The Impact of Aging-Preventive Algorithms on BESS Sizing under AGC Performance Standards

Modeling of battery energy storage systems for AGC performance analysis in wind power systems

In this paper, an optimizing model of battery energy storage system (BESS) capacity configuration for active distribution networks (ADNs) is proposed to mitigate the negative effect of distribution systems with distributed generations (DGs), in stability, economy and reliability perspectives. In details, this model involved the charging and discharging limitation and the operation constrains of BESS, as well as the power flow balance of distribution system. Moreover, three optimizing objectives have been presented for the optimal power profile of BESS, including minimizing the voltage fluctuation, reducing the feeder loss and maximizing the consecutive supplied power of ADN, respectively. After the optimization, the BESS capacity is calculated by estimation of the maximum charging and discharging energy. Finally, the simulation results are shown to illustrate the procedure of capacity configuration.

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.

With the large-scale uncertain renewable energy connected to the grid, the frequency regulation demand is growing day by day, and there is a great need to use energy storage to improve the automatic generation control (AGC) performance of the thermal power to meet the grid frequency regulation requirements. Therefore, a two-stage optimization method considering AGC performance and state of charge (SoC) control for energy storage coordinated with thermal power unit in AGC is proposed in this paper. Firstly, the AGC performance assessment index constraints are linearized, which is used to establish the first stage optimization model of the joint system participation in frequency regulation. Further, to reduce the depth charging/discharging situation of energy storage, the second stage optimization model is constructed with the objective of minimizing the SoC deviation. Finally, the simulation result shows that the proposed two-stage optimization method can improve both AGC performance and sustainability of energy storage, and effectively reduce the loss of energy storage working life.

The battery energy storage (BES) is very promising to be used for improving the performance of automatic generation control (AGC) in power system by offering fast active power compensation. However, the improper parameters of controller in AGC system may cause an unstable frequency problem. This paper presents an optimal controller design method based on bacteria foraging algorithm (BFA) for AGC system with BES. A two-area reheat thermal system is considered to be equipped with the proportional plus integral (PI) controllers. The BFA technique is employed to search for the optimum controller parameters by minimizing the integral of time multiply absolute error (ITAE) index. The performance of the proposed BFA tuning controller has been evaluated with that of the controllers tuned by particle swarm optimization (PSO) and genetic algorithm (GA). Simulation results emphasize the performance of AGC system with BES and demonstrate the superiority of the proposed BFA tuning controller compared to the optimized controller based on PSO and GA.

With the increasing penetration of wind power and other distributed renewable generation (DRG), its inherent intermittency and variability impose operational challenges on distribution systems. In order to alleviate this problem, this paper proposes an optimization framework to determine the optimal installation location, capacity and operation strategy of the battery energy storage system (BESS) in the 33-node distribution system with wind power access. The method first screens the BESS candidate installation location through power loss sensitivity analysis, and then adopts the particle swarm optimisation (PSO) algorithm to optimize the capacity and location of BESS with the goal of minimising network loss, capital expenditure (CAPEX) and operating expenditure (OPEX). This framework was systematically tested across three cases: the original 33-bus distribution network system, the system configured with the optimal capacity BESS, and the joint operation system integrating wind power and BESS. The results indicate that the coordinated operation scheme reduces the active power loss by nearly 27% while improving the reliability and operation flexibility.

This paper describes the demonstration and evaluation of stacking wind power time-shifting and regulation by a 1 MW/2 MWh battery energy storage system (BESS) within a 10 MW wind park. The BESS time-shifted 15.8 MWh of wind power to peak periods while responding to the automatic generation control (AGC) signals that provide regulation over the 24 day test period. Its fast response time and accurate output allowed the BESS to provide both services simultaneously. Limiting the charging to periods when wind power was available lowered the amount of time-shifting and AGC that could be provided. Some form of AGC was delivered from the BESS for 78.5% of the test period and, if it were remunerated at the PJM rate when the test was performed, it would have earned 8627 USD, with 97% from regulation and the rest from time-shifting wind power. Using PJM's performance template, the BESS averaged a performance score of 70.5% during hours when regulation was performed. This work adds to the growing empirical data that is informing models of and policy for grid storage.

The integration of battery energy storage systems (BESS) with renewable energy is a potential solution to address the disadvantages of renewable energy systems, which is irregular and intermittent power. In particular, residential BESS is advancing in numerous countries. The residential BESS connected to the photovoltaic system (PV) can store the PV power in the battery through charging, and supply the PV power, which was stored in the battery, to the load through discharging when there is no PV power. Therefore, the utilization of residential BESS with PV reduces the daily electric power consumption and the electricity bills that households have to charge. However, it is understood that there is no case of installing and using residential BESS in Korea yet. Most residential houses in Korea are apartment houses, and thus residential BESS can be used with balcony PV. This paper presents operation modes of residential BESS with balcony PV for apartment houses. The BESS capacity was estimated by considering the balcony PV capacity, which can be installed in households, and power consumption. The applicability of the residential BESS was analyzed through performance and economics evaluation under current and various conditions. The operation modes of BESS were divided into four types according to PV power supply priority and battery charging source, and a test took place in a demonstration house. The risk of fully discharging the battery has been discovered when PV power is first charged to the battery or when only PV power is charged with the battery. As a result, preferential charging of the battery with PV power and then with PV and grid power was found to be the most optimal operation mode. In addition, additional functions were proposed for residential BESS in apartment households. The results will contribute to effective application of residential BESS with balcony PV in the near future.

