Capacity Of Hybrid Energy Storage System Research Articles

Capacity Optimal Allocation Method and Frequency Division Energy Management for Hybrid Energy Storage System Considering Grid-Connected Requirements in Photovoltaic System

The coordination between a hybrid energy storage system (HESS) and photovoltaic (PV) power station can significantly reduce grid-connected PV power fluctuations. This study proposes a HESS capacity optimal allocation method considering the grid-connected PV requirements. Firstly, based on the power fluctuation requirements in the PV power station grid-connected regulations, the maximum power point tracking working point switching control is performed for the PV power station, from which the grid-connected PV power and HESS power are obtained. Then, a capacity optimal allocation method and frequency division energy management strategy (EMS) for HESS is proposed to find the energy response and power response of each energy storage source. Furthermore, a multi-objective optimization function with HESS cutoff frequency as the independent variable is constructed, and the input cost of HESS and the life loss of the lithium battery are optimized. Finally, the overall strategy is compared and analyzed under the scenarios of three typical PV power fluctuations. Simulation results show that the control strategy has a good smoothing effect on PV power fluctuations. From the perspective of the annual comprehensive input cost, HESS realizes the optimal capacity allocation when the cutoff frequency is 0.0066 Hz.

Capacity optimization of hybrid energy storage system for microgrid based on electric vehicles’ orderly charging/discharging strategy

The high penetration rate of electric vehicles (EVs) will aggravate the uncertainty of both supply and demand sides of the power system, which will seriously affect the security of the power system. A microgrid (MG) system based on a hybrid energy storage system (HESS) with the real-time price (RTP) demand response and distribution network is proposed to deal with uncertainties. Through the guidance of RTP, the electricity consumption behavior of consumers and car owners is more adaptable to the output uncertainty of renewable energy source (RES) to ensure that the system has sufficient elastic capacity within the schedule cycle. The bi-level programming optimization model of HESS capacity is established, maximizing the MG's reliability and minimizing the comprehensive operating cost (COC). In addition, an EV's orderly charging/discharging strategy is formed, which effectively reduces operating costs and peak-to-valley load differences. The results show that EVs can effectively mitigate the peak-to-valley load difference by 20.5% under 100% participation in orderly charging/discharging. Under RTP-based demand response, MG can reduce the COC by 25.5%. In addition, the number of EVs participating in vehicle-to-grid also has different effects on scheduling. Based on the experimental data from Guizhou, results show that 200 electric private vehicles (EPVs) and ten electric buses (EBs) are the optimal number of vehicles to be solved; namely, the optimal capacity of the HESS is achieved.

Dual Model Predictive Controlled Hybrid Energy Storage System for Naval DC Microgrids

Hybrid energy storage system (HESS) is an effective measure to improve the electrical performance of naval dc microgrids supplying pulsed power loads (PPLs). Coordination control scheme and capacity configuration of the HESS are two key issues to meet multiple control objectives and constraints. In response to the requirements of optimal operation for HESS under various complex scenarios, a dual model predictive control (D-MPC) strategy is proposed for the HESS integrated with the superconducting magnetic energy storage (SMES) and battery in this article. Firstly, the current reference of battery is obtained through the MPC integrated adaptive low-pass filtering (ALPF). Then, the local MPC controller of HESS converter is utilized to track the current reference of battery and SMES as well as the bus voltage reference to achieve transient power allocation of HESS and energy balancing of dc microgrid. Meanwhile, a cost function with adaptive weighting factor is designed to make tradeoff of conflict control objectives. Furthermore, the HESS capacity configuration and superconducting magnet optimal design according to its actual operation condition are presented. Comparative case studies are conducted in hardware-in-the-loop (HIL) experiment to demonstrate the superiority of the proposed scheme in terms of improving system operation performance under various PPLs.

Two-level optimization model of hybrid energy storage system in wind/solar distributed generation system

Hybrid energy storage system (HESS) composed of battery and supercapacitor can effectively compensate the power deviation between supply and demand in distributed generation system (DGs). However, the popularization application of which is limited by the high cost of capacity configuration and subsequent maintenance. Hence, considering dynamic change of frequency characteristics of power deviation, a two-level optimization model for power programming and capacity configuration of HESS is established. In the outer layer, aiming at minimizing the allocation of the capacity, power allocation strategy of HESS based on adaptive filtering is proposed, which adaptively adjusts the sliding window of moving average filter (MAF), and decomposes the supply–demand power deviation sequence into the scope of battery and supercapacitor. In the inner layer, considering grid-connected power fluctuations, a full life cycle HESS capacity optimization model is proposed. The model aims at the total cost of HESS and the renewable energy utilization (REU), and uses non-dominated sorting genetic algorithm II (NSGA-II) to solve the model. Furthermore, a case is introduced to verify the feasibility of the proposed strategy and the effectiveness of the model solution.

