Adaptive‐robust multi‐resolution generation maintenance scheduling with probabilistic reliability constraint

Bagheri, Bahareh; Amjady, Nima

doi:10.1049/iet-gtd.2018.6675

Cited by 9 publications

(5 citation statements)

References 43 publications

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“…Therefore, assigning appropriate big-M values can be a challenging task. On the other hand, the proposed BCD-based solution method does not have dualization or subsequent linearization, and, therefore, does not require setting the big-M values for the lower and upper limits of the dual variables [27], [34]. In addition, unlike the nested C&CG algorithm that uses binary modeling variables to construct polyhedral uncertainty sets (since these binary auxiliary variables are required by the disjunctive programming and linearization techniques), the BCD-based method uses the real continuous form of uncertainties in the polyhedral uncertainty set as indicated in ( 3) and ( 4).…”

Section: Solution Methodologymentioning

confidence: 99%

“…Therefore, one-side bounded intervals for the uncertain variables have been considered in the uncertainty set Θ. In addition, unlike other adaptive RO methods, the proposed BCD method does not require binary modeling variables to construct the polyhedral uncertainty set and can directly work with the continuous modeling variables Z ele tdh and Z Thermal tdh as indicated in ( 3) and ( 4) [27]. In the uncertainty set Θ, a pre-defined uncertainty budget in (5), i.e., Γ, has been used to control the robustness of the solution.…”

Section: Uncertainty Characterizationmentioning

confidence: 99%

“…For the first hour of each day, the previous hour is considered as the last hour of the previous day. The annual operation costs of the micro-CHP unit and the auxiliary boiler are calculated in (27) and (28), respectively. The annual maintenance cost of the components of the residential micro-CHP system is calculated in (29).…”

Section: The Third Levelmentioning

confidence: 99%

“…Finally, the costs of all out-of-sample scenarios are aggregated based on their normalized probabilities to obtain the out-of-sample cost for the considered case. More details of the out-of-sample analysis can be found in [27], [42].…”

Section: B Evaluating Solutions Using Out-of-sample Analysismentioning

confidence: 99%

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A Two-stage Adaptive Robust Model for Residential Micro-CHP Expansion Planning

Hamzehkolaei

Amjady

Bagheri

2021

Journal of Modern Power Systems and Clean Energy

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This paper addresses the planning problem of residential micro combined heat and power (micro-CHP) systems (including micro-generation units, auxiliary boilers, and thermal storage tanks) considering the associated technical and economic factors. Since the accurate values of the thermal and electrical loads of this system cannot be exactly predicted for the planning horizon, the thermal and electrical load uncertainties are modeled using a two-stage adaptive robust optimization method based on a polyhedral uncertainty set. A solution method, which is composed of column-and-constraint generation (C&CG) algorithm and block coordinate descent (BCD) method, is proposed to efficiently solve this adaptive robust optimization model. Numerical results from a practical case study show the effective performance of the proposed adaptive robust model for residential micro-CHP planning and its solution method. Index Terms--Micro combined heat and power (micro-CHP) planning, two-stage adaptive robust optimization model, block coordinate descent method, polyhedral uncertainty set.

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Section: Solution Methodologymentioning

confidence: 99%

Section: Uncertainty Characterizationmentioning

confidence: 99%

Section: The Third Levelmentioning

confidence: 99%

Section: B Evaluating Solutions Using Out-of-sample Analysismentioning

confidence: 99%

See 2 more Smart Citations

A Two-stage Adaptive Robust Model for Residential Micro-CHP Expansion Planning

Hamzehkolaei

Amjady

Bagheri

2021

Journal of Modern Power Systems and Clean Energy

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“…The model is formulated as non‐linear and solved by using the genetic algorithm. In [24], by considering the cost from energy not supplied, the GMS plan is based on the reliability‐constrained adaptive‐robust multi‐resolution. The energy not supplied is calculated by adapting the uncertainty of load, wind power generation, and equipment unavailability, whereas the bender decomposition is used to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

Optimal generation maintenance scheduling considering financial return and unexpected failure of distributed generation

Prukpanit

Kaewprapha

Leeprechanon

2021

IET Generation Trans & Dist

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Generation maintenance scheduling (GMS) is an important factor that can improve the reliability of power systems and decrease revenue achieved by generation companies (GenCos). Several GMS models, therefore, are based on these two crucial values. Nonetheless, there is no GMS problem that simultaneously considers unexpected failure of distributed generator (DG), financial return of GenCo, and reserve of system. This paper proposes the GMS model based on a global criterion approach to compromise functions that maximise the GenCo's annual return and probability that no DG fails unexpectedly. The system reserve (SR) is considered as a reliability constraint, while surplus reserve is exchanged with the main grid. To support alternative energy sources that have uncertain outputs and ensure continuous operation of DG, short-term GMS model including power from wind farm, photovoltaic system, energy system storage, and demand response (DR) is also run by adding the SR and inconstant cost of DR. Effectiveness of the proposed model is examined using the IEEE 6 and IEEE 18-bus test systems. Results show that not only the proposed model provides a better GMS solution for the GenCo, resulting in appropriate values of the two objectives, but also alternative energy sources are useful for the short-term GMS. INTRODUCTION Motivation and literature reviewGeneration maintenance scheduling (GMS), in restructured power system, is one of the most important factors used to improve system reliability. It can increase performance and prevent unexpected failure of distributed generator (DG). However, if the GMS model is solved improperly, the financial return of a generation company (GenCo) can be decreased as a result of costs required to support DG maintenance [1, 2], an outage of DG might occur unexpectedly [3], and the system reserve (SR) determined by the independent system operator (ISO) may not be satisfied [4]. Therefore, to ensure the stability of these three crucial values, a multi-objective GMS model should be developed and proposed. Recently, considerable works have focused on improving GMS models by considering various objectives, constraints, energy sources, and mathematical approaches. For the brief This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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