Distributed Energy Storage Research Articles

Understanding technological innovation and evolution of energy storage in China: Spatial differentiation of innovations in lithium-ion battery industry

China has attached great importance to technology innovation of lithium battery and expects to enhance its efficiency in distributed energy storage systems. The driving factors of technological innovation are often closely related to regional resources, spatial elements and intellectual factors. This has been confirmed by previous research such as in smart grid, but the exploration of lithium battery, an important field, has not been carried out. Based on the industrial location and spatial theory, this study obtained the patent data of lithium-ion batteries (LiB) and technology information. Based on spatial methods such as standard deviation ellipse and Moran index, this paper visually analyses the spatial patterns that influence the technological innovation of LiB in China, and discusses its driving factors in different development periods. The results show that: (1) The LiB innovation space in China is dominated by the Pearl River Delta, supplemented by the Yangtze River Delta and the Beijing-Tianjin-Hebei region, forming a multipolar pattern. (2) The driving force of innovation space evolution is different. Research institutions and enterprises occupy different leadership positions in different regions. (3) The concentration index of the LiB innovation indicates that innovation in neighbouring provinces of China is spatially dependent and increasingly significant. The market structure has a positive impact on the innovation efficiency of the lithium battery industry, and this positive impact also has characteristics of spatial heterogeneity.

A multi-stage sequential load restoration method for under-frequency load shedding with electric vehicles participation

As a low-cost and high-efficiency distributed energy storage resource, electric vehicles (EVs) play an increasingly prominent role in power system regulation. Since EVs are the regulation resources on the demand side, EVs should return to the initial states as soon as possible after completing the grid regulation task to avoid affecting the charging demand. Although EVs can provide a more flexible response to under-frequency load shedding (UFLS) than load shedding, the disorderly recharging of EVs during the load restoration stage may cause a significant second disturbance to the system frequency after UFLS. To ensure frequency stability and simultaneously restore the power supply, a multi-stage sequential load restoration method for UFLS with EVs participation is proposed to coordinate the priority and sequential restoration between the shedding load and recharging power of EVs. In the proposed method, the restoration willingness of EVs is quantified by the membership function, and the EV recharging optimization model with the objective function of maximizing the user’s restoration willingness satisfaction is established based on a two-stage robust under-frequency load shedding framework. Simulation results show that the proposed method can ensure system frequency stability and complete the load restoration quickly, effectively avoiding the secondary frequency drop caused by the disorderly recharging of EVs.

Charging private electric vehicles solely by photovoltaics: A battery-free direct-current microgrid with distributed charging strategy

Charging electric vehicles (EV) by photovoltaics (PV) contributes to achieving carbon neutrality, but puts pressure on urban renewal, e.g., large investments in distribution grid upgrade and energy storage (ES). To solve this problem, we proposed a charging system aiming at providing intermittent but free solar charging service for private EV drivers to cover their daily intra-urban transportation demand. It is a battery-free direct-current (DC) microgrid with a distributed charging strategy, taking variable DC bus voltage as a control signal. The system was optimized and tested by simulation on an office building with 80 private EVs. We performed a system performance comparison, when achieving the same guarantee level of EV use with the parking shed fully cover by PV. A 10-kW charger (DC Level 1) with the strategy can take the role of a 3.5-kW charger (AC Level 2) with ES (26.0 kWh/parking space) to coordinate PV generation and charging load. Furthermore, connecting the system with the adjacent building can increase the annual solar self-consumption rate from 33.3% to 67.9%, and avoid the distribution grid upgrade (average 5.6 kVA/charger). The discounted payback time of this building-connected system decreases from 9.4 to 4.5 years, compared with a conventional solar charging station.

Research on a Multi-Agent Cooperative Control Method of a Distributed Energy Storage System

For the flexible regulation requirements of new power systems with a high proportion of new energy, this paper proposes a multi-point distributed energy storage system control method based on the idea of multi-agent cooperative control. On the one hand, the method transforms and upgrades the strategies of each distributed battery energy storage control system to make it a terminal agent with active response and control functions; on the other hand, a coordination agent is set between the power grid and each terminal agent, the coordinated control of the distributed energy storage system is realized by the information interaction between the coordination agent and the terminal agent. The simulation results show that the method can fully exploit the aggregate regulation ability of the distributed energy storage system, quickly stabilize the frequency fluctuation of new energy, and support the safe and economical operation of the system.

