A Hierarchical Energy Management System Based on Hierarchical Optimization for Microgrid Community Economic Operation

Tian, Peigen; Xiao, Xi; Wang, Kui; Ding, Ruoxing

doi:10.1109/tsg.2015.2470551

Cited by 156 publications

(80 citation statements)

References 28 publications

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“…Most of the studies focused on economic operating optimization of HES [8][9][10][11]. Others have proposed theoretical modeling and optimization methods to ensure the stability and efficiency of HES [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Economic optimization on two time scales for a hybrid energy system based on virtual storage

Yang

Guo

2018

J. Mod. Power Syst. Clean Energy

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This paper proposes an economic optimization method with two time scales for a hybrid energy system based on the virtual storage characteristic of a thermostatically controlled load (TCL). The optimization process includes two time scales in order to ensure accuracy and efficiency. Based on the forecast load and energy supply of the system, the first time scale is day-ahead economic operating optimization, carried out to determine the minimum operating cost for the whole day, and to find the period of greatest cost to which the second time scale optimization is applied. Using the virtual storage characteristic, the second time scale is short term detailed optimization carried out for these particular hours. By dispatching thermal load in this period and adjusting energy supply accordingly, we can find the optimal economic performance, and customer requests are taken into account to ensure satisfaction. A case study in Tianjin illustrates the effectiveness of this method and proves that a TCL can make a great contribution to improving the economic performance of a hybrid energy system.

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Section: Introductionmentioning

confidence: 99%

Economic optimization on two time scales for a hybrid energy system based on virtual storage

Yang

Guo

2018

J. Mod. Power Syst. Clean Energy

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“…In order to provide high-quality and economical electricity, the microgrid operation needs to be scheduled reasonably [4]. As the brain of the microgrid, the energy management system (EMS) takes charge of coordinating the output power of the distributed resources and the exchanged power with the main grid to minimize the operational cost or maximize the total profits, according to the information from load prediction, power prediction of renewable energy and electricity price [5][6][7]. As far as the architecture of EMS is concerned, there exist three kinds of structures: the centralized structure [8], the hierarchical structure [7,9] and the distributed structure [5,10].…”

Section: Introductionmentioning

confidence: 99%

“…As the brain of the microgrid, the energy management system (EMS) takes charge of coordinating the output power of the distributed resources and the exchanged power with the main grid to minimize the operational cost or maximize the total profits, according to the information from load prediction, power prediction of renewable energy and electricity price [5][6][7]. As far as the architecture of EMS is concerned, there exist three kinds of structures: the centralized structure [8], the hierarchical structure [7,9] and the distributed structure [5,10]. The authors in [11] give a comprehensive review about the EMS of microgrids and summarize the recent state of the Power generation scheduling in the EMS plays an important role in the microgrid operation, which is used to dispatch various energy resources to meet the load demands at the minimum cost subject to the physical constraints of the microgrid [12].…”

Section: Introductionmentioning

confidence: 99%

“…Considering the users' thermal comfort and the system constraints, the authors in [16] propose an optimal day-ahead price-based power scheduling scheme for a community-scale microgrid for the purpose of maximizing the expected benefit and minimizing the operational cost. For a multi-microgrid interconnected system, the authors in [7] present a two-level hierarchical optimization method. Under the hierarchical framework, the lower level and the upper level are responsible for an individual microgrid and the entire system, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization

et al. 2017

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Abstract:In this paper, an optimal day-ahead scheduling problem is studied for a microgrid with multiple distributed resources. For the sake of coping with the prediction uncertainties of renewable energies and loads and taking advantage of the time-of-use price for buying/selling electricity, an interval-based optimization model for maximum profits is developed. To reduce the computational complexity in solving the model, the possibility degree comparison between an interval and a real number is used to convert the interval constraints into the general ones; meanwhile, some slack variables and complementary conditions are introduced to eliminate the absolute-value operation. Unlike the stochastic optimization, the interval optimization only needs the upper-lower bounds of the uncertain variables instead of their probability distribution functions, which is beneficial to the practical application. Furthermore, the possible profit interval and the expected optimal profit can be determined by solving the optimization model. Numerical simulations are performed on a microgrid system modified from the benchmark low voltage network in the European Union project "Microgrid", and the results demonstrate the effectiveness of the proposed method.

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“…With the increase in MG installations, integrated operation strategies for multiple MGs have been presented in several studies [6][7][8][9][10][11]. In community MGs, an optimal economic operating method is introduced through hierarchical optimization in [7].…”

Section: Introductionmentioning

confidence: 99%

Distributed Coordination Control Strategy for a Multi-Microgrid Based on a Consensus Algorithm

Lee¹,

Choi²,

Lee

et al. 2017

Energies

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Microgrids (MGs) in which power generation and consumption occur locally have gained prominence, and MG demonstration tests have been widely carried out. In accordance with the increase in the number of MG installations, studies regarding the cooperative control of multiple MGs are proceeding in various forms. In this paper, the distributed control strategy of a multi-microgrid (MMG) is proposed. Distributed control is the method in which agents of the electric power facility autonomously control their facility through communication with the neighboring agents only. In this process, a consensus algorithm is utilized to obtain the global information required to control the overall system. In this distributed control strategy, a single MG is operated at an optimal economic point using the equality incremental cost constraints while maintaining the balance between the generation and demand. The control strategy of a MMG is that the flow of the point of common coupling (PCC) is maintained at a particular value needed by the utility and the internal change in power is distributed to the MGs according to their reserves. The proposed algorithm is verified in the MG level and the MMG level through a simulation model using PSCAD/EMTDC software in the C language.

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A Hierarchical Energy Management System Based on Hierarchical Optimization for Microgrid Community Economic Operation

Cited by 156 publications

References 28 publications

Economic optimization on two time scales for a hybrid energy system based on virtual storage

Economic optimization on two time scales for a hybrid energy system based on virtual storage

Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization

Distributed Coordination Control Strategy for a Multi-Microgrid Based on a Consensus Algorithm

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