Economic model predictive control for scheduled switching operations

Space heating demand response potential of retrofitted residential apartment blocks

The dynamic economic optimization of the ultrasupercritical (USC) boiler-turbine unit has become an important task in modern power plants. Economic model predictive control (EMPC) has recently developed to be a promising method for realizing the dynamic economy. This EMPC essentially requires a highly reliable model for USC dynamic prediction which could reflect the internal mechanism of USC with big data feature. This article constitutes a deep-neural-network-based EMPC for the USC unit. Deep belief network (DBN) is used to model the USC unit with mathematical structure. To overcome the nonlinearity and time delay existing in the pulverized channel, an augmented model with predictor embedded is also incorporated into the EMPC design. The auxiliary controller and stability region have been constituted to guarantee closed-loop stability. Simulation results on a 1000-MW USC unit fully demonstrate the effectiveness of the proposed DBN-based EMPC.

Cost-efficient measurement of room-level heat output from hydronic radiators is a major barrier to large-scale implementation of Economic Model Predictive Control (EMPC) in residential space heating for demand-side management. This paper therefore presents a novel EMPC strategy for hydronic radiators that relies on measurements of radiator pipe temperatures as a proxy for the radiator heat output, thus eliminating the need for costly flow-based meters at each radiator in a building. Simulation-based experiments indicate that the proposed proxy-based EMPC matches the performance of its heat-based counterpart. The proxy-based EMPC achieved a 16.6 % cost reduction compared to the heat-based EMPC's 16.8 %, with no comfort violations in both cases. Furthermore, the strategy shows resilience towards uncertainties in the user-estimated radiator exponent and maximum heating capacity. The proposed EMPC scheme also allows system operators to fine-tune the balance between cost savings and return temperatures using the proxy's upper limit. The findings presented in this paper suggest that the proposed proxy-based EMPC scheme provides a practical pathway for broader applications of EMPC in hydronic-based space heating with the prospect of unlocking significant load shifting potential, cost savings for end-users, and enhanced efficiency in individual and collective energy systems.

A reinforcement learning-based economic model predictive control framework for autonomous operation of chemical reactors

This work focuses on the design of stochastic Lyapunov‐based economic model predictive control (SLEMPC) systems for a broad class of stochastic nonlinear systems with input constraints. Under the assumption of stabilizability of the origin of the stochastic nonlinear system via a stochastic Lyapunov‐based control law, an economic model predictive controller is proposed that utilizes suitable constraints based on the stochastic Lyapunov‐based controller to ensure economic optimality, feasibility and stability in probability in a well‐characterized region of the state‐space surrounding the origin. A chemical process example is used to illustrate the application of the approach and demonstrate its economic benefits with respect to an EMPC scheme that treats the disturbances in a deterministic, bounded manner. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3312–3322, 2018

In this work, we propose initial steps in the first systems safety approach that coordinates the control and safety systems through a common metric (a Safeness Index) and develop a controller formulation that incorporates this index. Specifically, this work presents an economic model predictive control (EMPC) scheme that utilizes a Safeness Index function as a hard constraint to define a safe region of operation termed the safety zone. Under the proposed EMPC, the closed-loop state of a nonlinear process can be guaranteed to enter the safety zone in finite time while maximizing the process economics. The proposed design is demonstrated using a chemical process example.

Economic Model Predictive Control for Optimal Operation of Home Microgrid with Photovoltaic-Combined Heat and Power Storage Systems

Economic Model Predictive Control: Designs and Implementation

Application of economic MPC to the energy and demand minimization of a commercial building

Non-linear economic model predictive control of water distribution networks

Cooperative economic model predictive control for linear systems with convex objectives

In microgrids, the volatility and uncertainty of renewable energy make it challenging to achieve optimal performance using hierarchical control strategies with time-scale separation for economic dispatch and real-time control. To address this issue and enhance transient optimization, a distributed economic model predictive control (EMPC) strategy based on the alternating direction method of multipliers (ADMM) is proposed in this paper. To reduce computational complexity and communication overhead in distributed control, the ADMM method is employed to solve the distributed EMPC problem in parallel. Using Lyapunov-based analysis, the study establishes the system's asymptotic stability. Moreover, it demonstrates that the EMPC strategy achieves an average performance equivalent to-or even exceeding-that of the optimal steady-state. Simulations validate the proposed algorithm's effectiveness in economic dispatch and frequency restoration, while demonstrating its advantages in minimizing computational load and communication requirements.

A tutorial review of economic model predictive control methods

State-estimation-based economic model predictive control of nonlinear systems

We address in the paper the problem of designing an economic model predictive control (EMPC) algorithm that asymptotically achieves the optimal performance despite the presence of plant-model mismatch. To motivate the problem, we present an example of a continuous stirred tank reactor in which available EMPC and tracking model predictive control (MPC) algorithms do not reach the optimal steady state operation. We propose to use an offset-free disturbance model and to modify the target optimization problem with a correction term that is iteratively computed to enforce the necessary conditions of optimality in the presence of plant-model mismatch. Then, we show how the proposed formulation behaves on the motivating example, highlighting the role of the stage cost function used in the finite horizon MPC problem.