Edge intelligence based Economic Dispatch for Virtual Power Plant in 5G Internet of Energy

Abstract Nowadays, with a large of complicated geography of Distributed Energy Sources (DES), how to integrate distributed renewable energy source and reduce the operational costs by Virtual Power Plant (VPP) becomes a mainstream problem in Internet of energy. The traditional method of energy integration and operational cost optimization utilizes the cloud computing technology to centralized control the computational task, which increases the burden of computing. According with the development of information communication technology, such as Internet of Things and 5G, edge computing technology is an effective way to offload computational task to the edge side of 5G networks. Moreover, with the increase of collected data, it becomes a key point to effectively improve the computing power of edge nodes in edge computing. Currently, machine learning is an effective way to process the big data. Based this situation, it leads the combination of machine learning and edge computing. In this paper, the Edge Intelligence (EI) structure is proposed to solve the Economic Dispatch Problem (EDP) in VPP of Internet of Energy. Compared with the traditional edge computing, the proposed EI structure inherits its original features which reduce the burden of cloud computing, and also the proposed EI structure improves the computational power of edge computing. Through the splitting model and deploying the particle model in the terminal, it is facility to real-time control and take the less costs of VPP. Due to the transmission between the splitting models with counterpart, it transmits the part information and gradient information, which effectively reduces the consumption of communication. The proposed method has verified the effectiveness and feasibility through the numerical experiments of real application data sets.

[1]  Jiaxin Li,et al.  Optimal Contract-Based Mechanisms for Online Data Trading Markets , 2019, IEEE Internet of Things Journal.

[2]  Ivana Podnar Žarko,et al.  Edge Computing Architecture for Mobile Crowdsensing , 2018, IEEE Access.

[3]  Zhipeng Cai,et al.  A Private and Efficient Mechanism for Data Uploading in Smart Cyber-Physical Systems , 2020, IEEE Transactions on Network Science and Engineering.

[4]  Kai Strunz,et al.  Wind and Solar Power Integration in Electricity Markets and Distribution Networks Through Service-Centric Virtual Power Plants , 2018, IEEE Transactions on Power Systems.

[5]  Muhammad Ali Imran,et al.  5G Backhaul Challenges and Emerging Research Directions: A Survey , 2016, IEEE Access.

[6]  Min Chen,et al.  Wearable Affective Robot , 2018, IEEE Access.

[7]  Min Chen,et al.  Opportunistic Task Scheduling over Co-Located Clouds in Mobile Environment , 2018, IEEE Transactions on Services Computing.

[8]  David Silver,et al.  Deep Reinforcement Learning with Double Q-Learning , 2015, AAAI.

[9]  Jiguo Yu,et al.  A Differential-Private Framework for Urban Traffic Flows Estimation via Taxi Companies , 2019, IEEE Transactions on Industrial Informatics.

[10]  Victor C. M. Leung,et al.  Cognitive Information Measurements: A New Perspective , 2019, Inf. Sci..

[11]  Yuval Tassa,et al.  Continuous control with deep reinforcement learning , 2015, ICLR.

[12]  Dong Yue,et al.  Economic dispatch of power systems with virtual power plant based interval optimization method , 2016 .

[13]  Yun Liu,et al.  A Robust Distributed Economic Dispatch Strategy of Virtual Power Plant Under Cyber-Attacks , 2018, IEEE Transactions on Industrial Informatics.

[14]  Dusit Niyato,et al.  Offloading in Mobile Cloudlet Systems with Intermittent Connectivity , 2015, IEEE Transactions on Mobile Computing.

[15]  Mahmud Fotuhi-Firuzabad,et al.  Commercial Demand Response Programs in Bidding of a Technical Virtual Power Plant , 2018, IEEE Transactions on Industrial Informatics.

[16]  Xu Chen,et al.  Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing , 2019, Proceedings of the IEEE.