Power-to-gas management using robust optimisation in integrated energy systems

Gu, Chenghong; Tang, Can; Xiang, Yue; Xie, Da

doi:10.1016/j.apenergy.2018.12.028

Cited by 42 publications

(20 citation statements)

References 23 publications

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“…Equations (40)-(41) simply model this process. 46 Line-packing is indeed short-term gas storage. It has been used to manage rapid short-term variations of gas consumption and also gas production.…”

Section: Natural Gas System Constraintsmentioning

confidence: 99%

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An efficient solution method for integrated unit commitment and natural gas network operational scheduling under “Duck Curve”

Fallahi

Maghouli

2020

Int Trans Electr Energ Syst

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Section: Natural Gas System Constraintsmentioning

confidence: 99%

“…Absorbed power especially power generated by renewable energy by helping PtG technology can easily convert to hydrogen which can be injected into the gas system. Equations (40)‐(41) simply model this process 46 …”

Section: Problem Formulationmentioning

confidence: 99%

An efficient solution method for integrated unit commitment and natural gas network operational scheduling under “Duck Curve”

Fallahi

Maghouli

2020

Int Trans Electr Energ Syst

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“…Due to the redundancy caused by meeting the worst case, RO is more suitable for situation that decision makers cannot take the risk of small probability. But the optimal solution solved by RO can avoid the effect of uncertainties 16‐18 . With the development of interval mathematics theory, interval method is also gradually expanded to power systems 21‐22 .…”

Section: Introductionmentioning

confidence: 99%

“…Varies methods can be adopted to accommodate the uncertainties of wind power, photovoltaic power, and various kinds of energy demands in the IES. In most literatures, one of the following approaches are generally presented: probabilistic constraints, 11,12 scenarios, 5,9,13,14 uncertainty set, [15][16][17][18][19][20] interval numbers, 21,22 and others. 23 For solving the optimization model, in conclusion, there are several primary approaches to address optimization problem under varies uncertainties incorporating in stochastic programming, 5,9,11,13,14,23 robust optimization, 12,[15][16][17][18][19][20] and interval optimization.…”

Section: Introductionmentioning

confidence: 99%

“…In most literatures, one of the following approaches are generally presented: probabilistic constraints, 11,12 scenarios, 5,9,13,14 uncertainty set, [15][16][17][18][19][20] interval numbers, 21,22 and others. 23 For solving the optimization model, in conclusion, there are several primary approaches to address optimization problem under varies uncertainties incorporating in stochastic programming, 5,9,11,13,14,23 robust optimization, 12,[15][16][17][18][19][20] and interval optimization. 21,22 To compare the current relevant researches, the detail comparison results of relevant literatures are shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Interval optimization based operational strategy of integrated energy system under renewable energy resources and loads uncertainties

Wang

Gao

et al. 2020

Int J Energy Res

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Summary Uncertainties from renewable energy resources (RESs) and energy demands have brought enormous challenges to the optimal operation of integrated energy system (IES). An interval optimization based operational strategy for IES is proposed to overcome uncertainties. Firstly, embarking from a deterministic IES operation model, an interval method is presented to quantify the uncertainties instead of possibility distribution so as to better characterize the impact of RESs and loads on the operation of the IES. Secondly, the interval optimization model under multiple uncertainties is presented. In the proposed model, the total daily cost is optimized and system operation constraints are fully considered. Thirdly, the order interval relation and possibility degree are adopted to transform the interval model to deterministic model, which is solved by CPLEX optimizer. Finally, case studies considering influence of different uncertainty objects and uncertainty possibility degree levels are performed and analyzed extensively. The simulation results show that the optimized interval numbers will be increased gradually as uncertainty fluctuation degree increased from ±5% to ±25%. Comparing with automatic robust convex optimization method, the robust optimized values are in accordance with the upper values of optimized interval number optimization method, and the midpoints of interval results optimized by interval method are 4.1%, 8.7%, 11.7%, 16.5%, and 8.0% less than robust optimization results, respectively.

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Multi‐objective optimization of integrated gas–electricity energy system based on improved multi‐object cuckoo algorithm

Yang

2021

Energy Science & Engineering

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With the depletion of sources of primary energy across the world, the prospect of a global energy crisis in the near future has raised considerable concern. Among the many sources of clean energy, wind energy is considered to be the most effective means of sustainable and environmentally friendly energy. Traditional systems of energy based on oil and natural gas still operate in a discrete manner, and the promise offered by the mutual coordination between energy systems to draw complementary benefits has not yet been fully exploited. This has prompted all energy sectors to examine the optimal integration of interconnected gas–electricity energy systems. In this context, this study proposes a multi‐objective collaborative operational optimization model of P2G for an interconnected, integrated gas–electricity energy system in case of uncertain wind power. Based on the effect of carbon capture by using P2G, the effect of coupling gas and electricity, and the cost of low‐capacity reserves, the authors use conditional risk values to design four targets of optimization—the operating cost of the system, rate of consumption of the natural gas system, total carbon emissions and capacity stand‐by cost—according to the power system, natural gas system, thermal energy system, and their elements of coupling from multiple perspectives to clarify the constraints on the model. Moreover, to reduce the likelihood of the optimal solution falling into the local optimum, the improved cuckoo algorithm is used to optimize the multi‐objective scheduling model. The authors used a 10‐node power system and a six‐node natural gas system as examples for simulation to verify the proposed model. The results of empirical analysis show the following: (a) Once the P2G equipment had been connected, it reduced the total operating cost and improved the consumption of wind power of the natural gas system regardless of whether backup services were provided. (b) The P2G equipment could be combined with gas‐fired CHP units to “cut peaks and fill valleys” in the electrical load of the power system. These results verify the effectiveness of the proposed multi‐objective optimal dispatch model of an interconnected gas–electricity energy system.

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Power-to-gas management using robust optimisation in integrated energy systems

Cited by 42 publications

References 23 publications

An efficient solution method for integrated unit commitment and natural gas network operational scheduling under “Duck Curve”

An efficient solution method for integrated unit commitment and natural gas network operational scheduling under “Duck Curve”

Interval optimization based operational strategy of integrated energy system under renewable energy resources and loads uncertainties

Multi‐objective optimization of integrated gas–electricity energy system based on improved multi‐object cuckoo algorithm

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