Related papers: Stochastic Virtual Power Plant Dispatch via Tempor…
This paper addresses the real-time energy dispatch of a hybrid system comprising cascaded hydropower plants, wind, and solar units, jointly participating in the day-ahead energy market under inflow, renewable generation, and price…
Energy hubs convert and distribute energy resources by combining different energy inputs through multiple conversion and storage components. The optimal operation of the energy hub exploits its flexibility to increase the energy efficiency…
Aggregating distributed energy resources in power systems significantly increases uncertainties, in particular caused by the fluctuation of renewable energy generation. This issue has driven the necessity of widely exploiting advanced…
This paper presents a distributionally robust model predictive control (DRMPC) framework for the optimal Virtual Power Plant (VPP) operation under electricity price uncertainty. A unified VPP model is formulated that captures the…
This paper presents a stochastic model predictive controller (SMPC) for linear time-invariant systems in the presence of additive disturbances. The distribution of the disturbance is unknown and is assumed to have a bounded support. A…
Emissions reduction and resilience to outages motivate the adoption of renewable microgrids. Surprisingly, research integrating both probabilistic grid outages and electric vehicle (EV) charging requirements remains limited. This paper…
This paper presents a distributionally robust stochastic model predictive control (SMPC) approach for linear discrete-time systems subject to unbounded and correlated additive disturbances. We consider hard input constraints and state…
This paper presents an online energy management system for an energy hub where electric vehicles are charged combining on-site photovoltaic generation and battery energy storage with the power grid, with the objective to decide on the…
Stochastic model predictive control (SMPC) has been a promising solution to complex control problems under uncertain disturbances. However, traditional SMPC approaches either require exact knowledge of probabilistic distributions, or rely…
Distributed model predictive control methods for uncertain systems often suffer from considerable conservatism and can tolerate only small uncertainties due to the use of robust formulations that are amenable to distributed design and…
The growing integration of distributed energy resources (DERs) into the power grid necessitates an effective coordination strategy to maximize their benefits. Acting as an aggregator of DERs, a virtual power plant (VPP) facilitates this…
In distributed model predictive control (MPC), the control input at each sampling time is computed by solving a large-scale optimal control problem (OCP) over a finite horizon using distributed algorithms. Typically, such algorithms require…
To manage huge amount of flexible distributed energy resources (DERs) in the distribution networks, the virtual power plant (VPP) is introduced in industry. The VPP can optimally dispatch these resources in a cluster way and provide…
This paper presents the development of a non-linear model predictive controller (MPC) for controlling variable speed hydropower (VSHP) plants. The MPC coordinates the turbine controller with the virtual synchronous generator (VSG) control…
This paper presents a model predictive control (MPC)-based online real-time adaptive control scheme for emergency voltage control in power systems. Despite tremendous success in various applications, real-time implementation of MPC for…
We present a stochastic model predictive control (MPC) framework for central heating, ventilation, and air conditioning (HVAC) plants. The framework uses real data to forecast and quantify uncertainty of disturbances affecting the system…
In this paper we propose a stochastic model predictive control (MPC) algorithm for linear discrete-time systems affected by possibly unbounded additive disturbances and subject to probabilistic constraints. Constraints are treated in…
Dispatchability of renewable energy sources and inflexible loads can be achieved using a volatility-compensating energy storage. However, as the future power outputs of the inflexible devices are uncertain, the computation of a dispatch…
This paper concerns the risk-aware control of stochastic systems with temporal logic specifications dynamically assigned during runtime. Conventional risk-aware control typically assumes that all specifications are predefined and remain…
Constraint tightening to non-conservatively guarantee recursive feasibility and stability in Stochastic Model Predictive Control is addressed. Stability and feasibility requirements are considered separately, highlighting the difference…