Dynamic Electricity Pricing Optimization Driven by Demand Response: Evidence from a Price–Load–Dispatch Coupling Model Using IPSO

High renewable penetration creates spatiotemporal mismatches between intermittent generation and rigid demand, making market-oriented electricity pricing critical for activating demand-side flexibility. Hypothesizing that state-contingent price adjustment, rather than temporal granularity alone, determines regulation efficiency, this study develops a unified price–load–dispatch coupling framework in which price, demand response and dispatch are solved jointly as a damped fixed point and compares four pricing mechanisms: regulated time-of-use (TOU) pricing, TOU–spot market coordination, real-time pricing (RTP), and a novel renewable-linked adaptive five-segment pricing (RL-AFSP) scheme. Using Shandong Province, China, a representative high-renewable-penetration region, as the case study, a customized improved particle swarm optimization (IPSO) algorithm quantifies each mechanism over 30 randomly drawn typical-day scenarios under a daily-energy-conserving demand-response model, with the tariff float ratios and time blocks taken from the official provincial schedule. Utilization is not monotone in price granularity: it rises from 86.33% under fixed TOU to 87.06% under TOU–spot coordination, falls back to 86.88% under RTP, and reaches its maximum of 88.19% under RL-AFSP, whose operating cost of 186.56 million CNY/day is also the lowest of the four (against 194.00 under fixed TOU). Real-time pricing therefore buys no technical advantage: RTP combines the widest intraday price swings (price coefficient of variation 81.7%), the highest revenue volatility (9.64%), the lowest daily profit (295.79 million CNY) and a price–load feedback that fails to converge within the 200-iteration cap. RL-AFSP converges in seven iterations and pairs the best technical performance with revenue volatility of 2.57% and the highest daily profit (575.68 million CNY). Against the static TOU benchmark its utilization and cost improvements are statistically significant (Holm-adjusted p = 1.86 × 10−8 and 1.68 × 10−8), and its advantage over RTP is robust to the price-sensitivity parameters, the adjustment band and demand elasticities from −0.15 to −0.40; at the weakest elasticity tested (−0.10) RTP attains a marginally lower cost, the single exception in the sweep, and is reported as such. Component ablation attributes the gain to state-contingent adjustment rather than to the bound itself, which instead guarantees convergence and limits revenue volatility. These simulation findings suggest that RL-AFSP is a candidate mechanism reconciling efficiency, economy and stability, offering policy-relevant insights for electricity pricing reform and source–grid–load–storage coordination, while requiring pilot validation before large-scale implementation.