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Shuai Fan
Key Laboratory of Control of Power Transmission and Conversion (Shanghai Jiao Tong University), Ministry of Education, Minhang District, Shanghai 200240, China

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Journal article
Published: 13 July 2021 in Applied Energy
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This paper introduces a decentralized economic dispatch method and an architecture suitable for the virtual power plant (VPP) aggregating massive distributed energy resources (DERs). The convergence condition is given for quadratic cost functions, and is extended to the case of general increasing function of incremental cost (IC). Further analysis shows that the step of this method is adaptive, which is generated from the bottom up according to the responsiveness of each DER unit (DERU). Combined with the decentralized architecture based on message queue (MQ), the algorithm design considers the hosting mechanism of the coordinator failure, which not only improves the efficiency of calculation and communication without losing privacy-protection, but also makes it more fault-tolerant. The correctness and effectiveness of the method are verified in the case studies. The iterative process can respond and converge quickly when DER units reach capacity limits or devices fail/join. Due to the adaptability of the step, the method has strong robustness to the quantity and parameters randomness of underlying units. Therefore, it can be applied to the VPP with a massive number of DERs in order to get consensus solution by rapid economic dispatch.

ACS Style

Lianxin Dong; Shuai Fan; Zhihua Wang; Jucheng Xiao; Huan Zhou; Zuyi Li; Guangyu He. An adaptive decentralized economic dispatch method for virtual power plant. Applied Energy 2021, 300, 117347 .

AMA Style

Lianxin Dong, Shuai Fan, Zhihua Wang, Jucheng Xiao, Huan Zhou, Zuyi Li, Guangyu He. An adaptive decentralized economic dispatch method for virtual power plant. Applied Energy. 2021; 300 ():117347.

Chicago/Turabian Style

Lianxin Dong; Shuai Fan; Zhihua Wang; Jucheng Xiao; Huan Zhou; Zuyi Li; Guangyu He. 2021. "An adaptive decentralized economic dispatch method for virtual power plant." Applied Energy 300, no. : 117347.

Journal article
Published: 12 January 2021 in Applied Energy
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To adequately utilize flexible resources on demand side, Virtual Power Plant (VPP) is an effective solution through the aggregation and application of distributed energy resources (DER). While centralized control approaches are easy to achieve global optimum for the scheduling of every DER, they have limitations when dealing with massive number of complex and heterogeneous DERs with time-varying states. Existing decentralized control approaches are mainly based on the assumption that all DERs are completely rational, which is quite far from the reality. In this paper, using a bottom-up approach, we propose a stimulus–response control strategy to realize exploitation of flexibility by VPP. In such a strategy, DERs are dynamically aggregated through autonomous decentralized system, and interact with each other via subscription and publication of topics, regardless of the source and recipient of the messages, thus removing the direct coupling relationship between VPP Operator and DERs. Furthermore, each DER makes an independent decision through edge computing at an agent that has a general End-to-End structure and is driven by the stimulus message received from VPP Operator. We develop a simple yet efficient double deep q-network (DDQN) algorithm to optimize the state sequence of DER agents. A simulation study is conducted with over 1000 DERs including photovoltaics, electric vehicles and air conditioners. Results indicate that the proposed approach can dynamically aggregate DERs and exploit their flexibility with each DER agent dynamically adapting to the change of stimulus signals, thus achieving dynamic, automatic and adaptive exploitation of flexibility by VPP.

ACS Style

Huan Zhou; Shuai Fan; Qing Wu; Lianxin Dong; Zuyi Li; Guangyu He. Stimulus-response control strategy based on autonomous decentralized system theory for exploitation of flexibility by virtual power plant. Applied Energy 2021, 285, 116424 .

AMA Style

Huan Zhou, Shuai Fan, Qing Wu, Lianxin Dong, Zuyi Li, Guangyu He. Stimulus-response control strategy based on autonomous decentralized system theory for exploitation of flexibility by virtual power plant. Applied Energy. 2021; 285 ():116424.

Chicago/Turabian Style

Huan Zhou; Shuai Fan; Qing Wu; Lianxin Dong; Zuyi Li; Guangyu He. 2021. "Stimulus-response control strategy based on autonomous decentralized system theory for exploitation of flexibility by virtual power plant." Applied Energy 285, no. : 116424.

Journal article
Published: 21 July 2020 in IEEE Transactions on Smart Grid
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This paper proposes a Lyapunov optimization-based online distributed (LOOD) algorithmic framework for active distribution networks (ADNs) with numerous photovoltaic inverters and inverter air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the social utility loss and ensuring the security of voltages. Conventional distributed optimization methods are rarely feasible to track the optimal solutions in fast variable environments using a fine-grained sampling interval where the underlying optimization problem evolves with the iterations of the algorithms. In contrast, based on the framework of online convex optimization (OCO), the developed approach uses a distributed algebraic update to compute the next round decisions relying on the current feedback of measurements. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder. Benchmark tests show that the proposed method is computationally and economically efficient, and outperforming existing algorithms.

