constructed in Europe. There are also incentives to refurbish older buildings and transform them into nZEBs. However, permission is not always granted for their connection to the grid to infuse surplus photovoltaic electricity due to the grid being overloaded with a large number of renewables. In this study, the case of a refurbished school building in Central Greece is examined. After refurbishing it, a significant amount of photovoltaic electricity surplus is observed during the summer and neutral months, which cannot be exported to the grid. The absence of an adequate battery storage capacity resulted in the rejection of an application for exporting the school's surplus to the network and the photovoltaic installation staying idle. An alternative approach is proposed in this work, involving a shift in the export of the photovoltaic electricity surplus to the evening hours, in order for the school to be granted permission to export it to the network. To this end, an optimal battery storage size is sought by employing a building energy system simulation. The mode of operation of the battery designed for this application is set to discharge daily, in order to export the electricity surplus in the afternoon hours to the evening hours, when it is favorable for the network. Additionally, the optimal size of the thermal energy storage of the heating system is studied to further improve its energy efficiency. Our battery and storage tank size optimization study shows that a significant battery capacity is required, with 12 kWh/kWp photovoltaic panels being recommended for installation. The ever-decreasing cost of battery installations results in the net present value (NPV) of the additional investment for the battery installation becoming positive. The solution proposed forms an alternative path to further increase the penetration of renewables in saturated networks in Greece by optimizing battery storage capacity. [ABSTRACT FROM AUTHOR]

Energy management control (EMC) becomes imperative in hybrid energy systems that involve multiple sources. The main objective of this study is to concentrate on the effective control and utilization of energy in a hybrid system combining photovoltaic solar and wind sources, together with a battery bank. A sophisticated intelligent supervisor, employing a fuzzy logic controller (FLC), adeptly balances the power generated by the proposed system in response to the load demand. Simulation results demonstrate that, despite the initial maximum battery state of charge value being set at 90%, the battery's state of charge does not exceed 60% during charging. Additionally, during discharge, the battery state of charge remains above 27%, surpassing the initially set minimum value of 25%. This achievement is attributed to the exchange of energy flow facilitated by the implemented intelligent management. The notable advantage of this intelligent management lies in its ability to precisely control the state of charge (SOC) of the battery across five distinct states, a feat not achievable with traditional management practices that rely on only two SOC levels (SOCmin–SOCmax). [ABSTRACT FROM AUTHOR]

The establishment of near-autonomous micro-grids in commercial or public building complexes is gaining increasing popularity. Short-term storage capacity is provided by means of large battery installations, or, more often, by the employees' increasing use of electric vehicle batteries, which are allowed to operate in bi-directional charging mode. In addition to the above short-term storage means, a long-term storage medium is considered essential to the optimal operation of the building's micro-grid. The most promising long-term energy storage carrier is hydrogen, which is produced by standard electrolyzer units by exploiting the surplus electricity produced by photovoltaic installation, due to the seasonal or weekly variation in a building's electricity consumption. To this end, a novel concept is studied in this paper. The details of the proposed concept are described in the context of a nearly Zero Energy Building (nZEB) and the associated micro-grid. The hydrogen produced is stored in a high-pressure tank to be used occasionally as fuel in an advanced technology hydrogen spark ignition engine, which moves a synchronous generator. A size optimization study is carried out to determine the genset's rating, the electrolyzer units' capacity and the tilt angle of the rooftop's photovoltaic panels, which minimize the building's interaction with the external grid. The hydrogen-fueled genset engine is optimally sized to 40 kW (0.18 kW/kWp PV). The optimal tilt angle of the rooftop PV panels is 39°. The maximum capacity of the electrolyzer units is optimized to 72 kW (0.33 kWmax/kWp PV). The resulting system is tacitly assumed to integrate to an external hydrogen network to make up for the expected mismatches between hydrogen production and consumption. The significance of technology in addressing the current challenges in the field of energy storage and micro-grid optimization is discussed, with an emphasis on its potential benefits. Moreover, areas for further research are highlighted, aiming to further advance sustainable energy solutions. [ABSTRACT FROM AUTHOR]

In order to solve the problems of wind power output volatility and wind power participation in frequency regulation, a method for optimizing the capacity allocation of wind farm storage batteries based on the dual grouping strategy and considering the simultaneous execution of the dual conditions of energy storage in fluctuation smoothing and primary frequency regulation is proposed. Firstly, a two-layer model is established to optimize the capacity allocation under dual operating conditions, i.e., the planning layer takes into account the lifetime, cost, and benefit, and the operation layer considers the wind turbine reserve backup and storage control to participate in the primary frequency regulation in a cooperative manner. Then, the dual battery pack operation strategy is embedded with the variational modal decomposition method to determine the charging and discharging operation strategy of energy storage after considering the grid-optimized reference power. An improved particle swarm algorithm with inverse learning pre-optimization combined with variational crossover post-optimization is embedded in the GUROBI computation to obtain the optimal battery storage capacity allocation scheme. Finally, the superiority of the model proposed in this paper in terms of improving energy storage utilization, service life, and economic efficiency as well as reducing wind power load shedding is verified by comparing it with a single execution working condition scenario and traditional battery control strategy. [ABSTRACT FROM AUTHOR]

