Optimization Of Energy Storage System Research Articles

A comprehensive survey of the application of swarm intelligent optimization algorithm in photovoltaic energy storage systems

With the rapid development of renewable energy, photovoltaic energy storage systems (PV-ESS) play an important role in improving energy efficiency, ensuring grid stability and promoting energy transition. As an important part of the micro-grid system, the energy storage system can realize the stable operation of the micro-grid system through the design optimization and scheduling optimization of the photovoltaic energy storage system. The structure and characteristics of photovoltaic energy storage system are summarized. From the perspective of photovoltaic energy storage system, the optimization objectives and constraints are discussed, and the current main optimization algorithms for energy storage systems are compared and evaluated. The challenges and future development of energy storage systems are briefly described, and the research results of energy storage system optimization methods are summarized. This paper summarizes the application of swarm intelligence optimization algorithm in photovoltaic energy storage systems, including algorithm principles, optimization goals, practical application cases, challenges and future development directions, providing new ideas for better promotion and application of new energy photovoltaic energy storage systems and valuable reference.

Hybrid Energy Storage System Optimization With Battery Charging and Swapping Coordination

Hybrid Energy Storage System Optimization With Battery Charging and Swapping Coordination

Machine learning-based prediction of lithium-ion battery life cycle for capacity degradation modelling

With a particular focus on capacity degradation modeling, this paper offers a ground-breaking examination of the use of machine learning approaches for the precise prediction of lithium-ion battery life cycles. Because lithium-ion batteries are essential to many technological applications, it's critical to comprehend and anticipate their life cycles in order to maximize performance and guarantee sustainable energy solutions. The study starts with an extensive examination of the literature, assessing current approaches critically and setting the stage for the introduction of models based on machine learning. The process entails the methodical collecting of data across a range of operational settings, environmental variables, and charging-discharging cycles. Thorough preprocessing guarantees the dataset's consistency and quality for further machine learning model training. Predictive models are created using a variety of machine learning algorithms, including regression models, support vector machines, and deep neural networks. In order to improve prediction accuracy, the paper focuses on the reasoning behind model selection, parameter tuning, and the incorporation of ensemble approaches. In order to uncover important elements influencing the life cycles of lithium-ion batteries and provide important insights into degradation mechanisms, feature selection approaches are used. Using cross-validation techniques and real-world lithium-ion battery datasets, the built machine learning models go through rigorous evaluation and validation processes to determine their robustness, capacity for generalization, and performance metrics. Comparing machine learning-based predictions with conventional models, the results are presented and discussed, offering insights into the interpretability of the models and the identification of important affecting elements. In order to promote proactive maintenance and optimize battery usage, predictive models are integrated into real-time monitoring systems. The consequences for battery management systems are examined. The paper continues by discussing the challenges that come with using machine learning to estimate the life cycle of batteries and outlining possible directions for further research and development, such as scalability, interpretability, and the incorporation of emerging technologies. This research contributes to the ongoing efforts to increase the reliability and sustainability of lithium-ion battery technologies by highlighting the potential impact of machine learning on energy storage system optimization.

Maximizing Efficiency in Energy Trading Operations through IoT-Integrated Digital Twins.

The Internet of Things (IoT) has brought about significant transformations in multiple sectors, including healthcare and navigation systems, by offering essential functionalities crucial for their operations. Nevertheless, there is ongoing debate surrounding the unexplored possibilities of the IoT within the energy industry. The requirement to better the performance of distributed energy systems necessitates transitioning from traditional mission-critical electric smart grid systems to digital twin-based IoT frameworks. Energy storage systems (ESSs) used within nano-grids have the potential to enhance energy utilization, fortify resilience, and promote sustainable practices by effectively storing surplus energy. The present study introduces a conceptual framework consisting of two fundamental modules: (1) Power optimization of energy storage systems (ESSs) in peer-to-peer (P2P) energy trading. (2) Task orchestration in IoT-enabled environments using digital twin technology. The optimization of energy storage systems (ESSs) aims to effectively manage surplus ESS energy by employing particle swarm optimization (PSO) techniques. This approach is designed to fulfill the energy needs of the ESS itself as well as meet the specific requirements of participating nano-grids. The primary objective of the IoT task orchestration system, which is based on the concept of digital twins, is to enhance the process of peer-to-peer nano-grid energy trading. This is achieved by integrating virtual control mechanisms through orchestration technology combining task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. The nano-grid energy trading system's architecture utilizes IoT sensors and Raspberry Pi-based edge technology to enable virtual operation. The evaluation of the proposed study is carried out through the examination of a simulated dataset derived from nano-grid dwellings. This research analyzes the efficacy of optimization approaches in mitigating energy trading costs and optimizing power utilization in energy storage systems (ESSs). The coordination of IoT devices is crucial in improving the system's overall efficiency.