A distributed PageRank-based dynamic partition algorithm to improve distributed energy storages participation in frequency regulation

In high renewable energy sources penetrated power systems, a large number of distributed small-capacity energy storages (ESs) are expected to form virtual power plants (VPPs) and provide comparable frequency regulation capacity to conventional power plants. In this paper, a dynamic and distributed partition method is proposed to cluster ESs into VPPs by selecting the electrical distance and local power demands as the index for partitioning. Utilizing the partition indices, the Personalized PageRank (PPR) scores are used to measure the similarity of all ESs with respect to other ESs in parallel. Then Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to partition the ESs into different VPPs. Within the VPPs, the secondary frequency control strategy is adopted to achieve the frequency regulation and state of charge (SoC) balancing. Finally, the performance comparison of the proposed partition method with several classic clustering algorithms is conducted to validate the efficiency and the quality of the partition. And numerical studies are performed in an IEEE 39-bus system to verify that the proposed control method can eliminate the frequency deviation and reduce power losses.

Recent advances in ammonia synthesis technologies: Toward future zero carbon emissions

As a carbon-free molecule, ammonia has gained great global interest in being considered a significant future candidate for the transition toward renewable energy. Numerous applications of ammonia as a fuel have been developed for energy generation, heavy transportation, and clean, distributed energy storage. There is a clear global target to achieve a sustainable economy and carbon neutrality. Therefore, most of the research's efforts are concentrated on generating cost-effective renewable energy on a large scale rather than fossil fuels. However, storage and transportation are still roadblocks for these technologies, for example, hydrogen technologies. Ammonia could be replaced as a viable fuel for a clean and sustainable future of global energy. More efforts from governments and scientists can lead to making ammonia a clean energy vector in most energy applications. In this review, ammonia synthesis was assessed, including conventional Haber–Bosch technology. Current hydrogen technologies as the key parameters for ammonia generation are also evaluated. The role of ammonia as a hydrogen-based fuel and generation roadmap are discussed for future utilization of energy mix. Further, ammonia generation processes are addressed in depth, including blue and green ammonia generation. A survey of ammonia synthesis catalytic materials was conducted and the role of catalyst materials in ammonia generation was compared, which showed that the Ru-based catalyst generated the maximum ammonia after 20 h of starting experiment. An end-use plan for using ammonia as a clean energy fuel in vehicles, marines, gas turbines as well as fuel cells, is briefly discussed to recognize the potential applications of ammonia use. The practical and future end-use vision of energy sources is proposed to achieve great benefits at low carbon emissions and costs. This review can provide prospective knowledge of large-scale aspects and environmental considerations of ammonia. Herein, we conclude that ammonia will become the “clean energy carrier link” that will achieve the global energy and economy sustainability targets. • The ammonia synthesis processes were assessed including conventional Haber-Bosch technology. • The catalytic materials of ammonia synthesis were reviewed. • The novel analysis of ammonia production methods has been compared. • Fundamental of ammonia combustion was reviewed. • The future end-use vision of energy sources was proposed to achieve low carbon emissions.

Cloud energy storage in power systems: Concept, applications, and technical challenges

AbstractCloud energy storage (CES) in the power systems is a novel idea for the consumers to get rid of the expensive distributed energy storages (DESs) and to move to using a cloud service centre as a virtual capacity. Although the different characteristics and applications of the energy storages are reviewed in some papers, there is no review study on the CES concepts, formulations, applications, and challenges. Therefore, the main contribution of this paper is to review the applications of the CES and its technical challenges in the power systems. For this purpose, the concept and fundamentals of the CES, as well as their role in supporting the consumers and the power network, are described first. The flow of information in a CES is then discussed, and the roles of the operator, consumers, and facilities, as the main sectors of the CES are explained. The existing studies are classified and discussed regarding the different applications of the CES in the power systems and their drawbacks are highlighted. The operation and planning (feasibility) problems of the CES are investigated. Reviewing the existing studies shows that comprehensive models are required to address the energy management (EM) and feasibility analysis of the CES applications. To address this challenge, the general formulations are presented for the planning and the operation scheduling problems of the CES. In addition, addressing different CES applications in the power systems leads to some technical challenges which are described. Finally, future directions are suggested for potential researchers to continue the studies on the CES integration and application.

