An overview of energy storage devices for distribution network

The primary goal of a power distribution system is to provide nominal voltages and power with minimal losses to meet consumer demands under various load conditions. In the distribution system, power loss and voltage uncertainty are the common challenges. However, these issues can be resolved by integrating distributed generation (DG) units into the distribution network, which improves the overall power quality of the network. If a DG unit with an appropriate size is not inserted at the appropriate location, it might have an adverse impact on the power system’s operation. Due to the arbitrary incorporation of DG units, some issues occur such as more fluctuations in voltage, power losses, and instability, which have been observed in power distribution networks (DNs). To address these problems, it is essential to optimize the placement and sizing of DG units to balance voltage variations, reduce power losses, and improve stability. An efficient and reliable strategy is always required for this purpose. Ensuring more stable, safer, and dependable power system operation requires careful examination of the optimal size and location of DG units when integrated into the network. As a result, DG should be integrated with power networks in the most efficient way possible to enhance power dependability, quality, and performance by reducing power losses and improving the voltage profile. In order to improve the performance of the distribution system by using optimal DG integration, there are several optimization techniques to take into consideration. Computational-intelligence-based optimization is one of the best options for finding the optimal solution. In this research work, a computational intelligence approach is proposed to find the appropriate sizes and optimal placements of newly introduced different types of DGs into a network with an optimized multi-objective framework. This framework prioritizes stability, minimizes power losses, and improves voltage profiles. This proposed method is simple, robust, and efficient, and converges faster than conventional techniques, making it a powerful tool of inspiration for efficient optimization. In order to check the validity of the proposed technique standard IEEE 14-bus and 30-bus benchmark test systems are considered, and the performance and feasibility of the proposed framework are analyzed and tested on them. Detailed simulations have been performed in “MATLAB”, and the results show that the proposed method enhances the performance of the power system more efficiently as compared to conventional methods.

An energy storage device is a multi-physic device with ability to store energy in different forms. Energy in electrical systems, so-called ?>electrical energy ?>, can be stored directly or indirectly, depending on the means of the storage medium. Devices that store the electrical energy without conversion from electrical to another form of energy are called direct electrical energy storage devices. Two major energy storage devices are ultra-capacitor energy storage (UCES) and super-conducting magnetic energy storage (SMES). Devices that convert and store the electrical energy in another form of energy are called indirect electrical energy storage devices. Electro-mechanical storage devices are flywheels, compressed air energy storage (CAES) and hydro pumped energy storage (HPES). Electrochemical energy storage devices are electrochemical batteries and fuel cells. Above mentioned energy storage technologies and devices are briefly described in this chapter. Different fields of application are also discussed.

Guest Editorial: Situational awareness of integrated energy systems

The presence of distributed generation (DG), represented by photovoltaic generation and wind generation, brings new challenges to distribution network operation. To accommodate the integration of DG, this study proposes a bi‐level optimisation model to determine the optimal installation site and the optimal capacity of battery energy storage system (BESS) in distribution network. The outer optimisation determines the optimal site and capacity of BESS aiming at minimising total net present value (NPV) of the distribution network within the project life cycle. Then optimal power flow (OPF) and BESS capacity adjustment are implemented in the inner optimisation. OPF optimises the scheduling of BESS and network losses. On the basis of optimal scheduling of BESS, a novel capacity adjustment method is further proposed to achieve the optimal BESS capacity considering battery lifetime for minimising the NPV of BESS. Finally, the proposed method is performed on a modified IEEE 33‐bus system and proven to be more effective comparing with an existing method without BESS capacity adjustment.

When the large amount of Distributed Generations (DGs) are connected to the grid, the whole distributed power network lost line, and the voltage fluctuates are obvious. In recent years, the concepts of power Internet of Things (IoT)technology and energy Internet are proposed. Therefore, power grid technology has entered a new stage. The IoT technology is characterized by high bandwidth, high transmission rate, and low latency. In addition, with the collected data are increased, IoT becomes an effective way to improve the computing power of edge nodes in edge computing. In order to reduce network losses and voltage fluctuations, reactive power optimization is need to be implement in distribution network system. This paper proposes a distributed power voltage regulation strategy based on the particle swarm optimization and deep belief network (PSO-DBN) under the IoT. Firstly, IoT technology and characteristics are introduced, and the current research status of voltage regulation technology are analyzed in distribution network. Secondly, the weight of DBN is improved with PSO. Finally, the IEEE33 bus system is taken as a simulation case, the effects of daily reactive power optimization and the amount of historical data is analyzed. It is concluded that the method proposed in this paper has obvious effects on reducing voltage fluctuation and network loss. KeywordsDistributed generationIoTPSO-DBNReactive power optimization