Capacity optimization of hybrid energy storage systems for offshore wind power volatility smoothing

Energy storage devices are frequently included to stabilize the fluctuation of offshore wind power’s output power in order to lessen the effect of intermittency and fluctuation on the electrical grid but doing so will raise operators’ investment costs. To obtain the best economic benefits, this paper presents a hybrid energy storage system based on batteries and super-capacitors and its capacity configuration optimization method. First, the wind power output is divided using the wavelet packet decomposition method, and then power is distributed between the batteries and the super-capacitors. Then, the mathematical model of energy storage system optimization is established to optimize the capacity configuration of hybrid energy storage with the objective of minimizing the daily input cost of energy storage, and the configuration schemes of single energy storage and hybrid energy storage are compared. Finally, using the measured data of a domestic offshore wind farm for simulation, several energy storage schemes are compared to verify the feasibility and effectiveness of the proposed method, at the same time, using the hybrid energy storage devices to participate in the grid-connected stabilization task of offshore wind power is more advantageous than the single energy storage, the results show that the daily input cost of the hybrid energy storage scheme is 2.79% and 3.84% lower than that of the single energy storage scheme using only battery or super-capacitor.

Optimization of Energy Storage Systems and Demand Side Management to Maximize Water Utility Savings: A Hawaii Case Study

Abstract Energy conservation is a concern in many industries, and consequently, facility operators are turning to various efficiency measures or alternative power sources to reduce electricity costs. With the expanding use of intermittent resources, energy storage systems (ESSs) and demand side management (DSM) options are also gaining interest to maximize potential energy savings. Here, we study the potential of ESSs versus DSM for water utilities through a case study of the National Energy Laboratory of the Hawaii Authority (NELHA). NELHA is a multizone water utility in which most of its electricity usage is dedicated to pumping water. In this study, the optimization of the overall electricity charges for NELHA, using both ESSs or DSM via pump load shifting and optimization of pump house output is investigated. An optimization algorithm is created to determine the optimal size of the batteries for installation considering the water demand and energy costs in each zone. An extended approach of considering the characteristics of individual pumps on each pump house in the optimization model is applied to provide insight into the proper optimization framework for dispatching pumps depending on the current zonal load, given pump efficiencies, and maximum flowrates from each pump. The outcome from mathematical models using general quadratic pump efficiency functions and a simplified linear version of pump efficiency is compared to determine the significance of this difference in modeling methodology in estimations and evaluations. Additionally, the effect of increasing solar power on electricity purchased is analyzed. This work will help to establish the role of ESS and DSM in energy savings for water utility industry as well as show what methods should be used for evaluation of the potential of ESS and DSM interventions.

Modeling of hydrogen production system for photovoltaic power generation and capacity optimization of energy storage system

Hydrogen production using solar energy is an important way to obtain hydrogen energy. However, the inherent intermittent and random characteristics of solar energy reduce the efficiency of hydrogen production. Therefore, it is necessary to add an energy storage system to the photovoltaic power hydrogen production system. This paper establishes a model of a photovoltaic power generation hydrogen system and optimizes the capacity configuration. Firstly, the mathematical model is modeled and analyzed, and the system is modeled using Matlab/Simulink; secondly, the principle of optimal configuration of energy storage capacity is analyzed to determine the optimization strategy, we propose the storage capacity configuration algorithm based on the low-pass filtering principle, and optimal time constant selection; finally, a case study is conducted, whose photovoltaic installed capacity of 30 MW, verifying the effectiveness of the proposed algorithm, analyzing the relationship between energy storage capacity and smoothing effect. The results show that as the cut-off frequency decreases, the energy storage capacity increases and the smoothing effect is more obvious. The proposed algorithm can effectively reduce the 1 h maximum power variation of PV power generation. In which the maximum power variation of PV generation 1 h before smoothing is 4.31 MW. We set four different sets of time constants, the maximum power variation of PV generation 1 h after smoothing is reduced to 0.751, 0.389, 0.078, and 0.04 MW, respectively.

