Model Of Energy Storage System Research Articles

Inverse identification of thermal behaviour of a paraffin-based phase change material in complete and partial phase change cycles

From the macroscopic point of view, phase change hysteresis (PCH) means that a phase change process (e.g. solidification) does not follow the same temperature-enthalpy path as the opposite phase change process (melting). Although the PCH is observed in most phase change materials (PCMs), it is often neglected in computational models, resulting in discrepancies when compared to experimental data. The PCH can particularly be an issue in the modelling of latent heat thermal energy storage systems, where incomplete (partial) phase transitions are quite common. Lab-scale experimental methods for characterisation of PCMs, such as differential scanning calorimetry or the temperature-history method, employ only small PCM samples, and the obtained results are often insufficient for predicting the thermal behaviour of large volumes of PCMs. The present study explores numerical modelling approaches to the PCH, addressing both complete and partial melting-to-solidification cycles. A set of validation experiments was performed, focusing on phase transitions in a paraffin-based PCM enclosed in a rectangular cavity. An inverse identification method was used to minimise the root mean square error (RMSE) of temperatures in the PCM using the particle swarm optimisation method. A two-curve approach showed the highest accuracy in complete phase change cycles, with a 62% improvement in the RMSE when compared to the manufacturer data. As for cycles with partial phase changes, a curve-scale approach showed superior behaviour, reducing the RMSE as much as 99%. Conversely, another investigated approach – a curve-track model – exhibited inferior performance, making it less suitable for the modelling of partial phase changes.

Numerical Study for the Design of a Thermal Energy Storage System with Multiple Tunnels Based on Phase Change Material: Case Study Mining in Chile (Thermal Storage in Off-Grid Industrial Applications)

This paper presents a numerical model for thermal energy storage systems’ design, development, and feasibility. The energy storage was composed of a tank that stores phase change material (AlSi12) and internal pipes with heat transfer fluid (Cerrolow 117), coupled to a power block to dispatch electrical energy on a small scale for off-grid industrial applications. Subsequently, the evolution of the temperature in charge/discharge cycles, temperature degradation, and storage efficiency was determined with the appropriate magnitudes and behavior through the resolution of a numerical model. In addition, for the proposed electric power generation plant for an off-grid pumping system in the mining industry of Chile, a numerical model was developed using the finite volumes method to simulate the thermocline performance. As a result, the temperature history reflects stable thermal behavior, low degradation, and high efficiency of approximately 92%, with a storage time increasing up to 13 [h] and 384.8 [kWh] capacity. Also, implementation was feasible on a small scale due to its compact, modular, and economically competitive characteristics in a concentrated solar power plant. Finally, the proposed design was proven to be an accurate and reliable alternative for small-scale off-grid mining applications.

Optimal PV‐storage capacity planning for rail transit self‐consistent energy systems considering extreme weather conditions

AbstractWith the rapid development of electrified rail transportation, the traction load demand of rail transportation has increased sharply, and its operational security under extreme conditions has been highlighted. Given the above background, this paper proposes a planning method for the optimal photovoltaic (PV)‐storage capacity of rail transit self‐consistent energy systems considering the impact of extreme weather. First, the basic structure of a rail transit self‐consistent energy system is presented. Second, considering a power transmission system with line trip‐off under extreme weather conditions, a traction load reduction model is established to obtain the maximum power exchange capability between the power transmission network and rail substations. Subsequently, an optimal planning model for a hybrid energy storage system (HESS) is proposed to minimize the total HESS investment and rail transit system operation costs. Finally, the model is linearized as mixed‐integer linear programming and solved using Gurobi and the Yalmip toolbox. The simulation results verify the effectiveness of the proposed optimal PV‐storage capacity planning for rail transit self‐consistent energy systems.

