Renewable Energy Storage Systems Research Articles

Realistic Home Energy Management System Considering the Life Cycle of Photovoltaic and Energy Storage Systems

Home Energy Management Systems (HEMSs) have become necessary due to energy security and climate change concerns. Scheduling the operating time of household appliances is one of the most effective strategies used by HEMSs to reduce electricity costs, with several studies proposing optimization strategies for scheduling home appliances to reduce the grid energy usage cost. This work considers energy usage costs from Renewable Energy Sources (RESs) and Energy Storage Systems (ESSs) in the appliance-scheduling strategy and energy flow management. The objectives are reducing the real electricity cost while maintaining a longer battery lifespan, reducing battery charging/discharging losses, and using PV power efficiently. To achieve this, we developed a pricing model of battery energy usage, in addition to modeling the PV energy usage cost based on the Levelized Cost of Energy (LCOE) for PV systems. PV-battery energy usage cost models were introduced into the optimization problem solved using the Augmented Grey Wolf Optimization (AGWO) and Particle Swarm Optimization (PSO) algorithms in MATLAB. We developed an efficient energy flow management algorithm. We collected real data from a home in Vigo, Spain, and simulated four scenarios. The results show that the proposed system using AGWO and PSO reduced the real cost by 25.87% and 25.98%, respectively. Compared with an existing energy-usage-pricing model, the AGWO reduced the energy losses by 40.429% and extended the battery lifespan by 68.282%. Similarly, the PSO reduced the energy losses by 45.540% and extended the battery lifespan by 84.56%. Moreover, the proposed system reached the breakeven point of the system in a shorter time.

A novel optimization for liquefied natural gas power plants based on the renewable energy

As the world's energy demand grows, the liquefied natural gas industry is gaining popularity as a cleaner fossil fuel source. The existing economic dispatch models of the liquefied natural gas-fueled oxy-fuel combined cycle integrating the renewable energy sources and the energy storage system are not applicable because of lacking modeling and optimizing of the renewable energy sources and energy storage system. This paper proposed a novel economic dispatch model to maximize the generation benefits for determining the optimal configuration of photovoltaic array panels, wind turbines, and the energy storage battery. We established and decoupled the energy-mass coupling model of a liquefied natural gas-fueled oxy-fuel combined cycle, developed the photovoltaic array panel, the wind turbine, and the storage battery model, and calculated the time-of-use electricity price, levelized cost of energy, and levelized cost of storage energy. The model determined the optimal ratio of the photovoltaic array panel and the wind turbine, the installed capacity of the storage battery, and allocated the power of the renewable energy sources and the online grid properly. The results showed that within 120 h, the renewable energy sources reduced the grid load by 40.81%, and the total generation of the photovoltaic array panel and the wind turbine was only 24.86% and 37.39% of the installed capacity. And the energy storage battery is in dynamic balance, with a net discharge capacity of 298.23 MW. The levelized cost of energy was the key factor affecting photovoltaic array panels/ wind turbines installed capacity. The installed percentage of wind turbines did not affect the energy storage systems capacity but the total economic benefit.

A hierarchical multi‐area capacity planning model considering configuration ratios of renewable energy and energy storage systems with multi‐area coordination

AbstractThe continuous growth of renewable energy sources (RESs) has increased the demand for flexibility in managing uncertainties of RES generation. Energy storage systems (ESSs) are recognized as one of the promising methods to address this challenge. For multi‐area power system planning problems, capacity allocations of RESs can vary considerably among areas accounting for the geographic diversities in RES generation and load patterns. This paper presents a hierarchical coordinated planning model considering the interaction of local‐area (LA) planning entities and the system‐wide (SW) planner. A novel multi‐objective LA planning model is proposed to compute optimal capacity configuration ratios of RESs and ESSs based on regional resource characteristics. The SW planner acts as a coordinator and further optimizes specific capacities of generation assets. Optimal configuration ratios produced by LA planning entities are important indices reflecting LA planning preferences and serve as a link between the two planning layers. Numerical results on a modified NREL‐118 test system show that the proposed planning model can reduce carbon emission by an average of 10.5% and improve the RES utilization rate by an average of 1.8% compared with planning models without considering configuration ratios and ESS installations.