Multi-source coordinated stochastic restoration for SOP in distribution networks with a two-stage algorithm

After suffering from a grid blackout, distributed energy resources (DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience. In this study, a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points (SOPs). Here, the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations. Owing to the uncertainty, a scenario-based stochastic optimization approach was employed, and the load restoration problem was formulated as a mixed-integer nonlinear programming model. A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods. The algorithm is organized into a two-stage structure, in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem. In the second stage, an integer programming problem is calculated to acquire the outputs of both SOPs and power resources. A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy.

Hierarchical Intelligent Operation of Energy Storage Systems in Power Distribution Grids

High penetration of distributed energy storage systems (ESS) offers an unparalleled opportunity to reinforce the distribution grid at the local level against upstream disruptions; however, their mass operation under uncertainty of load and renewable generation is computationally expensive. While deep reinforcement learning (DRL) has been suggested to train operator agents capable of handling uncertainty and high dimensionality of the problem, it falls short when safety and feasibility assurances are required in critical operations. This paper proposes a model for hierarchical coupling of DRL and mathematical optimization for operation of ESS in distribution grids, in order to take advantage of DRL fast response while keeping network constraints in check. In the proposed method, strategic scheduling of distributed ESS units are performed locally by fast DRL-trained agents, while critical grid-wide operations such as fault management and voltage control are performed by an optimization-based central controller. The local controller is trained by Twin Delayed Deep Deterministic Policy Gradient (TD3), whose response time is three orders of magnitude faster than stochastic optimization, while the optimality of solutions are similar in both cases.

Location and Capacity Determination for Energy Storage System Involved in Compensating Voltage Sag

For the energy storage system participating in the grid voltage sag compensation service, a location and capacity determination method based on the joint compensation strategy of distributed energy storage systems and dynamic voltage restorer is proposed. Firstly, a two-layer siting and determining capacity model for distributed energy storage systems is established, with the upper layer aiming to minimize the installation cost of distributed energy storage systems and the voltage crossing cost of sensitive loads, and determine the optimal access point for energy storage system. The lower layer determines the optimal configuration capacity of the energy storage system by targeting the compensation cost of the energy storage system, the dynamic voltage restorer, and the voltage satisfaction of sensitive loads. Then, the arithmetic simulation with IEEE33 bus system shows that the proposed method can select a reasonable access point for energy storage system and reduce the configuration capacity.

Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview

The increasing electric vehicle (EV) adoption in the context of transportation electrification and carbon neutrality pursuit brings both new challenges and opportunities for all the stakeholders in EV-grid integration. To fully unleash the potentials of EVs as flexible distributed energy storage to facilitate efficient EV-grid interactions, it is imperative to predicting spatio-temporal distributions of EV charging demand, optimizing charging infrastructure layout and implementing smart charging scheduling schemes. Appropriate EV behavior modeling plays a fundamental role to realize these targets. This paper aims to provide a comprehensive review on EV behavior modeling and its applications in EV-grid integration algorithm development. Various models have been developed to describe EV usage patterns, charging decision making process and response to smart charging schemes. In particular, the existing usage pattern models including temporal, spatial and energy sub-models are expounded, and different sub-models of charging choice and response to smart charging are also presented. An EV behavior modeling paradigm is proposed to provide guidance for EV behavior model selection in different application scenarios by developing different portfolios of temporal, spatial, energy usage, charging choice and response models. Accordingly, enabling EV behavior modeling for EV charging demands prediction and smart charging scheduling is covered in details. This study provides in-depth behavioural insights and viable approaches to developing efficient EV behavior models for advancing EV-grid integration and provides perspectives towards future research directions.