Due to its fewer components, the DC-DC three-phase converter has a simpler design and could be less expensive. However, it can present challenges in terms of precise voltage regulation and current balancing, due to the limited number of switching phases. On the other hand, the three-phase converter offers more precise voltage regulation and improved current balance owing to its higher number of phases. Although this results in increased design complexity and potentially higher cost, it allows for a more uniform distribution of current load among MOSFETs. The particular needs of the application, acceptable tradeoffs between complexity, cost, and performance, as well as the requirement for precise voltage regulation and ideal current balancing, can determine which option is the best. This work investigates a three-phase interlaced DC converter with a parallel MOSFET. A two-way DC-DC converter was used to assess PWM when charging and discharging a battery. The results demonstrate a great DC voltage gain without a very high cycle load. [ABSTRACT FROM AUTHOR]

Integrating residential energy storage and solar photovoltaic power generation into low-voltage distribution networks is a pathway to energy self-sufficiency. This paper elaborates on designing and implementing a 3 kW single-phase grid-connected battery inverter to integrate a 51.2-V lithium iron phosphate battery pack with a 220 V 50 Hz grid. The prototyped inverter consists of an LCL-filtered voltage source converter (VSC) and a dual active bridge (DAB) DC-DC converter, both operated at a switching frequency of 20 kHz. The VSC adopted a fast DC bus voltage control strategy with a unified current harmonic mitigation. Meanwhile, the DAB DC-DC converter employed a proportional-integral regulator to control the average battery current with a dynamic DC offset mitigation of the medium-frequency transformer's currents embedded in the single-phase shift modulation scheme. The control schemes of the two converters were implemented on a 32-bit TMS320F280049C microcontroller in the same interrupt service routine. This work presents a synchronization technique between the switching signal generation of the two converters and the sampling of analog signals for the control system. The prototyped inverter had an efficiency better than 90% and a total harmonic distortion in the grid current smaller than 1.5% at the battery power of ±1.5 kW. [ABSTRACT FROM AUTHOR]

Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. However, the PV installation should provide financial benefits for the utilities. Considering that the utility companies often incur costs for both energy and peak demand, PV installations should aim to reduce both energy and peak demand charges. Although PV systems can reduce energy needs during the day, their effectiveness in reducing peak demand, particularly in the early morning and late evening, is limited, as PV generation is zero or negligible at those times. To address this limitation, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV and the accompanying battery remains a challenge. While valuable optimization models have been developed to determine the optimal size of PV–battery systems, a certain gap remains where peak demand reduction has not been sufficiently addressed in the optimization process. Recognizing this gap, this study proposes a novel statistical model to optimize PV–battery system size for peak demand reduction. The model aims to flatten 95% of daily peak demands up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model's effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV–battery system successfully flattens 95% of daily peak demand with a selected threshold of 2000 kW, yielding a financial benefit of USD 812,648 over 20 years. [ABSTRACT FROM AUTHOR]

Abstract A Nanogrid (NG) model is described as a power distribution system that integrates Hybrid Renewable Energy Sources (HRESs) and Energy Storage Systems (ESSs) into the primary grid. However, this process is affected by several factors, like load variability, market pricing, and the intermittent nature of Wind Turbines (WTs) and Photovoltaic (PV) systems. Hence, other researchers in the past have used a few optimization-based processes to improve the development of Energy Management Systems (EMSs) and ESSs, which further enhanced the operational performance of NGs. It was seen that EMS acts as the distributed energy source in the NG setup and assists in power generation, usage, dissemination, and differential pricing. Hence this study employed the MATLAB Simulink software for modelling the grid-connected NG that included HRES; such as wind and PV; in addition to 3 Battery Storage Devices (BSDs) to design an effective EMS for the NG system and decrease its overall costs. For this purpose, a Rule-Based EMS (RB-EMS) that employs State Flow (SF) to guarantee a safe and reliable operating power flow to the NG has been developed. In addition to that, a Genetic Algorithm (GA)-based optimization system and Simulated Annealing optimization Algorithm (SAA) were proposed to determine an economical solution for decreasing the cost of the NG system depending on its operational constraints. Lastly, comparison about the cost between RB-EMS, GA and SAA has been presented. According to the simulation results, the proposed GA displayed an economical performance since it could achieve a 40% cost saving whereas the SAA system showed a 19.3% cost saving compared to the RB-EMS. It can be concluded from the findings that the GA-based optimization technique was very cost-effective displays many important features, like rapid convergence, simple design, and very few controlling factors.

This research focuses on the application of the Direct Torque Control (DTC) strategy to manage the terminal voltage of a Self-excited induction generator (SEIG) that supplies power to an autonomous load. The SEIG is connected to a three-level Neutral Point Clamped (3L_NPC) converter. The utilization of the 3L_NPC converter ensures the increase in generated voltage levels, leading to improved current waveform quality and reducing the Total Harmonic Distortion (THD) in the stator currents. To ensure a more stable and dependable power supply, this study also introduces an Energy Storage System (EESS) consisting of batteries (BT) along with a management algorithm designed to oversee power flows between different storage devices. Furthermore, the dynamic model of the SEIG incorporates the saturation effect of magnetic materials and is conducted in the frame using the Concordia transform. The efficacy of the proposed control strategy is verified through simulation tests conducted in MATLAB/Simulink.

