Distributed Energy Storage Research Articles

Double-layer optimized configuration of distributed energy storage and transformer capacity in distribution network

In order to solve the problem of low utilization of distribution network equipment and distributed generation (DG) caused by expansion and transformation of traditional transformer capacity, considering the relatively high cost of energy storage at this stage, a coordinated capacity configuration planning method for transformer expansion and distributed energy storage (DES) is proposed in this paper. First, the energy storage capacity requirements is analyzed on the basis of the transformer overload requirements, and analyzing the correspondence between different capacities of energy storage and transformer expansion capacities. Besides, taking into account the impact of different action mechanisms of energy storage on the node load within a day, a location optimization method for DES based on the standard deviation of network loss sensitivity is introduced. Then, considering the net cost of coordinated planning of energy storage and transformer are minimum and the benefit of energy storage operation is maximum, a two-layer optimization model of distributed energy storage and transformer capacity is established. Finally, the solution method of the two-layer optimization model is proposed, and the feasibility of the proposed model is verified by IEEE33 bus distribution system. The results show that the coordinated planning method proposed in this paper can greatly reduce the investment cost, and the net cost of the coordinated planning scheme is reduced by 17.558 million yuan compared with the scheme of separate configuration for energy storage, which effectively improves the economics of energy storage configuration.

Deep reinforcement learning based topology-aware voltage regulation of distribution networks with distributed energy storage

Both the high penetration of clean energy with strong fluctuation and the complicated variable operation condition bring great challenges to the voltage regulation of the distribution network. To deal with the problem, a topology-aware voltage regulation multi-agent deep reinforcement learning (MADRL) algorithm is proposed. The distributed energy storages (DESs) are modeled as agents to regulate voltage autonomously in real-time, which could fast adapt to dynamic topological scenarios. Firstly, taking the minimization of voltage fluctuation and maximization of reserve capacity as the target, the optimal voltage regulation model is established. Secondly, a topology extraction method considering voltage sensitivity is proposed for dynamic topology clustering, and the obtained typical topology is added to the observation set of agents. Then, the optimal voltage regulation model is formulated to the decentralized partially observable Markov decision process (Dec-POMDP) framework, in which only local information is required for the agent during the test process to decision-making to realize the hierarchical and partitioned control of voltage. Finally, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is used to solve the Dec-POMDP model. The feasibility and superiority of the proposed algorithm are verified and analyzed in the simulation under different scenarios.

Cutting-edge public policy proposal to maximize the long-term benefits of distributed energy resources

In the context of increasing integration and relevance of distributed energy resources, this paper proposes an advanced public policy to maximize their long-term benefits through optimal regulated electricity market management. More specifically, the focus is on renewable distributed generation and energy storage systems. The proposed policy arises from three models or techniques, i.e., the optimized tariff model (regulated electricity market model), Bass diffusion model (long-term forecasting of the integration of distributed energy resources), and random walk (risk assessment technique), along with a stochastic optimization problem. The policy leads to socioeconomic welfare maximization while ensuring fairness for all the market players and enhanced control by the regulatory agency on the integration of distributed energy resources. By applying the policy to a concession area located in northeastern Brazil, where photovoltaic distributed generation systems dominate the market share, results demonstrate that it is highly valuable as a 156 (MR$) mean socioeconomic welfare gain was achieved when optimizing the design variables (compared to a non-optimal solution).

Bidirectional quasi‐Z‐source DC‐DC converter with Lyapunov function‐based controller in stand‐alone photovoltaic‐connected system

SummaryThis article proposes a bidirectional quasi‐Z‐source DC‐DC converter (BQZSDC) to integrate distributed energy storage system with a stand‐alone photovoltaic (PV)‐connected system. The operational challenges like control and energy management of the renewable‐driven stand‐alone systems have been an interest of research. However, the challenge is managing the renewable energy source and the distributed energy storage system and simultaneously catering to the loads. This article analyzes the performance of a BQZSDC with a Lyapunov function‐based controller for regulating DC link voltage (load voltage) and battery current. A duty‐ratio feedforward control unit and a Lyapunov function‐based feedback control unit are used to develop the controller. The duty‐ratio feedforward control unit is used to reduce the burden on the feedback controller. The closed‐loop system is exponentially stable with the feedback controller, offering excellent transient responsiveness even under large disturbances. Simulations in MATLAB/Simulink show that the controller functions satisfactorily in achieving regulation objectives. Finally, experimental results validate the performance of the proposed controller.

