Energy Storage Resources Research Articles

Analysis of energy storage operation on the power supply side under a high proportion of wind power access based on system dynamics

With the rapid development of renewable energy sources such as wind energy and solar energy in China, structural problems such as wind and light abandonment, system operation imbalance, and insufficient energy supply have arisen in the process of accelerating the energy transformation process. Given the above problems, this paper uses the system dynamics method for modeling. First, the key variables are selected from the perspective of influencing the economic reliability of the power system. Second, the energy storage operation model of the power supply side under the high proportion of wind power access is established, and the impact of new energy access on the system balance and energy storage configuration is explored. Finally, the cost connection of each module body is used to optimize the allocation of energy storage resources, thereby improving the efficiency of the enterprise itself and providing a new idea for the power generation enterprise to participate in the operation and scheduling of the system.

Service restoration for resilient distribution system with coordination of battery swapping stations and distributed generations

Due to sufficient energy storage resources and central dispatch, the battery swapping station (BSS) offers a promising solution for the service restoration and resilience enhancement of the distribution system (DS). This paper proposes a joint service restoration strategy for the DS by coordinating BSSs and distributed generations (DGs) with distribution network reconfiguration. Firstly, the spatio-temporal distribution of battery swapping demand for electric vehicles (EVs) is analyzed. On this basis, the participation of the BSS in the DS service restoration is investigated, and the model of restoration capacity for the BSS is established, which can describe the power supply ability of the BSS to support the DS restoration. Secondly, a mathematical model for the resilient DS restoration with coordination between BSSs and DGs is established, aiming to minimize the total system cost by optimally allocating BSSs and DGs during the restoration stage. The second-order cone programming (SOCP) relaxation is also adopted to transform the proposed restoration model into a mixed-integer convex formulation, which can be tractably solved. Finally, numerical simulations are conducted based on the modified IEEE 33-bus distribution test system. The results demonstrate the effectiveness of the proposed strategy for the service restoration and resilience enhancement of the DS.

Optimal site selection study of wind-photovoltaic-shared energy storage power stations based on GIS and multi-criteria decision making: A two-stage framework

Wind-photovoltaic-shared energy storage system can improve the utilization efficiency of renewable energy resources while reducing the idle rate of energy storage resources. Using the geographic information system (GIS) and the multi-criteria decision-making (MCDM) method, a two-stage evaluation model is first developed for site selection of wind-photovoltaic-shared energy storage projects. In the first stage, infeasible alternatives are ruled out due to veto identification. In the second stage, the fuzzy MCDM method is applied to further merit selection. Firstly, the comprehensive criteria system is built, and evaluation data is collected using probabilistic linguistic term sets (PLTSs) to ensure the comprehensiveness and scientificity of decision. Then, the new score function based on compromise theory are proposed. Thirdly, the criteria weights are calculated using the improved Decision-Making and Trial Evaluation Laboratory (DEMATEL) approach. Finally, the improved gained and lost dominance score (GLDS) method is used to rank alternatives. To test model's feasibility, an empirical study is conducted in Chifeng City. The alternative A1 appears to be extremely prominent in the results. The model's stability and reliability are demonstrated by sensitivity and comparative analysis. This study aids in the layout of wind-photovoltaic-shared energy storage projects and broadens the application scopes of GIS and MCDM method.

Pricing model of reactive power services of shared energy storage considering baseline power for renewable energy accommodation

Driven by the gradual increase of the renewable energy installed capacity, shared energy storage (SES) has the potential to play an important role in the renewable energy accommodation scenario. In the process of accommodating renewable energy, SES users can also provide reactive power services to earn revenue and further improve the utilization rate of energy storage resources. In this context, a pricing model of reactive power services of SES is proposed in this paper to make SES users obtain reasonable revenue from the provided reactive power response capability. Firstly, based on the analysis of the cost composition, capacity constraint and application value of reactive power services, a cost-based time-variant capacity price is designed considering the investment cost and the opportunity cost of SES users. Moreover, the baseline power for renewable energy accommodation is proposed to quantify the opportunity cost caused by reactive power services in the renewable energy accommodation scenario. Finally, a case study based on the actual data of an SES trading pilot project in Qinghai, China, shows that the proposed pricing model can effectively reflect the costs of reactive power services which are highly coupled to the active power for renewable energy accommodation, and make SES users obtain the reasonable revenue covering the costs.