Due to its fewer components, the DC-DC three-phase converter has a simpler design and could be less expensive. However, it can present challenges in terms of precise voltage regulation and current balancing, due to the limited number of switching phases. On the other hand, the three-phase converter offers more precise voltage regulation and improved current balance owing to its higher number of phases. Although this results in increased design complexity and potentially higher cost, it allows for a more uniform distribution of current load among MOSFETs. The particular needs of the application, acceptable trade-offs between complexity, cost, and performance, as well as the requirement for precise voltage regulation and ideal current balancing, can determine which option is the best. This work investigates a three-phase interlaced DC converter with a parallel MOSFET. A two-way DC-DC converter was used to assess PWM when charging and discharging a battery. The results demonstrate a great DC voltage gain without a very high cycle load.

In this paper, a standalone photovoltaic (PV)-battery storage (BS) based microgrid (MG) is presented with a 415V-AC bus. The PV array is linked to the main voltage source converter (VSC1) DC-link to maximize power production in an efficient and cost-effective single-stage structure. This is achieved through an incremental conductance (INC) maximum power point (MPP) tracking approach. Moreover, a secondary converter (VSC2) is connected to BS for power flow management. MG performance is evaluated under various operating conditions. Steady-state and dynamic performances of MG are studied for variations such as changes in solar PV irradiance and load unbalances.

A Nanogrid (NG) model is described as a power distribution system that integrates Hybrid Renewable Energy Sources (HRESs) and Energy Storage Systems (ESSs) into the primary grid. However, this process is affected by several factors, like load variability, market pricing, and the intermittent nature of Wind Turbines (WTs) and Photovoltaic (PV) systems. Hence, other researchers in the past have used a few optimization-based processes to improve the development of Energy Management Systems (EMSs) and ESSs, which further enhanced the operational performance of NGs. It was seen that EMS acts as the distributed energy source in the NG setup and assists in power generation, usage, dissemination, and differential pricing. Hence this study employed the MATLAB Simulink software for modelling the grid-connected NG that included HRES; such as wind and PV; in addition to 3 Battery Storage Devices (BSDs) to design an effective EMS for the NG system and decrease its overall costs. For this purpose, a Rule-Based EMS (RB-EMS) that employs State Flow (SF) to guarantee a safe and reliable operating power flow to the NG has been developed. In addition to that, a Genetic Algorithm (GA)-based optimization system and Simulated Annealing optimization Algorithm (SAA) were proposed to determine an economical solution for decreasing the cost of the NG system depending on its operational constraints. Lastly, comparison about the cost between RB-EMS, GA and SAA has been presented. According to the simulation results, the proposed GA displayed an economical performance since it could achieve a 40% cost saving whereas the SAA system showed a 19.3% cost saving compared to the RB-EMS. It can be concluded from the findings that the GA-based optimization technique was very cost-effective displays many important features, like rapid convergence, simple design, and very few controlling factors. [ABSTRACT FROM AUTHOR]

DC micro-grids are gaining more attention and becoming more predominant due to its robustness and ease of control. To confront the stability issues of the DC Micro-grid system, a model free intellectual Control approach is proposed and recognized with a deep Q-learning controller to influence the functions of DC–DC boost converter. This self-reliant model of deep Q-learning controller establish a suitable communication link among the agent-environment by applying the reward/penalty algorithm for attaining the stable output voltage with its training statistics. Upon learning the state of agent, an optimal proposal is defined by the controller to attain the stabilized voltage at the output for all possible operating scenarios. The simulation and experimental studies validate the performance of the controller with higher accuracy, reduced overshoot, and faster settling time upon the transient states. [ABSTRACT FROM AUTHOR]

This work provides a novel model for solar PV – hydrogen (H 2) systems that uses weather data and electrical variables of the components to perform PV-H 2 design for different hybrid configurations. The objectives are to size and operate the systems optimally to reach a target production (Q H) and minimize cost of H 2. The component sizes and hydrogen production (Q H) are optimized by PV-EL direct coupling where the EL curves are determined by the number and area of EL cells connected to PV modules. The use of maximum power point (MPP) tracking increases the coupling factor (and Q H), but this gain is not significant vs. optimal PV-EL coupling. Battery assisted electrolysis has the advantage of reducing the EL size, showing how convenient is also to operate it at part loads (e.g., at night) so that the PV array and EL are larger to grant H 2 production, but the batteries are much more effective. The optimal configuration, design and operation will finally depend on the evolution of unit cost of the components (PV, MPPT, EL and batteries): direct coupling leads to a cost production of 5.03 Є / k g H 2 with 26 (20 cm2) EL cells per PV module; this is slightly increased by MPPT use (5.20 Є / k g H 2 ) and notably reduced with batteries (4.07 Є / k g H 2 ), but MPPT would be optimal if cost drops to 1/5 of base prices considered in this work and batteries are not favorable if cost per m2 of EL is reduced to less than 1/3 (depending on MPPT cost). • A new model for integrated hydrogen production with solar PV energy is proposed. • It considers different technologies, conditions of use and meteorological variations. • Optimum coupling of the electrolyzer and PV modules can be achieved. • Battery-assisted electrolysis for off-grid hydrogen production can be evaluated. • Economic analysis for customised designs that minimize hydrogen cost is provided. [ABSTRACT FROM AUTHOR]

