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Energy management of multi-microgrid system with renewable energy using data-driven distributionally robust optimization

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2024年第12期21卷 2699-2711页

作者： Shi, Zhichao Zhang, Tao Liu, Yajie Feng, Yunpeng Wang, Rui Huang, Shengjun Natl Univ Def Technol Coll Syst Engn Deya Rd 109 Changsha Peoples R China Natl Univ Def Technol Hunan Key Lab Multienergy Syst Intelligent Interco Changsha Peoples R China Beijing Adv Technol Res Ctr Beijing Peoples R China

By clustering multiple microgrids (MGs), a multi-microgrid (MMG) system plays a significant role in integrating a large amount of renewable generation. However, the large-scale utilization of renewable energy also brings uncertainties to MMG energy management. In this work, a novel comprehensive bi-level MMG energy management model considering uncertainties of renewable energy is first developed which includes unit commitment (UC) problem and other models in the lower MG level. Then considering the non-convexity of the bi-level problem, a decomposition method called analytical target cascading (ATC) is employed to deal with it by decentralization. With regard to the energy management of each individual MG, a two-stage distributionally robust model is developed which describes the uncertainties from probability distribution of renewable generation with a metric-based ambiguity set. Moreover, an efficient solution scheme based on column and constraint generation (CCG) algorithm and a decomposition method without duality is designed to handle the MG energy management problem. Finally, we implement extensive experiments to corroborate the effectiveness of the proposed approach. Particularly, the optimal solution from the proposed method can attain 2.98% less cost compared with that from robust optimization (RO) method and achieve 86.17% less energy trading amount than that in independent mode.

关键词： Multi-microgrid system energy management distributionally robust optimization uncertainty renewable generation

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Hierarchy Ranking Method for Multimodal Multiobjective Optimization With Local Pareto Fronts

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第1期27卷 98-110页

作者： Li, Wenhua Yao, Xingyi Zhang, Tao Wang, Rui Wang, Ling Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Natl Univ Def Technol Hunan Key Lab Multi Energy Syst Intelligent Interc Changsha 410073 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Multimodal multiobjective problems (MMOPs) commonly arise in real-world situations where distant solutions in decision space share a very similar objective value. Traditional multimodal multiobjective evolutionary algorithms (MMEAs) prefer to pursue multiple Pareto solutions that have the same objective values. However, a more practical situation in engineering problems is that one solution is slightly worse than another in terms of objective values, while the solutions are far away in the decision space. In other words, such problems have global and local Pareto fronts (PFs). In this study, we proposed several benchmark problems with several local PFs. Then, we proposed an evolutionary algorithm with a hierarchy ranking method (HREA) to find both the global and the local PFs based on the decision maker's preference. Regarding HREA, we proposed a local convergence quality evaluation method to better maintain diversity in the decision space. Moreover, a hierarchy ranking method was introduced to update the convergence archive. The experimental results show that HREA is competitive compared with other state-of-the-art MMEAs for solving the chosen benchmark problems.

关键词： Convergence Optimization Benchmark testing Clustering algorithms systems engineering and theory Statistics Space missions Diversity-preserving mechanisms evolutionary computation local Pareto front (PF) multimodal multiobjective optimization

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Hybridization of Evolutionary Algorithm and Deep Reinforcement Learning for Multiobjective Orienteering Optimization

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第5期27卷 1260-1274页

作者： Liu, Wei Wang, Rui Zhang, Tao Li, Kaiwen Li, Wenhua Ishibuchi, Hisao Liao, Xiangke Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Natl Univ Def Technol Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Peoples R China Southern Univ Sci & Technol Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Natl Univ Def Technol Collaborat Innovat Ctr High Performance Comp Changsha 410073 Peoples R China

Multiobjective orienteering problems (MO-OPs) are classical multiobjective routing problems and have received much attention in recent decades. This study seeks to solve MO-OPs through a problem-decomposition framework, that is, an MO-OP is decomposed into a multiobjective knapsack problem (MOKP) and a traveling salesman problem (TSP). The MOKP and TSP are then solved by a multiobjective evolutionary algorithm (MOEA) and a deep reinforcement learning (DRL) method, respectively. While the MOEA module is for selecting cities, the DRL module is for planning a Hamiltonian path for these cities. An iterative use of these two modules drives the population toward the Pareto front of MO-OPs. The effectiveness of the proposed method is compared against NSGA-II and NSGA-III on various types of MO-OP instances. Experimental results show that our method performs best on almost all the test instances and has shown strong generalization ability.