A Wasserstein Distributionally Robust Planning Model for Renewable Sources and Energy Storage Systems Under Multiple Uncertainties

Nowadays, electricity markets and carbon trading mechanisms can promote investment in renewable sources but also generate new uncertainties in decision-making. In this paper, a two-stage Wasserstein distributionally robust optimization (WDRO) model is presented to determine the optimal planning strategy for renewable energy generators (REGs) and energy storage systems (ESSs) in the distribution network. This model considers supply-side and demand-side uncertainties in the distribution network and the interaction uncertainty from the main grid which are depicted by the ambiguity sets based on the Wasserstein metric and historical data. Meanwhile, both 1-norm and -norm Wasserstein metric constraints are considered to satisfy the decision-makers different preference. Furthermore, to solve this WDRO model, a systematic solution method with a three-step process is developed. Numerical results from a modified IEEE 33-node system and a 130-node system in the real world demonstrate the advantages of the two-stage WDRO model and the effectiveness of the solution method.

Inverter Control Design for the optimal management of electrical Microgrids

Nowadays, the promotion of renewable energy sources is a necessity. Technically, the increase of the renewable penetration in the power system (PS) is a challenge. One solution for this problem is the use of self-controlled microgrids (smartgrid, SG). In this work, the design of an algorithm to optimal manage the SG energy through the power inverter control in the SG is presented. In general, a SG consists of renewable energy sources, electrical loads and storage systems. In an isolated SG (no connected to the power system), a DC/AC (direct current / alternating current) converter (power inverter, PInv) is necessary to provide an alternating voltage to supply the loads. If the SG is connected to the PS, the PInv is used to inject the active power from the renewable energy sources.

Investigation for improvement of discharging rate within a renewable energy storage system with adding nano-powders

Discharging process through a renewable energy unit in existence of nanoparticles as additives has been simulated numerically. The estimation of properties of working material (mixture of H2O and alumina) has been done via single phase technique. Model includes two equations dominated by conduction mode because the convective rate is negligible. FEM modeling has been verified based on utilized assumptions. With dispersing nanoparticles, the rate of heat release enhances and freezing time declines about 41.18 %. With increase in dp of alumina powders from 30 to 40, the period of process reduces around 19.96 %.

Using a Digital Twin of Hybrid Energy Complexes to Optimize Their Parameters and Operation Modes

The aim of the work is to develop a universal technique that will make it possible to determine -- both quickly and with high accuracy -- the optimal composition, parameters and operation modes of hybrid energy complexes containing power plants that use renewable energy sources and energy storage systems from the viewpoint of ensuring safe operation and guaranteed electricity and heat supply to isolated consumers and hard-to-reach areas. The article shows the effectiveness of research and development of recommendations on the choice of parameters and operation modes of hybrid energy complexes with a heterogeneous composition of generating plants and a high dependence on natural uncertain factors at all stages of the life cycle using the developed universal digital twin, which was verified on the example of real energy complexes with miscellaneous composition of equipment.

Facilitating Reconstruction of the Heterointerface Electronic Structure by the Enriched Oxygen Vacancy for the Oxygen Evolution Reaction

Exploring high-performance non-precious metal-based electrocatalysts for the sluggish oxygen evolution reaction (OER) process is fundamentally significant for the development of multifarious renewable energy conversion and storage systems. Oxygen vacancy (Vo) engineering is an effective leverage to boost the intrinsic activity of OER, but the underlying catalytic mechanism remains anfractuous. Herein, we realize the construction of oxygen vacancy-enriched porous NiO/ln2O3 nanofibers (designated as Vo-NiO/ln2O3@NFs hereafter) via a facile fabrication strategy for efficient oxygen evolution electrocatalysis. Theoretical calculations and experimental results uncover that, compared with the no-plasma engraving component, the presence of abundant oxygen vacancies in the Vo-NiO/ln2O3@NFs is conducive to modulating the electronic configuration of the catalyst, altering the adsorption of intermediates to reduce the OER overpotential and promote O* formation, upshifting the d band center of metal centers near the Fermi level (Ef), and also increasing the electrical conductivity and enhancing the OER reaction kinetics simultaneously. In situ Raman spectra proclaim that the oxygen vacancy can render the NiO/ln2O3 more easily reconstructible on the surface during the OER course. Therefore, the as-obtained Vo-NiO/ln2O3@NFs demonstrated distinguished OER activity, with an overpotential of only 230 mV at 10 mA cm-2 and excellent stability in alkaline medium, surmounting the majority of the previously reported representative non-noble metal-based candidates. The fundamental insights gained from this work can pave a new path for the electronic structure modulation of efficient, inexpensive OER catalysts via Vo engineering.

