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Sheng-Wei Mei
State Key Laboratory of Control and Simulation of Power System and Generation Equipments, Department of Electrical Engineering, Tsinghua University, Beijing, 100084, China

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Journal article
Published: 07 July 2021 in IEEE Transactions on Power Systems
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This paper proposes a multistage robust optimization model for distribution system operation with energy storage under uncertainty. Unlike the conventional robust optimization paradigm which minimizes the worst-case cost, the proposed formulation optimizes the cost in the nominal scenario. In analogy to dynamic programming, we define dynamic robust feasible regions in a recursive manner. In each period, the dynamic robust feasible region is shown to be polyhedral, and a linear programming based projection algorithm is developed to compute such regions offline. In the online stage, the method is executed following a rolling horizon manner: renewable output is observed at the beginning of each period, and the cost of remaining periods in the forecast scenario is to be minimized subject to operation constraints and dynamic robust feasible regions, giving rise to a linear program. In this way, the dispatch strategy ensures multistage operation security regardless of future realizations of renewable power. In numeric tests on a modified IEEE 33-bus distribution system, the dynamic robust feasible regions are visualized and analyzed, and the proposed method is compared with two prevailing robust optimization methods, verifying its advantages in terms of optimality and robustness

ACS Style

Mohammad Shahidehpour; Zhongjie Guo; Wei Wei; Laijun Chen; Shengwei Mei. Distribution System Operation with Renewables and Energy Storage: A Linear Programming Based Multistage Robust Feasibility Approach. IEEE Transactions on Power Systems 2021, PP, 1 -1.

AMA Style

Mohammad Shahidehpour, Zhongjie Guo, Wei Wei, Laijun Chen, Shengwei Mei. Distribution System Operation with Renewables and Energy Storage: A Linear Programming Based Multistage Robust Feasibility Approach. IEEE Transactions on Power Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Mohammad Shahidehpour; Zhongjie Guo; Wei Wei; Laijun Chen; Shengwei Mei. 2021. "Distribution System Operation with Renewables and Energy Storage: A Linear Programming Based Multistage Robust Feasibility Approach." IEEE Transactions on Power Systems PP, no. 99: 1-1.

Journal article
Published: 22 June 2021 in IEEE Transactions on Power Systems
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Since modern smart grids have various and deeply coupled cyber-physical components, they are vulnerable to malicious cyber attacks. Although regular defenses including firewall and IDS are deployed, they may be weakened by zero-day vulnerabilities and sophisticated attack schemes. Therefore, defense strategies to mitigate the risk of blackouts during cyber attacks are necessary. This paper proposes a cyber-physical coordinated defense strategy to overcome the disruption and minimize the risk as much as possible. At the cyber layer, a zero-sum multilevel Markovian Stackelberg game is proposed to model sequential actions of the attacker and the defender. The defender distributes defensive resources to protect lines in a real-time manner, according to the attackers action. If cyber attacks should result in physical outages, defense at the physical layer is then employed. A security-constrained optimal power flow reserving security margin of critical components will be performed to minimize the blackout scale and potential future risk. To solve the corresponding optimization problem and further get the optimal defense strategy, this paper devises a novel water-pouring algorithm. Lastly, test results show that the proposed dynamic defense strategy mitigates risk significantly and outperforms existing methods.

ACS Style

Zhimei Zhang; ShaoWei Huang; Ying Chen; Boda Li; Shengwei Mei. Cyber-Physical Coordinated Risk Mitigation in Smart Grids Based on Attack-Defense Game. IEEE Transactions on Power Systems 2021, PP, 1 -1.

AMA Style

Zhimei Zhang, ShaoWei Huang, Ying Chen, Boda Li, Shengwei Mei. Cyber-Physical Coordinated Risk Mitigation in Smart Grids Based on Attack-Defense Game. IEEE Transactions on Power Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Zhimei Zhang; ShaoWei Huang; Ying Chen; Boda Li; Shengwei Mei. 2021. "Cyber-Physical Coordinated Risk Mitigation in Smart Grids Based on Attack-Defense Game." IEEE Transactions on Power Systems PP, no. 99: 1-1.

Journal article
Published: 07 June 2021 in IEEE Transactions on Sustainable Energy
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Energy storage unit (ESU) is playing an increasingly important role in load shifting and uncertainty mitigation. This paper aims to quantify the value of ESU in the unit commitment (UC) with renewable generation. By treating the power and energy capacities of ESU as continuous parameters, the stochastic UC problem is cast as a multi-parametric mixed-integer linear program (mp-MILP), whose optimal value function (OVF) gives the relation between storage capacity and the daily operation cost in an analytical manner. It encompasses abundant sensitivity information, and its surface can be easily visualized. The reduced cost compared with the benchmark case without storage can be regarded as the value of ESU. As a potential application, the OVF is used to formulated an optimal storage sizing problem which maximizes the ratio between the reduced operation cost and the investment cost, ensuring the minimum time of cost recovery. The solution consists of two steps: the first step constructs the OVF of parameterized in storage capacity; the second step reformulates the fractional storage sizing program into an MILP, leveraging the expression of the OVF and variable transformations.