Call for Papers: Special Section on Advanced Modeling, Control, Applications and Safety of Energy Storage Systems

Call for Papers: Special Section on Advanced Modeling, Control, Applications and Safety of Energy Storage Systems

Closed loop model predictive control of a hybrid battery-hydrogen energy storage system using mixed-integer linear programming

The derivation of an efficient operational strategy for storing intermittent renewable energies using a hybrid battery-hydrogen energy storage system is a difficult task. One approach for deriving an efficient operational strategy is using mathematical optimization in the context of model predictive control. However, mathematical optimization derives an operational strategy based on a non-exact mathematical system representation for a specified prediction horizon to optimize a specified target. Thus, the resulting operational strategies can vary depending on the optimization settings.This work focuses on evaluating potential improvements in the operational strategy for a hybrid battery-hydrogen energy storage system using mathematical optimization. To investigate the operation, a simulation model of a hybrid energy storage system and a tailor-made mixed integer linear programming optimization model of this specific system are utilized in the context of a model predictive control framework. The resulting operational strategies for different settings of the model predictive control framework are compared to a rule-based controller to show the potential benefits of model predictive control compared to a conventional approach. Furthermore, an in-depth analysis of different factors that impact the effectiveness of the model predictive controller is done. Therefore, a sensitivity analysis of the effect of different electricity demands and resource sizes on the performance relative to a rule-based controller is conducted. The model predictive controller reduced the energy consumption by at least 3.9 % and up to 17.9% compared to a rule-based controller. Finally, Pareto fronts for multi-objective optimizations with different prediction and control horizons are derived and compared to the results of a rule-based controller. A cost reduction of up to 47 % is achieved by a model predictive controller with a prediction horizon of 7 days and perfect foresight.

Two-Stage Optimal Scheduling Based on the Meteorological Prediction of a Wind–Solar-Energy Storage System with Demand Response

With large-scale wind and solar power connected to the power grid, the randomness and volatility of its output have an increasingly serious adverse impact on power grid dispatching. Aiming at the system peak shaving problem caused by regional large-scale wind power photovoltaic grid connection, a new two-stage optimal scheduling model of wind solar energy storage system considering demand response is proposed. There is a need to comprehensively consider the power generation cost of various types of power sources, day-ahead load forecasting information, and other factors and plan the day-ahead output plan of the energy storage system with the minimum system operation cost as the optimization objective of day-ahead dispatching. The demand response strategy is introduced into the time-ahead optimal scheduling, and the optimization of the output value of the energy storage system in each period is studied with the goal of minimizing the system adjustment cost. The particle swarm optimization algorithm is used to solve the model, and the IEEE33 node system is used for an example simulation. The results show that using the demand response and the collaborative effect of the energy storage system can suppress the uncertainty of wind power and photovoltaic power, improve the utilization rate of the system, reduce the power generation cost of the system, and achieve significant comprehensive benefits.

Virtual Power Plant’s Optimal Scheduling Strategy in Day-Ahead and Balancing Markets Considering Reserve Provision Model of Energy Storage System

In recent years, with the rapid increase in renewable energy sources (RESs), a Virtual Power Plant (VPP) concept has been developed to integrate many small-scale RESs, energy storage systems (ESSs), and customers into a unified agent in the electricity market. Optimal coordination among resources within the VPP will overcome their disadvantages and enable them to participate in both energy and balancing markets. This study considers a VPP as an active agent in reserve provision with an upward reserve capacity contract pre-signed in the balancing capacity (BC) market. Based on the BC contract’s requirements and the forecasted data of RESs and demand, a two-stage stochastic optimization model is presented to determine the VPP’s optimal scheduling in the day-ahead (DA) and balancing energy (BE) markets. The probability of reserve activation in the BE market is considered in this model. The ESS’s reserve provision model is proposed so as not to affect its schedule in the DA market. The proposed optimal scheduling model is applied to a test VPP system; then, the effects of the BC contract and the probability of reserve activation on the VPP’s trading schedule are analyzed. The results show that the proposed model has practical significance.

Bidding strategy and economic evaluation of energy storage systems under the time-of-use pricing mechanism

Energy storage systems (ESSs) can smooth loads, effectively enable demand-side management, and promote renewable energy consumption. This study developed a two-stage bidding strategy and economic evaluation model for ESS. In the first stage, time-of-use (TOU) pricing model based on the consumer psychology theory and user demand response function is proposed. In the second stage, the benefits and life cycle costs of ESS were measured, and a bidding strategy optimization model was constructed to maximize profit. Subsequently, based on the environmental, social, and governance (ESG) concept, an integrated value model was constructed to evaluate the economics of ESS. Finally, the economics of ESSs were analyzed using the proposed model in a case study. The results indicate the following: (1) The TOU pricing model can effectively reduce the peak-to-valley load difference; (2) The integrated value of ESS is 249,930 yuan, and economic value of ESS is 22,016 yuan in scenarios 1, implying that the ESS has positive externalities under the TOU pricing mechanism; and (3) The net profit and economics of ESS are positively correlated with the rated power of the storage battery and negatively correlated with its rated capacity.

