The third use case tackles low-voltage grid management (i.e. 230/400v) and specifically targets DSOs. The use-case is an extension of use case 2, with the aim to demonstrate that local or district-level energy management, combined with effective grid monitoring and enhanced DSO / ESCO connections can significantly improve the flexibility and global balancing of the grid.

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There are several factors influencing the concept of monitoring and balancing the grid at this level, including:

  • the widespread increase in domestic and micro generation,
  • the need to respond to vehicle charging and domestic energy storage demands,
  • individual and corporate intent to reduce energy consumption and carbon emission, and
  • the wider economic benefits to be gained through deferral of investment by utilising the existing infrastructure to greater advantage.

This is becoming more achievable due to investment and development of enablers such as Smart Meters, making detailed consumption profiles and data available to consumers (and suppliers), improvements in communications and connectivity technology, and improved network monitoring, control and automation systems. All of this contributes to allowing the overall energy delivery system to be more flexible and cost-effective than has previously been possible.

Various forecasts indicate a predicted 50% penetration of electric vehicles and domestic heat pumps by 2030, contributing to a potential 40-60% increase in overall demand/load by 2050. This would require a long term and costly investment in reinforcing and replacing the existing distribution infrastructure using traditional design and construction techniques. However, some parties estimate that utilising ‘smart’ techniques and designing more flexible, interconnected, and demand responsive networks could reduce that investment significantly, even in the order of 50%.

The aims of this use-case are threefold:

  • to assess (based on simulation, limited-scale experimental deployment, and actual grid data) the best grid monitoring strategies for improving the awareness of the grid operator. The relevant technologies to be used to this end will be selected based on the outcomes of task 2.2 ‘Grid monitoring HW infrastructure design’.   Particular attention will be paid to the assessment of cost / benefits – i.e. which investment is required to reach the relevant level of awareness, and the related revenues;
  • to assess the impact of enhanced connection between district-level energy managers and the DSO. It is expected that grid balancing optimisation (and the savings that could result from) could be heavily improved if the DSO can influence consumption at a larger scale (district-scale) than the individual buildings or dwellings. We consider here a ‘district’ as a collection of consumption, production, and consumption nodes that are managed by a single entity (ESCO, aggregator, local authority energy department). The aim is to assess the gains in terms of grid balancing resulting from an effective connection between DSOs and such district-level energy managers;
  • to achieve quantified objectives, specifically:
    1. 5-7% reduction in grid losses
    2. 12-15% increase in renewable grid hosting capacity (i.e. renewable power production linkable to the grid at local level)
    3. Reduced switch-off time per year (to several minutes for urban grids)

This use case will be led by UPL, with the simulation and experimental deployment being implemented at the GDF Suez Laborelec facility situated at Linkebeek, near Brussels.

The simulation will be undertaken on several layers, including Physical (e.g. generation, consumption and power distribution connectivity), Communications (e.g. traffic, system failures, cyber attacks) and Social (e.g. economical interactions between actors, usages, generation of consumption patterns based on socio-economical models of consumers’ homes, electric vehicles, etc.).

The experimental facility includes a micro-windturbine, photovoltaic technologies, fuel cell, grid islanding system with batteries, additional loads and generating units, all of which will assist in emulating the behaviour of a ‘real’ district as described above.

The detailed use case scenarios are planned to be delivered by May 2016, with assessment, validation and final report being delivered by May 2017.

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