Fluctuation Of Renewable Energy Sources Research Articles

Virtual power plant management with hybrid energy storage system

The transition to renewable energy sources and distributed energy generation (DG) has spurred the global evolution of energy production methods. However, virtual power plants (VPPs) face challenges due to fluctuations in renewable energy sources (RES) production, such as those from photovoltaics and wind turbines. Factors like temperature, solar radiation, wind speed, and high-frequency interference contribute to unstable output power, potentially affecting power supply quality with voltage fluctuations and frequency changes. To address these challenges, it is crucial to smooth alternating current before grid transmission.This paper proposes a solution involving a smart grid with decentralized generators and controllable loads forming a VPP. The approach introduces a Hybrid Energy Storage System (HESS) comprising batteries, supercapacitors, and fuel cells. Equipped with proportional-integral (PI) and model predictive control (MPC) regulators, the HESS aims to regulate inverter voltage for renewable energy. By converting fluctuating electricity into high-quality power, the system enables seamless integration into the VPP, thereby preventing disruptions in generation processes and reducing potential costs associated with damage caused by power fluctuations to grid-connected devices.In the context of the HESS, a photovoltaic system and a wind turbine have been developed, with the proposed system connected to an RLC series load through an IGBT inverter. To evaluate the effectiveness of the HESS within the proposed VPP, two different scenarios were examined by varying the location of these systems in a 14-bus microgrid.

Anchoration of hydrothermally developed FeSe nanoparticles on rGO nanosheets as electrode for energy storage devices

A perpetual discrepancy exists between demand and supply due to the inherent fluctuation of renewable energy sources. A possible approach for addressing this persistent issue involves the advancement of appropriate materials for consumption in energy storage systems. An example of a device that can potentially serve as energy storage is a supercapacitor equipped with highly effective electrode materials. This study synthesizes FeSe, rGO and FeSe@rGO electrodes via hydrothermal route. Specific capacitance (Cs) of FeSe, rGO and FeSe@rGO electrode was measured by GCD analysis as 517, 799, and 1408 F g−1 at 1 A g−1. The synthesized FeSe@rGO nanohybrid exhibits outstanding performance and high cycling stability up to 10000th cycles. Furthermore, the enhanced supercapacitance of the nanohybrid has been ascribed to the synergistic interaction between FeSe and rGO, which delivers higher surface area and enhanced active sites. Further, symmetric two-electrode study revealed excellent electrode material with specific capacitance (Cs) of 553 at 1 A g−1 with capacitive retention of 66.36 % after 1000th cycle. However, the electrochemical investigation demonstrated that the FeSe@rGO nanohybrid structure exhibited superior performance and could be used in different electrochemical applications.

Making waves: Power-to-X for the Water Resource Recovery Facilities of the future

The wastewater industry and the energy system are undergoing significant transformations to address climate change and environmental pollution. Green hydrogen, which will be mainly obtained from renewable electricity water electrolysis (Power-to-Hydrogen, PtH), has been considered as an essential energy carrier to neutralize the fluctuations of renewable energy sources. PtH, or Power-to-X (PtX), has been allocated to multiple sectors, including industry, transport and power generation. However, considering its large potential for implementation in the wastewater sector, represented by Water Resource Recovery Facilities (WRRFs), the PtX concept has been largely overlooked in terms of planning and policymaking. This paper proposes a concept to implement PtX at WRRFs, where sourcing of water, utilization of the oxygen by-product, and PtX itself can be sustainable and diversified strategies. Potential value chains of PtX are presented and illustrated in the frame of a WWRF benchmark simulation model, highlighting the applications of oxygen from PtX through pure oxygen aeration and ozone disinfection. Opportunities and challenges are highlighted briefly, and so is the prospective outlook to the future. Ultimately, it is concluded that ‘coupling PtX to WRRFs’ is a promising solution, which will potentially bring sustainable opportunities for both WRRFs and the energy system. Apart from regulatory and economic challenges, the limitations in coupling PtX to WRRFs mainly come from energy efficiency concerns and the complexity of the integration of the water framework and the energy system.

