Energy Storage System Devices Research Articles

Blockchain Power Trading and Energy Management Platform

In this study, artificial intelligence, the Internet of things, and blockchain technology were combined to develop a vehicle-to-everything blockchain power trading and energy management platform with the objective of enabling multi-level power transactions for electric vehicle charging stations in or between commercial buildings. The proposed platform considers green power transactions across microgrids, bidirectional power and green power charging and discharging transactions in microgrids, demand response bidding, and vehicle-to-vehicle emergency rescue charging. This innovative architecture operates smart contracts and distributed ledgers independently; uses smart contracts to handle bidding, matching, and settlement of the power trading platform; and simultaneously employs the cloud and local distributed ledger nodes to chain the detailed real-time transaction information and power data on the ledger. In this manner, the security and fairness of data can be ensured; furthermore, a large amount of chaining information can be processed, and the chaining latency can be shortened. In addition, the communication protocols are integrated through the artificial intelligence of things. An energy management system (EMS) is proposed, which can perform optimal charging and discharging scheduling according to the power transaction matching results and then control charging piles and energy storage system devices. It can reduce the operating cost of microgrids in commercial buildings, increase the efficiency of green power utilization, and reduce power loss. In this system, power transactions are automatically and simultaneously executed in the EMS to achieve real-time supply and demand balance of regional microgrids.

New DTR line selection method in a power system comprising DTR, ESS, and RES for increasing RES integration and minimising load shedding

In this study, new technologies such as dynamic thermal rating (DTR) technology and energy storage system (ESS) are simultaneously used to optimise the integration of renewable energy sources (RESs) and minimise the load shedding. For achieving the mentioned aims, a new method is proposed to select the candidate lines for the implementation of DTR technologies. The DTR technology is responsible for increasing lines limited capacity. Moreover, optimally placed and sized ESSs save RESs generated power in non-peak-hours and inject it to the network in peak hours. For validating the performance of the proposed solution, comprehensive simulations are performed in several stages on IEEE RTS-24- and 30-bus test systems networks. To meet the increased power demand, RESs (wind and solar) are optimally allocated by using the genetic algorithm (GA) in the test systems. Then, ESS devices are optimally sized and placed by using GA. Finally, candidate lines are selected based on the proposed method and DTR devices are added to the systems. Comprehensive comparisons are presented for comparing the previously presented solutions and the proposed one. It is proved that using DTR technology and ESSs along with the proposed line selection method is the superior solution for system operators.

Energy Storage Requirements for Inverter-Based Microgrids Under Droop Control in d-q Coordinates

This article proposes a novel distributed control approach for Hamiltonian Surface Shaping and Power Flow Control (HSSPFC) method to determine Energy Storage Systems (ESSs) requirements for three-phase, inverter-based Microgrids (MGs). Here, local system references are obtained through a primary d-q droop control which is supported by a level-zero Hamiltonian controller. ESS devices are the actuators of the system to enforce reference points. The control approach as well as power flow and energy transfer model of the MG enables the ESS capacity and bandwidth to be obtained. As a result, a zero-output ESS element analysis is defined which can further be used to study storage requirements versus additional constraints. Communication network update-rate can affect ESSs and filtering requirements. The developed analysis is demonstrated in parallel and looped nine-bus WSCC reduced-order MG system examples to obtain ESS requirements versus communication network update-rate (bandwidth).

Using Dynamic Thermal Rating and Energy Storage Systems Technologies Simultaneously for Optimal Integration and Utilization of Renewable Energy Sources

Nowadays, optimal integration and utilization of renewable energy sources (RES) are of the most challenging issues in power systems. The wind and solar generation units' maximum production may or may not occur at peak consumption times resulting in non-optimal utilization of these resources. As a solution to this problem, energy storage systems (ESS) are embedded in networks. However, the power transfer from RES to ESS may lead to network congestion. In this paper, the simultaneous application of dynamic thermal rating (DTR) technology and ESS devices is proposed. The DTR is used to overcome the problem of transmission lines limited capacity and ESS is responsible for mitigating curtailment of RESs energy production by saving their generated energy in non-peak hours. The RESs generation and lines’ ratings are calculated based on hourly actual weather elements. For evaluating the proposed method, a linearized formulation of DC-OPF is used in the problem definition and also simulated on a modified IEEE 30-bus test system including a wind farm, solar park, and ESS devices by using MATLAB software. In addition, different comparisons are performed demonstrating the remarkable and better performance of the proposed method compared to previously introduced methods.

