Power-to-Gas: Electrolyzers as an alternative to network expansion – An example from a distribution system operator

Robinius, Martin; Raje, Tanmay; Nykamp, Stefan; Rott, Tobias; Müller, Martin; Grube, Thomas; Katzenbach, Burkhard; Küppers, Stefan; Stolten, Detlef

doi:10.1016/j.apenergy.2017.10.117

Cited by 85 publications

(50 citation statements)

References 34 publications

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“…Costs are expressed in terms of LCOE, calculated as shown in Equation :

LCOE = \frac{\sum_{t = 1}^{12} ((), \frac{italiccosts}{{(1 + r)}^{t}})}{\sum_{t = 1}^{12} ((), \frac{# {italicNm}^{3}}{{(1 + r)}^{t}})}

in which costs comprise investments (interest and payback) and operation and management (O&M) for all transformations, # Nm 3 is the volume of green gas injected each year, r is the discount rate and t is the time in years. Investments are amortised in 12 years.…”

Section: Methodsmentioning

confidence: 99%

“…Investments are amortised in 12 years. The LCOE is expressed in €ct Nm −3 with a lower result being preferable …”

Section: Methodsmentioning

confidence: 99%

“…The economic and environmental feasibility of bio-P2M technology has been investigated for four different scenarios ( Figure 1) by determining the levelised cost of energy (LCOE), the energy efficiency and the GHG emission saving. Costs are expressed in terms of LCOE, calculated as shown in Equation (1) [24][25][26] :…”

Section: Methodsmentioning

confidence: 99%

“…The LCOE is expressed in €ct Nm −3 with a lower result being preferable. [24][25][26] Energy efficiency is calculated as the ratio of fossil primary energy input and output (FPEIO), see Equation (2). This is the summation of fossil primary energy consumed (FPE consumed) over all the transformation blocks required for the injection of 1 Nm 3 green gas (89.7 mol% CH 4 and 10.3 mol% CO 2 ) into the natural gas grid, divided by the higher heating value HHV (35.6 MJ Nm −3 ) of 1 Nm 3 green gas 27 :…”

Section: Methodsmentioning

confidence: 99%

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Farm‐scale bio‐power‐to‐methane: Comparative analyses of economic and environmental feasibility

et al. 2019

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Summary Power‐to‐gas technologies are considered to be part of the future energy system, but their viability and applicability need to be assessed. Therefore, models for the viability of farm‐scale bio‐power‐to‐methane supply chains to produce green gas were analysed in terms of levelised cost of energy, energy efficiency and saving of greenhouse gas emission. In bio‐power‐to‐methane, hydrogen from electrolysis driven by surplus renewable electricity and carbon dioxide from biogas are converted to methane by microbes in an ex situ trickle‐bed reactor. Such bio‐methanation could replace the current upgrading of biogas to green gas with membrane technology. Four scenarios were compared: a reference scenario without bio‐methanation (A), bio‐methanation (B), bio‐methanation combined with membrane upgrading (C) and the latter with use of renewable energy only (all‐green; D). The reference scenario (A) has the lowest costs for green gas production, but the bio‐methanation scenarios (B‐D) have higher energy efficiencies and environmental benefits. The higher costs of the bio‐methanation scenarios are largely due to electrolysis, whereas the environmental benefits are due to the use of renewable electricity. Only the all‐green scenario (D) meets the 2026 EU goal of 80% reduction of greenhouse gas emissions, but it would require a CO2 price of 200 € t−1 to achieve the levelised cost of energy of 65 €ct Nm−3 of the reference scenario. Inclusion of the intermittency of renewable energy in the scenarios substantially increases the costs. Further greening of the bio‐methanation supply chain and how intermittency is best taken into account need further investigation.