Impact of climate on photovoltaic battery energy storage system optimization

The optimization of the battery energy storage (BES) system is critical to building photovoltaic (PV) systems. However, there is limited research on the impact of climatic conditions on the economic benefits and energy flexibility of building PV–BES systems. Taking an office building as an example, a method for minimizing the total cost of a PV–BES system was established using a mixed integer nonlinear programming model to optimize the BES capacity. Then, the effects of the grid import/export power limit and peak feed-in tariff on BES capacity optimization were investigated, and the various economic benefits of the PV–BES system were discussed and analyzed accordingly. The results show that the operating cost was reduced significantly when the PV–BES system was employed in any of the evaluated climates; the relative payback period of the PV–BES system in Changsha was the minimum at 1.4 years. The grid import power limit and the peak feed-in tariff had a much greater impact on the battery capacity, energy flexibility, and economic benefits of the PV–BES system than the grid export power limit. The results of this study provide guidance for the application of PV–BES systems in different climates.

Multi-service battery energy storage system optimization and control

Battery energy storage systems (BESS) have become a fundamental part of modern power systems due to their ability to provide multiple grid services. As renewable penetration increases, BESS procurement is also expected to increase and is envisioned to play a systematic and strategic role in power systems planning and operation. Therefore, in this paper we present a multiple grid service procurement and operation approach for BESS, including energy arbitrage, reserve/regulation services, power factor correction, and demand management. The proposed framework considers an optimal multi-temporal dimension and is designed to be operable for both planning and real-time operation. Moreover, the nonlinearity inherent to BESS services and the uncertainty associated with market forecast variables are addressed using techniques such as polyhedral norms and robust optimization approaches. The developed model is tested using a utility-scaled BESS, and the results show the effectiveness of the systematic BESS multi-service planning and operation approach.

Research on capacity configuration optimization of energy storage system participating in peak shaving

The “carbon double” goal calls for a further increase in the proportion of installed renewable energy capacity in China. The need to further expand the share of installed renewable energy capacity in China is increasing the need for ancillary peaking of the grid. There is also an ever-increasing need for peaking. The configuration of the energy storage system is one of the effective ways to reduce the peak load of each unit and increase the receiving space for renewable energy. Taking wind power generation as an example, the optimization model of thermal energy storage system is studied, the charging and discharging control strategy of energy storage system is designed, the integrated load impact on the grid due to wind power generation variation is considered, and the optimal economics and wind power generation are sought to maximize the comprehensive benefits.

Bi-level robust scheduling for multi-terminal DC distribution networks considering uncertainties of loads and renewable energies

The randomness of loads and renewable energies affects the security of DC distribution networks. In this paper, a bi-level robust scheduling method is proposed to improve the security and economy of droop-controlled DC distribution networks. The upper-level scheduling aims to minimize the variance of node voltage deviation from rated value with the optimization of voltage source converter (VSC) operation reference power. The lower-level scheduling aims to minimize the power purchase cost with the optimization of energy storage systems (ESSs). Based on the linear power flow model of droop-controlled DC distribution networks, the upper-level scheduling model is converted to a least square programming model and the lower-level scheduling model is converted to a linear programming model, which can be solved with high computation efficiency. In the scheduling model of VSC operation reference power, the uncertain scenarios are directly established based on the linear power flow model of droop-controlled DC distribution networks to improve the calculation speed of robust algorithm. The adjustable confidence intervals of load variations are used to coordinate the robustness and conservatism. The VSC operation reference power and ESS charging/discharging power are coordinated through iterative optimization. The simulation results of two DC distribution networks show that the proposed scheduling method can effectively improve the security and economy of DC distribution networks.