A Comprehensive Review on Power-Quality Issues, Optimization Techniques, and Control Strategies of Microgrid Based on Renewable Energy Sources

Microgrids (MGs) are systems that cleanly, efficiently, and economically integrate Renewable Energy Sources (RESs) and Energy Storage Systems (ESSs) to the electrical grid. They are capable of reducing transmission losses and improving the use of electricity and heat. However, RESs presents intermittent behavior derived from the stochastic nature of the renewable resources available on site. This can cause power-quality issues throughout the electrical grid, which can be solved by different optimization techniques and/or control strategies applied to power converters. This paper offers a detailed review of the literature regarding three important aspects: (i) Power-quality issues generated in MGs both in islanded mode and grid-connected mode; (ii) Optimization techniques used in the MGs to achieve the optimal operating conditions of the Energy Management System (EMS); and (iii) Control strategies implemented in the MGs to guarantee stability, mitigation of power-quality issues, power balance, and synchronization with the grid. It is worth mentioning that in this paper, we emphasize hybrid MGs (HMGs) since they combine the benefits of AC–MGs and DC–MGs while increasing system reliability. As the utility grid moves toward an optimal design of MG structures, this paper will serve as a foundation for future research, comparative analysis, and further development of novel techniques regarding HMGs.

Province-specific policies offer best option as China deploys renewable energy storage systems

How energy storage systems are implemented alongside renewable energy can have a large impact on energy prices.

An energy storage allocation method for renewable energy stations based on standardized supply curve

The goal of carbon emission peak and carbon neutrality requires China to vigorously develop renewable energy. However, renewable energy has obvious randomness and volatility. Therefore, it is necessary to configure energy storage systems for renewable energy stations to ensure the safe and stable operation of power systems. Given the problem of energy storage system configuration in renewable energy stations, it is necessary to consider the system load characteristics and design appropriate principles to formulate planned output curves for renewable energy stations, so that the joint output curve of renewable energy and energy storage system can match the system load curve as much as possible, which will reduce the system peak shaving pressure and promote the utilization of renewable energy. This paper first considers the impact of renewable energy stations with the different installed scales on the power system and designs the standardized supply curves differentially, and defines the supply curve deviation index to characterize the difference between the renewable energy–energy storage system joint output curve and the standardized supply curve. Then, to minimize energy storage system investment costs and supply deviation costs, an optimization model for energy storage system configuration in renewable energy stations is established, and output deviation control constraints are set to ensure that the operation of energy storage systems conforms to actual conditions. Finally, case studies analyze the energy storage system configuration results and the typical scenario operation results of a single renewable energy station and a renewable energy power generation base, which verify the rationality and effectiveness of the method proposed in this paper.

An energy-aware optimisation model to minimise energy consumption and carbon footprint in a flexible manufacturing system

This paper proposes an energy-aware production schedule model for flexible manufacturing systems (FMSs) that aims to minimise energy costs and carbon tax while considering rising energy costs and environmental policies motivated by climate change concerns. The model is based on a sequence-dependent five-machine FMS scheduling problem, which is flexible in levels of parts, tools, machines, and routings. The proposed model is solved as a multi-objective problem, with tardiness and energy consumption as primary goals and carbon footprint reduction policies incorporated into the framework as control strategies. The machine ON/OFF strategy is also introduced to reduce idle energy. The study analyses the trade-off between minimising tardiness and carbon emissions to achieve both service level and environmental sustainability of the FMS. The proposed model reduced total energy consumption and carbon emissions by 20% and 21%, respectively, without compromising manufacturing deadlines. Future research may explore the integration of renewable energy sources and storage systems, considering more complex manufacturing scenarios, such as stochastic demands, machine failures, and workforce constraints.

Optimizing electricity demand scheduling in microgrids using deep reinforcement learning for cost‐efficiency

AbstractRenewable energy sources (RES) are increasingly being developed and used to address the energy crisis and protect the environment. However, the large‐scale integration of wind and solar energy into the power grid is still challenging and limits the adoption of these new energy sources. Microgrids (MGs) are small‐scale power generation and distribution systems that can effectively integrate renewable energy, electric loads, and energy storage systems (ESS). By using MGs, it is possible to consume renewable energy locally and reduce energy losses from long‐distance transmission. This paper proposes a deep reinforcement learning (DRL)‐based energy management system (EMS) called DRL‐MG to process and schedule energy purchase requests from customers in real‐time. Specifically, the aim of this paper is to enhance the quality of service (QoS) for customers and reduce their electricity costs by proposing an approach that utilizes a Deep Q‐learning Network (DQN) model. The experimental results indicate that the proposed method outperforms commonly used real‐time scheduling methods significantly.