Despite the unstoppable global drive towards electric mobility, the electrification of sub-Saharan Africa's ubiquitous informal multi-passenger minibus taxis raises substantial concerns. This is due to a constrained electricity system, both in terms of generation capacity and distribution networks. Without careful planning and mitigation, the additional load of charging hundreds of thousands of electric minibus taxis during peak demand times could prove catastrophic. This paper assesses the impact of charging 202 of these taxis in Johannesburg, South Africa. The potential of using external stationary battery storage and solar PV generation is assessed to reduce both peak grid demand and total energy drawn from the grid. With the addition of stationary battery storage of an equivalent of 60 kWh/taxi and a solar plant of an equivalent of 9.45 kW pk /taxi, the grid load impact is reduced by 66%, from 12 kW/taxi to 4 kW/taxi, and the daily grid energy by 58% from 87 kWh/taxi to 47 kWh/taxi. The country's dependence on coal to generate electricity, including the solar PV supply, also reduces greenhouse gas emissions by 58%. [ABSTRACT FROM AUTHOR]

As the residential sector has a significant environmental impact, there is growing pressure to move away from heavy reliance on fossil fuels and towards renewable technologies. One approach to avoid burning fossil fuels in this sector is a hybrid solar-hydrogen energy system based on photovoltaic panels for electricity supply, a battery for short-term storage, and a hydrogen system consisting of a fuel cell, electrolyzer, and hydrogen tank for long-term storage. This study uses the life cycle assessment method to analyze the environmental impact of such a system and compares the results to a reference system with no hydrogen components in both off-grid and grid-connected scenarios. The results show that the solar-hydrogen system causes lower greenhouse gas emissions in the off-grid scenario. Overall, it is significantly beneficial in 6 out of 16 impact categories. In contrast, the off-grid reference system, equipped with a diesel generator as a backup power source, has lower burdens in 7 impact categories and the single score result. The production of the hydrogen tank is identified as the primary driver of emissions of the solar-hydrogen system. In the on-grid scenario, the grid-connected reference system is beneficial in all impact categories. • LCA of a solar-hydrogen energy system for buildings. • Combination of a Matlab Simulink simulation and LCA. • Dynamic electricity generation data used in the LCA model. • Solar-hydrogen system causes lower greenhouse gas emissions in off-grid scenario. • Hydrogen tank is identified as main driver of emissions. [ABSTRACT FROM AUTHOR]

The reliability of the microgrid supply is often relying on the energy storage mediums used. Hydrogen is considered as a long-term energy storage solution whereas battery provides the energy for short-term. Fuel cell converts the stored hydrogen energy into electricity. As these storage mediums have low inertia in the system, there can be excessive current stress on the devices, leading to degradation. Virtual inertia can improve the behavior of the devices during load variation. These storage devices can be modeled as virtual machines, and these virtual inertia-based devices can provide a delayed response to the load variation. Therefore, the life of the storage mediums can be improved. In this paper, a DC microgrid system has been studied, having the photovoltaic power generator, battery, supercapacitor, and fuel cell as the other power sources. The photovoltaic generator works in MPPT mode and generates electricity to meet the load demand. The virtual inertia-based control embedded with each kind of storage medium enables the limitation of the sudden variation of power. The fuel cell provides the rated power with a certain delay w.r.t. battery and supercapacitor so that it has less dynamic power stress. The sudden variation in the load demand is met by the supercapacitor, and the battery provides the remaining power owing the high inertia as compared to the supercapacitor. The power allocation among each kind of storage medium takes into account the hydrogen storage state of charge also. The simulation results have been obtained by using the OPAL-RT to verify the effectiveness of the control strategy in real-time. The rate of power change for the fuel cell is achieved as ≈ 160 W/s. Whereas the rate of power change of the battery and supercapacitor are found to be ≈ 321 W/s, and ≈ 626 W/s, respectively. The DC bus voltage is well regulated to the nominal value in the transient load condition. [ABSTRACT FROM AUTHOR]

Over the period 2016--2021 Australia's National Electricity Market (NEM) experienced an investment supercycle with 16,000MW of new utility-scale renewable plant commitments in a power system with a peak demand of 35,000MW, and the disorderly loss of 5,000MW of synchronous coal-fired plant. This placed strains on system security, most visibly in the distribution of the power systems' frequency, requiring material changes to the NEM's suite of Frequency Control Ancillary Service (FCAS) markets. Utility-scale batteries are ideally suited for FCAS duties, but there is no forward price curve for FCAS markets, nor is there any systematic framework for determining equilibrium prices that might otherwise be used for investment decision-making. In this article, we develop an approach for quantifying long run equilibrium costs and stochastic spot prices in the markets for Frequency Control Ancillary Services, with the intended application being to guide the suitability of utility-scale battery investments under conditions of uncertainty and missing forward FCAS markets. [ABSTRACT FROM AUTHOR]

