A Long-term Strategy Framework for Flexible Energy Operation of Residential Buildings

Abstract

Within the European power system, the electricity mix is experiencing more presence of variable power production due to the green shift. This shift causes increased need for flexibility measures to combat instability, which is achievable on both the generation and consumption side of the power system. Buildings, neighborhoods, and most end-users are able to assist with flexibility through demand side management, adjusting their consumption profile to react to the price signals given. This flexibility can be activated through the use of home energy management systems, which can control the flexible assets present in a given building. However, most of these applications only consider the short-term period of operation, up to a couple of days in the future when operating the flexible assets. Flexibility also has a value in operation beyond this period, and can assist in the long-term strategy of operation of the energy system in buildings, which could be many weeks or months into the future. This is important when accounting for long-term price signals or when operating seasonal flexible assets. Finding accurate and descriptive measures of representing the long-term value of flexibility use is needed to enable this. The work presented in this thesis investigates the long-term value of flexibility in residential buildings at the end-user level, and how the value of flexibility can be represented for a short-term operational model. The work applies and describes a long-term strategy framework specifically aimed at generating cost curves representing the long-term value of flexibility. The cost curves describe the future consequence of operation based on the future price signals they include. The developed models enable price signals of different categories to be included in the strategy framework. These price signals could be grid tariffs, but also flexible assets themselves, for instance, seasonal storage. The strategy framework creates a coupling between short-term and long-term operation of buildings, to achieve better overall use of flexibility within buildings. Overall, this thesis is made up of four scientific papers, where three are published and one is submitted for review at the present time. These publications comprise the contribution and discussion constituted in this thesis. A summary of the main results of this PhD is given below: • A long-term strategy framework and toolbox for building operation has been created, to provide more information on the long-term value of flexibility for building operation. The toolbox Long-term strategy frame-work for future building operation (LOSTFUTURE) calculates the long-term value of flexibility, and represents this as future cost curves. The framework enables long-term price signals to be embedded into the strategy, such that the cost curves represent the consequence of operation on these signals as well. Through the use of flexible assets, operation of the building can be controlled to react to the short-term and long-term consequences of operation. • The LOSTFUTURE toolbox has been used in combination with several different long-term price signals, to analyze the long-term value of flexibility and strategic decisions during operation. For a monthly demand charge, penalizing peak-import of electricity, the strategy finds the costoptimal peak-import level to aim for over the whole month, accounting for both the demand charge cost and value of operation from real-time price variation. For a price signal related to CO2eq-inventory, motivating costoptimal net zero-emission during yearly operation, the strategy captures the cost-optimal value of emission compensation and the optimal timing of performing this compensation. For long-term price signal as input to seasonal thermal energy storage operation, the strategy captures the value of using the seasonal storage unit to benefit from seasonal variation in operation. • With the LOSTFUTURE toolbox, the flexible assets are controlled such that they are able to influence the short-term and long-term value of flexibility. The flexible assets in this work included a small-scale battery energy storage system, a controllable electric vehicle charger, and control of space heating to influence indoor temperature. For a case study surrounding a monthly demand charge price signal in a Norwegian building, each of these flexible assets individually provided means of flexibility use to react to the long-term price signal during operation. Despite only being able to perform flexibility within a day at the time, the flexible assets are able to react to the long-term price signals and cost-optimally balance cost of operation. While the battery and electric vehicle charger saw cost-reduction primarily in the demand charge price signal, space heating found a cost-optimal peak-import level that balanced savings from real-time prices. This showed that each flexible asset reacts to the price signals differently based on their characteristics. • Multi-period price signals coupled to other price signals have been investigated and applied to the LOSTFUTURE toolbox. This enables the framework to include multiple price signals at once, including price signals that are repeatedly activated and only valid for a limited period at a time. This coupling was performed on a case study surrounding a Norwegian building with seasonal thermal energy storage and monthly demand charge, to find the operational strategy over a whole year. The model managed to accurately couple each monthly demand charge with the strategy surrounding the seasonal storage unit, such that the long-term value of flexibility on both price signals were preserved. The results showed a strategy that captured the accurate peak-import level of the demand charge for each month, and also the cost-effective use of seasonal storage.