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Solar electricity has become one of the most important renewable power sources due to rapid developments in the manufacturing of photovoltaic (PV) cells and power electronic techniques as well as the consciousness of environmental protection. In general, PV panels are connected to DC-DC converters and/or DC-AC inverters to implement the maximum power point tracking algorithm and to fulfill the load requirements. Thus, power conversion efficiency and power density need to be taken into consideration when designing PV systems. Three-port and partial power conversion technologies are proposed to improve the efficiency of a whole PV system and its power density. In this paper, three types of three-port converters (TPCs), including fully isolated, partly isolated, and non-isolated TPCs, are studied with detailed discussions of advantages, disadvantages, and comparisons. In addition, based on partial power conversion technologies, partial power two-port and three-port topologies are analyzed in detail. Their efficiency and power density can be further improved by the combination of three-port and partial power conversion technologies. Moreover, comparisons among seven different types of distributed PV systems are presented with their advantages and disadvantages. Compared to distributed PV systems without energy storage, distributed PV systems with hybridization of energy storage and with partial power regulation can use solar energy in a more efficient way. [ABSTRACT FROM AUTHOR]

The incorporation of clean sources into the power grid, along with the introduction of power electronic gadgets to regulate loads that are nonlinear, had a significant effect on power quality (PQ). Nevertheless, PQ emerges as the primary issue for the integration of these intermittent renewable sources. This study combines the advantageous characteristics of the Sliding mode controller (SM-C) and fractional-order proportional integral derivative controller (FPIDC) to manage both the shunt and series active filters of the Unified Power Quality Conditioner (UPQC) integrated with the wind power system (WPS) and battery systems (BS). The primary objective of the proposed system is to maintain a consistent voltage across the DC Link capacitor, even when there are fluctuations in load and wind velocity. Additionally, the system aims to reduce the presence of harmonics in waveform of current signal, improve power factor (PF), and mitigate any disruptions or imbalances in the source voltage. The effectiveness of the created model has been assessed by four case studies involving diverse load types and grid voltage situations, including fluctuations. Besides, to validate the developed method the comparative analysis is conducted with respect to other methods like PI and fuzzy logic controllers, as well as with other ways documented in the literature. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a rule‐based energy management system (EMS) designed for a standalone DC microgrid incorporating solar photovoltaic (PV), fuel cell, battery energy storage system (BESS), and electric vehicle. The unpredictable nature of renewable energy sources and the instability of loads pose challenges for maintaining DC bus voltages and power‐sharing arrangements, impacting the microgrid's smooth operation. The proposed EMS aims to ensure power balance between generation and demand, mitigating vulnerabilities of the DC bus to voltage instability caused by fluctuations from both the load and source sides. This is achieved through an autonomous DC bus voltage stabilization strategy, involving the maintenance of a nominal state of energy (SoE) for the BESS and hydrogen fuel consumption for the fuel cell within predefined lower and upper limits. By regulating these two factors, the EMS algorithm facilitates optimal performance of the PV, battery, and fuel cell components. Consequently, the EMS provides decision‐making instructions to each individual energy source, ensuring efficient operation under various conditions. The effectiveness of the proposed EMS is evaluated through hardware‐based testing on a DC microgrid and simulations in the MATLAB Simulink environment across multiple operating scenarios.

In order to mitigate the impact of the intermittent nature of renewable energy sources on the safe and secure operation of the power grid, and to enhance energy efficiency, the application of energy storage systems is a preferred solution that received much attention in many countries. This paper presents a strategy for managing and controlling the charging and discharging modes of a battery storage system based on ensuring the load requirements for consumers who have installed rooftop photovoltaic (PV) systems. The control algorithm is simulated to implement the charging mode of the battery during surplus solar power periods and off-peak hours, and the discharging mode of the battery during peak hours to reduce electricity costs for households and level the load profile. Simulation results show that, the payment of electricity has been significantly reduced which confirms the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a rule‐based energy management system (EMS) designed for a standalone DC microgrid incorporating solar photovoltaic (PV), fuel cell, battery energy storage system (BESS), and electric vehicle. The unpredictable nature of renewable energy sources and the instability of loads pose challenges for maintaining DC bus voltages and power‐sharing arrangements, impacting the microgrid's smooth operation. The proposed EMS aims to ensure power balance between generation and demand, mitigating vulnerabilities of the DC bus to voltage instability caused by fluctuations from both the load and source sides. This is achieved through an autonomous DC bus voltage stabilization strategy, involving the maintenance of a nominal state of energy (SoE) for the BESS and hydrogen fuel consumption for the fuel cell within predefined lower and upper limits. By regulating these two factors, the EMS algorithm facilitates optimal performance of the PV, battery, and fuel cell components. Consequently, the EMS provides decision‐making instructions to each individual energy source, ensuring efficient operation under various conditions. The effectiveness of the proposed EMS is evaluated through hardware‐based testing on a DC microgrid and simulations in the MATLAB Simulink environment across multiple operating scenarios. [ABSTRACT FROM AUTHOR]

