Using artificial intelligence and machine learning to manage the electricity grids of the future
Existing power grids were designed to transmit electricity over relatively short distances, however, increasingly grids are required to supply major cities from remote offshore wind farms at the same time as integrating local generation. With generators feeding variable amounts of energy from renewable sources into the grid at all voltage levels, it is more difficult to balance supply and demand, and the risks of overloads and fluctuations increase.
By 2020 it is estimated that there will be over 50 billion smart devices connected to the internet, creating vast quantities of data which can be harnessed to develop smart systems for managing electricity systems, both at a local and national level to reduce the costs of balancing the electricity system.
The management and operation of the future power system and its components – particularly active power distribution grids and microgrids – will require new control functionality, including the following key functions and services :
Advanced monitoring and diagnostics: Monitoring and state estimation capabilities and real-time condition monitoring of components in the medium and low-voltage distribution grids, including self-diagnostic capabilities.
Optimisation/self-optimisation capabilities: Fluctuating electricity generation from renewable sources requires the ability to (self-) optimise operations in medium- and low-voltage grids, including effective integration of flexible loads and storage systems.
Automatic grid (topology) reconfiguration: Support of automatic or semiautomatic adjustment of the distribution grid topology due to optimisation processes or fault management and power system restoration.
Adaptive protection: Automatic or semiautomatic adaption of protection devices (eg protection relays and breakers) with respect to the actual power grid conditions (eg adaptation of the protection system settings due to the bidirectional power flow caused by DERs).
Distributed power system management: Distributed control with automatic decision finding processes and proactive fault prevention have to be provided for the power system infrastructure operators in medium- and low-voltage grids.
Islanding possibilities/microgrids: Local operation of islands/micro grids can improve the availability of the electricity supply due to failures on higher voltage levels.
Distributed generation/distributed energy resources with ancillary services: Use of ancillary services provided by DER (eg local voltage or frequency control and virtual inertia) improves power grid optimisation.
Demand response/energy management support: Electric loads and energy storage systems and demand response provide additional flexibility in power system operation.
Advanced forecasting support: Forecasting of (distributed) generation and load profiles for optimised grid operation.
Self-healing: Automatic or semiautomatic restoration of grid operation in case of component/grid faults helps power system and infrastructure operators.
Asset management/condition-dependent power system maintenance: Preventive maintenance according to component/device conditions and remaining lifetime.
Relying on traditional linear mathematical models to manage these processes is not feasible, since both the manpower required to encode the models and the computing power to process them would be extremely large. that would be required to solve it. A more real-time approach is required.
Using AI, an efficient and adaptive framework can be developed that can look across multiple assets and infrastructure, and, given all the operational parameters, intelligently optimises their behaviour.
