New Market Model Shows How to Squeeze More Wind and Solar Into the Grid

China’s power system has just crossed a historic threshold. By the end of 2025, the combined installed capacity of wind and solar photovoltaics exceeded 1,840 gigawatts, accounting for 47 percent of total power-generation capacity and, for the first time, overtaking coal-fired capacity. Yet the same year exposed the Achilles heel of this renewable boom: the national renewable-energy utilization rate slipped below 95 percent, roughly two percentage points lower than the previous year, while combined wind and solar curtailment climbed above 5 percent. In western provinces the problem is far more severe, with photovoltaic curtailment reaching 35.1 percent in Tibet, 16.6 percent in Qinghai, 13.7 percent in Xinjiang, and 10.4 percent in Gansu. A new study published in Energy Reports argues that the missing piece is not more hardware alone, but a smarter market-clearing mechanism that treats electricity, heat, carbon, and grid exchange as one tightly coupled optimization problem.

The research, led by Chen Mingyuan, Yang Youhui, Zheng Wenbin, Li Huayuan, Xuan Peizheng, and Peng Chaoyi, tackles a structural blind spot in existing electricity markets. Most day-ahead clearing frameworks were designed around pure electricity systems and do not fully represent the multi-energy coupling that defines modern integrated energy systems, or IES. These systems coordinate the conversion and delivery of electricity, heat, cooling, and natural gas, and their combined heat-and-power units, known as CHP, produce electricity and heat simultaneously from a single fuel input. When a CHP plant must run to satisfy a heat demand, it injects electricity whether or not the grid needs it, squeezing out room for wind and solar. The authors set out to build a day-ahead clearing formulation in which supply bids, coupled energy balances, carbon costs, cross-border grid transactions, and shiftable demand all appear inside one transparent mathematical program.

Technically, the model is a linear program solved over 96 quarter-hour intervals, covering a full day at 15-minute resolution. The objective function minimizes the sum of operating costs and monetized direct carbon emissions. Operating costs include accepted generation bids from wind, photovoltaic, CHP, and gas-boiler resources, net grid purchases and sales, and the cost of shifting demand between intervals. Carbon costs are computed from output-based emission factors: 0.400 kilograms of CO2 per kilowatt-hour for CHP electricity and 0.202 kilograms per kilowatt-hour for useful boiler heat, multiplied by the prevailing carbon price. The constraint system enforces an electricity balance and a heat balance in every interval, a fixed heat-to-power ratio for the CHP unit, rated-capacity and ramp-rate limits for both the CHP plant and the gas boiler, forecast-based availability ceilings on wind and solar, tie-line import and export limits, and an energy-neutral demand-shifting product in which total shifted-in energy must equal total shifted-out energy over the day.

One elegant modeling choice deserves attention. In many market-clearing formulations, preventing simultaneous buying and selling of grid power requires a binary variable, which pushes the problem into the harder mixed-integer domain. The authors show that because the buying price exceeds the selling price in every interval of their case, simultaneous purchase and sale can never be optimal: canceling the common component strictly reduces cost while preserving all physical constraints. The optimum therefore satisfies the mutual-exclusivity condition automatically, keeping the entire model affine and solvable with standard linear-programming tools. The implementation uses Python with SciPy’s linear-programming interface and the HiGHS solver, and every solution is verified through equality residuals, inequality slacks, and bound-violation checks before results are accepted.

The numerical case draws on real public data. Hourly German electricity load, wind generation, photovoltaic generation, and day-ahead prices for 15 November 2018 come from the Open Power System Data package, whose upstream sources include ENTSO-E and German transmission-system operators. The heat-demand shape comes from the When2Heat German total-heat-demand series. These profiles are scaled to a park-level system with a peak electricity demand of 2,400 kilowatts, maximum available wind power of 1,700 kilowatts, maximum photovoltaic power of 900 kilowatts, and peak heat demand of 2,300 kilowatts. The CHP unit ranges up to 2,000 kilowatts of electrical output with a base heat-to-power ratio of 1.2, the gas boiler up to 2,600 kilowatts of heat, and the reference carbon price of 505 yuan per tonne of CO2 is converted from the European Commission’s reported 2024 EU Emissions Trading System average auction price of 64.74 euros per tonne.