Energy demand is growing exponentially due to industrialization and population growth, and existing conventional energy systems (CES) cannot meet it totally due to the limited availability of power supply. Furthermore, CES has a substantial environmental impact. In comparison to CES, hybrid renewable energy systems (HRES) are a suitable combination of renewable and non-renewable energy systems that consider the system's benefits, allowing for lower system implementation and maintenance costs, lower emissions levels, and improved overall system reliability, and so forth. Therefore, this article offers an extensive review of the research conducted on numerous aspects that must be considered while developing and implementing an HRES. The numerous aspects of HRES considered are configuration, modeling, storage technologies, design parameters, sizing methodologies, energy management, control, and sensitivity analysis. This research assists the designer in choosing the best HRES configuration, appropriate energy management and HRES control. In addition, the selection of various design parameters, and sensitivity parameters for performance evaluation of HRES. Further the selection of mode of operation (off-grid or grid-connected modes) of HRES depending on the location's requirements. [ABSTRACT FROM AUTHOR]

E-mobility represents an important part of the EU's green transition and one of the key drivers for reducing CO2 pollution in urban areas. To accelerate the e-mobility sector's development it is necessary to invest in energy infrastructure and to assure favorable conditions in terms of competitive electricity prices to make the technology even more attractive. Large peak consumption of parking lots which use different variants of uncoordinated charging strategies increases grid problems and increases electricity supply costs. On the other hand, as observed lately in energy markets, different, mostly uncontrollable, factors can drive electricity prices to extreme levels, making the use of electric vehicles very expensive. In order to reduce exposure to these extreme conditions, it is essential to identify the optimal way to supply parking lots in the long term and to apply an adequate charging strategy that can help to reduce costs for end consumers and bring higher profit for parking lot owners. The significant decline in photovoltaic (PV) and battery storage technology costs makes them an ideal complement for the future supply of parking lots if they are used in an optimal manner in coordination with an adequate charging strategy. This paper addresses the optimal power supply investment problem related to parking lot electricity supply coupled with the application of an optimal EV charging strategy. The proposed optimization model determines optimal investment decisions related to grid supply and contracted peak power, PV plant capacity, battery storage capacity, and operation while optimizing EV charging. The model uses realistic data of EV charging patterns (arrival, departure, energy requirements, etc.) which are derived from commercial platforms. The model is applied using the data and prices from the Croatian market. [ABSTRACT FROM AUTHOR]

Telecom services play a vital role in the socio-economic development of a country. The number of people using these services is growing rapidly with further enhance growth expected in future. Consequently, the number of telecom towers that are critical for providing such services has also increased correspondingly. Such an increase in the number of telecom towers in a country implies a corresponding increase in the electricity demand of the country. Moreover, in view of growing concerns about climate change, it is necessary that the decarbonization of telecom towers is prioritized so as to minimize their carbon footprint. In view of the above, the primary objective of this paper is to provide a comprehensive analysis of various renewable energy-based systems and the advantages they offer for powering telecom towers, based on a review of the existing literature and field installations. Telecom towers are powered by hybrid energy systems that incorporate renewable energy technologies such as solar photovoltaic panels, wind turbines, fuel cells, and microturbines. Utilizing these systems helps to reduce the consumption of fossil fuels and consequently mitigates the anthropogenic carbon emissions. Moreover, information related to growth of the telecom industry, telecom tower configurations and power supply needs, conventional power supply options, and hybrid system combinations and their benefits. Several field installations of renewable energy-based hybrid systems have also been summarized. This review can help to evaluate appropriate low-carbon technologies and also to develop policy instruments to promote renewable energy-based telecom tower power systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przeglad Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper conducts a comprehensive analysis of offshore wind-photovoltaic hybrid systems, presenting autonomous components tailored for maritime applications. Focusing on the critical role of these systems in supplying electricity to regions with limited infrastructure, particularly maritime zones, the study explores economic advantages over traditional power line installations, emphasizing remote areas. Key components, including battery storage, autonomy, battery selection (with a spotlight on lead-acid batteries), charge controllers, inverters, and the integration of Automatic Identification System (A.I.S.) for maritime safety, are discussed. The article concludes by underscoring the significance of power supply buoys for ship safety and efficient refueling near hybrid systems. [ABSTRACT FROM AUTHOR]

Three different types of MPPT techniques will be studied and compared in this work. MPPT is a technology for maximizing the power produced by solar energy systems. Fuzzy logic-based control (FLC), one of the most widely utilized MPPT approaches, is examined, perturbation and monitoring (P&O), and plug-in (Inc). The power and effectiveness of a solar energy system with lead-acid batteries for storage, an energy management plan, and associated photovoltaics In order to confirm the benefit, FL-MPPT was examined in this study, and the performance of its algorithm was compared and evaluated. Using both techniques, P&O and Inc. Using Matlab/Simulink 2018-a tools, simulation results showed that the power quality was higher using MPPTFLC. [ABSTRACT FROM AUTHOR]

