Two-layer optimization methodology for wind distributed generation planning considering plug-in electric vehicles uncertainty: A flexible active-reactive power approach

Ahmadian, Ali; Sedghi, Mahdi; Golkar, Masoud Aliakbar; Fowler, Michael; Elkamel, Ali

doi:10.1016/j.enconman.2016.07.025

Cited by 56 publications

(29 citation statements)

References 32 publications

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“…The planning results suggest that optimal allocations of ESSs and capacitors are beneficial to improving penetration and utilization level of RDGs and achieving the upgrade deferral. Similar with [75], the benefits brought by optimal allocation of voltage control devices on accommodation of RDGs are also verified in [41,68,79,88,96]. …”

Section: Planning Objectivesmentioning

confidence: 57%

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A Review of Optimal Planning Active Distribution System: Models, Methods, and Future Researches

Wang

Chen³

et al. 2017

Energies

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Due to the widespread deployment of distributed energy resources (DERs) and the liberalization of electricity market, traditional distribution networks are undergoing a transition to active distribution systems (ADSs), and the traditional deterministic planning methods have become unsuitable under the high penetration of DERs. Aiming to develop appropriate models and methodologies for the planning of ADSs, the key features of ADS planning problem are analyzed from the different perspectives, such as the allocation of DGs and ESS, coupling of operation and planning, and high-level uncertainties. Based on these analyses, this comprehensive literature review summarizes the latest research and development associated with ADS planning. The planning models and methods proposed in these research works are analyzed and categorized from different perspectives including objectives, decision variables, constraint conditions, and solving algorithms. The key theoretical issues and challenges of ADS planning are extracted and discussed. Meanwhile, emphasis is also given to the suitable suggestions to deal with these abovementioned issues based on the available literature and comparisons between them. Finally, several important research prospects are recommended for further research in ADS planning field, such as planning with multiple micro-grids (MGs), collaborative planning between ADSs and information communication system (ICS), and planning from different perspectives of multi-stakeholders.

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Section: Planning Objectivesmentioning

confidence: 57%

“…The process is shown in Figure 9. Then, combined with the approaches of probabilistic optimal power flow [44,60,79], chance constrained programming [62,70], etc., these simulated data of RDGs and load demand could be used in ADS planning models considering high-level uncertainties.…”

Section: Probabilistic Approachesmentioning

confidence: 99%

A Review of Optimal Planning Active Distribution System: Models, Methods, and Future Researches

Wang

Chen³

et al. 2017

Energies

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“…Through the substitution of variables, the formula (34) satisfies the requirements of the linear constraint conditions of cone optimization; equations (39) and (40) constitute the Descartes product form of the quadratic rotating cone, and the search space satisfies the range of the convex cone. When the optimization model is processed into linearization form, the search space of the decision variable is satisfied with the range of the convex cone, which is beneficial to improve the efficiency of the optimization.…”

Section: Cone Transformation Methods For Constrained Conditionsmentioning

confidence: 99%

A power balancing method of distributed generation and electric vehicle charging for minimizing operation cost of distribution systems with uncertainties

Jiekang

et al. 2017

Energy Science & Engineering

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The output power of distributed generation depends on the wind speed, solar light intensity, and other climatic factors, which have intermittent and uncertain characteristics [4]. Because of the difference in energy state of battery, the expected driving distance in a day, the charging power, the expected charging time, and so on, electric vehicle charging also has uncertain characteristics. The uncertainties of distributed generation and electric vehicle charging may bring a great impact on distribution systems. The randomness and uncertainties of distributed MODELING AND ANALYSISA power balancing method of distributed generation and electric vehicle charging for minimizing operation cost of distribution systems with uncertainties AbstractA power balancing method of distributed generation (DG) and electric vehicle charging is presented for minimizing operation costs of distribution systems with uncertainties, which includes the uncertainties in output power of DG and the randomness of charging power of electric vehicles (EV). A probability model is established for the uncertain characteristics of DG and electric vehicle charging. A multi-state optimization coordination method for DG of renewable energy systems and electric vehicle charging in distribution systems based on quadratic rotation cone programming is presented to minimize the expected generation cost of generators in the main power grid, the expected operation cost of DG systems in distribution systems, and the expected social outage loss. An objective function maximizing the operation efficiency of distribution systems with DGs and EVs is proposed. Using quadratic rotated conic programming, the nonlinear objective function and constraint functions are transformed into a linear form. An IEEE 14-node distribution system is used as a study example to illustrate adaptability of the proposed model and the feasibility of the proposed method. The simulation results show that the proposed method simplifies the original problem of the optimization problem and makes its solution faster, more stable, and better. 168

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“…Park and Law conducted a study of using sequential convex programming for wind farm layout optimization for maximizing power production. Ahmadian et al presented two‐layer optimization methodology for wind‐distributed generation planning using a flexible active‐reactive power approach. Zha et al used risk model for selection of time window for wind power ramp prediction.…”

Section: Introductionmentioning

confidence: 99%

Technoeconomic study for the optimization of turbine size and wind farm layouts

Nada

Elsayed

El‐Sayed³

2019

Int J Energy Res

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Summary A technoeconomic analysis and optimization of wind turbine size and layout are performed using WAsP software. A case study of a 100‐MW wind farm located in Egypt is considered. Wind atlas for Egypt was used as the input data of the WAsP software. Two turbine models of powers 52 and 80 MW are considered for this project. The wind turbine size and distributions are selected based on the technoeconomic optimization, namely minimum wake effect, maximum annual energy production (AEP) rate, optimum cash flow, and payback period. The future worth method is adopted in economic comparison between the two alternatives, and the cash flow diagram provided the payback period and future worth after the lifetime of the plant. The results showed that (1) the AEP dramatically decreases for a wind farm area less than 15 km2; (2) the turbine spacing, spacing‐to‐diameter ratio, and the setback distances decrease and the wind turbine density and wake losses increase with decreasing the wind turbines size; (3) the total net AEP using G52 is lower than that of using G80 by about 16%; (4) the technoeconomic analysis recommended using G80 as it has higher profit than those of G52 by about $20 million.

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Two-layer optimization methodology for wind distributed generation planning considering plug-in electric vehicles uncertainty: A flexible active-reactive power approach

Cited by 56 publications

References 32 publications

A Review of Optimal Planning Active Distribution System: Models, Methods, and Future Researches

A Review of Optimal Planning Active Distribution System: Models, Methods, and Future Researches

A power balancing method of distributed generation and electric vehicle charging for minimizing operation cost of distribution systems with uncertainties

Technoeconomic study for the optimization of turbine size and wind farm layouts

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