Optimal Energy Storage Capacity Research Articles

Optimal capacity configuration of the wind-storage combined frequency regulation system considering secondary frequency drop

With wind power integrated into the power system on a large scale, the system has become vulnerable to the frequency stability issue. The battery energy storage system (BESS) is considered the key solution to improving the system frequency regulation performance due to its fast response ability. Furthermore, the construction of wind-storage combined frequency regulation systems has been developed for many years, in which the optimal capacity configuration of the wind-storage system is getting more attention. However, the secondary frequency drop (SFD) caused by wind turbines (WTs) participating in primary frequency regulation (PFR) is neglected in most existing capacity configurations, which is worthy of further study. In this paper, the optimal capacity of the wind-storage combined frequency regulation system is studied from the perspective of SFD. The time-domain expressions of two-stage system frequency response considering SFD are derived based on the wind-storage combined frequency regulation model. Next, considering the technical and economic characteristics of wind-storage combined frequency regulation, an optimization model of the energy storage capacity configuration is established with the objective of minimizing the sum of the maximum frequency deviations in two stages and the energy storage cost. The optimization model is solved by the multi-objective salp swarm algorithm (MSSA) to obtain the setting value of wind-storage combined frequency regulation parameters and the optimal energy storage capacity. The effectiveness of the proposed method is verified in MATLAB. The simulation results show that the proposed model can effectively improve the frequency regulation effect of the system and ensure the optimal capacity configuration with better economy.

Optimal Configuration of Energy Storage Systems in High PV Penetrating Distribution Network

In this paper, a method for rationally allocating energy storage capacity in a high-permeability distribution network is proposed. By constructing a bi-level programming model, the optimal capacity of energy storage connected to the distribution network is allocated by considering the operating cost, load fluctuation, and battery charging and discharging strategy. By constructing four scenarios with energy storage in the distribution network with a photovoltaic permeability of 29%, it was found that the bi-level decision-making model proposed in this paper saves 2346.66 yuan and 2055.05 yuan, respectively, in daily operation cost compared to the scenario without energy storage and the scenario with single-layer energy storage. After accessing IEEE-33 nodes for simulation verification, it was found that the bi-level decision-making model proposed in this paper has a good inhibition effect on voltage fluctuation and load fluctuation after energy storage configuration. In addition, this paper analyzes the energy storage that can be accessed by photovoltaic distribution networks with different permeability and finds that when photovoltaic permeability reaches 45% and corresponding energy storage is configured, the economic and energy storage benefits of the system are the best.

Optimal configuration of wind storage capacity based on VMD and improved GWO

In view of the fluctuation of the output power of wind power generation, a hybrid energy storage capacity optimization configuration strategy combining variational mode decomposition (VMD) and improved Grey Wolf Optimizer (GWO) is proposed, taking the wind storage joint system as the research object. The sliding average method is used to obtain the target power of the wind power grid connection and use the VMD decomposition processing to allocate the energy storage target power to the different energy storage devices in the hybrid energy storage system. The capacity optimization model of hybrid energy storage system with the minimum annual configuration cost of energy storage system as the target function is established, and the improved Grey Wolf Optimizer is used to solve the model, and the simulation and analysis are conducted on Matlab / Simulink, so as to verify the effectiveness and economy of the proposed capacity optimization configuration method.

Energy Storage Capacity Optimization of Non-Grid-Connected Wind-Hydrogen Systems: From the Perspective of Hydrogen Production Features

This paper intends to improve the hydrogen production efficiency of the electrolysis cells, fully utilize wind energy, and ensure the reliability of power supply. For this purpose, the authors put forward a capacity optimization configuration for non-grid-connected wind-hydrogen hybrid energy storage system, in view of the features of hydrogen production efficiency. The working interval of the electrolytic cell was optimized by analyzing the said features. Considering the features of battery charge/discharge, equipment capacity and power, the authors formulated the energy management strategy applicable to six working conditions, established the quantitative multi-objective function of system cost and reliability, and solved the optimization model by the fast non-dominant sorting genetic algorithm (NSGA)-II. In this way, the optimal combination of energy storage capacity was determined. Next, the wind velocity data of a pastoral area in Inner Mongolia was measured, and analyzed in details. The analysis results show that the electrolytic cell always operates in the optimal working area, and the optimized wind-hydrogen system is economic and reliable in power supply. The research provides a reference for practical engineering applications.

