The capacity allocation method of photovoltaic and energy storage hybrid system considering the whole life cycle

Li, Junhui; Zhang, Zheshen; Shen, Baoxing; Gao, Zhuo; Ma, Dexuan; Yue, Pengcheng; Pan, Junliang

doi:10.1016/j.jclepro.2020.122902

Cited by 52 publications

(8 citation statements)

References 19 publications

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“…24 Differently, in Ref. [ 25 ], the authors proposed a particle swarm optimization model to study the capacity allocation configuration method of the photovoltaic energy storage system, joined to a set of control strategies, which can carry out arbitrage in the Time-of-Use price mode and ensure the continuous power operation of the system.…”

Section: Storage Energy Feasibilitymentioning

confidence: 99%

Energy storage for photovoltaic power plants: Economic analysis for different ion‐lithium batteries

et al. 2022

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Energy storage has been identified as a strategic solution to the operation management of the electric power system to guarantee the reliability, economic feasibility, and a low carbon footprint. In this sense, this article analyzes the economic feasibility of a storage system using different Li‐ion batteries applied to a real case of the photovoltaic power plant at Alto Rodrigues, Rio Grande do Norte, Brazil. The System Advisor Model software was used to simulate the systems which allowed showing the difference between the revenue obtained from energy sales and the total generation cost. According to the results, the viability of the energy storage system can be achieved in different ways. The first way would be to reduce current investment costs in storage systems. In the second way, the energy sale price is higher than the current sale price. The third and fourth ways are a combination of cost storage reduction and the increase of energy sale price in different annual proportions.

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Section: Storage Energy Feasibilitymentioning

confidence: 99%

Energy storage for photovoltaic power plants: Economic analysis for different ion‐lithium batteries

et al. 2022

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“…The battery Rint model is shown in Figure 4. The operating cycle of the ESS was set at 10 years without calculating the cost of the battery floor space [27]. Therefore, the average hourly purchase cost of the ESS is described as follows:…”

Section: Construction Of System Operating Cost Modelmentioning

confidence: 99%

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

Gao

Hong

2021

Processes

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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.

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“…J. Li et al (2020) propose a capacity optimization method for combined PV and storage systems, which considers the power allocation for PV and storage systems with the objective of economic optimality; P. D. Lund (2018) considered the PV self-consumption, as well as the sensitivity of the storage system size of weather, and finally obtained the economically optimal PV energy storage system configuration; R. Luthander et al (2019) consider the matching of PV generation with demand and the transfer of solar energy through energy storage systems, which can achieve zero-energy buildings that meet the relevant requirements of EU regulations; and R. Domínguez et al (2020) analyzed the operation and construction of the European power system in 2050 and proposed a stochastic model to study the future renewable energy construction planning. The results showed that new renewable energy generation capacity in Europe should reach 881 GW by 2050, taking into account the installation of energy storage system, and until then, CO 2 emissions will be reduced by 77%.…”

Section: Introductionmentioning

confidence: 99%

An optimal energy storage system sizing determination for improving the utilization and forecasting accuracy of photovoltaic (PV) power stations

Li²,

Yan³

et al. 2023

Front. Energy Res.

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As a new type of flexible regulation resource, energy storage systems not only smooth out the fluctuation of new energy generation but also track the generation scheduling combined with new energy power to enhance the reliability of new energy system operations. In recent years, installing energy storage for new on-grid energy power stations has become a basic requirement in China, but there is still a lack of relevant assessment strategies and techno-economic evaluation of the size determination of energy storage systems from the perspective of new energy power stations. Therefore, this paper starts from summarizing the role and configuration method of energy storage in new energy power stations and then proposes multidimensional evaluation indicators, including the solar curtailment rate, forecasting accuracy, and economics, which are taken as the optimization targets for configuring energy storage systems in PV power stations. Lastly, taking the operational data of a 4000 MWPV plant in Belgium, for example, we develop six scenarios with different ratios of energy storage capacity and further explore the impact of energy storage size on the solar curtailment rate, PV curtailment power, and economics. The method proposed in this paper is effective for the performance evaluation of large PV power stations with annual operating data, realizes the automatic analysis on the optimal size determination of energy storage system for PV power stations, and verifies the rationality of the principle for configuring energy storage for PV power stations in some regions of China.

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The capacity allocation method of photovoltaic and energy storage hybrid system considering the whole life cycle

Cited by 52 publications

References 19 publications

Energy storage for photovoltaic power plants: Economic analysis for different ion‐lithium batteries

Energy storage for photovoltaic power plants: Economic analysis for different ion‐lithium batteries

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

An optimal energy storage system sizing determination for improving the utilization and forecasting accuracy of photovoltaic (PV) power stations

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