Fuel cell electric vehicles (FCEVs) hold great promise for achieving sustainable and environmentally friendly transportation. However, their widespread adoption depends on the efficient development of the hydrogen supply chain (HSC) required for refueling these vehicles. Given the interdependence between the HSC and power distribution networks (PDNs), this paper presents an innovative model to jointly plan the HSC and reinforce PDNs. In this new model, the inherent uncertainties related to wind energy generation, load demand, and FCEV consumption are modeled. Furthermore, coordinating the planning of hydrogen storage and battery energy storage systems is incorporated. The results underscore the critical importance of simultaneous planning for the HSC and PDN reinforcement. Moreover, the findings reveal that hydrogen storage alone can provide sufficient energy arbitrage, leading to a 9.6% reduction in total costs compared to the scenario where storage is disregarded. [Display omitted] • A novel planning model for hydrogen supply chains and power distribution networks. • Both hydrogen storage and battery energy storage are included. • Uncertainties related to wind generation and loads are modeled. • Model results show that simultaneous planning cuts costs by 25.9%. • Hydrogen storage alone reduces costs by 9.6%. [ABSTRACT FROM AUTHOR]

Abstract Although several studies have been performed on energy systems, there is a gap in examining the impact of energy storage systems (ESSs) technologies on the technoeconomic design of optimal multicarrier microgrids (MCMGs), considering environmental concerns. This paper aims to address this research gap by developing an optimal technoeconomic reliability‐oriented design of MCMGs, specifically focusing on battery storage systems (BSSs) and hydrogen storage systems (HSSs). The study was applied to MCMG at an actual MCMG, using HOMER Pro software. Additionally, a reliability‐oriented sensitivity analysis is conducted to understand the effects of reliability constraints, such as desired maximum capacity shortage, on the performance of various ESS technologies. One of the main contributions is analyzing the HSS and BSS from different viewpoints, such as cost, emission, and other technical–economic features for MCMGs. The comparative test results show that if a highly reliable MCMG is desired, the BSS‐based MCMG is the more practical option in terms of technoeconomic indices. However, regardless of reliability constraints, the HSS‐based MCMGs offer better environmental conditions. The total net present cost of the HSS‐based MCMG is 1.68% higher than the BSS‐based one, while it has 17.99% less CO2 emissions, while the HSS one has 17.99% less CO2 emissions.

A hybrid energy system is made up of intermittent, nonlinear, and fluctuating renewable energy sources like wind and solar. The cost of implementing and maintaining hybrid energy system can be a significant drawback, particularly due to the high upfront investment required for renewable energy infrastructure and energy storage technologies. The demand for electricity, the quantity of energy generated, the climate, and power management are some of the variables that affect how well HES performs. A hybrid renewable power system's energy management is proposed in this manuscript. The proposed method is Mother Optimization Algorithm (MOA). The system is made up of several components that use renewable energy sources, including a solar energy conservation system (SECS), battery storage system (BSS) and a wind energy conservation system (WECS). The proposed method's primary aim is to improves the system's efficiency and to lessen the system's operating expenses. Additionally, an energy management (EM) based on the battery state of charge is established in order to maintain an energy balance in the hybrid power system. The proposed MOA approach controls the DC/AC converter. The specific contributions include enhancing system efficiency and reducing operating expenses in hybrid renewable power systems using the MOA technique and also the practical implications involve promoting economic viability and environmental sustainability while advancing the transition to cleaner energy systems. By then, the operational MATLAB platform has adopted the proposed model, and the current processes are used to calculate its execution. Compared to all other ways, the proposed strategy yields better outcomes like Genetic Algorithm (GA), Mixed Integer Linear Problem and Particle Swarm Optimization. The proposed method cost is 7100$ and the existing methods cost are 8100$, 1100$, 9100$. From the result, the proposed method displays the cost is lower compared to existing methods. [ABSTRACT FROM AUTHOR]

A novel integrated DC-DC converter is proposed for the first stage of two-stage grid connected photovoltaic (PV) systems with energy storage systems. The proposed three-port converter (TPC) consists of a buck–boost converter, interposed between the battery storage system and the DC-AC inverter, in series with PV modules. The buck–boost converter in the proposed TPC is utilized for maximum power point tracking by regulating two power switches. The output power of the proposed converter is regulated by controlling the DC-AC converter. During the battery-charging mode, partial power regulation is employed with a direct power flow path (the series-connection of the PV panel, the battery and the output). As resistances in this path are almost negligible, the power conversion efficiency is higher than existing topologies. During battery-discharging mode, the power conversion is processed through a buck–boost converter with only two active power switches and one inductor. With fewer components, higher power conversion efficiency is also achieved. The circuit operation and analysis are presented in detail. To illustrate the simplicity of the converter control, the performance of the converter is tested with a straightforward maximum power point tracking on a PV system with battery cells. Simulation and experimental tests are carried out to demonstrate circuit operation and power conversion efficiency. [ABSTRACT FROM AUTHOR]

Although several studies have been performed on energy systems, there is a gap in examining the impact of energy storage systems (ESSs) technologies on the technoeconomic design of optimal multicarrier microgrids (MCMGs), considering environmental concerns. This paper aims to address this research gap by developing an optimal technoeconomic reliability‐oriented design of MCMGs, specifically focusing on battery storage systems (BSSs) and hydrogen storage systems (HSSs). The study was applied to MCMG at an actual MCMG, using HOMER Pro software. Additionally, a reliability‐oriented sensitivity analysis is conducted to understand the effects of reliability constraints, such as desired maximum capacity shortage, on the performance of various ESS technologies. One of the main contributions is analyzing the HSS and BSS from different viewpoints, such as cost, emission, and other technical–economic features for MCMGs. The comparative test results show that if a highly reliable MCMG is desired, the BSS‐based MCMG is the more practical option in terms of technoeconomic indices. However, regardless of reliability constraints, the HSS‐based MCMGs offer better environmental conditions. The total net present cost of the HSS‐based MCMG is 1.68% higher than the BSS‐based one, while it has 17.99% less CO2 emissions, while the HSS one has 17.99% less CO2 emissions. [ABSTRACT FROM AUTHOR]

