Research on Economic Feasibility of Multi-Energy Complementary Systems in Medium- and Long-Term Markets

doi:10.16628/j.cnki.2095-8188.2025.07.009

Abstract

Abstract:

Driven by the “Dual Carbon” goals,the large-scale development of renewable energy faces integration challenges due to its inherent intermittency and volatility.To enhance system flexibility and promote renewable energy accommodation, the economic and stability advantages of multi-energy complementary systems in electricity markets are investigated, guided by China’s national policy on “Integrated Source-Grid-Load-Storage and Multi-Energy Complementarity.” By incorporating electricity market rules and multi-energy complementarity characteristics, a medium- and long-term market-clearing model is established. Using nonlinear programming theory and a particle swarm optimization algorithm, simulations are conducted to solve the bidding strategies of power generation and large consumers’ electricity purchase decisions.Through market-clearing result analysis and policy adaptability research, the superiority of multi-energy complementary systems is validated in terms of economic benefits, market resilience, and cost structure, which can provide theoretical support for market-based renewable energy integration.

Key words: medium- and long-term markets, multi-energy complementary system, renewable energy consumption, power system flexibility

CLC Number:

TM73

CHEN Yeshuai, BIAN Guoliang. Research on Economic Feasibility of Multi-Energy Complementary Systems in Medium- and Long-Term Markets[J]. LOW VOLTAGE APPARATUS, 2025, 0(7): 59-70.

Figures/Tables 21

机组	SO₂/ (元·当量^-1)	NO_x/ (元·当量^-1)	烟尘/ (元·当量^-1)	$A —$ / (元·MWh^-1)	$A —$ / (元·MWh^-1)	λ/ (元·MWh^-2)	ρ_coal/ (元·kg^-1)	B_i/ (kg·MWh^-1)
多能互补系统	2.4	2.4	1.2	562.35	449.88	0.205 7	1.3	791
600 MW火电-1	2.4	2.4	1.2	562.35	449.88	0.384 5	1.3	791
600 MW火电-2	2.4	2.4	1.2	562.35	449.88	0.419 9	1.3	791
1 000 MW火电	2.4	2.4	1.2	562.35	449.88	0.290 7	1.3	791

References 22

[1]	HU S, XIANG Y, LIU J, et al. Agent-based coordinated operation strategy for active distribution network with distributed energy resources[J]. IEEE Transactions on Industry Applications, 2019, 55:3310-3320.
[2]	ZHANG M M, YANG Z K, LIU L Y, et al. Impact of renewable energy investment on carbon emissions in China-An empirical study using a nonparametric additive regression model[J]. Science of the Total Environment, 2021, 785:1-11.
[3]	吕力行, 刘骅, 徐雷, 等. 基于自动调价公式的区块链储能系统安全交易机制的研究[J]. 电器与能效管理技术, 2021(11):30-35.
[4]	YANG B, ZHAO Y, GAN Z, et al. Coordinated control strategy of source-load-storage considering the regulatory ability in market environment[C]// 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), 2020:366-371.
[5]	LUKAS S, ENRIQUE L, LUIS R. Energy storage systems providing primary reserve and peak shaving in small isolated power systems:An economic assessment[J]. International Journal of Electrical Power & Energy Systems, 2013, 53:675-683.
[6]	夏耀杰. 考虑不确定因素的虚拟电厂竞价优化策略[J]. 电器与能效管理技术, 2022(9):45-50.
[7]	程光, 徐飞, 胡博, 等. 可再生能源与火电集成耦合的协同性能及组合设计研究[J]. 电网技术, 2021, 45(6):2178-2191.
[8]	李咸善, 胡家旗, 张远航, 等. 风光水储联合体多时间尺度市场化运营调度策略[J/OL]. 中国电机工程学报,1-17.(2025-02-28)[2025-06-12] http://kns.cnki.net/kcms/detail/11.2107.tm.20250228.1604.007.html.
[9]	HE Q, LIN Z, CHEN H, et al. Bi-level optimization based two-stage market clearing model considering guaranteed accommodation of renewable energy generation[J]. Protection and Control of Modern Power Systems, 2022, 7(3):1-13.
[10]	常乃超, 崔晖, 韩彬, 等. 风光储联合发电站参与省间电力市场出清[J]. 电力需求侧管理, 2024, 26(3):82-88.
[11]	赵建平, 胡家华, 李东辉, 等. 储能参与下的中长期电力市场合约转让机制灵活性研究[J]. 热力发电, 2021, 50(8):18-23.
[12]	王艺舒, 蔡国伟, 杨德友, 等. 考虑小干扰稳定性的风火打捆配比优化[J]. 电力系统及其自动化学报, 2021, 33:131-137.
[13]	李娟, 李方媛, 王鹏, 等. 限流式SSSC提高DFIG型风火打捆系统暂态稳定分析[J]. 电力系统及其自动化学报, 2021, 33(10):68-76.
[14]	MEHDIPOURPICHA H, BO R. Optimal bidding strategy for physical market participants with virtual bidding capability in day-ahead electricity markets[J]. IEEE Access, 2021, 9:85392-85402.
[15]	TANG W, YANG H T. Optimal operation and bidding strategy of a virtual power plant integrated with energy storage systems and elasticity demand response[J]. IEEE Access, 2019, 7:79798-79809.
[16]	XIAO D, ALASHERY M K, QIAO W. Optimal price-maker trading strategy of wind power producer using virtual bidding[J]. Journal of Modern Power Systems and Clean Energy, 2022, 10(3):766-778.
[17]	李知远, 张翼飞, 张政, 等. 基于多代理决策模型的电力市场月度集中竞价博弈分析[J]. 电气自动化, 2022, 44(5):50-52.
[18]	姜涛, 周慧娟, 张启蒙, 等. 基于多源热泵协同优化的区域综合能源系统经济调度[J]. 电器与能效管理技术, 2024(4):13-21.
[19]	赵凤展, 李奇, 张启承, 等. 考虑多能互补供应特性的综合能源系统经济优化调度[J]. 电器与能效管理技术, 2021(9):45-50.
[20]	MENG Y, CAO T, LI J, et al. The real cost of deep peak shaving for renewable energy accommodation in coal-fired power plants:Calculation framework and case study in China[J]. Journal of Cleaner Production, 2022, 367:132913.
[21]	赵凤展, 李奇, 张启承, 等. 考虑多能互补供应特性的综合能源系统经济优化调度[J]. 电器与能效管理技术, 2021(9):45-50.
[22]	侯婷婷, 方仍存, 王治华, 等. 提升源荷多元调峰主动性的优化调度及费用补偿分摊机制[J]. 高电压技术, 2024, 50(12):5529-5538.