关键词： Decomposition deep reinforcement learning (DRL) evolutionary algorithms (EAs) multiobjective optimization orienteering problems (OPs) pointer networks (PNs)

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Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval Prediction

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DISCRETE DYNAMICS IN NATURE AND SOCIETY 2021年第1期2021卷

作者： He, Huihui Huang, Shengjun Liu, Yajie Zhang, Tao Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Hunan Peoples R China

With the integration of Renewable Energy Resources (RERs), the Day-Ahead (DA) scheduling for the optimal operation of the integrated Isolated Microgrids (IMGs) may not be economically optimal in real time due to the prediction errors of multiple uncertainty sources. To compensate for prediction error, this paper proposes a Robust Model Predictive Control (RMPC) based on an interval prediction approach to optimize the real-time operation of the IMGs, which diminishes the influence from prediction error. The rolling optimization model in RMPC is formulated into the robust model to schedule operation with the consideration of the price of robustness. In addition, an Online Learning (OL) method for interval prediction is utilized in RMPC to predict the future information of the uncertainties of RERs and load, thereby limiting the uncertainty. A case study demonstrates the effectiveness of the proposed with the better matching between demand and supply compared with the traditional Model Predictive Control (MPC) method and Hard Charging (HC) method.

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Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 13142-13155页

作者： Li, Kaiwen Zhang, Tao Wang, Rui Wang, Yuheng Han, Yi Wang, Ling Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc HKL MSI2T Changsha 410073 Peoples R China Natl Univ Def Technol Grad Coll Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

This article introduces a new deep learning approach to approximately solve the covering salesman problem (CSP). In this approach, given the city locations of a CSP as input, a deep neural network model is designed to directly output the solution. It is trained using the deep reinforcement learning without supervision. Specifically, in the model, we apply the multihead attention (MHA) to capture the structural patterns, and design a dynamic embedding to handle the dynamic patterns of the problem. Once the model is trained, it can generalize to various types of CSP tasks (different sizes and topologies) without the need of retraining. Through controlled experiments, the proposed approach shows desirable time complexity: it runs more than 20 times faster than the traditional heuristic solvers with a tiny gap of optimality. Moreover, it significantly outperforms the current state-of-the-art deep learning approaches for combinatorial optimization in the aspect of both training and inference. In comparison with traditional solvers, this approach is highly desirable for most of the challenging tasks in practice that are usually large scale and require quick decisions.

关键词： Urban areas Deep learning Optimization Task analysis Approximation algorithms Reinforcement learning Search problems Attention covering salesman problem (CSP) deep learning deep reinforcement learning (DRL)

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A deep belief network approach to remaining capacity estimation for lithium-ion batteries based on charging process features

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JOURNAL OF ENERGY STORAGE 2022年 48卷

作者： Cao, Mengda Zhang, Tao Wang, Jia Liu, Yajie Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Hunan Peoples R China Beijing Inst Tracking & Telecommun Technol Beijing 100094 Peoples R China

Accurate remaining capacity estimation for lithium batteries can help people understand the working state of a battery, which ensures the reliability and safety of electric equipment. Since the remaining capacities of lithium-ion batteries are related to internal physicochemical reactions, such as loss of lithium inventory and loss of active material, their remaining capacities are usually difficult to directly estimate. Advancements in deep learning have prompted the development of new data-driven approaches to solve this problem that can capture potential degradation correlation information from high-dimensional data via hidden layers of the deep learning model. This paper presents a novel deep learning method for lithium-ion battery capacity estimation based on charging process features with the following process. First, multiple health indicators are analysed and extracted according to different working conditions of batteries. Next, a grey relation analysis is combined with a cross-validation test, which is employed to eliminate information redundancy and improve prediction accuracy. Moreover, the optimized health indicators are further extracted through the restricted Boltzmann machine component of the deep belief network model, and a fully connected layer is adopted for estimation of lithium-ion battery capacity. A total of 23 battery datasets, including six working condition types, are employed for verification experiments. The maximum root mean square error of a single battery prediction is 3%, and that of multiple battery prediction is still within 6%, which confirms the effectiveness and accuracy of the proposed method.

关键词： Lithium-ion battery Deep belief network (DBN) Health indicator (HI) Sample entropy (SE) Capacity estimation

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Sizing a Hybrid Renewable Energy system by a Coevolutionary Multiobjective Optimization Algorithm

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COMPLEXITY 2021年第1期2021卷

作者： Li, Wenhua Zhang, Guo Yang, Xu Tao, Zhang Xu, Hu Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Peoples R China State Key Lab Astronaut Dynam Xian 710043 Peoples R China

Hybrid renewable energy system (HRES) arises regularly in real life. By optimizing the capacity and running status of the microgrid (MG), HRES can decrease the running cost and improve the efficiency. Such an optimization problem is generally a constrained mixed-integer programming problem, which is usually solved by linear programming method. However, as more and more devices are added into MG, the mathematical model of HRES refers to nonlinear, in which the traditional method is incapable to solve. To address this issue, we first proposed the mathematical model of an HRES. Then, a coevolutionary multiobjective optimization algorithm, termed CMOEA-c, is proposed to handle the nonlinear part and the constraints. By considering the constraints and the objective values simultaneously, CMOEA-c can easily jump out of the local optimal solution and obtain satisfactory results. Experimental results show that, compared to other state-of-the-art methods, the proposed algorithm is competitive in solving HRES problems.