ACS Style

Zhongjie Guo; Wei Wei; Laijun Chen; Mohammad Shahidehpour; Shengwei Mei. Economic Value of Energy Storages in Unit Commitment with Renewables and its Implication on Storage Sizing. IEEE Transactions on Sustainable Energy 2021, PP, 1 -1.

AMA Style

Zhongjie Guo, Wei Wei, Laijun Chen, Mohammad Shahidehpour, Shengwei Mei. Economic Value of Energy Storages in Unit Commitment with Renewables and its Implication on Storage Sizing. IEEE Transactions on Sustainable Energy. 2021; PP (99):1-1.

Chicago/Turabian Style

Zhongjie Guo; Wei Wei; Laijun Chen; Mohammad Shahidehpour; Shengwei Mei. 2021. "Economic Value of Energy Storages in Unit Commitment with Renewables and its Implication on Storage Sizing." IEEE Transactions on Sustainable Energy PP, no. 99: 1-1.

Journal article
Published: 05 June 2021 in Energy Conversion and Management
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Faced with environmental pollution and energy crisis, energy hub yields an improvement on efficiency and flexibility of multi-energy supply. Advanced adiabatic compressed air energy storage (AA-CAES) is a promising large-scale energy storage technology and is attracting increasing attention due to its heat-electricity co-storage potentials. This paper investigates the external characteristics of advanced adiabatic compressed air energy storage and exploits its ability to implement an energy hub. First, a dual state-of-charge (SoC) model of advanced adiabatic compressed air energy storage is presented, taking into account the system off-design features and the impact of ambient temperature. The state-of-charge of the air storage tank depends on the mass of stored air, whose mass flow rate affects the charging and discharging electric power. The state-of-charge of the high-temperature thermal energy storage depends on the mass of heat transfer oil, whose mass flow rate determines the reserving and releasing heating power. Adjusting the mass flow rates of air and oil offers flexible control on the power and thermal outputs. An energy hub is built based upon the advanced adiabatic compressed air energy storage. To address the daily self-dispatch of the energy hub facing the uncertainties of load and ambient temperature, a data-driven stochastic dynamic programming model is proposed which allows a rolling horizon implementation. The Kernel regression is employed to estimate the conditional probability distribution of uncertainties. The cost-to-go functions in the Bellman equation are approximated via sampling and interpolation. Case studies validate the effectiveness of the proposed approach. The results indicate that: 1) The proposed dynamic programming method outperforms model predictive control in computational efficiency. 2) Neglecting the temperature effect on compressed air energy storage operation leads to 4.5%, 7.8%, and 9.2% regulation errors of charging, discharging and heating power, respectively.

ACS Style

Jiayu Bai; Wei Wei; Laijun Chen; Shengwei Mei. Rolling-horizon dispatch of advanced adiabatic compressed air energy storage based energy hub via data-driven stochastic dynamic programming. Energy Conversion and Management 2021, 243, 114322 .

AMA Style

Jiayu Bai, Wei Wei, Laijun Chen, Shengwei Mei. Rolling-horizon dispatch of advanced adiabatic compressed air energy storage based energy hub via data-driven stochastic dynamic programming. Energy Conversion and Management. 2021; 243 ():114322.

Chicago/Turabian Style

Jiayu Bai; Wei Wei; Laijun Chen; Shengwei Mei. 2021. "Rolling-horizon dispatch of advanced adiabatic compressed air energy storage based energy hub via data-driven stochastic dynamic programming." Energy Conversion and Management 243, no. : 114322.

Journal article
Published: 04 June 2021 in IEEE Transactions on Power Systems
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Since modern cyber-physical power systems are vulnerable to coordinated wide-area cyber attacks, it is necessary to mitigate the potential risk as much as possible. At the planning stage, the defender can utilize software diversity, which is a common phenomenon that the cyber software of different substations comes from different competing vendors. Therefore, different kinds of software may not be exposed to the same zero-day security loophole, preventing the attacker from taking charge of multiple substations at the same time. In this paper, the optimal scheme of software deployment considering long-term risk mitigation is studied. Firstly, the framework of diversity-based cyber defense against malicious attacks is formulated. Secondly, the risk index based on representative attack patterns is constructed, which is the objective to be minimized. Thirdly, considering that the deployment scheme is long-term stable while the operating mode varies with time, we construct a multiobjective nonlinear stochastic programming to mitigate the average risk of operating modes. Then the optimization problem is solved by the multiobjective genetic algorithm. Lastly, results of the IEEE 39-node CPPS and and the Virtual European Grid demonstrate that the proposed method can considerably reduce the attack risk.

ACS Style

Zhimei Zhang; ShaoWei Huang; Ying Chen; Boda Li; Shengwei Mei. Diversified Software Deployment for Long-Term Risk Mitigation in Cyber-Physical Power Systems. IEEE Transactions on Power Systems 2021, PP, 1 -1.

