Electricity scheduling optimisation based on energy cloud for residential microgrids

Abstract

Nowadays with the development of smart residential microgrid (RMG), the distributed energy storage system (DESS) can help consumers to not only balance generation and consumption but also participate in demand respond. However, the unadjustable capacity of DESS and the lack of energy sharing among users have become the major challenges to the further development of RMG. This paper proposes a novel electricity scheduling architecture based on energy cloud (EC) for RMGs and designs an electricity scheduling optimisation. The EC is used in order to link different end-users and promote coordination. In the proposed EC-based electricity scheduling architecture, the mathematical model for the RMG is provided. Moreover, considering the depreciation cost of battery, the optimisation model is established with the objective of minimising the electricity cost. Compared with the traditional RMG, simulation results show that the proposed strategy can not only allow consumers to adjust their optimal energy storage capacity but also further reduce electricity payment costs. The designed strategy provides a new and effective research perspective for electricity scheduling of RMGs.

Management Challenges and Opportunities for Energy Cloud Development and Diffusion

“…The integration of electrical and information infrastructures is the basis of a cloud-based energy management system [35]. The energy cloud idea is to connect different end-users and promote coordination in excess electricity with the participation of these end-users, which can adjust their optimal storage capacity, according to energy consumption and generation [36]. The users' energy grid is connected to the public distribution network [7] and the automation devices for electrical systems are integrated into communication networks to exchange information between various devices and supervisory systems [6].…”

“…Thus, considering that the basic infrastructure of this layer contains installations for generation, transmission, and distribution of energy, renovation, and automation [6,13] as well as improvements in protection systems [6,31], which are the main technological and economic challenges to overcome. Regarding energy storage systems, the main challenges are the high value of initial investment [36,77] and the short life cycle of batteries [93]. Concerning electric vehicles, three important opportunities for technological development are the optimization of energy consumption by electric vehicles [94], the installation of a greater number of charging points [95], and the addition of renewable energy in these charging stations [96].…”

Research on Optimized Energy Scheduling of Rural Microgrid

“…They can solve various linear or nonlinear, optimization problems. This method is applied to the microgrid dispatching optimization problem [21], which proves to be a feasible method.…”

“…In addition, the energy-storage system (ESS) can also make a significant contribution to energy development in rural areas. With the reduction of renewable energy generation costs and the improvement of energy-storage technology, an ESS plays an important role in stabilizing the intermittent of renewable energy generation and ensuring the reliability of microgrid energy dispatching [19][20][21], and the reasonable configuration of ESS can improve the economy of the microgrid [22]. An improved distributed energy-storage model is proposed by [23] to maintain the frequency stability and supply-demand balance of a microgrid; and [24] utilizes the optimal capacity of ESS in hybrid power systems to minimize costs.…”

Reinforcement-Learning-Based Virtual Energy Storage System Operation Strategy for Wind Power Forecast Uncertainty Management

“…The initial state s 1 is set as the current VESS condition and the feasible action range A 1 is determined by the current state s 1 in step 9. During the operation time horizon T , the VESS operation action is selected using (22) and the values of the state and the state-action value function are updated according to the selected action in steps 12 and 13.…”