Optimal battery capacity of grid-connected PV-battery systems considering battery degradation

2022 ◽  
Vol 181 ◽  
pp. 10-23
Author(s):  
Yaling Wu ◽  
Zhongbing Liu ◽  
Jiangyang Liu ◽  
Hui Xiao ◽  
Ruimiao Liu ◽  
...  
Keyword(s):  
Battery Capacity ◽  
Battery Systems ◽  
Battery Degradation

A Dispatch Optimization Model for Hybrid Renewable and Battery Systems Incorporating a Battery Degradation Model

2021 ◽  
pp. 1-13
Author(s):  
Sahana Upadhya ◽  
Michael J. Wagner
Keyword(s):  
Optimization Model ◽  
Time Horizon ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Global Optimality ◽  
Battery Capacity ◽  
Battery Systems ◽  
Battery Degradation ◽  
Number Of Cycles

Abstract A recent increase in the integration of renewable energy systems in existing power grids along with a lack of integrated dispatch models has led to waste in power produced. This paper presents a mixed-integer nonlinear optimization model for hybrid renewable-generator-plus-battery systems, with the objective of maximizing long-term profit. Prior studies have revealed that both high and low state of charge (SOC) of the battery is detrimental to its lifetime and results in reduced battery capacity over time. In addition, increased number of cycles of charge and discharge also causes capacity reduction. This paper models these two factors with a constraint relating capacity loss to the SOC and number of cycles completed by the battery. Loss in capacity is penalized in the objective function of the optimization model, thereby disincentivizing high and low SOCs and frequent cycling. A rolling time horizon optimization approach is used to overcome the computational difficulties of achieving global optimality within a long-term time horizon. By incorporating battery degradation, the model is capable of maximizing the profits from the power dispatch to the grid while also maximizing the life of the battery. This paper exercises the model within a case study using a sample photovoltaic system generation time series that considers multiple battery capacities. The results indicate that the optimal battery lifetime is extended in comparison to conventional models that ignore battery degradation in dispatch decisions. Finally, we analyze the relationship between battery operational decisions and the resultant capacity fade.

A Dispatch Optimization Model for Hybrid Renewable and Battery Systems Incorporating a Battery Degradation Model

2021 ◽  
Author(s):  
Sahana Upadhya ◽  
Michael J. Wagner
Keyword(s):  
Optimization Model ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Global Optimality ◽  
Operational Parameters ◽  
Time Model ◽  
Battery Capacity ◽  
Battery Systems ◽  
Battery Degradation ◽  
Number Of Cycles

Abstract Recently, there has been an increased level of integration of renewable energy systems in existing power grids. Lack of integrated dispatch models has led to waste in power produced. This paper proposes a mixed-integer linear optimization model for hybrid renewable-generator-plus-battery systems, with the objective of maximizing long-term profit. Prior studies have revealed that both high and low state of charge (SOC) of the battery is detrimental to its lifetime and results in reduced battery capacity over time. In addition, increased number of cycles of charge and discharge also causes capacity reduction. This paper models these two factors with a constraint relating capacity loss to the SOC and number of cycles completed by the battery. Finally, the loss in capacity is penalized in the objective function of the optimization model, thereby indirectly penalizing high and low SOCs and frequent cycling. To overcome the computational difficulties of achieving global optimality, a rolling time horizon optimization approach is used. By incorporating battery degradation in the real-time model, the model is capable of maximizing the profits from the power dispatch to the grid while also maximizing the life of the battery. This paper exercises the model by assessing sample generator time series profiles with a range of battery capacities. The results demonstrate that the battery lifetime is extended in comparison to conventional models that ignore battery degradation in dispatch decisions. Finally, we analyze the relationship between operational parameters of the battery and the capacity fade.

scholarly journals Derating Guidelines for Lithium-Ion Batteries

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3295 ◽  
Author(s):  
Yongquan Sun ◽  
Saurabh Saxena ◽  
Michael Pecht
Keyword(s):  
Lithium Ion ◽  
Stress Factors ◽  
Li Ion Batteries ◽  
Degradation Data ◽  
Capacity Loss ◽  
Battery Capacity ◽  
Operational Life ◽  
Battery Degradation ◽  
Li Ion

Derating is widely applied to electronic components and products to ensure or extend their operational life for the targeted application. However, there are currently no derating guidelines for Li-ion batteries. This paper presents derating methodology and guidelines for Li-ion batteries using temperature, discharge C-rate, charge C-rate, charge cut-off current, charge cut-off voltage, and state of charge (SOC) stress factors to reduce the rate of capacity loss and extend battery calendar life and cycle life. Experimental battery degradation data from our testing and the literature have been reviewed to demonstrate the role of stress factors in battery degradation and derating for two widely used Li-ion batteries: graphite/LiCoO2 (LCO) and graphite/LiFePO4 (LFP). Derating factors have been computed based on the battery capacity loss to quantitatively evaluate the derating effects of the stress factors and identify the significant factors for battery derating.

scholarly journals Ageing and Efficiency Aware Battery Dispatch for Arbitrage Markets Using Mixed Integer Linear Programming

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 999 ◽  
Author(s):  
Holger Hesse ◽  
Volkan Kumtepeli ◽  
Michael Schimpe ◽  
Jorn Reniers ◽  
David Howey ◽  
...  
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Mixed Integer ◽  
Time Variability ◽  
Data Set ◽  
Electricity Cost ◽  
Battery Capacity ◽  
Battery Degradation ◽  
Installed Capacity

