A hybrid prediction-based microgrid energy management strategy considering demand-side response and data interruption

Yuan, Dongmei; Lu, Zhengding; Zhang, Jiangfeng; Li, Xueping

doi:10.1016/j.ijepes.2019.05.045

Cited by 38 publications

(14 citation statements)

References 42 publications

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“…At first, we should assign weights for u 1 ∼ u 3 . In this study, the entropy weighting method [35] is used to assign the weights of the three factors. This method can assign higher weight to important factor.…”

Section: A Fuzzy Suitability Evaluation Processmentioning

confidence: 99%

Integrative Design of an Emergency Resource Predicting-Scheduling-Repairing Method for Rail Track Faults

Yuan

Zhang

et al. 2019

IEEE Access

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To solve the problem of track failure in railway system, integrative design of an emergency resource forecasting-scheduling-repairing method is proposed in this paper. In the current railway repair process, resource dispatching centers dispatch transport resources to the fault point for the repair work. In order to optimize this process, a hybrid prediction method consisting of radial basis function neuron network (RBFNN) and expert prediction method to rationally predict repair resources is proposed. In order to optimize the resource transportation path and reduce the transportation cost, a bi-objective resource scheduling method is presented in this paper. A fuzzy evaluation method combining with the prospect theory to optimally select the repairing for the repair work is formulated. Finally, experiment studies are carried out and the results illustrate the effectiveness of the proposed method.

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Section: A Fuzzy Suitability Evaluation Processmentioning

confidence: 99%

Integrative Design of an Emergency Resource Predicting-Scheduling-Repairing Method for Rail Track Faults

Yuan

Zhang

et al. 2019

IEEE Access

Self Cite

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“…In many published research results, conducted in various countries, there are individual approaches to the application of DSM&R at the level of TSO, DSO [6][7][8][9][10][11][12][13][14][15][16] and the final consumer [13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Some of them relate to the narrow scope of DSM&R. For example, the paper [6] presents the impact of aggregators as important entities on the development of DSM&R in Great Britain.…”

Section: Introductionmentioning

confidence: 99%

“…The developed genetic algorithm is designed to provide DSM&R while minimizing the cost of purchasing energy and maximizing the use of RES. In [14], concerning micro-networks also containing RES, an energy management strategy was proposed based on the response on the demand side, taking into account the application of the PSO algorithm. The PSO algorithm was also used to minimize the costs of energy consumption by the household consumer [15].…”

Section: Introductionmentioning

confidence: 99%

Household Energy Management

et al. 2021

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Ensuring flexibility and security in power systems requires the use of appropriate management measures on the demand side. The article presents the results of work related to energy management in households in which renewable energy sources (RES)can be installed. The main part of the article is about the developed elastic energy management algorithm (EEM), consisting of two algorithms, EEM1 and EEM2. The EEM1 algorithm is activated in time periods with a higher energy price. Its purpose is to reduce the power consumed by the appliances to the level defined by the consumer. In contrast, the EEM2 algorithm is run by the Distribution System Operator (DSO) when peak demand occurs. Its purpose is to reduce the power of appliances in a specified time period to the level defined by the DSO. The optimization tasks in both algorithms are based on the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic algorithm. The EEM1 and EEM2 algorithms also provide energy consumer comfort. For this purpose, both algorithms take into account the smart appliance parameters proposed in the article: sections of the working devices, power reduction levels, priorities and enablingof time shifting devices. The EEM algorithm in its operation also takes into account the information about the production of power, e.g., generated by the photovoltaic systems. On this basis, it makes decisions on the control of smart appliances. The EEM algorithm also enables inverter control to limit the power transferred from the photovoltaic system to the energy system. Such action is taken on the basis of the DSO request containing the information on the power limits. Such a structure of EEM enables the balancing of energy demand and supply. The possibility of peak demand phenomenon will be reduced. The simulation and experiment results presented in the paper confirmed the rationality and effectiveness of the EEM algorithm.

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“…The prediction may be incorrect due to the uncertainty and stochastic behavior of the load profile or solar irradiation. A vulnerability assessment method for interruptible load employing weights of vulnerability indexes has been proposed [30]. In this paper, a simple method using a voltage gap and delay filter is applied to cope with a quick state change and prediction error.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Optimal Control for Photovoltaic Systems Using Prediction in the Distribution Systems

Phan-Tan

Hill

2021

Energies

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The high penetration of photovoltaic (PV) systems and fast communications networks increase the potential for PV inverters to support the stability and performance of microgrids. PV inverters in the distribution network can work cooperatively and follow centralized and decentralized control commands to optimize energy production while meeting grid code requirements. However, there are older autonomous inverters that have already been installed and will operate in the same network as smart controllable ones. This paper proposes a decentralized optimal control (DOC) that performs multi-objective optimization for a group of PV inverters in a network of existing residential loads and autonomous inverters. The interaction of independent DOC groups in the same network is considered. The limit of PV inverter power factor is included in the control. The DOC is done by the power flow calculation and an autoregression prediction model for estimating maximum power point and loads. Overvoltage caused by prediction errors resulting in non-optimal commands from the DOC is avoided by switching to autonomous droop control (ADC). The DOC and ADC operate at different time scales to take account of communication delays between PV inverters and decentralized controller. The simulation of different scenarios of network control has proved the effectiveness of the control strategies.

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A hybrid prediction-based microgrid energy management strategy considering demand-side response and data interruption

Cited by 38 publications

References 42 publications

Integrative Design of an Emergency Resource Predicting-Scheduling-Repairing Method for Rail Track Faults

Integrative Design of an Emergency Resource Predicting-Scheduling-Repairing Method for Rail Track Faults

Household Energy Management

Decentralized Optimal Control for Photovoltaic Systems Using Prediction in the Distribution Systems

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