The paper presents the design and implementation of decentralized control for a PV–wind–battery hybrid off-grid system with limited power electronics devices and sensors. To perform well without using any maximum power point tracking (MPPT) technique from the wind turbine (WT) based on a permanent-magnet brushless DC generator (PMBLDCG) and solar panels (PVs) and balance the power in the system, a cascade control structure strategy based on a linear active disturbance rejection controller (LADRC) is developed for the two-switch DC-DC buck-boost converter. Moreover, to ensure an uninterruptible power supply to the connected loads with a constant voltage and frequency, a cascade d-q control structure based on LADRC is developed for the interfacing single-phase inverter. Furthermore, the modeling and controller parameters design are presented. The performance under all operation conditions of the hybrid off-grid configuration and its decentralized control is validated by simulation using MATLAB/Simulink and in real-time using a small-scale hardware prototype. [ABSTRACT FROM AUTHOR]

Wind energy conversion systems (WECS) are considered to be a promising alternative to traditional power generation systems. The principal purpose of this study is to enhance the performance of a stand-alone wind-driven five-phase PMSG system. The initial step involves providing a clear explanation of the system model, which includes the wind turbine, five-phase PMSG, and a battery storage system. Then, the maximum power point tracking (MPPT) and pitch angle control (PAC) technique is employed to optimize the power delivered to an isolated load. Furthermore, the system incorporates the machine-side converter (MSC) and battery converter, which require control to efficiently regulate the power delivered from them to an isolated load. In addition, the MSC is regulated by two predictive control algorithms: predictive torque control (PTC) and a newly formulated predictive voltage control (PVC). The PTC faces limitations such as considerable ripple, significant load commutation, and the weighting factor of its cost functions. The proposed prediction technique aims to overcome these constraints by using a simple cost function and eliminating the need for weighting factors to address stability issues also another study's objective revolves around proposing a PVC controller that achieves an optimal design. The results demonstrate that the newly proposed predictive controller outperforms the conventional control method for the wind generator. The developed controller produces consistently distributed sinusoidal currents with significantly fewer ripples and current harmonics. This fact is verified mathematically as the total harmonic distortion (THD) is reduced to 1.168 % as an average value.

Solar electricity has become one of the most important renewable power sources due to rapid developments in the manufacturing of photovoltaic (PV) cells and power electronic techniques as well as the consciousness of environmental protection. In general, PV panels are connected to DC-DC converters and/or DC-AC inverters to implement the maximum power point tracking algorithm and to fulfill the load requirements. Thus, power conversion efficiency and power density need to be taken into consideration when designing PV systems. Three-port and partial power conversion technologies are proposed to improve the efficiency of a whole PV system and its power density. In this paper, three types of three-port converters (TPCs), including fully isolated, partly isolated, and non-isolated TPCs, are studied with detailed discussions of advantages, disadvantages, and comparisons. In addition, based on partial power conversion technologies, partial power two-port and three-port topologies are analyzed in detail. Their efficiency and power density can be further improved by the combination of three-port and partial power conversion technologies. Moreover, comparisons among seven different types of distributed PV systems are presented with their advantages and disadvantages. Compared to distributed PV systems without energy storage, distributed PV systems with hybridization of energy storage and with partial power regulation can use solar energy in a more efficient way.

In this article, the aim is to develop a model for efficient energy management using hybrid energy to power a drilling rig. This involves utilizing wind turbines and emergency generators, as well as charging battery storage systems with recycled energy from the depot through regenerative braking. The goal is to decrease the fuel consumption of diesel generators, while also reducing energy costs and environmental impact. The model takes into account the combined energy demand for diesel generators and battery charging as input. The results indicate that the proposed model can reduce operating costs and greenhouse gas emissions by over 25% compared to using only diesel generators. Analysis of 365 days in the last year reveals that three-quarters of operating costs could be saved due to random loading on the drilling rig. The investment payback period is estimated to be 1 year and 4 months. The proposed micro-drilling rig network with the optimal battery system and energy recovery has a lower life cycle cost of $1,500,000 with a $114,000 investment, compared to $5,400,000 for the baseline method. A reservation cost of $4,000,000 can be achieved within three-quarters of the specified period. For system performance analysis, a complete Model Power Supply of Drilling Rig is established on MATLAB/Simulink environment. Overall, this study demonstrates that the rig's generator load demand is reduced, leading to decreased environmental pollutants and operating costs. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of the abolition of annual net metering in Slovenia and compares the electric energy costs for the studied active user after the abolition. The article also provides an exploration of the role played by an aggregator, which serves as a central entity that enables individuals to participate in the electric energy market. An analysis of the case study of an active user was made, where an analysis was made of the measurements of household consumption and photovoltaic plant production for the year 2022. This article presents an economic analysis with and without net metering and an analysis of the aggregator involvement strategies. In addition, a battery energy storage system was also considered in the analysis. An important part of the article is the identification of the flexibility potential for shiftable loads, which enable an aggregator to acquire insight into the energy consumption profile and energy production profile of active users. The following indicators were used to compare the strategies: annual electric energy cost and the indicators including self-sufficiency, self-consumption, and grid dependency. The findings indicate that, even in the absence of annual net metering, the active user can lower their costs for electric energy with the help of an aggregator. [ABSTRACT FROM AUTHOR]