Demand response-oriented virtual power plant evaluation based on AdaBoost and BP neural network

As an advanced technology that efficiently aggregates and optimizes renewable energy, controllable loads, and energy storage systems, virtual power plants (VPPs) can effectively promote the green and low-carbon transformation of power systems. A comprehensive assessment of VPPs is important for VPP investment and operation. However, most of the existing evaluation methods focus on the reliability, economy, and mobilizability of VPPs. In this paper, in order to better address the characteristics of demand response-oriented VPPs, three aspects of VPP operation indices, new energy indices, and demand response indices are analyzed. In this manner, it is possible to meet the principles of the construction of VPP evaluation system and also to measure the effect of demand response of the VPP. Then, on the basis of AdaBoost algorithm, combined with back propagation (BP) neural network for the evaluation and classification of demand response-oriented VPPs. The entropy value method and gray correlation are also compared to validate the superiority of the proposed method.

Coordinated smart home energy sharing with a centralized neighbourhood energy management

This paper presents a centralized neighbourhood energy management with coordinated smart home energy-sharing model for neighbourhood smart homes, which are integrated with in-house renewable energy resources and energy storage systems. The main aim of this neighbourhood energy management model is to minimize the aggregated electricity bill of all smart home consumers in the neighbourhood area by increasing the utilization of local renewable energy generation. The effective combination of time of use pricing, feed-in-tariff scheme and fixed incentive method is considered in this model to encourage the more electricity transactions among the neighbourhood smart homes. A group of ten smart homes with different level of renewable energy integration and load profiles is considered as a test system to determine the performance of the proposed neighbourhood smart home energy management model. The simulation results show that the centralized neighbourhood coordinated smart home energy management model can provide significant economic benefits to all smart home consumers in the neighbourhood area, when compared to the without neighbourhood power-sharing case. From the results, it is noted that all smart homes together get 56.23% of cost savings in a day with the proposed model when compared to the baseline model. Finally, an economic analysis has also done in this paper to investigate the capital investment benefits of all smart home consumers in neighbourhood area. From the economic analysis, it is clearly noticed that with the proposed model the annual profits of all smart homes (SHs) in the neighbourhood area are improved by 54.48% for SH1, 59.88% for SH2, 85.03% for SH3, 41.55% for SH4, 30.68% for SH5, and 34.70% for SH6 respectively when compared to without neighbourhood energy sharing concept.

Biomass derived metal free hierarchical porous activated carbon for efficient oxygen evolution reaction

ABSTRACT The development of high electro-efficient and sustainable metal-free catalysts to replace noble materials is essential for the development of prospective renewable energy conversion and storage systems. Due to its ability to provide clean energy, the oxygen evolution reaction (OER) recently received a lot of interest. Here, we provide an effective “waste to clean energy” concept-based porous-activated carbon from waste rice husk with hierarchical pore architecture for OER. The physicochemical properties of the synthesized activated carbon material were examined by XRD, FESEM, and BET analysis. Characterization of the synthesized activated carbon derived from the rice husk shows a high BET surface area (755 m2/g) with high pore volume. Electro-catalytic performance of the prepared porous-activated carbon has been studied, viz. linear sweep voltammetry, Tafel slope, electrochemical impedance spectroscopy, and cyclic voltammetry in 1 M KOH solution. As an electrocatalytic performance in alkaline media, it shows low-onset potential (1.56 V vs. reversible hydrogen electrode), with a small Tafel slope (61 mV/dec), an overpotential (η10) of 0.47 V, low Rct (212 Ω), and good stability toward OER performance even after 500 cycles. Here, we have also reported the effect of electrochemically active surface area and mass activity effect on the OER performance.

Optimization Technique for Renewable Energy Storage Systems for Power Quality Analysis with Connected Grid

Power quality is the main problem with the power system network. Poor electricity quality may cause disruptions and financial challenges for consumers. Additionally, it could cause electronic gadgets to overheat, be damaged, or operate inadvertently. Transformers and other components of the power distribution system may also overheat and experience core saturation. This study investigates potential problems with the quality of the electricity in a photovoltaic linked power system. This paper suggests a new optimization method for day-ahead trading and control in DC microgrid power management (MG). The goal of the multiobjective optimization dispatch (MOOD) problem is to lower overall operational costs as well as the costs associated with power loss in efficient conservation systems and exhaust emission quantities such as nitrogen oxides, sulphur dioxide, and carbon dioxide. Using the weighted sum approach, the multiobjective optimization problem is reduced to a single optimization problem. The analytical hierarchy process (AHP) method is then used to calculate the weight coefficients while accounting for each objective function’s preferences. Power balancing, high levels of renewable energy penetration, the most effective scheduling of battery charging and discharging, control of load curtailment, and the technical limitations of the system are all taken into account when evaluating the system’s performance in both grid-connected and standalone operation modes. The ant lion optimizer (ALO) technique is taken into account to tackle MOOD, comparing the effectiveness of the proposed method to other well-known heuristic optimization techniques. The simulation’s results demonstrate how effectively the proposed strategy can address the coordinated control and optimization dispatch difficulties. They also found that operating the MG system economically in grid-connected mode can save overall costs by roughly 4.70% compared to doing it in independent mode.

