To optimise the distribution grids evolution, Hive Power offers a simulation tool to have a better understanding of present and future scenarios.
The primary tool of the Hive Modeller is a simulation framework to analyse future scenarios on distribution grids, from both a technical and economic points of view.
The simulation takes into account the thermal behaviour of buildings and heating systems, statistics on typical users habits, and the typical weather conditions for a specific location.
Novel business models for the energy exchange, such as dynamic tariffs, P2P energy trading, auction systems or flexibility markets can be studied. Depending on the business models, the controlling algorithms are implemented.
The future scenarios include the penetration of solar rooftops, heat-pumps, EVs and batteries into the local district. Results from the simulation show how voltages and currents change in the predicted scenarios, therefore suggesting the necessary grid investments.
The comparison of different business models provides you with valuable information on the best strategy to keep your grid safe and efficient.
The Hive Modeller provides a tool for the design of novel schemes of dynamic tariffs for DSOs, energy retailers and aggregators in general.
Hive Power customises the scheme for any customer's specific needs, such as peak-shaving, energy trading or voltage control.
The users will face different tariffs every day and can benefit from their flexible loads, such as heat-pumps and EVs, by optimising the consumption when the tariff is lower.
Optimal Hydropower Scheduler
By leveraging the results of the stochastic forecasts, the Hive Modeller proposes optimal control algorithms for hydropower plants and uses modern optimisation techniques to schedule the operation of hydro turbines.
The schedule takes into account the efficiency curves of the turbines and several other technical constraints, such as minimum operation time, environmental constraints and available water.
The optimisation can have multiple objectives, such as peak-shaving or optimal energy trading on the spot market, alternating the goals depending on specific conditions.