To achieve maximum profit by dispatching a battery storage system in an arbitrage operation, multiple factors must be considered. While revenue from the application is determined by the time variability of the electricity cost, the profit will be lowered by costs resulting from energy efficiency losses, as well as by battery degradation. In this paper, an optimal dispatch strategy is proposed for storage systems trading on energy arbitrage markets. The dispatch is based on a computationally-efficient implementation of a mixed-integer linear programming method, with a cost function that includes variable-energy conversion losses and a cycle-induced battery capacity fade. The parametrisation of these non-linear functions is backed by in-house laboratory tests. A detailed analysis of the proposed methods is given through case studies of different cost-inclusion scenarios, as well as battery investment-cost scenarios. An evaluation with a sample intraday market data set, collected throughout 2017 in Germany, offers a potential monthly revenue of up to 8762 EUR/MWh cap installed capacity, without accounting for the costs attributed to energy losses and battery degradation. While this is slightly above the revenue attainable in a reference application—namely, primary frequency regulation for the same sample month (7716 EUR/MWh cap installed capacity)—the situation changes if costs are considered: The optimisation reveals that losses in battery ageing and efficiency reduce the attainable profit by up to 36% for the most profitable arbitrage use case considered herein. The findings underline the significance of considering both ageing and efficiency in battery system dispatch optimisation.

scholarly journals Memristive Equivalent Circuit Model for Battery

2021 ◽  
Vol 13 (20) ◽  
pp. 11204
Author(s):  
Mazin Mohammed Mogadem ◽  
Yan Li
Keyword(s):  
Electrochemical Impedance ◽  
Real Life ◽  
Equivalent Circuit Model ◽  
Battery Materials ◽  
Materials Modeling ◽  
Proposed Model ◽  
Battery Capacity ◽  
Battery Degradation ◽  
Sustainability Challenges ◽  
Nonlinear Autoregressive

The design of mathematical models is based on conservation laws and also on the fundamental principles of modeling: structure, parameters, and physical meaning. Those kinds of modeling should have specific capabilities to deal with different working conditions and environments coping with challenges that include but are not limited to battery capacity, life-cycle, or the attempts to manipulate the current profiles during operation. Introducing memristive elements in batteries will be ideal to satisfy these fundamentals and goals of modeling, whereas addressing the recycling and sustainability concerns on the environmental impact by the placement of TiO2 memristor into this model can promote a recovery hierarchy via recycling and dispatching a slight amount to disposal as the previous focus was mainly concentrated on availability. As for battery materials, modeling, performing, and manufacturing all have proliferated to grasp the possible sustainability challenges inherited in these systems. This paper investigated electrochemical impedance spectroscopy to study this model and the dynamic behavior inside the battery. We found a solution to address the existing battery limitations that elucidate the battery degradation without affecting the performance, correspondingly by employing the dampest least-squares combination with nonlinear autoregressive exogenous for identifying such model and its associated parameters because of its embedded memory and fast convergence to diminish the influence of the vanishing gradient. Lastly, we found that this model is proven to be efficient and accurate compared to actual experimented data to validate our theory and show the value of the proposed model in real life while assuming Normal Gaussian distribution of data error with outstanding results; the auto-correlations were within the 95% confidence limit, the best validation was 2.7877, and an overall regression of 0.99993 was achieved.

scholarly journals Economic and Ecological Design of Hybrid Renewable Energy Systems Based on a Developed IWO/BSA Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 687
Author(s):  
Mohammed Kharrich ◽  
Salah Kamel ◽  
Rachid Ellaia ◽  
Mohammed Akherraz ◽  
Ali S. Alghamdi ◽  
...  
Keyword(s):  
Optimal Design ◽  
Wind Turbine ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Invasive Weed ◽  
Battery Capacity ◽  
Battery Systems

In this paper, an optimal design of a microgrid including four houses in Dakhla city (Morocco) is proposed. To make this study comprehensive and applicable to any hybrid system, each house has a different configuration of renewable energies. The configurations of these four houses are PV/wind turbine (WT)/biomass/battery, PV/biomass, PV/diesel/battery, and WT/diesel/battery systems. The comparison factor among these configurations is the cost of energy (COE), comparative index, where the load is different in the four houses. Otherwise, the main objective function is the minimization of the net present cost (NPC), subject to several operating constraints, the power loss, the power generated by the renewable sources (renewable fraction), and the availability. This objective function is achieved using a developed optimization algorithm. The main contribution of this paper is to propose and apply a new optimization technique for the optimal design of a microgrid considering different economic and ecological aspects. The developed optimization algorithm is based on the hybridization of two metaheuristic algorithms, the invasive weed optimization (IWO) and backtracking search algorithm (BSA), with the aim of collecting the advantages of both. The proposed hybrid optimization algorithm (IWO/BSA) is compared with the original two optimization methods (IWO and BSA) as well as other well-known optimization methods. The results indicate that PV/biomass and PV/diesel/battery systems have the best energy cost using the proposed IWO/BSA algorithm with 0.1184 $/kWh and 0.1354 $/kWh, respectively. The best system based on its LCOE factor is the PV/biomass which represents an NPC of 124,689 $, the size of this system is 349.55 m2 of PV area and the capacity of the biomass is 18.99 ton/year. The PV/diesel/battery option has also good results, with a system NPC of 142,233 $, the size of this system is about 391.39 m2 of PV area, rated power of diesel generator about 0.55 kW, and a battery capacity of 12.97 kWh. Otherwise, the proposed IWO/BSA has the best convergence in all cases. It is observed that the wind turbine generates more dumped power, and the PV system is highly suitable for the studied area.

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