Fractional-Order Control Techniques for Renewable Energy and Energy-Storage-Integrated Power Systems: A Review

The worldwide energy revolution has accelerated the utilization of demand-side manageable energy systems such as wind turbines, photovoltaic panels, electric vehicles, and energy storage systems in order to deal with the growing energy crisis and greenhouse emissions. The control system of renewable energy units and energy storage systems has a high effect on their performance and absolutely on the efficiency of the total power network. Classical controllers are based on integer-order differentiation and integration, while the fractional-order controller has tremendous potential to change the order for better modeling and controlling the system. This paper presents a comprehensive review of the energy system of renewable energy units and energy storage devices. Various papers are evaluated, and their methods and results are presented. Moreover, the mathematical fundamentals of the fractional-order method are mentioned, and the various studies are categorized based on different parameters. Various definitions for fractional-order calculus are also explained using their mathematical formula. Different studies and numerical evaluations present appropriate efficiency and accuracy of the fractional-order techniques for estimating, controlling, and improving the performance of energy systems in various operational conditions so that the average error of the fractional-order methods is considerably lower than other ones.

Maximum hosting capacity estimation for renewables in power grids considering energy storage and transmission lines expansion using hybrid sine cosine artificial rabbits algorithm

The broad spread of renewable energy sources (RESs) and storage systems increases modern power systems’ challenges and may conflict with system operation requirements. Determination of the maximum hosting capacity (HC) is crucial for utilities to estimate the maximum capacity of RESs and storage units that a network can accommodate efficiently. Several models were developed in the literature that lacks studying the impact of reliability requirements and the size of other network components on the HC levels. This paper proposes a framework to effectively maximize the hosting of RESs and storage systems in power systems. An AC optimal power flow is used to formulate the problem and consider the networks’ real operation. The objective function is formulated to expand transmission lines, allocate fault current limiters (FCLs), and ensure HC enhancement. The AC models are always complex and non-convex, and finding optimal solutions is critical. A hybrid artificial rabbits sine–cosine algorithm is developed to solve the problem, and its performance is compared to some well-known metaheuristics. The numerical studies on the Garver network and the IEE 24-bus system demonstrated that the size of different power technologies directly impacted the amount of RESs deployed. The penetration of RESs and storage systems was maximized by increasing the number of candidate circuits. The results revealed a 22 % increase in the size of RESs installed in the Garver network. Also, a 25.7 % increase in the size of RESs for the 24-bus system was obtained due to the increased candidate transmission lines. Further, an increase of 49 % was noticed in the size of storage systems built for the 24-bus system. The use of FCLs was necessary to maintain the short-circuit current below the required values. For both systems, the size of FCLs required was increased by 24.3 % and 28 %, respectively. The results also showed that the hybrid artificial rabbits sine–cosine optimizer was more efficient than other algorithms in solving the investigated problem.

Demand-Side Regulation Provision of Virtual Power Plants Consisting of Interconnected Microgrids Through Double-Stage Double-Layer Optimization

This study proposes a double-stage double-layer optimization model for a virtual power plant (VPP) consisting of interconnected microgrids (IMGs) with integrated renewable energy sources (RESs) and energy storage systems (ESSs) to realize demand-side ancillary service, considering intra energy sharing among the IMGs within the VPP. In particular, the first stage, day-ahead scheduling, is carried out to predict the hourly electricity consumption baseline and regulation capacity for the next day, the latter of which results in a reward from the market operator. In the second stage, real-time power consumption control is performed by following the dynamic regulation (or RegD) signal. The second stage is further divided into two layers: the upper layer distributes demand response (DR) signals from the main grid according to the electricity unit price of each microgrid (MG) and exchanges electricity among MGs based on a new energy sharing mechanism to reduce RegD-following violations. The lower layer performs real-time power consumption control for each MG to minimize operation costs. The overall goal is to maximize the reward in the day-ahead stage and minimize the RegD-following violation penalty in the real-time stage, so as to minimize the overall operation cost of the VPP. The optimization is written in five objective functions, which are solved using mixed integer linear programming (MILP) in Gurobisolvers. Extensive simulation and comparison studies are carried out, and numerical results show that compared with traditional MG operations, VPPs comprised of IMGs can reduce operation costs and provide better frequency support for the grid through superior RegD signal following performances.