In most situations, fuel cells (FCs) are insufficient to supply power demands in hybrid electric vehicles (HEVs), thus battery storage systems (BSSs) are used to make the system more efficient like as rapid starting, high power density, and enhanced dynamic set response. How the power sources are regulated and distributed determines the accomplishment efficiency of the driving forces in such a configuration. The most critical aspect should be how to guarantee torque stability of the traction motor which is in our case a permanent magnet synchronous motor (PMSM) while attaining quick reaction. This study discusses a hybrid battery‐FCs energy storage and management system for a hybrid electric vehicle (HEV), as well as an integrated PMSM's passivity‐based control (PBC) technique to enable power integration and increase the hybrid electric vehicle (HEV)'s operating speed. The present paper is separated into two sections. First, a power management control (PMC) technology is used to manage the FCs‐battery system to guarantee that the HEV gets continuous power from the hybrid energy resources, where a fuzzy logic controller is used as an artificial intelligence system for the FCs converter to maximize the power produced. The second is dedicated to the new proposed PBC control of the EV's PMSM to rectify non‐linearities, external disturbances, and parametric fluctuations. The standard proportional‐integral (PI) control and sliding mode control (SMC) are built for comparison with the proposed PBC dedicated to the PMSM in order to properly assess the efficacy of the proposed control strategy. The simulation results obtained using MATALB/Simulink show that the proposed PBC technique and hybrid battery‐ proton‐exchange membrane PEMFCs energy management enables high power integration and increase the EV's operational speed. [ABSTRACT FROM AUTHOR]

The insertion of renewable energy sources into the grid, as well as the development of power electronics technology to regulate loads that are not linear, had an impact on power quality (PQ). This study focuses on the PQ enhancement of grid-connected and standalone solar PV systems (SPVS) with battery energy storage device (BESD) for the Electric vehicle (EV) charging station (EVCS) load in addition to the local load. Here, a hybrid control strategy that uses both the superior qualities of the sliding mode controller (SMC) and the fuzzy logic controller (FLC) is suggested for the unified power quality conditioner (UPQC's) shunt filter. It's major goal is to achieve steady DC capacitance voltage (SVDC) during load (like EVCS, non linear balanced/unbalanced etc.) and irradiation variations, diminish of total harmonic distortion (THD) in source current and load voltage, and mitigate sag, swell disturbances, and source voltage unbalances. The created model's performance is assessed using two scenarios (grid and island) under four case studies with varying combinations of loads and grid voltage circumstances. However, to establish the superiority of the proposed technology, comparative research with standard technologies such as proportional integral controller (PIC) and SMC controllers is required. THD is reduced by the proposed method to 2.25 %, 2.36 %, and 1.71 %, It is inferior to the current approaches found in the survey.

A proposal is presented for a low voltage (LV) grid integrated singlestage solar photovoltaic (SPV) system, accompanied by a hybrid control methodology aimed at optimizing system performance. To address prevailing challenges, the hybrid method incorporates the Adaptive Neuro-Fuzzy Inference System (ANFIS). Anticipated benefits of this initiative include efficient power distribution, load connectivity facilitated by the system, and operational functionalities such as mode zero voltage regulation and power factor adjustment. These functionalities collectively enhance energy quality by mitigating harmonic components, compensating for reactive power, and ensuring load balance. The proposed control strategy for a photovoltaic (PV) system interfaced with the grid is designed to exhibit rapid response times in both static and dynamic conditions. Comparative analyses were conducted between the output of our method and that of several competing approaches. The MATLAB/Simulink platform is employed for the purpose of demonstrating the developed system. The results show the extent to which the proposed controller works with reactive power compensation and load balancing to minimize network harmonics and maximize power consumption while keeping power factor functions at unity. [ABSTRACT FROM AUTHOR]

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Residential chargers are going to become the standard in the near future. Their operational cycles are closely tied to users' daily routines, and the power consumption fluctuates between zero and peak levels. These types of installations are particularly challenging for the grid, especially concerning the balance of electricity production and consumption. Using battery storage in conjunction with renewable sources (e.g., photovoltaic power plants) represents a flexible solution for grid stabilization, but it is also associated with additional costs. Nowadays, grid authorities penalize a destabilization of the grid resulting from an increased imbalance between electricity generation and consumption and reward contributions to the system balance. Hence, there is a motivation for larger prosumers to make use of this mechanism to reduce their operational costs by better aligning their energy needs with the grid. This study explores the possibility of utilizing battery storage when it is not needed to fulfil its primary function of supporting charging electric vehicles, to generate some additional profit from providing a counter-imbalance. To test this idea, we develop an optimization model that maximizes the economic profit, considering system imbalance penalties/rewards, photovoltaic production, electric vehicle charging demand, and battery storage utilization. By means of computer simulation, we assess the overall operational costs while varying key installation parameters such as battery capacity and power, the installed power of photovoltaic panels and the prediction model's accuracy. We identify conditions when counter-imbalance has proven to be a viable way to reduce installation costs. These conditions include temporal distribution of charging demand, electricity prices and photovoltaic production. For the morning time window, with a suitable setting of the installation parameters, the cost reduction reaches up to 14% compared to the situation without counter-imbalance. [ABSTRACT FROM AUTHOR]

Via the analysis of a set of parking and journey information for vehicles traveling to the parking site at the University of Brescia (Italy), we evaluated the possibility of managing the electric recharging of these vehicles, which are hypothesized to be electric. The paper investigates charging optimization techniques that can limit the charge power peaks and distribute the energy demand throughout the day. A cost assessment for an auxiliary system consisting of a photovoltaic energy source (PV) and battery stationary storage (BSS) is also carried out. Optimal power management at the station with PV and BSS is introduced, and the performance of two feedback controllers based on the optimized results is compared with that of a real-time management algorithm in the presence of randomness in charging requests and insolation. The results show that the BSS degradation cost plays a primary role in determining the strategy to adopt to minimize the operating expenditure of a charging station.

