Energy storage technologies as techno-economic parameters for master-planning and optimal dispatch in smart multi energy systems

Mazzoni, Stefano; Ooi, Sean; Nastasi, Benedetto; Romagnoli, Alessandro

doi:10.1016/j.apenergy.2019.113682

Cited by 111 publications

(27 citation statements)

References 53 publications

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“…The general form (6) to represent the final energy stored in the TES is extended with respect to the constraint of having the same energy at the beginning and the end of the study period, used in various references, e.g., [40][41][42]. This extended form is more suitable to be incorporated into calculation tools that use uncertain or predicted values, for example, within an approach based on model predictive control (MPC).…”

Section: Modelling Aspectsmentioning

confidence: 99%

Thermal Energy Storage for Grid Applications: Current Status and Emerging Trends

et al. 2020

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Thermal energy systems (TES) contribute to the on-going process that leads to higher integration among different energy systems, with the aim of reaching a cleaner, more flexible and sustainable use of the energy resources. This paper reviews the current literature that refers to the development and exploitation of TES-based solutions in systems connected to the electrical grid. These solutions facilitate the energy system integration to get additional flexibility for energy management, enable better use of variable renewable energy sources (RES), and contribute to the modernisation of the energy system infrastructures, the enhancement of the grid operation practices that include energy shifting, and the provision of cost-effective grid services. This paper offers a complementary view with respect to other reviews that deal with energy storage technologies, materials for TES applications, TES for buildings, and contributions of electrical energy storage for grid applications. The main aspects addressed are the characteristics, parameters and models of the TES systems, the deployment of TES in systems with variable RES, microgrids, and multi-energy networks, and the emerging trends for TES applications.

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Section: Modelling Aspectsmentioning

confidence: 99%

Thermal Energy Storage for Grid Applications: Current Status and Emerging Trends

et al. 2020

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“…Photovoltaics (PV) is a key technology since it is already deployed and economically viable [18]. Further development in materials and efficiency as well as products' integration can be promoted by a simplified modelling tool [19] but obstacles still present have to be considered, such as storage needs [20] or energy market mechanisms for its future prospects [21]. As regards impact, according to Reference [22], PV has a low impact if integrated in existing buildings or being part of new ones not entailing further land use [23].…”

Section: Introductionmentioning

confidence: 99%

Solar Energy Data Analytics: PV Deployment and Land Use

Mancini

Nastasi

2020

Energies

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EU targets for sustainable development call for strong changes in the current energy systems as well as committed protection of environmental resources. This target conflicts if a policy is not going to promote the compatible solutions to both the issues. This is the case of the additional renewable energy sources to be exploited for increasing the share in the electricity mix and in the gross final energy consumption. Solar energy is, currently, the cheapest solution in Southern European Countries, like Italy. In this paper, thanks to the availability of three open databases provided by National Institutions, the authors compared the historic trends and policy scenarios for soil consumption, electricity consumption, and renewable electricity production to check correlations. The provincial scale was chosen as resolution of the analysis. The deviations from the policy scenarios was then addressed to identify the demand for policy recommendations and pathways to promote in order to achieve the target for renewable electricity share as well as the reduction in soli consumption trend in 2030. The role of renewables integrated in the existing contexts, such as building integrated photovoltaics, is considered a key driver for solving this issue.

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“…On a global scale, renewable energy technologies produced 26.2% of the global electricity demand in 2018, and this is expected to climb to 45% by 2040 [1]. A large number of investigations have been applied in order to optimise various characteristics of renewable energy systems such as dealing with the uncertainty in renewable energy accessibility, support decision-making in the built environment [2] and the appropriation of energy storage operations for dampening the chaotic problems [3]. Among the different renewable energy sources, ocean wave energy is the cleanest, safest, most reliable and predictable source of renewable energy [4] with a power density significantly higher than that of solar and wind [5].…”

Section: Introductionmentioning

confidence: 99%

A New Bi-Level Optimisation Framework for Optimising a Multi-Mode Wave Energy Converter Design: A Case Study for the Marettimo Island, Mediterranean Sea

et al. 2020

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To advance commercialisation of ocean wave energy and for the technology to become competitive with other sources of renewable energy, the cost of wave energy harvesting should be significantly reduced. The Mediterranean Sea is a region with a relatively low wave energy potential, but due to the absence of extreme waves, can be considered at the initial stage of the prototype development as a proof of concept. In this study, we focus on the optimisation of a multi-mode wave energy converter inspired by the CETO system to be tested in the west of Sicily, Italy. We develop a computationally efficient spectral-domain model that fully captures the nonlinear dynamics of a wave energy converter (WEC). We consider two different objective functions for the purpose of optimising a WEC: (1) maximise the annual average power output (with no concern for WEC cost), and (2) minimise the levelised cost of energy (LCoE). We develop a new bi-level optimisation framework to simultaneously optimise the WEC geometry, tether angles and power take-off (PTO) parameters. In the upper-level of this bi-level process, all WEC parameters are optimised using a state-of-the-art self-adaptive differential evolution method as a global optimisation technique. At the lower-level, we apply a local downhill search method to optimise the geometry and tether angles settings in two independent steps. We evaluate and compare the performance of the new bi-level optimisation framework with seven well-known evolutionary and swarm optimisation methods using the same computational budget. The simulation results demonstrate that the bi-level method converges faster than other methods to a better configuration in terms of both absorbed power and the levelised cost of energy. The optimisation results confirm that if we focus on minimising the produced energy cost at the given location, the best-found WEC dimension is that of a small WEC with a radius of 5 m and height of 2 m.

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Energy storage technologies as techno-economic parameters for master-planning and optimal dispatch in smart multi energy systems

Cited by 111 publications

References 53 publications

Thermal Energy Storage for Grid Applications: Current Status and Emerging Trends

Thermal Energy Storage for Grid Applications: Current Status and Emerging Trends

Solar Energy Data Analytics: PV Deployment and Land Use

A New Bi-Level Optimisation Framework for Optimising a Multi-Mode Wave Energy Converter Design: A Case Study for the Marettimo Island, Mediterranean Sea

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