PDF(3307 KB)
PDF(3307 KB)
PDF(3307 KB)
基于ADMM-GBS的考虑风光不确定性的智能电网实时电价策略
Real-Time Price Strategy for Smart Grid Considering Wind and Solar Power Uncertainty Based on ADMM-GBS
实时电价是智能电网需求侧管理的有效方法,对于维持电力供需平衡、削峰填谷至关重要。为提高实时电价模型的低碳经济性和精确性,在充分考虑用户与供电侧双方利益前提下提出碳交易机制,并根据新能源的发电特性构建风光出力不确定模型,建立以用户总效用最大、供电侧成本最小为目标的社会福利最大化实时电价模型。提出基于改进交替方向乘子法(alternating direction method of multiplier, ADMM),即高斯回代交替方向乘子法(ADMM with Gaussian back substitution, ADMM-GBS)的分布式优化调度方法,通过将不确定模型转化为确定模型求解。仿真结果表明,所提实时电价策略能够提升社会福利,验证了模型和算法的有效性。
Real-time price is an effective method for demand side management in smart grids, which is crucial for maintaining power supply and demand balance and peak shaving and valley filling. In order to improve the low-carbon economy and accuracy of the real-time price model, and fully consider the interests of both users and the power supply side, the paper proposes a carbon trading mechanism and constructs an uncertain model of wind and solar output based on the power generation characteristics of new energy. A real-time price model for maximizing social welfare is established with the goal of maximizing the total utility of users and minimizing the cost of power supply. A distributed optimization scheduling method is proposed based on the improved alternating direction method of multiplier (ADMM), namely ADMM with Gaussian back substitution (ADMM-GBS). The method solves the problem by transforming the uncertain model into a deterministic model. The simulation results show that the proposed real-time price strategy can improve social welfare, which verifies the effectiveness of the proposed model and algorithm.
智能电网 / 需求侧管理 / 实时电价 / 新能源发电 / 交替方向乘子法(ADMM)
smart grid / demand-side management / real-time price / new energy generation / alternating direction method of multiplier (ADMM)
| [1] |
高岩. 基于需求侧管理实时电价优化方法综述[J]. 上海理工大学学报,2022, 44(2): 103-111, 121.
|
| [2] |
|
| [3] |
徐健玮,马刚,高丛,等. 基于风光场景生成的综合能源系统日前-日内优化调度[J]. 分布式能源,2022, 7(4): 18-27.
|
| [4] |
|
| [5] |
杨军伟,杜露露,刘夏,等. 高风电渗透率下考虑需求侧管理策略的智能微电网调度方法[J]. 智慧电力,2021, 49(3): 32-39.
|
| [6] |
王书峰,钟明,许贤泽,等. 计及需求侧管理的新能源微电网多目标优化调度方法[J]. 智慧电力,2022, 50(12): 55-62, 69.
|
| [7] |
高岩. 智能电网实时电价社会福利最大化模型的研究[J]. 中国管理科学,2020, 28(10): 201-209.
|
| [8] |
李军祥,王宇倩,孙权,等. 电力零售商引导下智能电网多方利益均衡策略[J]. 运筹与管理,2022, 31(2): 209-215.
|
| [9] |
|
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
张衡,张沈习,程浩忠,等. Stackelberg博弈在电力市场中的应用研究综述[J]. 电工技术学报,2022, 37(13): 3250-3262.
|
| [14] |
张涛,杨建华,靳开元,等. 基于Stackelberg博弈的配电网分布式光伏低碳化消纳方法[J]. 电力自动化设备,2023, 43(1): 48-54, 63.
|
| [15] |
代业明,齐尧,高红伟,等. 基于PMSC管理及奖惩机制的智能电网实时电价研究[J]. 中国管理科学,2022, 30(7): 88-98.
|
| [16] |
|
| [17] |
王菁祺,高岩,吴志强,等. 计及负荷不确定性的强化学习实时电价策略[J]. 计算机应用研究,2022, 39(9): 2640-2646, 2659.
|
| [18] |
|
| [19] |
李军祥,潘婷婷,高岩. 智能电网互补能源供用电实时电价算法研究[J]. 计算机应用研究,2020, 37(4): 1092-1096.
|
| [20] |
|
| [21] |
|
| [22] |
姚文亮,王成福,赵雨菲,等. 不确定性环境下基于合作博弈的综合能源系统分布式优化[J]. 电力系统自动化,2022, 46(20): 43-53.
|
| [23] |
|
| [24] |
|
| [25] |
|
| [26] |
陈健,张维桐,林达,等. 基于改进交替方向乘子法的电-气-热系统分布式优化调度[J]. 电力系统自动化,2019, 43(7): 50-58.
|
| [27] |
米阳,赵海辉,付起欣,等. 考虑风光不确定与碳交易的区域综合能源系统双层博弈优化运行[J]. 电网技术,2023, 47(6): 2174-2188.
|
| [28] |
赵泽明,刘敏. 考虑碳捕集技术的虚拟电厂热电联合优化[J]. 分布式能源,2023, 8(1): 30-38.
|
/
| 〈 |
|
〉 |