Distributed Smart Decision-Making for a Multimicrogrid System Based on a Hierarchical Interactive Architecture

In this paper, a comprehensive real-time interactive energy management system (EMS) framework for the utility and multiple electrically coupled MGs is proposed. A hierarchical bi-level control scheme (BLCS) with primary and secondary level controllers is applied in this regard. The proposed hierarchical architecture consists of sub-components of load demand prediction, renewable generation resource integration, electrical power-load balancing, and responsive load demand. In the primary level, EMSs are operating separately for each microgrid (MG) by considering the problem constraints, power set-points of generation resources, and possible shortage or surplus of power generation in the MGs. In the proposed framework, minimum information exchange is required among MGs and the distribution system operator. It is a highly desirable feature in future distributed EMS. Various parameters such as load demand and renewable power generation are treated as uncertainties in the proposed structure. In order to handle the uncertainties, Taguchi's orthogonal array testing approach is utilized. Then, the shortage or surplus of the MGs power should be submitted to a central EMS in the secondary level. In order to validate the proposed control structure, a test system is simulated and optimized based on multiperiod imperialist competition algorithm. The obtained results clearly show that the proposed BLCS is effective in achieving optimal dispatch of generation resources in systems with multiple MGs

Optimal energy scheduling for a grid connected Microgrid based on Multi-period Imperialist competition algorithm

More flexibility, better reliability achievement and optimal usage of distributed generation are all together caused to develop Microgrids (MGs) in the power systems. The main duty of an optimal Energy Management System (EMS) is to find the best solution for operation and scheduling of generation resources with the least possible of the operation cost, demand side management and power exchange with the main grid. An EMS based on Multi-dimension Imperialist Competitive Algorithm (thereafter it is called EMS-MICA briefly) is proposed in this paper to consider the non-linear nature of MG system. The fulfillment of the load requirement, the technical specifications of the generation resources and the main grid constraints are included in the proposed problem. Moreover, the obtained results are compared to an EMS based on Mixed Integer Non-linear Programming (EMS-MINLP) approach to achieve the lower Market Clearing Price (MCP) during day-ahead scheduling. The results are proved that the efficiency of the proposed algorithm is significantly improved in comparison with the EMS-MINLP algorithm

Optimal energy management for stand-alone microgrids based on multi-period imperialist competition algorithm considering uncertainties: experimental validation

Microgrid (MG) constitutes non-dispatchable resources and responsive loads, which can serve as a basic tool to reach desired objectives while distributing electricity more effectively, economically, and securely. However, high penetration of distributed generations into the grid leads to fundamental and critical challenges to ensure a reliable power system operation. This paper presents a general formulation of optimum operation strategy with the objective of cost optimization plan and demand response regulation. MG energy management problem can be formulated as an optimization problem in order to minimize the cost-related to generation resources and responsive loads. An expert heuristic approach based on multi-period imperialist competition algorithm is applied to implement an energy management system for optimization purposes. A comparison is carried out between the proposed algorithm and classical techniques, including particle swarm optimization and a modified conventional energy management system algorithms. An artificial neural network combined with Markov-chain approach is used to predict non-dispatchable power generation and load demand under uncertainty conditions. The proposed algorithm is evaluated experimentally on an MG testbed, and the obtained results demonstrate the efficiency of the proposed algorithm to minimize the total generation cost with a fast calculation time, which makes it useful for real-time applications