Energy storage, analogous to data storage in a computer system, is one of the enabling technologies that has emerged alongside the widespread use of renewable energy sources in the nation's power grid. This article shows that the underlying platforms for storing data and energy are quite similar. Batteries and hydrogen storage offer significant energy potential, much like a hard disk for storing vast amounts of data in a computer's central processing unit (CPU). A supercapacitor or flywheel storage device can be used to have emergency power on hand, with access times as fast as random access memory (RAM) in modern computers. In this study, we propose an energy-control scheme for caches that is akin to computer engineering and is used to coordinate the operation of multilevel storage systems that incorporate both capacity and access-oriented storage. By supporting the energy-management system, which in turn provides modern plug-and-play functionality, cache energy control helps optimise the system as a whole. Such an integrated system calls for renewable energy generation, local loads, fueling stations, and connections to gas and electric distribution grids. Distribution energy concepts with various storage systems can be easily grasped by drawing parallels between computer engineering and power system integration. [ABSTRACT FROM AUTHOR]

Abstract Unified power quality conditioner is chiefly employed to offer power quality improvement, especially in grid connected mode of operation in microgrid applications. This article proposes an artificial ecosystem optimized neural network for control of photovoltaic system and battery powered UPQC for microgrid applications. The intelligent routine implemented by the proposed controller helps tune parameters such as the error between load voltage references and measured load voltage signals so that the optimal performance of the system can be reached as its exploratory and exploitation capabilities are leveraged in controller design. A prototype of a three-phase system with a dually powered conditioner is tested and validated in MATLAB-Simulink environment in a variety of dynamic scenarios that are commonly present in a contemporary distribution network, such as grid voltage changes, grid inaccessibility, variation in photovoltaic power output, and nonlinear load. It is shown that the proposed controller, being aware of the instantaneous values of grid voltages, was able to adequately compensate in magnitude and phase under all dynamic scenarios to maintain the load voltage constant at the nominal value and sinusoidal. When the system switches automatically from grid-connected mode to islanded mode due to a grid fault, it was observed that the controller prioritizes delivering uninterrupted power to critical loads and enables fast discharge from the battery. The total harmonic distortion percentages of grid currents and load voltages are found to be within the limits as per IEEE-519 standards.

To facilitate the integration of renewable energy in electric power systems, the installed capacity of distributed renewable energy sources has been increased rapidly worldwide in recent years, and the prosumers and prosumer aggregators have become important players in smart grid. To maximize the economic benefits of a prosumer aggregator in power markets, a stochastic model used for generating the day-ahead (DA) energy and operating reserve scheduling strategies is proposed. The uncertainties associated with solar power productions, demands and prices in energy and operating reserve markets are handled via stochastic optimization technique. To manage the potential risks faced by the prosumer aggregator, the conditional value at risk is adopted to measure and manage its potential low profits or losses. Finally, case studies are performed by using the data in the energy and operating reserve markets operated by the Southwest Power Pool (SPP), and the optimal energy and operating reserve bids are generated effectively by solving the proposed model. The values of the operating reserve markets and battery storages are analyzed in detail via simulation, and it shows that higher reserve prices and larger battery storage capacity would lead to higher expected profit and lower risks for the prosumer aggregator.

Microgrids are a promising solution for decentralized energy generation and distribution, offering reliability, efficiency, and resilience. These small-scale power systems can operate independently or connect to the main grid, providing greater reliability and resilience. However, integrating renewable energy into microgrids presents challenges due to their unpredictable nature and fluctuating load of electricity. Energy management strategies play a crucial role in optimizing the operation of microgrids, aiming to balance electricity supply and demand, maximize renewable energy utilization, and minimize operational costs. Various approaches have been proposed for energy management in microgrids, including optimization algorithms, machine learning techniques, and intelligent control systems. This study proposes an optimized and efficient strategy for microgrids operating in both independent and grid-connected modes, focusing on microgrids that utilize a combination of solar and green energy sources. The proposed approach, based on the Promoted Remora Optimization (PRO) algorithm, aims to meet load power requirements at the lowest possible cost while ensuring constant DC bus voltage and safeguarding batteries against overcharging and depletion. The CRO method effectively optimized the charging process, maintaining a consistent level of charge and achieving a final SoC of 33.37 %–33.60 %. It also demonstrated high system efficiency, with an average of 87.99 %, and a range of 87.80 %–88.03 %. The optimizer efficiency ranged from 83.12 % to 86.52 %, with an average of 86.46 %. The CRO method also achieved reasonable operating costs, with a cost per power of $0.1687/kW to $0.1699/kW and a daily cost of $1,379,595 to $1,479,998. Overall, the CRO method showed promise in optimizing the charging process in terms of efficiency and cost-effectiveness. Comparative analysis with existing literature is conducted to evaluate the effectiveness of the proposed approach, demonstrating its superior results compared to other energy management strategies for microgrids. This study contributes to the field of microgrid energy management by providing a novel approach based on the PRO algorithm and demonstrating its effectiveness through comparative analysis.

