Hierarchical Distributed Model Predictive Control of Interconnected Microgrids

Abstract

In this work, we propose a hierarchical distributed model predictive control strategy to operate interconnected microgrids (MGs) with the goal of increasing the overall infeed of renewable energy sources. In particular, we investigate how renewable infeed of MGs can be increased by using a transmission network allowing the exchange of energy. To obtain an model predictive control scheme which is scalable with respect to the number of MGs and preserves their independent structure, we make use of the alternating direction method of multipliers leading to local controllers communicating through a central entity. This entity is in charge of the power lines and ensures that the constraints on the transmission capacities are met. The results are illustrated in a numerical case study.

Collaborative Autonomous Optimization of Interconnected Multi-Energy Systems with Two-Stage Transactive Control Framework

“…First, X * 2 apparently satisfies constraints(3)-(8), (10), (12)-(16), (18), (20), (24) since the modified variables are not included in these constraints. Besides, the transformation method introduced in (29) guarantees that X * 2 always meets the power balance constraint (1) and the mutual exclusiveness constraint (11). As for constraints (9), (17), (19), (23):…”

Resilient distributed model predictive control for energy management of interconnected microgrids

“…Distributed optimization methods have been considered as suitable approaches to solve large-scale economic dispatch problems. [5][6][7][8] One of the main reasons is the fact that the solution obtained by applying a distributed optimization algorithm approximates the optimal solution obtained from the centralized approach. The main challenge when one wants to solve an economic dispatch problem, such as Problem (10), in a distributed manner is that the problem is not trivially separable.…”

“…denote the per-unit cost of storage operation, the per-unit cost of producing energy, the per-unit cost of buying energy from the main grid, and the per-unit cost of transferring energy to/from the neighbor due to losses, respectively. 7 Remark 2. The per-unit cost of transferring energy is considered to be equal for all neighbors, ie, with a common c t i , for simplicity of the exposition in the proposed attack identification and mitigation approach.…”

“…Different from the conventional economic dispatch that computes a power production plan, which typically consists of hourly decisions spanned over one day, MPC-based economic dispatch keeps optimizing the decision of several time steps ahead based on the measurement at each time instant. In this regard, different distributed MPC (DMPC) approaches, such as dual decomposition, 5 alternating direction method of multipliers, 6,7 optimality condition decomposition 4,8 and population dynamics, 9 have been proposed to solve economic dispatch problems. These distributed optimization approaches are suitable since they are able to obtain an optimal solution given that the related optimization problem is convex, with some mild assumptions.…”

Toward economic NMPC for multistage AC optimal power flow

“…Approaches to consider voltage control via minimizing the absolute value of total reactive power injection are presented in the work of Zima and Andersson. 38 A hierarchical economic mpc scheme considering generator cost and electricity prices for power from an upper grid level is proposed in the work of Hans et al 42 In the work of Bansal et al, 43 a multistage voltage control scheme for electric vehicle charging ensuring recursive feasibility and exponential stability is proposed. Scheduling based on linearized power flow equations and setpoint tracking is investigated in the works of Almassalkhi and Hiskens.…”