Risk‐based self‐scheduling for GenCos in the day‐ahead competitive electricity markets

Yamin, H.Y.; Altawil, Ibrahim; Al-Ajlouni, Ahmad F.; Shahidehpour, Mohammad

doi:10.1002/etep.245

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…For instance, the constraint (31) is related to the flow limit of branches. According to Table VI, for the single objective case, the sub-constraint related to branch B22 (16)(17)(18)(19), shown in the second column of the table, has the highest sensitivity factor among the 46 sub-constraints of the constraint (31) (there are 46 branches in the test system). Zero marginal value in Table VI means that the objective function has no sensitivity with respect to the sub-constraints of the corresponding constraint (this constraint is not binding for the optimization problem).…”

Section: Numerical Resultsmentioning

confidence: 99%

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Stochastic multi-objective congestion management in power markets improving voltage and transient stabilities

Esmaili

Amjady

Shayanfar

2010

Euro. Trans. Electr. Power

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Congestion management is traditionally done using deterministic values of power system parameters. After relieving congestion using conventional methods, the network may be operated with a low voltage or transient stability margin because of hitting security limits. In this paper, a stochastic multi-objective framework considering power system uncertainties is proposed to enhance voltage and transient stabilities after congestion management. The uncertainty sources incorporate contingencies of generating units and branches. The proposed multi-objective framework simultaneously optimizes competing objective functions of congestion management cost, voltage security, and dynamic security under a stochastic framework. The proposed framework not only captures a more uncertainty spectrum of the power system compared with deterministic methods, but also establishes a sufficient level of voltage and dynamic security after congestion management. Results of testing the proposed framework and previous congestion management methods on the New-England power system, discussed in detail, elaborate the efficiency of the proposed framework.

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Section: Numerical Resultsmentioning

confidence: 99%

“…However, a real power system has a stochastic behavior in practical operations due to uncertainties in the availability of generation, load, and transmission equipment [16]. The solution of congestion management could become more cumbersome when both security concerns and equipment uncertainties are considered simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Stochastic multi-objective congestion management in power markets improving voltage and transient stabilities

Esmaili

Amjady

Shayanfar

2010

Euro. Trans. Electr. Power

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“…It is worth to mention that if the system operator opts to utilise reserve capacity, VPP should be paid for both energy and reserve services [30].…”

Section: Objective Functionmentioning

confidence: 99%

Stochastic approach to represent distributed energy resources in the form of a virtual power plant in energy and reserve markets

Karimyan

Abedi

Hosseinian

et al. 2016

IET Generation, Transmission & Distribution

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Today, traditional networks are changing to active grids due to the burgeoning growth of distributed energy resources (DER), which demands scrupulous attention to technical infrastructures, as well as economic aspects. In this study, from economic point of view, the aggregation of DERs in a distribution network to participate in joint energy and reserve markets is investigated. This approach, which is predicated upon price-based unit commitment method, has considered virtually all the technical data in the proposed model. It is worth to mention that uncertainties of loads and market prices, as an inherent characteristic of the electricity markets, are treated in this study, and their effect on the operation of virtual power plants in energy and reserve markets has been thoroughly discussed. To this end, for both uncertain parameters, a good number of scenarios are generated and using the backward reduction method the number of these scenarios is reduced. The problem is formulated as a MINLP model and is implemented in GAMS software, while its authenticity is validated using particle swarm optimisation method. NomenclatureIndices i,j index for buses t index for hours N total number of buses Sets S DG set of distributed generation (DG) units S IL set of interruptible load (IL) S EES set of electrical energy storage (EES) S int set of tie-lines S b set of buses Variables P DG t i; Q DG t i amount of active and reactive power generated by ith DG unit at hour t for the energy market R DG t i amount of active power generated by ith DG unit at hour t for reserve marketamount of curtailed load of ith IL allotted to spinning reserve market at hour t P min DG i ; P max DG i minimum and maximum active power generation of ith DG unit P int t i ; Q int t i amount of active and reactive exchanged power between virtual power plant (VPP) and the ith neighbouring grid (positive sign for purchasing from the neighbouring grid and negative sign for selling to it) P E t ; Q E t amount of active and reactive exchanged power between VPP and upstream network (positive sign for purchasing from the neighbouring grid and negative sign for selling to it) R t sum of curtailed load and DG generation allotted to spinning reserve market at hour t P Load t ; Q Load t supplied active and reactive load by VPP to end customers P ch t i ; P Dch t i amount of power charged/discharged into ith EES at hour t P Str t i amount of charged/discharged capacity of ith EES at hour t in kW (positive sign for charging state and negative sign for discharging state) P min Str ; P max Str minimum and maximum capacity of ith EES in kWh R ch i ; R Dch i maximum charge/discharge rate of ith EES in kW Cap t i state of charge of the ith EES at hour t C(P DG t i ) generation cost function of ith DG unit C(P Str t i ) operation cost function of ith EES C(P IL t i )contracted cost function of IL to curtail its load in specified hours I t ; L t ; J i,t ; K i,t ; I t i binary variables V i,t /d i,t voltage phasor at bus i at hour t Y ij /u ij polar form of ijth element of a...

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“…In the competitive electricity markets, a GENCO is responsible for estimating the future electricity price trend by using electricity price prediction model [5]. A given GENCO optimizes its unit generation at each period of time, and formulates the optimal generation bidding plan, thus maximizing profits.…”

Section: Introductionmentioning

confidence: 99%

Self-scheduling of a power generating company: Carbon tax considerations

Liu

Chevallier

Zhu

2016

Computers & Operations Research

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Risk‐based self‐scheduling for GenCos in the day‐ahead competitive electricity markets

Cited by 6 publications

References 25 publications

Stochastic multi-objective congestion management in power markets improving voltage and transient stabilities

Stochastic multi-objective congestion management in power markets improving voltage and transient stabilities

Stochastic approach to represent distributed energy resources in the form of a virtual power plant in energy and reserve markets

Self-scheduling of a power generating company: Carbon tax considerations

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