Medium-term multi-stage distributionally robust scheduling of hydro–wind–solar complementary systems in electricity markets considering multiple time-scale uncertainties

Li, Zhuangzhuang; Yang, Ping; Guo, Yi; Lu, Guanpeng

doi:10.1016/j.apenergy.2023.121371

Cited by 14 publications

(3 citation statements)

References 43 publications

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“…Using the stochastic optimization is predicated on knowing these representative scenarios in advance [22]. Different researchers use different ways to generate the representative scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Data-Driven Tighten-Constraint Method for Wind-Hydro Hybrid Power System to Improve Day-Ahead Plan Performance in Real-Time Operation

Lai,

Kazemtabrizi

2023

Preprint

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Data-Driven Tighten-Constraint Method for Wind-Hydro Hybrid Power System to Improve Day-Ahead Plan Performance in Real-Time Operation

Lai,

Kazemtabrizi

2023

Preprint

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“…There are few studies on the coordinated operation of hydropower plants with wind and photovoltaic power considering market functionality. Li et al [28] proposed a medium-term multi-stage distributionally robust optimization scheduling approach for price-taking hydro-wind-solar complementary systems in the electricity market to address the uncertainties of multiple energy resources and market prices that affected the trading strategies. The profits of a complementary wind-solar-hydropower system which can be increased by coordinating the spot market and the forward market may be influenced by complex market mechanisms and uncertainties of multiple energy resources and market prices; Cheng et al [29] established a stochastic scheduling model to address this issue.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Stochastic Scheduling of Cascaded Hydropower–Wind–Photovoltaic Hybrid Systems Considering Contract Decomposition and Spot Market

Li,

Fang,

et al. 2024

Sustainability

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With the advancement of China’s electricity markets and the continuous development of renewable energy sources (RESs), it is of great importance to investigate the strategic behavior of RESs in electricity markets. In this paper, a two-stage stochastic optimization model is proposed for a hybrid energy system composed of cascade hydropower plants, wind farms, and photovoltaic stations. Firstly, typical scenarios are generated based on Latin hypercube sampling (LHS) and the K-means clustering algorithm to represent uncertainties of wind–photovoltaic power outputs. Then, with an analysis of China’s electricity market structure, a two-stage coordinated scheduling model of hydropower–wind–photovoltaic hybrid systems in electricity markets is established with the objective of maximizing total revenues considering bilateral contract decomposition, the day-ahead energy market, and the real-time balance market. In addition, the proposed model is transformed into a mixed-integer linear programming (MILP) problem for computational convenience. As shown in an analysis of case studies, cascade hydropower plants can compensate for the fluctuation in wind and photovoltaic power outputs to reduce financial risks caused by uncertainties of wind and photovoltaic power generation. Simulation results show that compared with uncoordinated operation, the coordinated operation of hydropower–wind–photovoltaic hybrid systems increases total revenue by 1.08% and reduces the imbalance penalty by 29.85%.

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“…To further explore the multi-energy coupling capacity and carbon reduction potential of the integrated energy systems, Yang et al (2023) proposed a cooling-heat-electricity-gas collaborative optimization model of integrated energy systems given a ladder carbon trading mechanism and multi-energy demand response. Li et al (2023) proposed a medium-term multi-stage distributionally robust optimization scheduling approach for a price-taking of hydro-wind-solar complementary systems in the EM. A multiagent deep reinforcement learning approach combining the multiagent actor-critic algorithm with the twin delayed deep deterministic policy gradient algorithm was proposed by Chen et al (2022), and the proposed approach can handle the highdimensional continuous action space and aligns with the nature of peer-to-peer energy trading.…”

Section: Introductionmentioning

confidence: 99%

Electricity market clearing for multiple stakeholders based on the Stackelberg game

Wu,

Ye,

Chen

et al. 2024

Front. Energy Res.

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In order to improve the operating benefits of the distribution network and reduce the energy consumption costs of small–micro-industrial parks, an electricity market clearing considering small–micro-industrial parks is proposed based on the Stackelberg game. First, an optimal operating model of multiple stakeholders is established for integrated energy suppliers, the electricity market, and small–micro-industrial parks. In this model, an optimal electricity supply model for integrated energy suppliers is established with the goal of maximizing the operating benefits. A market clearing optimization model is established for the electricity market with the goal of maximizing the social surplus profit. In addition, an energy utilization optimization model is established for the small–micro-industrial parks with the goal of minimizing the energy procurement costs. Second, with the electricity market as the leader, the integrated energy suppliers and the small–micro-industrial parks as the followers, a leader–follower game strategy is proposed based on the Stackelberg game theory to achieve the maximizing benefits for multiple stakeholders. Finally, the simulation indicates that the proposed strategy can find the best profit point during the game process and achieve a balance between supply and demand.

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Medium-term multi-stage distributionally robust scheduling of hydro–wind–solar complementary systems in electricity markets considering multiple time-scale uncertainties

Cited by 14 publications

References 43 publications

A Novel Data-Driven Tighten-Constraint Method for Wind-Hydro Hybrid Power System to Improve Day-Ahead Plan Performance in Real-Time Operation

A Novel Data-Driven Tighten-Constraint Method for Wind-Hydro Hybrid Power System to Improve Day-Ahead Plan Performance in Real-Time Operation

Two-Stage Stochastic Scheduling of Cascaded Hydropower–Wind–Photovoltaic Hybrid Systems Considering Contract Decomposition and Spot Market

Electricity market clearing for multiple stakeholders based on the Stackelberg game

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