Fast state-of-charge balancing control strategies for battery energy storage systems to maximize capacity utilization

To improve the carrying capacity of the distributed energy storage system, fast state of charge (SOC) balancing control strategies based on reference voltage scheduling (RVSF) function and power command iterative calculation (PIC) are proposed in this paper, respectively. First, as an improvement to the traditional droop SOC balancing control, the working principle and parameters design of the RVSF-based method is introduced, which can realize the energy interaction among the storage units during the balancing process. The PIC-based fast SOC balancing strategy is then proposed for a system with a current bus sensor. The PIC strategy realizes the fastest SOC equalization speed by ensuring there is always at least one storage unit operating in the maximum power flow during the SOC balancing process. In addition, the proposed strategy is extended to the system with inconsistent energy storage unit capacity. Finally, circuit-based simulation and code-based models for a plan with different energy storage units are constructed in PLECS and MATLAB simulation software, respectively. The hardware in the loop simulation results validate the correctness and feasibility of the proposed strategies and show that the proposed techniques can significantly improve the load capacity and duration of the system under many operating conditions.

Decentralized optimal management of a large-scale EV fleet: Optimality and computational complexity comparison between an adaptive MAS and MILP

Increasing the penetration of variable and uncertain renewables and electric vehicles in power systems may give rise to problems (such as network congestion and commitment mismatches) if not controlled strategically. This demands control solutions in the form of energy management strategies for active distribution networks which would control the connected distributed energy resources and storage units in real-time to address the mentioned challenges. Centralized strategies may fail to serve this purpose for large-scale distribution networks due to their inherent shortcomings like vulnerability to single point of failures and large computing times. Unlike centralized approaches, decentralized control strategies show more potential. This paper presents one such solution, based on an adaptive multi-agent system, to control a large-scale distribution network in real-time. Its performance is compared with the results obtained with the corresponding centralized optimization problem, modeled as a mixed integer linear programming problem. Both the centralized version and the decentralized multi-agent version of the problem under consideration are presented and a case study is designed for the comparison. The comparison shows that the designed multi-agent system produces a near-optimal solution in real-time while the centralized optimization strategy struggles in terms of computational complexities for larger distribution networks.© 2017 Elsevier Inc. All rights reserved.

Distribution Network-Oriented Distributed Energy Storage Optimization Planning and Economic Quantitative Evaluation

Distributed energy storage can provide node support for energy consumption, but there are still some shortcomings in the formulation of technical standards and overall development planning. Due to the current cost of electric energy storage, it is difficult to give full play to the advantages of energy storage application under the current electricity price mechanism and favorable policy conditions. From the perspective of external support and technical methods for electric energy storage, this paper studies the typical application mode and comprehensive quantitative evaluation of distributed energy storage in the distribution network and takes the actual operation scheme as a verification case.

Distribution Network Distributed Energy Storage Configuration Optimization Method Considering Variance of Network Loss Sensitivity

With the wide application of distributed generation technology, in order to maintain the stable and safe operation of the distribution network, this paper considers the uncertainty of power generation and a load of distributed energy storage. It takes the distribution network with distributed energy storage as the research object, models and analyzes the optimization problem, and studies the problem of DG configuration by using the reinforcement learning method. In the case of distributed grid connection, the voltage distribution change of the distribution network and the impact of network loss are analyzed. The line power flow of the distribution network is calculated by using the MATLAB platform as a platform, and the power flow calculation of the distribution network is simulated and analyzed by the MATLAB platform. The system loss is significantly reduced. The experimental results show that the system loss variance is 29.19. After adding energy storage, the loss variance is 14.77, which is reduced by 49.41%. According to the scheme, the voltage stability and operating loss of the system can be effectively improved.