A Capacity Optimization Method for a Hybrid Energy Storage Microgrid System Based on an Augmented ε- Constraint Method

In general, microgrids have a high renewable energy abandonment rate and high grid construction and operation costs. To improve the microgrid renewable energy utilization rate, the economic advantages, and environmental safety of power grid operation, we propose a hybrid energy storage capacity optimization method for a wind–solar–diesel grid-connected microgrid system, based on an augmented ε- constraint method. First, the battery is coupled with a seasonal hydrogen energy storage system to establish a hybrid energy storage model that avoids the shortcomings of traditional microgrid systems, such as a single energy storage mode and a small capacity. Second, by considering the comprehensive cost and carbon emissions of the power grid within the planning period as the objective function, the abandonment rate of renewable energy as the evaluation index, and the electric energy storage and seasonal hydrogen energy storage system operating conditions as the main constraints, the capacity allocation model of the microgrid can be constructed. Finally, an augmented ε- constraint method is implemented to optimize the model above; the entropy–TOPSIS method is used to select the configuration scheme. By comparative analysis, the results show that the optimization method can effectively improve the local absorption rate of wind and solar radiation, and significantly reduce the carbon emissions of microgrids.

Energy storage capacity optimization strategy for combined wind storage system

The rapid development of wind power has imposed many challenges on the operation of the power system. Energy storage system has broad application prospects in promoting wind power to the grid. However, the high price of the energy storage restricts the development of the combined wind energy-storage system. In order to deal with the power fluctuation of the large-scale wind power grid connection, we propose an allocation strategy of energy storage capacity for combined wind-storage system considering the wind power output volatility and battery storage system’s own operational constraints. The model aims to maximize the annual avenue of the cogeneration system, which allows for the investment fee, operation and maintenance fee and penalty cost of the combined system. The calculation results validate the effectiveness of the optimal allocation strategy.

Sizing of centralized shared energy storage for resilience microgrids with controllable load: A bi-level optimization approach

To improve the utilization of flexible resources in microgrids and meet the energy storage requirements of the microgrids in different scenarios, a centralized shared energy storage capacity optimization configuration model for microgrids based on bi-level optimization is proposed. First, the response characteristics of the shared energy storage and controllable load in the resilience microgrid are analyzed, and the centralized shared energy storage operation mode meeting the regulatory demand of multi-scenarios is designed. Then, a bi-level optimal allocation model is constructed, which takes the maximum net income of centralized shared energy storage as the upper layer and the minimum payment cost of load in the microgrid as the lower layer. Furthermore, the multi-objective whale optimization algorithm is used to solve the bi-level optimization model. The results show that the shared energy storage can jointly meet the regulation demand of multi-scenarios by coordinating the transferable load and cuttable load in the microgrid and improving the utilization rate of shared energy storage.

Energy storage capacity optimization of wind-energy storage hybrid power plant based on dynamic control strategy

The construction of wind-energy storage hybrid power plants is critical to improving the efficiency of wind energy utilization and reducing the burden of wind power uncertainty on the electric power system. However, the overall benefits of wind-energy storage system (WESS) must be improved further. In this study, a dynamic control strategy based on the state of charge (SOC) for WESS is proposed to maintain a healthy SOC for energy storage system (ESS). Then, four scenarios with different operation strategies are set based on the historical operation data of a wind farm in China. Particle swarm optimization is used to solve the problem, with the goal of optimizing the overall system benefit. The results show that the dynamic control strategy is the most cost-effective while maintaining reliability. When compared to other scenarios, the profit of the dynamic control strategy is extended by 7.63 %, 327.69 % and 9.75 % respectively, and the energy storage life is extended by 10.02 %, 62.89 % and 21.61 % respectively, demonstrating that different working state of energy storage will significantly affect the storage life and, ultimately, affect the economic benefit of the system. Finally, the influences of feed-in tariff, frequency regulation mileage price and energy storage investment cost on the optimal energy storage capacity and the overall benefit of wind-energy storage hybrid power plant are discussed.