Development of a simulation model for controlling energy storage systems on electric trains

The article discusses the experience of using electrical energy storage devices on multiple unit rolling stock, both in Russia and abroad. It is noted that the use of energy storage systems in railway transport in Russia lags somewhat behind its foreign colleagues. The main assumptions taken when performing simulation modeling of the operation of energy storage systems on an electric train are presented. Simulations of the operation of energy storage systems were carried out using the MatLab software package. Simulation models of an electric train with an energy storage device, a model of a heater for heating an electric train car, a model of a hybrid energy storage system, a model of a supercapacitor unit, and batteries are presented. The algorithm of operation of the active topology of a hybrid drive is considered. The developed simulation models have been tested. Graphs of the voltage at the pantograph and the current consumed by the electric train were obtained, as well as graphs of the operation of the battery and supercapacitor in charge/discharge modes.

Cooperative Hierarchical Control of Isolated Microgrids Considering Energy Storage System Aggregation

The integration of numerous energy storage systems (ESSs) improves the reliable and economic operation of microgrids but also enlarges the burden of control and communication systems. This paper proposes a cooperative hierarchical control for isolated microgrids with ESSs, which fully frees from the centralized paradigm and is therefore superior in flexibility and scalability. Firstly, the ESS cooperative control based on the distributed frequency controller is introduced for the state of charge (SoC) balance and the frequency regulation. Then, the ESS aggregation is considered. The aggregated ESS model is established and the real-time model parameters are derived by proposed distributed state observers. Finally, energy management is employed to allocate the output power between generators and the aggregated ESS. A detailed theoretical analysis of the dynamic and steady-state performance of proposed controllers and observers is provided. Simulations on the IEEE 33-bus and the 69-bus system verify the effectiveness of proposed methods.

Energy storage: Status and future perspective in Arab countries

AbstractIn this paper, the present status of energy storage implementation and research in Arab countries (ACs) is investigated. The different technologies of energy storage are reviewed then projects and capacities of installed or planned energy storage systems in the ACs are summarized based on published literature. In ACs, the installed and planned capacity of pumped hydro storage is 4365 MW, while for battery storage it is 5597 MW. No compressed energy storage projects are installed or planned in the near future. Green hydrogen as a fuel is planned in Egypt, Algeria, and Morocco. Renewable energy as a main employer of energy storage is predicted for the next 30 years; similarly, energy storage capacity is forecasted for the next 30 years. To enhance energy storage implementation it is recommended to include energy storage in all new commercial renewable energy projects. In addition to creating a regulatory framework that encourages the development of energy storage schemes. To advance research in the energy storage, modeling and simulation of energy storage systems in residential, commercial, and industrial sectors are recommended. In addition, cold energy storage for cooling and air‐conditioning using phase change material to be investigated.

A Stackelberg Game-Based Model of Distribution Network-Distributed Energy Storage Systems Considering Demand Response

In the context of national efforts to promote country-wide distributed photovoltaics (DPVs), the installation of distributed energy storage systems (DESSs) can solve the current problems of DPV consumption, peak shaving, and valley filling, as well as operation optimization faced by medium-voltage distribution networks (DN). In this paper, firstly, a price elasticity matrix based on the peak and valley tariff mechanism is introduced to establish a master–slave game framework for DN-DESSs under the DPV multi-point access environment. Secondly, the main model optimizes the pricing strategy of peak and valley tariffs with the objective of the lowest annual operating cost of the DN, and the slave model establishes a two-layer optimization model of DESSs with the objective of the maximum investment return of the DESSs and the lowest daily operating costs and call the CPLEX solver and particle swarm optimization algorithm for solving. Finally, the IEEE33 node system is used as a prototype for simulation verification. The results show that the proposed model can not only effectively reduce the operating cost of the distribution network but also play a role in improving the energy storage revenue and DPV consumption capacity, which has a certain degree of rationality and practicality.

