Optimal Energy Storage System Positioning and Sizing with Robust Optimization

Chowdhury, Nayeem; Pilo, Fabrizio; Pisano, Giuditta

doi:10.3390/en13030512

Cited by 27 publications

(19 citation statements)

References 32 publications

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“…The restrictions for wind generation (i.e. wind power loss) are defined in (15). The expression corresponds to the reduction of use of potentially available wind energy.…”

Section: Formulationmentioning

confidence: 99%

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Power system operation considering detailed modelling of energy storage systems

Cantillo

Moreno

2021

IJECE

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The power system operation considering energy storage systems (ESS) and renewable power represents a challenge. In a 24-hour economic dispatch, the generation resources are dispatched to meet demand requirements considering network restrictions. The uncertainty and unpredictability associated with renewable resources and storage systems represents challenges for power system operation due to operational and economical restrictions. This paper develops a detailed formulation to model energy storage systems (ESS) and renewable sources for power system operation considering 24-hour period. The model is formulated and evaluated with two different power systems (i.e. 5-bus and IEEE modified 24-bus systems). Wind availability patterns and scenarios are used to assess the ESS performance under different operational circumstances. With regard to the systems proposed, there are scenarios in order to evaluate ESS performance. In one of them, the increase in capacity did not represent significant savings or performance for the system, while in the other it was quite the opposite especially during peak load periods.

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“…The restrictions for wind generation (i.e. wind power loss) are defined in (15). The expression corresponds to the reduction of use of potentially available wind energy.…”

Section: Formulationmentioning

confidence: 99%

“…The ESS can absorb energy when generation exceeds the load especially when this surplus come from renewable sources and supply this energy to the grid during load peak hours [12,13]. Thus, the ESS provides flexibility under the integration of renewable resources given that the power dispatch can be settled to a desirable supply profile [14,15].…”

mentioning

confidence: 99%

Power system operation considering detailed modelling of energy storage systems

Cantillo

Moreno

2021

IJECE

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“…The chance-constrained information decision gap method is another way of handling uncertainty [38]. Robust optimization [39] techniques consider the worst-case scenario and also require a transformation of the problem. However, these methods are difficult to apply to black-box systems like the one we have here.…”

Section: The Bonus Algorithmmentioning

confidence: 99%

“…method is another way of handling uncertainty [38}. Robust optimization[39] techniques consider 308 the worst-case scenario and also require a transformation of the problem. However, these methods…”

mentioning

confidence: 99%

Optimization under Uncertainty to Reduce the Cost of Energy for Parabolic Trough Solar Power Plants for Different Weather Conditions

2020

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Renewable energy use can mitigate the effects of climate change. Solar energy is amongst the cleanest and most readily available renewable energy sources. However, issues of cost and uncertainty associated with solar energy need to be addressed to make it a major source of energy. These uncertainties are different for different locations. In this work, we considered four different locations in the United States of America (Northeast, Northwest, Southeast, Southwest). The weather and cost uncertainties of these locations are included in the formulation, making the problem an optimization-under-uncertainty problem. We used the novel Better Optimization of Nonlinear Uncertain Systems (BONUS) algorithm to solve these problems. The performance and economic models provided by the System Advisory Model (SAM) system from NREL were used for this optimization. Since this is a black-box model, this adds difficulty for optimization and optimization under uncertainty. The objective function and constraints in stochastic optimization (stochastic programming) problems are probabilistic functionals. The generalized treatment of such problems is to use a two-loop computationally intensive procedure, with an inner loop representing probabilistic or stochastic models or scenarios instead of the deterministic model, inside the optimization loop. BONUS circumvents the inner sampling loop, thereby reducing the computational intensity significantly. BONUS can be used for black-box models. The results show that, using the BONUS algorithm, we get 41%–47% of savings on the expected value of the Levelized Cost of Electricity (LCOE) for Parabolic Trough Solar Power Plants. The expected LCOE in New York is 57.42%, in Jacksonville is 38.52%, and in San Diego is 17.57% more than in Las Vegas. This difference is due to the differences in weather and weather uncertainties at these locations.

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“…From a different view of mathematical programming, robust optimization can be implemented for mathematical formulation. Several models for optimal capacity of ESSs in transmission and distribution systems are found in [11,12]. In selecting a method for mathematical programming, robust optimization can be an alternative offering a tractable optimization problem without known probability distributions of uncertain factors.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Planning Model for Battery Energy Storage Systems Coupled with Utility-Scale Solar Photovoltaics

Park¹

2021

Energies

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With recent technology advances and price drop, battery energy storage systems (BESSs) are considered as a promising storage technology in power systems. In this paper, a stochastic BESS planning model is introduced, which determines optimal capacity and durations of BESSs to co-locate utility-scale solar photovoltaic (PV) systems in a high-voltage power system under the uncertainties of renewable resources and electric load. The optimization model minimizing total costs aims to obtain at least 20% electric energy from renewable sources, while satisfying all the physical constraints. Furthermore, two-stage stochastic programming is applied to formulate mathematical optimization problem to find out optimal durations and capacity of BESSs. In scheduling BESSs, chronology needs to be considered to represent temporal changes of BESS states; therefore, a scenario generation method to generate random sample paths with 1-h time step is adopted to explicitly represent uncertainty and temporal changes. The proposed mathematical model is applied to a modified IEEE 300-bus system that comprises 300 electric buses and 411 transmission lines. Optimal BESS durations and capacity are compared when different numbers of scenarios are employed to see the sensitivity to the number of scenarios in the model, and “value of stochastic solution” (VSS) is calculated to verify the impacts of inclusion of stochastic parameters. The results show that the building costs and capacity of BESSs increase when the number of scenarios increases from 10 to 30. By inspecting VSSs, it is observed that an explicit representation of stochastic parameters affects the optimal value, and the impacts become larger when the larger number of scenarios are applied.

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Optimal Energy Storage System Positioning and Sizing with Robust Optimization

Cited by 27 publications

References 32 publications

Power system operation considering detailed modelling of energy storage systems

Power system operation considering detailed modelling of energy storage systems

Optimization under Uncertainty to Reduce the Cost of Energy for Parabolic Trough Solar Power Plants for Different Weather Conditions

A Stochastic Planning Model for Battery Energy Storage Systems Coupled with Utility-Scale Solar Photovoltaics

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