Pricing and remunerating electricity storage flexibility using virtual links

Ambitious renewable portfolio standards motivate the mass deployment of energy storage resources (ESR) as sources of flexibility. As such, the design of electricity markets that properly remunerate the provision of flexibility services by ESRs is under active development. In this paper, we propose a new market clearing framework that incorporates ESR systems. Compared to existing market designs, our approach models ESR systems using the concept of virtual links (VLs), which capture the transfer/shift of power across time. The VL representation reveals economic incentives available for ESR operations and sheds light into how electricity markets should remunerate ESRs. Our framework also allows us to explore the role of ESR physical parameters on market behavior; specifically, we show that, while energy and power capacity defines the amount of flexibility each ESR can provide, charge/discharge efficiencies play a fundamental role in ESR remuneration and in the ability of the power grid to mitigate market price volatility. The new market design is also computationally attractive in that it is a linear program and thus avoids mixed-integer formulations and formulations with complementarity constraints (used in current designs to capture binary charge/discharge logic). We use our market framework to analyze the interplay between ESRs and market operators and to provide insights into optimal deployment strategies for ESRs in power grids.

Energy management programming to reduce distribution network operating costs in the presence of electric vehicles and renewable energy sources

Due to growing environmental and economic constraints, countries are exploring renewable sources such as wind, solar, and fuel cells to save energy, and develop the use of dispersed generations. Thus, the use of electric vehicles (EVs) is on the rise. On a large scale, either of these technologies can have damaging effects on the electricity grid; however, with suitable consumption-side management and programming, technologies and energy storage resources can reduce these effects. Thus, energy management optimization has become an interesting topic of research. Accordingly, the effect of the integrated aggregation of PEVs to the grid for the charge/discharge process and the resulting grid instability, especially at load peak time, is the main challenge to the use of these vehicles. The contribution of this paper includes the presentation of a model for managing the coordinated and uncoordinated charging system of grid-connected EVs with wind power and photovoltaic power units as dispersed generation sources and dividing the EVs into 4 classes by considering the share of each in the grid and considering a random number of vehicles per class using the normal distribution function and implementing the incoordination in wind speed and solar irradiation. The proposed model uses a novel Reinforcement Learning (RL) based onDeep Q Network (DQN) algorithm to solve the multi-objective problem. In this model, the costs of annual energy losses and the operation of dispersed generation units are discussed in an integrated manner as the objective function. The simulation is performed on a 57-bus IEEE grid and the results show the efficiency and improved performance of the model.

Using inventory as energy storage for demand-side management of manufacturing operations

Traditionally, electrical grids were designed to have unidirectional flow of power and related services from the utility to the end-user. Modern grids are now tasked with bi-directional flow of power and information. To capitalize on this reality, utilities have designed programs that financially incentivize energy consumers to adjust their energy profile in such a way that is advantageous to the utility. At the same time, users seek to maximize the benefits offered by the utilities. Since this is a complex decision-making process, particularly as users continue to adopt energy storage, solar photovoltaics, and other distributed energy resources, the need for decision support tools is increasingly important. To maximize the financial incentive received by the user, and the benefit gained by the utility, this paper proposes an optimization model to aid manufacturers in operating a battery energy storage device while also using product inventory as additional energy storage. Using inventory as additional energy storage is accomplished by scheduling production to build buffers of inventory during low electricity cost times so that production may be reduced during high electricity cost times. We refer to this problem as the energy job-shop scheduling problem (EJSSP). To accomplish these efforts, a deterministic optimization model is developed in order to determine optimal production and energy storage schedules. It is shown that under the proper conditions, utilities can work directly with energy users such that both parties stand to gain from cooperation with one another. Using several synthetic scenarios, it is found that more flexible manufacturing configurations can have as high as 25% more relative energy cost savings when compared to their more rigid counterparts. Moreover, production scheduling allows inventory to be effectively used as energy storage. Ultimately, the methodology forms the foundation of a gateway for electric utilities and their customers to align their interests and act cooperatively.

