Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Day-ahead Risk-constrained Stochastic Scheduling of Multi-energy System
Author:
Affiliation:

1.Department of Electrical Engineering, Sichuan University, Chengdu 610065, China;2.Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA

Fund Project:

This work was supported by the National Natural Science Foundation of China (No. 52007125).

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    Abstract:

    As an increasing penetration of renewable energy sources can potentially impact voltage profile and compromise system security, the security continues to be the most critical concern in power system operations. A risk-constrained stochastic scheduling model is proposed to leverage the latent scheduling capacity of a multi-energy system to seek an economic operation solution while maintaining system operation risk level against uncertain renewable generation. Overvoltage risk constraints, as compared to the straightforward voltage boundary limits, are incorporated into the stochastic scheduling model to guarantee the operation security and economics. Linearized AC power flow model is applied to enable overvoltage risk assessment within the coordinated scheduling model. The proposed stochastic scheduling model is tackled via the improved progressive hedging approach with an enhanced relax-round-polish process, which overcomes the convergence issues of the traditional progressive hedging in handling nonconvex stochastic scheduling model with binary variables on both stages. Numerical simulation results of IEEE 30-bus system and IEEE 118-bus system illustrate the efficacy of the proposed model in ensuring voltage security and improving economic operation of systems.

    表 6 Table 6
    表 1 Table 1
    表 9 Table 9
    表 5 Table 5
    表 10 Table 10
    表 3 Table 3
    表 11 Table 11
    表 8 Table 8
    图1 Schematic of IEHES.Fig.1
    图2 Electrothermal coupling characteristics of CHP unit with HSD.Fig.2
    图3 Schematic of DHN pipeline connection.Fig.3
    图4 Thermal inertia of PHN.Fig.4
    图5 Thermal inertia of equivalent building.Fig.5
    图6 Electrothermal coupling characteristics of CHP unit with EB.Fig.6
    图7 Schematic of heat source.Fig.7
    图8 Schematic of optimal scheduling process.Fig.8
    图9 Electric power and temperature curve on a typical day.Fig.9
    图10 Diagram of 6-bus EPS and 6-node DHS test system.Fig.10
    图11 Scheduling results with HSD and EB. (a) Wind power. (b) Photovoltaic. (c) Heat storage of HSD. (d) Heat power of EB.Fig.11
    图12 Scheduling results with thermal inertia. (a) Wind power. (b) Photovoltaic. (c) PHN inlet and outlet temperatures. (d) Indoor temperature.Fig.12
    图13 Scheduling results with PDR. (a) Wind power. (b) Photovoltaic. (c) Electric load. (d) Day-ahead electricity price.Fig.13
    图14 Comparison of optimal scheduling results. (a) Wind power. (b) Photovoltaic. (c) Electric power of CHP unit. (d) Heat power of CHP unit.Fig.14
    图15 Renewable energy curtailment and cost reduction. (a) Wind power curtailment. (b) Photovoltaic curtailment. (c) Cost reduction.Fig.15
    图16 Diagram of 30-bus and 12-node large system.Fig.16
    图17 Comparison of optimal scheduling results. (a) Wind power. (b) Photovoltaic.Fig.17
    表 4 Table 4
    表 2 Table 2
    表 7 Table 7
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History
  • Received:June 14,2020
  • Revised:
  • Adopted:
  • Online: August 04,2021
  • Published: