Optimal Generation Scheduling of Power System for Maximum Renewable Energy Harvesting and Power Losses Minimization

Banhthasit, Bounthanh; Jamroen, Chaowanan; Dechanupaprittha, Sanchai

doi:10.11591/ijece.v8i4.pp1954-1966

Cited by 21 publications

(17 citation statements)

References 18 publications

(19 reference statements)

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“…Our research team is planning to develop a citywide charging coordination integrating the renewable energy generation. Here, a variety of datasets are collected regarding solar energy generation and climate history records [15].…”

Section: Discussionmentioning

confidence: 99%

Price effect analysis and pre-reseravtion scheme on electric vehicle charging networks

Lee¹,

Park²

2019

IJECE

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<p>This paper investigates the price effect to the charging demand coming from electric vehicles and then evaluates the performance of a pre-reservation mechanism using the real-life demand patterns. On the charging network in Jeju city, the occupancy rates for 3 price groups, namely, free, medium-price, and expensive chargers, are separated almost evenly by about 9.0 %, while a set of chargers dominates the charging demand during hot hours. The virtual pre-reservation scheme matches electric vehicles to a time slot of a charger so as not only to avoid intolerable waiting time in charging stations systematically but also to increase the revenue of service providers, taking into account both bidding levels specified by electric vehicles and preference criteria defined by chargers. The performance analysis results obtained by prototype implementation show that the proposed pre-reservation mechanism improves the revenue of service providers by up to 9.5 % and 42.9 %, compared with the legacy FCFS and reservation-less walk-in schemes for the given performance parameter sets.</p>

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Section: Discussionmentioning

confidence: 99%

Price effect analysis and pre-reseravtion scheme on electric vehicle charging networks

Lee¹,

Park²

2019

IJECE

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“…Where and are functions of fitness functions and given as in (3) and (4). Similarly, and are weighing factors in which is weight as applied to total real power loss minimization, and is weight given to voltage deviation minimization.…”

Section: (2)mentioning

confidence: 99%

“…Many researchers have carried out research concerning the impact that distributed generation (DG) has on a distributed system. DG concepts that aims at loss minimization have been presented in [4]. DG planning and optimization have been implemented and reviewed by [5], cost of DG and cost of tariff [6], and issues and challenges of integration of DG are discussed in [7].…”

Section: Introductionmentioning

confidence: 99%

Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm

Musa

Hashim

2019

IJEECS

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This paper presents a Genetic Algorithm (GA) for optimal location and sizing of multiple distributed generation (DG) for loss minimization. The study is implemented on a 33-bus radial distribution system to optimally allocate different numbers of DGs through the minimization of total active power losses and voltage deviation at power constraints of 0 – 2 MW and 0 – 3 MW respectively. The study proposed a PQ model of DG and Direct Load Flow (DLF) technique that uses Bus Incidence to Branch current (BIBC) and Branch Current to Bus Voltage (BCBV) matrices. The result obtained a minimum base case voltage level of 0.9898 p.u at bus 18 with variations of voltage improvements at other buses after single and multiple DG allocations in the system. Besides, the total power loss before DG allocation is observed as 0.2243 MW, and total power loss after DG allocation was determined based on the power constraints. Various optimal locations were seen depending on the power limits of different DG sizes. The results have shown that the impact of optimal allocation and sizing of three DG is more advantageous concerning voltage improvement, reduction of the voltage deviation and also total power loss in the distribution system. The results obtained in the 0 – 2 MW power limit is consistent to the 0 – 3 MW power limits regarding the influence of allocating DG to the network and minimization of total power losses.

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“…The models includes the unit commitment formulation to integrate wind power along with thermal power, for instance, in [1] the authors propose a bioinspired algorithm to solve the problem given that the problem is nonlinear. [2][3][4][5][6]. A probabilistic approach has been addressed to capture the intermittency in day-ahead dispatching models.…”

Section: Introductionmentioning

confidence: 99%

Quantification of operating reserves with high penetration of wind power considering extreme values

Obando¹,

González²,

Moreno³

2020

IJECE

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The high integration of wind energy in power systems requires operating reserves to ensure the reliability and security in the operation. The intermittency and volatility in wind power sets a challenge for day-ahead dispatching in order to schedule generation resources. Therefore, the quantification of operating reserves is addressed in this paper using extreme values through Monte-Carlo simulations. The uncertainty in wind power forecasting is captured by a generalized extreme value distribution to generate scenarios. The day-ahead dispatching model is formulated as a mixed-integer linear quadratic problem including ramping constraints. This approach is tested in the IEEE-118 bus test system including integration of wind power in the system. The results represent the range of values for operating reserves in day-ahead dispatching.

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Optimal Generation Scheduling of Power System for Maximum Renewable Energy Harvesting and Power Losses Minimization

Cited by 21 publications

References 18 publications

Price effect analysis and pre-reseravtion scheme on electric vehicle charging networks

Price effect analysis and pre-reseravtion scheme on electric vehicle charging networks

Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm

Quantification of operating reserves with high penetration of wind power considering extreme values

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