AMA Style

Zhimei Zhang, ShaoWei Huang, Ying Chen, Boda Li, Shengwei Mei. Diversified Software Deployment for Long-Term Risk Mitigation in Cyber-Physical Power Systems. IEEE Transactions on Power Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Zhimei Zhang; ShaoWei Huang; Ying Chen; Boda Li; Shengwei Mei. 2021. "Diversified Software Deployment for Long-Term Risk Mitigation in Cyber-Physical Power Systems." IEEE Transactions on Power Systems PP, no. 99: 1-1.

Journal article
Published: 19 February 2021 in IEEE Transactions on Smart Grid
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Electric buses (EBs) possess large-capacity batteries and are dispatched by a central operator, exhibiting great potential to enhance the resilience of distribution systems (DSs) against meteorological disasters. This paper proposes an optimization model for joint post-disaster DS restoration, considering coordinated dispatching with EBs. By assuming that the DS can rent some EBs from the bus company, an EB scheduling problem with adjustable timetables is established. Idle buses are placed at designated areas and feed power back to the grid via charging piles or charging stations in case of need. The schedule of the remaining buses should meet the passenger transport demand, which is smaller than usual because of bad weather. The objective is to maximize the total benefits and minimize the EB rental cost of the grid company. Techniques in integer algebra are used to reformulate the proposed restoration problem with bus scheduling constraints as a mixed-integer linear program, which can be processed by off-the-shelf solvers. The proposed method is tested on a modified 15-bus system and IEEE 123-bus system. The results demonstrate that the resilience of the system is enhanced, and the benefits of the grid company increase significantly, because of the flexibility brought by EBs.

ACS Style

Boda Li; Ying Chen; Wei Wei; ShaoWei Huang; Shengwei Mei. Resilient Restoration of Distribution Systems in Coordination With Electric Bus Scheduling. IEEE Transactions on Smart Grid 2021, 12, 3314 -3325.

AMA Style

Boda Li, Ying Chen, Wei Wei, ShaoWei Huang, Shengwei Mei. Resilient Restoration of Distribution Systems in Coordination With Electric Bus Scheduling. IEEE Transactions on Smart Grid. 2021; 12 (4):3314-3325.

Chicago/Turabian Style

Boda Li; Ying Chen; Wei Wei; ShaoWei Huang; Shengwei Mei. 2021. "Resilient Restoration of Distribution Systems in Coordination With Electric Bus Scheduling." IEEE Transactions on Smart Grid 12, no. 4: 3314-3325.

Conference paper
Published: 01 February 2021 in IOP Conference Series: Earth and Environmental Science
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As a new type of mechanical energy storage, compressed air energy storage (CAES) has attracted wide attention in recent years. This paper studies the optimal sizing problem of CAES in power distribution network (PDN). CAES plays a role in shaving the peak and filling the valley at demand side and thus reduces the operation cost of PDN to purchase electricity from the main grid. A multi-parametric linear programming (MP-LP) model is formulated where the power and energy capacities of CAES are regarded as parameters; the parameterized optimal value function (OVF) provides a graphical tool to describe the impact on operation cost of CAES configuration, which helps determine the planning strategy in a visual manner. The visualized results reveal not only the optimal solution, but also some useful information, like the sensitivity of operation cost to parameters. Case studies conducted on the IEEE 33-bus distribution system verifies the effectiveness of the proposed method.

ACS Style

Fan Chen; Zhongjie Guo; Qiyou Lin; Xiaodai Xue; Yuguang Xie; Shengwei Mei. Optimal power and energy sizing of compressed air energy storage in distribution network using multiparametric programming. IOP Conference Series: Earth and Environmental Science 2021, 675, 012093 .

AMA Style

Fan Chen, Zhongjie Guo, Qiyou Lin, Xiaodai Xue, Yuguang Xie, Shengwei Mei. Optimal power and energy sizing of compressed air energy storage in distribution network using multiparametric programming. IOP Conference Series: Earth and Environmental Science. 2021; 675 (1):012093.

Chicago/Turabian Style

Fan Chen; Zhongjie Guo; Qiyou Lin; Xiaodai Xue; Yuguang Xie; Shengwei Mei. 2021. "Optimal power and energy sizing of compressed air energy storage in distribution network using multiparametric programming." IOP Conference Series: Earth and Environmental Science 675, no. 1: 012093.

Journal article
Published: 28 January 2021 in Energy
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Liquid air energy storage (LAES) is a promising large-scale energy storage technology in improving renewable energy systems and grid load shifting. In baseline LAES (B-LAES), the compression heat harvested in the charging process is stored and utilized in the discharging process to enhance the power generation. Due to the low liquid air yield, a large amount of compression heat is wasted. In order to improve the round-trip efficiency (RTE) and extend the application field, a novel combined cooling, heating and power system based on the LAES (LAES-CCHP) is proposed and investigated. In the proposed system, an organic Rankine cycle (ORC) is employed to recover the high-temperature surplus compression heat to generate electricity and an absorption refrigeration system (ARS) is introduced to utilize the low-temperature compression heat to realize district cooling and heating. Based on a mathematical model, performance evaluation and exergy analysis of the system is performed. It is found that the effective and cascaded utilization of the compression heat could significantly improve the efficiency and performance of the system. With optimal operational parameters, the RTE and exergy efficiency of the LAES-CCHP could reach 69.64% and 57.02%, respectively, which are 37.66% and 12.71% higher than those of the B-LAES.