An Energy Storage Assessment: Using Frequency Modulation Approach to Capture Optimal Coordination

To reduce the allocation of energy storage capacity in wind farms and improve economic benefits, this study is focused on the virtual synchronous generator (synchronverter) technology. A system accompanied by wind power, energy storage, a synchronous generator and load is presented in detail. A brief description of the virtual synchronous generator control strategy is given. The capacity allocation is based on different optimization goals and the optimal energy storage capacity configuration of the coordinated frequency modulation (FM) control strategy. The detail of the dual-loop control strategy is carried out by establishing the grid-connected transfer function model of the synchronverter energy storage and a theoretical model of life cycle cost is established. The optimal control strategy of coordinated FM for wind storage is implemented using MATLAB software. The simulation showed that the proposed strategy provided the energy storage capacity at high wind speed, which is configured to be 5.9% of the installed capacity of the wind turbine, marking a reduction of 26% compared with the 8% capacity required for independent support. In addition, the proposed method has improved the energy storage capacity configuration of the coordinated FM control strategy.

Ultraflexible, Breathable, and Form-Stable Phase Change Fibrous Membranes by Green Electrospinning for Personal Thermal Management

Advanced textiles for personal thermal management (PTM) are more precise and energy-saving than traditional heating/cooling strategies. Applying phase change materials (PCMs) in PTM is sustainable and effective but challenging due to the intrinsic rigid character of PCMs and the leakage issue in phase transition. In this work, we employed poly(ethylene glycol) (PEG) as a functional material and poly(vinyl alcohol) (PVA) as a polymer skeleton by green electrospinning to endow the composite membranes with superior flexibility, breathability, and thermal regulation capability. The introduction of carbon nanotubes (CNTs) promoted the mechanical properties and thermal conductivity of the prepared membranes. The thermal conductivity of resultant membranes was enhanced by 40.4% with only 1.5% weight content of CNTs. Furthermore, surface chemical cross-linking was applied to improve water-resistance properties and minimize the confinement on the PEG segment. The prepared membranes have a reasonable temperature range (∼26.9–38.9 °C) in the phase change process with a melting latent heat of 60.1 J/g and freezing latent heat of 59.1 J/g as the mass ratio of PEG reached 55 wt %. These flexible fibrous membranes with optimal energy storage capacity and eco-friendly characteristics indicate they will be a promising candidate for personal thermal management.

Energy storage capacity optimization of residential buildings considering consumer purchase intention: A mutually beneficial way

This paper aims to study the energy storage capacity allocation of residential buildings in a way of mutual benefit between investors and users. The relationship between the interests of investors and users can be established through consumer purchase intention. Firstly, this paper analyzes the running process of the energy storage system and constructs the charging state discrimination model. Secondly, based on the consumer utility theory, the purchase intention of users to such assets is analyzed, and the optimal capacity allocation is modeled by maximizing investors' profits. Finally, considering that the problem is NP-hard, particle swarm optimization algorithm based on inertial weight is applied to solve it. This article creatively and quantitatively depicts consumers' purchase intention, and takes into account the interests of users while carrying out capacity allocation, although from the perspective of maximizing the interests of investors. In the example analysis, after optimization, consumers' electricity bills are reduced by more than 50%, and investors' profits are increased by 1420 dollars per house, indicating: compared with the construction of traditional residential buildings or buildings with only photovoltaic system, the configuration of energy storage system can bring more profits to investors, users can also save a considerable amount of electricity bills, this paper also fits the requirements of carbon reduction.

An Energy Storage Capacity Optimization Method for Aggregator Based on Users’ Participation Evaluation

With the increase of the installed capacity of electrochemical batteries, how to maximize the benefit of large-scale energy storage system(ESS) has become an urgent problem. Therefore, this paper intends to establish an optimization model of aggregator based on users’ participation evaluation. Firstly, the idea of hierarchical capacity optimization for ESS is introduced, and the charging and discharging power distribution of distributed energy storage system(DESS) is evaluated. Then, based on the price-based demand response model, a DESS aggregation model based on herd psychology is established to evaluate the functional relationship between subsidy and participation. Finally, the capacity optimization model of aggregator participating in peak load regulation based on users’ participation evaluation is established. An example is given to verify the effectiveness of the proposed model.

