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Heat market for interconnected multi-energy microgrids: A distributed optimization approach

ENERGY NEXUS 2024年 14卷

作者： Gonzalez-Castellanos, Alvaro Bischi, Aldo Corp Red Solvers Barranquilla Atlantico Colombia Univ Pisa Dept Energy Syst Terr & Construct Engn Largo Lucio Lazzarino 1 I-56122 Pisa Italy

Thermal networks, part of heat -and -power multi -energy microgrids, may face capacity issues, generation and distribution ones, either due to the increase in the requested demand or capacity underused, which is sized for peak hours. Under -capacity issues may be addressed with generation and pipeline capacity expansion, resulting in considerable capital costs and extra maintenance costs. In the case of over -capacity, better usage of the existing assets may bring further revenues and increase the multi -energy microgrid's overall energy efficiency. In the electricity sector, it is being considered the interconnection of microgrids via the distribution system network, since microgrids can operate in both islanded and network -connected modes. In this work, in a similar fashion, we propose the interconnection of adjacent thermal networks enabling direct heat trading among them to increase the micro -grids' supply flexibility, help meeting demand peaks, and reduce operational costs. Examples of integrated heat -and -power microgrids that could benefit from thermal interconnections are industrial parks, university campuses, hospitals, and even residential complexes with a shared heat generator. This paper presents a market model for the optimal heat transfer between thermally interconnected heat -and -power microgrids. The resulting model is a convex quadratic programming model that enables the derivation of heat transfer prices that guarantee a competitive equilibrium. Furthermore, we performed numerical tests to explore the impact of connection topology, thermal power transfer capacity, and interconnection efficiency on transferred energy and prices.

关键词： Interconnected microgrids Multi-energy microgrids Heat market Combined heat and power distributed optimization

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A Novel State Decomposition-Based Privacy-Preserving Algorithm for distributed optimization over Directed Networks 14

A Novel State Decomposition-Based Privacy-Preserving Algorit...

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14th Asian Control Conference (ASCC)

作者： Zhang, Jianhang Ma, Dan Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9798331540845;9789887581598

With the widespread application of distributed optimization, various of algorithms have been developed. However, the rapid development of the Internet has led to a proliferation of network attacks. Therefore, to design the distributed optimization algorithm, it is necessary to consider the ability to protect the privacy information of agents in the face of malicious attacks. In this paper, a distributed optimization algorithm based on state decomposition for fixed directed graphs is proposed. Firstly, in each iteration, the algorithm updates not only the estimate of the optimal decision variable but also an auxiliary variable that estimates the average gradient of the agent's objective function. The algorithm convergence to the optimal solution of the distributed optimization problem is also discussed. Furthermore, through the state decomposition, the algorithm ensures that the gradient information of any agent cannot be precisely inferred in the presence of external eavesdroppers. Simulation results show the effectiveness of the proposed algorithm.

关键词： distributed optimization directed graph state decomposition privacy-preserving

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Fully-distributed optimization with Network Exact Consensus-GIANT 25

Fully-distributed optimization with Network Exact Consensus-...

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25th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Maritan, Alessio Sharma, Ganesh Dey, Subhrakanti Schenato, Luca Univ Padua Dept Informat Engn Padua Italy Maynooth Univ Hamilton Inst Maynooth Kildare Ireland Uppsala Univ Dept Elect Engn Uppsala Sweden

ISBN: (纸本)9798350393194;9798350393187

We consider a fully-distributed optimization problem involving multiple collaborative agents, where the global objective is to minimize a sum of local cost functions. Agents are part of a communication network and can only exchange information with their neighbors. We introduce a novel optimization algorithm called NEC-GIANT, which improves over both GIANT, a popular federated learning algorithm, and Network-GIANT, our previously proposed fully-distributed counterpart of GIANT. NEC-GIANT extends GIANT to the fully-distributed scenario, removing the need for a central server to orchestrate the agents. Unlike the existing Network-GIANT, which suffers from the inefficiency of standard asymptotic consensus, the novel NEC-GIANT is based on finite-time distributed consensus and retains all the convergence properties of the original GIANT. Numerical simulations prove the efficiency and superiority of the proposed algorithm in terms of both iterations and machine run-time.

