Parallel stochastic programming for energy storage management in smart grid with probabilistic renewable generation and load models

Wang, Yue; Liang, Hao; Dinavahi, Venkata

doi:10.1049/iet-rpg.2018.5737

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…Specifically, the maximum and minimum energy storage states are 600W and 5400W, respectively. The typical home appliance parameters can be found in [17]. We assume that PV power is randomly generated by Monte Carlo simulation to simulate the real cases.…”

Section: Numerical Resultsmentioning

confidence: 99%

Stochastic Demand Response under Random Renewable Power Generation in Smart Grid

Wang

Liang

Dinavahi

2019

2019 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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Rooftop photovoltaic (PV) generation combined with battery energy storage provides a promising solution for solar energy integration in smart grid. Specifically, the home battery energy storage systems can improve the efficiency and reliability of PV integration while reducing the greenhouse gas emissions. In this paper, we investigate the randomness of home PV generation and the residential random load demand, which may affect the efficiency and reliability of the power grid. A bilevel stochastic programming problem is formulated to provide a pricing strategy to customers for the optimal demand response in smart grid. In particular, the operators model represents the cost minimization of the power system operation, while the customers' model represents the cost minimization of their household energy demand. In the operators model, power loss calculated based on power flow analysis is used as the system loss, while the stochastic model of the household load demand is used instead of the expected value to characterize the human random behaviour. The performance of the proposed stochastic demand response scheme is evaluated through extensive simulations. Simulation results indicate that this novel scheme can help both power system operators and electrical customers to better decide on their operating schedule and energy usage, respectively.

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Section: Numerical Resultsmentioning

confidence: 99%

Stochastic Demand Response under Random Renewable Power Generation in Smart Grid

Wang

Liang

Dinavahi

2019

2019 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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“…In this simulation, several categories of typical household appliances are considered, whose characteristics can be found in [30]. There are in total 21 kinds of electrical appliances, and 4132 scenarios of power demand

ξ_{m, l_{f}, t}^{f}

considered.…”

Section: Case Studymentioning

confidence: 99%

Decentralized stochastic programming for optimal vehicle‐to‐grid operation in smart grid with renewable generation

Wang

Liang

Dinavahi

2021

IET Renewable Power Gen

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This paper presents a decentralized stochastic programming operation scheme for a vehicle‐to‐grid system in a smart grid, which includes a series of equipment with random power generation and demands. For households with electric devices, renewable solar power generation, energy storage systems and electric vehicles, we consider utility operating expenses, including power loss and energy consumption cost as the objective function. For customers, we consider the cost of electricity, including battery degradation. To investigate the uncertainty of the devices, a bottom‐up approach is proposed to develop a random device usage model for analyzing customers' uncertain behaviour. Besides, a random renewable power generation model and an electric vehicle random driving model are implemented. The proposed approach is implemented with OpenMP to simulate the decentralized process on a multi‐core CPU while reducing the computational burden. A case study based on the IEEE 33‐bus distribution system with different scenarios is used to evaluate the performance of the proposed approach. The simulation results show that by introducing an optimal household operation schedule, the expense of distribution system utility company can be reduced in which both customers and operators can benefit from the optimization of the system schedules.

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“…In study [13], a mathematical model of an intelligent multi‐objective home energy management (HEM) with the integration of renewable energy sources was presented. The main aim of the proposed model is to handle the residential load demand in a smart way to minimise both the consumer's energy bill and the system peak demand simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Smart household management systems with renewable generation to increase the operation profit of a microgrid

et al. 2019

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During the past few years, due to the growth of electric power consumption, generation costs as well as rises in the level of greenhouse gases efficiency bring special focus on distributed generation. Developing distributed generation resources, especially renewable energy resources, is one of the safest ways to solve such problem. These resources have been decentralised by being installed close to the houses producing few kilowatts. Therefore, there are no losses in transmission lines and provide response for demand. Based on their benefits, the use of such energy resources should be developed in the future, but its management and optimal use is a major challenge. This has become one of the main concerns ofenergy systems researchers. In the current study, an innovative model is provided as a strategic management. It is intended to optimise the operation in smart homes consisting of generation units such as a wind turbine, solar panels, storages, and un/controllable loads. The main objective of this optimisation management is to maximise microgrid profitability for 24 h. The overall results of the model proved that the profit of microgrid increased significantly.

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Parallel stochastic programming for energy storage management in smart grid with probabilistic renewable generation and load models

Cited by 5 publications

References 45 publications

Stochastic Demand Response under Random Renewable Power Generation in Smart Grid

Stochastic Demand Response under Random Renewable Power Generation in Smart Grid

Decentralized stochastic programming for optimal vehicle‐to‐grid operation in smart grid with renewable generation

Smart household management systems with renewable generation to increase the operation profit of a microgrid

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