Two-stage robust optimization for expansion planning of active distribution systems coupled with urban transportation networks

Xie, Shiwei; Hu, Zhijian; Wang, Jueying

doi:10.1016/j.apenergy.2019.114412

Cited by 87 publications

(30 citation statements)

References 40 publications

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“…For the first generation scenario, Figure 3 compares the results obtained from the proposed model, considering and disregarding Equations (11)- (15), which limits the number of charging and discharging cycles. To that end, for such simulations including the impact of Equations (11)-(15), the user-defined parameter, M, has been set to 2.…”

Section: Resultsmentioning

confidence: 99%

“…The volume of energy stored in period t is modelled in (9), which must comply with the energy capacity limits according to the Equation (10). Besides, as suggested in [26], to prevent a high the number of cycles performed by the energy storage systems and thus, minimize degradation issues, Equations (11)- (15) are included in the formulation. Finally, the binary nature of scheduling variables δ C st and δ D st is modelled in (16).…”

Section: Proposed Optimization Model For the Active Power Scheduling mentioning

confidence: 99%

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Optimal Scheduling for Energy Storage Systems in Distribution Networks

et al. 2020

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Distributed energy storage may play a key role in the operation of future low-carbon power systems as they can help to facilitate the provision of the required flexibility to cope with the intermittency and volatility featured by renewable generation. Within this context, this paper addresses an optimization methodology that will allow managing distributed storage systems of different technology and characteristics in a specific distribution network, taking into account not only the technical aspects of the network and the storage systems but also the uncertainties linked to demand and renewable energy variability. The implementation of the proposed methodology will allow facilitating the integration of energy storage systems within future smart grids. This paper’s results demonstrate numerically the good performance of the developed methodology.

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Section: Resultsmentioning

confidence: 99%

Section: Proposed Optimization Model For the Active Power Scheduling mentioning

confidence: 99%

Optimal Scheduling for Energy Storage Systems in Distribution Networks

et al. 2020

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“…The power balancing constraint (15) and the high sharing RES integration constraint (16) are modified to represent the RES uncertainty as defined in (21), (22), and (23).…”

Section: Renewable Energy Overcapacity Methodsmentioning

confidence: 99%

“…Renewable energies and demand uncertainties are described through a robust set. 23 The robust set is defined with different operating conditions via a multiscenario set. The probabilistic approach is useful in case a sufficient historical data of the system variables are available.…”

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confidence: 99%

Robust generation expansion planning considering high penetration renewable energies uncertainty

Abdalla

Adma

Ahmed

2020

Engineering Reports

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The proper optimal generation expansion planning (GEP) should meet the reliability criteria requirements over a planning horizon under the presence of uncertainties. The intermittent nature of renewable energy sources (RES) introduces an enormous uncertainties impact within the planning model. A simulation model for RES uncertainty is developed using the capacity factor (CF) of the RES historical data. The RES simulation model is handled via the probability density function (PDF). The uncertainty parameter of different RES is described as a flexible polyhedral uncertainty set and incorporated within the proposed GEP model. The influence of different uncertainty scenarios for each RES uncertainty on the GEP model can be analyzed separately. The RES uncertainty scenarios are predefined and incorporated within the proposed GEP model through a proposed parameter named as a confidence level. The proposed confidence level parameter is beneficial to the power system planner to control the degree of robustness. Different GEP results are presented for various RES uncertainty scenarios. Three methods are proposed as appropriate solutions to deal with the RES uncertainty impact. The most economical method among the three proposed methods is determined by developing an objective function tailored to achieve the optimality of the economic factor.

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“…It is shown that the traffic demand can affect the strategies of the distribution system since higher traffic demand can require more charging facilities. Another study of the same authors 30 proposes a two‐stage cost minimization model for a similar system. Both studies deal with uncertain traffic demand, which affects charging requirements and so distribution system indirectly.…”

Section: Related Workmentioning

confidence: 99%

Economic model for an electric vehicle charging station with v ehicle‐to‐grid functionality

et al. 2020

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Summary Energy consumption is increasing in parallel with the population and technological advancements. Albeit the energy efficiency efforts, industrial consumption, and transportation have quite important roles in this increase. The electric vehicles (EVs) are expected to constitute a positive solution to mitigate energy use in transportation. Increasing penetration of EVs into public transport provides new opportunities both for environmental sustainability and transportation infrastructure. To project the potential operator revenue, we set up a hybrid model (solar and storage), which optimizes scheduling for the EV charging unit and evaluates environmental and economic benefits. Different operational scenarios of PV‐assisted charging stations will be simulated and solved with Rule‐based decision making. Since regulations have an important role in building the optimization model, scenarios are created considering different European regulations. Uncertainties depending on the traffic jam (arrival‐departure times), technological differences of the charging stations are encountered in the model. The results are compared among three different scenarios. Net present value of the system changes for different scenarios and electricity sale price is one of the main factors. This value tends to increase after an annual electricity sale price increase rate of 11%. Novelty statement In this study, the effect of different economic parameters on an electric vehicle charging station were analyzed. A system with electric vehicles, solar panel support, and external battery was mathematically modeled and a case study was done.

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Two-stage robust optimization for expansion planning of active distribution systems coupled with urban transportation networks

Cited by 87 publications

References 40 publications

Optimal Scheduling for Energy Storage Systems in Distribution Networks

Optimal Scheduling for Energy Storage Systems in Distribution Networks

Robust generation expansion planning considering high penetration renewable energies uncertainty

Economic model for an electric vehicle charging station with v ehicle‐to‐grid functionality

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