电源类型	现有问题
火电机组	可再生能源的大规模并网和低发电成本挤压了火电企业的市场份额;火电企业低出力情况下的高燃料成本进一步造成了利润空间的压缩
可再生能源	可再生能源出力的间歇性和波动性特征,影响其在中长期市场中的竞争力和合约履行能力,并带来相应的偏差考核费用

交易类型	年度双边交易	集中交易
交易方式	双边协商,价格博弈	集中竞价,边际出清
价格形成	长期稳定	短期波动
市场主导	80%以上电量,市场主导	20%以下电量,辅助调节
适用主体	大用户及稳定电源	中小用户及可再生能源
风险特征	锁定长期价格	价格市场波动

机组	最大出力/MW	最小出力/MW	爬坡速率/ [MW·(15 min)^-1]	耗量参数特性			煤价/ (元·kg^-1)
机组	最大出力/MW	最小出力/MW	爬坡速率/ [MW·(15 min)^-1]	a/(kg·MWh^-2)	b/(kg·MWh^-1)	c/kg	煤价/ (元·kg^-1)
多能互补系统	1 200	360	540	1.71×10^-4	-0.389 7	853.947 0	1.3
600 MW火电-1	600	180	270	3.44×10^-4	-0.385 2	410.496 5	1.3
600 MW火电-2	600	180	270	3.97×10^-4	-0.420 7	414.357 2	1.3
1 000 MW火电	1 000	300	400	9.29×10^-5	-0.178 3	370.121 3	1.3

时间	可再生能源偏差考核基准	可再生能源偏差考核方法
2020—2022	风电预测准确率≥75%;光伏预测准确率≥85%	每低于基准1个百分点,每MW容量扣10元
2023—2024	风电预测曲线的误差带宽为±20%;光伏率预测曲线的误差带宽为±15%;误差带以外的偏差电量为考核电量	误差带以外偏差电量按天计算,按照100元/MWh进行考核

偏差考核	工作日/万元	双休日/万元	节假日/万元
一季度	3.706 3	5.126 3	5.080 1
二季度	2.830 2	3.255 1	2.684 1
三季度	0.450 9	0.996 2	1.503 9
四季度	3.607 6	4.467 4	3.628 8
总成本	10.595 0	13.845 0	12.896 9