Double-layer Optimization Study of Virtual Power Plants with Source-load-storage

In order to effectively solve the supply-demand imbalance of the grid caused by intermittent new energy penetration and improve the scheduling flexibility of virtual power plants, a two-layer optimization model of virtual power plants with source-load-storage is proposed. The upper layer of the model is designed to reduce the forecast error of wind power output and improve the revenue of the virtual power plant. On the basis of the maximum wind power consumption, distributed energy storage and fuel cells are used to coordinate and compensate for the deviation between the planned and actual output of renewable energy. The active output of the upper layer is transferred to the lower layer. The lower layer of the model is solved by using the improved linear decreasing inertia weight particle swarm algorithm for the output of the gas turbine unit. The analysis results show that the model can absorb the excess new energy generation in the grid, achieve the balance of supply and demand regulation in the grid, and effectively reduce the economic operation cost of the system.

A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective

Today’s plug-in electric vehicle (PEV) technology is one of the important ways to address the dependence of fossil energy and greenhouse gas emissions. With the explosive growth of the number of PEVs worldwide, surging PEVs charging has offered new opportunities and challenges for large amounts of existing buildings where more and more charging infrastructures have been added. By means of microgrid technology, energy-flexible buildings are nowadays integrated with multiple distributed energy sources, energy storages and various types of consumers, allowing the possibility of providing measures to mitigate the growing impact of surging PEVs charging demand and accommodate more PEVs in buildings. Collaborative management between PEVs and energy-flexible buildings is attracting more attention of energy professionals and provides incentives to make more efforts in developing more extensive and optimal control methods for the integrated building energy systems. However, there is little review focusing on the impact analysis and co-management technologies on PEVs charging in buildings. This paper firstly provides an overview of the literatures related to the impact of PEV charging on the building power demand profile and the associated distribution transformer performance as well as the key impact factors. Additionally, a comprehensive review and analysis are conducted on the state-of-the-art researches with regard to the co-management of PEVs and energy-flexible buildings with various energy flexibility sources for the co-benefits of the integrated building energy system. The further efforts needed for developing more applicable co-management methods for facilitating surging penetration of PEVs in buildings are also addressed.

Multi-objective Optimal Configuration Analysis of Energy Storage System in Distribution Network Based on Improved Lion Colony Algorithm

Aiming to enhance the new energy consumption capacity and lessen the impact of new energy access on the distribution network as the size of distributed power access expands annually, energy storage is being progressively incorporated into the design of the distribution network. When examining the distributed power source’s output in its entirety, the access point and capacity configuration of the distributed energy storage system are carried out, and the energy storage system’s best scheduling is also considered. The goal is to conduct research on the optimization of the two-layer model of the energy storage system using the suggested improved lion colony method. It is crucial to the expansion of the distribution network’s economic benefits.

Research on configuration optimization of distributed energy storage system based on multi-objective genetic algorithm

In view of some problems existing in China’s power market, this paper uses the multi-objective genetic algorithm to analyze the problems in the power market. The equivalent mathematical model of energy distribution is established and optimized, and its performance is optimized. The results indicates that the scheme can significantly improve the new energy consumption rate of the distribution network and the security of the distribution network and achieve the design purpose.

分散型蓄電システム管理のためのマイクログリッド周波数制御

This paper proposes a novel frequency control method for the management of distributed energy storage resources in AC microgrid. Distributed autonomous controls by the individual prosumers perform an efficient coordination of SOCs which are automatically controlled to a same value that indicates the total SOC in the microgrid. The coordination is realized by the proposed control law that allows frequency deviation in a specified bandwidth. The proposed method has been developed in order for the microgrid operation by SSI, the single-phase synchronous inverters developed by the authors, while useful for general AC microgrid to increase the resilience of power supply.