ACS Style

Shuai Fan; Guangyu He; Xinyang Zhou; Mingjian Cui. Online Optimization for Networked Distributed Energy Resources With Time-Coupling Constraints. IEEE Transactions on Smart Grid 2020, 12, 251 -267.

AMA Style

Shuai Fan, Guangyu He, Xinyang Zhou, Mingjian Cui. Online Optimization for Networked Distributed Energy Resources With Time-Coupling Constraints. IEEE Transactions on Smart Grid. 2020; 12 (1):251-267.

Chicago/Turabian Style

Shuai Fan; Guangyu He; Xinyang Zhou; Mingjian Cui. 2020. "Online Optimization for Networked Distributed Energy Resources With Time-Coupling Constraints." IEEE Transactions on Smart Grid 12, no. 1: 251-267.

Journal article
Published: 27 December 2019 in Electric Power Systems Research
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Large-scale demand response (DR) is a critical enabler to integrate significant renewable energy sources (RES) into power systems. Current customer baseline load (CBL)-based DR schemes face obstacles in large-scale deployments due to their centralized form, unfair DR performance measurement, and poor effect on decision making approach of customers. To bridge the gaps, this paper proposes the concept of customer directrix load (CDL), which is the desired load profile for customers from the view of the entire DR program, and a novel CDL-based DR scheme. Additionally, an optimization problem considering time-coupling constraints is formulated to help customers respond to the CDL. The computationally intensive problem is then translated into a quadratic programming problem in each time slot using Lyapunov optimization approach. A closed-form solution exists and ensures that the optimal decision is reached in real-time efficiently. Test systems are generated using data from PJM and Open Energy Information. The online algorithm and fairness performance of the proposed scheme are validated in a small system through benchmark comparisons. Further tests on a large-scale system show that the CDL-based DR scheme can help the power system integrate considerably more RES.

ACS Style

Shuai Fan; Zuyi Li; Lin Yang; Guangyu He. Customer directrix load-based large-scale demand response for integrating renewable energy sources. Electric Power Systems Research 2019, 181, 106175 .

AMA Style

Shuai Fan, Zuyi Li, Lin Yang, Guangyu He. Customer directrix load-based large-scale demand response for integrating renewable energy sources. Electric Power Systems Research. 2019; 181 ():106175.

Chicago/Turabian Style

Shuai Fan; Zuyi Li; Lin Yang; Guangyu He. 2019. "Customer directrix load-based large-scale demand response for integrating renewable energy sources." Electric Power Systems Research 181, no. : 106175.

Preprint content
Published: 24 October 2019
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This paper proposes a Lyapunov optimization-based online distributed (LOOD) algorithmic framework for active distribution networks with numerous photovoltaic inverters and invert air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the utility loss and ensuring the security of voltages. In contrast to conventional distributed optimization methods that employ the setpoints for controllable devices only when the algorithm converges, the proposed LOOD can carry out the setpoints immediately relying on the current measurements and operation conditions. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder.

ACS Style

Shuai Fan; Guangyu He; Xinyang Zhou; Mingjian Cui. Online Optimization for Networked Distributed Energy Resources with Time-Coupling Constraints. 2019, 1 .

AMA Style

Shuai Fan, Guangyu He, Xinyang Zhou, Mingjian Cui. Online Optimization for Networked Distributed Energy Resources with Time-Coupling Constraints. . 2019; ():1.

Chicago/Turabian Style

Shuai Fan; Guangyu He; Xinyang Zhou; Mingjian Cui. 2019. "Online Optimization for Networked Distributed Energy Resources with Time-Coupling Constraints." , no. : 1.

Preprint content
Published: 24 October 2019
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This paper proposes a Lyapunov optimization-based online distributed (LOOD) algorithmic framework for active distribution networks with numerous photovoltaic inverters and invert air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the utility loss and ensuring the security of voltages. In contrast to conventional distributed optimization methods that employ the setpoints for controllable devices only when the algorithm converges, the proposed LOOD can carry out the setpoints immediately relying on the current measurements and operation conditions. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder.

ACS Style

Shuai Fan; Guangyu He; Xinyang Zhou; Mingjian Cui. Online Optimization for Networked Distributed Energy Resources with Time-Coupling Constraints. 2019, 1 .

AMA Style

Shuai Fan, Guangyu He, Xinyang Zhou, Mingjian Cui. Online Optimization for Networked Distributed Energy Resources with Time-Coupling Constraints. . 2019; ():1.

Chicago/Turabian Style

Shuai Fan; Guangyu He; Xinyang Zhou; Mingjian Cui. 2019. "Online Optimization for Networked Distributed Energy Resources with Time-Coupling Constraints." , no. : 1.

