An Intelligent Multi-Stage Optimization Approach for Community Based Micro-Grid Within Multi-Microgrid Paradigm

Abstract

Smart community setups nowadays are subjected to complicated issues such as instability, intermittent integration of the load at the demand side, and lack of intelligent two-way communication process. These issues need to be addressed in terms of a balanced power demand dispatch (DD) in the realtime or day-ahead duplex signal regime under multi-microgrids. This paper offers an intelligent multi-agentbased approach that works between different levels of communication and their respective layers for a community-based system to optimize the power in community-based multi-microgrids model. This will further enhance user personal comfort. Constraints relative to cost minimization also have a relation with this model. A three-level structure with various layers of autonomous agents take intelligent decisions based on prioritized particle swarm optimization (P-PSO), prioritized plug and play (PPnP), and knapsack; considering DD as the main driver of the system to address objectives like price and power consumption uncertainties. Distinct smart home models, depending upon their living habits, are keenly observed providing their power infrastructure and personal comfort. Load appliances considered as load agents are individually contemplated for maximum proficiency. Furthermore, two-way communication between utility and consumers lowers down the risk of the inefficiency of the system.INDEX TERMS Demand dispatch, demand response, multi-agent system, multi-microgrid, python agent development, prioritized plug-and-play, prioritized particle swarm optimization, real-time pricing and usage.

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

“…Therefore, the emerging management solution should be efficient and low-cost for an economically viable smart grid. The main research directions of current MMG energy management and operation control in different cooperative approaches can be summarized as the following three aspects:1) energy transaction strategy [76][77][78][79][80]; 2) information interaction strategy [81][82][83][84]; 3) artificial intelligence technology in MMG [88][89][90][91][92][93][94]. Table 3 summarized and compared the different solution methods and optimization objectives of energy management and operation control of MMG under different cooperative approaches.…”

“…Aiming at the information interaction strategy of MMG, researchers put forward an information interaction strategy based on multi-agent technology [37], [81][82][83][84][85]. It regards each MG as an agent, and each agent can spontaneously communicate and exchange information with the neighboring agents according to the pre-set communication protocol, to realize power flow and intelligent control between MMG [82]. At the spatial scale, MMG information interaction based on multi-agent system adopts a three-level linkage hierarchical control strategy of system layer, node layer and device layer.…”

State-of-the-art review on energy sharing and trading of resilient multi microgrids

“…The following list summarizes the drawbacks of conventional scheduling techniques. 88 It is ambitious to implement the necessary functionalities in large-scale and wide-area DG access, including global optimization, multi-point connectivity, and vertical hierarchical control. Dynamic optimization is crucial in MMG dispatch because of the significant probabilistic and time-varying attributes.…”

An intelligent multi-layer, multi-agent MMG framework with amelioration of energy efficiency and future investment outlook, under the deregulated day-ahead and real-time market regime

“…A synced publish-subscribe channel is used for taking part in REMMC activities. The solar and wind power output at any instant t is calculated using Eqs 24, 25 (Malik et al, 2020):…”