scholarly journals Coordinated Energy Scheduling of a Distributed Multi-Microgrid System Based on Multi-Agent Decisions

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4077
Author(s):  
Yuyan Sun ◽  
Zexiang Cai ◽  
Ziyi Zhang ◽  
Caishan Guo ◽  
Guolong Ma ◽  
...  
Keyword(s):  
Lower Layer ◽  
Pso Algorithm ◽  
Control Agent ◽  
Mixed Integer ◽  
Hierarchical Optimization ◽  
Limited Information ◽  
Multi Stage ◽  
Coordinated Optimization ◽  
Multi Agent ◽  
Internal Operations

Regarding the different ownerships and autonomy of microgrids (MGs) in the distributed multi-microgrid (MMG) system, this paper establishes a multi-stage energy scheduling model based on a multi-agent system (MAS). The proposed mechanism enables a microgrid agent (MGA), a central energy management agent (CEMA), and a coordination control agent (CCA) to cooperate efficiently during various stages including prescheduling, coordinated optimization, rescheduling and participation willingness analysis. Based on the limited information sharing between agents, energy scheduling models of agents and coordinated diagrams are constructed to demonstrate the different roles of agents and their interactions within the MMG system. Distributed schemes are introduced for MG internal operations considering demand response, while centralized schemes under the control of the CCA are proposed to coordinate MGAs. Participation willingness is defined to analyze the MGA’s satisfaction degree of the matchmaking. A hierarchical optimization algorithm is applied to solve the above nonlinear problem. The upper layer establishes a mixed-integer linear programming (MILP) model to optimize the internal operation problem of each MG, and the lower layer applies the particle swarm optimization (PSO) algorithm for coordination. The simulation with a three-MG system verifies the rationality and effectiveness of the proposed model and method.

scholarly journals HADES: A Multi-Agent Platform to Reduce Congestion Anchoring Based on Temporal Coordination of Vessel Arrivals—Application to the Multi-Client Liquid Bulk Terminal in the Port of Cartagena (Spain)

2021 ◽  
Vol 11 (7) ◽  
pp. 3109
Author(s):  
Pilar Jiménez ◽  
José María Gómez-Fuster ◽  
Pablo Pavón-Mariño
Keyword(s):  
Mixed Integer ◽  
Berth Allocation ◽  
Limited Information ◽  
Key Factors ◽  
Berth Allocation Problem ◽  
Mixed Integer Linear Program ◽  
Agent Platform ◽  
Liquid Bulk ◽  
Multi Agent ◽  
Time Changes

Ports are key factors in international trade, and new port terminals are quite costly and time consuming to build. Therefore, it is necessary to optimize existing infrastructure to achieve sustainability in logistics. This problem is more complex in multi-client port terminals, where quay infrastructure is shared among terminal operators who often have conflicting interests. Moreover, the berth allocation problem in liquid bulk terminals implies demanding restrictions due to the reduced flexibility in berth allocation for these types of goods. In this context, this paper presents HADES, a multi-agent platform, and the experience of its pilot use in the Port of Cartagena. HADES is a software platform where agents involved in vessel arrivals share meaningful but limited information. This is done to alleviate potential congestion in multi-client liquid bulk terminals, promoting a consensus where overall congestion anchoring is reduced. A study is presented using a mixed integer linear program (MILP) optimization model to analyze the maximum theoretical reduction in congestion anchoring, depending on the flexibility of vessel arrival time changes. Results show that 6 h of flexibility is enough to reduce congestion anchoring by half, and 24 h reduces it to negligible values. This confirms the utility of HADES, which is also briefly described.

scholarly journals A Faithful Mechanism for Incremental Multi-Agent Agreement Problems with Self-Interested and Privacy-Preserving Agents

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Farzaneh Farhadi ◽  
Nicholas R. Jennings
Keyword(s):  
Information Leakage ◽  
Optimal Strategies ◽  
Privacy Preserving ◽  
Limited Attention ◽  
Limited Information ◽  
Multi Agent Systems ◽  
Negotiation Strategies ◽  
Multi Agent ◽  
Theoretical And Numerical Analysis ◽  
Negotiation Mechanism