Capacity Configuration Strategy of Hybrid Energy Storage System Considering Photovoltaic Power Fluctuation Suppression and Operation Optimization

Photovoltaic (PV) is becoming popular in many countries. However, due to the influence of weather, the PV output power often fluctuates greatly in a short time, and its intermittence makes it difficult to meet the load demand. The Hybrid energy storage system (HESS) can smooth the PV power fluctuation and optimize the operation of the whole system. Therefore, this paper proposes a capacity configuration strategy for the HESS composed of a battery and super capacitor. The strategy aims to minimize the construction and operation costs. It contains a two-layer optimization model. The upper layer configures the capacity and output power of the HESS, and the lower model optimizes the operation of the PV-HESS system and solves the model through deep reinforcement learning. Finally, a PV demonstration project in Shandong Province, China, is taken as an example to verify the effectiveness of the HESS capacity configuration strategy.

Capacity Configuration Method of Hybrid Energy Storage Participating in AGC Based on Improved Meta-Model Optimization Algorithm

To improve the performance and economy of the hybrid energy storage system (HESS) coordinating thermal generators to participate in automatic generation control (AGC), a HESS bi-layer capacity configuration model that considers the control strategy and net benefits of HESS is proposed. In addition, an improved mode-pursuing sampling (MPS) optimization algorithm based on meta-model is presented to improve the accuracy of model solving. In the lower layer, to improve the performance of HESS participating in AGC, a model predictive control (MPC) strategy is presented to distribute HESS power reasonably. Based on this, the upper layer develops a life-cycle net benefit model of HESS participating in AGC to improve its economy. The bi-layer model realizes iterative optimization of HESS capacity and operation through parameter transmission. Furthermore, to improve the solution accuracy of the bi-layer model, the convergence speed of the MPS algorithm is improved, so that the global search and local convergence speed can be taken into account. The case study results show that the bi-layer model can comprehensively consider the interaction between the economy and operating strategy of HESS. The proposed MPC strategy has better frequency regulation performance and the improved MPS algorithm has better solution performance.

Optimal selection and sizing of hybrid energy storage systems for wind power dispatching

Wind power uncertainty is one of the problems in large-scale wind farms integration to the network. The use of Energy Storage Systems (ESSs) is a practical solution to enhance availability and power dispatching possibility of renewable energy sources (RESs). RESs need an ESS with high power and energy capacity while none of ESSs has this feature at the same time. The accepted solution for this problem is using the hybrid energy storage system (HESS). In this paper, HESS optimal sizing and power dispatching of wind-HESS system are considered, simultaneously, and the problem of high storage capacity in the modified min-max wind power dispatching method is resolved by utilizing the limited min-max wind power dispatching method. The optimal types and capacity of HESS are determined based on multi-objective optimization function with objectives of maximizing the net present value and storage lifetime. Furthermore, in short-term power management control, the wind-HESS performance and delivering the prescheduled and constant power to the network are investigated and HESS charge-discharge cycles are controlled to work in safety range. Finally, the proposed method and short-term power management are evaluated by a wind farm real data, which is scaled down to 3 MW power level for better comparison with other studies.

Research on demand management of hybrid energy storage system in industrial park based on variational mode decomposition and Wigner–Ville distribution

In the industrial park microgrids, the curves of industrial load and photovoltaic output are unstable and unadjustable. The implementation of energy storage system (ESS) has proven successful in tackling these issues. Compared with the single-type battery energy storage (SBES), the hybrid energy storage system (HESS) is composed by energy-type energy storage and power-type energy storage, which can effectively improve the controllability and schedulability of renewable energy generation. The governments of most countries have implemented a two-part time-of-use tariff mechanism for industrial users. In this context, the paper proposes a solution for the reasonable low-cost configuration and operation of HESS in industrial parks. First of all, this paper creatively combines the Variational Mode Decomposition and Wigner–Ville Distribution (VMD-WVD) algorithm to break down the net load of the industrial park system, and installs the super-capacitors and lithium batteries to smooth the high frequency and low frequency components of the original net load. Secondly, this paper establishes a two-stage monthly and day-ahead optimization model, which is solved by the Chaos Particle Swarm Optimization (CPSO) algorithm. The monthly HESS capacity optimization configuration model is to minimize the total installation cost. And the day-ahead scheduling model maximizes the net income of life cycle, which further improves the user's peak-to-valley arbitrage. The results show that, compared with SBES, the installation of HESS can better achieve peak shaving and reduce grid connection fluctuations. Besides, the installation of HESS can greatly reduce the electricity cost and the basic electricity cost of industrial parks, so that it can save industrial users' production costs.

