Towards Uncertainty in Micro-grids: Planning, Control and Investment.

摘要

The advent of micro-grids and their potential participation in the wholesale market makes the development of new business models necessary. It is anticipated that the future wholesale market for electricity and its ancillary services will include the existing major players as well as micro-grids, which will act both as buyers and sellers. The wholesale market will shift more towards a distributed system with the centers of gravity dynamically changing depending on how the smaller micro-grids play out their supply and demand and also how the market aggregation takes place. Having the obligation to fully satisfy its demand at each point of time, any shortage in available power supply within the micro-grid will lead to the purchase of electricity from the macro-grid at spot market price. The sources of variability rising from the forecast of renewable energy resources and electricity demand, would introduce uncertainty in making decisions at planning layer. Also, a micro-grid may fail to honor its contractual obligations to supply to the market due to these volatilities. To optimize the planning and operation of micro-grids, it is important for the planner and controller to take into account uncertainties inherent to the micro-grid and the overall supply and demand network, including the energy market place. With high capital costs involved in building a micro-grid, sequential investment strategies, which promote gradual increase in capacity of generation, are needed.;This work aims to develop a set of models and tools that address the optimal decision-making processes involved in both operation and investment in micro-grids. These models account for short-term operational and long-term investment uncertainties in decision making and adopt the following analytics: • Two-stage stochastic programming and certainty equivalent models to obtain optimal decisions for day-ahead planning in micro-grid's operation; • Contingent claim analysis and Monte Carlo simulation to examine the value in delaying the investment due to uncertainty around the investment.;Capital budget planning model along with Monte Carlo stochastic scenario generation to derive the optimal investment decisions for micro-grid's portfolio considering its optimal operation under uncertainty.