Short-Term Voltage Stability Improvement via Dynamic Voltage Support Capability of ESS Devices

Modern power systems are undergoing a steady increase in the loading level and higher penetration of renewables. Induction motor (IM) loads and fixed-speed induction generator (FSIG) based wind turbines (WTs) are broadly used in many power systems. Both IMs and FSIGs draw high amounts of reactive power during large disturbances, which pose major problems for short-term voltage stability (ST-VS). In this paper, the effects of IMs, FSIG-based WTs, and over-excitation limiter (OEL) of synchronous generators on ST-VS are analyzed and illustrated. Then, the use of energy storage systems (ESSs) to provide dynamic voltage support (DVS) to power system during and after large disturbances is investigated, as a countermeasure against short-term voltage instability. To implement ESS's DVS capability, the required power injection by the ESS is dynamically determined and then tracked after the occurrence of a disturbance. The effects of implementing fault ride through and time-overload capabilities of ESS's inverter, on ST-VS of the system, are also analyzed. Optimal power factor adjustment for power injection by ESS, from an ST-VS viewpoint, is discussed as well. It is shown that the DVS capability of an ESS can be effectively used against detrimental effects of IMs, FSIGs, and OELs on ST-VS.

Risk-Based Stochastic Allocation of ESS to Ensure Voltage Stability Margin for Distribution Systems

High intermittency in today's renewable-rich distribution systems, and the prohibitive cost of upgrading the system infrastructure, has rendered voltage instability a real concern for heavily loaded systems. The voltage instability of distribution systems can spread to the corresponding transmission grid, as proven by real-life incidents. Recent advances in energy storage systems (ESSs), however, have offered a viable means of enhancing the reliability and robustness of power systems. In this paper, the optimal placement, sizing, and operation of ESS devices in wind-intensive distribution power systems are studied. The objectives of this paper are to minimize the ESS size (power and energy), required to ensure a desired voltage stability margin (VSM), and to minimize the reactive power loss and reactive power import from the upstream network. Wind uncertainty has been accounted for through a risk-based stochastic optimization framework. Besides, active network management (ANM) tools, such as the tap position of on-load tap changers, modelled by using a new method, and reactive power capabilities of both ESS devices and wind farms, are used as additional means to reduce the required ESS size. The results show that the required ESS significantly increases with increase in the desired VSM. Nevertheless, the increase can be considerably curtailed by using risk-based VS constraints and ANM tools.

Optimal sizing of energy storage system for microgrids

Microgrids (MGs) are Low Voltage distribution networks comprising various distributed generators (DG), storage devices and controllable loads that can operate either interconnected or isolated from the main distribution grid as a controlled entity. Energy storage system (ESS) is a vital part of an MG. In this paper, a methodology is proposed for the optimal allocation and economic analysis of ESS in MGs on the basis of net present value (NPV). As the optimal operation of an MG strongly depends on the arrangement and allocation of its ESS, economic operation strategies and optimal allocation methods of the ESS devices are required for the MG. Self-adaptive Bee Swarm Optimization (SBSO) algorithm is applied to optimize the operation strategies and capacities of ESS in MGs in order to find maximal NPV, the generation schedule of ESS and distributed generation sources. This paper is to suggest, among those available ESS, the optimal sizes and types of them and their optimal arrangement, such that the total NPV achieved during the system operational lifetime period is maximized. After introducing the methodology, a case study is presented for illustration.

Long-term optimization based on PSO of a grid-connected renewable energy/battery/hydrogen hybrid system

Abstract This paper presents and evaluates three energy management systems (EMSs) based on Particle Swarm Optimization (PSO) for long-term operation optimization of a grid-connected hybrid system. It is composed of wind turbine (WT) and photovoltaic (PV) panels as primary energy sources, and hydrogen system (fuel cell –FC–, electrolyzer and hydrogen storage tank) and battery as energy storage system (ESS). The EMSs are responsible for making the hybrid system produce the demanded power, deciding on the energy dispatch among the ESS devices. The first PSO-based EMS tries to minimize the ESS utilization costs, the second one to maximize the ESS efficiency, and the third one to optimize the lifetime of the ESS devices. Long-term simulations of 25 years (expected lifetime of the hybrid system) are shown in order to demonstrate the right performance of the three EMSs and their differences. The simulations show that: 1) each EMS outperforms the others in the designed target; and 2) the third EMS is considered the best EMS, because it needs the least ESS devices, and presents the lowest total acquisition cost of hybrid system, whereas the rest of parameters are similar to the best values obtained by the other EMSs.

Optimal Allocation and Economic Analysis of Energy Storage System in Microgrids

This paper presents a methodology for the optimal allocation and economic analysis of energy storage system (ESS) in microgrids (MGs) on the basis of net present value (NPV). As the performance of an MG strongly depends on the allocation and arrangement of its ESS, optimal allocation methods and economic operation strategies of the ESS devices are required for the MG. To optimize the operation strategies and capacities of ESS in MGs, the financial benefit and dynamic models of ESS are discussed. And then, a matrix real-coded genetic algorithm is applied to find maximal NPV, in which each GA chromosome consists of a 2-D real number matrix representing the generation schedule of ESS and distributed generation sources. This paper is to suggest, among those available ESS, the optimal sizes and types of them and their optimal arrangement, such that the total NPV achieved during the system operational lifetime period is maximized. Finally, some computational simulation results are presented to verify the effectiveness of the proposed method.