Ecole des Mines - St Etienne
February 14, 2019, 11:00TBD
As the European power system is progressing towards more liberalization and while climate policy targets are increasingly ambitious, the power sector is facing radical changes. Intermittent and decentralised renewable energies are developing at a fast pace, innovations in energy efficiency and power demand management are being deployed at a large-scale, and increased interconnections between market zones result in an unprecedented level of system integration, bringing new challenges for planning and operations. System level studies are necessary for decision-makers as to understand the issues and opportunities raised by these changes. In particular, major difficulties lie in making the right decisions for long-term planning while taking into account operational constraints representing the system's behaviour at a local level. In this sense, mathematical optimisation represents a powerful tool to model and simulate this type of complex problems. The first part of this presentation will give an overview of the actors involved in the European power system strategic decisions and will illustrate the type of studies typically conducted. The second part will detail a mathematical framework used for modelling the problems aforementioned with an emphasis on flexibility, providing means to avoid or defer unnecessary capital-intensive investments. A particular focus will be given on demand side flexibility with an illustration of demand-response schemes contributing to system's reliability as well as electric vehicles and heat pumps driven by price signals. The third part will illustrate a dedicated methodology for optimal investment with two studies conducted for the European Commission and the ADEME (French Environment and Energy Management Agency), respectively: 'Mainstreaming RES, Design of flexibility portfolios at Member State level to facilitate a cost-efficient integration of high shares of renewables' and 'The evolution of the French electricity mix between 2020 and 2060'. In the last part, the shortcomings of system-level studies regarding local issues will be discussed. The contrast between smooth aggregated and highly-variable local demand profiles will be used as to illustrate the challenges of transposing a system-level approach at local levels. Hence, typical questions such as 'how to predict the energy behaviour of a neighbourhood by 2040?' or 'how to define a market or regulatory mechanism to encourage households to invest in a particular type of heating equipment?' require a different approach and call for new methodologies.
Pierre Attard is an engineer specialized in energy systems modeling and statistics. He holds the Engineering diploma from Mines Saint-Etienne, and a master's degree in energy economics/market from IFP (French institute for oil and energy). Pierre co-authored prospective studies and policy assessments for actors such as the European Commission, the European Climate Foundation as well as ministries and regulators. From his previous experiences, he has also worked on network-specific topics such as pricing or investing in production assets. His expertise in energy systems has led him to contribute to multi-energy modelling for local energy systems (Grand Lyon, Pays de Gex). He is proficient in R as well as in Python - including Machine Learning Libraries such as Scikit-Learn. He is also familiar with Machine Learning environments such as ElasticSearch, Kibana and Docker. Pierre participates in numerous projects requiring the implementation of forecasting tools and their industrialization.