Two kinds of decentralized robust economic dispatch framework combined distribution network and multi-microgrids

Zhou, Xinping; Ai, Qian; Yousif, Muhammad

doi:10.1016/j.apenergy.2019.113588

Cited by 81 publications

(34 citation statements)

References 43 publications

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“…The system is set with one supplier and three MGs in this article, as is described in References 43 and 44. We assume that 24 hours is an operating cycle, and 1 hour is a time slot.…”

Section: Case Studiesmentioning

confidence: 99%

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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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“…The system is set with one supplier and three MGs in this article, as is described in References 43 and 44. We assume that 24 hours is an operating cycle, and 1 hour is a time slot.…”

Section: Case Studiesmentioning

confidence: 99%

“…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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“…Some studies have developed a stochastic approach to model and handle the various types of uncertainties. A two-stage adaptive robust optimization based on the column-and-constraint generation (C&CG) method has been suggested by [24] and [25], which minimizes the total cost of the system under the worst case of photovoltaic (PV) output. The study in [26] has presented optimal scheduling for NMGs considering uncertainty features in the generation of renewable energy resources and hourly load using a metaheuristic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Resilient Scheduling of Networked Microgrids Against Real-Time Failures

et al. 2021

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Integrating microgrids within distribution systems can significantly improve the power system's reliability while reducing operating costs. However, due to the unintentional disaster conditions, sometimes distribution systems and microgrids cannot support each other, and the microgrids are forced to work in the islanded mode. Accordingly, we developed an optimal resilient scheduling scheme that guarantees networked microgrids (NMGs) reliable operation in the normal and islanding modes. To achieve this aim, the problem is decomposed into day-ahead normal operation (grid-connected) and real-time islanded examination by benders decomposition algorithm perspective. The specified scheme of NMGs in the normal operation will be scrutinized in the real-time islanded mode. According to the benders decomposition theory, the scheduling of NMGs would be revised in the next iteration if the current schedule is not feasible for possible real-time islanding conditions. The status of thermal units, charging, and discharging of energy storage systems respecting their other constraints are changed depending on the type and severity of mismatches between generation and demand. Three different interconnection topologies are tested for assessing the performance of the proposed method and the impact of transaction energy between NMGs on that. Numerical simulations illustrate the advantages of the proposed scheme and explain its merits.

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“…In addition, Choobineh and Mohagheghi [21] proposed an MMG optimisation model that seeks a solution that is Pareto optimal and meets all constraints associated with individual users rather than allowing individuals to compete for resources directly. Zhou et al [22] proposed a distributed robust optimisation framework based on an integrated analytical target cascading and robust model for application in networked MMGs. The model was then solved using the C&CG method.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical two‐stage robust optimisation dispatch based on co‐evolutionary theory for multiple CCHP microgrids

Tan

Chen

Zheng

2020

IET Renewable Power Generation

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Combined cooling, heating, and power (CCHP) microgrids are a special form of a microgrid that is attracting increasing attention. This study contributes to the goal of minimising the operation cost of CCHP microgrids by proposing a hierarchical two‐stage robust optimisation dispatch model for multiple CCHP microgrid systems. The uncertainties associated with wind power output, electric power, heating, and cooling loads, and transmission line failures are considered in the proposed model. Moreover, the electricity purchasing and selling prices of each microgrid are independently determined. The proposed model applies the outputs of fuel cells, energy storage devices, and gas turbines, the distribution factor of waste heat, and the power transmission between the microgrids and an external grid as control variables. The optimised dispatch problem is solved using McCormick envelopes relaxation and a novel column and constraint generation algorithm that provides enhanced optimisation performance by implementing co‐evolutionary theory. In this way, the microgrid system is divided into several sections, and each section is represented as an individual min–max–min problem. The rationality and validity of the proposed model and the superiority of the solution performance of the improved algorithm are verified through simulation case studies involving a system composed of four CCHP microgrids.

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Two kinds of decentralized robust economic dispatch framework combined distribution network and multi-microgrids

Cited by 81 publications

References 43 publications

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

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

Resilient Scheduling of Networked Microgrids Against Real-Time Failures

Hierarchical two‐stage robust optimisation dispatch based on co‐evolutionary theory for multiple CCHP microgrids

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