关键词： distributed optimization gradient tracking finite-time consensus network learning Newton-type algorithms

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distributed optimization With Event-Triggered Communication via Input Feedforward Passivity

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IEEE CONTROL SYSTEMS LETTERS 2021年第1期5卷 283-288页

作者： Li, Mengmou Su, Lanlan Liu, Tao Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Univ Leicester Sch Engn Leicester LE1 7RH Leics England

In this letter, we address the distributed optimization problem with event-triggered communication by the notion of input feedforward passivity (IFP). First, we analyze the distributed continuous-time algorithm over uniformly jointly strongly connected balanced digraphs in an IFP-based framework. Then, we propose a distributed event-triggered communication mechanism for this algorithm. Next, we discretize the continuous-time algorithm by the forward Euler method with a constant stepsize irrelevant to network size, and show that the discretization can be seen as a stepsize-dependent passivity degradation of the input feedforward passivity. Thus, the discretized system preserves the IFP property and enables the same event-triggered communication mechanism but without Zeno behavior due to the discrete-time nature. Finally, a numerical example is presented to illustrate our results.

关键词： optimization Feedforward systems Communication networks Radio frequency Heuristic algorithms Indexes distributed algorithms distributed optimization input feedforward passivity event-triggered control

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distributed optimization and statistical learning for large-scale penalized expectile regression

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JOURNAL OF THE KOREAN STATISTICAL SOCIETY 2021年第1期50卷 290-314页

作者： Pan, Yingli Hubei Univ Fac Math & Stat Hubei Key Lab Appl Math Wuhan Peoples R China

Large-scale data from various research fields are not only heterogeneous and sparse but also difficult to store on a single machine. Expectile regression is a popular alternative for modeling heterogeneous data. In this paper, we devise a distributed optimization approach to SCAD and adaptive LASSO penalized expectile regression, where the observations are randomly partitioned across multiple machines. We construct a penalized communication-efficient surrogate loss (CSL) function. Computationally, our method based on the CSL function requires only the master machine to solve a regular M-estimation problem, while other worker machines compute the gradient of the loss function on local data. Our method matches the estimation error bound of the centralized method during consecutive rounds of communication. Under some mild assumptions, we establish the oracle properties of the SCAD and adaptive LASSO penalized expectile regression. We then develop a modified alternating direction method of multipliers (ADMM) algorithm for the implementation of the proposed estimator. A series of simulation studies are conducted to assess the finite-sample performance of the proposed estimator. Applications to an HIV study demonstrate the practicability of the proposed method.

关键词： distributed optimization Expectile regression SCAD Adaptive LASSO Modified ADMM algorithm

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distributed optimization of multi-integrator agent systems with mixed neighbor interactions

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AUTOMATICA 2023年第1期157卷

作者： Chen, Zhao Nian, Xiaohong Meng, Qing Cent South Univ Sch Automat Changsha 410075 Peoples R China

In this paper, we consider a constrained distributed optimization problem for multi-integrator agent systems with mixed neighbor interactions, where the neighbors of the agents can be classified as cooperative or noncooperative by introducing a cooperation-competition network into the modeling of the communication graph. The agent's goal is to optimize the total objective function of the cooperators. Since agents can only communicate with their neighbors, they are unable to obtain complete information about cooperators. The distributed optimization algorithm is proposed to classify cooperative subnetworks by introducing additional auxiliary variables, and the outputs of all agents exponentially converge to the optimal solution of the optimization problem under the condition of satisfying the global equality constraint of the subnetwork. The Lyapunov stability theory is used to analyze the algorithm's convergence. Simulation results show the effectiveness of the proposed algorithm in two examples of economic dispatch in smart grids and optimal area coverage in multi-sensor networks. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： distributed optimization Multi-integral agent systems Mixed neighbor interactions Cooperation-competition network

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Resilient distributed optimization Against Cyber-Attacks

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IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 3956-3961页

作者： Gusrialdi, Azwirman Qu, Zhihua Tampere Univ Automat & Mech Engn Unit Tampere 33720 Finland Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

This letter presents a novel resilient distributed optimization algorithm for a network of agents. It guarantees that the local estimates of the agents converge close to the optimal solution of the original optimization problem, even in the presence of unknown but bounded attacks on both the agent's local computation and communication network. To this end, a virtual state variable is introduced, which also enables each agent to identify the compromised communication links in real-time and in a distributed manner. The distributed algorithm imposes no restrictions on the maximum tolerable number of attacks, and it does not require high network connectivity and trusted/secured agents or edges. A numerical example is provided to illustrate the results.