ACS Style

Xiao-Dai Xue; Tong Zhang; Xue-Lin Zhang; Lin-Rui Ma; Ya-Ling He; Ming-Jia Li; Sheng-Wei Mei. Performance evaluation and exergy analysis of a novel combined cooling, heating and power (CCHP) system based on liquid air energy storage. Energy 2021, 222, 119975 .

AMA Style

Xiao-Dai Xue, Tong Zhang, Xue-Lin Zhang, Lin-Rui Ma, Ya-Ling He, Ming-Jia Li, Sheng-Wei Mei. Performance evaluation and exergy analysis of a novel combined cooling, heating and power (CCHP) system based on liquid air energy storage. Energy. 2021; 222 ():119975.

Chicago/Turabian Style

Xiao-Dai Xue; Tong Zhang; Xue-Lin Zhang; Lin-Rui Ma; Ya-Ling He; Ming-Jia Li; Sheng-Wei Mei. 2021. "Performance evaluation and exergy analysis of a novel combined cooling, heating and power (CCHP) system based on liquid air energy storage." Energy 222, no. : 119975.

Review article
Published: 28 January 2021 in Energy Conversion and Management
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Importance for the accurate forecast of wind region with multiple wind farms is gradually emerging. As influenced by the geographical features of the wind region, the power output from each wind farm is closely correlated to the local-patterns of its covered weather. However, modeling the highly time-varying nature of the local-patterns’ spatial distribution remains the key challenge to regional wind power forecast. For this purpose, a sub-region is proposed to represent the spatial scale of wind farms covered by the same local-pattern. All wind farms in the wind region are divided into multiple sub-regions. This classification is defined as the partition which represents a typical state of the wind region. To deal with the time-varying nature, partitions are considered on the adaptive process. In this paper, a regional wind power forecasting method based on adaptive partition and long-short-term matching is proposed. First, a refined partition set of wind region is determined by the Regional Hierarchical Clustering algorithm. Second, to identify the current states of the wind region, the partition with minimum forecasting error is chosen as Optimal Partition. Third, the long-short-term matching strategy is proposed to find the adaptive partition among the refined partition set with the indication of recent and historical Optimal Partitions. Eventually, for each time horizon, the forecasted power of each sub-regions in the adaptive partition is aggregated to achieve the final regional wind power forecasting results. The superior performance and robustness of the proposed methods are validated with actual wind generation data from a wind region which contains nine wind farms in China. The ability to capture wind farm local-pattern of the proposed method is also approved.

ACS Style

Chenyu Liu; Xuemin Zhang; Shengwei Mei; Feng Liu. Local-pattern-aware forecast of regional wind power: Adaptive partition and long-short-term matching. Energy Conversion and Management 2021, 231, 113799 .

AMA Style

Chenyu Liu, Xuemin Zhang, Shengwei Mei, Feng Liu. Local-pattern-aware forecast of regional wind power: Adaptive partition and long-short-term matching. Energy Conversion and Management. 2021; 231 ():113799.

Chicago/Turabian Style

Chenyu Liu; Xuemin Zhang; Shengwei Mei; Feng Liu. 2021. "Local-pattern-aware forecast of regional wind power: Adaptive partition and long-short-term matching." Energy Conversion and Management 231, no. : 113799.

Journal article
Published: 26 January 2021 in IEEE Transactions on Energy Conversion
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Consensus-based control has been widely applied in the frequency/voltage restoration and power sharing of microgrids. Considering the existence of plug-and-play devices and accidental communication failures in microgrids, the physical and cyber structure of a microgrid may be time-varying, where the performance of traditional consensus-based control methods under ideal cases may not be guaranteed. This paper proposed a novel finite-time consensus-based secondary frequency control strategy, i.e., a control strategy with finite-time convergence performance. The convergence time of the proposed control strategy is independent of both the structure and operation states of the microgrid, which guarantees the effectiveness of the control strategy under flexible operating conditions. Moreover, a detailed performance analysis of the finite-time control strategy is presented, and an optimal design method of the control parameters is developed, which significantly improves the performance of the finite-time control strategy. The flexibility of the proposed control strategy, as well as the effectiveness of the optimal design method, is verified by both simulation and experimental results.

ACS Style

Sicheng Deng; Laijun Chen; Xiaonan Lu; Tianwen Zheng; Shengwei Mei. Distributed Finite-Time Secondary Frequency Control of Islanded Microgrids With Enhanced Operational Flexibility. IEEE Transactions on Energy Conversion 2021, 36, 1733 -1742.

AMA Style

Sicheng Deng, Laijun Chen, Xiaonan Lu, Tianwen Zheng, Shengwei Mei. Distributed Finite-Time Secondary Frequency Control of Islanded Microgrids With Enhanced Operational Flexibility. IEEE Transactions on Energy Conversion. 2021; 36 (3):1733-1742.