Planning battery energy storage system in line with grid support parameters enables circular economy aligned ancillary services in low voltage networks

The dynamic performance of automatic generation control (AGC) primarily relies upon the performance index criteria (PIC) being used to optimize the supplementary controller gains. Most of the previous AGC studies were focused on developing various intelligent controllers and meta-heuristic algorithms to improve the system dynamic performance of the AGC system. However, no significant efforts were made to formulate effective PICs in order to acquire better supplementary control and improved AGC dynamic performance. In this paper, a maiden attempt has been made and new hybrid-peak-area-based PICs have been proposed for the optimal tuning of supplementary controller gains. The proposed PICs have been implemented upon single area as well as standard New England IEEE 39 bus system. Sensitivity analysis has also been carried out to demonstrate the superiority of the proposed PICs for wide changes in AGC power system model parameters. The proposed PICs demonstrate better dynamic performance in comparison to existing PICs in terms of overshoot, subsequent oscillations, and settling time.

In recent years, wind power which is an intermittent and random energy source affects the frequency stability of automatic generation control (AGC) system with the large-scale wind power integration. So, it is necessary to improve the conventional frequency control method and model. Firstly, a dynamic wind farm model is set up based on wind turbines clustering for the accuracy of data in this paper. Secondly, it is analyzed that the difference between multi-area interconnection AGC model and area control error (ACE) with wind farm and without wind farm. Thirdly, an intelligent control strategy of cascade control with outer loop using fuzzy PI algorithm and inner loop using internal model controller is designed to reduce the impacting of wind power changes on AGC system frequency and improve the performance of AGC system. Finally, frequency control effects of conventional AGC system and improved AGC system are contrasted by simulation study of four-area system. It is proved that the improved AGC system has stronger ability to resist fluctuation of wind power and better frequency control performance.

Coordinated intelligent frequency control incorporating battery energy storage system, minimum variable contribution of demand response, and variable load damping coefficient in isolated power systems

Maintaining the balance between real-time generation and load is one of the important tasks of power system. Automatic generation control (AGC) is an important part of power grid frequency control, so grid-connected thermal power units must have good AGC performance. Many factors may cause the AGC regulation performance of generating units to fail to meet the requirements, such as unreasonable system design, imperfect control strategy, poor equipment characteristics and so on. This paper introduces the performance index of thermal power units participating in AGC regulation and analyses the influence of flow characteristics of steam turbine valves on AGC performance. By optimizing the steam turbine valve management function, the performance can be effectively improved. The effectiveness of this method is verified by its implementation on a typical 300 MW thermal power unit.

Development and implementation of new Automatic Generation Control (AGC) algorithms requires testing them on a model that adequately simulates primary energetic, information and control processes. In this article an implementation of a test platform based on HRTSim (Hybrid Real Time Simulator) and SCADA CK-2007 (which is widely used by the System Operator of Russia) is proposed. Testing of AGC algorithms on the test platform based on the same SCADA system that is used in operation allows to exclude errors associated with the transfer of AGC algorithms and settings from the test platform to a real power system. A power system including relay protection, automatic control systems and emergency control automatics can be accurately simulated on HRTSim. Besides the information commonly used by conventional AGC systems HRTSim is able to provide a resemblance of Phasor Measurement Unit (PMU) measurements (information about rotor angles, magnitudes and phase angles of currents and voltages etc.). The additional information significantly expands the number of possible AGC algorithms so the test platform is useful in modern AGC system developing. The obtained test results confirm that the proposed system is applicable for the tasks mentioned above.

This study aims to show methods of determining the installation site and the optimal capacity of a battery energy storage system (BESS) to attain load leveling. The methods are based on the hardware characteristics of 20 kW/100 kWh and 1 MW/4 MWh prototype BESSs, which are already developed and are being developed, respectively. The candidate site of BESS installation is the secondary side of a distribution substation's main transformer (MTR). These tasks are performed by first classifying the load pattern of an MTR and comparing it to the load pattern of a total power system. If the load factor improves, then the location is determined to be a BESS installation site. Subsequently, the optimal BESS capacity for an MTR is determined using the given operation pattern, the charging/discharging characteristics and efficiency. Computational experimentation was performed on an actual distribution system in Korea. After determining the optimal capacity, the authors verified that installing the BESS on the selected MTR improved both daily and nightly load factors.