Capacity optimization allocation method for off-grid RES-H2 microgrid system considering the load-follow-source mechanism

Based on the uncertainty of the output of renewable energy such as wind and photovoltaic (PV), a capacity optimization allocation method considering the load-follow-source mechanism is proposed to solve the shortages of traditional methods, which is low in utilization rate of energy and low in economy. Firstly, considering the fluctuations of renewable energy sources (RES) and the operating characteristics of the electrolyzer, this paper proposes a capacity optimization method for off-grid hydrogen(H2) production system of renewable energy sources considering the load-follow-source operation mechanism. Then, combined with this mechanism, a capacity optimization allocation model is established, the objectives contain minimizing the levelized cost of hydrogen (LCOH), minimizing the capacity of energy storage, and optimizing the efficiency of power supply. Finally, an adaptive non-dominated sorting differential evolution algorithm (ANSDE) with external archiving is proposed to solve the established model. The calculation results show that the load-follow-source mechanism is effective for improving the economy of the system, and the proposed algorithm is high in efficiency and good in convergence comparing with traditional algorithms.

The improved aquila optimization approach for cost-effective design of hybrid renewable energy systems

The growing demand for renewable energy systems is driven by climate change concerns, government support, technological advancements, economic viability, and energy security. These factors combine to create a strong momentum towards a clean and sustainable energy future. Governments, governments, and individuals are increasingly aware of the environmental impacts of traditional energy sources and adopting renewable energy solutions. Hybrid Renewable Energy Systems (HRES) are developed as an effective way of meeting the energy demands in remote locations. The complexity of the system components and the fluctuation of renewable energy sources make it difficult to design an economical and effective HRES. In this study, the Improved Aquila Optimization (IAO) approach has been suggested as a powerful tool to optimize the HRES design. The study addresses the implementation of the IAO approach in the design of HRES and emphasizes its advantages over other optimization techniques. Through extensive simulations and analyses, our findings demonstrate the superior performance of the IAO algorithm in improving the efficiency and cost-effectiveness of HRES. The optimization process using IAO resulted in a significant reduction in overall system costs, achieving an estimated Net Present Cost (NPC) of $201,973. It translates to a cost reduction of 25% compared to conventional optimization techniques. Furthermore, our analysis reveals that the IAO approach enhances the utilization of renewable energy sources, leading to a 15% increase in overall energy generation efficiency. These results highlight the effectiveness of the IAO approach in addressing the challenges associated with designing HRES. By significantly reducing costs and improving efficiency, it facilitates the adoption of sustainable energy systems in remote areas. The outcomes of this study emphasize the importance of utilizing advanced optimization techniques, such as IAO, to ensure the economic viability and environmental sustainability of HRES.

Analysis of inertia, damping, and synchronization characteristics in grid-connected photovoltaic systems with fuzzy logic control

The integration of renewable energy sources (RES) into DC-distributed power systems (DC-DPSS) is gaining traction as a sustainable energy solution. However, the inherent variability of RES output can introduce instability into the grid, posing challenges for maintaining system reliability and stability. Fuzzy logic controllers (FLCs) have emerged as a promising approach to mitigate these instability issues, offering a robust and adaptable control strategy that can effectively handle the complexities of DC-DPSS. This paper examines the application of FLCs in DC-DPSS, exploring their effectiveness in addressing instability caused by RES fluctuations. FLCs are a control system that leverages fuzzy logic, a form of logic that utilizes linguistic variables to represent uncertainty, make decisions, and improve the stability of DC-distributed power systems. The research analyzes various system parameters, including inertia, damping, and synchronization characteristics, using a static synchronous generator (SSG) model. The study builds upon prior findings by adding a fuzzy logic controller to the existing system. The results showed better performance which resulted in improved inertia, damping, and synchronization characteristics. The efficiency of the proposed controller is demonstrated using MATLAB/Simulink.