Chicago/Turabian Style

Sicheng Deng; Laijun Chen; Xiaonan Lu; Tianwen Zheng; Shengwei Mei. 2021. "Distributed Finite-Time Secondary Frequency Control of Islanded Microgrids With Enhanced Operational Flexibility." IEEE Transactions on Energy Conversion 36, no. 3: 1733-1742.

Journal article
Published: 26 January 2021 in Applied Sciences
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The uncertain natures of renewable energy lead to its underutilization; energy storage unit (ESU) is expected to be one of the most promising solutions to this issue. This paper evaluates the impact of ESUs on renewable energy curtailment. For any fixed renewable power output, the evaluation model minimizes the total amount of curtailment and is formulated as a mixed integer linear program (MILP) with the complementarity constraints on the charging and discharging behaviors of ESUs; by treating the power and energy capacities of ESUs as parameters, the MILP is transformed into a multi-parametric MILP (mp-MILP), whose optimal value function (OVF) explicitly maps the parameters to the renewable energy curtailment. Further, given the inexactness of uncertainty’s probability distribution, a distributionally robust mp-MILP (DR-mp-MILP) is proposed that considers the worst distribution in a neighborhood of the empirical distribution built by the representative scenarios. The DR-mp-MILP has a max–min form and is reformed as a canonical mp-MILP by duality theory. The proposed method was validated on the modified IEEE nine-bus systems; the parameterized OVFs provide insightful suggestions on storage sizing.

ACS Style

Zhongjie Guo; Wei Wei; Maochun Wang; Jian Li; ShaoWei Huang; Laijun Chen; Shengwei Mei. Characterizing and Visualizing the Impact of Energy Storage on Renewable Energy Curtailment in Bulk Power Systems. Applied Sciences 2021, 11, 1135 .

AMA Style

Zhongjie Guo, Wei Wei, Maochun Wang, Jian Li, ShaoWei Huang, Laijun Chen, Shengwei Mei. Characterizing and Visualizing the Impact of Energy Storage on Renewable Energy Curtailment in Bulk Power Systems. Applied Sciences. 2021; 11 (3):1135.

Chicago/Turabian Style

Zhongjie Guo; Wei Wei; Maochun Wang; Jian Li; ShaoWei Huang; Laijun Chen; Shengwei Mei. 2021. "Characterizing and Visualizing the Impact of Energy Storage on Renewable Energy Curtailment in Bulk Power Systems." Applied Sciences 11, no. 3: 1135.

Journal article
Published: 20 January 2021 in Applied Sciences
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Hot dry rock (HDR) power stations have the potential to serve as an energy storage system for large-scale photovoltaic (PV) plants. For flexible operation, thermal storage (TS) power stations are required to coordinate with HDR power stations. In this study, a hybrid power system is constructed by combining the HDR, TS, and PV plants. Game theory is then introduced into the optimal dispatch of the hybrid power system. Considering HDR, TS, and PV as players, non-cooperative and cooperative game dispatching models are established and verified by a case in the Gonghe basin of Qinghai. Finally, the stability of the coalitions and the rationality of allocation of the hybrid power system is verified, and the sensitivity of critical parameters is analyzed. The results demonstrate that the overall payoff of the hybrid power system is increased by 10.15%. The payoff of the HDR power station is increased by 16.5%. The TS power station has obtained 50% of the total extra profits. The PV plant reduces the impact on the grid to obtain the priority of grid connection. Based on these results, a theoretical basis can be provided for developing generation systems based on the HDR resources in the Gonghe Basin.

ACS Style

Yang Si; Laijun Chen; Xuelin Zhang; Xiaotao Chen; Tianwen Zheng; Shengwei Mei. Game Approach to HDR-TS-PV Hybrid Power System Dispatching. Applied Sciences 2021, 11, 914 .

AMA Style

Yang Si, Laijun Chen, Xuelin Zhang, Xiaotao Chen, Tianwen Zheng, Shengwei Mei. Game Approach to HDR-TS-PV Hybrid Power System Dispatching. Applied Sciences. 2021; 11 (3):914.

Chicago/Turabian Style

Yang Si; Laijun Chen; Xuelin Zhang; Xiaotao Chen; Tianwen Zheng; Shengwei Mei. 2021. "Game Approach to HDR-TS-PV Hybrid Power System Dispatching." Applied Sciences 11, no. 3: 914.

Journal article
Published: 26 December 2020 in Energy
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Hydrogen is a promising form of secondary energy in the future. This paper studies the equilibrium state of supply-demand flow in a regional hydrogen market. We consider peer-to-peer transactions between renewable energy-based suppliers, profit-driven retailers, and transportation costs. A game model is proposed to characterize the market equilibrium taking into account the strategic behaviors of individual participants. The uncertainty of available renewable energy is described by an inexact probability distribution, and suppliers’ problems give rise to distributionally robust optimization. The market clearing price is endogenously determined from the supply and demand, precipitating an equilibrium in the market. Based on Karush-Kuhn-Tucker optimality conditions and linearization techniques, a mixed-integer linear program is developed to compute the market equilibrium. Case studies and numerical analysis conducted on a testing system demonstrate that the proposed method can provide useful insights on hydrogen market design and analysis.

