Development and application of linear ventilation and temperature models for indoor environmental prediction and HVAC systems control

Multi-microgrid (MMG) system is a new method that concurrently incorporates different types of distributed energy resources, energy storage systems and demand responses to provide reliable and independent electricity for the community. However, MMG system faces the problems of management, real-time economic operations and controls. Therefore, this study proposes an energy management system (EMS) that turns an infinite number of MMGs into a coherence and efficient system, where each MMG can achieve its goals and perspectives. The proposed EMS employs a cooperative game to achieve efficient coordination and operations of the MMG system and also ensures a fair energy cost allocation among members in the coalition. This study considers the energy cost allocation problem when the number of members in the coalition grows exponentially. The energy cost allocation problem is solved using a column generation algorithm. The proposed model includes energy storage systems, demand loads, real-time electricity prices and renewable energy. The estimate of the daily operating cost of the MMG using a proposed deep convolutional neural network (CNN) is analyzed in this study. An optimal scheduling policy to optimize the total daily operating cost of MMG is also proposed. Besides, other existing optimal scheduling policies, such as approximate dynamic programming (ADP), model prediction control (MPC), and greedy policy are considered for the comparison. To evaluate the effectiveness of the proposed model, the real-time electricity prices of the electric reliability council of Texas are used. Simulation results show that each MMG can achieve energy cost savings through a coalition of MMG. Moreover, the proposed optimal policy method achieves MG's daily operating cost reduction up to 87.86% as compared to 79.52% for the MPC method, 73.94% for the greedy policy method and 79.42% for ADP method.

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Towards Real-Time Energy Management of Multi-Microgrid Using a Deep Convolution Neural Network and Cooperative Game Approach

Distributed model predictive control for economic dispatch of power systems with high penetration of renewable energy resources

An agent-based distributed real-time optimal control strategy for building HVAC systems for applications in the context of future IoT-based smart sensor networks

A review of optimization approaches for controlling water-cooled central cooling systems

A decentralized algorithm for optimal distribution in HVAC systems

Decentralized economic dispatch of an isolated distributed generator network

Under a self-organizing and flat sensors network structure, a novel decentralized sensors fault detection and self-correction method is proposed. In the self-organizing sensors network, each sensor is updated as smart agent and is connected based on basic physical relations and can communicate with adjacent nodes. This network possess the advantages of plug-and-play and can reduce the high labor and maintenance cost, without having to build central monitor. For the proposed decentralized method, each smart sensor is treated as an individual which has its own utility with local coupling between adjacent nodes. And a decentralized swarm intelligence optimization algorithm is designed and executed in all the smart nodes in parallel to achieve the sensors fault detection and self-correction. Moreover, convergence property of the presented method is analyzed theoretically. Simulation results and hardware platform test on an actual HVAC system illustrate the effectiveness of the proposed method.

A novel sensors fault detection and self-correction method for HVAC systems using decentralized swarm intelligence algorithm

To solve the challenges of high labour and maintenance cost while saving energy in engineering, a decentralized agent-based model-free global optimization method for heating, ventilation and air co...

A decentralized, model-free, global optimization method for energy saving in heating, ventilation and air conditioning systems:

To solve the high labor and maintenance cost while save energy problems in actual engineering, a decentralized control structure and optimization method for the heating, ventilation, and air conditioning (HVAC) systems are proposed. In this decentralized HVAC control system, each updated smart equipment is connected according to the physical correlations and can communicate with adjacent nodes collaboratively. Furthermore, a novel fully distributed and self-organizing swarm intelligence optimization algorithm is presented. And the algorithm runs in each smart equipment to achieve the optimal operation and avoid the conflict among correlated equipment. Moreover, the convergence property of the novel method is analyzed theoretically. This method is confirmed effective for realizing the global optimal operation of HVAC system through simulation results and hardware tests on actual engineering.

/pdf/a-decentralized-swarm-intelligence-algorithm-for-global-p7rwawete9.pdf

Jiang Ziyan

Papers

A decentralized algorithm for optimal distribution in HVAC systems

Decentralized economic dispatch of an isolated distributed generator network

A novel sensors fault detection and self-correction method for HVAC systems using decentralized swarm intelligence algorithm

A decentralized, model-free, global optimization method for energy saving in heating, ventilation and air conditioning systems:

A Decentralized Swarm Intelligence Algorithm for Global Optimization of HVAC System