Feasibility study of energy storage using hydraulic fracturing in shale formations

Electric energy storage is currently the primary solution for addressing the intermittency and fluctuation of renewable energy sources. Traditional energy storage methods often struggle to simultaneously meet the demands of long storage duration, large capacity, high efficiency, and low cost. In this study, we present and verify the feasibility of a new energy storage method that utilizes hydraulic fracturing technology to store electrical energy in artificial fractures. Our study analyzed factors that impact energy storage capacity and efficiency, which provides a theoretical basis for optimizing hydraulic fracturing design for energy storage. This study also shows a promising direction for transforming depleted shale oil and gas wells into energy storage wells.

A flexible load adaptive control strategy for efficient photovoltaic hydrogen generation system

The adaptive adjustment of the input current in proton exchange membrane electrolyzers (PEMEL) can provide several benefits, including the enhancement of PEMEL efficiency and the extension of its operating lifespan within an optimal range. This study presents a novel configuration for a photovoltaic (PV) hydrogen generation system that allows for the direct integration of PV. Moreover, the utilization of the triple-phase shift (TPS) modulation technique is implemented in order to achieve maximum power point tracking (MPPT) control within dual active bridge (DAB) DC/DC converters. The proposed strategy offers a pre-control approach for active adjustment of flexible loads to mitigate the mismatch between power output and load caused by fluctuations in renewable energy sources. This mismatch can lead to excessive current ripple, which in turn can impact the safe functioning of the PEMEL. In tandem with the analysis of the factors that contribute to ripple and the properties of the Electrical Spring (ES), relevant multiple variables are selected as control signals for the control strategy. This is done to effectively regulate the magnitude of non-essential loads and consequently reduce the ripple in the PEMEL system. The use of the ES pre-control technique effectively addresses the issue of delayed response of the PEMEL to PV oscillations, which is attributed to the large time-lag characteristics of electrolyzers. The simulation results at steady-state conditions and sudden changes in lighting conditions demonstrate concurrently that the overall control strategy yields a reduction in current stress in the DAB to 30.09% and 51.59%, respectively, enables MPPT in PV systems, and decreases the input current ripple of PEMEL to 80.1% and 57.67%, showing stronger anti-disturbance ability.

Event-triggered reactive power tracking optimization for second-level power fluctuations of renewables and stochastic loads

The power fluctuations of renewable energy sources and stochastic loads (RESL) makes it difficult for system operators to achieve optimal control. This paper proposes an event-triggered reactive power optimization strategy based on the event-triggered algorithm to rapidly track the optimal operating states for accommodating the second-level power fluctuations of RESL, with less computation and communication. Firstly, on a longer temporal scale, event-triggered multi-stage global optimization (MGO) is performed on a rolling basis to obtain the time-interval optimal operating states according to chance constraint programming, and the system operation security is improved by narrowing the voltage-feasible regions. Next, at each moment within the time interval, event-triggered local tracking optimization (LTO) is performed at each controller of RESL by rapidly regulating reactive power to adapt to its second-level active power fluctuations. The LTO is completed at the local level by using the sensitivity information from MGO, without running the optimal power flow program, to relieve the computation and communication burdens. Then, event-triggered LTOs performed at all controllers synchronously form the system-level real-time optimization. The unnecessary MGO and LTO are skipped by using the event-triggered algorithm when small power fluctuations are occurring. Finally, a case study on the IEEE-39 bus power system validates the feasibility of the proposed strategy.

A Gershgorin Circle Theorem Inspired Controller for Renewable Energy Source Integrated Power Systems and Multimicrogrids

Increasing load demands, variations in the operating conditions, continuous integration of the renewable energy sources constitute a multifarious challenge for the researchers to maintain the quality in performances of a load frequency controller. To contemplate such issues, a Gershgorin circle theorem inspired controller assimilated with the particle swarm optimization is proposed for multiarea power systems. The proposed method ensures system stability while abating the frequency variations in the operational areas and the power deviations in the interconnected tie-lines. The present analysis is substantiated through an interconnected photovoltaic (PV) integrated thermal power systems. Furthermore, the competence of the proposed method against increasing complexities is validated by considering an interconnected multimicrogrid systems consisting of renewable and nonrenewable energy sources. The performances of the proposed controller are demonstrated considering fixed and time-varying load disturbances, and fluctuations in renewable energy sources. Results corroborate that the proposed method is successful in maintaining the system stability while mitigating the frequency and the power deviations in all such operating conditions. A comparative analysis of the proposed method with prevalent control techniques (e.g., PI and PID-based controllers, tilt–integral–derivative, integral–tilt–derivative) and optimization algorithms (e.g., genetic algorithm, gravitational search algorithm, imperialist competitive algorithm) is presented to highlight the substantial performance improvements in the considered systems.

