A microgrid energy management system based on chance-constrained stochastic optimization and big data analytics

Marino, Carlos; Marufuzzaman, Mohammad

doi:10.1016/j.cie.2020.106392

Cited by 34 publications

(11 citation statements)

References 66 publications

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“…Reference [25] proposed a hybrid microgrid optimization model based on mixed integer linear programming, and solved the problem based on stochastic optimization method. Reference [26] combined the chance-constrained stochastic optimization with big data analysis and applied them to the micro-grid energy management system. Reference [27] used stochastic optimization framework to solve the energy scheduling problem of micro-energy grid with stochastic renewable energy generation and vehicle activity mode.…”

Section: Literature Reviewmentioning

confidence: 99%

A Two-Stage Optimal Dispatching Model for Micro Energy Grid Considering the Dual Goals of Economy and Environmental Protection under CVaR

et al. 2021

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With the development of distributed renewable energy, a micro-energy grid (MEG) is an important way to solve the problem of energy supply in the future. A two-stage optimal scheduling model considering economy and environmental protection is proposed to solve the problem of optimal scheduling of micro-energy grid with high proportion of renewable energy system (RES) and multiple energy storage systems (ESS), in which the risk is measured by conditional value-at-risk (CVaR). The results show that (a) this model can realize the optimal power of various energy equipment, promote the consumption of renewable energy, and the optimal operating cost of the system is 34873 USD. (b) The dispatch of generating units is different under different risk coefficients λ, which leads to different dispatch cost and risk cost, and the two costs cannot be optimal at the same time. The risk coefficient λ shall be determined according to the degree of risk preference of the decision-maker. (c) The proposed optimal model could balance economic objectives and environmental objectives, and rationally control its pollutant emission level while pursuing the minimum operation costs. Therefore, the proposed model can not only reduce the operation cost based on the consideration of system carbon emissions but also provide decision-makers with decision-making support by measuring the risk.

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Section: Literature Reviewmentioning

confidence: 99%

A Two-Stage Optimal Dispatching Model for Micro Energy Grid Considering the Dual Goals of Economy and Environmental Protection under CVaR

et al. 2021

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“…Smart meters play a key role in the modernization of the power grid. They create new opportunities for a better management, optimization and control of the power grid at all levels [27]. They allow two-way communication between consumers and energy providers enabling a real-time monitoring of energy usage and more efficient control of power flow and grid assets.…”

Section: Energy Management Systemmentioning

confidence: 99%

A new approach to demand response in a microgrid based on coordination control between smart meter and distributed superconducting magnetic energy storage unit

Kouache

Sebaa

Bey

et al. 2020

Journal of Energy Storage

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Energy storage and demand response are becoming an increasingly valuable solution for the enhancement of stability and reliability of the electricity grid with high penetration of distributed energy sources such as wind and solar. This paper proposes an intelligent energy management scheme to allow different distributed generation sources and energy storage technologies to actively participate in demand response via the deployed smart meter infrastructure. A Distributed Superconducting Magnetic Energy Storage (D-SMES) device is integrated into the network to deliver instantaneous and large bursts of power to support the grid under shortterm disturbances. The proposed demand response-based energy management algorithm receives, via the smart meter infrastructure, the information from the electricity network such as the energy cost, upper load adjustment limit, and the State-of-Charge (SOC) of the storage system then, under very specific conditions, a decision is formulated as a control signal for charging or discharging the storage system, aiming at minimizing the total energy while avoiding any load shedding. The effectiveness of the proposed demand response approach for controlling generation, storage and demand coordination and energy cost reduction is demonstrated on a sample power system simulated in MATLAB/SimPowerSystems toolbox and evaluated under different case study scenarios.

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“…There are also the issues of scalability due to spatial or other constraints such as a pandemic etc. across various sectors (Tran and Smith [7]; Marino and Marufuzzaman [8]; Ivanov and Das [9]; Steen and Brandsen [10]). These uncertainties give rise to the need for research, teaching and learning of various stochastic optimization techniques.…”

Section: Preparing Next Generation Business Analysts and Policy Plannersmentioning

confidence: 99%

Randomized Constraint Limit Linear Programming in Risk Management

Ridley¹,

Khan²

2020

JAMP

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Traditional linear program (LP) models are deterministic. The way that constraint limit uncertainty is handled is to compute the range of feasibility. After the optimal solution is obtained, typically by the simplex method, one considers the effect of varying each constraint limit, one at a time. This yields the range of feasibility within which the solution remains feasible. This sensitivity analysis is useful for helping the analyst get a feel for the problem. However, it is unrealistic because some constraint limits can vary randomly. These are typically constraint limits based on expected inventory. Inventory may fall short if there are overdue deliveries, unplanned machine failure, spoilage, etc. A realistic LP is created for simultaneously randomizing the constraint limits from any probability distribution. The corresponding distribution of objective function values is created. This distribution is examined directly for central tendencies, spread, skewness and extreme values for the purpose of risk analysis. The spreadsheet design presented is ideal for teaching Monte Carlo simulation and risk analysis to graduate students in business analytics with no specialized programming language requirement.

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A microgrid energy management system based on chance-constrained stochastic optimization and big data analytics

Cited by 34 publications

References 66 publications

A Two-Stage Optimal Dispatching Model for Micro Energy Grid Considering the Dual Goals of Economy and Environmental Protection under CVaR

A Two-Stage Optimal Dispatching Model for Micro Energy Grid Considering the Dual Goals of Economy and Environmental Protection under CVaR

A new approach to demand response in a microgrid based on coordination control between smart meter and distributed superconducting magnetic energy storage unit

Randomized Constraint Limit Linear Programming in Risk Management

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