IGDT-Based Robust Optimization for Multicarrier Energy System Management

Afrashi, K.; Bahmani-Firouzi, Bahman; Nafar, Mehdi

doi:10.1007/s40998-020-00356-y

Cited by 23 publications

(59 citation statements)

References 30 publications

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“…e economic dispatching model of the hub considering minimization of the hub cost is presented in [22]. Besides, some research expresses a market model for hubs, such as in [23], where the day-ahead (DA) electrical energy market method is used for grid-connected hubs, including RESs, an electrical storage system (ESS), and CHP. Also, the DA energy market for electrical, gas, and heating energies is presented in [24] for networked hubs.…”

Section: Introductionmentioning

confidence: 99%

“…us, they can increase their financial benefit by engaging in ancillary services markets like the reserve market, which has rarely been studied. Also, in most research such as [14][15][16][17][18][19][20][21][22][25][26][27][28][29], the operation model of hubs has been considered, while considering the model of the energy market can obtain a suitable financial benefit for it, and this is also the case that has rarely been addressed [23,24]. (iii) e operation model of the energy hub in different energy networks such as NLP and MINLP is nonconvex.…”

Section: Introductionmentioning

confidence: 99%

“…Also, their solvers generally cannot find the optimal single solution. To compensate for this, some research works such as [20,23,24] have used linear modeling for the main problem, the solvers of which can calculate the unique response in the shortest possible time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Participation of Energy Storage-Based Flexible Hubs in Day-Ahead Reserve Regulation and Energy Markets Based on a Coordinated Energy Management Strategy

Kazemi

Salehpour

Shahbaazy

et al. 2022

International Transactions on Electrical Energy Systems

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In order to increase energy efficiency, the energy hub is considered as a form of aggregator and coordinator of various resources and storage. With the optimal performance of resources and storage generators based on a proper energy management system, it is expected that hubs can gain financial benefits from energy markets and ancillary services. So, the paper presents the participation of networked energy hubs in day-ahead (DA) reserve regulation and energy markets, where the hub operator incorporates a coordinated energy management (CEM) strategy to manage power sources and energy storage devices within the hub. Hence, this problem maximizes the total profit of hubs in the DA energy and up and down reserve markets. Also, the problem is constrained by optimal power flow (OPF) constraints in gas, electricity, and thermal networks, reserve limits, and hub constraints, including the model of the combined heat and power (CHP), renewable energy source (RES), electrical/thermal storage, parking lots of electric vehicles (EVs), and boiler. Following that, a linear format is obtained for the nonlinear equation using traditional linearization methods so that an optimal solution is found in less time considering less computational error. Eventually, a standard case system is used to test the strategy, and thus, the capabilities of the approach are investigated. The obtained findings validate the potential of the proposed design in enhancing the economic situation of power sources and storage in hub form, which can enhance operation indices by optimal management of the hub so that the energy management of resources and storage in the form of a hub based on CEM compared to their independent management plan has been able to increase the profit of these elements in energy and up and down reserve markets by about 17%, 28%, and 15%, respectively. Regarding technical indices of energy networks, the proposed scheme by creating low energy losses in the gas network and providing pressure drop, overvoltage, and overtemperature within their permissible limits succeeded in reducing the energy losses in electricity and heat networks by about 83% and 38%, respectively, compared to power flow studies. Also, in these conditions, it has reduced the maximum voltage and temperature drop by 45% and 39%, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Participation of Energy Storage-Based Flexible Hubs in Day-Ahead Reserve Regulation and Energy Markets Based on a Coordinated Energy Management Strategy

Kazemi

Salehpour

Shahbaazy

et al. 2022

International Transactions on Electrical Energy Systems

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“…In the framework, the uncertainties of load demands and wind power generation are studied using the Monte Carlo simulation method and IGDT, respectively. In [18], the authors proposed an IGDT-based robust problem model for multi-carrier energy system management; it uses an IGDT model for handling the uncertainties associated with VRE. Although the interval-and IGDT-based robust models are useful for uncertainties, optimization results are conservative [10].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Multi-Energy Microgrid Operation in the Presence of PV, Heterogeneous Energy Storage and Integrated Demand Response

Wang

Liang

et al. 2021

Applied Sciences

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In this paper, a model is proposed for the optimal operation of multi-energy microgrids (MEMGs) in the presence of solar photovoltaics (PV), heterogeneous energy storage (HES) and integrated demand response (IDR), considering technical and economic ties among the resources. Uncertainty of solar power as well as the flexibility of electrical, cooling and heat load demand are taken into account. A p-efficient point method is applied to compute PV power at different confidence levels based on historical data. This method converts the uncertain PV energy from stochastic to deterministic to be included in the optimization model. The concept of demand response is extended and mathematically modeled using a linear function based on the quantized flexibility interval of multi-energy load demand. As a result, the overall model is formulated as a mixed-integer linear program, which can be effectively solved by the commercial solvers. The proposed model is implemented on two typical summer and winter days for various cases. Results of case studies show the important benefits for maximum PV utilization, energy efficiency and economic system operation. Moreover, the influence of the different confidence levels of PV power and effectiveness of IDR in the stochastic circumstances are addressed in the optimization-based operation.

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“…Therefore, IGDT has a certain degree of blindness. 33,34 In contrast, scene analysis can generate a large number of samples and cause computational burden, but it can use backward scenario reduction technique to reduce. This article adopts the scenario analysis method and uses the backward scenario reduction technology to classify the scenarios into six types, which can handle the computational burden well.…”

mentioning

confidence: 99%

Bi‐level scheduling of large‐scale electric vehicles based on the generation side and the distribution side

Chang

Niu

Jia

2021

Int Trans Electr Energ Syst

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With the intensification of energy shortages and climate warming, how to achieve the coordinated dispatch of electric vehicles (EVs) and high-proportion renewable energy systems (RESs) in the power system has been attracting more and more attentions. From the generation side and the distribution side, this work investigates the problem of large-scale EVs charging and discharging scheduling via the collaborative optimization among thermal power plants, wind and solar power, and EVs. To this end, an energy scheduling system (ESS) including power transmission and distribution is established, based on which a bi-level scheduling strategy is proposed. The upper level coordinately optimizes EVs, thermal power units, wind and solar power generation, and grid base loads to achieve the minimization of power supply costs. By introducing the node loss sensitivity (NLS) and node electricity price (NEP), the lower level allocates EVs to charging nodes to attain the minimization of the grid loss cost and charging cost. Finally, via the IEEE33-node standard power grid and the ESS model, it is verified that the proposed bi-level scheduling strategy can reduce the overall cost (node loss and charging cost) by 28.5%, and the wind curtailment rate and photovoltaic curtailment rate are 0.0% and 0.4%, respectively, making full use of renewable energy and adapting to its uncertainty.

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IGDT-Based Robust Optimization for Multicarrier Energy System Management

Cited by 23 publications

References 30 publications

Participation of Energy Storage-Based Flexible Hubs in Day-Ahead Reserve Regulation and Energy Markets Based on a Coordinated Energy Management Strategy

Participation of Energy Storage-Based Flexible Hubs in Day-Ahead Reserve Regulation and Energy Markets Based on a Coordinated Energy Management Strategy

Optimization of Multi-Energy Microgrid Operation in the Presence of PV, Heterogeneous Energy Storage and Integrated Demand Response

Bi‐level scheduling of large‐scale electric vehicles based on the generation side and the distribution side

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