Solar photovoltaic (PV) systems have become an integral part of today's advanced energy infrastructure due to its low kinetic energy, its abundance availability, and its freedom from human interference. Solar PV systems have the potential to greatly reduce our reliance on fossil fuels, but their intermittent nature means they cannot provide a constant source of electricity. The system's security should be well thought out, and it should be able to withstand a lot of abuse. The current energy system faces a significant difficulty in ensuring continuous supply. In this study, a threephase, two-stage photovoltaic system that is managed by artificial neural networks (ANN). A DC-DC boost converter with maximum power point tracking (MPPT) based on the incremental conductance (INC) method is incorporated in the first stage. In the next step, an ANN-based controller optimizes the performance of a three-phase switching PWM inverter that is connected to the grid by controlling currents along the d-q axis. Comprehensive simulations were carried out using MATLAB or Simulink to evaluate the system's performance under various illumination and temperature conditions. Results show that the suggested approach outperforms the baseline in a number of areas. Better dynamic reactions, accurate tracking of reference currents within permissible bounds, and quick settling periods after startup are all displayed by it. These findings show that our method has the potential to greatly improve the efficiency and dependability of solar PV systems. The results of this study have implications for renewable energy in general and present a viable path toward enhancing the resilience and sustainability of energy infrastructure. [ABSTRACT FROM AUTHOR]

Hybrid renewable power generation becomes essential in most of electric power networks. Battery storage is commonly used in renewable energy systems (RESs) with distributed generation, such as solar and wind energy systems, to reduce power fluctuations caused by the intermittent behavior of renewable energy sources. A battery has been connected with the dc grid through a dc–dc bi-directional converter. The control scheme of the dc–dc bi-directional converter (BDC) is having two controlled loops. One is voltage-controlled loop, and another is the current-controlled loop. In this work, two-degree-of-freedom fractional order proportional integral derivative + proportional integral controller (2-DOF FOPID + PI) is proposed and applied in the voltage-controlled loop to stabilize the dc bus voltage and power flow effectively for both islanded mode and grid-connected mode. A novel meta-heuristic algorithm, i.e., modified sine cosine algorithm (m-SCA), is used to tune the controller parameters of the proposed controller. To test the performance of the modified SCA algorithm, various well-known uni-modal and multi-modal benchmark functions are taken in this paper. To confer the viability of the proposed controller, its performance is compared with other literature-based conventional controllers. This paper presents time-domain analysis, stability, and robustness test to illustrate the effectiveness of the proposed controller. MATLAB/Simulink has been used for computational work to analyze the system's efficiency. Finally, the proposed technique is validated by using OPAL-RT platform (Opal-RT-OP4510 model) with the power management of RES (PV and wind) and battery energy storage. Experimental work proved the effectiveness of the proposed m-SCA optimized 2-DOF FOPID + PI controller. [ABSTRACT FROM AUTHOR]

In this paper, a new method to address the scheduling problem of a renewable energy community while considering network constraints and users' privacy preservation is proposed. The method decouples the optimization solution into two interacting procedures: conic projection (CP) and linear programming (LP) optimization. A new optimal CP method is proposed based on local computations and on the calculation of the roots of a fourth-order polynomial for which a closed-form solution is known. Computational tests conducted on both 14-bus and 84-bus distribution networks demonstrate the effectiveness of the proposed method in obtaining the same quality of solutions compared with that by a centralized solver. The proposed method is scalable and has features that can be implemented on microcontrollers since both LP and CP procedures require only simple matrix-vector multiplications.

Electricity storage technologies in buildings are evolving, mainly to reduce their environmental impact and to improve self-sufficiency of buildings that produce their own energy through Building-Integrated Photovoltaics (BIPV) installations. To maximize self-consumption - minimizing the import of grid electricity - photovoltaic (PV) systems can be coupled with a hydrogen storage system converting the electricity to hydrogen by electrolysis during the summer season - when the on-site production is higher - and employing it during the winter season with fuel cells. This study focuses on the sizing constraints of solar hydrogen systems at building-scale using an innovative research-centre that will be built in Fribourg (Switzerland). It presents four stories and a mix-usage (office spaces and research facilities areas) and multi-oriented PV installation in order to produce enough electricity to achieve at least 50 % of electricity self-sufficiency ratio. Using the PV production, this study aims to optimise the sizing of a hydrogen storage system allowing to reach the required self-sufficiency ratio with the lowest environmental impact possible. Ultimately, the global energy and financial efficiency of the system will be analysed.

