WP4: Intelligent Aggregation and Markets [High Level Integration]

WP Leader: Pierre Pinson, pierre.pinson@smart-cities-centre.org

Objective: Develop aggregate models and market structures for city level systems (and subsystems).

Tasks

WP4.1 Develop methodologies for aggregated simulation, based on detailed models from WP1 and WP2. Investigate methods for classification and aggregation of such components, including the network and associated constraints, and express the technologies at more generic, statistical and scalable levels.
WP4.2 Develop a system modelling framework to investigate and optimise the interactions between the aggregate resources from WP4.1. The experience gained from models such as WILMAR and Balmorel will be built upon, while recognising the need for novel approaches when considering the entire energy system and the stochasticity introduced by increased amounts of renewables.
WP4.3 Develop market structures and mechanisms for the optimal management of integrated energy systems, to ensure that the optimised operations identified in WP4.2 are incentivised in a socio-economically optimal manner. Practical examples include, for instance, (i) the coupling of markets for electricity, gas and heating, and (ii) new market mechanisms with high uncertainties on both supply and demand sides.

PhD projects

Classification and aggregation of energy components

PhD student: Thibaut Richert, thibaut.p.richert@smart-cities-centre.org

The energy system is dominated by the electrical, gas and heating domains. Mainly the power grid and district heating (DH) observe significant operational changes due to the high penetration of renewables but also the proliferation of distributed energy resources (DERs), i.e. small production, consumption or storage units connected to the distribution grid. The DH could be a key to enabling flexibility in the next generation of energy systems supporting a high penetration of renewables in the power grid but also facilitating the accommodation of DERs in both the electrical and heating domains.

This project aims at developing methods for classification and aggregation of energy components, including the network, and express the technologies at a more generic, statistical and scalable level in a multi energy system environment. These methods will eventually allow to simulate, analyse and design operational strategies for domain-linking energy components.

Market Mechanisms for Integrated Energy Systems

PhD student: Christos Ordoudis, christos.ordoudis@smart-cities-centre.org

The research of this project is focused on the development of market designs for multi-carrier energy systems under high shares of renewable energy production. Each system’s own complexities and interactions among them will be identified in order to efficiently design these new market structures. Particular emphasis will be placed on examining different levels of coordination in terms of system integration and time coupling of trading floors. Our analysis will cover the full spectrum of potential outcomes ranging from ideal coupled solutions to current decoupled approaches. The goal is to propose innovative market designs for an integrated energy system, as well as market-based coupling mechanisms that can be readily applied to the current setups. In this project, tools based on optimisation and decision making under uncertainty techniques will be developed. Advanced market structures will eventually result in the efficient coordination of various energy systems and facilitate the deployment of renewable energy sources.

Implementation Strategies for Balanced Renewable Energy Systems

PhD student: Louise Krog Jensen, louise.k.jensen@smart-cities-centre.org

This PhD project focuses on developing and identifying tools and approaches that can ensure development and implementation of local strategic energy strategies, through involvement and activating local actors in a way that makes them committed to participate in the transition of the energy system, so that a balance between the production and demand side can be ensured in the transition to a 100 % renewable energy system. This requires both a technical and societal understanding of how the energy system is constructed and how the society interacts with this system.

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