Multi-objective optimization of combined cooling, heating and power system considering the collaboration of thermal energy storage with load uncertainties

A thermoelectric coupling limitation imposes the combined cooling, heating, and power (CCHP) systems low performance under dynamic uncertainty. Energy storage is an effective way to address the problem. This paper integrates an energy storage device into the CCHP system to decouple the limitation and proposes a multi-objective optimization model considering load uncertainty for the optimal capacity of energy storage. The design space is a set of all feasible configuration schemes that meet the requirements of the system’s confidence level. A chance-constrained method is employed to address the load uncertainty and transform the uncertain optimization into deterministic optimization. Load uncertainty is closely related to the given confidence level. Therefore, the indicator loss of load expectation is used to evaluate the reliability of the CCHP system. The economic and environmental performance of the CCHP system is assessed by using the annual total cost and carbon dioxide emission, respectively. A hotel example validates the feasibility of the proposed methodology. The results show that increasing the confidence level from 0.5 to 0.99 results in the capacity increase of the waste heat boiler from 326.8 kW to 408.2 kW in the heating mode. The increased change reduces the loss of load expectation by 108.4% and increases the annual operation cost by 110.14%.

Machine Learning Based Optimization Model for Energy Management of Energy Storage System for Large Industrial Park

Renewable energy represented by wind energy and photovoltaic energy is used for energy structure adjustment to solve the energy and environmental problems. However, wind or photovoltaic power generation is unstable which caused by environmental impact. Energy storage is an important method to eliminate the instability, and lithium batteries are an increasingly mature technique. If the capacity is too large, it would cause waste and cost would increase, but too small capacity cannot schedule well. At the same time, the size of energy storage capacity is also constrained by power consumption, whereas large-scale industrial power consumption is random and non-periodic. This is a complex problem which needs a model that can not only dispatch but also give a reasonable storage capacity. This paper proposes a model considering the cycle life of a lithium battery and the installation parameters of the battery, and the electricity consumption data and photovoltaic power generation data of an industrial park was used to establish an energy management model. The energy management system aimed to reduce operating costs and obtain optimal energy storage capacity, which is constrained by lithium battery performance and grid demand. With the operational cost and reasonable battery capacity as the optimization objectives, the Deep Deterministic Policy Gradient (DDPG) method, the greedy dynamic programming algorithm, and the genetic algorithm (GA) were adopted, where the performance of lithium battery and the requirement of power grid were the constraints. The simulation results show that compared with the current forms of energy, the three energy management methods reduced the cost of capacity and operating of the energy storage system by 18.9%, 36.1%, and 35.9%, respectively.

Optimization of Energy Storage Capacity to Smooth Wind Power Fluctuation

The uncertainty and randomness of wind power generation bring hidden trouble to the safe operation of power distribution network. Combining energy storage system with wind power generation can effectively improve the output characteristics of wind power generation. In this paper, considering the investment cost of energy storage and the effect of suppressing the fluctuation of wind power output, the optimization of energy storage capacity under the scenario of wind power grid connection is studied. Firstly, the multi-objective capacity optimization model of the energy storage system is established to minimize the cost of the energy storage system and the variance of wind power system output equipped with energy storage. Then, the multi-objective optimization problem is transformed into a single objective optimization problem by introducing the weight coefficient of the objective function and the particle swarm optimization algorithm (PSO) is used to solve the problem. Finally, through the test of IEEE-33 bus distribution network system, the relationship curve between energy storage capacity and wind power fluctuation suppression effect is obtained. The results of numerical examples show the effectiveness of the proposed method.

Analyzing and Quantifying the Intrinsic Distributional Robustness of CVaR Reformulation for Chance-Constrained Stochastic Programs

Chance-constrained program (CCP) is a popular stochastic optimization method in power system planning, and operation problems. Conditional Value-at-Risk (CVaR) provides a convex approximation for chance constraints which are nonconvex. Although CCP assumes an exact empirical distribution, and the optimum of a stochastic programming model is thought to be sensitive in the designated probability distribution, this letter discloses that CVaR reformulation of a chance constraint is intrinsically robust. A pair of indices are proposed to quantify the maximum tolerable perturbation of the probability distribution, and can be computed from a computationally-cheap dichotomy search. An example on the coordinated capacity optimization of energy storage, and transmission line for a remote wind farm validates the main claims. The above results demonstrate that stochastic optimization methods are not necessarily vulnerable to distributional uncertainty, and justify the positive effect of the conservatism brought by the CVaR reformulation.