Abstract Large‐scale penetration of renewable resource based generation will displace conventional synchronous machine‐based power generation. This will lower the overall system inertia and will result in lower short circuit levels at point of connection (POC) of renewable resources. Renewable resources such as wind and solar photovoltaic (PV) are interfaced to the power system via power electronic inverters. Operation of inverter‐based equipment under low inertia and low short circuit conditions is challenging. Specific challenges include fault recovery response, unstable oscillatory interactions, and impact on torsional oscillations of thermal generating units. In this paper, the effectiveness of inverters, controlled as a virtual synchronous machine (VSM), to overcome some of these issues is investigated. A battery energy storage system (BESS) inverter is controlled as a VSM. The input signals provided to the BESS inverter are derived from solving the time domain equations of a synchronous machine. The response of exciter, governor, and power system stabilizer (PSS) are also included in the VSM strategy. The dynamic characteristics of the proposed VSM are verified using a single machine infinite bus (SMIB) test model. The effectiveness of the proposed VSM approach to mitigate specific technical challenges is verified by performing dynamic response studies on two test systems. The two test systems represent selected regions of practical power systems with high renewable penetration. The VSM response is compared with a conventional synchronous machine response in all test cases, to demonstrate the close correlation of dynamic response. The simulation results demonstrate that the VSM's response is as expected and is capable of mitigating stability‐related issues due to low system inertia and low short circuit strength.

Nearly zero-emission buildings (nZEBs) are increasingly being constructed in Europe. There are also incentives to refurbish older buildings and transform them into nZEBs. However, permission is not always granted for their connection to the grid to infuse surplus photovoltaic electricity due to the grid being overloaded with a large number of renewables. In this study, the case of a refurbished school building in Central Greece is examined. After refurbishing it, a significant amount of photovoltaic electricity surplus is observed during the summer and neutral months, which cannot be exported to the grid. The absence of an adequate battery storage capacity resulted in the rejection of an application for exporting the school’s surplus to the network and the photovoltaic installation staying idle. An alternative approach is proposed in this work, involving a shift in the export of the photovoltaic electricity surplus to the evening hours, in order for the school to be granted permission to export it to the network. To this end, an optimal battery storage size is sought by employing a building energy system simulation. The mode of operation of the battery designed for this application is set to discharge daily, in order to export the electricity surplus in the afternoon hours to the evening hours, when it is favorable for the network. Additionally, the optimal size of the thermal energy storage of the heating system is studied to further improve its energy efficiency. Our battery and storage tank size optimization study shows that a significant battery capacity is required, with 12 kWh/kWp photovoltaic panels being recommended for installation. The ever-decreasing cost of battery installations results in the net present value (NPV) of the additional investment for the battery installation becoming positive. The solution proposed forms an alternative path to further increase the penetration of renewables in saturated networks in Greece by optimizing battery storage capacity.

Integrating residential energy storage and solar photovoltaic power generation into low-voltage distribution networks is a pathway to energy self-sufficiency. This paper elaborates on designing and implementing a 3 kW single-phase grid-connected battery inverter to integrate a 51.2-V lithium iron phosphate battery pack with a 220 V 50 Hz grid. The prototyped inverter consists of an LCL-filtered voltage source converter (VSC) and a dual active bridge (DAB) DC-DC converter, both operated at a switching frequency of 20 kHz. The VSC adopted a fast DC bus voltage control strategy with a unified current harmonic mitigation. Meanwhile, the DAB DC-DC converter employed a proportional-integral regulator to control the average battery current with a dynamic DC offset mitigation of the medium-frequency transformer’s currents embedded in the single-phase shift modulation scheme. The control schemes of the two converters were implemented on a 32-bit TMS320F280049C microcontroller in the same interrupt service routine. This work presents a synchronization technique between the switching signal generation of the two converters and the sampling of analog signals for the control system. The prototyped inverter had an efficiency better than 90% and a total harmonic distortion in the grid current smaller than 1.5% at the battery power of ±1.5 kW.

The establishment of near-autonomous micro-grids in commercial or public building complexes is gaining increasing popularity. Short-term storage capacity is provided by means of large battery installations, or, more often, by the employees’ increasing use of electric vehicle batteries, which are allowed to operate in bi-directional charging mode. In addition to the above short-term storage means, a long-term storage medium is considered essential to the optimal operation of the building’s micro-grid. The most promising long-term energy storage carrier is hydrogen, which is produced by standard electrolyzer units by exploiting the surplus electricity produced by photovoltaic installation, due to the seasonal or weekly variation in a building’s electricity consumption. To this end, a novel concept is studied in this paper. The details of the proposed concept are described in the context of a nearly Zero Energy Building (nZEB) and the associated micro-grid. The hydrogen produced is stored in a high-pressure tank to be used occasionally as fuel in an advanced technology hydrogen spark ignition engine, which moves a synchronous generator. A size optimization study is carried out to determine the genset’s rating, the electrolyzer units’ capacity and the tilt angle of the rooftop’s photovoltaic panels, which minimize the building’s interaction with the external grid. The hydrogen-fueled genset engine is optimally sized to 40 kW (0.18 kW/kWp PV). The optimal tilt angle of the rooftop PV panels is 39°. The maximum capacity of the electrolyzer units is optimized to 72 kW (0.33 kWmax/kWp PV). The resulting system is tacitly assumed to integrate to an external hydrogen network to make up for the expected mismatches between hydrogen production and consumption. The significance of technology in addressing the current challenges in the field of energy storage and micro-grid optimization is discussed, with an emphasis on its potential benefits. Moreover, areas for further research are highlighted, aiming to further advance sustainable energy solutions.

