This paper presents a hierarchical control architecture for the regulation of frequency and nodal voltages of a microgrid in islanded operation. Considering systems with both dispatchable and nondispatchable generation, as well as noncontrollable loads, the suggested approach allows to coordinate the MG devices in order to maintain the network variables inside the desired operational ranges. Moreover, the proposed algorithm, based on model predictive control, introduces the possibility to define different resource management strategies while taking into account the constraints of the available devices. Simulation examples are reported and described in the final part of this paper.Note to Practitioners-This paper proposes a method to control the nodal voltages and the frequency of a microgrid (MG) in islanded operation. The implementation of this control scheme requires defining a control logic for the inverters connecting the distributed generation units to the MG. A centralized supervising system has also to be deployed for the coordination of their actions. This paper shows how the flexibility of this structure allows for the implementation of different resource management strategies.
A two-layer control scheme based on Model Predictive Control (MPC) operating at two different timescales is proposed for the energy management of a grid-connected microgrid (MG), including a battery, a microturbine, a photovoltaic system, a partially non predictable load, and the input from the electrical network. The high-level optimizer runs at a slow timescale, relies on a simplified model of the system, and is in charge of computing the nominal operating conditions for each MG component over a long time horizon, typically one day, with sampling period of 15 minutes, so as to optimize an economic performance index on the basis of available predictions for the PV generation and load request. The low-level controller runs at higher frequency, typically 1 minute, relies on a stochastic MPC (sMPC) algorithm, and adjusts the MG operation to minimize the difference, over each interval of 15 minutes, between the planned energy exchange and the real one, so avoiding penalties. The sMPC method is implemented according to a shrinking horizon strategy and ensures probabilistic constraints satisfaction. Detailed models and simulations of the overall control system are presented.
Due to the widespread diffusion of renewables, the energy management and control of microgrids with non-dispatchable generation units, storage systems, and uncontrollable loads is becoming a topic of paramount interest. This problem is made complex by the need to consider both the continuous dynamics of generators and loads, and a number of logic constraints representing the feasible operating conditions of the devices, such as latency times or charge/discharge constraints. A promising solution, already proposed in various papers, is to resort to hybrid modeling and optimization; however, this leads to very large and intractable mixed integer optimization problems when the number of devices increases. For these reasons, in this paper a multilayer control scheme of microgrids is proposed: the higher layer defines the daily energy exchange with the main network based on a simplified model of the microgrid where dispatchable devices with similar characteristics are organized in clusters. The intermediate, or supervisory, layer allocates the energy requests among the units of each cluster. Finally, the lower layer is made by local controllers of the units. Simulation results are reported to witness the potentialities of this approach
A two-layer control scheme based on Model Predictive Control (MPC) operating at two different timescales is proposed for the energy management of a micro-grid (MG), including a battery, a gas-turbine generator, a photovoltaic (PV) generator and the input from the electrical network. The highlevel optimizer, which acts at a slow timescale and relies on a simplified model of the system, is in charge of computing the nominal operating conditions for each MG component so as to optimize an economic performance index on the basis of available predictions for the PV generation and load request. The low-level controller acts at higher frequency, adjusts the MG operation, and relies on a stochastic MPC algorithm that ensures probabilistic constraints satisfaction. Detailed models and simulations of the overall control system are presented.
This paper presents a two-level control architecture for the intra-day energy management of a microgrid. The aim is to comply with an agreed energy exchange profile with the main grid, minimizing the operating cost. The higher level is entitled for the definition of the nominal profiles of the controllable devices, based on the forecasts of demand and production. The task of the lower level control is to reach the target energy exchange setpoint, correcting the scheduled setpoint of the controllable devices.While the high level optimization consists in the solution of a mixed integer linear programming problem, the low level controller has been implemented as a daisy chaining control scheme with a PI controller.This control architecture potentially helps to increase the reliability of the whole grid, by means of the aggregation of unpredictable energy resources and making their overall behaviour more predictable from the DSO point of view.
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