A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures

Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a learning model, we put forward corresponding solutions using the concept of style migration solutions, such as training samples. A lack of variability and poor generalization of practical applications presents a challenge for underwater object identification. The UW_YOLOv3 lightweight model was proposed to solve the problems of calculating energy consumption and storage resource limitations in underwater application scenarios. The detection and recognition module, based on deep learning, can deal with the degradation process of underwater imaging by embedding an image enhancement module into the detection and recognition module for the joint tuning and transferring of knowledge. Results: The detection accuracy of the UW_YOLOv3 model designed in this paper outperformed the lightweight algorithm YOLOV3-TINY by 7.9% at the same image scale input. Compared with other large algorithms, the detection accuracy was lower, but the detection speed was much higher. Compared with the SSD algorithm, the detection accuracy was only 4.7 lower; the speed was 40.9 FPS higher; and the rate was nearly 16 times higher than Faster R-CNN. When the input scale was 224, although part of the accuracy was lost, the detection speed doubled, reaching 156.9 FPS. Conclusion: Based on our framework, the problem of underwater optical image target detection and recognition can be effectively solved. Relevant studies have not only enriched the theory of target detection and glory, but have also provided optical glasses with a clear vision for appropriate underwater application systems.

Distributed energy resource interconnection: An overview of challenges and opportunities in the United States

Decarbonizing the energy sector and electrifying buildings and transportation requires the rapid and cost-efficient build-out of solar, energy storage, demand management, and other distributed energy resources (DERs). Many jurisdictions have recognized the critical importance of DERs to the clean energy transition and are developing solutions to enable the more rapid and cost-efficient integration of DERs onto the electric grid. At the same time, as the scale and speed of DER deployment grows, more challenges are emerging, including lengthy interconnection timelines, costly grid upgrades, and regulatory and technical barriers to enabling the variety of benefits DERs can provide to the grid. Interconnection, in particular, can pose a significant challenge to growing DER penetration. Interconnection rules and regulations spell out the procedural and technical requirements for connecting DERs to the grid. When these rules are well-designed, they can support faster and more cost-effective DER integration, but when they are outdated or poorly designed, they can slow or even halt DER growth. This article describes some of the key challenges that U.S. states are facing with DER interconnection to the distribution system, and highlights leading solutions to address those challenges. While the article focuses on DER interconnection rules and regulations in the United States, many of the lessons learned and solutions are likely applicable to other jurisdictions that are experiencing rapid DER market growth.

Resilience Oriented Planning of Urban Multi-Energy Systems With Generalized Energy Storage Sources

In the last decade, a number of severe urban power outages have been caused by extreme natural disasters, e.g., hurricanes, snowstorms and earthquakes, which highlights the need for rethinking current planning principles of urban energy systems and expanding the classical reliability-oriented view. In addition to being reliable to low-impact and high-probability outages, power system should also have high level of resilience to withstand high-impact and low-probability (HILP) events. Compared with power system, multi-energy systems (MESs) have advantages in improving resilience through energy shifting across multiple energy sectors, a variety of generalized energy storage resources and thermal inertia of heat/cooling loads. This paper proposes a resilience-oriented planning method to determine optimal configuration of distribution level MES, e.g., urban energy supply systems, considering comprehensive impacts from supply, network and demand sides in MES. Impacts of energy shifting at supply side, pipe storage at network side and thermal inertia at demand side are described in the same linear modeling framework using energy hub (EH) model. Generalized energy storage resources including centralized and distributed energy storage devices, pipe network storage and building heat capacity are all modeled into centralized energy storage to facilitate an efficient configuration planning of MES.

