AI Agents Cut Prosumer Energy Costs 37% in Local Market Simulation
Smart bidding strategies slash community electricity bills by over a third.
The paper evaluates how autonomous cyber-physical energy systems (prosumer agents) can optimize local electricity markets. The simulation models 33 households equipped with photovoltaic systems, battery storage, electric vehicles, and heat pumps, trading in a uniform-price double-sided call auction at 15-minute intervals across summer, winter, and spring. Four strategies of increasing complexity are compared: a zero-intelligence constrained baseline, a boundary-price strategy, an extended storage cascade, and a market-adaptive pricing strategy.
Results show that smarter resource control dramatically improves outcomes. The extended storage cascade achieved a total community cost of 39.06 EUR compared to 62.38 EUR for the baseline — a 37.4% reduction. Under summer conditions, the market-adaptive strategy delivered the highest aggregate financial gain from local market participation: 14.40 EUR vs 10.28 EUR, a 40.1% increase. The study also highlights that strategy effectiveness depends on both portfolio composition and seasonal supply conditions, emphasizing the need for joint evaluation of resource control and pricing decisions.
- Extended storage cascade strategy reduced total community energy costs by 37.4% (from 62.38 EUR to 39.06 EUR).
- Market-adaptive strategy increased aggregate financial gain by 40.1% in summer (14.40 EUR vs 10.28 EUR).
- Simulation ran 33 prosumer agents with PV, battery, EV, and heat pump assets at 15-minute resolution across three seasons.
Why It Matters
AI-driven bidding strategies can cut energy bills and boost local grid efficiency for prosumer communities.