Currently, there is a tendency to apply new approaches to optimize the capacity balance and reduce electric energy losses in 10 kV distribution networks, which is reflected in the updated federal standards and standards of PJSC “ROSSETI”. In this regard, the issues of the use of controlled energy storage devices become relevant. The study purpose is to assess the impact of a controlled electricity storage device on the operating mode of the distribution network. Materials and methods. Simulation modeling methods are used in the paper: simulation models of a section of a distribution network and a controlled electric power storage device are developed in the Matlab/Simulink dynamic modeling environment using typical mathematical blocks of Simulink base libraries. The distribution network section is represented by six transformer substations of 10/0.4 kV, which are connected to a power source according to the highway power supply scheme. A controlled electric power storage device is implemented in the model by an electric power storage subsystem and combines a battery subsystem, which consists of eight cells of series-connected Li-Ion batteries with DC-DC converters, and a controller subsystem with PI current and voltage regulators. The energy exchange between the network and the energy storage system is realized in the model by means of an AC-DC converter with an intermediate DC link C0. The controlled electric energy storage device in such system is used as an additional source of electrical energy. A mathematical description of the relationship between the physical parameters of the power supply network and the controlled electric energy storage device is performed. The work results. On the basis of the developed simulation model, the study of the possibility of increasing the efficiency of the electrical energy system through the use of a controlled electric energy storage device was carried out. Transient processes were observed in a controlled electric power storage by measuring the parameters of the battery such as state of charge SOC, %; voltage VB, V and current IB, A. Voltage and power measurements were carried out in nodes on the sides of the lower (0.4 kV) and higher (10 kV) voltage. Conclusions. The analysis results of simulation of the distribution network with a connected controlled electric energy storage device at the end of the trunk line showed a possible increase in the efficiency of the system.

This manuscript presents a case study to determine the optimal location and size of photovoltaic systems in an electric distribution networks using particle swarm optimization (PSO) such that network voltage profile is improved and losses are minimized. The distribution network of Masirah Island, Oman, is considered as a case study system. The test system is modeled and simulated using MATLAB load flow toolbox.

Electrical protection for the grid-interconnection of photovoltaic-distributed generation

Transition metal nitride electrodes as future energy storage devices: A review

The necessity and installation of energy storage device are increasing due to the change of power system such as an increase in large-capacity distributed power source, and therefore, it is required to study the power system analysis and control method considering energy storage system (ESS). The type of grid-connected converter of the battery system is assumed to be the voltage source converter. The controller is configured to allow independent control of P and Q, as the effective power output controller of the energy storage device according to the frequency change of the power system, the droop control method using the controllable droop coefficient was used. In this paper, we compare and analyze the effects of fixed and adaptive droop control methods on the stability of the independent power system in the transient situations such as the dropout of generators. A method has been studied to improve system stability by appropriately controlling ESS using adaptive droop control. Simulations showed the adverse effects of inappropriate control on the power system stability. An adequate capacity and droop control for an ESS active power control depending on the power system operation condition has been studied, and the effectiveness of the droop control was verified by simulation. The adaptive droop control strategy can appropriately adjust the droop coefficient according to the system condition so that the ESS contributes appropriately to the stability improvement of the power system.

More and more distributed generation (DG) and energy storage (ES) devices are being connected to the distribution network (DN). They have the potential of maintaining a stable supply load during failure periods when using islanding operations. Therefore, DG and ES have capacity value, i.e., improving the power supply capability of the system. However, there are strong fluctuations in DG outputs, and the operations of ES devices have sequential characteristics. The same capacity of DG has different load-bearing capabilities compared to conventional thermal or hydroelectric units. This paper proposes a method for evaluation of power supply capability improvement in DNs. First, the temporal fluctuation in both power source and load demand during fault periods is considered. A DN island partition model considering the secondary power outage constraint is established. Then, a modified genetic algorithm is designed. The complex island partition model is solved to achieve accurate power supply reliability evaluation. And the incremental power supply capability associated to DG and ES devices is calculated. Finally, a case study is conducted on the PG&E 69-bus system to verify the effectiveness of the proposed method. It is found that with a 20% configuration ratio of ES devices, the power supply capability improvement brought about by 6 MW DG can reach about 773 kW.

This paper researches the different configuration modes of distributed generation, such as installation location, installation capacity and influence on distribution network, analyzes the running power factor, energy loss, network topology, voltage control mode with traditional voltage control equipment, and gives relevant conclusions, so that power system planning, dispatching and running engineer can process in time, effectively guide customers to connect distributed power to distribution power grid, minimize the distributed power impact to distribution power system. As for the access point and capacity optimization of distributed generation in distribution network, this paper proposes the intelligent optimization algorithm to solve the optimal access mode of distributed power supply. The objective is to maximize the capacity of distributed generation and minimize the system energy loss. At the same time, the upper and lower limits of branch current carrying capacity, main transformer capacity, section active power and node voltage are taken as constraints. By the calculation and analysis of the access capability of a real distribution network, the effectiveness of the proposed method is verified.

