A Two-Layer Stochastic Model Predictive Control Scheme for Microgrids

Abstract

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.

A Comparative Study of Stochastic Model Predictive Controllers

“…Stochastic model predictive control approaches were inspired by this type of systems, in which δ and w are stochastic in nature, independent and with known probability distributions. Since this statistical information is taken into account in the solution of the OCP [18][19][20][21], stochastic predictive control has been widely accepted and has been applied in different areas such as building air conditioning [37][38][39], renewable energy management [40,41], process control [3,42], robotics and automotive [5,22,[43][44][45]. A more extensive review of these and other applications is presented in [18,19,21,25,46], where network control systems, air traffic, finance, path planning and training control are discussed.…”

A hierarchical distributed predictive control approach for microgrids energy management

“…(MPC), which is well suited to deal with a large amount of constraints of different types that have to be imposed in real time in microgrids. This technique has been exploited for example in [2,3,11,12] and [17][18][19][20][21][22][23]. In [2], [3] and [21] the flexible loads are modelled as a predefined range of possible load consumption and the microgrid controller can directly determine a load profile as long as it fulfils the range.…”

“…However, it is well-known that this scheme presents issues of scalability, computational burden, failure of single unit, adaptability, etc. Recent works are putting more attention to the distributed MPC and hierarchical control schemes, such as [11], [19]. In particular, in [19], a two-layer control scheme based MPC operating at two different timescales has been studied.…”

70% Decrease of Hot-Spotted Photovoltaic Modules Output Power Loss Using Novel MPPT Algorithm

“…OT-SPOTTING is a reliability problem in Photovoltaic (PV) modules, this phenomena is well-identified when a mismatched solar cell temperature increases significantly, reducing the overall PV module output power [1]. PV hot-spots occur when a cell, or group of cells activates at reverse-bias, dissipating power instead of delivering it, and consequently operating at anomalous temperature levels [2] and [3].…”