The headline results are striking in their precision. In the baseline scenario, with a 350-kilowatt tie-line and a 1,000 kilowatt-hour daily shiftable-energy limit, the curtailment rate is exactly 12.70 percent. Doubling the shiftable-energy limit to 2,000 kilowatt-hours per day cuts curtailment to 9.96 percent, a 2.74 percentage-point improvement, because more demand can be pushed into renewable-rich hours. But the most dramatic effect comes from spatial flexibility: raising the tie-line capacity from 350 to 800 kilowatts eliminates curtailment entirely, achieving 100 percent renewable accommodation. In that case, all 26.927 megawatt-hours of available wind and solar energy are accepted, and 11.082 megawatt-hours are exported. The comparison cleanly separates two distinct levers: demand shifting reallocates consumption in time, while tie-line capacity creates spatial transfer capability.

Perhaps the most provocative finding concerns carbon pricing. At the reference price of 505 yuan per tonne, monetizing emissions raises the objective value from 8,843.42 yuan to 15,849.03 yuan, yet the physical dispatch and the 12.70 percent curtailment rate remain completely unchanged. The heat balance and the constrained tie-line bind before the carbon-adjusted merit order can alter technology selection. Only when the carbon price reaches 1,500 yuan per tonne does the system cross a technology-substitution threshold: curtailment falls to zero and emissions decline from 13,872.50 to 13,333.46 kilograms of CO2, dropping further to 12,126.03 kilograms at 2,000 yuan per tonne. The lesson is that a carbon signal, however politically meaningful, has no operational effect unless it is large enough to overcome binding physical constraints and the relative costs of competing technologies.

The study also quantifies how the CHP coupling configuration itself shapes renewable accommodation. Sweeping the prescribed fixed heat-to-power ratio across values of 1.0, 1.2, 1.4, and 1.6 produces curtailment rates of 33.10, 12.70, 4.72, and 1.23 percent respectively. The mechanism is intuitive: for a fixed heat-demand profile, a higher ratio means less CHP electricity is needed to deliver the same thermal output, freeing electrical balancing space for wind and photovoltaic generation. The authors are careful to note that this sweep compares alternative fixed-ratio technology configurations rather than the within-unit operating flexibility of a single plant, but the implication for planners is clear: the choice of CHP coupling parameters is a first-order determinant of how much renewable energy a coupled system can absorb.

Beyond the principal scenarios, the team solved 25 combinations of tie-line capacity and daily shiftable energy to map the joint sensitivity surface, revealing strong complementarity and diminishing returns. With a 200-kilowatt tie-line, no amount of demand shifting beyond 1,000 kilowatt-hours per day lowers curtailment below 23.33 percent, because transfer capacity remains the binding bottleneck. At 350 kilowatts, shifting reduces curtailment from 16.42 to 9.96 percent but cannot eliminate it. Zero curtailment is achieved only at sufficient combinations: 650 kilowatts with at least 1,500 kilowatt-hours of daily shifting, or 800 kilowatts with at least 500 kilowatt-hours. Additional flexibility, in other words, is valuable only when the complementary constraint has headroom.

The practical message for policymakers is that carbon pricing, CHP configuration, demand response, and transmission planning should be evaluated and coordinated jointly rather than as isolated measures. Temporal and spatial flexibility should be assessed together, with demand shifting deployed before transfer capacity becomes the dominant bottleneck and grid expansion prioritized where flexible demand already exists. As China targets 3,600 gigawatts of combined wind and solar capacity by 2030 and a non-fossil share of primary energy above 25 percent, frameworks like this one, which make the physics of coupled energy systems legible to market clearing, may determine whether the next wave of renewable capacity is actually used or simply switched off.

Subject of Research: Low-carbon day-ahead joint electricity-heat market clearing for integrated energy systems with high renewable penetration

Article Title: Low-carbon day-ahead joint market clearing for integrated energy systems with high renewable energy penetration

Article References: Mingyuan, C., Youhui, Y., Wenbin, Z., Huayuan, L., Peizheng, X., & Chaoyi, P. (2026). Low-carbon day-ahead joint market clearing for integrated energy systems with high renewable energy penetration. Energy Reports, 16, Article 109773. https://doi.org/10.1016/j.egyr.2026.109773

Image Credits: AI Generated

DOI: 10.1016/j.egyr.2026.109773

Keywords: integrated energy systems, day-ahead market clearing, renewable curtailment, carbon pricing, combined heat and power, demand response, tie-line capacity, linear programming, wind and solar integration, energy transition, market design, low-carbon dispatch