Mingyu Yan

Prof. Mingyu Yan

orcid

Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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22 October 2020 in IEEE Transactions on Smart Grid
Verified
Journal Article
Closed access
MYMingyu Yan
Mingyu Yan
Mohammad ShahidehpourMohammad Shahidehpour
Mohammad Shahidehpour
APAleksi Paaso
Aleksi Paaso
LZLiuxi Zhang
Liuxi Zhang
AAAhmed Alabdulwahab
Ahmed Alabdulwahab
AAAbdullah Abusorrah
Abdullah Abusorrah
This paper proposes a two-level networkconstrained peer-to-peer (P2P) transactive energy for multimicrogrids (MGs), which guarantees the distribution power network security and allows MGs to trade energy with each other flexibly. At the lower level, a P2P transactive energy is employed for multi-MGs to trade energy with each other. A multi-leader multi-follower (MLMF) Stackelberg game approach is utilized to model the energy trading process among MGs. We prove the existence and the uniqueness of the Stackelberg equilibrium (SE) and provide the closed-form expression for SE. For privacy concerns, we provide several distributed algorithms to obtain SE. At the upper level, the distribution system operator (DSO) reconfigures the distribution network based on the P2P transactive energy trading results by applying the AC optimal power flow considering the distribution network reconfiguration. If there are any network violations, DSO requests trading adjustments at the lower level for network security. We reformulate the DSO operation problem in a mixed-integer second-order cone programming (MISOCP) model and ensure its solution accuracy. Numerical results for a 4-Microgrid system, a modified IEEE 33-bus and 123-bus distribution power system show the effectiveness of the proposed transactive model and its solution technique.

29 December 2017 in IEEE Transactions on Power Systems
Verified
Journal Article
Closed access
YHYubin He
Yubin He
MYMingyu Yan
Mingyu Yan
Mohammad ShahidehpourMohammad Shahidehpour
Mohammad Shahidehpour
ZLZhiyi Li
Zhiyi Li
CGChuangxin Guo
Chuangxin Guo
Yi DingYi Ding
Yi Ding
A large-scale integrated energy system can represent several subsystems representing areas that are tied by electricity and natural gas networks. Accordingly, we propose a decentralized optimal energy flow (DOEF) calculation as compared with a centralized solution method. The proposed approach demonstrates the merits of the decentralized operation and control of a multi-area integrated electricity-natural gas system (IEGS), in terms of large-scale modeling requirements, faster computations, and data management for local sensitivity analyses. Using the proposed decentralized structure, the communication burden is relatively light as individual area operators in a multi-area IEGS will make optimal dispatch decisions independently and the corresponding information is shared with adjacent subsystems. The reformulation of the second-order cone (SOC) is proposed using advanced sequential cone programming (SCP) to handle the nonlinear steady-state natural gas flow, which provides a feasible solution with a high degree of computational efficiency. Furthermore, an iterative alternating direction method of multipliers (I-ADMM) is adopted to manage the nonconvexity of integer variables, which guarantees a satisfactory convergence performance. Case studies on three multi-area IEGS validate the effectiveness of the proposed model in a multi-area IEGS.

14 September 2018 in IEEE Transactions on Smart Grid
Verified
Journal Article
Closed access
MYMingyu Yan
Mingyu Yan
YHYubin He
Yubin He
Mohammad ShahidehpourMohammad Shahidehpour
Mohammad Shahidehpour
XAXiaomeng Ai
Xiaomeng Ai
ZLZhiyi Li
Zhiyi Li
JWJinyu Wen
Jinyu Wen
This paper proposes a coordinated regional-district operation of integrated energy system (IES) for enhancing resilience in extreme conditions. A bi-directional flow model is established for regional-district IES with the utilization of power-to-gas technologies which shows how regional and district IES can jointly enhance the IES resilience. The regional IES co-optimizes power grid and natural gas networks, while the district IES coordinates the energy conversion and storage facilities. The district IES is modeled as an energy hub which supplies electricity to the regional IES. A tri-level two-stage robust model is established to accommodate random outages caused by natural disasters in both natural gas and electricity generation and delivery systems. A two-level algorithm which utilizes Benders decomposition and column-and-constraint generation algorithms is proposed to solve the proposed robust IES problem. Numerical results tested on a 6-bus electrical -6-node natural gas system and a modified IEEE RTS-79 40-node natural gas system show the effectiveness of the proposed model and solution technique for enhancing the IES resilience.