RETRACTED: Stochastic scheduling of a power grid in the presence of EVs, RESs, and risk index with a developed lightning search algorithm

- Due to the rise in environmental and economic limitations in today's world, the use of dispersed generation that often applies renewable energies, and electric vehicles is markedly increasing. On a large scale, either of these technologies can have damaging effects on the electricity grid; however, with proper energy management and programming, technologies, and energy storage resources can reduce these effects. Accordingly, the impact of the integrated aggregation of EVs to the grid for the charge/discharge process and the resulting grid instability, especially at the load peak time, is the main challenge usig of these vehicles. A challenge associated with these resources is their intrinsic uncertainty and stochastic scheduling. Herein, the stochastic scheduling of a power system in the presence of electric vehicles (EVs) and renewable energy sources (RESs) is solved via the optimization algorithm developed based on lightning search. The proposed model uses the probabilistic method to generate different scenarios for wind speed and solar irradiation coefficient; then, it modifies them via the scenario reduction method. In the proposed method, the conditional value at risk (CVaR) method is adopted to evaluate and manage the risk of the problem uncertainties. The use of the capacity stored in the EV batteries for overcoming the uncertainty of generation by wind and solar energy sources is also evaluated. Finally, the proposed method is tested on a modified sample system. The simulation results show that the power capacity stored in the vehicle-to-grid (V2G) stations greatly contributes to covering the uncertainty of wind and solar farms. The optimization algorithm is superior to other methods in local and global search.

Effect of Capacitor Voltage Ripples on Submodule Active Power Control Limits of Cascaded Multilevel Converters

In the operation of cascaded H-bridge converters and modular multilevel converters with energy storage or renewable power resources, unbalanced active power distribution among the submodules (SMs) is unavoidable. Depending on the operating conditions, there are certain upper and lower limits on the active power that can be processed by a single SM or a subset of SMs. The control system needs to restrict the SM power references to these limits, hence, accurate knowledge of the power limits is important. In existing methods to derive the power limits, the SM capacitor voltages are assumed to have negligible ripples, whereas in practice the ripples can be considerable. This article analyzes the effect of capacitor voltage ripples on the SM active power control limits and highlights the importance of considering the ripple effect. A methodology is proposed to accurately incorporate capacitor voltage ripples in the derivation of SM active power control limits. Simulation and experimental results are provided to evaluate the effectiveness of the proposed methodology.

Energy storage resources management: Planning, operation, and business model

Energy storage resources management: Planning, operation, and business model

A robust optimization framework for energy management of CCHP users with integrated demand response in electricity market

Micro-energy grid for CCHP users can make full use of local renewable energy and integrated demand response (IDR) resources to meet the electric, cooling, heating and other load demands in electricity market. In this paper, aiming at the uncertainties of renewable energy output and electric, cooling and heating load in micro-energy grid, a mode of IDR resources flexibly participating in day-ahead scheduling and intra-day real-time regulation stage in micro-energy grid is established, and a two-stage robust scheduling optimization model with IDR considered is constructed. In the model, the cost and constraints of IDR resources participating in two-stages is considered, and the uncertain budget parameter is introduced for regulating economy and robustness of the system. In day-ahead stage, the day-ahead scheduling cost is taken as the goal to determine the output of each unit, energy storage and IDR resource response mode. Based on the optimization results of day-ahead stage, each regulation quantity of the system is determined with the goal of minimizing the real-time regulation cost within a day. Strong duality theory and column constraint generation algorithm is used to transform and solve the robust optimization problem of min–max-min structure. The results show that the robust optimization model can improve the system's ability to resist uncertain risks, and the integrated demand response can improve the economy of the robust optimization model and the self-sufficiency of micro-energy grid.