Abstract The presence of turbulent oscillations results in instability within power systems. These oscillations, while not directly impacting the synchronization of machinery, can incite specific oscillatory mode, causing voltage imbalance, and desynchronization of the machine. This research introduces a novel control approach in the form of a Static synchronous compensator (STATCOM) with a battery storage, tuned by a Whale Optimization Algorithm (WOA) to alleviate and dampen the turbulence of the system. The versatile controller comprising two PI controllers (AC and DC voltage regulators) manages the control signals via a current regulator using PWM (Pulse width modulation). The battery integrated with the VSC (Voltage source converter) is meant to operate as a stable voltage source, while the control block ensures to maintain the battery voltage. The system is exposed to the most prominent perturbances like varying electric torque and changing reference voltages. Zero natural damping has been considered to aggravate the system conditions so that the efficacy of the proposed controller could be truly examined. The analysis of rotor dynamics has been conducted under various conditions, including no control, STATCOM alone, and WOA-based STATCOM. The results demonstrate the mitigation capabilities of the proposed controller while operating under the worst scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a novel real-time hybrid battery state of charge (SoC) and state of health (SoH) estimation technique with less computational effort for optimal operation in renewable energy integrated microgrid applications. The proposed SoC estimation technique utilizes battery terminal voltage and current information along with stress factors like battery charge–discharge rates and temperature effects to accurately estimate the SoC. In addition, it considers the open-circuit voltage (OCV) and SoC relation to dynamically recalibrate the SoC during idle conditions. The proposed SoH estimation technique uses a modified coulomb counting method and variation of battery capacity at different charge–discharge rates to precisely estimate the SoH of the battery. Simulation studies are carried out by considering the aging factor, temperature effect, and charge–discharge rates to analyze the performance of the proposed techniques under various dynamic conditions. A LabVIEW-based application is developed, and experimental verification in terms of estimation accuracy, real-time monitoring is carried out to verify the efficacy of the proposed technique. A comparative analysis with state-of-the-art estimation techniques is presented for validating the effectiveness and usefulness in real-time applications. [ABSTRACT FROM AUTHOR]

Due to climate change and the increasing scarcity of resources, the sustainability performance of companies is increasingly becoming the focus of science and practice. Consequently, bicriteria energy-efficient production planning under price-dynamic electricity tariffs—e.g., real-time-pricing (RTP) or time-of-use (TOU)—is meanwhile well established, often fathoming the tradeoffs between electricity costs of production and another criterion such as makespan. However, tradeoffs between electricity costs and electricity consumption in general are rarely the focus of such analyses. So-called green power purchase agreements (PPAs), which are becoming increasingly popular in the European business community as a means of improving corporate sustainability performance, are also largely ignored. Thus, for the first time in the scientific literature, we put this type of electricity tariff to the test by analyzing the tradeoffs between electricity costs and electricity consumption in a lot-sizing and scheduling context. Here, we additionally consider a real-world redox flow battery storage system that may be the system of the future, which is also new to the literature on lot-sizing and scheduling. Even more: due to the complex nature of our bicriteria mixed-integer problem, we develop and present suitable heuristics. These include an energy-efficient allocation heuristic in the case of PPA and, among others, a fix-relax-and-optimize heuristic combined with a decomposition approach in the case of RTP and TOU. Ultimately, a scenario analysis demonstrates the performance of these heuristics. [ABSTRACT FROM AUTHOR]

Hydrogen energy is regarded as a key path to combat climate change and promote sustainable economic and social development. The fluctuation of renewable energy leads to frequent start/stop cycles in hydrogen electrolysis equipment. However, electrochemical energy storage, with its fast response characteristics, helps regulate the power of hydrogen electrolysis, enabling smooth operation. In this study, a multi-objective constrained operation optimization model for a wind/battery storage/alkaline electrolyzer system is constructed. Both profit maximization and power abandonment rate minimization are considered. In addition, some constraints, such as minimum start/stop times, upper and lower power limits, and input fluctuation limits, are also taken into account. Then, the non-dominated sorting genetic algorithm II (NSGA-II) algorithm and the entropy method are used to optimize the operation strategy of the hybrid energy system by considering dynamic hydrogen production efficiency, and through optimization to obtain the best hydrogen production power of the system under the two objectives. The change in dynamic hydrogen production efficiency is mainly related to the change in electrolyzer power, and the system can be better adjusted according to the actual supply of renewable energy to avoid the waste of renewable energy. Our results show that the distribution of Pareto solutions is uniform, which indicates the suitability of the NSGA-II algorithm. In addition, the optimal solution indicates that the battery storage and alkaline electrolyzer can complement each other in operation and achieve the absorption of wind power. The dynamic hydrogen production efficiency can make the electrolyzer operate more efficiently, which paves the way for system optimization. A sensitivity analysis reveals that the profit is sensitive to the price of hydrogen energy. [ABSTRACT FROM AUTHOR]