Optimal Allocation of Hybrid Energy Storage System Based on Smoothing Wind Power Fluctuation and Improved Scenario Clustering Algorithm

Against the backdrop of the global energy transition, wind power generation has seen rapid development. However, the intermittent and fluctuating nature of wind power poses a challenge to the stability of grid operation. To solve this problem, a solution based on a hybrid energy storage system is proposed. The hybrid energy storage system is characterized by fast and precise control and bidirectional energy throughput, which can improve the impact of wind power fluctuations on grid stability. An ensemble empirical modal decomposition method was used to assign the raw wind power data to the grid-connected power and energy storage power commands with two reasonable corrections to meet the power allocation of the hybrid energy storage characteristics. In addition, a hybrid energy storage system model considering the whole life cycle cost was developed, and the optimal energy storage power cutoff was determined by exhaustively enumerating the high- and low-frequency power cutoffs. Finally, a comparison with a single storage capacity optimization model was carried out to verify the technical and economic advantages of hybrid energy storage in smoothing wind power fluctuations. To address the shortcomings of the traditional fuzzy c-means clustering algorithm, such as the need to specify the number of clusters in advance and sensitivity to the selection of the initial clustering centers, a combination of the cloud modeling theory and fuzzy c-means was used to make the process more automated and efficient. The improved clustering method algorithmic scheme had capacity error, power error, and cost error of around 3%, and the computational time was also significantly reduced and was computationally efficient compared to the full-year time series simulation. Through MATLAB (2020b) experimental simulation, it was found that the algorithm had a better balance of computational accuracy and efficiency.

Battery energy storage system planning for promoting renewable energy consumption using a constraint boundarization technique

AbstractThe battery energy storage system (EES) deployed in power system can effectively counteract the power fluctuation of renewable energy source. In the planning and operation process of grid side EES, however, the incorporation of power flow constraints into the optimization problem will strongly affect the solving efficiency. Therefore, a bi‐level planning and operation co‐optimization model for EES is proposed based on a constraint boundarization technique. Firstly, a constraint boundarization method for considering power flow constraints is proposed to solve the efficiency problem of the optimization model. Secondly, in terms of the upper and lower bounds of storage's operation curve obtained by the constraint boundarization method, an operation risk evaluation index is defined to describe the potential risk of storage planning scheme. Thirdly, this paper establishes a bi‐level planning and operation co‐optimization model for EES, in which the planning problem with a goal of minimizing total cost is taken as an outer layer and the operation problem for pursuing renewable energy consumption and reducing the load curtailment is taken as an inner layer. Finally, case studies are carried out, and results demonstrate the feasibility and effectiveness of the proposed model and method.

Dynamic modeling and techno-economic assessment of hybrid renewable energy and thermal storage systems for a net-zero energy greenhouse in South Korea

Dynamic modeling and techno-economic assessment of hybrid renewable energy and thermal storage systems for a net-zero energy greenhouse in South Korea

Experimental Validation of Switched Moving Boundary Modeling of Phase Change Thermal Energy Storage Systems

Abstract Thermal Energy Storage (TES) devices use Phase Change Materials (PCMs) to store and release thermal energy. Control-oriented models are needed to predict the behavior of TES devices and experimental validation is necessary to demonstrate the predictive capabilities of these models. This paper presents an experimental validation of a switched Moving Boundary (MB) approach for modeling TES devices, where the dynamics of the device are captured with fewer states than traditional models. A graph-based modeling approach is used to model heat flow, while the moving boundary captures the time-varying liquid and solid regions of the TES. The model uses a Finite State Machine (FSM) to switch between four modes of operation based on the State-of-Charge (SOC) of the TES. Results show that the switched MB approach has similar accuracy and lower computational cost compared to traditional modeling approaches when predicting the SOC of an experimental TES device.