ACS Style

Zhongjie Guo; Wei Wei; Laijun Chen; Xiaoping Zhang; Shengwei Mei. Equilibrium model of a regional hydrogen market with renewable energy based suppliers and transportation costs. Energy 2020, 220, 119608 .

AMA Style

Zhongjie Guo, Wei Wei, Laijun Chen, Xiaoping Zhang, Shengwei Mei. Equilibrium model of a regional hydrogen market with renewable energy based suppliers and transportation costs. Energy. 2020; 220 ():119608.

Chicago/Turabian Style

Zhongjie Guo; Wei Wei; Laijun Chen; Xiaoping Zhang; Shengwei Mei. 2020. "Equilibrium model of a regional hydrogen market with renewable energy based suppliers and transportation costs." Energy 220, no. : 119608.

Journal article
Published: 23 December 2020 in IEEE Systems Journal
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Prosumers are agents that both consume and produce energy. This article studies the optimal energy management of a residential prosumer which consists of a renewable power plant and an energy storage unit. Energy could stream among power grid, renewable plant, storage unit, and demand, providing a highly flexible energy supply and the opportunity of arbitrage. To capture the uncertainty of renewable generation and electricity price, as well as the rolling horizon feature of the multiperiod energy management, the problem is formulated as a robust data-driven dynamic programming (RDDP). Kernel regression is utilized to build the empirical conditional distribution in a data-driven manner, and all candidates that reside in a Wasserstein metric-based ambiguity set are taken into account to tackle the inexactness of the empirical distribution. The RDDP can be transformed into a series of convex optimization problems with cost-to-go functions in their constraints. The piecewise linear expression of the cost-to-go function is retrieved from dual linear programs. Through such an analytical expression of cost-to-go functions, the RDDP can be solved via backward induction, unlike the popular stochastic dual dynamic programming technique that incorporates forward and backward passes. Case studies validate the performance and advantage of the proposed RDDP approach.

ACS Style

Zhongjie Guo; Wei Wei; Laijun Chen; Zhaojian Wang; Joao P. S. Catalao; Shengwei Mei. Optimal Energy Management of a Residential Prosumer: A Robust Data-Driven Dynamic Programming Approach. IEEE Systems Journal 2020, PP, 1 -10.

AMA Style

Zhongjie Guo, Wei Wei, Laijun Chen, Zhaojian Wang, Joao P. S. Catalao, Shengwei Mei. Optimal Energy Management of a Residential Prosumer: A Robust Data-Driven Dynamic Programming Approach. IEEE Systems Journal. 2020; PP (99):1-10.

Chicago/Turabian Style

Zhongjie Guo; Wei Wei; Laijun Chen; Zhaojian Wang; Joao P. S. Catalao; Shengwei Mei. 2020. "Optimal Energy Management of a Residential Prosumer: A Robust Data-Driven Dynamic Programming Approach." IEEE Systems Journal PP, no. 99: 1-10.

Journal article
Published: 20 December 2020 in Entropy
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As a fundamental infrastructure of energy supply for future society, energy Internet (EI) can achieve clean energy generation, conversion, storage and consumption in a more economic and safer way. This paper demonstrates the technology principle of advanced adiabatic compressed air energy storage system (AA-CAES), as well as analysis of the technical characteristics of AA-CAES. Furthermore, we propose an overall architectural scheme of a clean energy router (CER) based on AA-CAES. The storage and mutual conversion mechanism of wind and solar power, heating, and other clean energy were designed to provide a key technological solution for the coordination and comprehensive utilization of various clean energies for the EI. Therefore, the design of the CER scheme and its efficiency were analyzed based on a thermodynamic simulation model of AA-CAES. Meanwhile, we explored the energy conversion mechanism of the CER and improved its overall efficiency. The CER based on AA-CAES proposed in this paper can provide a reference for efficient comprehensive energy utilization (CEU) (93.6%) in regions with abundant wind and solar energy sources.

ACS Style

Chenyixuan Ni; Xiaodai Xue; Shengwei Mei; Xiao-Ping Zhang; Xiaotao Chen. Technological Research of a Clean Energy Router Based on Advanced Adiabatic Compressed Air Energy Storage System. Entropy 2020, 22, 1440 .

AMA Style

Chenyixuan Ni, Xiaodai Xue, Shengwei Mei, Xiao-Ping Zhang, Xiaotao Chen. Technological Research of a Clean Energy Router Based on Advanced Adiabatic Compressed Air Energy Storage System. Entropy. 2020; 22 (12):1440.

Chicago/Turabian Style

Chenyixuan Ni; Xiaodai Xue; Shengwei Mei; Xiao-Ping Zhang; Xiaotao Chen. 2020. "Technological Research of a Clean Energy Router Based on Advanced Adiabatic Compressed Air Energy Storage System." Entropy 22, no. 12: 1440.

