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Joint Energy Hub and Electric-Bus Fleet Management under Bidirectional Charging
Battery-powered electric buses can be interpreted as large-scale, mobile, electricity storage devices. The schedules and locations of electric buses are relatively predictable with regards to fixed routes, such as in the twice daily runs of school buses. When an electric bus is not serving its route, it can schedule its charging/discharging to provide ancillary services to the main grid in exchange for monetary incentives. This is often referred to as Vehicle-to-Grid (V2G). Simultaneously, a fleet of electric buses can play a key role as a source of demand-side flexibility to support the system in managing operational uncertainty, resulting in the generation of new revenue streams. The onsite coupling of electric buses with site resources in a Vehicle-to-Everything (V2X) setting has shown extremely promising performance in terms of both site self-sufficiency maximization and demand-side flexibility provision. This project will investigate economic model predictive control (MPC) to reduce energy costs and maximize service revenues in the scenario of joint control of an energy hub (e.g., depot, school campus, parking lot) and its buses. Flexibility envelopes will be developed to estimate the flexibility potential and the corresponding market revenues generated with this joint control architecture, as compared to unpredictable arrival/departure times and with separate control policies. Since the flexibility provision market is highly regulated, we plan to include Swiss/EU regulations as hard constraints in our formulation. Extensions will include the effects of different depreciation models and cases where the energy hub is equipped with Photovoltaic generation, electricity storage (battery/hydrogen), and/or thermal storage.
Keywords: Bus, Electricity Storage, Battery, Vehicle-to-Grid, Flexibility, Energy Hub, Model Predictive Control
The aim of this project is threefold:
1. quantify the forecast error when predicting arrival/departure times and energy demands of the buses and impact on the V2X capabilities,
2. design an economic MPC-based controller for the optimal V2X operation of an energy hub together with its fleet of electric buses [1, 3], and
3. analyze the performance of the MPC-based controller in terms of energy cost reduction, technical/operational constraint violation and service revenue generation.
The aim of this project is threefold:
1. quantify the forecast error when predicting arrival/departure times and energy demands of the buses and impact on the V2X capabilities, 2. design an economic MPC-based controller for the optimal V2X operation of an energy hub together with its fleet of electric buses [1, 3], and 3. analyze the performance of the MPC-based controller in terms of energy cost reduction, technical/operational constraint violation and service revenue generation.
As a result of this project, the student will:
- Learn about energy hub management and V2X capabilities of electric fleets
- Develop model predictive control schemes in energy hub operation and fleet dispatch for demand-side flexibility provision
- Validate synthesized controllers on simulations (or/and in real systems)
- Engage in regular meetings with members from IfA and EMPA
- Prepare a final presentation (∼20 minutes) and report (∼20 pages for SA, ∼50 pages for MA)
As a result of this project, the student will: - Learn about energy hub management and V2X capabilities of electric fleets - Develop model predictive control schemes in energy hub operation and fleet dispatch for demand-side flexibility provision - Validate synthesized controllers on simulations (or/and in real systems) - Engage in regular meetings with members from IfA and EMPA - Prepare a final presentation (∼20 minutes) and report (∼20 pages for SA, ∼50 pages for MA)
Federica Bellizio, federica.bellizio@empa.ch
Marta Fochesato, mfochesato@ethz.ch
Jared Miller, jarmiller@ethz.ch