Microgrids Energy Management Using Robust Convex Programming

Giraldo, Juan S.; Castrillon, Jhon A.; López, Juan Camilo; Rider, Marcos J.; Castro, Carlos A.

doi:10.1109/tsg.2018.2863049

Cited by 123 publications

(53 citation statements)

References 42 publications

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“…The BP-PID scheme has proven helpful in suppressing DC current based on the LCL-type three-phase grid-connected inverter. Several recent scientific studies on power grid show that receding robust optimization instead of classical optimization is a viable technique to deal with uncertainty of parameters [19][20][21]. The BP neural network can automatically regulate the coefficients of the PID controller based on the dynamic DC current.…”

Section: Methodsologymentioning

confidence: 99%

A Genetic-Algorithm-Based DC Current Minimization Scheme for Transformless Grid-Connected Photovoltaic Inverters

et al. 2020

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Transformerless grid-connected inverters are of great industrial value in photovoltaic power generation. However, the direct current (DC) induced into the inverter's output degrades the power quality of the grid. Recently, a back-propagation neural work proportional-integral-derivative (BP-PID) scheme has proven helpful in solving this problem. However, this scheme can be improved by reducing the suppressing time and overshoot. A genetic algorithm (GA)-based DC current minimization scheme, namely the genetic-algorithm-based BP-PID (GA-BP-PID) scheme, was established in this study. In this scheme, GA was used off-line to optimize the initial weights within the BP neural network. Subsequently, the optimal weight was applied to the online DC current suppression process. Compared with the BP-PID scheme, the proposed scheme can reduce the suppressing time by 59% and restrain the overshoot. A prototype of the proposed scheme was implemented and tested on experimental hardware as a proof of concept. The results of the scheme were verified using a three-phase inverter experiment. The novel GA-PB-PID scheme proposed in this study was proven efficient in reducing the suppressing time and overshoot.

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Section: Methodsologymentioning

confidence: 99%

A Genetic-Algorithm-Based DC Current Minimization Scheme for Transformless Grid-Connected Photovoltaic Inverters

et al. 2020

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“…Nonetheless, the performance of the controller could be further improved by considering a robust MPC scheme. This type of controller can achieve solutions that remain feasible even if uncertain variables are changing, which has been shown to be really useful on similar power grids problems [43,44].…”

Section: Parameter Uncertainty and Robustnessmentioning

confidence: 99%

Optimal Energy Management in a Standalone Microgrid, with Photovoltaic Generation, Short-Term Storage, and Hydrogen Production

et al. 2020

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This paper addresses the energy management of a standalone renewable energy system. The system is configured as a microgrid, including photovoltaic generation, a lead-acid battery as a short term energy storage system, hydrogen production, and several loads. In this microgrid, an energy management strategy has been incorporated that pursues several objectives. On the one hand, it aims to minimize the amount of energy cycled in the battery, in order to reduce the associated losses and battery size. On the other hand, it seeks to take advantage of the long-term surplus energy, producing hydrogen and extracting it from the system, to be used in a fuel cell hybrid electric vehicle. A crucial factor in this approach is to accommodate the energy consumption to the energy demand and to achieve this, a model predictive control (MPC) scheme is proposed. In this context, proper models for solar estimation, hydrogen production, and battery energy storage will be presented. Moreover, the controller is capable of advancing or delaying the deferrable loads from its prescheduled time. As a result, a stable and efficient supply with a relatively small battery is obtained. Finally, the proposed control scheme has been validated on a real case scenario.

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“…where P m, t r is the PV active power output from the household m at a time t, I β, t is the actual irradiance, A is the total area of the PV [11] two-point estimate method ✓ ✓ ✓ D [16] tabu search method ✓ ✓ ✓ D [25] roulette wheel mechanism ✓ ✓ ✓ D [12] Stackelberg game ✓ ✓ D [13] routing algorithm ✓ ✓ D [23] semi-Markov process ✓ S [24] bottom-up model (random case) ✓ D [26] robust optimisation ✓ D [10] bi-level optimisation ✓ D [9] robust optimisation ✓ ✓ ✓ D [4,8] robust optimisation ✓ ✓ D [3] maximum power point tracking ✓ ✓ D [7] affine arithmetic method ✓ ✓ D [6] bi-level, imperialist competition algorithm ✓ ✓ D [5] two-stage stochastic programming ✓ ✓ D [19] chance constrained programming ✓ ✓ D [20][21][22] robust optimisation ✓ ✓ D [28] approximate dynamic programming and Monte Carlo simulation ✓ ✓ S [18] robust transient stability constrained ✓ D [2] two-period stochastic programming (historical data) ✓ ✓ ✓ D [14] approximate dynamic programming (expected value) ✓ ✓ D [15] graphical array, ρ is the short-circuit temperature coefficient and T and T ref are practical and reference temperature coefficients, respectively. According to Tan et al [40], the variation in solar irradiance will ultimately result in a change in the cell temperature.…”

Section: Probabilistic Model Of Pv Generationmentioning

confidence: 99%

“…Energy storage (ES) is usually integrated with renewable generation to improve the reliability and efficiency of the power grid [2,3]. Energy management system (EMS) integrates the renewable generation and ES is invested in [4][5][6][7][8]. Specifically, to maximise utility for the demands with uncertain distributed renewable energy and customers' power demand, Rahimiyan et al in [4] propose a robust optimisation algorithm, which allows the customer to operate at a suitable time.…”

Section: Introductionmentioning

confidence: 99%

Parallel stochastic programming for energy storage management in smart grid with probabilistic renewable generation and load models

Wang¹,

Liang²,

Dinavahi³

2019

IET Renewable Power Generation

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Renewable power generation combined with energy storage (ES) is expected to bring enormous economical and environmental benefits to the future smart grid. However, the ES management in smart grid is facing significant technical challenges due to the volatile nature of renewable energy sources and the buffering effect of ES units. The challenges are further complicated by the increasing size and complexity of the system, as well as the consideration of random usage patterns of electrical appliances by customers. To address these challenges, this study proposes a parallel decomposition method for large-scale stochastic programming in a distribution system with renewable energy sources and ES units. By leveraging nested decomposition, the problem can be converted into independent sub-problems with a series of time periods. In addition, the reformulated problem is fully parallel for speed up in execution. The performance of the proposed method is evaluated based on the IEEE 4-bus and 33-bus test distribution systems with real photovoltaic generation and electrical appliance usage data. The case study demonstrates that the proposed scheme can substantially reduce the system operation cost, with low computational complexity.

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Microgrids Energy Management Using Robust Convex Programming

Cited by 123 publications

References 42 publications

A Genetic-Algorithm-Based DC Current Minimization Scheme for Transformless Grid-Connected Photovoltaic Inverters

A Genetic-Algorithm-Based DC Current Minimization Scheme for Transformless Grid-Connected Photovoltaic Inverters

Optimal Energy Management in a Standalone Microgrid, with Photovoltaic Generation, Short-Term Storage, and Hydrogen Production

Parallel stochastic programming for energy storage management in smart grid with probabilistic renewable generation and load models

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