Journal article
Published: 06 December 2020 in Energy
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Advanced adiabatic compressed air energy storage (AA-CAES) is a scalable storage technology with a long lifespan, fast response and low environmental impact, and is suitable for grid-level applications. In power systems with high-penetration renewable generation, AA-CAES is expected to play an active role in flexible regulation. This paper proposes a state-space set-point control model of AA-CAES for the application in the power tracking mode considering off-design characteristics. The part-load features of the multi-stage turbine and heat exchanger are captured by simplified models, and then tailored for improving computational efficiency in the applications with a timescale of one minute. The set-point control (power tracking) of AA-CAES entails the coordination of turbine inlet pressure, air mass flow rate and heat transfer fluid (HTF) mass flow rate, while ensuring the secure pressure at the throttle valve linking the air storage tank and the expansion train. The set-point control problem is cast to a differential-algebraic equation (DAE) constrained optimization problem, and is reformulated as a nonlinear program via the simultaneous collocation method. Case studies validate the accuracy and applicability of the proposed AA-CAES model for power tracking under off-design generating conditions.

ACS Style

Jiayu Bai; Feng Liu; Xiaodai Xue; Wei Wei; Laijun Chen; Guohua Wang; Shengwei Mei. Modelling and control of advanced adiabatic compressed air energy storage under power tracking mode considering off-design generating conditions. Energy 2020, 218, 119525 .

AMA Style

Jiayu Bai, Feng Liu, Xiaodai Xue, Wei Wei, Laijun Chen, Guohua Wang, Shengwei Mei. Modelling and control of advanced adiabatic compressed air energy storage under power tracking mode considering off-design generating conditions. Energy. 2020; 218 ():119525.

Chicago/Turabian Style

Jiayu Bai; Feng Liu; Xiaodai Xue; Wei Wei; Laijun Chen; Guohua Wang; Shengwei Mei. 2020. "Modelling and control of advanced adiabatic compressed air energy storage under power tracking mode considering off-design generating conditions." Energy 218, no. : 119525.

Research article
Published: 24 November 2020 in IET Renewable Power Generation
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The limited reserve of fossil fuels and public awareness of environmental issues prompt the rapid development of renewable energy generation. However, the centralised utilisation of renewable energy in bulk power systems is impeded mainly by its volatile nature and transmission congestion, leading to the spillage of renewable power. The energy storage unit is expected to be a promising measure to smooth the output of renewable plants and reduce the curtailment rate. This study addresses the energy storage sizing problem in bulk power systems. To capture the operating status of the power system more accurately, the authors use a dedicated power flow model which involves voltage and reactive power. The uncertainty of renewable generation is described via inexact probability distributions encapsulated in a data-driven Wasserstein-metric based ambiguity set, based on which the renewable energy curtailment rate is formulated as a distributionally robust chance constraint. The objective is to minimise the total investment cost, and the optimal sizing problem gives rise to a distributionally robust chance-constrained program, and is reformulated as a tractable linear program via conservative approximation. Case studies conducted on the modified IEEE 30-bus and 118-bus systems demonstrate the effectiveness and performance of the proposed approach.

ACS Style

Zhongjie Guo; Wei Wei; Laijun Chen; Rui Xie; Shengwei Mei. Sizing energy storage to reduce renewable power curtailment considering network power flows: a distributionally robust optimisation approach. IET Renewable Power Generation 2020, 14, 3273 -3280.

AMA Style

Zhongjie Guo, Wei Wei, Laijun Chen, Rui Xie, Shengwei Mei. Sizing energy storage to reduce renewable power curtailment considering network power flows: a distributionally robust optimisation approach. IET Renewable Power Generation. 2020; 14 (16):3273-3280.

Chicago/Turabian Style

Zhongjie Guo; Wei Wei; Laijun Chen; Rui Xie; Shengwei Mei. 2020. "Sizing energy storage to reduce renewable power curtailment considering network power flows: a distributionally robust optimisation approach." IET Renewable Power Generation 14, no. 16: 3273-3280.

Journal article
Published: 08 November 2020 in Applied Sciences
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Ultra-short-term wind power prediction is of great importance for the integration of renewable energy. It is the foundation of probabilistic prediction and even a slight increase in the prediction accuracy can exert significant improvement for the safe and economic operation of power systems. However, due to the complex spatiotemporal relationship and the intrinsic characteristic of nonlinear, randomness and intermittence, the prediction of regional wind farm clusters and each wind farm’s power is still a challenge. In this paper, a framework based on graph neural network and numerical weather prediction (NWP) is proposed for the ultra-short-term wind power prediction. First, the adjacent matrix of wind farms, which are regarded as the vertexes of a graph, is defined based on geographical distance. Second, two graph neural networks are designed to extract the spatiotemporal feature of historical wind power and NWP information separately. Then, these features are fused based on multi-modal learning. Third, to enhance the efficiency of prediction method, a multi-task learning method is adopted to extract the common feature of the regional wind farm cluster and it can output the prediction of each wind farm at the same time. The cases of a wind farm cluster located in Northeast China verified that the accuracy of a regional wind farm cluster power prediction is improved, and the time consumption increases slowly when the number of wind farms grows. The results indicate that this method has great potential to be used in large-scale wind farm clusters.