关键词： Multiobjective optimization

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Research on the Multiobjective Optimization of Microwave Wireless Power Receiving in an Unmanned Aerial Vehicle Network

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COMPLEXITY 2020年第1期2020卷

作者： Song, Yuanming Liu, Yajie Xu, Wanli Yang, Xu Wang, Rui Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Hunan Peoples R China Acad Mil Med Sci Inst Syst Engn Beijing 100091 Peoples R China

With the wide application of various wireless energy transmission technologies and unmanned aerial vehicle clusters in both production and life, the use of microwave wireless energy transmission to provide a real-time energy supply for an unmanned aerial vehicle network in flight has become an effective way to extend its working time. This paper focuses on the optimization of the energy transmission efficiency and cost in the microwave wireless power receiving process of an unmanned aerial vehicle network. Considering the overall energy transmission efficiency from the power supply terminal to the power receiving network and the cost of the wireless power transmission equipment of the network, we have established a multiobjective wireless power receiving optimization model including a microwave energy emission source and an unmanned aerial vehicle network that receives energy. The model is optimized to select the best wireless power access point and the number of wireless power receiving modules in a network node. In the case study, the optimization model is solved using an evolutionary algorithm, and the solution results verify the effectiveness and correctness of the model.

关键词： Microwaves

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Constraint multi-objective optimal design of hybrid renewable energy system considering load characteristics

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COMPLEX & intelligent systEMS 2022年第2期8卷 803-817页

作者： Chen, Yingfeng Wang, Rui Ming, Mengjun Cheng, Shi Bao, Yiping Zhang, Wensheng Zhang, Chi Jiangsu Univ Sch Management Zhenjiang 212013 Jiangsu Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Hunan Inst Traff Engn Hengyang 421000 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Chinese Acad Sci Inst Automat Beijing 100084 Peoples R China Hunan Datang Xianyi Technol Co Ltd Changsha 430103 Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Peoples R China

Finding the optimal size of a hybrid renewable energy system is certainly important. The problem is often modelled as an multi-objective optimization problem (MOP) in which objectives such as annualized system cost, loss of power supply probability etc. are minimized. However, the MOP model rarely takes the load characteristics into account. We argue that ignoring load characteristics may be inappropriate when designing HRES for a place with intermittent high load demand. For example, in a training base the load demand is high when there are training tasks while the demand decreases to a low level when there is no training task. This results in an interesting issue, that is, when the loss of power supply probability is determined at a specific value, say 15%, then it is very likely that most of loss of power supply would occur right in the training period which is unexpected. Therefore, this study proposes a constraint multi-objective model to deal with this issue-in addition to the general multi-objective optimization model, the loss of power supply probability over a critical period is set as a constraint. Correspondingly, the non-dominated sorting genetic algorithm II with a relaxed epsilon constraint handling strategy is proposed to address the constraint MOP. Experimental results on a real world application demonstrate that the proposed model and algorithm are both effective and efficient.

关键词： Constraint optimization Multi-objective optimization Hybrid renewable energy system Evolutionary algorithms

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Resilience Assessment for Microgrid with Pre-Position and Reconfiguration of Emergency Distribution Generations under Natural Hazard

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY systEMS 2022年第1期2022卷

作者： Lei, Hongtao Huang, Shengjun Liu, Yajie Zhang, Tao Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Hunan Peoples R China

Recently, increasing the number and severity of the natural hazards requires the resilience assessment and enhancement of the power system, especially the microgrid system. The emergency distribution generations have great potential to enhance the resilience of microgrid against blackouts under the emergency environment. This paper investigates the resilience assessment of the microgrid system under natural hazard, where emergency distribution generations are firstly pre-positioned on system nodes and then reconfigured after system damage occurs. A new resilience metric index and an efficient approximation computation method are provided for the resilience assessment of the focused problem. A new pre-position strategy and a new reconfiguration strategy on emergency distribution generations are proposed for the microgrid system emergency restoration under natural hazard. Also, a framework of resilience assessment is provided for problem-solving. The results of extensive experiments on the modified IEEE 30-Bus system and modified IEEE 118-Bus system confirm the effectiveness of the resilience assessment methodology and the superiority of proposed restoration strategies.

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