# Stochastic Predictive Energy Management of Multi-Microgrid Systems
## 1 Introduction  
In recent years, the multi-microgrid (MMG) concept has emerged to improve the operational and economic performance of distribution networks [1, 2]. Coordinating the operation of interconnected microgrids (IMGs) will result in more efficient energy usage and less frequent power exchange with the main grid (upstream network). Moreover, distribution system reliability will be enhanced by microgrids (MGs) involvement in supporting activities including sharing surplus power with the adjacent MGs. However, to exploit these potential benefits, developing an efficient energy management system (EMS) for the entire MMG network is required [3]. The most important issues in establishing appropriate models and control strategies are related to the complexity that is resulted from interaction of multiple MGs as well as the uncertainties that are caused by the intermittent nature of the renewable energy (RE)-based distributed energy resources (DERs) and variability of loads. An overview of the integration challenges of REs as well as smart grid control and reliability issues can be found in [4, 5]. The development of efficient EMSs for IMGs has drawn the attention of many researchers. Especially, centralized energy management approaches have been widely studied during the last years. In [6], a two-stage scenario-based programming approach is developed in the centralized form to manage the operation of IMGs in a cooperative manner. In [7], model predictive control (MPC) is adopted to coordinate the operation of a renewable-based MMG system. However, no uncertainty management strategy is used. Accommodating the intermittent nature of renewable energy sources (RESs) production and load fluctuations robust techniques are used in [8; 9]. In all of the reviewed papers, a central controller is responsible for devising the operation strategy of the whole system considering the internal dynamics of individual MGs and interacting parts. Although adopting this optimization approach could result in an optimal solution, the problem may not be always computationally tractable especially when the number of system players (e.g., MGs) increases. Accordingly, distributed and hierarchical approaches have been developed in which decisions are made at different control levels. Although the final solution of distributed approaches will be sub-optimal compared to the centralized strategies, a substantial reduction in computational time will be acquired. Multi-agent systems [10; 11], game theory [12; 13], decentralized optimization techniques based on Alternating Direction Method of Multipliers (ADMM) [14], and distributed MPC [15; 16; 17] are among the most common methodologies that have been used to decompose the energy management problem of a network of IMGs into several smaller optimization problems. In [18], power transfer between multiple AC microgrids is scheduled by using distributed cooperative control. Two inter-cluster and intra-cluster schemes are proposed to manage power sharing and regulate DER's voltage and frequency, respectively. In [19], smart MGs are modeled as a team of cooperative agents. Decentralized MPC is used to manage the energy level of storage devices and power exchange of MGs around pre-defined reference levels. In [20], a two-level hierarchical EMS is proposed for an MMG system connected to the main grid. Karush-Kuhn-Tucker optimality conditions are used to decompose the problem into smaller subproblems. In [2], a cooperative distributed MPC is proposed to achieve an economic performance of MMG systems. Supply and demand uncertainties are handled through chance constraints. However, MGs can not exchange power directly and the distribution system operator (DSO) plays an intermediary role. In [21], line agents are responsible for determining optimal amount of power to be transferred through connecting lines of cooperating MGs. The distributed robust technique is utilized to manage the uncertainty of generation and consumption.