Economic Bidding Strategy of Electric Vehicles in Real-Time Electricity Markets based on Marginal Opportunity Value

TL;DR

The paper tackles real-time EV aggregator bidding by formulating EV flexibility and market dynamics as an MDP, linking inter-temporal opportunity value to a post-decision value function. It delivers fully analytical, closed-form expressions for marginal charging values and discharging costs under both risk-neutral and risk-averse settings, enabling market-compliant stepwise bid curves without heavy computation. Dynamic risk via CVaR is embedded in a time-consistent MDP framework, allowing risk-aware decisions that adapt to evolving price distributions. Training combines short-term forecasts with historical price data, achieving linear-time complexity and solver-free deployment, then validating with Macao EV data and NYISO prices to demonstrate cost reductions, robustness to price volatility, and fast decision times. The approach offers a practical, interpretable, and scalable solution for price-responsive EVA bidding in volatile real-time electricity markets.

Abstract

The participation of electric vehicle (EV) aggregators in real-time electricity markets offers promising revenue opportunities through price-responsive energy arbitrage. A central challenge in economic bidding lies in quantifying the marginal opportunity value of EVs' charging and discharging decisions. This value is implicitly defined and dynamically shaped by uncertainties in electricity prices and availability of EV resources. In this paper, we propose an efficient bidding strategy that enables EV aggregators to generate market-compliant bids based on the underlying marginal value of energy. The approach first formulates the EV aggregator's power scheduling problem as a Markov decision process, linking the opportunity value of energy to the value function. Building on this formulation, we derive the probability distributions of marginal opportunity values across EVs' different energy states under stochastic electricity prices. These are then used to construct closed-form expressions for marginal charging values and discharging costs under both risk-neutral and risk-averse preferences. The resulting expressions support a fully analytical bid construction procedure that transforms marginal valuations into stepwise price-quantity bids without redundant computation. Case studies using real-world EV charging data and market prices demonstrate the effectiveness and adaptability of the proposed strategy.

Figures (7)

• Figure 1: Illustrative example of EVA piecewise bidding.
• Figure 2: Aggregated energy and power boundaries of an EV fleet. In both subplots, $t_{\min }^{\text{a}}$ and $t_{\max }^{\text{d}}$ denote the earliest arrival and latest departure times of all EVs. In subplot (a), ${E^{{\text{max}}}}$, ${E^{{\text{min}}}}$ and ${E^{{\text{need}}}}$ denote the maximum, minimum, and required cumulative energy levels. In subplot (b), ${P^{{\text{max}}}}$ and ${P^{{\text{min}}}}$ are the maximum and minimum allowable charging/discharging power, determined by the physical limits of the EV fleet.
• Figure 3: Illustrative example of EVA bids generation. (a) Post-decision marginal value function $v_t^{\text{post}}(e)$. (b) Feasible energy transition range $[e_t^{\min}, e_t^{\max}]$. (c) Marginal price functions for charging and discharging. (d) Economic bid curves composed of price–quantity pairs.
• Figure 4: The statistical characteristics of EV charging and electricity prices.
• Figure 5: Operational cost comparison under different strategies.

Theorems & Definitions (5)

• Proposition 1
• Proposition 2
• Proposition 3
• Proposition 4
• Proposition 5