Development of Superconducting Cable With Energy Storage Function and Evaluation of its Functionality in DC Microgrid With Renewable Energy Sources

We propose a superconducting cable with energy storage and its operation in a DC microgrid as a measure to mitigate output fluctuations of renewable energy sources. This not only enables high-speed and high-power charge-discharge operation, which is difficult with conventional energy storage devices, but also minimizes the additional equipment required for energy storage because it also serves as power transmission. In the previous study, we reported its effectiveness through conceptual cable design and simple numerical simulation. In this study, we experimentally verified such functionality by hardware-in-the-loop simulation with the use of small model cables and clarified the effects on DC microgrids by numerical simulation reflecting the experimental results. The results showed that in a 10 MW scale microgrid, a superconducting cable with 40 MJ of stored energy could fully compensate for second-scale fluctuations which could not be compensated without it. Furthermore, a superconducting cable with 1 GJ of stored energy eliminated the even need for other batteries for such a microgrid.

Segmentation of interconnected power systems considering microgrids and the uncertainty of renewable energy sources

AbstractThis paper proposes an approach for segmentation of power systems to prevent uncontrolled islanding. With the development of microgrids (MGs) and increased penetration of renewable energy sources (RESs) in the power system, it is necessary to consider the effect of MGs and the stochastic behaviour of RESs in the controlled islanding process. First, the enormous search space for finding the boundary points is reduced by the clustering of the coherent generators and the buses which are placed in sections that cannot be separated from the coherent groups. Then, linear AC power flow is defined as a sub‐problem and solved by linear programming to satisfy the power system and MG constraints. In this step, an optimization model is defined, and a mixed‐integer linear programming (MILP)‐based model is suggested to find cut‐sets and islands arrangement with reduced load shedding. In view of the fact that the fluctuation of RESs may have the significant effects on the power imbalance, to cover the uncertainties a scenario based stochastic simulation is considered. Finally, the effect of uncertainties on cut‐sets and load curtailment is evaluated. The effectiveness of the proposed methodology is demonstrated by the simulation on the test system.

Zero-Carbon Power-to-Hydrogen Integrated Residential System Over a Hybrid Cloud Framework

The study pioneers in proposing a hybrid cloud-based framework for designing a totally stand-alone, green residential house, stable over load variations and fluctuations of renewable energy sources (RES). The framework uses wind turbine (WT) and photovoltaic panels (PV) as the main power supplies, while employing a fuel cell (FC), fed with an electrolyzer, as the secondary source of energy. A battery is also used, which together with the FC and electrolyzer, constitute the compensation system in the proposed framework. Taking into account the various types of residential electrical loads, including an electrical vehicle (EV), and applying a deep learning method, the proposed framework makes the day-ahead scheduling of all components in the house based on the forecasted profile of load demand and the energy generated by the RES. The compensation system comes into use to balance the real-time scheduling error caused by the uncertainties of the main sources of power. To ascertain the practicality of the proposed framework for real-life implementation, it is examined on a residential house considering components with authentic technical features. The real-time operation of the suggested residential system is also tested on the SpeadGoat real-time simulator, whose results corroborate the practicability of both the real-time and day-ahead operation of the proposed framework.