ACS Style

Hang Fan; Xuemin Zhang; Shengwei Mei; Kunjin Chen; Xinyang Chen. M2GSNet: Multi-Modal Multi-Task Graph Spatiotemporal Network for Ultra-Short-Term Wind Farm Cluster Power Prediction. Applied Sciences 2020, 10, 7915 .

AMA Style

Hang Fan, Xuemin Zhang, Shengwei Mei, Kunjin Chen, Xinyang Chen. M2GSNet: Multi-Modal Multi-Task Graph Spatiotemporal Network for Ultra-Short-Term Wind Farm Cluster Power Prediction. Applied Sciences. 2020; 10 (21):7915.

Chicago/Turabian Style

Hang Fan; Xuemin Zhang; Shengwei Mei; Kunjin Chen; Xinyang Chen. 2020. "M2GSNet: Multi-Modal Multi-Task Graph Spatiotemporal Network for Ultra-Short-Term Wind Farm Cluster Power Prediction." Applied Sciences 10, no. 21: 7915.

Journal article
Published: 11 September 2020 in IEEE Transactions on Sustainable Energy
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The high penetration of volatile renewable energy challenges power system operation. Energy storage units (ESUs) can shift the demand over time and compensate real-time discrepancy between generation and demand, and thus improve system operation flexibility and reduce renewable energy curtailment. This paper proposes two parametric optimization models to quantify how the power (MW) and energy (MWh) capacity of ESU would impact renewable energy utilization from two aspects: renewable energy curtailment and system flexibility for uncertainty mitigation. The two indicators are characterized as multivariate functions in the capacity parameters of ESUs. A severity ranking algorithm is suggested to pick up critical scenarios of fluctuation patterns from the uncertainty set; consequently, the proposed models come down to multi-parametric mixed-integer linear programs (mp-MILPs) which can be solved by a decomposition algorithm. The proposed method provides analytical expressions of the two indicators as functions in MW and MWh capacity. Such a characterization delivers abundant sensitivity information on the impact of ESU capacity parameters, and provides a powerful tool for visualization and useful reference for storage sizing. Case studies verify the effectiveness of the proposed method and demonstrate how to use the geometric information.

ACS Style

Zhongjie Guo; Wei Wei; Laijun Chen; Zhao Yang Dong; Shengwei Mei. Impact of Energy Storage on Renewable Energy Utilization: A Geometric Description. IEEE Transactions on Sustainable Energy 2020, 12, 874 -885.

AMA Style

Zhongjie Guo, Wei Wei, Laijun Chen, Zhao Yang Dong, Shengwei Mei. Impact of Energy Storage on Renewable Energy Utilization: A Geometric Description. IEEE Transactions on Sustainable Energy. 2020; 12 (2):874-885.

Chicago/Turabian Style

Zhongjie Guo; Wei Wei; Laijun Chen; Zhao Yang Dong; Shengwei Mei. 2020. "Impact of Energy Storage on Renewable Energy Utilization: A Geometric Description." IEEE Transactions on Sustainable Energy 12, no. 2: 874-885.

Journal article
Published: 02 September 2020 in IEEE Transactions on Power Systems
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Chance constrained program (CCP) is a popular stochastic optimization method in power system planning and operation problems. Conditional Value-at-Risk (CVaR) provides a convex approximation for chance constraints which are nonconvex. Although CCP assumes an exact empirical distribution, and the optimum of a stochastic programming model is thought to be sensitive in the designated probability distribution, this letter discloses that CVaR reformulation of chance constraint is intrinsically robust. A pair of indices are proposed to quantify the maximum tolerable perturbation of the probability distribution, and can be computed from a computationally-cheap dichotomy search. An example on the coordinated capacity optimization of energy storage and transmission line for a remote wind farm validates the main claims. The above results demonstrate that stochastic optimization methods are not necessarily vulnerable to distributional uncertainty, and justify the positive effect of the conservatism brought by the CVaR reformulation.

ACS Style

Yang Cao; Wei Wei; Shengwei Mei; Miadreza Shafie-Khah; Joao P. S. Catalao. Analyzing and Quantifying the Intrinsic Distributional Robustness of CVaR Reformulation for Chance-Constrained Stochastic Programs. IEEE Transactions on Power Systems 2020, 35, 4908 -4911.

AMA Style

Yang Cao, Wei Wei, Shengwei Mei, Miadreza Shafie-Khah, Joao P. S. Catalao. Analyzing and Quantifying the Intrinsic Distributional Robustness of CVaR Reformulation for Chance-Constrained Stochastic Programs. IEEE Transactions on Power Systems. 2020; 35 (6):4908-4911.

Chicago/Turabian Style

Yang Cao; Wei Wei; Shengwei Mei; Miadreza Shafie-Khah; Joao P. S. Catalao. 2020. "Analyzing and Quantifying the Intrinsic Distributional Robustness of CVaR Reformulation for Chance-Constrained Stochastic Programs." IEEE Transactions on Power Systems 35, no. 6: 4908-4911.