Energy Storage Sizing Strategy for Grid-Tied PV Plants under Power Clipping Limitations

Müller, Nicolás; Kouro, Samir; Zanchetta, Pericle; Wheeler, Patrick; Bittner, Gustavo; Girardi, Francesco

doi:10.3390/en12091812

Cited by 18 publications

(7 citation statements)

References 35 publications

(40 reference statements)

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“…About the optimization problem in this paper, we should consider some aspects: in the future, the cost conditions will change; for different purposes, a different compensation degree will be required. Therefore, in this section, we will deeply discuss how these influencing factors affect the optimal result [28][29][30][31][32][33][34][35].…”

Section: The Analysis Of Influencing Factors On the Optimal Resultsmentioning

confidence: 99%

Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation

Dong

Pang

et al. 2019

Energies

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To better track the planned output (forecast output), energy storage systems (ESS) are used by wind farms to compensate the forecast error of wind power and reduce the uncertainty of wind power output. When the error compensation degree is the same, the compensation interval is not unique, different compensation intervals need different ESS sizing. This paper focused on finding the optimal compensation interval not only satisfied the error compensation degree but also obtained the max profit of the wind farm. First, a mathematical model was proposed as well as a corresponding optimization method aiming at maximizing the profit of the wind farm. Second, the effect of the influencing factors (compensation degree, electricity price, ESS cost, and wind penalty cost) on the optimal result was fully analyzed and deeply discussed. Through the analysis, the complex relationship between the factors and the optimal results was found. Finally, the comparison between the proposed and traditional method was given, and the simulation results showed that the proposed method can provide a powerful decision-making basis for ESS planning in current and future market.Energies 2019, 12, 4755 2 of 21 fluctuation of wind power output [3,4], dealing with the peak shaving problem of power systems with wind power [5,6], and compensating the forecast error of wind power output, and so on.The forecast error of wind power is inevitable because of the characteristics of wind power generation. The ESS is installed at the outlet of the wind farm to compensate the forecast error of wind power and ensure that the wind farm can output the power reliably according to the pre-declared plan. In real-time dispatching, the ESS can store or release energy to balance the corresponding power error when the real-time power value of a wind farm is different from the planned output. In terms of compensating wind power forecast error, there are many references discussing this top from different aspects [7,8].The authors of [9] optimized the capacity of energy storage devices by controlling the wind power forecast error within a certain range to minimize the cost of energy storage equipment. In [10] the authors optimized the capacity of energy storage devices with the objective of minimizing wind power prediction errors. By quantifying the functional relationship between energy storage capacity and unserved energy, the minimum energy storage capacity corresponding to different unserved energy is analyzed. The authors of [11] synthetically considered the energy efficiency, environmental benefit and one investment of wind power and ESS, constructed the corresponding economic evaluation model, and evaluated the feasibility of using ESS to improve the wind power acceptance scale in different dispatching risk scenarios. The authors of [12] reduced the impacts of wind power forecast errors while prolonging the lifetime of ESS. In [13] the authors used a simple autoregressive model to capture the stochastic behavior of day-ahead forecast errors, in particular the au...

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Section: The Analysis Of Influencing Factors On the Optimal Resultsmentioning

confidence: 99%

Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation

Dong

Pang

et al. 2019

Energies

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“…For performing proper hybrid energy source sizing, the load profile is defined first. In contrast to most previous works [24][25][26][27], we will consider a statistical rather than analytical load profile to approach as close as possible a realistic situation. The minimization target function is defined next for one or more variables as minimum fuel, maintenance cost, operating cost, initial cost, volume, weight, etc.…”

Section: Sizing Proceduresmentioning

confidence: 99%

“…Every power system is governed by a real time energy management controller that behaves in a specific manner according to its design rules. A sizing algorithm from previous works [24][25][26][27] does not utilize the real time system management controller in the analysis procedures. Since the real time controller dramatically affects the operation of each source, it must take place as a system manager in the sizing procedure as well.…”

Section: Sizing Proceduresmentioning

confidence: 99%

“…In systems where several requirements are applied, the weight of each one determines the overall target. The process for setting each source quantity is known as sizing [23][24][25][26]. The procedure's methodologies [27,28] are well known and based on traditional methods such as the graphic construction method, iterative method, numerical method, probabilistic methods, and analytical method.…”

Section: Introductionmentioning

confidence: 99%

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Sizing Procedure for System Hybridization Based on Experimental Source Modeling in Grid Application

et al. 2021

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Hybridization of sources is spreading worldwide by utilizing renewable sources and storage units as standard parts of every grid. The conjunction of energy source and storage type open the door to reshaping the sustainability and robustness of the mains while improving system parameters such as efficiency and fuel consumption. The solution fits existing networks as well as new ones. The study proposes the creation of an accurate optimal sizing procedure for setting the required rating of each type of source. The first step is to model the storage and energy sources by using real experimental results for creating the generic database. Then, data on the mission profile, system constraints, and the minimization target function are inserted. The mission profile is then analyzed to determine the minimum and maximum energy source rating. Next, the real time energy management system controller is used to find the set of solutions for each available energy source and the optimal compatible storage in the revealed band to fulfil the mission task. A Pareto-curve is then plotted to present the optimal findings of the sizing procedure. Ultimately, the main research contribution is the far more accurate sizing results. A case study shows that relying on the standard method leads to noncompliance of sizing constraints, while the proposed procedure leads to fulfilling the mission successfully. First, by utilizing experimentally based energy and a storage unit. Second, by using the same real time energy management system controller in the sizing procedure.

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“…In this context, energy storage can be used to compensate for the difference between the predicted and actual generation, relieve the generation output variability, and arbitrage by scheduling charge and discharge profiles according to the expected electricity prices and expected RES generation. Other works have been conducted on the ESS capacity that enhances the performance of RES power plants [20], [24], [25]. In addition, the proper ESS size for stabilizing the power and frequency variance due to fluctuating RES outputs has been studied in [26] [27].…”

Section: Introductionmentioning

confidence: 99%

Optimal Energy Storage Sizing With Battery Augmentation for Renewable-Plus-Storage Power Plants

Shin

Hur

2020

IEEE Access

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The renewable-plus-storage power plant is becoming economically viable for power producers given the maturing technology and continued cost reduction. However, as batteries and power conversion systems remain costly, the power plant profitability depends on the capacity determination of the battery energy storage system (BESS). This study explored an approach for optimal capacity determination of a BESS combined with renewable energy considering the complex degradation of lithium-ion batteries. The proposed sizing algorithm iteratively evaluates the effect of BESS operation on battery degradation and estimates the cash flows of the power plant. In addition, we studied battery augmentation that adds the storage capacity in the base system to sustain the BESS capacity throughout the project planning horizon. Using data from South Korea, we showed that both the optimal storage capacity and project profitability are higher when the BESS is combined with solar generation than when combined with wind generation. Moreover, simulation results demonstrated that the proposed battery augmentation scheme improves the project profitability by deferring the upfront cost of batteries and increasing the total revenue. The proposed approach can provide a comprehensive framework for the parties involved in a BESS project to accurately calculate the BESS sizes and maximize the project profitability.

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Energy Storage Sizing Strategy for Grid-Tied PV Plants under Power Clipping Limitations

Cited by 18 publications

References 35 publications

Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation

Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation

Sizing Procedure for System Hybridization Based on Experimental Source Modeling in Grid Application

Optimal Energy Storage Sizing With Battery Augmentation for Renewable-Plus-Storage Power Plants

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