An improved multi-timescale coordinated control strategy for an integrated energy system with a hybrid energy storage system

In view of the complex energy coupling and fluctuation of renewable energy sources in the integrated energy system, this paper proposes an improved multi-timescale coordinated control strategy for an integrated energy system (IES) with a hybrid energy storage system (HESS). The power response characteristics of various devices in IES at different time scales are analyzed which shows that the multi-timescale coordinated control method can be well coupled with the IES including HESS. The advantages of HESS over single energy storage system in stabilizing power fluctuation and extending energy storage life are compared and analyzed while the control method of supercapacitor under multi-time scale coordinated control strategy is proposed. The primary energy consumption, operating costs, carbon dioxide emissions and load shedding costs are considered the optimization objectives in the day-ahead rolling optimization stage according to the day-ahead forecast renewable energy (RE) data. In the intraday rolling adjusting stage, the proposed method can reduce the effect of the RE day-ahead forecasting errors to achieve the intraday energy scheduling balance and ensure the safe operation of the devices of IES. Considering the background of IES with a high proportion of renewable energy, this paper makes innovative use of the advantages of HESS to improve the power response characteristic of the system. The high-frequency part of RE is balanced by HESS in the real-time coordinated control stage. A typical structure of IES is applied and the simulation results are analyzed to show the performance of the proposed method in this paper. Moreover, general rules for sizing of HESS are also proposed. The influence of the hybrid energy storage capacity allocation ratio on battery capacity loss and carbon emission reduction is discussed. The Pareto front including the configuration and cost of supercapacitor has been obtained. As shown in the case, the application of HESS in the IES can improve the power response characteristic, extend the lithium-ion battery (LiB) life and has more benefits in reducing carbon emissions.

Multilayer Deep Deterministic Policy Gradient for Static Safety and Stability Analysis of Novel Power Systems

More and more renewable energy sources are integrated into novel power systems. The randomness and fluctuation of such renewable energy sources bring challenges to the static stability and safety analysis of novel power systems. In this work, a multilayer deep deterministic policy gradient is proposed to address the fluctuation of renewable energy sources. The proposed method is stacked with multilayer deep reinforcement learning methods that can be continuously updated online. The proposed multilayer deep deterministic policy gradient is compared with other deep learning algorithms. The feasibility, effectiveness, and superiority of the proposed method are verified by numerical simulations.

Non-aqueous organic redox active materials for a bicontinuous microemulsion-based redox flow battery

To mitigate fluctuations in renewable energy sources, redox flow batteries (RFBs) attract much attention for stationary energy storage applications by taking advantage of decoupling power and energy. However, there are still some limitations for conventional RFBs, including low energy density, a narrow electrochemical window (∼1.23 V) for an aqueous electrolyte, and low ionic conductivity for a non-aqueous electrolyte. Herein, we demonstrate a bicontinuous microemulsion-based RFB using methyl-p-benzoquinone and 2,2,6,6-Tetramethyl-1-piperidinyloxy as anolyte and catholyte redox active materials, respectively. By stabilizing an aqueous and organic medium at a nanometer scale through a surfactant, the microemulsion electrolyte exhibits a wider electrochemical window and good ionic conductivity, a promising alternative to increase the energy density by applying non-aqueous redox species with high solubility and high redox potential. The resulting cell exhibits Coulombic efficiency of 80%–85% and capacity retention of 50% over 30 cycles. We provide a systemic analysis of performance limitation and capacity decay and proposed a possible dimerization mechanism for an anolyte through a density functional theorycalculation, ending with a discussion of the efforts needed to realize the application of microemulsion-based RFBs.

Mathematical model and dynamic simulink simulation of PEM electrolyzer system

Hydrogen, being the most abundant element in the universe, holds great promise as an energy carrier for decarbonizing various economic sectors. In particular, green hydrogen production through water electrolysis is essential for achieving this goal, with polymer electrolyte membrane (PEM) water electrolyzers playing a crucial role. PEM water electrolyzers are known for their rapid response, enabling them to effectively adapt to fluctuations in renewable energy sources. Nevertheless, rapid load changes can result in the rapid build-up of heat within the electrolytic cell, leading to a sharp increase in temperature and potentially harming the cell. To address this challenge, we developed an electrolysis water system model using MATLAB and validated its accuracy through experiments. This model allowed us to explore the factors influencing stack temperature and propose a fast and secure dynamic process control strategy. By laying the groundwork for subsequent control studies on PEMEC (Proton Exchange Membrane Electrolysis Cell) stacks and systems, this research facilitates further progress in their control and regulation.