Alterable Electricity Pricing Mechanism Considering the Deviation of Wind Power Prediction

Fluctuation and prediction errors of wind power would cause a large amount of automatic generation control (AGC) adjustment costs, which lead to the problem of power curtailment. A reasonable mechanism of grid-connection electricity price may encourage wind farms to take measures to reduce the deviation between output power and schedule power, which is helpful for source-network coordination and reducing wind power curtailment. An alterable electricity pricing mechanism considering wind power deviation rate is proposed. In each schedule cycle, electricity price is adjusted according to the deviation rate and its historical change trend. In this way, wind farms will be encouraged to configure energy storage to promote the accordance of wind output power with schedule power to the greatest extent. Given the statistical characteristic of prediction errors of wind power, this paper proposes a schedule power model, taking least squares of output power deviation as objective function, and then puts forward an engineering application method for determining schedule power. This paper analyzes the overall cost and revenue of a wind farm to configure energy storage and determine the optimal energy storage capacity with the goal of maximizing the profit of the wind farm. In the case analysis, the effect of the deviation rate and its historical change trend, the deviation rate tolerance coefficient on electricity price is analyzed. The case analysis demonstrates the effectiveness of the proposed alterable electricity pricing mechanism and shows that the mechanism is helpful at reducing wind power output deviation and wind curtailment.

An energy storage capacity optimization method based on partition

With the vision of large-scale deployment of grid-connected distributed energy storage system (ESS) in the distribution network, it is necessary to study the capacity optimization of ESS for striking a balance among investment, renewable energy (RE) consumption, power supply security and price arbitrage. In this paper, an ESS partition model based on the improved flame propagation model is proposed. The results of ESS partition are obtained by constructing indexes such as the flammability of nodes, the wind direction of flame propagation, and the speed of flame propagation. Then, an optimization method for ESS capacity in partition is established, which is to maximize the annual net profit and the integer programming method is used to solve the model. Finally, the effectiveness and feasibility of the method are verified by conducting case studies on the IEEE 33 bus distribution network system. The case study results show that the rational configuration of ESS capacity can improve the economy of planning, and the proposed method can improve the Photovoltaic (PV) local consumption ability and power supply reliability of distribution network.

Chance-Constrained Optimization of Energy Storage Capacity for Microgrids

The optimal storage capacity is a crucial parameter for stable and reliable operation of microgrids in an islanded mode. In this context, an analytical method is developed to robustly formulate and analyze energy storage capacity deploying chance constrained stochastic optimization. More specifically, the goal is to determine an appropriate size for an energy storage to reach a specific loss of load probability (LOLP) in a microgrid with large penetration of renewables considering generation and load forecast error. The total cost is minimized over optimal storage capacity as well as over generators power, while accounting for generation and storage power and energy constraints. It is postulated that the shortage/surplus power will be derived from/injected to the storage system. However, due to stochastic nature of load and renewables and an inevitable forecast error, the renewable generation output or the load power may not be accurately acquired. Thus, the total storage power and energy constraints are posed as chance constraints, for which conservative convex approximations are employed for tractability. In particular, to overcome the difficulty brought about by the large size of the optimization problem, a separable (distributed) structure is pursued, and the dual decomposition method is adopted to obtain optimal solutions. Numerical tests verify the effect of prior knowledge in modeling the uncertainty in optimal choice of storage capacity.

Research on Energy Storage Optimization for Large-Scale PV Power Stations under Given Long-Distance Delivery Mode

Western China has good conditions for constructing large-scale photovoltaic (PV) power stations; however, such power plants with large fluctuations and strong randomness suffer from the long-distance power transmission problem, which needs to be solved. For large-scale PV power stations that do not have the conditions for simultaneous hydropower and PV power, this study examined long-distance delivery mode and energy storage optimization. The objective was to realize the long-distance transmission of electrical energy and maximize the economic value of the energy storage and PV power storage. For a large-scale PV power station, the energy storage optimization was modelled under a given long-distance delivery mode, and the economic evaluation system quantified using the net present value (NPV) of the battery was based on the energy dispatch optimization model. By contrast, a lithium battery performance model was developed. Therefore, further analysis of the economics of the energy storage and obtaining the best capacity of the energy storage battery and corresponding replacement cycle considered battery degradation. The case study of Qinghai Gonghe 100 MWp demonstration base PV power station showed that the optimal energy storage capacity was 5 MWh, and the optimal replacement period was 2 years. Therefore, the annual abandoned electricity was reduced by 3.051 × 10 4 MWh compared with no energy storage. The utilization rate of both the PV power station and quality of the delivered electricity were modelled to realize a long-distance transmission to the grid net. This will have an important guiding significance to develop and construct large-scale single PV power stations.