关键词： Cyber-attacks distributed optimization resilient distributed algorithm attack identification

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Learning Infused Quantum-Classical distributed optimization Technique for Power Generation Scheduling

IEEE TRANSACTIONS ON QUANTUM ENGINEERING

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IEEE TRANSACTIONS ON QUANTUM ENGINEERING 2023年第1期4卷 1页

作者： Mahroo, Reza Kargarian, Amin Louisiana State Univ Dept Elect & Comp Engn Baton Rouge LA 70803 USA

The advent of quantum computing can potentially revolutionize how complex problems are solved. This article proposes a two-loop quantum-classical solution algorithm for generation scheduling by infusing quantum computing, machine learning, and distributed optimization. The aim is to facilitate employing noisy near-term quantum machines with a limited number of qubits to solve practical power system optimization problems, such as generation scheduling. The outer loop is a three-block quantum alternating direction method of multipliers (QADMM) algorithm that decomposes the generation scheduling problem into three subproblems, including one quadratically unconstrained binary optimization (QUBO) and two non-QUBOs. The inner loop is a trainable quantum approximate optimization algorithm (T-QAOA) for solving QUBO on a quantum computer. The proposed T-QAOA translates interactions of quantum-classical machines as sequential information and uses a recurrent neural network to estimate variational parameters of the quantum circuit with a proper sampling technique. The T-QAOA determines the QUBO solution in a few quantum-learner iterations instead of hundreds of iterations needed for a quantum-classical solver. The outer three-block alternating direction method of multipliers coordinates QUBO and non-QUBO solutions to obtain the solution to the original problem. The conditions under which the proposed QADMM is guaranteed to converge are discussed. Two mathematical and three generation scheduling cases are studied. Analyses performed on quantum simulators and classical computers show the effectiveness of the proposed algorithm. The advantages of T-QAOA are discussed and numerically compared with QAOA, which uses a stochastic-gradient-descent-based optimizer.

关键词： distributed optimization generation scheduling machine learning quantum computing variational quantum algorithm distributed optimization generation scheduling machine learning quantum computing variational quantum algorithm

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distributed optimization for network-constrained peer-to-peer energy trading among multiple microgrids under uncertainty

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年第1期149卷

作者： Wang, Luhao Wang, Zhuo Li, Zhengmao Yang, Ming Cheng, Xingong Univ Jinan Sch Elect Engn Jinan 250022 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

This paper addresses network-constrained peer-to-peer (P2P) energy trading problems for multiple microgrids (MGs) under uncertainty. A bi-level distributed optimization framework is proposed to bridge the gap between physical power flows supervised by distribution system operators and logical P2P transactions among multiple MGs under uncertainty. At the upper level, a conditional optimal power flow model is formulated to minimize power losses and guarantee the operating security of local distribution networks. At the lower level, a stochastic programming-based P2P trading model for multiple MGs is formulated to pursue the flexibility of energy transactions among different entities. To realize the consistency of decision-making processes between the two levels and among different MGs, a nested bi-level distributed algorithm including a parallel analytical target cascading algorithm and an alternating direction multiplier method is designed to solve the proposed model in a distributed manner. Furthermore, an adaptive updating method for penalty parameters is adopted to decrease the sensitivity to the initialization. Finally, numerical tests are implemented in a modified IEEE 33-node distribution network with four MGs to testify to the validity of the proposed energy trading framework. The results confirm that the obtained P2P trading schemes can protect against uncertainties and satisfy network constraints, especially since the proposed parallel distributed algorithm has better computing performances compared to the traditional sequential distributed algorithm.

关键词： Multiple microgrids (MGs) Peer-to-peer (P2P) energy trading Uncertainty Optimal power flow (OPF) distributed optimization

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Incentivizing Control Design for Blockchain-Enabled distributed optimization in Electricity Market Systems

Incentivizing Control Design for Blockchain-Enabled Distribu...

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SICE International Symposium on Control Systems (ISCS)

作者： Motoe, Kansei Wasa, Yasuaki Waseda Univ Dept Elect Engn & Biosci Tokyo Japan

ISBN: (纸本)9784907764814;9798350358285

This paper provides an incentivizing control design for blockchain-enabled distributed optimization in electricity market systems. After a wholesale multi-spot bidding problem in electricity market systems is first formulated, an incentivizing distributed optimization inspired by the mechanism design in economics is proposed, and we discuss the relationship between the proposed utility/incentive function design and the potential game. Moreover, it is explained that the proposed mechanism can be implemented on a blockchain system based on Solidity. The effectiveness of the proposed control architecture is finally illustrated by simulation.

关键词： Electricity market systems blockchain distributed optimization mechanism design

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