Pulsewidth Modulated DC-to-DC Power Conversion: Circuits, Dynamics, Control, and DC Power Distribution Systems, Second Edition [Book News

DC grids are believed to be an important contributing technology for energy sustainability. DC grids can increase the penetration of renewable energy resources in electrical networks and enhance the coordinated use of distributed energy storage while improving the efficiency of energy supply as most loads are now dc. For dc grids including photovoltaic energy, constant power loads, and battery storage, dc–dc converters are the key technology for the establishment of a stable dc grid, conveying dc power throughout the grid.

Multi-objective optimization method for distributed energy storage configuration under distribution network operation constraints

The multi-objective optimization method for distributed energy storage configuration has the problem of high network loss expectation. A multi-objective optimization method for distributed energy storage configuration under distribution network operation constraints is designed to solve the above problems. The intra-day charge/discharge balance is used as a criterion to identify the characteristics of distributed energy storage configuration, calculate the network loss sensitivity of nodes, construct a siting and capacity setting model, and integrate multiple power quality indicators to improve the multi-objective optimization model under the distribution network operation constraint. The experimental results show that the mean values of network loss expectations for the distributed energy storage configuration multi-objective optimization method in the paper and the other two distributed energy storage configuration multi-objective optimization methods are: 226.731 kW, 270.762 kW, and 276.728 kW, respectively, indicating that the designed distributed energy storage configuration multi-objective optimization method is more feasible after fully considering the distribution network operation constraints.

Life-cycle economic analysis of thermal energy storage, new and second-life batteries in buildings for providing multiple flexibility services in electricity markets

More energy flexibility is required to alleviate the stress of power systems caused by intermittent and weather-dependent solar and wind power. The utilization of demand-side flexibility (e.g., flexibility management strategies and distributed storage technologies) can be considered a win-win approach for demand-side users and power grids. However, the economic benefits of distributed energy storage systems in buildings are usually underestimated without considering the full-scale flexibility utilization, which may impede storage investments. Meanwhile, the disposal problem of retired EV batteries is becoming more serious. Repurposing these batteries for stationary applications in buildings seems cost-effective and eco-friendly. Therefore, this study first proposes novel optimal dispatch strategies for different storage systems in buildings to maximize their benefits from providing multiple grid flexibility services simultaneously, and then conducts a comparative life-cycle economic analysis on thermal energy storage, new and second-life batteries. The optimal configuration of hybrid storage systems is also analyzed to facilitate the decision-making of building owners/operators. Test results show that thermal energy storage and electrical energy storage can increase the economic benefits by 13% and 2.6 times, respectively. Battery storage may no longer be an expensive option for building-scale investment due to downward trends in capacity costs and environmental impacts.

Hierarchical cooperative control strategy of distributed hybrid energy storage system in an island direct current microgrid

This paper presents a distributed hybrid energy storage system (HESS) for an island DC microgrid (MG) with a central superconducting magnetic energy storage (SMES) system and multiple distributed battery energy storage systems (BESS). A hierarchical cooperative control scheme is proposed to realize coordinate power sharing among distributed HESS and uninterrupted operation of SMES. The control scheme adopts filtration control to decouple imbalance power into high frequency component and low frequency component, which will be compensated by SMES and distributed BESSs. The proposed mode switch control (MSC) enables SMES to have higher transient large power regulation capability than filtration control. When the DC microgrid suffers from 400 kW power drops and power surges, the maximum DC bus voltage overshoot is 5.6 % and 5.3 % under MSC control, while the overshoot increases to 26.9 % and 8.2 % under the filtration control. Meanwhile, an autonomous state-of-charge (SOC) recovery control is designed for SMES to regulate its SOC within a set range (0.64, 0.725), which enables uninterrupted operation of SMES without performance trade off. A design scheme is proposed to make sure the desired transient power sharing dynamics and SOC recovery with negligible interactions. Additionally, the proposed distributed HESS structure and cooperative control method allows acceptable communication failure and physical faults. Cases verification conducted in PSCAD/EMTDC demonstrates the feasibility and superiority of the proposed system and control scheme against various kinds of power fluctuations.