Journal article
Published: 02 April 2019 in Future Internet
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With the development of techniques, such as the Internet of Things (IoT) and edge computing, home energy management systems (HEMS) have been widely implemented to improve the electric energy efficiency of customers. In order to automatically optimize electric appliances’ operation schedules, this paper considers how to quantitatively evaluate a customer’s comfort satisfaction in energy-saving programs, and how to formulate the optimal energy-saving model based on this satisfaction evaluation. First, the paper categorizes the utility functions of current electric appliances into two types; time-sensitive utilities and temperature-sensitive utilities, which cover nearly all kinds of electric appliances in HEMS. Furthermore, considering the bounded rationality of customers, a novel concept called the energy-saving cost is defined by incorporating prospect theory in behavioral economics into general utility functions. The proposed energy-saving cost depicts the comfort loss risk for customers when their HEMS schedules the operation status of appliances, which is able to be set by residents as a coefficient in the automatic energy-saving program. An optimization model is formulated based on minimizing energy consumption. Because the energy-saving cost has already been evaluated in the context of the satisfaction of customers, the formulation of the optimization program is very simple and has high computational efficiency. The case study included in this paper is first performed on a general simulation system. Then, a case study is set up based on real field tests from a pilot project in Guangdong province, China, in which air-conditioners, lighting, and some other popular electric appliances were included. The total energy-saving rate reached 65.5% after the proposed energy-saving program was deployed in our project. The benchmark test shows our optimal strategy is able to considerably save electrical energy for residents while ensuring customers’ comfort satisfaction is maintained.

ACS Style

Guoying Lin; Yuyao Yang; Feng Pan; Sijian Zhang; Fen Wang; Shuai Fan. An Optimal Energy-Saving Strategy for Home Energy Management Systems with Bounded Customer Rationality. Future Internet 2019, 11, 88 .

AMA Style

Guoying Lin, Yuyao Yang, Feng Pan, Sijian Zhang, Fen Wang, Shuai Fan. An Optimal Energy-Saving Strategy for Home Energy Management Systems with Bounded Customer Rationality. Future Internet. 2019; 11 (4):88.

Chicago/Turabian Style

Guoying Lin; Yuyao Yang; Feng Pan; Sijian Zhang; Fen Wang; Shuai Fan. 2019. "An Optimal Energy-Saving Strategy for Home Energy Management Systems with Bounded Customer Rationality." Future Internet 11, no. 4: 88.

Journal article
Published: 01 January 2019 in IEEE Access
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ACS Style

Shuai Fan; Zhengshuo Li; Zuyi Li; Guangyu He. Evaluating and Increasing the Renewable Energy Share of Customers’ Electricity Consumption. IEEE Access 2019, 7, 129200 -129214.

AMA Style

Shuai Fan, Zhengshuo Li, Zuyi Li, Guangyu He. Evaluating and Increasing the Renewable Energy Share of Customers’ Electricity Consumption. IEEE Access. 2019; 7 ():129200-129214.

Chicago/Turabian Style

Shuai Fan; Zhengshuo Li; Zuyi Li; Guangyu He. 2019. "Evaluating and Increasing the Renewable Energy Share of Customers’ Electricity Consumption." IEEE Access 7, no. : 129200-129214.

Journal article
Published: 16 March 2018 in Applied Sciences
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Large-scale demand response (DR) is a useful regulatory method used in high proportion renewable energy sources (RES) integration power systems. Current incentive-based DR schemes are unsuitable for large-scale DR due to their centralized formulation. This paper proposes a distributed scheme to support large-scale implementation of DR. To measure DR performance, this paper proposes the customer directrix load (CDL), which is a desired load profile, to replace the customer baseline load (CBL). The uniqueness of CDL makes it more suitable for distributed schemes, while numerous CBLs have to be calculated in a centralized manner to ensure fairness. To allocate DR tasks and rebates, this paper proposes a distributed approach, where the load serving entity (LSE) only needs to publish a total rebate and corresponding CDL. As for each customer, s/he needs to claim an ideal rebate ratio that ranges from 0 to 1, which indicates the proportion of rebate s/he wants to get from LSE. The rebate value for each customer also determines his or her DR task. Then, the process of customer claims for the ideal rebate ratio is modeled as a non-cooperative game, and the Nash equilibrium is proved to exist. The Gossip algorithm is improved in this paper to be suitable for socially connected networks, and the entire game process is distributed. Finally, a large-scale DR system is formulated. The simulation results show that the proposed DR can promote the consumption of RES. Additionally, this scheme is suitable for large-scale customer systems, and the distributed game process is effective.

ACS Style

Shuai Fan; Guangyu He; Kunqi Jia; Zhihua Wang. A Novel Distributed Large-Scale Demand Response Scheme in High Proportion Renewable Energy Sources Integration Power Systems. Applied Sciences 2018, 8, 452 .

AMA Style

Shuai Fan, Guangyu He, Kunqi Jia, Zhihua Wang. A Novel Distributed Large-Scale Demand Response Scheme in High Proportion Renewable Energy Sources Integration Power Systems. Applied Sciences. 2018; 8 (3):452.

Chicago/Turabian Style

Shuai Fan; Guangyu He; Kunqi Jia; Zhihua Wang. 2018. "A Novel Distributed Large-Scale Demand Response Scheme in High Proportion Renewable Energy Sources Integration Power Systems." Applied Sciences 8, no. 3: 452.