Energy balancing using charge/discharge storages control and load forecasts in a renewable-energy-based grids

Small-scale systems are vulnerable to changes. The development of solution strategies for changing conditions should, therefore, be considered from the outset in planning. For planners and installers of mini-grids for rural villages without grid connection, first of all, the question of the expected electricity demand arises. So far, this is determined primarily by interviewing the population. However, studies have shown that there are still major differences between the power demand estimated and the real load after electrification. Reinforcing here is the inadequate assessment of load development through immigration and increased prosperity. On the basis of a currently developing mini-grid in the Eastern Cape of South Africa, we could show that a load increase of ∼60% within 10 years is to be expected. The following possibilities were outlined as possible ways of counteracting the increase in load: (i) communicating with the population, i.e. on energy efficiency to raise awareness and understanding, (ii) early planning of capacity building and identification of key performance parameters to trigger the expansion based on local socio-cultural conditions and grid supporting qualities. An adequate database for the initial electrification and power development should also be established and available on open-source basis for researchers, developers, communities, and installers.

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“…Another option would be intraday load forecasting [14]. However, these methods require a larger network and a larger storage capacity.…”

Section: Discussionmentioning

confidence: 99%

Load predictions: vulnerability of micro‐grids based on renewable energies due to increasing population and individual demand

Kühnel¹,

Ravanbach²,

Hanke³

et al. 2020

IET Renewable Power Generation

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“…Here α is the regularisation parameter. Following [36] the publicly available dataset of the German power grid load, provided by ENTSO-E, for the period of 48 hours from 29.11.2013 22:00 till 01.12.2013 21:00 was used to demonstrate the superiority of regularised Volterra models when it comes to noisy input data. It is assumed that the exact load is unknown and only its forecast is available.…”

Section: Regularised Volterra Modelmentioning

confidence: 99%

A Dynamic Analysis of Energy Storage With Renewable and Diesel Generation Using Volterra Equations

Sidorov

Muftahov

Tomin

et al. 2020

IEEE Trans. Ind. Inf.

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Energy storage systems will play a key role in the power system of the twenty first century considering the large penetrations of variable renewable energy, growth in transport electrification and decentralisation of heating loads. Therefore reliable real time methods to optimise energy storage, demand response and generation are vital for power system operations. This paper presents a concise review of battery energy storage and an example of battery modelling for renewable energy applications and second details an adaptive approach to solve this load levelling problem with storage. A dynamic evolutionary model based on the first kind Volterra integral equation is used in both cases. A direct regularised numerical method is employed to find the least-cost dispatch of the battery in terms of integral equation solution. Validation on real data shows that the proposed evolutionary Volterra model effectively generalises conventional discrete integral model taking into account both state of health and the availability of generation/storage.

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“…The storage system optimization should be clearly distinguished from the storage system modeling. The latter can be attacked using generalization of the recently proposed Volterra balance model [35].…”

Section: The Relationship Between Local and Centralized Levelsmentioning

confidence: 99%

Toward Zero-Emission Hybrid AC/DC Power Systems with Renewable Energy Sources and Storages: A Case Study from Lake Baikal Region

et al. 2020

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Tourism development in ecologically vulnerable areas like the lake Baikal region in Eastern Siberia is a challenging problem. To this end, the dynamical models of AC/DC hybrid isolated power system consisting of four power grids with renewable generation units and energy storage systems are proposed using the advanced methods based on deep reinforcement learning and integral equations. First, the wind and solar irradiance potential of several sites on the lake Baikal’s banks is analyzed as well as the electric load as a function of the climatic conditions. The optimal selection of the energy storage system components is supported in online mode. The approach is justified using the retrospective meteorological datasets. Such a formulation will allow us to develop a number of valuable recommendations related to the optimal control of several autonomous AC/DC hybrid power systems with different structures, equipment composition and kind of AC or DC current. Developed approach provides the valuable information at different stages of AC/DC hybrid power systems projects development with stand-alone hybrid solar-wind power generation systems.

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Energy balancing using charge/discharge storages control and load forecasts in a renewable-energy-based grids

Cited by 16 publications

References 25 publications

Load predictions: vulnerability of micro‐grids based on renewable energies due to increasing population and individual demand

Load predictions: vulnerability of micro‐grids based on renewable energies due to increasing population and individual demand

A Dynamic Analysis of Energy Storage With Renewable and Diesel Generation Using Volterra Equations

Toward Zero-Emission Hybrid AC/DC Power Systems with Renewable Energy Sources and Storages: A Case Study from Lake Baikal Region

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