Energy storage system optimization based on a multi-time scale decomposition-coordination algorithm for wind-ESS systems

Wind power is one of the most important renewable energy sources to build a sustainable power system. Energy storage technologies provide an effective control method for the operation of power systems with high penetration wind power. In existing energy storage system (ESS) optimization methods for wind-ESS systems, different ESS devices are deployed for several typical scenarios separately, including wind output power fluctuation smoothing, power imbalances mitigating, and peak load shaving. But the operational coupling of different ESS devices among these scenarios is usually omitted or simplified improperly. In this paper, the wavelet analysis algorithm was used to obtain three decomposed components of the wind output power with different time scales, and then the decomposition-coordination ESS optimization method was used to solve the constructed corresponding three submodels for different ESS application scenarios. Several coupling variables are designed to coordinate these sub-optimization models with different time scales. In the meantime, the emerging battery ESS has also been discussed in our optimization process based on the battery cost and the net earnings of the whole system to enhance the effectiveness of our proposed optimization method. Compared with traditional ESS deployment methods, our proposed method can reduce the total ESS equivalent investment annual cost by 20% in the selected regional power system in China. Our proposed multi-time scale decomposition-coordination algorithm is suitable for the optimal ESS devices deployment because the coordinated charge–discharge operation of different ESS devices can be inherited taken into consideration for short-term power fluctuation smoothing, power balancing during several hours, and peak load shaving in awhole day.

Performance analysis of an adiabatic compressed air energy storage system with a pressure regulation inverter-driven compressor

Adiabatic compressed air energy storage provides an efficient and emission free approach for large-scale energy storage. In adiabatic compressed air energy storage system with isochoric air storage tank, the throttle valves cause large exergy losses. To reduce throttling loss, a novel system is proposed by regulating the discharging pressure with an inverter-driven air compressor. Variable- and constant-output power operation modes of the novel system are proposed and studied from the aspects of exergy, economic and dynamic characteristic analysis. Furthermore, parametric analysis of the novel system is conducted from the perspective of the system discharge total output power, inverter-driven compressor efficiency and equipment purchase cost. The results indicate that for the novel system, the round-trip efficiency is improved by 1.8–2.7%, and the levelized cost of electricity decreases by 0.57–0.85 ¢/kWh. Hence, the proposed novel system is verified an effective way for adiabatic compressed air energy storage system optimization.

Solar Irradiance Forecast Based on Cloud Movement Prediction

Solar energy production based on a photovoltaic system is closely related to solar irradiance. Therefore, the planning of production is based on the prediction of solar irradiance. The optimal use of different energy storage systems requires an accurate prediction of solar irradiation with at least an hourly time horizon. In this work, a solar irradiance prediction method is developed based on the prediction of solar shading by clouds. The method is based on determining the current cloud position and estimating the velocity from a sequence of multiple images taken with a 180-degree wide-angle camera with a resolution of 5 s. The cloud positions for the next hour interval are calculated from the estimated current cloud position and velocity. Based on the cloud position, the percentage of solar overshadowing by clouds is determined, i.e., the solar overshadowing curve for the next hour interval is calculated. The solar irradiance is determined by normalizing the percentage of the solar unshadowing curve to the mean value of the irradiance predicted by the hydrometeorological institute for that hourly interval. Image processing for cloud detection and localization is performed using a computer vision library and the Java programming language. The algorithm developed in this work leads to improved accuracy and resolution of irradiance prediction for the next hour interval. The predicted irradiance curve can be used as a predicted reference for solar energy production in energy storage system optimization.

Integrated energy system–Hydrogen natural gas hybrid energy storage system optimization model based on cooperative game under carbon neutrality

Under the background that carbon emission reduction has become an important research issue in the world, China has put forward the goal of achieving carbon neutrality by 2060. In order to realize the carbon neutralization of Integrated energy system (IES), this paper first constructs the cooperative game model of Integrated energy system- Hydrogen natural gas hybrid energy storage system (IES-HGESS) considering carbon neutralization. Based on the cooperative game theory, this paper analyzes the driving force of cooperative alliance between IES and HGESS, and puts forward the cooperative game model of IES-HGESS. Secondly, based on the cooperative game, it constructs the clearing model of IES-HGESS in the day ahead market and real-time market. Thirdly, in order to realize the long-term and stable cooperation between IES and HGESS, the cooperation gain coefficient was introduced and the income distribution strategy of IES-HGESS was designed. Finally, the paper takes an IES-HGESS as an example to analyze the case, and the analysis of the calculation example shows that the IES-HGESS alliance can increase the net profit of both parties and achieve mutual benefit and win-win results.