AbstractDistributed multi-agent agreement problems (MAPs) are central to many multi-agent systems. However, to date, the issues associated with encounters between self-interested and privacy-preserving agents have received limited attention. Given this, we develop the first distributed negotiation mechanism that enables self-interested agents to reach a socially desirable agreement with limited information leakage. The agents’ optimal negotiation strategies in this mechanism are investigated. Specifically, we propose a reinforcement learning-based approach to train agents to learn their optimal strategies in the proposed mechanism. Also, a heuristic algorithm is designed to find close-to-optimal negotiation strategies with reduced computational costs. We demonstrate the effectiveness and strength of our proposed mechanism through both game theoretical and numerical analysis. We prove theoretically that the proposed mechanism is budget balanced and motivates the agents to participate and follow the rules faithfully. The experimental results confirm that the proposed mechanism significantly outperforms the current state of the art, by increasing the social-welfare and decreasing the privacy leakage.

Implementation of a Multi-Agent System to Support ZDM Strategies in Multi-Stage Environments

2018 ◽  
Author(s):  
Jose Barbosa ◽  
Paulo Leitao ◽  
Adriano Ferreira ◽  
Jonas Queiroz ◽  
Carla A. S. Geraldes ◽  
...  
Keyword(s):  
Multi Agent System ◽  
Agent System ◽  
Multi Stage ◽  
Multi Agent

scholarly journals Hierarchical Scheduling Scheme for AC/DC Hybrid Active Distribution Network Based on Multi-Stakeholders

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2830 ◽  
Author(s):  
Chang Ye ◽  
Shihong Miao ◽  
Yaowang Li ◽  
Chao Li ◽  
Lixing Li
Keyword(s):  
Optimization Model ◽  
Distribution Network ◽  
Hierarchical Optimization ◽  
Active Distribution Network ◽  
Scheduling Scheme ◽  
Multi Stage ◽  
Flexible Loads ◽  
Level Model ◽  
Load Regulation ◽  
Upper Level

This paper presents a hierarchical multi-stage scheduling scheme for the AC/DC hybrid active distribution network (ADN). The load regulation center (LRC) is considered in the developed scheduling strategy, as well as the AC and DC sub-network operators. They are taken to be different stakeholders. To coordinate the interests of all stakeholders, a two-level optimization model is established. The flexible loads are dispatched by LRC in the upper-level optimization model, the objective of which is minimizing the loss of the entire distribution network. The lower-level optimization is divided into two sub-optimal models, and they are carried out to minimize the operating costs of the AC/DC sub-network operators respectively. This two-level model avoids the difficulty of solving multi-objective optimization and can clarify the role of various stakeholders in the system scheduling. To solve the model effectively, a discrete wind-driven optimization (DWDO) algorithm is proposed. Then, considering the combination of the proposed DWDO algorithm and the YALMIP toolbox, a hierarchical optimization algorithm (HOA) is developed. The HOA can obtain the overall optimization result of the system through the iterative optimization of the upper and lower levels. Finally, the simulation results verify the effectiveness of the proposed scheduling scheme.

scholarly journals Risk-Averse Multi-Stage Stochastic Programming to Optimizing Vaccine Allocation and Treatment Logistics for Effective Epidemic Response

2021 ◽  
Author(s):  
Xuecheng Yin ◽  
Esra Buyuktahtakin
Keyword(s):  
Value At Risk ◽  
Virus Disease ◽  
Risk Measure ◽  
Adverse Outcomes ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
Risk Averse ◽  
Expected Number ◽  
Multi Stage ◽  
Stochastic Mixed Integer Programming

Existing compartmental-logistics models in epidemics control are limited in terms of optimizing the allocation of vaccines and treatment resources under a risk-averse objective. In this paper, we present a data-driven, mean-risk, multi-stage, stochastic epidemics-vaccination-logistics model that evaluates various disease growth scenarios under the Conditional Value-at-Risk (CVaR) risk measure to optimize the distribution of treatment centers, resources, and vaccines, while minimizing the total expected number of infections, deaths, and close contacts of infected people under a limited budget. We integrate a new ring vaccination compartment into a Susceptible-Infected-Treated-Recovered-Funeral-Burial epidemics-logistics model. Our formulation involves uncertainty both in the vaccine supply and the disease transmission rate. Here, we also consider the risk of experiencing scenarios that lead to adverse outcomes in terms of the number of infected and dead people due to the epidemic. Combining the risk-neutral objective with a risk measure allows for a trade-off between the weighted expected impact of the outbreak and the expected risks associated with experiencing extremely disastrous scenarios. We incorporate human mobility into the model and develop a new method to estimate the migration rate between each region when data on migration rates is not available. We apply our multi-stage stochastic mixed-integer programming model to the case of controlling the 2018-2020 Ebola Virus Disease (EVD) in the Democratic Republic of the Congo (DRC) using real data. Our results show that increasing the risk-aversion by emphasizing potentially disastrous outbreak scenarios reduces the expected risk related to adverse scenarios at the price of the increased expected number of infections and deaths over all possible scenarios. We also find that isolating and treating infected individuals are the most efficient ways to slow the transmission of the disease, while vaccination is supplementary to primary interventions on reducing the number of infections. Furthermore, our analysis indicates that vaccine acceptance rates affect the optimal vaccine allocation only at the initial stages of the vaccine rollout under a tight vaccine supply.

