Day-ahead bidding strategy of cloud energy storage serving multiple heterogeneous microgrids in the electricity market

Cloud energy storage (CES) receives increasing attention as an efficient and viable paradigm for the provision of distributed energy storage services. This paper exploits CES's service modes to both energy storage and electricity trading for its users, e.g., microgrid (MG). The optimal day-ahead bidding strategy is investigated for CES as an independent entity in the electricity market. Besides, two energy service modes are introduced considering MG's requirements and preferences. Each mode consists of a set of schemes for energy storage system (ESS) rent (power-based and capacity-based) and electricity trading (internal price with an improved pricing method and market clearing price). Finally, a stochastic programming (SP)-based optimization model is formulated to maximize the CES's expected profits fully considering the electricity market's settlement mechanism and uncertainty as well as the participation interest of MG. The proposed solution is extensively assessed through a case study of CES's energy services to five heterogeneous MGs with distinct electricity generation and consumption characteristics and two energy service modes are assessed through comparative experiments. The numerical results confirm the effectiveness and benefits of the proposed optimal day-ahead bidding solution.

Automated summary

This paper investigates the optimal day-ahead bidding strategy for cloud energy storage (CES) as an independent entity in the electricity market. Two energy service modes are introduced, considering the requirements and preferences of microgrids (MG). A stochastic programming-based optimization model is formulated to maximize CES's expected profits, taking into account the settlement mechanism and uncertainty of the electricity market. The proposed solution is extensively assessed through a case study of CES's energy services to five heterogeneous MGs. The numerical results confirm the effectiveness and benefits of the proposed optimal day-ahead bidding solution.