Comprehensive optimized hybrid energy storage system for long-life solar-powered wireless sensor network nodes

Solar energy harvesting is promising to provide long-term power autonomy for wireless sensor networks. Energy storage devices like lithium-ion batteries are usually integrated to solar-powered sensor nodes to overcome the intermittency of solar power. However, the cycle life of lithium-ion batteries is short, which limits the lifetime of the nodes. Therefore, supercapacitor-battery hybrid energy storage system has been used to extend the cycle life of battery. The design of hybrid energy storage systems significantly affects the performance of wireless sensor network nodes in many ways. However, hybrid energy storage system optimization is a comprehensive problem because the configuration and power-split strategy need to be considered concurrently, which has not been done in the existing studies of solar-powered sensor nodes. In this study, a two-port hybrid diode topology is proposed, accompanied by an adaptive supercapacitor buffer energy management strategy to achieve integrated optimization. The hybrid diode semi-active topology uses a unidirectional DC/DC converter accompanied by two diodes to replace the bidirectional DC/DC converter in the existing semi-active topology. Accordingly, the energy lose is decreased by 15.5% while the cost, size, and controlling complexity are also decreased. Moreover, an adaptive supercapacitor buffer energy management strategy is designed based on the new topology to decrease the battery degradation. Simulation and experimental results indicate that the proposed hybrid energy storage system increases the battery lifetime to at least 3 times that of existing hybrid energy storage systems. Finally, a long-life solar-powered wireless sensor network node with a relatively small size and cost is achievable.

Storage-Transmission Joint Planning Method to Deal with Insufficient Flexibility and Transmission Congestion

The insufficient power system flexibility and transmission congestion are two fundamental reasons for wind power curtailment. As the scale of the wind power in the power system is growing rapidly, the two factors of wind power curtailment events coexist and have a certain coupling relationship. The configuration of the energy storage system can not only increase the flexibility of the system but also alleviate transmission congestion. Therefore, the joint planning of energy storage and transmission grid that takes into account the flexibility of the system and the transmission congestion is of great significance to solve the wind curtailment. Hence, this paper first decouples the insufficient flexibility and transmission congestion wind power curtailment, and quantitatively analyzes the impact of transmission capacity on the coupling relationship between the two; second, reveals the principle of joint planning of energy storage system and transmission congestion, and constructs an optimization model, and proposes to set up the capacity of the wind power which connects the power network. The solution procedure, which deals with grid planning and the energy storage system optimization in turn, not only ensures the accuracy of the model, but also significantly reduces the calculation cost. Finally, a case study of a wind power base in Northeast China and an improved Garver-6 system are carried out to verify the effectiveness of the proposed method.

Impact of Climate on Photovoltaic Battery Energy Storage System Optimization

Impact of Climate on Photovoltaic Battery Energy Storage System Optimization

Design of clean energy dispatching system for wind and solar storage

In order to optimize the capacity dispatch of energy storage system in grid-connected wind-solar hybrid power generation system, a method for optimizing the capacity of hybrid energy storage system composed of supercapacitors and batteries in grid-connected wind-solar hybrid power generation system is proposed by utilizing the generation data of wind power and photovoltaic and the dispatch data of power grid. Based on the method of Particle Swarm Optimizer (PSO), it was simulated that wind-solar hybrid power joined into the dispatch according to the rules of dispatch system. The best power dispatch was achieved by multi-objective particle swarm optimization (MPSO). The results show that the accuracy of capacity optimization of energy storage system can be improved by considering both the total continuous power loss and the instantaneous power loss. The hybrid energy storage system composed of supercapacitors and batteries can improve the overall security and economy of the power grid.

Optimization of Energy Storage System Considering Battery Degradation

Energy storage system(ESS) is operated to save and release electric energy. And the battery in an ESS performs an essential function. For this purpose its capacity needs to be finely optimized for specific applications(peak shaving, frequency control, arbitrage, UPS etc).Some papers have already showed the studies about the topic of optimization of battery capacity in an ESS, but a battery degradation factor and irregular operation event have not much been considered. Actually, ESS may experience a very irregular operational event and the decrease of battery capacity during repeated charge/discharge is a major issue to be addressed. In the ESS industry, usually several factors such as state of charge, time, temperature, and C-rate interdependently affect the decrease of battery capacity in a complicated mode. In order to install the ESS in a specific application looking for a reasonable benefit and a sufficent realiability, ESS lifetime prediction technique considering degradation of battery is highly required.In this study, the battery capacity fading through test of dynamic stress factors was quantifized and reflected to designing of ESS battery. The goal is to make an accurate estimating methodology of ESS lifetime and benefit from its installation in specific applications.