An optimal probabilistic spinning reserve quantification scheme considering frequency dynamic response in smart power environment

Malekshah, Soheil; Alhelou, Hassan Haes; Siano, Pierluigi

doi:10.1002/2050-7038.13052

Cited by 15 publications

(3 citation statements)

References 32 publications

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“…Renewables are not always available, and their power output can fluctuate rapidly due to weather conditions [14]. The study [25] proposes a scheme that utilizes probabilistic techniques to model the uncertainties associated with renewable energy sources, load forecasting errors, and other stochastic factors. To maintain stability in the face of disturbances, it is crucial to take the power system's dynamic response into consideration.…”

Section: Challenges In Power Distribution and Demand-side Managementmentioning

confidence: 99%

Categorization of Loads in Educational Institutions to Effectively Manage Peak Demand and Minimize Energy Cost Using an Intelligent Load Management Technique

Ramu

Gangatharan

Rangasamy³

et al. 2023

Sustainability

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The inclusion of photovoltaics (PV) in electric power supply systems continues to be a significant factor in global interest. However, solar power exhibits intermittent uncertainty and is further unpredictable. Accurate solar generation prediction and efficient utilization are mandatory for power distribution management and demand-side management. Peak demand management and reducing energy costs can be effectively tackled through the implementation of a reliable solar power forecasting system and its efficient utilization. In this regard, the proposed work is related to efficiently managing solar PV power and optimizing power distribution using an enhanced reinforced binary particle swarm optimization (RBPSO) technique. This DSM (demand-side management) strategy involves utilizing a forecast of solar PV generation for the upcoming day and adjusting the consumption schedule of the load to decrease the highest energy demand. The proposed approach improves user comfort by adjusting the non-interruptible and flexible institutional load through clipping and shifting techniques. To evaluate the effectiveness of this approach, its performance is assessed by analyzing the peak demand range and PAR (peak-to-average ratio). It is then compared to the conventional genetic algorithm to determine its effectiveness. Simulation results obtained using MATLAB show that the PAR peak demand before DSM was found to be 1.8602 kW and 378.06 kW, and after DSM, it was reduced to 0.7211 kW and 266.54 kW. This indicates a 29% reduction in Peak demand and performance compared to the conventional genetic algorithm (GA).

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Section: Challenges In Power Distribution and Demand-side Managementmentioning

confidence: 99%

Categorization of Loads in Educational Institutions to Effectively Manage Peak Demand and Minimize Energy Cost Using an Intelligent Load Management Technique

Ramu

Gangatharan

Rangasamy³

et al. 2023

Sustainability

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“…The effective power electronics-based dynamic-voltage restorer has been proposed to mitigate the power quality disturbance in secondary distribution networks [12]. Malekshah et al propose an optimal probabilistic spinning reserve quantification scheme for future smart-grid frequency enhancement considering wind generation, ESSs, thermal generating units, shiftable loads, and power-system frequency response characteristics [13]. In [14], the short-time least square prony's (STLSP) method is based on calculating and monitoring a pertinent severity factor to prevent failure breakdowns and increase the reliability and safety of industrial facilities under unbalanced supply voltage and load variations.…”

Section: Introductionmentioning

confidence: 99%

Optimal Phase Balancing in Electricity Distribution Feeders Using Mixed-Integer Linear Programming

Lin

et al. 2023

Sustainability

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A mixed-integer linear programming (MILP) model that includes reductions in neutral current, feeder energy-loss cost, customer interruption cost, and labor cost is developed to derive the optimal phase-swapping strategy to enhance the phase balancing of distribution feeders. The neutral current of the distribution feeder is reduced by the phase-swapping strategy so that the tripping of the low-energy overcurrent relay can be prevented and customer-service interruption costs and the labor cost to execute the phase-swapping works can be justified by the energy-loss reduction obtained. The novelty of the study is its derivation of the phase-swapping strategy using mixed-integer linear programming to solve the problem of the unbalance of the distribution feeders. A Taipower distribution feeder is used to derive the phase-swapping strategy to demonstrate the proposed MILP model for phase balancing. The comparison of the phase currents and neutral current before phase-swapping reveals that the three-phase balance was not only significantly improved, but that the voltage unbalance was also decreased dramatically using the proposed phase-swapping strategy.

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“…Although there is no widely used agreement on the amount of active power which should be kept as SR, traditionally, SR requirement has been based on deterministic criteria such as loss of the largest online generator or a given percentage of the load [1][2][3]. Deterministic criteria normally provide suboptimal SR because they consider only basic factors including unit size, unit availability and etc., without taking in to account the stochastic nature of power system components such as probability of generation and transmission outage, uncertainties in load forecast, variation of solar power especially in cloudy sky days, and security criteria [4][5].…”

Section: Introductionmentioning

confidence: 99%

Cost-based Risk Approach for Spinning Reserve Assessment in Bulk Power Systems

Rashidi

Harifi

2022

IJE

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Spinning reserve (SR) is one of the most prevalent methods for balancing grid uncertainties, such as generator faults, to maintain grid reliability. Literature review shows that several deterministic as well as probabilistic methos have been proposed for determining SR. It is always a challenge for a system operator to decide which approach better from security and reliability point of view. This is important because the allocated SR may provide in some cases a misleading sense of confidence with respect to safe, secure, reliable and economic operation of power systems. This paper presents a cost-based risk index approach for assessing the spinning reserve requirements in a power system. To that end, the performance of spinning reserve is classified in three types, namely, not-effective, partially-effective, and not-meeting-load. Then probability of each type and its consequences are subsequently computed and finally that the risk associated with any spinning reserve value is determined. It is shown that one might consider various spinning reserve values for an operating condition (randomly or using approaches proposed in literature), then calculate risks associated with each value, and finally use the calculated risk indices to determining the optimal level of spinning reserve. As an example, we have shown that in the studied network with 6600MW load, maintaining 240MW SR will increase cost of 1MW hour energy by $0.154 while the optimal value of 200MW SR will increase cost of 1MWhour energy by just $0.126. This paper initially focuses on providing a measure of the quality of an ex-ante specified spinning reserve, latter on the flowchart of using the proposed approach for determining optimal level of spinning reserve is presented. The proposed risk index can also be used for comparing different deterministic as well as probabilistic approaches presented in literature for spinning reserve requirements.

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An optimal probabilistic spinning reserve quantification scheme considering frequency dynamic response in smart power environment

Cited by 15 publications

References 32 publications

Categorization of Loads in Educational Institutions to Effectively Manage Peak Demand and Minimize Energy Cost Using an Intelligent Load Management Technique

Categorization of Loads in Educational Institutions to Effectively Manage Peak Demand and Minimize Energy Cost Using an Intelligent Load Management Technique

Optimal Phase Balancing in Electricity Distribution Feeders Using Mixed-Integer Linear Programming

Cost-based Risk Approach for Spinning Reserve Assessment in Bulk Power Systems

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