One of the technical solutions for improving the stability of power system is incorporation of Static Synchronous Compensators (STATCOM) and Static Synchronous Series Compensator (SSSC) controllers. However, the impact of hybridized STATCOM and SSSC on the generator damping stability of the power system to improve the post disturbance recovery voltages of the generator is necessary. Thus, in this study, hybridized model of STATCOM and SSSC controllers were incorporated on the Nigerian 31-bus power system to improve the system generator damping stability during disturbance. Transient stability of electrical power system with contingency was performed using swing equations technique. Line-Voltage Stability Index (L-VSI) technique was employed to determine the critical load bus for the placement of the controllers. Hybridized model of the STATCOM and SSSC was developed and incorporate into the selected load buses and its impact on stability of the generator oscillation damping was examined. Simulation was done in MATLAB R2023a. The generator damping ratio, total active power losses and total cost of controllers were determined. Results verified the effectiveness of hybridized model of STATCOM and SSSC controllers in improving the stability of power generator oscillation damping. Keywords : STATCOM, SSSC, Line-Voltage Stability Index, Contingency, Generator Oscillation Damping, Power Loss. DOI: 10.7176/NCS/15-04 Publication date: February 28 th 2025

Premium power park (PPP) provides corresponding power supply for different levels of load using multiple PQDs, which is an effective measures to solve power quality problem. Existing international and regional or national power quality standards was given firstly. Then according to Premium Power Park user's feature, from the power supply capacity, load-sensitive, power quality level requirements etc, designed real-time monitoring systems for power quality, custom power and energy storage device's distribution program, function and performance indicators. To study energy saving of PPP operating with power supply scheme adding PQDs, and satisfy limits of power quality, proposed a an energy saving quantization to meet the Premium Power Park and various user needs. The calculation model show that under the new power supply scheme, energy loss of line and transformer have decreased and supply efficiency of system has been greatly improved.

With large wind energy integration into power systems, wind farms begin to influence power systems in a much more significant manner. As wind energy systems utilize different generator technologies from the one utilized in the conventional power plants, the steady-state, transient and small-signal dynamics, as well as, power system stability will thus be significantly affected. The impact of wind energy systems on the power system dynamics and stability is thus of practical importance. As there is a significant increase in installation of wind turbines equipped with doubly-fed induction generator (DFIG) in recent years, a dynamic model of the DFIG wind turbine is firstly developed in this thesis. The model is validated against field measurement data, and the validation gives confidence about the accuracy and applicability of the developed model. DFIG wind farms consist of tens to hundreds of identical DFIG wind turbines increasing the complexity of the wind farm model and simulation time. A novel aggregation technique is developed in this thesis that incorporates a multiplication factor, namely mechanical torque compensation factor (MTCF), to the mechanical torque of the full aggregated wind farm model. The MTCF is initially constructed to approximate a Gaussian function by using fuzzy logic method. By optimizing the MTCF on a trial and error basis, less than 10 percent discrepancy is then achieved between the proposed aggregated model and the complete model. The proposed aggregation technique is then applied to a 120 MVA offshore wind farm comprising of 72 DFIG wind turbines and shows higher accuracy in approximating the wind farm dynamics as it appears at the point of common coupling (PCC) as compared to the full aggregated model. The proposed aggregated model computes faster than the complete wind farm model by 90.3 percent during normal operations and 87 percent during grid disturbances. To overcome the adverse effects due to the fluctuating nature of wind, two wind power smoothing methods are proposed using a fuzzy logic pitch angle controller for a smooth performance with a minimum drop in output power. One method performs partial smoothing with only 4.74 percent drop in the output power while the other method offers complete smoothing with a 8.28 percent drop in output power. The impact of wind energy integration on power system transient stability (PSTS) is studied quantitatively with the transient energy margin (TEM), which is calculated through the evaluation of the transient energy function (TEF). This study is carried out in two ways in the thesis. One is to analyse the impact of transient fault on the DFIG wind turbine as compared to SGs for different factors, like the fault clearing time, the grid coupling, the inertia constant and the voltage sag. The study reveals that transient stability of the DFIG wind turbine is hardly affected by the grid coupling, the inertia constant and the generator terminal voltage sag variations indicating its consistent transient performance within a wide range of these factors. The fault clearing time should be almost 11 percent faster for the system with the DFIG than the synchronous generator (SG). The other is to investigate the impact of the DFIG wind farm on the PSTS with the variation of different factors, which are the voltage sag, the fault clearing time, the load and the wind power penetration level. The study reveals that power systems integrated with DFIG wind farms are sensitive to transient events with high voltage sag, high fault clearing time, low load operation and high wind power penetration level. Machines at different locations individually possess distinct fault response and suffer from a large-scale power imbalance. As a result, reliable operation of DFIG wind farm integrated power systems demands upgraded equipment, such as advanced switchgear, fast breakers/isolators, efficient power reserve systems and advanced reactive power compensating device, etc.