Electrical power systems represent complex and dynamic systems, consisting of a large number of interconnected subsystems for supply, demand, and distribution. These subsystems consist of smart electric meters, consumers, electrical grids, transformers, power-plants, and decentralized generators to name a few. These subsystems are being increasingly strained by the rapid growth of electric mobility, like cars and trains, and the increasing demand for high power and fast charging. Moreover, the transition to renewable energy sources and the decommissioning of conventional power plants creates supply-stability problems. Both the increase in demand and instability in the supply pose a major challenge that requires innovative solutions to regain the balance in the supply chain.
From a business perspective, this problem is vital to electricity providers as their core business is changing. Since the technology to generate and store electricity at one’s home is becoming affordable, more people are adopting it and the value proposition for being connected to the grid is no longer just about energy, but about service reliability and power quality that the grid can provide. Such a situation creates a new business opportunity where electricity providers can charge for the reliable service offered, not the energy itself. For example, charge customers’ batteries when wholesale prices are low, or discharge them when prices are high; schedule energy intensive devices and/or operations at times when demand is low; and support local grids when they are stressed. The new business model should revolve around managing the behind-the-meter assets, not managing the generation assets.
Demand Side Management (DSM) enables the adjustment of the loads in the grid to ensure a balanced operation, while simultaneously optimizing the utilization of the resources in the electrical power system. It also provides an opportunity for energy consumers to function as providers, energy storage buffers, and demand pattern regulators. Today’s DSM systems are limited to local energy grids, and the load-balancing solutions within the local grid itself. A larger rollout of the same idea can be achieved by utilizing mathematical planning and machine learning methods. e-INDEX proposes a more holistic level of data integration and decision making spanning a large scale inter-regional connection. Specifically, we propose three solution avenues. First, channel the capabilities of the suppliers, storage units, and consumer contributions into an inter-regional ecosystem instead of separated local ones. Second, investigate novel aspects of non-stationary storage resources, where to place them, and how to route them within the network in order to further enhance the stability and balance in the overall system. Third, introduce data models connected to live feeds from the real systems in order to create a digital twin with an AI-supported planning and load-balancing tool.