Quantifying and Monetizing Demand-Side Potential at the Grid Edge: Methods for Aggregation, Bidding and Retail Optimization

This comprehensive review maps how China’s demand-side resources are aggregated, bid into markets, and monetized at the grid edge. We synthesize original studies and pilots to compare edge architectures for local estimation and privacy-preserving coordination, bidding frameworks that span deterministic, stochastic, chance-constrained, and robust designs, and retailer plan optimization that turns wholesale signals into simple user choices. Our headlined findings are fourfold. First, risk-aware bidding frameworks that use chance constraints or conditional value at risk (CVaR) reduce shortfalls without eroding expected revenue when penalties are strict and data are noisy. Second, joint design of retail prices with storage dispatch stabilizes delivery and consumer bills, raising participation and persistence. Third, intraday refresh of envelopes and redispatch improves balance and profit when provincial rules allow updates. Fourth, transparent baselines and settlement rules determine realized value and should be co-designed with aggregation and pricing. We organize reproducible metrics for revenue, reliability, latency, and consumer welfare, and provide simulation templates aligned with Chinese spot practice to enable head-to-head comparisons. The review closes with a research agenda on correlation modeling for heterogeneous portfolios, distribution-aware coordination, and long-run equipment impacts as areas where larger field trials and open data would unlock credible evaluation and faster deployment in China.