The expected outcome of this project is to develop a methodology for prediction and trading of power flexibility, including the development of new business models for future flexibility markets.
Increasing energy consumption and greater environmental concerns have led to more focus on decarbonization of the power sector. One of the possible solutions is electrification, in which the consumption of fossil energy is replaced by electric energy from renewable sources. This applies to the traditional electric power generation, as well as to the demand side. A typical example of the latter is the introduction of batteries and electric vehicles (EVs).
Electrification can potentially reduce the total energy consumption, since electrical technology is generally more energy efficient than fossil-based technology. On the other hand, this transition will lead to increased high-intensive decentralized demand of electricity. In addition, an increased share of variable renewable energy sources like solar PV and wind power, leads to more dynamics and less predictability. A significant part of this production comes at decentralized level. Decentralized energy production also introduces a two-way flow of power into the network topography, and traditional consumers become producing consumers - so-called "prosumers". Overall, the complexity of the future power system will increase rapidly, especially at the distribution level, and this will result in more local congestions and voltage-quality challenges. The traditional way to meet such challenges is to invest in grid capacity upgrades. An alternative to grid reinforcements is the use of demand side flexibility. Commercial buildings and households have a number of flexible power components (assets). Examples of these are batteries, diesel generators, electric boilers for hot water, cold stores and solar cells. New local flexibility markets are emerging and aim to provide an open platform where end users / prosumers can sell their flexibility to a network operator or other buyers who need this flexibility.
The project will start with a literature review of power flexibility. The aim is to get an overview of existing and emerging markets, as well as to conduct a mapping of potential sources of flexibility that can be used in such a market. Parallel, contact will be established with potential data providers and end-users.
Based on available data, the next step is to estimate power flexibility for selected assets. In order to estimate flexibility, it is important to establish a baseline for potential flexibility assets. This can be expected production from production components (e.g. solar cells) or expected consumption from load components (e.g. electric boiler). For some components the baseline will be planned (e.g. optimized battery management), while in other components prediction is required. Many prediction methods exist, e.g. different statistical methods or machine learning algorithms. In this project, machine learning will be used to predict future production and / or consumption based on historical data and external parameters such as temperature or building activity. Various machine learning algorithms should be tested and compared, with the emphasis on supervised learning. Based on baseline information, physical properties of the asset and agreed constraints for baseline deviation, available flexibility volumes are estimated.
The focus of the project will further be switched towards prediction of aggregated power flexibility for selected end-users. These can in turn be formatted into flexibility bids into a future flexibility market. Fundamentally, the bids contain information about the prosumer’s ability and willingness to provide flexibility per asset, defined by the sales price, volumes per period and potential limitations. The bids should be of a format that can be submitted to a potential flexibility marketplace. As this is a future market, there are still uncertainties about the size of such bids - whether it will be appropriate to offer flexibility from individual components, aggregated flexibility for buildings, or aggregate flexibility for larger areas.
The final step of the project is to develop business models the flexibility market. This will require a significant insight in the Norwegian power system, and close contact with potential end-users.
The project is interdisciplinary, and the successful PhD candidate will be part of a team including expertise from energy physics, data science and industrial economy. Energy physics is the backbone of the project, as this is important in order to understand the physical behaviour of the flexibility components and the Norwegian power system. Data science will be used as a tool for data analysis and prediction of power flexibility. Machine learning is expected to give more accurate prediction compared to traditional physical models. Industrial economy plays a key role in the project related to the development of new business models for the future flexibility market, which will be essential for implementation in real markets.
A PhD position is available for the project.
Project leader and main supervisor for the PhD candidate within the project
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Kart: Mazemap / (pdf)