Via the analysis of a set of parking and journey information for vehicles traveling to the parking site at the University of Brescia (Italy), we evaluated the possibility of managing the electric recharging of these vehicles, which are hypothesized to be electric. The paper investigates charging optimization techniques that can limit the charge power peaks and distribute the energy demand throughout the day. A cost assessment for an auxiliary system consisting of a photovoltaic energy source (PV) and battery stationary storage (BSS) is also carried out. Optimal power management at the station with PV and BSS is introduced, and the performance of two feedback controllers based on the optimized results is compared with that of a real-time management algorithm in the presence of randomness in charging requests and insolation. The results show that the BSS degradation cost plays a primary role in determining the strategy to adopt to minimize the operating expenditure of a charging station. [ABSTRACT FROM AUTHOR]

For increased penetration of energy production from renewable energy sources at a utility scale, battery storage systems (BSSs) are a must. Their levelized cost of electricity (LCOE) has drastically decreased over the last decade. Residential battery storage, mostly combined with photovoltaic (PV) panels, also follow this falling prices trend. The combined effect of the COVID-19 pandemic and the war in Ukraine has caused such a dramatic increase in electricity prices that many consumers have adjusted their strategies to become prosumers and self-sufficient as feed-in subsidies continue to drop. In this study, an investigation is conducted to determine how profitable it is to install BSSs in homes with regards to battery health and the levelized cost of total managed energy. This is performed using mixed-integer linear programming (MILP) in MATLAB, along with its embedded solver Intlinprog. The results show that a reasonable optimized yearly cycling rate of the BSS can be reached by simply considering a non-zero cost for energy cycling through the batteries. This cost is simply added to the electricity cost equation of standard optimization problems and ensures a very good usage rate of the batteries. The proposed control does not overreact to small electricity price variations until it is financially worth it. The trio composed of feed-in tariffs (FITs), electricity costs, and the LCOE of BSSs represents the most significant factors. Ancillary grid service provision can represent a substantial source of revenue for BSSs, besides FITs and avoided costs. [ABSTRACT FROM AUTHOR]

For national energy capacity improvement and CO2 emission reductions, Mongolia has focused its attention on grid-connected residential PV systems. Due to the feed-in tariff (FIT), the aggregated residential PV systems are expected to increase with the PV penetration level. Currently, there is no power injection limitation in Mongolia. A new policy for the PV penetration level of residential PV systems needs to be developed. This study analyzed the techno-economic performances of distributed PV-battery systems, considering PV generation, the historical load demand, and the tariff structure. We studied the performances of 40 combinations of PV sizes (2 kW–9 kW) and battery capacities (4.4 kWh, 6.6 kWh, 10 kWh, 12 kWh, and 15 kWh) to find feasible system sizes. The aggregated PV-battery systems in a low-voltage (LV) distribution system located in Ulaanbaatar, Mongolia, are also discussed. The results show that six combinations satisfied the technical and economic requirements. The maximum profit was determined to be an NPV of 1650 USD with a 9-year payback period using combination 3 (6 kW PV and 6.6 kWh battery capacity). Combination 6 (8 kW PV and 15 kWh battery capacity) shows that the energy management strategy for residential houses with battery storage has the potential to increase the installed capacity of PV systems without voltage violence in the LV network. For the distributed PV-battery storage system (BSS), the environmental analysis indicates that CO2 and SO2 emissions were reduced by 3929 t/year and 49 t/year, respectively. The findings obtained from this analysis will be used for power system planning. [ABSTRACT FROM AUTHOR]

Abstract The multi-purpose usage of battery energy storage systems (BESSs) increases the exploitation of their flexibility potential. This can be further enhanced when a large number of small BESSs are combined into a swarm and marketed collectively by an aggregator. To this end, a unified representation of remaining flexibility for each BESS is needed that meets the requirements of both, a multi-purpose usage and a distributed swarm design. In this work, we present a compact model which we call abstract multi-purpose-limited flexibility (Amplify). It can be used by an aggregator to determine how much flexibility remains after accepting obligations and includes an integrated detection of conflicts in the planned schedule of a BESS. It is shown that the model is quickly computable and does not need much data volume during transmission.

In recent years, the need for efficient and sustainable energy solutions has become increasingly important. One potential solution is the use of solar power for battery charging systems. In this project, an Arduino-based solar-powered battery charging system is designed and implemented. The system consists of a solar panel that collects energy from the sun, an Arduino microcontroller that regulates the battery's charging, and a battery that stores the energy for later use. The solar panel converts sunlight into DC electrical energy, which is then fed to the battery through a charging circuit. The implementation of this system is fairly straightforward. The solar panel is connected to an input pin of the Arduino microcontroller, which then controls the charging of the battery through a charging circuit connected to an output pin. The charging circuit is designed to limit the charging current and voltage to prevent damage to the battery.

Variations in state-of-charges (SOCs) of batteries in a cascaded H-bridge multilevel converter (CHB-MLC) based battery storage system (BSS) could lead to undesired efficiency and performance drops, even failure of the whole system. Hence, SOC balancing is crucial for BSSs. Avoiding over-modulation region, ensuring zero common-mode voltage and reaching balanced SOC condition as quickly as possible are the key points to consider while performing SOC balancing. In this paper, a gain-scheduling based adaptive SOC balancing method is proposed for single-phase CHB-MLC based BSSs. In the proposed method, gains of the proportional controllers are updated at each sampling time based on the mathematical relationship between instantaneous SOCs and voltage reference of the CHB-MLC. Performance of the proposed method is validated through a Monte Carlo simulation based numerical analysis and experimental studies on a single-phase three-module CHB-MLC based BSS. Results reveal that the proposed method achieves SOC balancing at least two times faster than the traditional constant gain methods while avoiding over-modulation region and having zero common-mode voltage. • Over-modulation region is avoided for all instantaneous SOC conditions. • Zero CM voltage throughout the SOC balancing operation is ensured. • SOC balancing speed is increased at least two times compared to traditional methods. • Battery capacity losses are reduced at least 40 % compared to traditional methods. • SOC balancing speed and battery capacity losses are verified by Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