Distributed Energy Storage Cluster Control Method for DC Microgrid Considering Flexibility

As the core support, when we develop some new energies, the energy storage industry and energy storage technology cover both the power supply, grid and the user side, residential side, and socialized functional store of the energy facilities. If we can implement the storage of energy by the distributed unit, we can save the time and space translation of energy in function and has the characteristics of flexible layout and installation at the equipment level. It is important to promote the production and consumption of renewable energy and improve the operational reliability of alternating current or direct current AC/DC hybrid power grids. It is a virtual form and develops a tendency for energy storage resources in the power grid in the future. However, as an emerging technology and resource, the use of distributed energy storage still has problems such as low efficiency, high idle rate, and single functional scenarios. Distributed energy storage systems can be used almost everywhere around the system of power, have broad application prospects and huge application potential, and will become more and more significant for the power grid in the near future. In general, the distributed energy storage system now is at a primary level of engineering application, and deep study about it still exists various key technologies which needed to lay the foundation for its popularization and application. In this paper, by constructing a microgrid experimental system containing a variety of distributed energy storage systems, research is carried out around the modeling, control, efficiency analysis, and energy management of distributed energy storage systems.

Highly graphitized porous biocarbon nanosheets with tunable Micro-Meso interfaces and enhanced layer spacing for CO2 capture and LIBs

• Generation of porosity cum graphitization in carbon materials. • Controlled tuning of the porous features and degree of graphitization. • Easily manipulated content of micropores and mesopores. • High CO 2 adsorption of 5.9/16.7 mmol g −1 at 0 °C and 1/30 bar. • Graphitization and porosity led to a good specific capacity of 646 mAh g −1 in LIBs. Porous carbon materials with tunable micro and mesoporous structure, graphitic wall structure, and enhanced layer spacing are considered attractive materials for several applications, including adsorption and energy storage and conversion. However, it is challenging to design porous carbon with all these properties in a single system. Here, we present a facile and rational synthesis route to fabricate graphitized porous biocarbon nanosheets from a low-cost precursor through a simple integration of simultaneous “activation and graphitization” process using iron acetate and potassium acetate at a high temperature. The prepared materials show tunable micro and mesoporosity with an enhanced layer spacing and high crystallinity. These sophisticated materials exhibit a smooth switch over the micro and mesopores for either low (5.9 mmol g −1 / 0 °C/ 1 bar) or high pressure (16.7 mmol g −1 / 0 °C/ 30 bar) CO 2 capture. The careful manipulation of the porous texture and the graphitization degree also allows for enhanced performance as LIB anodes (646 mAh g −1 ), which show good cycling and surpass the specific capacity of conventional graphite anode (372 mAh g −1 ). These findings exemplify the importance of designing intriguing materials for addressing climate change by reducing greenhouse gases as well as providing low-cost alternative energy storage resources.

Automatic Demand Response Method for the Energy Storage Resource System Based on the Blockchain Technology Combined with Sensors

In order to improve the efficiency of the automatic demand response of the energy storage resource system, a user authentication and key agreement scheme for wireless sensor networks based on blockchain is proposed. The user does not need to register with the gateway node. As the light node of the blockchain network, the two can directly authenticate each other. The user obtains the public information of the sensor node from the blockchain through the client and verifies its legitimacy. It solves the problem that the traditional wireless sensor network user authentication scheme cannot be applied to the distributed Internet of things environment and the authentication process is low in security and efficiency. Mode 1 is the disordered charging mode. The charging facility provides continuous constant power charging service for the connected response subject until the user leaves. If the response subject is full before this, the charging will stop. Mode 2 is the BADR operation mode under charging optimization only, that is, the V2G function of PEV is not considered. In this case, 0 ≤ P l , k ≤ P l max c . Mode 3 is the BADR operating mode with V2G functionality in mind. The ELN load characteristics under the above three operating modes are discussed, respectively. Calculate and compare with economy on both sides of supply and demand. The experimental results show that the discharge compensation coefficient will not affect mode 2. In mode 3, as the compensation coefficient increases (from 0.4 to 1.2 at intervals of 0.2), the average total cost of the PEV cluster continues to decrease and the average total cost of the energy local network (ELN) system increases monotonically. Comparing the curves of the economic impact of the compensation coefficient on both sides of mode 2 and mode 3, it can be seen that the reasonable setting of the compensation coefficient is one of the key parameters for controlling the distribution of benefits. It proves that the proposed blockchain-based automated demand response (BADR) method can not only meet the satisfaction of energy demand but also coordinate the available energy storage resources in the system, so that the load level curve can track the output of new energy to the greatest extent and improve the balance between supply and demand of the system, which is suitable for a large-scale decentralized energy storage system.