A Car-Following Model with the Acceleration Generalized Force Coupled with External Resistance and the Temporal-Spatial Distribution of Battery Decline

A novel energy storage mode based on the vehicle-to-grid (V2G) and vehicle-to-vehicle (V2V) concept will be greatly researched and applied as a new green solution to energy and environmental problems. However, the existing research on battery capacity decline in V2G applications has mainly focused on modeling the battery capacity to investigate its decline during vehicle charging and discharging, in order to reduce the battery capacity decline and evaluate its economics. A car-following model with the acceleration generalized force coupled with external resistance is proposed in the paper. A linear stability analysis was used to analyze the stability of the model. The stability of the traffic flow was improved when the value of the resistance coefficient increases. Then, the currents of different vehicles were also calculated according to the velocities. Moreover, the effect of different physical characteristics of driving on the decline of distributed energy storage batteries in the Internet of Vehicles (IoV) was investigated. The results suggest that in different road types and road slopes, vehicles which are at the end of the platoon position have less battery capacity degradation and better battery condition. It provides a reference for subsequent research related to V2G energy storage in the context of vehicle networking.

An adaptive decentralized regulation strategy for the cluster with massive inverter air conditionings

To deal with the power fluctuation caused by renewable energy integration and load growth, it has been a general recognition to provide regulation services for local power system or main grid by flexible loads. There is a certain range of adaption to temperature for users and thermal inertias for building components, making it possible for air conditioning (AC) loads to participate in demand response (DR). Large cluster of ACs offers a resource akin to that of a distributed energy storage system, which is also an important regulation module of virtual power plant (VPP). Inverter air conditioning (IAC) is gradually occupying the market proportion owing to comfort and power savings. It has become a key technology in the new power system on how to coordinate massive IAC resources with heterogeneous parameters and status to output reliable load regulation services. To this end, this paper proposes a consensus control strategy for the IACs cluster based on lightweight decentralized architecture. The task is distributed fairly while ensuring the occupants’ comfort. The target load curve can be accurately tracked with high adaptability and robustness. The interactive parameters are generated from the bottom up based on individuals’ response ability, which facilitates rapid convergence. Together with the intrinsic adaptability and robustness, the hosting mechanism for the coordinator is designed to guarantee high fault-tolerance, and therefore plug-and-play can be satisfied. Besides, dimensionality reduction and locality for the interaction data ensure a higher level of privacy protection. The advantages are illustrated in case studies and compared with other approaches. To the best of our knowledge, the proposed method has the overwhelming convergence performance over the present literatures. These characteristics indicate a sufficient basis for its application in the variable conditions with massive users and can be extended to more scenarios using consensus control.

The novel multiagent distributed SOC balancing strategy for energy storage system in DC microgrid without droop control

For the distributed energy storage system (ESS) in a DC microgrid, the novel distributed control strategy based on multiagent control is designed to achieve state of charge (SOC) balancing. In the proposed scheme, the output current of the converter is not required, which is an attractive feature to avoid the measurement error. The voltage loop can stabilize the bus voltage and provide the original current reference for each ESS. The SOC information is introduced to design the current closed loop, so that the operating current of the ESS can be dynamically adjusted to realize SOC balance control according to the SOC and capacity of each ESS during the charging and discharging process. Furthermore, in order to avoid the circulating current in ESSs, an adaptive current segment regulation algorithm is designed to improve the SOC balance speed, especially in the case of low SOC difference among ESSs. The proposed control strategy not only can enhance the current regulation performance under steady and transient states, but also has favorable voltage control capability and stability. Finally, the real-time test results in OPAL-RT platform are presented to validate the effectiveness of the proposed method.