Large‐scale battery storage systems have become popular for various grid services in recent years. A worldwide market growth for battery storage has led to increased competition in several grid service markets. Modular large‐scale battery storage systems require a safe, highly available, and intelligent energy management system (EMS) in order to be economically competitive. One component of this EMS is the control for distributing the power requests between individual battery units of the large‐scale battery storage system. As the EMS is usually undisclosed intellectual property of the system manufacturers, there is only little information on real‐world operation available. To contribute, we present a rule‐based power distribution algorithm (SPDA) in this paper and validate it through field tests on a 6 MW/7.5 MWh system that is providing frequency containment reserve to the German power grid. The results show that especially when combining different battery technologies, the SPDA can exploit individual technological strengths. In this way, the state of charge of the batteries, energy throughput and power load of the batteries can be controlled to extend the lifetime. Moreover, the SPDA managed to shift nearly 80 % of the energy throughput to one battery unit to protect less cyclic stable batteries and make use of the advantage of cyclic stable battery technologies, while fulfilling all grid service requirements. By shifting those large quantities of the energy throughput to more cyclic stable battery units, the large‐scale battery storage system experienced in sum up to 45 % less cyclic aging with the SPDA than with a symmetrical power distribution algorithm. Furthermore, the operational efficiency of a large‐scale battery storage system can be significantly improved via additional software adaptations of the power distribution, depending on the system layout. [ABSTRACT FROM AUTHOR]

In a DC microgrid, the main aim of power management is to maintain the balance of active power between renewable sources, storage batteries, loads, and distribution grid which can be represented by stable DC-link voltage. This paper proposes an adaptive strategy for power flow management of a stand-alone hybrid renewable energy system (HRES) consisting of a wind turbine generator, photovoltaic cell, battery energy storage (BES), and load by optimizing the gain of the PI controller in the battery storage system using the particle swarm optimization-based firefly algorithm (PSO-FA). A part of the PSO algorithm is utilized for improving velocity and attractiveness of the firefly algorithm thereby increasing the rate of convergence in the PI controller. This control technique improves gain constant by generating control pulses for charging and discharging of the BES system in order to make optimum use of renewable energy sources and to stabilize the voltage of the DC-link. From the simulations done in MATLAB/SIMULINK, it is observed that conventional trial- and error-based PI tuning produces high overshoot, whereas the proposed method produces better performance with less overshoot. Maintaining constant DC-link voltage, the instantaneous power generation under various conditions of generation and load consumption is managed by monitoring the state of charge of battery. This approach can be used as a complement for improving the performance of PI controllers designed by conventional methods. [ABSTRACT FROM AUTHOR]

As the price of solar modules has decreased, oversizing PV system becomes a general practice. Without proper energy management, the oversized systems could lead to over-generation waste which cause a loss in revenue. Battery energy storage system (BESS) can be integrated to the PV system for utilizing the over-consumption energy and increasing the system's financial benefits. This paper highlights the influence of technical and financial factors on the photovoltaic (PV) and PV with BESS design. To analyze the impacts of the factors on PV systems, thirty-six system configurations with a variety of geographic locations, tariff structures, billing methods, and PV system technology are simulated using System Advisor Model (SAM) as a tool with result validation from measurements on a real oversized residential grid-tied bifacial PV+BESS system. To study the financial criteria that affect the optimal system design, we conducted the sensitivity analysis of the DC/AC ratio, battery replacement threshold, and battery size to observe the optimal system design based on three financial outputs: levelized cost of energy (LCOE), net present value (NPV), and payback period. The findings show that the optimal DC/AC ratio for residential-scale PV systems lies between 1.1 - 1.3 and can go up to 1.6 with BESS. PV+BESS systems can reduce the payback period by 20% and boost NPV by 80% compared to the PV-only systems. The sensitivity study of the battery replacement shows that the battery can be degraded until 50% of its maximum capacity for the lowest LCOE. To achieve the optimal grid-tied PV system design, each economic parameter should be evaluated together. [ABSTRACT FROM AUTHOR]

The superior access to renewable sources in modern power systems increases the fluctuations in system voltage and power. Additionally, the central dilemmas in using renewable energy sources (RESs) are the intermittent nature of and dependence on wind speed and solar irradiance for wind and photovoltaic (PV) systems, respectively. Therefore, utilizing a vigorous and effective energy storage system (ESS) with RESs is crucial to overcoming such challenges and dilemmas. This paper describes the impacts of using a battery storage system (BSS) and superconducting magnetic energy storage (SMES) system on a DC bus microgrid-integrated hybrid solar–wind system. The proposed method employs a combination of BSS and SMES to improve the microgrid stability during different events, such as wind variation, shadow, wind turbine (WT) connection, and sudden PV outage events. Distinct control approaches are proposed to control the system’s different components in order to increase overall system stability and power exchange. Both the PV and wind systems are further equipped with unique maximum power point tracking (MPPT) controllers. Additionally, each of the ESSs is controlled using a proposed control method to supervise the interchange of the active power within the system and to keep the DC bus voltage constant during the different examined instabilities. Furthermore, to maintain the load voltage /frequency constant, the prime inverter is controlled using the proposed inverter control unit. The simulation results performed with Matlab/Simulink show that the hybrid BSS + SMES system successfully achieves the main targets, i.e., DC voltage, interchange power, and load voltage/frequency are improved and smoothed out. Moreover, a comparison among three case studies is presented, namely without using ESSs, using the BSS only, and once more using both BSS and SMES systems. The findings prove the efficacy of the proposed control method based on the hybrid BSS + SMES approach over BSS only in preserving the modern power system’s stability and reliability during the variable events.

