Brief Description

• The Vehicle-to-Grid Kit from Accenture optimizes electric vehicle feed-in to and charging from an energy network to shift demand peaks to times with lower market prices. 
• The usage of V2G is implemented as a so-called Feed-in Reward Program (FiRP): the utility provider offers (participating) EV users a reward price to incentivize them to feed into the energy grid at specific times. The utility provider can control the amount of feed-in energy depending on the prices. The optimal reward prices for this demo maximize the utility provider’s profit. Finding them is the goal of a complex optimization problem that can be solved with classical, quantum, and quantum-inspired methods.

Problem Statement

With increasing regenerative power generation, adapting the energy supply to the demand becomes increasingly difficult. This results in network instabilities and utility provider losses due to expensive energy and fluctuating market prices. Vehicle-to-grid (V2G) technology allows electric vehicles (EVs) to be connected to the power grid and send and receive electricity. V2G can help to balance the grid by enabling EVs to provide power back to it when demand is high and to charge their batteries when demand is low. It also allows energy providers to optimize their profits by shifting energy loads to times of cheaper market energy. Additionally, V2G can help increase EVs' utilization and value by allowing them to be used as a distributed energy storage. This can also help reduce the need for expensive, centralized energy storage solutions and promote using renewable energy sources.

Key Highlights

• Leveraging real-world data from the German energy market and user profiles based on a scientific study.
• Based on real-world German market data, the capability estimates a substantial profit gain of up to €2B in 2022.
• The app's custom optimization framework offers various backends and algorithms, some showcased in this demo. The full version can access/includes, e.g., Operator-Splitting Quadratic Program (OSQP) solver, Covariance Matrix Adaption Evolution Strategy (CMA-ES) algorithm, Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing and Quantum-inspired Algorithms (Microsoft QIO)

Disclaimer

This Demo is for demonstration purposes only and not for any other intent. Accenture owns all rights, titles, and interests in this Demo. Accenture makes no representations or warranties regarding this Demo. To the extent permitted by law, Accenture disclaims all liability, including direct and indirect, incidental or consequential damages, including loss of profits, arising out of or relating to the use of this Demo. The laws designated by the Accenture entity providing this Demo will govern without referencing conflicts of law principles.