8 August 2018 in IEEE Transactions on Sustainable Energy
Verified
Journal Article
Closed access
MYMingyu Yan
Mingyu Yan
NZNing Zhang
Ning Zhang
XAXiaomeng Ai
Xiaomeng Ai
Mohammad ShahidehpourMohammad Shahidehpour
Mohammad Shahidehpour
CKChongqing Kang
Chongqing Kang
JWJinyu Wen
Jinyu Wen
This paper proposes a robust day-ahead scheduling method for the multi-carrier energy system (MES) which enhances the power system flexibility when a large sum of variable wind power is deployed. We build an MES model and propose an optimal schedule which helps MES reduce wind power curtailment in power system scheduling. At first, electricity and natural gas networks are coordinated at the transmission (regional) level for accommodating the large penetration of wind power at regional MES. The distribution (district) level MES coordinates energy conversion and storage to jointly supply the electricity, natural gas, and heat loads. The transmission level, MES is modeled using detailed network equations while the distribution level MES is modeled using the energy hub (EH) model. A two-stage robust model is established to consider the variability of wind power at regional-district MES levels. The proposed problem is solved by a nested column-and-constraint generation method. The first-stage problem which schedules the hourly unit commitment is solved in the outer loop, while the inner loop solves the second-stage problem to realize the worst scenario. Numerical results offered for a 6-bus 3-node system and a modified IEEE 118-bus 10-node system show the effectiveness of the proposed MES model and solution technique for enhancing the power system flexibility.

23 April 2020 in IEEE Transactions on Smart Grid
Verified
Journal Article
Closed access
WGWei Gan
Wei Gan
Mohammad ShahidehpourMohammad Shahidehpour
Mohammad Shahidehpour
MYMingyu Yan
Mingyu Yan
JGJianbo Guo
Jianbo Guo
Wei YaoWei Yao
Wei Yao
JWJinyu Wen
Jinyu Wen
The interdependency of transportation and electric power networks is becoming tighter due to the proliferation of electric vehicles (EVs), which introduces additional difficulties in the planning of the two networks. This paper presents the enhanced solution for the coordinated planning of multiple facilities in the two networks, including electric power lines, transportation roads, energy storage systems and fast charging stations. In order to calculate the optimal solution for the proposed coordinated planning problem, we introduce the applications of linear optimization theory including Karush–Kuhn–Tucker conditions, the big M method, and a linear expression of power loss to transform the nonlinear planning problem into a mixed-integer quadratically constrained programming (MIQCP) formulation, which is solved by commercial solvers. The proposed MIQCP formulation is decomposed into two corresponding subproblems by Lagrangian relaxation to represent transportation and electric power networks. The case studies validate the proposed planning model and demonstrate that the proposed solution can enhance the coordinated network planning with the proliferation of EVs.

24 January 2019 in IEEE Transactions on Sustainable Energy
Verified
Journal Article
Closed access
XXXiaoyuan Xu
Xiaoyuan Xu
ZYZheng Yan
Zheng Yan
Mohammad ShahidehpourMohammad Shahidehpour
Mohammad Shahidehpour
ZLZhiyi Li
Zhiyi Li
MYMingyu Yan
Mingyu Yan
XKXiangrui Kong
Xiangrui Kong
This paper proposes a data-driven optimization method to solve the integrated energy and reserve dispatch problem with variable and correlated renewable energy generation. The proposed method applies the kernel density estimation to establish an ambiguity set of continuous multivariate probability distributions and the optimization model for the integrated dispatch is formulated as a combination of stochastic and robust optimization problems. First, a risk-averse two-stage stochastic optimization model is formulated to hedge the distributional uncertainty. Next, the second-stage worst-case expectation is evaluated, using the equivalent model reformulation, as a combination of conditional value-at-risk (CVaR) and the extreme cost in the worst-case scenario. The CVaR is calculated using a scenario-based stochastic optimization problem. After describing the wind power correlation in ellipsoidal uncertainty sets, the robust optimization problem for finding the worst-case cost is cast into a mixed-integer second-order cone programming problem. Finally, the column-and-constraint generation method is employed to solve the proposed risk-averse two-stage problem. The proposed method is tested on the 6-bus and IEEE 118-bus systems and validated by comparing the results with those of conventional stochastic and robust optimization methods.