A robust optimization based approach for microgrid operation in deregulated environment

Gupta, Rajan; Gupta, Nitin

doi:10.1016/j.enconman.2015.01.008

Cited by 97 publications

(51 citation statements)

References 16 publications

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“…Wind and solar unpredictability and batteries management methods are presented in [15][16][17]. Optimal schedule in a microgrid is discussed in [18] for both islanded and grid-connected operation. Other methods such as mixed integer linear programming [19], prediction intervals [20] or load demand prediction [21] exist.…”

Section: Fig 1 Islanded Renewable Energy Microgridmentioning

confidence: 99%

An islanded microgrid energy management controller validated by using hardware-in-the-loop emulators

Petreuş

Etz

Pătărău

et al. 2019

International Journal of Electrical Power & Energy Systems

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A novel microgrid emulator used to test multiple microgrid configurations and energy management control strategies is presented. The system includes Hardware-In-the-Loop (HIL) emulators for geothermal and biogas energy sources. It also includes actual photovoltaic energy together with a lead acid battery bank for storage and it is controlled by a two level control system. The control system consists of a primary level voltage-reactive power and frequency-active power and a secondary level energy management algorithm based on the balance between the power produced by the renewable energy generators, state of charge of the battery bank and the loads. The energy management control strategy is based on cycle-charging the batteries at a reference value in order to efficiently use the available resources. The primary energy source is considered the geothermal energy, while the most cost effective one is the photovoltaic energy. A buffer zone is kept in the battery bank in order to store as much energy produced by the photovoltaic system as possible. The presented microgrid, used for testing the energy management strategies, employs emulators for the geothermal and biogas generators; however, the results are relevant and can be scaled for a real-life microgrid.Keywords: microgrid, power management, renewable energies, two level control, cycle charge control. Auxiliary equipment and communicationA method to send the commands to the microgrid and to measure the process variables from the microgrid is needed for the control algorithm based on battery cycle charging implementation. A bidirectional three wire RS485 serial communication network is built for this purpose. The communication protocol used in the network uses Modbus compatible data frameworks but is also compatible with the data protocols used by the inverters. Beside the communication modules needed by the inverters, there is a need for other equipment in order to give the geothermal and biomass emulators communication capabilities. The command signals are fed to the emulators through Modbus to analog converters and Modbus output compatible power meters are used for the electrical measurement. The network communication master is the central computing unit. The program that implements the master's communication functions and the cycle charging battery management algorithm is written in ANSI C, thus being highly portable. The physical interface between the network and the master is made by an USB to RS-485 interface. The connections of the equipment are presented in Fig. 22. The set points for the SMA equipment are directly sent through the communication network, but the emulators need Modbus to analog transducers for their commands.

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Section: Fig 1 Islanded Renewable Energy Microgridmentioning

confidence: 99%

An islanded microgrid energy management controller validated by using hardware-in-the-loop emulators

Petreuş

Etz

Pătărău

et al. 2019

International Journal of Electrical Power & Energy Systems

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“…However, in standard robust optimization, the uncertainties are determined before solving the problem, i.e., static robust optimization. Therefore, a variant of the standard robust optimization, adaptive robust optimization, is proposed to overcome the drawbacks of static robust optimization [19][20][21][22][23][24][25]. The microgrid operation problem is solved in two steps, inner and outer, in the adaptive robust optimization method.…”

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confidence: 99%

“…The on/off decisions of dispatchable generators are made in the outer problem, and uncertainties are determined in the inner problem. The output of DGs, the charging/discharging amount of storage elements, and the amount of power trading with the utility grid are adapted in accordance with the revealed uncertainties in the inner problem.The two-step adaptive robust optimization method is widely used to incorporate uncertainties into the operation model of microgrids [19][20][21][22][23][24]. The authors in [19] developed a method to derive an exact solution for two-step mixed integer programming problems in finite steps.…”

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confidence: 99%

“…Similarly, column and constraint cuts were introduced in [20] to expedite the convergence process of adaptive robust optimization problems. These approaches have been widely used in the literature to solve two-step adaptive robust optimization problems [21][22][23][24]. Load uncertainty and component outages are considered in [21] for the unit commitment problem of microgrids.…”

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confidence: 99%

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Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles

2018

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The optimal operation of microgrids is challenging due to the presence of various uncertain factors, i.e., renewable energy sources, loads, market price signals, and arrival and departure times of electric vehicles (EVs). In order to incorporate these uncertainties into the operation model of microgrids, an adaptive robust optimization-based operation method is proposed in this paper. In particular, the focus is on the uncertainties in arrival and departure times of EVs. The optimization problem is divided into inner and outer problems and is solved iteratively by introducing column and constraint cuts. The unit commitment status of dispatchable generators is determined in the outer problem. Then, the worst-case realizations of all the uncertain factors are determined in the inner problem. Based on the values of uncertain factors, the generation amount of dispatchable generators, the amount of power trading with the utility grid, and the charging/discharging amount of storage elements are determined. The performance of the proposed method is evaluated using three different cases, and sensitivity analysis is carried out by varying the number of EVs and the budget of uncertainty. The impact of the budget of uncertainty and number of EVs on the operation cost of the microgrid is also evaluated considering uncertainties in arrival and departure times of EVs.

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“…In [13], a dynamic optimization model is proposed to minimize operating costs and CO 2 emissions, and is applied to the University of Genova Savona Campus test-bed facilities. A robust optimization approach for optimal microgrid management considering wind power uncertainty is presented in [14], in which a time-series based autoregressive integrated moving average model is used to characterize the wind power uncertainty through interval forecasting. A decentralized EMS for microgrids is described in [15], based on a multi-agent system, and a, centralized EMS is compared with the proposed decentralized EMS.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective energy management system for DC microgrids based on the maximum membership degree principle

Wang

Meng

et al. 2017

J. Mod. Power Syst. Clean Energy

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With the increasing quantity of DC electrical equipment, DC microgrids have been paid more and more attention. This paper proposes an approach to multi-objective optimisation of an energy management system (EMS) for a DC microgrid that includes a hybrid energy storage system (HESS). The operating and maintenance cost and the loss of power supply probability (LPSP) of the system are used as optimisation targets. The power flows of all distributed generators (DGs) in the DC microgrid during operating period are optimized. Based on the improved differential evolution (DE) algorithm, and by using the multi-objective non-dominated sorting method and the maximum membership degree principle (MMDP) of fuzzy control, the overall satisfaction degree of Pareto solutions to power flow optimization can be obtained. Simulation results verify the effectiveness of the proposed EMS optimization scheme, which is able to achieve an effective trade-off between the economy and the reliability of microgrid operation.

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A robust optimization based approach for microgrid operation in deregulated environment

Cited by 97 publications

References 16 publications

An islanded microgrid energy management controller validated by using hardware-in-the-loop emulators

An islanded microgrid energy management controller validated by using hardware-in-the-loop emulators

Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles

Multi-objective energy management system for DC microgrids based on the maximum membership degree principle

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