Towards 100% renewable energy systems: The role of hydrogen and batteries

New challenges arise for the accurate modelling of energy systems with a high share of renewable energy. In this context, energy storage technologies become key elements to manage fluctuations in renewable energy sources and electricity demand. The aim of this work is to investigate the role of batteries and hydrogen storage in achieving a 100% renewable energy system. First, the impact of time series clustering on the multi-year planning of energy systems that rely heavily on energy storage is assessed. The results show good accuracy, even for a small number of representative days, which is necessary to limit the computational burden of the optimisation problem. Then, different configurations of carbon-free energy systems are considered by varying the energy storage solution: only-battery, only‑hydrogen, and hybrid scenarios. An island energy system based on photovoltaics and floating offshore wind turbines is used as a demonstrative case study. It is shown that the cost of the only-battery configuration is 155% higher than the cost of the hydrogen-based scenarios. The reason is that the long-term hydrogen-based storage, despite its low round-trip efficiency, avoids costly oversizing of batteries and wind turbines throughout the analysed period. In the selected case study, hydrogen storage reduces the total rated power of the wind farm by about 5 times compared to the only-battery system. Hydrogen-based solutions are therefore crucial in 100% renewable energy systems to achieve energy self-sufficiency in a cost-effective way.

Distributed Event-Triggered Optimal Control Method for Heterogeneous Energy Storage Systems in Smart Grid

Increasing implementation of energy storage systems (ESS) is an available way to mitigate the fluctuation of renewable energy sources (RES) in smart grid. This paper proposes distributed control strategies without/with event-triggered information delivery for a group of heterogeneous energy storage systems (HESS) with different parameters such that the power fluctuation of renewable generation and load variation can be smoothed, meanwhile ensuring power and energy sharing among various ESS. To mitigate the impacts of parameter differences in HESS, a weighted control matrix is designed, and the method of deriving optimal feedback control gain is designed with the help of Riccati control theory. Aiming at reducing communication burdens meanwhile guaranteeing system performance, an event-triggered information delivery strategy without Zeno behavior is introduced to the distributed control method. Convergence and stability of the proposed methods are verified by rigorous mathematical analysis. Simulation studies are conducted to verify the effectiveness of the proposed method. The results show that the proposed method can achieve the state of charge (SoC), the state of temperature (SoT) and the state of energy (SoE) balance of HESS, proportional power sharing and power tracking objectives, even if the states of HESS are interfered by random disturbances.

Energy storage with Power-to-Power systems relying on photovoltaic and hydrogen: modelling the operation with secondary reserve provision

Energy storage is increasingly required in order to cope with the fluctuations of renewable energy sources, especially in power generation. In many countries, the electric market is undergoing regulatory transformations that aim at increasing the type and number of technologies that can provide grid services, either alone or as virtual aggregates. In this context, this work investigates the economic profitability of Secondary Frequency Reserve (SFR) provision to the electric grid through integrated generation-and-storage systems. In particular, the case of Italy is assessed, where recent projects opened the Ancillary Service Market (ASM) to relevant generation units or aggregates of units, including programmable and non-programmable renewable sources, consumption units, and energy storage systems. The operation of a hydrogen-based Power-to-Power (P2P) system, designed to supply an electric load with locally generated electricity from a solar PV field, is studied when acting on both the Day-Ahead Market (DAM) and the ASM. A mixed-integer linear optimization problem is set up that minimizes the annual average cost of electricity to the load. The analysis considers a broad range of options in terms of electricity prices on the DAM and price-quantity combinations of accepted offers on the ASM. Results show that the economic advantages deriving from the utilization of P2P systems increase with more fluctuating prices. The provision of SFR services with an optimal bidding strategy and asymmetric bids allows one to reduce the average cost of electricity up to about 15 % with respect to the operation on the DAM only.