Robust economic model predictive control of a community micro-grid

Pereira, M.; Peña, David Muñoz de la; Limón, Daniel

doi:10.1016/j.renene.2016.04.086

Cited by 36 publications

(29 citation statements)

References 22 publications

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“…Economic cost functions are not necessarily quadratic or positive definite with respect to the given trajectories or references as tracking MPC. Economic MPC has been applied to a variety of industrial applications, such as water distribution networks [6][7][8], wastewater treatment processes [9], smart grids [10,11] and chemical processes [12,13].…”

Section: Introductionmentioning

confidence: 99%

Economic model predictive control based on a periodicity constraint

Wang

Salvador

Peña

et al. 2018

Journal of Process Control

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This paper addresses a novel economic model predictive control (MPC) formulation based on a periodicity constraint to achieve an optimal periodic operation for discrete-time linear systems. The proposed control strategy does not rely on forcing the terminal state by means of a terminal equality constraint and hence it does not require a priori knowledge of a periodic steady trajectory. Instead, at each sampling time step the economic cost function is optimized based on a periodicity constraint over all the periodic trajectories that include the current state. The recursive feasibility and the closed-loop convergence to a periodic steady trajectory are discussed. Moreover, an optimality certificate of this steady trajectory is provided based on the Karush-Kuhn-Tucker (KKT) optimality conditions. Finally, an application to a well-known water distribution network benchmark is presented to demonstrate the proposed economic MPC in which the closed-loop simulation results obtained with a linear model and a virtual-reality simulator are both provided.

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Section: Introductionmentioning

confidence: 99%

Economic model predictive control based on a periodicity constraint

Wang

Salvador

Peña

et al. 2018

Journal of Process Control

View full text Add to dashboard Cite

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“…Other major challenges of PV system integration with existing electricity supply system are the variable PV generation and dynamic energy demand trends. The work reported in [41] used the on-site battery energy storage and PV system to solve the supply-demand mismatch problem. The work discussed in [42] focussed on efficient energy use for cost saving through an optimization algorithm with the integration of battery storage systems.…”

Section: Literature Reviewmentioning

confidence: 99%

A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid

et al. 2019

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The smart grid (SG) has emerged as a key enabling technology facilitating the integration of variable energy resources with the objective of load management and reduced carbon-dioxide (CO 2 ) emissions. However, dynamic load consumption trends and inherent intermittent nature of renewable generations may cause uncertainty in active resource management. Eventually, these uncertainties pose serious challenges to the energy management system. To address these challenges, this work establishes an efficient load scheduling scheme by jointly considering an on-site photo-voltaic (PV) system and an energy storage system (ESS). An optimum PV-site matching technique was used to optimally select the highest capacity and lowest cost PV module. Furthermore, the best-fit of PV array in regard with load is anticipated using least square method (LSM). Initially, the mathematical models of PV energy generation, consumption and ESS are presented along with load categorization through Zero and Finite shift methods. Then, the final problem is formulated as a multiobjective optimization problem which is solved by using the proposed Dijkstra algorithm (DA). The proposed algorithm quantifies day-ahead electricity market consumption cost, used energy mixes, curtailed load, and grid imbalances. However, to further analyse and compare the performance of proposed model, the results of the proposed algorithm are compared with the genetic algorithm (GA), binary particle swarm optimization (BPSO), and optimal pattern recognition algorithm (OPRA), respectively. Simulation results show that DA achieved 51.72% cost reduction when grid and renewable sources are used. Similarly, DA outperforms other algorithms in terms of maximum peak to average ratio (PAR) reduction, which is 10.22%.

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“…Few cases of EMS implement optimization over control policies. While a computationally inefficient (optimization problem with exponentially increasing size with the prediction horizon) robust MPC based EMS is proposed in [28], a more efficient formulation [29] optimizes a predicted sequence of nominal control actions which is corrected by linear terms of disturbances that would affect the system.…”

Section: Introductionmentioning

confidence: 99%

“…This controller considers a robust optimization over a control policy parameterized by gains that compensates the uncertainties of the predictions, which are modeled based on fuzzy intervals. The control policy is similar to that of [29], but it was designed according to the particular microgrid considered in this work so that the uncertainty of the power predictions can be compensated either by the battery or main grid power consumption. This compensation enables the controller to find better solutions than other robust MPC formulations with no uncertainty compensation.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control

et al. 2019

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This paper presents a two-level hierarchical energy management system (EMS) for microgrid operation that is based on a robust model predictive control (MPC) strategy. This EMS focuses on minimizing the cost of the energy drawn from the main grid and increasing self-consumption of local renewable energy resources, and brings benefits to the users of the microgrid as well as the distribution network operator (DNO). The higher level of the EMS comprises a robust MPC controller which optimizes energy usage and defines a power reference that is tracked by the lower-level real-time controller. The proposed EMS addresses the uncertainty of the predictions of the generation and end-user consumption profiles with the use of the robust MPC controller, which considers the optimization over a control policy where the uncertainty of the power predictions can be compensated either by the battery or main grid power consumption. Simulation results using data from a real urban community showed that when compared with an equivalent (non-robust) deterministic EMS (i.e., an EMS based on the same MPC formulation, but without the uncertainty handling), the proposed EMS based on robust MPC achieved reduced energy costs and obtained a more uniform grid power consumption, safer battery operation, and reduced peak loads.

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Robust economic model predictive control of a community micro-grid

Cited by 36 publications

References 22 publications

Economic model predictive control based on a periodicity constraint

Economic model predictive control based on a periodicity constraint

A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid

Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control

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