Short-Term Reactive Power Planning to Minimize Cost of Energy Losses Considering PV Systems

Alkaabi, Sultan; Zeineldin, H. H.; Khadkikar, Vinod

doi:10.1109/tsg.2018.2815434

Cited by 46 publications

(16 citation statements)

References 24 publications

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“…Accordingly, (14f) and (14g) represent the power flow equation and the second-order cone constraints, respectively. Due to the fact that the actual probability distribution of variable DG power in the practical application, a general datadriven DRO model is applied based on the huge historical data in the distribution management system, as shown in (15). At the first stage, we search for the optimal DERs investment plan.…”

Section: Data-driven Distributionally Robust Modelmentioning

confidence: 99%

“…The effectiveness of the doubly-fed induction generator (DFIG) model in the reactive power compensation was studied in [6]. Moreover, photovoltaic (PV) inverters are adopted to reduce overvoltage or power loss by providing ancillary services [14,15]. Thus, joint planning considering DGs with reactive power output, DESSs and different types of CLs is the work of this paper.…”

Section: Introductionmentioning

confidence: 99%

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Data‐driven distributionally robust joint planning of distributed energy resources in active distribution network

Gao

Wang

Liu

et al. 2020

IET Generation, Transmission & Distribution

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Section: Data-driven Distributionally Robust Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data‐driven distributionally robust joint planning of distributed energy resources in active distribution network

Gao

Wang

Liu

et al. 2020

IET Generation, Transmission & Distribution

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“…In the second part, cost is further reduced by using the RESs and power management algorithm, where a column and constraint generation (C&CG) mechanism is used to solve a cost minimization problem. In [53,54], the researchers aim to improve the system efficiency with the help of RESs and home energy storage system (HESS) in an SG. RESs support the SG in terms of energy production in a place where the energy consumption is increasing day by day.…”

Section: Distributed Energy Managementmentioning

confidence: 99%

“…Moreover, SG also facilitates the energy retailers and end users to incorporate distributed energy resources and DR programs to achieve the aforementioned objective. In this regard, various optimization algorithms are being developed to manage load demand within given limits and requirements [16,[54][55][56][57][58][59][60][61][62]. In doing so, the common objectives are: reduced carbon emission, electricity cost, and user discomfort, where user discomfort and cost reduction are contradictory objectives, and users have to prioritize one from another.…”

Section: Motivationmentioning

confidence: 99%

“…In this way, the required objective could be more complex, as both objectives have to be achieved without compromising end user requirements. The previous works [45][46][47][48][49][50][51][52][53][54] are being developed to achieve this objective; however, there is still a need to optimise the given objective, i.e., cost, and rebound peaks, to further improve the results. Hence, by considering consumer and utility requirements, we have proposed a weight based load management mechanism, where users have been given the facility to set their priority level.…”

Section: Motivationmentioning

confidence: 99%

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Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources

et al. 2018

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Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the incorporation of energy resources (RESs) i.e., solar energy. For this purpose, a real-time electricity price (RTP), energy demand, user preferences and renewable energy parameters are taken as an inputs and genetic algorithm (GA) has been used to manage and schedule residential load with the objective of cost, user discomfort, and peak-to-average ratio (PAR) reduction. Initially, RTP is used to reduce the energy consumption cost. However, to minimize the cost along with reducing the peaks, a combined pricing model, i.e., RTP with inclining block rate (IBR) has been used which incorporates user preferences and RES to optimally schedule load demand. User comfort and cost reduction are contradictory objectives, and difficult to maximize, simultaneously. Considering this trade-off, a combined pricing scheme is modelled in such a way that users are given priority to achieve their objective as per their requirements. To validate and analyze the performance of the proposed algorithm, we first propose mathematical models of all utilized loads, and then multi-objective optimization problem has been formulated. Furthermore, analytical results regarding the objective function and the associated constraints have also been provided to validate simulation results. Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort..

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Reactive power management by using a modified differential evolution algorithm

Kar

Kumar

Singh

et al. 2021

Optim Control Appl Methods

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In this article, a modified differential evolution (MDE) algorithm is proposed and applied to provide the solution for reactive power management by incorporating the flexible alternating current transmission systems (FACTS) controllers. The proper siting of FACTS controller has been achieved with an objective to minimize the losses and to improve the loading capability. The power flow analysis is performed to determine the optimal position for FACTS controllers. These controllers are incorporated in the most heavily loaded lines and hence controls the power flow in that particular line and allow more power to be transmitted in the remaining lines. The proposed MDE algorithm uses a novel DE/best3/1/bin mutation operator to produce three temporary mutant vectors which are averaged to obtain the mutant vector. Hence, the decision vectors of a generation simultaneously move toward the three best decision vectors of the population thereby maintains a better trade between exploration and exploitation. The proposed MDE algorithm is applied on different standard test bus (i.e., IEEE30, IEEE57, and IEEE118) systems with varying active and reactive loading (i.e., 100%, 110%, and 120%). The proposed method's performance is compared to those obtained from some well‐known meta‐heuristic algorithms. The proposed MDE algorithm optimized FACTS controllers reduce transmission loss by 60.90% in IEEE30 bus, 49.72% in IEEE57 bus and 8.37% in IEEE118 bus test system under base loading. The statistical analysis of the obtained results is carried out using the Wilcoxon signed rank test and the Friedman and Nemenyi hypothesis test, which ensures the reliability and robustness of the proposed method.

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Short-Term Reactive Power Planning to Minimize Cost of Energy Losses Considering PV Systems

Cited by 46 publications

References 24 publications

Data‐driven distributionally robust joint planning of distributed energy resources in active distribution network

Data‐driven distributionally robust joint planning of distributed energy resources in active distribution network

Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources

Reactive power management by using a modified differential evolution algorithm

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