Data-Driven Nonparametric Chance-Constrained Optimization for Microgrid Energy Management

Ciftci, Okan; Mehrtash, Mahdi; Kargarian, Amin

doi:10.1109/tii.2019.2932078

Cited by 37 publications

(26 citation statements)

References 34 publications

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“…EM is one of the constituents of future distribution grids' control and management system [3]. The primary function of EM is to optimally allocate power generation and delivery to sources of energy in a well-defined sense while adhering to system constraints [4][5][6][7][8][9][10]. Distributed EM is considered superior to the centralized counterpart in terms of resiliency and scalability.…”

Section: A Literature Reviewmentioning

confidence: 99%

A Dual Distributed Optimal Energy Management Method for Distribution Grids With Electric Vehicles

Hoang

Özkan

Badr

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Future distribution grids are expected to face an increasing penetration of electric vehicles (EVs) and heterogeneous distributed energy resources (DERs). This demands a distributed energy management (EM) to manage power generation and delivery of energy sources to maintain power quality under the impact of EV charging, to save operating costs, and to enhance resiliency. However, the global optimality of the distributed EM's optimization problem is still an issue in existing work because of the non-convex nature of the optimization problem. In this paper, a distributed EM strategy for grid-connected distribution networks is proposed. In particular, the EM strategy is composed of two steps. In the first step, some conditions of the EM optimization task are relaxed to apply an algorithm converging to the global optimality. The results of the first step are used to reconfigure constraints of the full optimization problem in Step 2. The proposed scheme is validated by implementing the real-time controller-hardware-in-the-loop (CHIL) experimentation on the IEEE 33 bus system. To study the impact of EV charging, EV data is

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Section: A Literature Reviewmentioning

confidence: 99%

A Dual Distributed Optimal Energy Management Method for Distribution Grids With Electric Vehicles

Hoang

Özkan

Badr

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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“…Here, the uncertainty sets are defined using probability distributions. In [18], the uncertainty sets are defined by using the historical data without imposing any specific probability distribution, which makes this approach more realistic in comparison to the chance-constrained RO. However, all of these RO techniques produce conservative solutions with considerable curtailment of the available renewable energy.…”

Section: Introductionmentioning

confidence: 99%

“…, , (P k,i BD ) = P out P in = P k,i BD P k,i BD + P k,i BD,loss (18) Where the discharging losses I BESS,BD…”

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confidence: 99%

An Integrated Energy Management Approach for the Economic Operation of Industrial Microgrids Under Uncertainty of Renewable Energy

Mohy-ud-din

Vu²,

Muttaqi

et al. 2020

IEEE Trans. on Ind. Applicat.

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© 1972-2012 IEEE. Many modern industries are equipped with onsite renewable generation and are normally connected to the grid. A battery energy storage system (BESS) can complement the intermittency of the available onsite renewable generation. The combination of the BESS and the renewable generation can operate as a microgrid. If the microgrid is properly sized and managed, it is possible to reduce the electricity bill to have a huge saving in the electricity cost. This article proposes an energy management system for such an industrial microgrids. The decisions to charge and discharge the BESS in the proposed energy management are usually constrained by the size of the energy storage. The proposed energy management strategy aims to optimize the operation of the industrial microgrids subject to the scalability of the BESS under uncertainties. The proposed optimization involves two stages. In the first stage of optimization, it determines the optimum size of the energy storage taking into account the cost of the BESS, and in the second stage, it minimizes the cost of the microgrid operation based on the decision made in the first stage. This proposed two-stage energy management strategy is formulated as a single-stage linear program that incorporates stochastic scenarios for addressing uncertainties. In addition, the proposed strategy also considers the various operating limits of the energy storage, such as the efficiency and the charging and the discharging rates, and considers the fading effect of the batteries of the BESS. The proposed strategy is then validated using two typical datasets from two different industrial units in New South Wales, Australia. The simulation results show that the proposed strategy 1 Abstract-Many modern industries are equipped with on-site renewable generation and are normally connected to the grid. A battery energy storage system (BESS) can complement the intermittency of the available on-site renewable generation. The combination of the BESS and the renewable generation can operate as a microgrid. If the microgrid is properly sized and managed, it is possible to reduce the electricity bill to have a huge saving in the electricity cost. This paper proposes an energy management system for such an industrial microgrids. The decisions to charge and discharge the BESS in the proposed energy management are usually constrained by the size of the energy storage. The proposed energy management strategy aims to optimize the operation of the industrial microgrids subject to the scalability of the BESS under uncertainties. The proposed optimization involves two stages. In the first stage of optimization, it determines the optimum size of the energy storage taking into account the cost of the BESS, and in the second stage, it minimizes the cost of the microgrid operation based on the decision made in the first stage. This proposed two-stage energy management strategy is formulated as a single stage linear program that incorporates stochastic scenarios for addressing uncertainties. In addition...

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“…building-integrated battery energy storage (BES) or heat storage tank, can be economically beneficial for the ZEB owner as well as for the grid (e.g. in congestion relieving manner) [3][4][5][6]. This paper focuses on finding the optimum size of each energy production, storage and energy-efficient device at ZEB planning stage that satisfies the zero energy constraint.…”

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confidence: 99%

“…Constraint (4) imposes that at each time interval one of the binary variables ‫ܫ‬ ା ሺ‫ݐ‬ሻ and ‫ܫ‬ ି ሺ‫ݐ‬ሻ is equal to one, and the other one must be zero. In the case of having excess of electricity at time interval ‫,ݐ‬ the excess of electric power can be calculated by (5). Similarly, for intervals that deficit of electricity exists, the deficit of electric power can be obtained by (6).…”

mentioning

confidence: 99%

A New Bi-Objective Approach for Optimal Sizing of Electrical and Thermal Devices in Zero Energy Buildings Considering Environmental Impacts

Mehrtash

Capitanescu

Heiselberg

et al. 2021

IEEE Trans. Sustain. Energy

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This paper proposes a new bi-objective optimization model, trading-off cost and environmental impacts, for sizing the key electrical and thermal devices in a zero energy building (ZEB), i.e. a building that roughly generates as much renewable energy as it consumes annually. A salient novel feature is the consideration of the environmental impacts, computed through a rigorous life cycle assessment approach, of buying electricity from the grid and manufacturing devices. Furthermore, an enhancement of the proposed model, as compared to the existing models, is to prioritize storing the ZEB excess of energy rather than selling it to the grid. The proposed solution approach of the initial mixedinteger nonlinear programming model relies on McCormick relaxation linearization to obtain a more tractable mixed-integer linear model. An augmented ε-constraint method is applied to solve the obtained bi-objective model. Finally, considering the building owners' willingness-to-pay for environmental impacts, a decision-making criterion is proposed to select the optimal size of the devices among all non-dominated solutions of the Pareto front.

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Data-Driven Nonparametric Chance-Constrained Optimization for Microgrid Energy Management

Cited by 37 publications

References 34 publications

A Dual Distributed Optimal Energy Management Method for Distribution Grids With Electric Vehicles

A Dual Distributed Optimal Energy Management Method for Distribution Grids With Electric Vehicles

An Integrated Energy Management Approach for the Economic Operation of Industrial Microgrids Under Uncertainty of Renewable Energy

A New Bi-Objective Approach for Optimal Sizing of Electrical and Thermal Devices in Zero Energy Buildings Considering Environmental Impacts

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