Multi-Agent-Based Controller for Microgrids: An Overview and Case Study

A microgrid can be defined as a grid of interconnected distributed energy resources, loads and energy storage systems. In microgrid systems containing renewable energy resources, the coordinated operation of distributed generation units is important to ensure the stability of the microgrid. A microgrid needs a successful control scheme to achieve its design goals. Undesirable situations such as distorted voltage profile and frequency fluctuations are significantly reduced by installing the appropriate hardware such as energy storage systems, and control strategies. The multi-agent system is one of the approaches used to control microgrids. The application of multi-agent systems in electric power systems is becoming popular because of their inherent benefits such as autonomy, responsiveness, and social ability. This study provides an overview of the agent concept and multi-agent systems, as well as reviews of recent research studies on multi-agent systems’ application in microgrid control systems. In addition, a multi-agent-based controller and energy management system design is proposed for the DC microgrid in the study. The designed microgrid is composed of a photovoltaic system consisting of 30 series-connected PV modules, a wind turbine, a synchronous generator, a battery-based energy storage system, critical and non-critical DC loads, the grid and the control system. The microgrid is controlled by the designed multi-agent-based controller. The proposed multi-agent-based controller has a distributed generation agent, battery agent, load agent and grid agent. The roles of each agent and communication among the agents are designed properly and coordinated to achieve control goals, which basically are the DC bus voltage quality and system stability. The designed microgrid and proposed multi-agent-based controller are tested for two different scenarios, and the performance of the controller has been verified with MATLAB/Simulink simulations. The simulation results show that the proposed controller provides constant DC voltage for any operation condition. Additionally, the system stability is ensured with the proposed controller for variable renewable generation and variable load conditions.

The energy storage and optimal dispatch supply chain for new energy grids using edge computing and the internet of things

AbstractThe intermittent and uncertainty of new energy in the grid connection process affects the overall quality of the grid. To resolve the scattered geographical locations, small individual capacities and poor controllability of distributed energy storage (DES) devices, edge computing is applied in conjunction with aggregate service providers to build an edge computing‐based aggregate model of DES. Regarding its characteristics, the schedulable potential of the energy storage device is analysed through modelling. A multi‐objective scheduling model with multiple parties participating in the subject's behaviour is established using the Internet of Things (IoT) optimization strategy. The simulation example of concrete data further verifies the validity of the model. Results demonstrate that the edge computing‐based aggregate model of DES effectively reduces the calculation load's peak‐valley load and reduces the wind power abandonment. In the meantime, the multiple starts and stops of the thermal power unit reduce the operation cost of the unit. The multi‐objective dispatch model can reduce the opportunity cost and payment of DES effectively. This model achieves load peak reduction and valley filling and reduces the peak dispatch cost of the power grid. The research results can provide some ideas for storing and utilizing the new energy.

MILP Model for Optimal Day-Ahead PDS Scheduling Considering TSO-DSO Interconnection Power Flow Commitment Under Uncertainty

We propose a comprehensive framework for the optimal day-ahead scheduling of power distribution systems (PDS), based on a mixed-integer linear programming (MILP) model. The interaction between transmission system operator (TSO) and distribution system operator (DSO) is considered, where TSO informs DSO of the day-ahead forecast concerning the expected power flow (PF) commitment at the TSO-DSO interconnection points and the violation costs. We propose a MILP model to determine the optimal voltage and power settings of distributed energy resources (DERs), such as dispatchable distributed generators and energy storage units and optimal adjustments of capacitor banks controlled by current. The objective function includes the minimization of energy losses, voltage violations, power curtailment of DERs using volt-watt strategy, and violation of TSO-DSO interconnection PF commitment. Furthermore, we propose a method to estimate the degree of uncertainty in the PF commitment. Therefore, the proposed method can help achieve an optimal operation of the distribution system; in addition, the TSO can best model uncertainty at TSO-DSO interface points and thereby procure reduced amounts of resources to address these uncertainties. Numerical results obtained for a system based on real data highlight the several potential applications of the proposed framework.

Study of the impact of DC distribution network under large-scale electric-powered vehicle access

As a high-standard verdant method of transportation, electric-powered-vehicles have become the development hotspot of the automotive business in recent years. Large scale access to electric-powered vehicles can form a distributed energy storage system, which can improve the operational performance of power grid peak shaving and frequency modulation, and improve the comprehensive utilization efficiency of system energy. But at the same time, large-scale electric-powered vehicles connected to the DC distribution network will have a certain impact on its topology. Therefore, this paper studies the impact of the access location and number of electric-powered vehicles on the network topology and establishes a multi-dimensional evaluation system of the DC distribution network topology. The application of this result can play an evaluation and guidance role in the operation stability of the DC distribution network in the case of large-scale access to electric-powered vehicles.

