Coordinative optimal dispatch of multi-park integrated energy system considering complementary cooling， heating and power and energy storage systems

doi:10.19799/j.cnki.2095-4239.2021.0631

Abstract

Abstract:

The randomness of new energy output and user load makes the optimal dispatching of the integrated energy system full of challenges, and a multi-park integrated energy coordinated optimal dispatching method considering the complementary cooling, heating and power and energy storage systems is proposed. First, the virtual power plant technology was introduced to aggregate the internal resources of the integrated energy system, and the energy storage devices in the park and the energy transmission devices between the parks were considered, and a multi-park integrated energy system structure under the framework of the virtual power plant was constructed; secondly, the thermal power system was established. Internal resource models such as co-supply units, energy storage systems, and cooling and heating pipelines, with the goal of maximizing system revenue, established a coordinated scheduling model for a multi-virtual power plant integrated energy system; finally, a deep reinforcement learning framework was built, and the Q value was migrated The method is introduced into the deep reinforcement learning algorithm. Based on the improved deep deterministic policy gradient algorithm, the optimal scheduling decision is made in the continuous state and action space. The effectiveness of the proposed method is verified through the analysis of calculation examples, and the results show that the coordinated and optimized dispatch of the multi-park integrated energy system based on the improved algorithm can effectively realize the reasonable allocation of resources and the complementary energy supply between the parks, and reduce the operating cost of the system.

Key words: multi-park integrated energy system, energy storage system, Q value migration, deep reinforcement learning, optimal scheduling

CLC Number:

TM 73

Hao LI, Chang LIU, Bo MIAO, Jing ZHANG. Coordinative optimal dispatch of multi-park integrated energy system considering complementary cooling， heating and power and energy storage systems[J]. Energy Storage Science and Technology, 2022, 11(5): 1482-1491.

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设备类型	最小功率/kW	最大功率/kW
热电联供	0	600
燃气锅炉	0	800
余热回收装置	0	750
电锅炉	0	900
电制冷空调	0	1650
吸收式制冷机	0	600
蓄电池	-160	160
热储能	-230	230
热管道	0	600
冷管道	0	600

参数	数值	参数	数值
η^MT	0.35	η^ESC	0.95
α	1.20	η^ESD	0.95
η^HR	0.75	η^TSC	0.90
η^GB	0.75	η^TSD	0.90
η^EB	0.90	SOC^ES，MIN	0.20
η^EC	4.00	SOC^ES，MAX	0.80
η^AC	1.20	SOC^TS，MIN	0.00
η^TF	0.15	SOC^TS，MAX	1.00
η^CF	0.05

参数	数值
评论家网络学习率	0.001
演员网络学习率	0.0001
奖励折扣系数	0.9
经验池容量	30000
小批量采样规模	128
软更新系数	0.01

收益与成本/元	场景1			场景2
收益与成本/元	VPP1	VPP2	VPP3	VPP1	VPP2	VPP3
电力市场	-1005	-736	-677	-890	-627	-574
热电联供	1787	1347	1557	1894	1421	1605
燃气锅炉	348	276	328	269	236	256
中断负荷	466	355	401	466	355	401
总成本	3606	2714	2963	3519	2639	2836
总计	9283			8994

成本/元	QT-CDDPG	DDPG	DQN	遗传算法
最大值	10352	10635	11232	11952
最小值	8132	8214	8533	9071
平均值	9013	9201	9662	10210