An ADMM-Based Resilient Distributed Economic MPC Algorithm for Frequency Restoration and Economic Dispatch in Networked Microgrids

Space heating demand response potential of retrofitted residential apartment blocks

Non-linear economic model predictive control of water distribution networks

In this paper, a periodic economic model predictive control (EMPC) strategy with nonlinear algebraic constraint relaxations for water distribution networks (WDNs) is presented. A WDN is usually modeled by a series of differential-algebraic equations. When the hydraulic pressure/head and flow relations in the interconnected pipes are considered, the nonlinear algebraic equations will appear in the control-oriented model of WDNs. Specifically, two types of nonlinear algebraic equations are studied in terms of unidirectional and bidirectional flows in pipes. These nonlinear algebraic constraints are iteratively relaxed by a series of linear constraints. Therefore, the proposed EMPC strategy can be implemented by solving an optimization problem using the linear programming technique. Finally, the EMPC strategy with nonlinear algebraic constraint relaxations is verified in the Richmond water network. The comparison results of applying nonlinear EMPC strategy are also provided. The proposed nonlinear-constraint relaxation technique turns out to be much faster than the one obtained by a standard nonlinear optimization solver.

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

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

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

Cooperative economic model predictive control for linear systems with convex objectives

This papers proposes new ways to enforce convergence to equilibria for Economic Model Predictive Control schemes. Economic Model Predictive Control is a control technique capable of optimizing an economic performance index while enforcing state and input constraints. For nonlinear systems and/or non-convex cost functionals, performance optimization may result in non converging behaviours. While this might be acceptable in some cases (i.e. operation of chemical reactors), it may be undesirable for other types of applications. In the present paper we discuss ways of enforcing convergence to equilibrium by trading it off with asymptotic performance. Indeed, while all trajectories converging to a given equilibrium yield the same asymptotic average cost, transient costs may differ and trade-offs are naturally highlighted between the latter and speed of convergence.

Multi-market demand response using economic model predictive control of space heating in residential buildings

This article presents an optimization-based control framework for the autonomous forced landing of a fixed-wing unmanned aircraft (UA). A two-level model-predictive control (MPC) scheme is proposed to realize this framework, where an economic model-predictive control (EMPC) in a long piecewise constant fashion is proposed at the high level, while a short fixed-horizon linear time-varying MPC at the low level responds to fast dynamics of UA and tracks the optimal path provided by the high-level controller, alleviating computational burden compared to the high-frequency single-layer MPC scheme. Compared with a single EMPC setup with high sampling frequency, this hierarchical EMPC controller can significantly reduce the computational complexity and make it feasible to be implemented in real time. In addition, it also responds to disturbances (e.g., wind) and aircraft fast dynamics in a timely manner. The recursive feasibility and stability of the high- and low-level MPC schemes are established. The performance of the proposed EMPC forced landing function is illustrated by simulation case studies on both Aerosonde and Skywalker X8, compared favorably with competing schemes.

In this work, we consider economic model predictive control (EMPC) for scheduled switching operations. The proposed EMPC scheme takes advantage of a set of auxiliary controllers that locally stabilizes the optimal steady state of each operating mode. In the proposed approach, EMPC operations are divided into two phases - an infinite-time operation phase and a mode transition phase, depending on the current sampling time and the scheduled mode switching time. The proposed EMPC design is computationally efficient and enjoys enlarged feasibility regions than the auxiliary controllers. Sufficient conditions to ensure recursive feasibility of the proposed EMPC design are established. Simulation results of a chemical process example demonstrate the applicability and effectiveness of our approach.

The Economic Dispatch (ED) is one of the most important optimization problems in power systems the ultimate goal of the ED is to minimize the cost of operations in a power generation. In this paper, the Woodpecker Mating Algorithm (WMA) is used to solve the ED problem considering the nonlinear properties of generators such as valve point effects (VPE), prohibited operating zones (POZ), ramp rate limits, multiple fuel options, and transmission loss. The WMA algorithm is a novel metaheuristic algorithm inspired by the mating behavior of woodpeckers and sound intensity (a physical quantity). The WMA is implemented on six test systems of different operational dimensions and characteristics to show its capacity for solving the ED problem. The results are compared with the latest and most efficient methods introduced in the literature. Proving the efficiency of the WMA to solve the ED problem, simulation results are promising and offer the optimal fuel cost of production.

Direct Contact Membrane Distillation (DCMD) is an emerging sustainable desalination technology that can utilize solar energy to desalinate seawater. The low water production rate associated with this technology prevents it from becoming commercially feasible. To overcome this challenge, advanced control strategies may be utilized. An optimization-based control scheme termed Model Predictive Control (MPC) provides a natural framework to optimally operate DCMD processes due to its unique control advantages. Among these advantages are the flexibility provided in formulating the objective function, the capability to directly handle process constraints, and the ability to work with various classes of nonlinear systems. Motivated by the above considerations, this paper proposes two MPC schemes that can maximize the water production rate of DCMD systems. The first MPC scheme is formulated to track an optimal set-point while taking input and stability constraints into account. The second MPC scheme termed Economic Model Predictive Control (EMPC) is formulated to maximize the distilled water flux while meeting input, stability and other process operational constraints. To illustrate the effectiveness of the two proposed control paradigms, the total water production under both control designs is compared. Simulation results show that the DCMD process produces more distilled water when it is operated by EMPC than when it is operated by MPC.

Recently, the alternating direction method of multipliers (ADMM) has been used for distributed consensus optimization and is shown to converge faster than conventional approaches based on consensus subgradient. In this paper, we consider a convex optimization problem with a linearly coupled equality constraint and employ a dual consensus ADMM (DC-ADMM) method for solving the problem in a fully distributed fashion. In particular, by considering a non-ideal network where the agents can be ON and OFF randomly and the communications among agents can fail probabilistically, we propose a randomized DC-ADMM method that is robust against these non-ideal effects. Moreover, we show that the proposed randomized method is provably convergent to an optimal solution and has a worst-case O(1/k) convergence rate, where k is the iteration number. Simulation results are presented to examine the practical convergence behavior of the proposed method in the presence of randomly ON/OFF agents and non-ideal communication links.