STOCHASTIC DYNAMIC PRICING FOR EV CHARGING STATIONS WITH RENEWABLE ENERGY INTEGRATION AND ENERGY STORAGE

Abstract

This project describes the problem of stochastic dynamic pricing and energy management policy for electric vehicle (EV) charging service providers. In the presence of renewable energy integration and energy storage system, EV charging service providers must deal with multiple uncertainties — charging demand volatility, inherent intermittency of renewable energy generation, and wholesale electricity price fluctuation. The motivation behind our work is to offer guidelines for charging service providers to determine proper charging prices and manage electricity to balance the competing objectives of improving profitability, enhancing customer satisfaction, and reducing impact on power grid in spite of these uncertainties. A new metric to assess the impact on power grid without solving complete power flow equations was proposed. To protect service providers from severe financial losses, a safeguard of profit is incorporated in the model. Two algorithms — stochastic dynamic programming (SDP) algorithm and greedy algorithm (benchmark algorithm) — are applied to derive the pricing and electricity procurement policy. A Pareto front of the multi objective optimization is derived. Simulation results show that using SDP algorithm can achieve up to 7% profit gain over using greedy algorithm. Additionally, we observe that the charging service provider is able to reshape spatial-temporal charging demands to reduce the impact on power grid via pricing signals.

Existing System

In the existing system, Game theory based approaches have been used to model the interplay among multiple EVs or between EVs and power grid was used.

Proposed System

In the Proposed system, a multi-objective optimization framework to solve the problem, and the solutions provide us insights into how to make a tradeoff among multiple objectives of the profitability, the customer satisfaction, and impact on power grid, and offer guidance to set charging prices to balance the charging demand across the power system was proposed. Newton’s method to derive a fast-computing metric to assess the impact of EV charging on power grid, which frees us from solving the complete nonlinear power flow equations was used. This metric also can be used to analyze other electric load’s impact on power grid. The active power and reactive power sensitivities for the load buses in a power system which can serve as a guideline for EV charging station placement to alleviate the charging stress on the power grid. In terms of market risk, we introduced a safeguard of profit for EV charging service providers, which raises a warning when the profit is likely to reach a dangerous threshold. This mechanism is beneficial for the charging service provider to safely manage its capital and avoid severe financial losses.

Architecture

Business Model of EV Charging Service Provider