A Reserve-Based Smart Tariff Mechanism to Combat Local Dynamic Grid Congestion

Abstract

Local grids face growing challenges from sporadic congestion. To address this, distribution system operators (DSOs) require ready-to-use and reliable flexibility that they can directly control. Leveraging consumer flexibility through demand adjustments and energy storage can help but is hindered by multistakeholder complexities, regulatory constraints, and market barriers. Existing solutions, including direct load control and price-based incentives, face privacy concerns, slow response times, and misalignment with real-time congestion needs. This work proposes a novel reserve-based tariff mechanism for local dynamic congestion management that balances the interests of consumers, aggregators, and DSOs. We formulate the problem as a bi-level optimization model and transform it into a single-stage, tractable Mixed-Integer Nonlinear Problem. Numerical results show that with appropriate prices, the proposed program significantly lowers consumer costs, increases aggregator revenues, and enables DSOs to secure demand and supply interruptions for congestion management. It also aligns behind-the-meter operations with congestion needs by prioritizing battery use for demand interruption and solar power for supply interruption and demand reduction. When solar PV adoption is mature but not saturated, the mechanism favors battery investments over additional solar panels. Adding batteries improves consumer cost reductions by 2.5×, aggregator profitability by 10×, and demand interruption flexibility for DSOs by 10×.

Keywords: Bilevel optimization, aggregators, consumers, interruptible tariff