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“…Costs are expressed in terms of LCOE, calculated as shown in Equation :

LCOE = \frac{\sum_{t = 1}^{12} ((), \frac{italiccosts}{{(1 + r)}^{t}})}{\sum_{t = 1}^{12} ((), \frac{# {italicNm}^{3}}{{(1 + r)}^{t}})}

Section: Methodsmentioning

confidence: 99%

“…Investments are amortised in 12 years. The LCOE is expressed in €ct Nm −3 with a lower result being preferable …”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Farm‐scale bio‐power‐to‐methane: Comparative analyses of economic and environmental feasibility

et al. 2019

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“…By producing fuel gases with fluctuating electricity and storing the renewable energy that may otherwise be curtailed, the power-to-gas (P2G) approach presents an attractive solution for meeting the increasing energy demand in an ecofriendly manner [2,3]. Moreover, P2G plays an important role in the context of multi-energy systems by functioning as the interaction points to couple the gas network and the power system [4]. Numerous P2G pilot systems have been constructed around the world for research or demonstration purposes as seen in [5] and [6].…”

mentioning

confidence: 99%

Optimization of hydrogen yield of a high-temperature electrolysis system with coordinated temperature and feed factors at various loading conditions: A model-based study

et al. 2018

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High-temperature electrolysis (HTE) is a promising technology for achieving high-efficiency power-to-gas, which mitigates the renewable curtailment while promoting decarbonization by transforming wind or solar energy into fuels. Different from low-temperature electrolysis, a considerable amount of the input energy is consumed by auxiliary equipment in an HTE system for maintaining the temperature, so the studies on systematic energy description and parameter optimization of HTE are essentially required. A few published studies investigated HTE's systematic optimization based on simulation, yet there is not a commonly used analytical optimization model which is more suitable for integration with power grid. To fill in this blank, a concise analytical operation model is proposed in this paper to coordinate the necessary power consumptions of auxiliaries under various loading conditions of an HTE system. First, this paper develops a comprehensive energy flow model for an HTE system based on the fundamentals extracted from the existing work, providing a quantitative description of the impacts of condition parameters, including the temperature and the feedstock flow rates. A concise operation model is then analytically proposed to search for the optimal operating states that maximize the hydrogen yield while meeting the desired system loading power by coordinating the temperature, the feedstock flows and the electrolysis current. The evaluation of system performance and the consideration of constraints caused by energy balances and necessary stack requirements are both included. In addition, analytical optimality conditions are obtained to locate the optimal operating states without performing nonlinear programming by further investigating the optimization method. In the case study, a numerical case of an HTE system is employed to validate the proposed operation model and optimization method, 1 arXiv:1810.06439v1 [physics.app-ph]

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Stochastic multi‐objective expansion of renewable resources in distribution systems incorporating responsive loads towards achieving zero energy structure

Azari

Tabar

Tohidi

2022

Intl J of Energy Research

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Summary Due to the increase in global warming and environmental pollution, heavy penalties have been imposed on pollution producers such as electrical energy networks. Hence, the role of renewable resources expansion in power systems is unavoidable. However, their high penetration increases the uncertainties and decreases the reliability. This paper investigates the expansion of renewable resources in distribution systems based on the zero energy concept. In this regard, different scenarios are designed to determine the optimal location and sizing of wind turbines and photovoltaics as the most matured technologies. To achieve such a goal, a multiobjective linear programming consisting of investment cost and environmental pollution is suggested, which is solved by the augmented epsilon constraint method. Moreover, an incentive‐based demand response program is implemented using shiftable and curtailable responsive loads. Eventually, the uncertainties of renewable resources are modeled by a scenario generation and reduction approach to simulate realistic conditions. The results show that the system that includes only wind turbines or only photovoltaics cannot completely overcome the pollution problem due to the uncertainties. By contrast, a zero energy structure is achievable by their combination, where the investment cost is increased by 73.17% and 48.13% compared to the separate expansion of photovoltaics and wind turbines, respectively. The participation of responsive loads decreases the investment cost by 49.29% as well. Furthermore, the uncertainties of renewable resources increase the total cost by only about 5.95% in the presence of responsive loads; however, the system will be robust against at least 6% of wind turbines and photovoltaics fluctuations.

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Power-to-Gas: Electrolyzers as an alternative to network expansion – An example from a distribution system operator

Cited by 85 publications

References 34 publications

Farm‐scale bio‐power‐to‐methane: Comparative analyses of economic and environmental feasibility

Farm‐scale bio‐power‐to‐methane: Comparative analyses of economic and environmental feasibility

Optimization of hydrogen yield of a high-temperature electrolysis system with coordinated temperature and feed factors at various loading conditions: A model-based study

Stochastic multi‐objective expansion of renewable resources in distribution systems incorporating responsive loads towards achieving zero energy structure

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