Optimized Dispatch of Electricity-Hydrogen Integrated Energy System Considering Chinese Certified Emission Reduction, Green Certificate, and Green Electricity Trading
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摘要:
针对"双碳"目标下综合能源系统在电碳多元市场机制协同下经济与减排效益不明确的问题,本文面向含氢能子系统的综合能源系统,提出了一种融合CCER核证减排量、绿色电力证书与绿电交易的多元市场协同优化调度方法. 首先,构建电-氢综合能源系统架构,建立各设备的数学模型并深入分析了系统内部能量流与碳流关系;其次,考虑系统可再生能源的内部消纳与外部供应,基于电-碳-氢耦合特性量化了系统实际核证减排量,提出了系统参与CCER、绿证与绿电多元市场的协同交易方法;基于全生命周期评估方法,建立了涵盖设备生产、运输建设、运行投产和退役处理全阶段的碳排放核算模型;最后,以系统运行成本最小为优化目标,构建了考虑阶梯式碳交易成本的综合能源系统优化调度模型,采用混合整数线性规划方法求解,并通过算例验证了所提方法的有效性. 研究结果表明:多元化市场交易实现了激励效应叠加,系统总成本较基准场景降低19.38%,达成了系统“降本-减排-消纳”的协同优化.
Abstract:Objective Under the carbon peaking and carbon neutrality goals, integrated energy systems (IESs) cut regional carbon emissions through multi-energy complementarity and cascaded utilization, yet their combined economic and emission reduction benefits stay unclear when several electricity-carbon market mechanisms operate jointly. Existing models often treat carbon capture and power-to-gas (P2G) as independent modules and overlook the bridging role of hydrogen, so the synergistic mitigation potential of electricity-carbon-hydrogen coupling stays underused. Moreover, most carbon-trading studies count only direct operational emissions and thus misjudge the real reduction under full-life-cycle accounting. The green certificate (GC), green electricity (GE), and Chinese certified emission reduction (CCER) mechanisms have mostly been studied independently, and their interaction in IES dispatch has seldom been examined. The objective was to quantify and coordinate the economic and low-carbon performance of an electricity-hydrogen IES that participates simultaneously in the CCER, GC, and GE markets under life-cycle carbon accounting.
Method An electricity-hydrogen IES architecture was established, and mathematical models were built for the wind turbine (WT), the photovoltaic array (PV), the methane reactor (MR), the carbon capture, utilization and storage (CCUS) unit, the electrolyzer (EL), the hydrogen fuel cell (HFC), the hydrogen storage tank (HST), and other energy-conversion and energy-storage devices. Electricity-carbon-hydrogen coupling was achieved through water electrolysis and CO2 methanation, which formed a carbon capture, hydrogen conversion, and gas utilization cycle. The actual certified emission reduction was quantified from internally consumed renewable generation, directly stored CO2, and CO2 converted in the methane reactor. Under the principle of unique environmental rights certification, a synergistic trading method was formulated for the CCER, GC, and GE markets, and a price-coupling coefficient was introduced to merge the three market signals into a single marginal criterion for renewable allocation. A life-cycle-assessment (LCA) carbon-accounting model was developed for equipment production, transportation and construction, operation, commissioning, and decommissioning, in which a carbon-intensity-per-unit-electricity (CIUE) index was defined to unify device-level emissions. A tiered carbon-trading cost was embedded, and a dispatch model that minimizes the total cost, namely annualized investment, operation and maintenance, energy purchase, carbon, GC-trading, and CCER costs, was constructed. The mixed-integer linear programming model was solved by the GUROBI solver through YALMIP in Matlab. A typical-day case of an industrial park in southwest China supplied the inputs, with measured wind speed, solar irradiance, and ambient temperature, and five scenarios were compared: a conventional IES; the same system with a hydrogen subsystem; the hydrogen system further combined with GC and GE, with CCER, and with all three mechanisms together.