scholarly journals Impact Size and Determinants of Indirect Cost of Financial Distress: Role of Receivable and Inventory Management

2020 ◽  
Vol 16 (2) ◽  
pp. 179-207
Author(s):  
Umar Farooq ◽  
Muhammad Ali Jibran Qamar ◽  
Krishna Reddy
Keyword(s):  
Indirect Cost ◽  
Inventory Management ◽  
Financial Distress ◽  
Multi Stage ◽  
Initial Stage ◽  
Non Linear ◽  
Management Policies ◽  
Internal Operations ◽  
The Impact

This research investigates the opportunity cost as an indirect cost of financial distress from two perspectives. First, indirect cost is estimated using multi-stage financial distress and non-linear proxy of debt. Second, receivable and inventory management are studied as determinants of indirect cost. The sample includes ongoing Pakistani firms that were healthy in the previous year and documenting positive gross profit. Results showed that firms bear opportunity loss primarily due to leverage rather than multistage financial distress. However, a non-linear relationship is found between leverage and indirect cost. Results further explored the impact of multistage financial distress on internal operations, i.e., working capital policies. It is found that firms manage receivable and inventory simultaneously during the multistage financial distress. Results revealed that increasing receivables and decreasing inventory is suitable during the transition of healthy firms to initial stage of financial distress, i.e., profit reduction. However, decreasing receivables, along with holding more inventory, is recommended for healthy firms that face liquidity problems subsequently. It is concluded that managers can reduce the indirect cost after deploying the optimal debt ratio and recommended receivable and inventory management policies.

The determination of the most suitable inertia weight strategy for particle swarm optimization via the minimax mixed-integer linear programming model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Volkan Soner Özsoy
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Pso Algorithm ◽  
Complex Problem ◽  
Mixed Integer ◽  
Swarm Optimization ◽  
Content Type ◽  
Inertia Weight

Purpose This paper aims to consider each strategy of the particle swarm optimization (PSO) as a unit in data envelopment analysis (DEA) and uses the minimax mixed-integer linear programming DEA approach to find the most suitable inertia weight strategy. A total of 15 inertia weight strategies were empirically examined in a suite of 42 benchmark problems in the view of DEA. Design/methodology/approach PSO is very sensitive to inertia weight strategies, and therefore, an important amount of research attempts has been concentrated on these strategies. There is no research into the determination of the most suitable inertia weight strategy; however, there are a large number of comparisons related to the inertia weight strategies. DEA is one of the performance evaluation methods, and its models classify the set of strategies into two distinct sets as efficient and inefficient. However, only one of the strategies should be used in the PSO algorithm. Some effective models were proposed to find the most efficient strategy. Findings The experimental studies demonstrate that an approach is a useful tool in the determination of the most suitable strategy. Besides, if the author encounters a new complex problem whose properties are known, it will help the author to choose the best strategy. Practical implications A heavy oil thermal cracking three lumps model for the simplification of the reaction system was used because it is an important complicated chemical process. In addition, the soil water retention curve (SWRC) plays an important role in diverse facets of agricultural engineering. As the SWRC can be regarded as a nonlinear function between the water content and the soil water potential, Van Genuchten model is proposed to describe this function. To determinate these model parameters, an optimization problem is formulated, which minimizes the difference between the measured and modeled data. Originality/value In this paper, the PSO algorithm is integrated with minimax mixed-integer linear programming to find the most suitable inertia weight strategy. In this way, the best strategy could be chosen for a new more complex problem.

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