Reinforcement learning-based optimal scheduling model of battery energy storage system at the building level

Installing the battery energy storage system (BESS) and optimizing its schedule to effectively address the intermittency and volatility of photovoltaic (PV) systems has emerged as a critical research challenge. Nonetheless, some existing studies still have limitations in terms of the efficiency of the BESS scheduling due to the lack of comprehensive consideration of diverse user objectives. As a response to this gap, this study aimed to develop a reinforcement learning (RL)-based optimal scheduling model to better reflect the continuous behaviors in the complex real world. To this end, focused on residential buildings connected to the grid and equipped with a BESS and PV system, its optimal scheduling models were developed using four algorithms from among the various RL techniques according to training methods. The results of the case study showed that the developed RL-based optimal scheduling model using Proximal Policy Optimization (PPO) can be applied to effectively operate the BESS with a PV system, considering possible uncertainties in the real world. The case study demonstrated the effectiveness and feasibility of the developed RL-based optimal scheduling model. Compared to other algorithms, the PPO-based RL model has better decision-making for optimal BESS scheduling strategies to maximize their self-sufficiency rate and economic profits by coping with changing variables in the real world. Therefore, the RL-based BESS scheduling model will offer an optimal solution, specifically tailored for use within a virtual power plant, where numerous buildings continuously share electricity.

Application of energy storage allocation model in the context of mitigating new energy source power fluctuation

To address the impact of new energy source power fluctuations on the power grid, research has been conducted on energy storage allocation applied to mitigate the power fluctuations of new energy source. Firstly, based on the first-order low-pass filtering algorithm and discrete Fourier transform algorithm, the original power data of new energy sources were preprocessed to achieve the reconstruction of power signal. Subsequently, a more secure and reliable energy storage allocation model is constructed by taking into account the boundary conditions of energy storage charging and discharging efficiency, energy balance, state of charge, and target power output fluctuation. Then, a comprehensive Life-Cycle-Cost model for energy storage systems was developed and applied to economic evaluation of energy storage under two algorithms. Finally, the calculation case study analysis shows that the energy storage allocation model effectively improves the power fluctuations of new energy sources, represented by wind power, and ensure the safe and stable operation of energy storage system throughout the entire cycle, thus verifying the effectiveness and feasibility of the energy storage configuration model. Under fluctuation rate constraints of 10%, 15%, and 20%, the rated power required for the first-order low-pass filtering algorithm is 8.795 MW, 6.485 MW, and 4.022 MW, respectively, representing 18.32%, 13.51%, and 8.38% of the new energy source rated power(48 MW). The corresponding rated capacity required is 7.763 MWh, 3.675 MWh, and 1.123 MWh. In contrast, for the discrete fourier transform algorithm, the rated power required is 9.159 MW, 8.84 MW, and 5.78 MW, respectively, representing 19.08%, 18.42%, and 12.04% of the new energy source rated power. The corresponding rated capacity required is 2.01 MWh, 0.97 MWh, and 0.5 MWh. Form an economic feasibility perspective, the economic efficiency of energy storage allocation by discrete fourier transform algorithm is significantly better than allocated by the first-order low-pass filtering algorithm. For instance, when U10minup= 10%, the cost difference reaches a maximum of 1256 million ¥.

Convex Hull Formulations for Linear Modeling of Energy Storage Systems

Convex Hull Formulations for Linear Modeling of Energy Storage Systems

A CFD results-based reduced-order model for latent heat thermal energy storage systems with macro-encapsulated PCM

Macro-encapsulation of phase change material (PCM) is a promising approach to overcome a serious drawback of many latent heat thermal energy storage systems (LHTESSs): their low thermal power. Simulations are often used to support the design of these storage systems, but the simulation of the charging process of such an LHTESS with detailed CFD models is too computationally expensive. To obtain information about the behavior of a complete LHTESS, highly simplified system simulation models are usually applied. A new approach to create a reduced-order model is herein presented that aims to increase the accuracy of these system simulation models. The first step consists of performing a set of detailed CFD simulations of one capsule with different boundary conditions. The results are written into look-up tables that contain the charging power of one capsule as a function of the enthalpy stored and the boundary conditions. These look-up tables are then implemented into the reduced-order model. The temporal mean deviation of the energy content in the storage unit between experiments and the reduced-order model is only 5 % and the simulation time of the fastest reduced-order model was 5 s, while the CFD simulations took up to about two weeks on a workstation. Finally, for the conditions tested, the heat transfer fluid (HTF) does not have to be included in the CFD simulation, but can be replaced by a properly defined convective boundary condition. The capsule wall, however, needs to be included in the CFD model (especially for capsule wall materials with a distinctively higher thermal conductivity than the PCM) to account for the heat flow towards the bottom of the capsule supporting close contact melting.