Analytical uncertainty propagation for multi-period stochastic optimal power flow

The increase in renewable energy sources (RESs), like wind or solar power, results in growinguncertainty also in transmission grids. This affects grid stability through fluctuating energy supplyand an increased probability of overloaded lines. One key strategy to cope with this uncertainty isthe use of distributed energy storage systems (ESSs). In order to securely operate power systemscontaining renewables and use storage, optimization models are needed that both handle uncertaintyand apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained DC optimalpower flow (CC-OPF) model, that minimizes generation costs and includes distributed ESSs. AssumingGaussian uncertainty, we use affine policies to obtain a tractable, analytically exact reformulation asa second-order cone problem (SOCP). We test the new model on five different IEEE networks withvarying sizes of 5, 39, 57, 118 and 300 nodes and include complexity analysis. The results showthat the model is computationally efficient and robust with respect to constraint violation risk. Thedistributed energy storage system leads to more stable operation with flattened generation profiles.Storage absorbed RES uncertainty, and reduced generation cost.

Distributed multi-energy storage cooperative optimization control method for power grid voltage stability enhancement

To solve the problem of grid voltage fluctuation in multi-energy systems, this study proposes a voltage optimization control method based on the coordination of battery storage, heat storage, and gas storage. First, the different operation modes of electricity, heat, and gas in the system were analysed, and a coupling model of an energy system with multi-energy storage was established. Second, according to the energy storage characteristics of distributed energy storage, a collaborative optimization model of distributed energy storage was established by studying the influence mechanism of battery storage, gas storage, and heat storage on grid voltage stability. Taking the minimum voltage fluctuation as the optimization objective, a coordinated operation strategy of distributed energy storage is proposed under the premise of meeting the constraints of distributed energy storage. Combined with the real-time charging and discharging states of each distributed energy storage system, the particle swarm optimization method was used to maintain the node voltage stability. Finally, a regional multi-energy system is taken as an example to verify that the proposed collaborative optimization control method can effectively improve grid voltage stability. Through the results, it can be seen that the node voltage in the regional power grid has increased by 0.012p.u., 0.012p.u. 0.011p.u. 0.014p.u.

Exploring the profitability of using electric bus fleets for transport and power grid services

Electric buses (E-buses) are increasingly replacing internal combustion powered bus fleets. They can further function as distributed energy storage units. We investigate the feasibility of the novel “Bus-to-Grid” (B2G) or “E-Buses as Power Storage” (EBaPS) concept, which allows battery E-buses to provide transportation as well as power-grid services. By aggregating the electrified bus fleets into Virtual Power Plants and discharging the energy to the grid, a bus operator can provide grid services and open up a secondary revenue source in addition to fareboxes. We discuss two contracts the bus operator can arrange with the grid. The first one is “Sale by Market Price (SbMP)”, presuming that the buses can discharge and sell the energy at real-time market prices. The second one is “Frequency Control Reserve (FCR)”, in which the buses discharge on immediate requests of the grid. We formulate the two contracts and their associated fleet allocation problems as two non-linear optimization problems maximizing the operator’s profit. Optimal bus charging, discharging, and dispatching plans are solved on an aggregate level for strategic planning, allowing scalability to large fleet sizes. The profitability of the contracts is illustrated based on the schedule and demand of a real bus line and observed time-of-day dependent price variations of the energy market.

Day-ahead bidding strategy of cloud energy storage serving multiple heterogeneous microgrids in the electricity market

Cloud energy storage (CES) receives increasing attention as an efficient and viable paradigm for the provision of distributed energy storage services. This paper exploits CES’s service modes to both energy storage and electricity trading for its users, e.g., microgrid (MG). The optimal day-ahead bidding strategy is investigated for CES as an independent entity in the electricity market. Besides, two energy service modes are introduced considering MG's requirements and preferences. Each mode consists of a set of schemes for energy storage system (ESS) rent (power-based and capacity-based) and electricity trading (internal price with an improved pricing method and market clearing price). Finally, a stochastic programming (SP)-based optimization model is formulated to maximize the CES’s expected profits fully considering the electricity market’s settlement mechanism and uncertainty as well as the participation interest of MG. The proposed solution is extensively assessed through a case study of CES’s energy services to five heterogeneous MGs with distinct electricity generation and consumption characteristics and two energy service modes are assessed through comparative experiments. The numerical results confirm the effectiveness and benefits of the proposed optimal day-ahead bidding solution.

Guest editorial: Application of cloud energy storage systems in power systems

Guest editorial: Application of cloud energy storage systems in power systems