Result The hydrogen subsystem builds an electricity-hydrogen-methane path that consumes captured CO2; relative to the conventional system, it lowers total carbon emissions by 10.53%, carbon trading cost by 16.32%, and gas-purchase cost by 3.74%, raises operation and maintenance cost by 43.63%, and reduces total cost by 2.14%. Under the GC and GE mechanism, the value of self-consumed renewable power, including certificate revenue plus avoided purchase and lower carbon-trading cost, exceeds the revenue from electricity sales, so on-site sales fall by 37.89%, with renewable curtailment of 1 702.17 kWh; the carbon-capture rate stays at 75.32%, as end-of-pipe capture receives little direct incentive. The CCER mechanism shifts the strategy from source-side cleaning to coordinated source-and-end abatement, cuts carbon-trading cost by 51.15%, raises the capture rate to 79.40%, and lowers curtailment to 1 123.81 kWh. The combination of all three mechanisms yields the lowest total cost, 39 375.96 yuan, 19.38% below the baseline scenario; stronger hydrogen-production economics push the methane reactor and the capture unit to high-intensity operation, raise the capture rate to 83.91% and captured CO2 to 38 354.28 kg, hold the net traded carbon to 6 828.04 kg despite the largest CO2 generation of 45 708.78 kg, and achieve zero renewable curtailment. The three mechanisms couple through the renewable-allocation constraint rather than act as independent additive incentives.
Conclusion The hydrogen subsystem links carbon capture with gas supply and turns captured CO2 into a usable carbon source, so the electricity-carbon-hydrogen coupling improves both economy and emissions over a conventional IES. The CCER, GC, and GE mechanisms act on dispatch through a shared renewable-allocation constraint and produce synergistic amplification rather than a linear sum of separate incentives. Coordinated participation in the three markets achieves the joint optimization of cost reduction, emission mitigation, and renewable accommodation and provides a reference for the planning and operation of IESs in industrial parks.
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表 1 场景设置
Table 1. Setting of scenarios
场景 考虑
氢能考虑GC
和GE考虑CCER GC、GE和
CCER交互1 × × × × 2 √ × × × 3 √ √ × × 4 √ × √ × 5 √ √ √ √ 表 2 场景1、2成本对比分析
Table 2. Cost analysis for scenarios 1 and 2
元 成本 场景1 场景2 运维成本 3587.99 5153.37 燃煤成本 19800.33 17855.82 碳封存成本 1350.22 1541.38 碳交易成本 7702.47 6445.25 购电成本 5724.92 5448.61 售电成本 − 6427.90 − 5103.98 购气成本 18172.59 17492.64 总成本/元 49910.63 48843.09 表 3 场景1、2碳排放对比分析
Table 3. Comparative analysis of carbon emissions for scenarios 1 and 2
kg 项目 场景1 场景2 碳排放总量 39795.62 35604.87 碳交易净值 25674.91 21484.16 表 4 场景3、4、5成本对比分析
Table 4. Comparative analysis of cost for scenarios 3, 4, and 5
元 成本 场景3 场景4 场景5 运维成本 5134.10 5004.37 5133.44 燃煤成本 15217.04 17429.14 16233.03 碳封存成本 1480.43 1465.58 1615.54 碳交易成本 3460.10 3148.41 2911.62 购电成本 5410.80 5509.01 5685.22 售电成本 − 3170.10 − 2724.08 − 4371.90 购气成本 15933.92 14938.12 15338.70 绿证成本 −842.54 0 −949.71 CCER成本 0 − 1446.39 − 2219.98 总成本 42623.78 43024.16 39375.96 表 5 场景3、4、5碳捕集效益对比
Table 5. Comparison of carbon capture benefits for scenarios 3, 4, and 5
项目 场景3 场景4 场景5 总发电量/(kW•h) 39135.80 41497.96 45792.92 净发电量/(kW•h) 11523.04 8996.72 12176.31 碳捕集耗电/(kW•h) 28612.77 32501.24 33616.61 CO2生成量/kg 39098.52 42327.92 45708.78 CO2捕获量/kg 29449.01 33608.65 38354.28 碳捕集率/% 75.32 79.40 83.91 表 6 场景3、4、5碳排放对比分析
Table 6. Comparison of carbon emissions for scenarios 3, 4, and 5
项目 场景3 场景4 场景5 碳排放总量/kg 25654.39 23698.96 20948.78 碳交易净值/kg 11533.65 9578.22 6828.04 -
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