Distributed generators (DG) with inverter based on renewable sources are generally utilized in microgrids. Most of these sources work in droop control mode to effectively share the load. Higher droop is chosen on these systems to recover dynamic power sharing. This paper proposes a Hybrid Control Technique for Small Signal Stability Analysis for Microgrids under Uncertainty. The proposed topology is to recover the capacity of power system is used to restore the normal operating condition. The proposed hybrid technique is the combination of chaotic Henry gas solubility optimization (CHGSO) and recalling-enhanced recurrent neural network (RENNN) and therefore called the CHGSO-RENNN technique. The proposed technique is used to optimally predict the internal and external current loop control parameters in light and the variety of power and current parameters. The small stability is revealed through the working conditions of the whole machine. The overall stability of the small signal is investigated in a linear model so that both source and load are used to characterize the state matrix of the frame that is used for eigenvalue examination. The PI controller gain parameters are optimally tuned and the controller offers reliable frame operation. The proposed technique is performed on MATLAB/Simulink work platform. [ABSTRACT FROM AUTHOR]

Electricity storage technologies in buildings are evolving, mainly to reduce their environmental impact and to improve self-sufficiency of buildings that produce their own energy through Building-Integrated Photovoltaics (BIPV) installations. To maximize self-consumption - minimizing the import of grid electricity - photovoltaic (PV) systems can be coupled with a hydrogen storage system converting the electricity to hydrogen by electrolysis during the summer season - when the on-site production is higher - and employing it during the winter season with fuel cells. This study focuses on the sizing constraints of solar hydrogen systems at building-scale using an innovative research-centre that will be built in Fribourg (Switzerland). It presents four stories and a mix-usage (office spaces and research facilities areas) and multi-oriented PV installation in order to produce enough electricity to achieve at least 50% of electricity self-sufficiency ratio. Using the PV production, this study aims to optimise the sizing of a hydrogen storage system allowing to reach the required self-sufficiency ratio with the lowest environmental impact possible. Ultimately, the global energy and financial efficiency of the system will be analysed. [ABSTRACT FROM AUTHOR]

Due to the lack of grid power availability in rural areas, hybrid renewable energy sources are integrated with microgrids to distribute reliable power to remote locations. This optimal hybrid system is created using a solar photovoltaic system, wind turbine, diesel generator, battery storage system, converter, electrolyzer and hydrogen tank to provide uninterrupted power and meet different load demands of different communities in Doddipalli village, Chittoor, Andhra Pradesh, India. Optimization and techno-economic analysis are performed to design the proposed system using HOMER Software. Various configurations are obtained from the software among which the best four combinations are considered for case studies. This research article aims to design the optimal hybrid renewable energy system, wherein the design consists of PV/BS (1476 kW-solar PV, 417 batteries, electrolyser-200 kW, hydrogen tank-20 kg and 59.6 kW-converter) by comparing the minimum net present cost (NPC: $7.01 M), levelized cost of energy (LCOE: 0.244 $/kWh), and the high renewable fraction (RF: 84.1%). In this research, the proposed system would be more economical when solar energy becomes the primary source and is integrated with the battery. This research also presents a sensitivity analysis of the off-grid HRES system with various electrical load demands, project lifetime, and derating factors. [ABSTRACT FROM AUTHOR]

In this research, we introduce an artificial gorilla troop optimizer for use in artificial neural networks that manage energy consumption in DC–AC hybrid distribution networks. It is being proposed to implement an energy management system that takes into account distributed generation, load demand, and battery-charge level. Using the profile data, an artificial neural network was trained on the charging and discharging characteristics of an energy storage system under a variety of distribution-network power situations. As an added bonus, the percentage of mistakes was maintained far below 10%. An artificial neural network is used in the proposed energy management system, and it has been taught to operate in the best possible manner by using an optimizer inspired by gorillas called artificial gorilla troops. The artificial gorilla troops optimizer optimize the weights and bias of the neural network based on the power of the distributed generator, the power of the grid, and the reference direct axis current to obtain most suitable energy management system. In order to simulate and evaluate the proposed energy management system, small-scale hybrid DC/AC microgrids have been created and tested. When compared to other systems in the literature, the artificial gorilla troops optimizer enhanced neural network energy management system has been shown to deliver 99.55% efficiency, making it the clear winner. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic maximum power point tracking controller for a wind energy conversion system (WECS) with a battery storage system (BSS). Here, the multilayer feed-forward neural network (MLFF-NN) is used to generate the duty cycle for the DC-DC boost converter and tracks the maximum power from the WECS, whereas the charge controller is used to control the bi-directional converter of BSS to compensate for the uncertainty in the wind conversion system and load changes. Initially, a conventional P&O algorithm is implemented and obtained data are used to train the MLFF-NN with various combinations of input parameters. The best combination of input parameters is selected based on structure, target MSE, epochs, and cost. The proposed controller replaces the conventional technique and provides accurate and dynamic tracking of maximum power for trained and untrained conditions which increase the efficiency of the system. The proposed work is implemented in MATLAB/Simulink platform and results are validated in a different condition. Also, an experimental setup is developed with a 1 kW rating of WECS, whereas in hardware to reduce the computational burden on FPGA, the Elliot function with 16-bit precision is realized without losing accuracy. Finally, the simulation and hardware results are verified and comparative analysis with other existing techniques is performed. [ABSTRACT FROM AUTHOR]