Real-Time Optimal Operation Control of Micro Energy Grid Coupling With Electricity- Thermal -Gas Considering Prosumer Characteristics

Abstract

Under the increasingly stringent environmental regulations, the installed capacity of distributed renewable energy is increasing rapidly. How to fully absorb renewable energy without affecting power grid security and ensuring power quality is the key problem to be solved. The rapid development of energy production, conversion and storage equipment of prosumer makes the operation of micro energy grid possible. Therefore, according to the energy characteristics of different prosumer, this paper divides the micro energy grid into two types: micro energy grid of electricity selling and micro energy grid of energy supplying. The constrained nonlinear optimization method is used to solve the optimization problem of this high-dimensional nonlinear system with time delay. In this paper, the comparative study of optimal operation control between the two kinds of micro energy grid is carried out. In order to deal with the uncertainty of distributed renewable energy output and load forecasting deviation, this paper proposes a real time adaptive dynamic optimization control strategy based on deep learning. The strategy uses deep learning technology to pretrain the action network, so as to learn the optimal operation behavior of micro energy grid. Finally, the online simulation of micro energy grid for consecutive days is used to verify the correctness and real-time performance of the algorithm. INDEX TERMS Micro energy grid, distributed renewable energy, adaptive dynamic optimization, deep learning.

Optimal Dispatch and Control Strategy of Park Micro-Energy Grid in Electricity Market

“…We used the method proposed in [14] to achieve a rolling dispatch for the park microenergy grid in Guangdong Province. The simulation results are shown in Figure 5.…”

“…Zhao et al [13] proposed a control model for grid-connected wind turbines after they are connected to a microgrid, which provides solution ideas for the control of energy units such as wind turbines in microgrids. An et al [14] proposed a real time adaptive dynamic optimization control strategy based on deep learning. Zhang et al [15] developed a robust optimization method to guarantee an optimal and reliable multi-energy supply under the uncertainties, which can achieve high energy utilization efficiency and high operating robustness against the uncertainties.…”

Review of Control Strategies for DC Nano-Grid

“…In the context of the deep coupling of electricity, heat, natural gas and other energy sources, DCNG should consider the impact of comprehensive energy loads (An et al, 2020), while complex physical properties of various energy sources and interacted energy flow in different time scales should also be considered. 3) Future DCNG will integrate high-penetration renewable energy, energy storage devices, and flexible loads.…”

“…Another requirement is an efficient algorithm that can coordinate a large number of DCNGs (An et al, 2020). Since AlphaGo defeated human Go masters, AI algorithms have received extensive attention in various fields.…”

A Cloud-Edge Computing Method for Integrated Electricity-Gas System Dispatch

“…In the research related to integrated energy systems, reference [15] used a deep reinforcement learning (DRL) approach to solve the optimal dispatch problem of an IEGS. Reference [16] proposed a real-time adaptive dynamic optimal control strategy based on deep learning to learn the optimal operation behavior of micro-energy grids to realize the operation control of electricity-thermal-gas energy grids. However, the above reference adopts a centralized calculation and centralized execution approach, and the power system and natural gas system are solved as a whole, without considering the data protection between different systems.…”