Stochastic Optimal Control for Energy Internet: A Bottom-Up Energy Management Approach

Hu, Hua; Qin, Yuchao; Hao, Chuantong; Cao, Junwei

doi:10.1109/tii.2018.2867373

Cited by 104 publications

(42 citation statements)

References 23 publications

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“…A class of energy routing algorithm based on graph theory within local area energy network has been studied in [28]. In reference [29], stochastic optimal controllers are designed in ERs and micro-turbines (MT), such that the bottom-up energy management principle in EI is achieved. In reference [30], energy routing for delay-tolerant loads and mobile energy buffers has been investigated.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Based on the operational principle of EI, the target of autonomous power supply-demand balance of each ER is expected to be achieved with priority [2,29]. Thus, the event of power imbalance within any ER would lead to complicated energy dispatching issues within the whole ER network, which brings extra time and operational cost.…”

Section: H 2 Control Problem Formulationmentioning

confidence: 99%

“…Firstly, it is remarkable that, if two interconnected ERs are far away from each other, the energy transmission cost may be relatively high. Besides, it is costly if power is frequently transmitted between multiple connection nodes within ER network [1,29]. Typically, in this note, since we are focusing on one specified ER system, i.e., ER 1 , it is important to restrict power transmitted between ER 1 and ER 2 .…”

Section: H 2 Control Problem Formulationmentioning

confidence: 99%

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Robust Mixed H 2 / H ∞ Controller Design for Energy Routers in Energy Internet

Qin

Geng

et al. 2019

Energies

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In this paper, a class of mixed H2/H∞ controller is designed for an energy router (ER) within the scenario of an energy Internet (EI). The considered ER is assumed to have access with photovoltaic panels, wind turbine generators, micro-turbines, fuel cells, diesel engine generators, battery energy storage devices, flywheel energy storage devices, loads, and other ERs. Two types of control targets are considered. First, due to the access of large-scale renewable energy sources, the DC bus voltage deviation within the ER system shall be regulated. Second, an optimal energy management strategy shall be achieved, such that the autonomous power supply-demand balance within each ER is achieved with priority and the rational utilization of controllable power generation devices and energy storage devices are realized. When these objectives are considered simultaneously, the control issues with respect to ER is formulated as a mixed robust H2/H∞ control problem with analytical solutions provided. Several numerical examples are given, and the feasibility and effectiveness of the proposed method are demonstrated.

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Section: Literature Reviewmentioning

confidence: 99%

Section: H 2 Control Problem Formulationmentioning

confidence: 99%

Section: H 2 Control Problem Formulationmentioning

confidence: 99%

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Robust Mixed H 2 / H ∞ Controller Design for Energy Routers in Energy Internet

Qin

Geng

et al. 2019

Energies

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“…Typically, an EI scenario can have access to the utility grid. Alternatively, when disconnected from the main power grid, multiple sub-grids interconnected via energy routers are able to function normally [6,7]. For the detailed definition, architecture and key technologies of EI, readers can refer to [8,9], and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach

et al. 2019

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The existence of high proportional distributed energy resources in energy Internet (EI) scenarios has a strong impact on the power supply-demand balance of the EI system. Decision-making optimization research that focuses on the transient voltage stability is of great significance for maintaining effective and safe operation of the EI. Within a typical EI scenario, this paper conducts a study of transient voltage stability analysis based on convolutional neural networks. Based on the judgment of transient voltage stability, a reactive power compensation decision optimization algorithm via deep reinforcement learning approach is proposed. In this sense, the following targets are achieved: the efficiency of decision-making is greatly improved, risks are identified in advance, and decisions are made in time. Simulations show the effectiveness of our proposed method.

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“…The ST commonly consists of AC-DC rectifier, DC-DC converter and DC-AC inverter [2]. This structure can also provide the DC port to integrate the DC subsystems, i.e., renewable energy sources [3], electric energy storages [4,5] and electric vehicle charger stations [6]. Active control of the ST manages the power flow between the subsystems [7].…”

Section: Introductionmentioning

confidence: 99%

Methodology for Assessment of the Impact of Smart Transformers on Power System Reliability

Chen

Nouri

Keane

et al. 2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

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The smart transformer (ST) has been proposed as an alternative to the traditional low frequency transformer as a means to provide extra control functionality in the smart power system. The ST has merits in terms of reactive power decoupling and voltage decoupling at the primary and secondary side. This provides flexibility for reactive power compensation in the transmission system and demand reduction in the distribution system. Using its ability to control demand through voltage regulation, the ST provides the possibility to reduce demand while keeping the entire load online, which can provide an alternative to load curtailment. Thus, it may provide a means to improve power system reliability. However, no of previous research has investigated these potential system reliability benefits of the ST. The paper presents a methodology which can be used to quantify the system reliability impacts of the use of STs as an interface between the transmission and distribution systems. Using the methodology, the ST impacts on the system reliability are assessed using the IEEE 39-bus system as an example.

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Stochastic Optimal Control for Energy Internet: A Bottom-Up Energy Management Approach

Cited by 104 publications

References 23 publications

Robust Mixed H 2 / H ∞ Controller Design for Energy Routers in Energy Internet

Robust Mixed H 2 / H ∞ Controller Design for Energy Routers in Energy Internet

Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach

Methodology for Assessment of the Impact of Smart Transformers on Power System Reliability

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