Stochastic electricity market model in networked microgrids considering demand response programs and renewable energy sources

Bahmani, Ramin; Karimi, Hamid; Jadid, Shahram

doi:10.1016/j.ijepes.2019.105606

Cited by 81 publications

(27 citation statements)

References 41 publications

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“…This section provides the results of SOC constraints for PS, including constraints (8)(9)(10)(11)(12)(13). Similarly, the SOC constraints for the HS could be inferred by the same algorithm.…”

Section: B the Results Of Soc Constraintsmentioning

confidence: 99%

Optimal Risk Operation for a Coupled Electricity and Heat System Considering Different Operation Modes

et al. 2021

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The coupled electricity and heat system (CEHS) is considered one of the most efficient energy utilization schemes for the energy transition to solve energy crises and environmental problems. However, the individual data between the power system (PS) and heat system (HS) probably limit the high share and optimization operation. Moreover, the uncertain renewables and complex coupled network create challenges regarding the operation risk of CEHS. Given that the CEHS may be affiliated with different operation entities, this paper proposes two operation modes to achieve the solution of the optimal risk operation model (OROM), including the distributionally robust chance constraint (DRCC)-based centralized risk operation mode (CROM) and the DRCC-based alternating direction method of multipliers (ADMM) distributed risk operation mode (DROM). By formulating the moment-based ambiguity set estimated from historical data and introducing the operation risk constraints, a tractable reformulation of two-stage DRCC OROM problems is presented under CROM. Moreover, the DRCC-based ADMM distributed algorithm with the guarantee of convergence is developed to optimize the PS and HS independently under DROM considering the operation risk. The two operation modes achieve the minimum amount of information shared between the two systems regarding the underlying distribution. In the numerical case, the approximate OROM solution is obtained between CROM and DROM. The two proposed approaches based on different operation modes outperform the existing methods according to the risk level depicted by different forms of the violation probability and the risk cost compared with Gaussian chance constraint (GCC) under CROM, while the safety coefficient is set as 0.25. Then, the impact of the iteration number on the convergence is also discussed with comparison of the classical ADMM under DROM.

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Section: B the Results Of Soc Constraintsmentioning

confidence: 99%

Optimal Risk Operation for a Coupled Electricity and Heat System Considering Different Operation Modes

et al. 2021

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“…In [9], a multi-agent-based rolling optimization is developed for restoration scheduling of distribution grids with distributed generation. A cooperative market mechanism is proposed in [10] to define the energy transactions and market price in multi-microgrids. In [11], a cooperative multi-objective optimization for the networked microgrids is carried out.…”

Section: A Literature Review and Paper Contributionsmentioning

confidence: 99%

“…The subindex ref are the reference values for each variable of the subsystems. The system constraints is given by expressions (6)- (10). The controller finds an optimal operation point for the cooperative DMPC problem defined by a set of optimal control variables OP…”

Section: Fig 2 Controller Block Diagrammentioning

confidence: 99%

Cooperative Optimization of Networked Microgrids for Supporting Grid Flexibility Services Using Model Predictive Control

García-Torres¹,

Báez-González

Tobajas³

et al. 2021

IEEE Trans. Smart Grid

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“…The characteristics of the WT, PV system and their maintenance cost parameters in each MG are presented in Table 2 44,45 . Furthermore, the output of PV and WT are shown in Figures 3 and 4, respectively.…”

Section: Case Studiesmentioning

confidence: 99%

A bilevel programming approach for real‐time pricing strategy of smart grid considering multi‐microgrids connection

Yuan

Gao

et al. 2021

Int J Energy Res

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Summary As an important method for demand‐side management, the real‐time pricing can not only adjust the power balance between the supply and demand‐sides, but also effectively promote the internal energy dispatching behavior of the microgrid. In this article, a bilevel programming model is proposed based on the demand response to coordinate the master‐slave hierarchical relationship in the electricity market. At the upper level, the main grid determines the prices and generation for the supplier's maximum profit. At the lower level, we consider multiple microgrids, which operate in isolated‐island or grid‐connected mode, with photovoltaic generation system, energy storage system, and loads. Then, the optimal energy strategy is emerged for the user's maximum welfare according to the dynamic prices. Furthermore, we solve the upper and lower models separately to avoid user privacy being violated. The upper problem uses the genetic algorithm (GA), while the lower problem is handled by the branch and bound algorithm (BBA). A novel distributed algorithm named GA‐BBA is developed accordingly. Finally, taking an example of a smart grid system with three microgrids, the model and algorithm are simulated and compared. The results show that the peak‐to‐average ratio under the real‐time pricing is reduced from 1.6547 (fixed electricity price) and 1.4513 (time‐of‐use) to 1.2545. The peak‐to‐valley difference is also decreased by approximately 14.33%. In addition, the total social welfare of the system is increased by 145.53 kCNY (Chinese Yuan) through comparison with the existing model. The conclusions verify that the proposed bilevel programming model not only effectively reduces peak load but also increases the social welfare, which is more reasonable and realistic. Novelty Statement A novel bilevel optimization energy management system is proposed to optimize the hierarchical market structure between the main grid and multi‐microgrids (MMGs). A RTP mechanism based on demand response is formulated to balance the benefits between the main grid and MMGs. Moreover, the optimal energy strategy is obtained along with the dynamic price. Besides the operating cost, users' utility in microgrids are taken to our model. A hybrid distributed GA‐BBA is developed to protect the user's utility privacy.

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Stochastic electricity market model in networked microgrids considering demand response programs and renewable energy sources

Cited by 81 publications

References 41 publications

Optimal Risk Operation for a Coupled Electricity and Heat System Considering Different Operation Modes

Optimal Risk Operation for a Coupled Electricity and Heat System Considering Different Operation Modes

Cooperative Optimization of Networked Microgrids for Supporting Grid Flexibility Services Using Model Predictive Control

A bilevel programming approach for real‐time pricing strategy of smart grid considering multi‐microgrids connection

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