A techno-economic assessment framework for hydrogen energy storage toward multiple energy delivery pathways and grid services

Hydrogen energy storage (HES) transforms and stores electric energy from the grid into hydrogen, and supplements other energy storage and demand response resources in addressing challenges in renewable-intensive power systems. Understanding how to optimally utilize an HES system to maximize its economic benefits from stacked value streams is highly important to its development and deployment. This paper presents a techno-economic assessment framework for an HES system considering three common energy delivery pathways and multiple grid and end-user services. Models are developed to capture the operational capability, flexibility, and constraints associated with hydrogen production, compression, storage, and utilization as well as different grid services in an economic assessment. To define the technically achievable benefits, an optimal dispatch formulation is proposed to maximize the economic benefits over a representative year with an hourly time step considering the trade-offs among different value streams. Representative case studies are designed and carried out to show how system configuration, energy delivery pathways, and grid services may affect economic benefits. It was found that value streams from bundling grid services account for up to 76% of the total benefits and are critical for an HES project to be financially viable.

Optimal and stochastic performance of an energy hub-based microgrid consisting of a solar-powered compressed-air energy storage system and cooling storage system by modified grasshopper optimization algorithm

Simultaneous operation of different energy generation and transmission infrastructures is a subject that has been considered under the concept of energy hub. This subject is highly regarded in the field of microgrids. One of the basic issues for investors is to properly utilize the energy hub for optimally managing energy carriers, especially in the energy price prediction. In the present paper, a new strategy is introduced for the energy hub in order to achieve the optimal performance of a microgrid (MG) that includes different energy carriers for each day. The objective of this strategy is to minimize the operation cost and consider the environmental issues. The proposed energy hub consists of a combined cooling-heating-power (CCHP) system along with a wind turbine and photovoltaic cells. The studied energy hub system is composed of an ice storage conditioner (ISC) system and an energy storage system (ESS) as the energy storage resource (ESR). One of the goals of the present work is to investigate the effect of solar-powered compressed-air energy storage (SPCAES) on the performance of the energy hub. The proposed strategy takes into account the uncertainty of the energy resources such as the wind and sun for meeting the electric, thermal, and cooling needs in different scenarios. In the present paper, to produce various scenarios, the Latin hypercube sampling (LHS) method is used. Also, the k-means method is used to reduce the number of scenarios. The objective function is solved using the modified grasshopper optimization algorithm (MGOA). According to the modeling results, the ESS can exhibit successful performance in the energy management strategy. • Considering the effects of the SPCAES and ISC on optimal performance of the energy hub. • Considering the effects of the SPCAES and ISC on the optimal performance. • Application of generations sources as PV, WT, CHP system, and TESS.

Planning Method and Principles of the Cloud Energy Storage Applied in the Power Grid Based on Charging and Discharging Load Model for Distributed Energy Storage Devices

The cloud energy storage system (CES) is a shared distributed energy storage resource. The random disordered charging and discharging of large-scale distributed energy storage equipment has a great impact on the power grid. This paper solves two problems. On one hand, to present detailed plans for designing an orderly controlled CES system in a realistic power system. On the other hand, Monte Carlo simulation (MCS) is used for analyzing the load curves of five types of distributed energy storage systems to manage and operate the CES system. A method of its planning and the principles of CES for applied in a power grid, are presented by analyzing the impact based on five load curves including the electric vehicle (EV), the ice storage system, the demand response, the heat storage system, and the decentralized electrochemical energy storage system. The MCS simulates the random charging and discharging of the system over a five-year planned scaling of distributed energy storage from 2021 through 2025. The influence of distributed energy storage systems on power grid capacity, load characteristics, and safety margins is researched to summarize the applicable fields of CES in supporting large power grids. Finally, important conclusions are summarized and other research possibilities in this field are presented. This paper represents a significant reference for planners.