TY - JOUR
T1 - Deep Reinforcement Learning for Economic Dispatch of Virtual Power Plant in Internet of Energy
AU - Lin, Lin
AU - Guan, Xin
AU - Peng, Yu
AU - Wang, Ning
AU - Maharjan, Sabita
AU - Ohtsuki, Tomoaki
N1 - Funding Information:
Manuscript received September 30, 2019; revised December 18, 2019; accepted December 27, 2019. Date of publication January 13, 2020; date of current version July 10, 2020. This work was supported by the Science and Technology Projects of State Grid Corporation of China under Grant SGHL0000DKJS1900883. (Corresponding author: Xin Guan.) Lin Lin and Xin Guan are with the School of Data Science and Technology, Heilongjiang University, Harbin 150080, China (e-mail: ll.linlin@hotmail.com; guanxin.hlju@gmail.com).
Publisher Copyright:
© 2014 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - With the high penetration of large-scale distributed renewable energy generation, the power system is facing enormous challenges in terms of the inherent uncertainty of power generation of renewable energy resources. In this regard, virtual power plants (VPPs) can play a crucial role in integrating a large number of distributed generation units (DGs) more effectively to improve the stability of the power systems. Due to the uncertainty and nonlinear characteristics of DGs, reliable economic dispatch in VPPs requires timely and reliable communication between DGs, and between the generation side and the load side. The online economic dispatch optimizes the cost of VPPs. In this article, we propose a deep reinforcement learning (DRL) algorithm for the optimal online economic dispatch strategy in VPPs. By utilizing DRL, our proposed algorithm reduced the computational complexity while also incorporating large and continuous state space due to the stochastic characteristics of distributed power generation. We further design an edge computing framework to handle the stochastic and large-state space characteristics of VPPs. The DRL-based real-time economic dispatch algorithm is executed online. We utilize real meteorological and load data to analyze and validate the performance of our proposed algorithm. The experimental results show that our proposed DRL-based algorithm can successfully learn the characteristics of DGs and industrial user demands. It can learn to choose actions to minimize the cost of VPPs. Compared with the deterministic policy gradient algorithm and DDPG, our proposed method has lower time complexity.
AB - With the high penetration of large-scale distributed renewable energy generation, the power system is facing enormous challenges in terms of the inherent uncertainty of power generation of renewable energy resources. In this regard, virtual power plants (VPPs) can play a crucial role in integrating a large number of distributed generation units (DGs) more effectively to improve the stability of the power systems. Due to the uncertainty and nonlinear characteristics of DGs, reliable economic dispatch in VPPs requires timely and reliable communication between DGs, and between the generation side and the load side. The online economic dispatch optimizes the cost of VPPs. In this article, we propose a deep reinforcement learning (DRL) algorithm for the optimal online economic dispatch strategy in VPPs. By utilizing DRL, our proposed algorithm reduced the computational complexity while also incorporating large and continuous state space due to the stochastic characteristics of distributed power generation. We further design an edge computing framework to handle the stochastic and large-state space characteristics of VPPs. The DRL-based real-time economic dispatch algorithm is executed online. We utilize real meteorological and load data to analyze and validate the performance of our proposed algorithm. The experimental results show that our proposed DRL-based algorithm can successfully learn the characteristics of DGs and industrial user demands. It can learn to choose actions to minimize the cost of VPPs. Compared with the deterministic policy gradient algorithm and DDPG, our proposed method has lower time complexity.
KW - Deep reinforcement learning (DRL)
KW - distributed generation
KW - economic dispatch
KW - edge computing
KW - virtual power plants (VPPs)
UR - http://www.scopus.com/inward/record.url?scp=85089302935&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089302935&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2020.2966232
DO - 10.1109/JIOT.2020.2966232
M3 - Article
AN - SCOPUS:85089302935
VL - 7
SP - 6288
EP - 6301
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
SN - 2327-4662
IS - 7
M1 - 8957677
ER -