Capacity allocation of HESS in micro-grid based on ABC algorithm

Abstract The hybrid energy storage system (HESS) is a key component for smoothing fluctuation of power in micro-grids. An appropriate configuration of energy storage capacity for micro-grids can effectively improve the system economy. A new method for HESS capacity allocation in micro-grids based on the artificial bee colony (ABC) algorithm is proposed. The method proposed a power allocation strategy based on low pass filter (LPF) and fuzzy control. The strategy coordinates battery and supercapacitor operation and improves the battery operation environment. The fuzzy control takes the state of charge (SOC) of the battery and supercapacitors as the input and the correction coefficient of the time constant of the LPF filter as the output. The filter time constant of the LPF is timely adjusted, and the SOC of the battery and supercapacitor is stable within the limited range so that the overcharge and over-discharge of the battery can be avoided, and the lifetime of the battery is increased. This method also exploits sub-algorithms for supercapacitors and battery capacity optimization. Besides, the Monte Carlo simulation of the statistic model is implemented to eliminate the influence of uncertain factors such as wind speed, light intensity and temperature. The ABC algorithm is used to optimize the capacity allocation of hybrid energy storage, which avoids the problem of low accuracy and being easy to fall into the local optimal solution of the supercapacitors and battery capacity allocation sub-algorithms, and the optimal allocation of the capacity of the HESS is determined. By using this method, the number of supercapacitors required for the HESS is unchanged, and the number of battery is reduced from 75 to 65, which proves the rationality and economy of the proposed method.

Impact of Power Sharing Method on Battery Life Extension in HESS for Grid Ancillary Services

Hybrid energy storage system (HESS) based on Li-ion and supercapacitor (SC) can play a potential role to stabilize the grid by providing the fast frequency ancillary services. The SC helps to reduce the battery charge/discharge stress and, hence, assists to extend the battery lifespan. The power sharing method (PSM) is the heart in control part to improve the HESS performance and reduce the battery stress. This paper proposes a hybrid PSM and investigates its impact on battery life extension along with its relation to the system design and regulation signal. The performance of hybrid PSM is compared with three other PSMs (low-pass filter, first and second rule based) in a 10 MW/10 MWh full-active parallel HESS for frequency regulation service in two networks: U.K. (national grid) and USA (PJM). Considering maximum possible battery lifetime upto 25 years, result shows that the hybrid PSM approach allows a degree of the better performance for both grid while the sharing of SC is kept maximum 2.0% and 2.5% (for U.S. and U.K. grid, respectively) of the HESS capacity. This study also analyses the impact of PSM on shared capacity and design of HESS for different grid regulation signals.

Optimal Capacity Configuration of a Hybrid Energy Storage System for an Isolated Microgrid Using Quantum-Behaved Particle Swarm Optimization

The capacity of an energy storage device configuration not only affects the economic operation of a microgrid, but also affects the power supply’s reliability. An isolated microgrid is considered with typical loads, renewable energy resources, and a hybrid energy storage system (HESS) composed of batteries and ultracapacitors in this paper. A quantum-behaved particle swarm optimization (QPSO) algorithm that optimizes the HESS capacity is used. Based on the respective power compensation capabilities of ultracapacitors and batteries, a rational energy scheduling strategy is proposed using the principle of a low-pass filter and can help to avoid frequent batteries charging and discharging. Considering the rated power of each energy storage type, the respective compensation power is corrected. By determining whether the charging state reaches the limit, the value is corrected again. Additionally, a mathematical model that minimizes the daily cost of the HESS is derived. This paper takes an isolated micrgrid in north China as an example to verify the effectiveness of this method. The comparison between QPSO and a traditional particle swarm algorithm shows that QPSO can find the optimal solution faster and the HESS has lower daily cost. Simulation results for an isolated microgrid verified the effectiveness of the HESS optimal capacity configuration method.

Optimal configuration of energy storage system based on frequency hierarchical control in ship power system with solar photovoltaic plant

This study presents the results of hybrid energy storage system (HESS) and improved maximum power point tracking (MPPT) algorithm simulation analysis undertaken to improve the smoothing performance of solar photovoltaic (PV) plant installed in the ship power system. Frequency hierarchical control algorithm is used to assign high-frequency oscillation part to the supercapacitor and low-frequency oscillation part to the battery. Meanwhile, improved MPPT algorithm is also used to limit the maximum output power of PV plant, which can reduce the necessary installed capacity of HESS further. The effectiveness of the proposed method was verified under a typical ship power system model.

Optimal capacity design for hybrid energy storage system supporting dispatch of large-scale photovoltaic power plant

This paper presents a methodology to evaluate the optimal capacity and economic viability of a hybrid energy storage system (HESS) supporting the dispatch of a 30MW photovoltaic (PV) power plant. The optimal capacity design is achieved through a comprehensive analysis of the PV power plant performance under numerous HESS capacity scenarios. The analysis has been conducted using a high performance computing cluster which generated a large amount of simulation data based on a PV power profile of one month. The HESS, consisting of a vanadium redox battery and a supercapacitor bank with a power rating ratio between the two energy storage technologies of 5:1, is connected at the point of common coupling to support the PV power plant to comply with the dispatch rules in the Australian national electricity market. A quantitative relation is developed based on surface fitting, which relates the HESS capacity with the dispatch performance of the PV power plant in terms of energy yield and required ancillary services. The paper concludes the optimal capacity of HESS which will provide the maximum profit improvement under the actual market conditions.