Energy management system of low voltage dc microgrid using mixed-integer nonlinear programing and a global optimization technique

AbstractThis paper presents a real time price based energy management system for DC microgrid. The DC distribution system is considered as a prospective system according to the increase of DC loads and DC output type distribution energy resources (DERs) such as photovoltaic (PV) systems, battery bank (BB), and hybrid car (HC). The control objective is to achieve the optimal cost of energy. The proposed control scheme is developed based on the source as well as load scheduling of the DC microgrid. The source scheduling algorithm is based on the selection of cheapest power source to supply the load of DC microgrid and achieve the optimal electricity price. The BB and HC charges in regular hours at the less electricity price to supply the future load during the higher electricity price of the public utility. The load scheduling algorithm shifts the deferrable load of the building from peak hours to the regular hours to obtain the lowest cost of energy for the building. The proposed scheme significantly decreases the peak demand, which is the main cause of load shedding. Dynamic simulation is presented to access the control performance with price fluctuations and robustness of the system.

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Optimal load balancing strategy for hybrid energy management system in DC microgrid with PV, fuel cell and battery storage

2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA) ◽

10.1109/icrera.2016.7884456 ◽

2016 ◽

Cited By ~ 13

Author(s):

Sneha Mane ◽

Pratik Kadam ◽

Gopal Lahoti ◽

Faruk Kazi ◽

N. M. Singh

Keyword(s):

Fuel Cell ◽

Load Balancing ◽

Energy Management ◽

Management System ◽

Energy Management System ◽

Battery Storage ◽

Dc Microgrid ◽

Hybrid Energy ◽

Optimal Load

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Mixed integer smart off-grid home energy management system

Energy Reports ◽

10.1016/j.egyr.2021.11.227 ◽

2021 ◽

Vol 7 ◽

pp. 9094-9107

Author(s):

Rasha Elazab ◽

Omar Saif ◽

Amr M.A. Amin Metwally ◽

Mohamed Daowd

Keyword(s):

Energy Management ◽

Management System ◽

Mixed Integer ◽

Energy Management System ◽

Home Energy Management ◽

Home Energy Management System

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An Embedded Platform for Testbed Implementation of Multi-Agent System in Building Energy Management System

Energies ◽

10.3390/en12193655 ◽

2019 ◽

Vol 12 (19) ◽

pp. 3655 ◽

Cited By ~ 1

Author(s):

Aryuanto Soetedjo ◽

Yusuf Ismail Nakhoda ◽

Choirul Saleh

Keyword(s):

Energy Management ◽

Management System ◽

Fuzzy Logic Controller ◽

Optimization Technique ◽

Building Energy ◽

Energy Management System ◽

Multi Agent System ◽

Agent System ◽

Building Energy Management ◽

Multi Agent

This paper presents a hardware testbed for testing the building energy management system (BEMS) based-on the multi agent system (MAS). The objective of BEMS is to maximize user comfort while minimizing the energy extracted from the grid. The proposed system implements a multi-objective optimization technique using a genetic algorithm (GA) and the fuzzy logic controller (FLC) to control the room temperature and illumination setpoints. The agents are implemented on the low cost embedded systems equipped with the WiFi communication for communicating between the agents. The photovoltaic (PV)-battery system, the air conditioning system, the lighting system, and the electrical loads are modeled and simulated on the embedded hardware. The popular communication protocols such as Message Queuing Telemetry Transport (MQTT) and Modbus TCP/IP are adopted for integrating the proposed MAS with the existing infrastructures and devices. The experimental results show that the sampling time of the proposed system is 16.50 s. Therefore it is suitable for implementing the BEMS in a real-time where the data are updated in an hourly or minutely basis. Further, the proposed optimization technique shows better results in optimizing the comfort index and the energy extracted from the grid compared to the existing methods.

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Comparative Study on Optimization Solvers for Implementation of a Two-Stage Economic Dispatch Strategy in a Microgrid Energy Management System

Energies ◽

10.3390/en13051096 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1096 ◽

Cited By ~ 1

Author(s):

Gi-Ho Lee ◽

Jae-Young Park ◽

Seung-Jun Ham ◽

Young-Jin Kim

Keyword(s):

Comparative Study ◽

Energy Management ◽

Management System ◽

Economic Dispatch ◽

Optimal Schedule ◽

Mixed Integer ◽

Energy Management System ◽

Two Stage ◽

Microgrid Energy Management ◽

Optimization Solvers

A microgrid energy management system (MEMS) optimally schedules the operation of dispatchable distributed energy resources to minimize the operation costs of microgrids (MGs) via an economic dispatch (ED). Actual ED implementation in the MEMS relies on an optimization software package called an optimization solver. This paper presents a comparative study of optimization solvers to investigate their suitability for ED implementation in the MEMS. Four optimization solvers, including commercial as well as open-source-based ones, were compared in terms of their computational capability and optimization results for ED. Two-stage scheduling was applied for the ED strategy, whereby a mixed-integer programming problem was solved to yield the optimal operation schedule of battery-based energy storage systems. In the first stage, the optimal schedule is identified one day before the operating day; in the second stage, the optimal schedule is updated every 5 min during actual operation to compensate for operational uncertainties. A modularized programming strategy was also introduced to allow for a comparison between the optimization solvers and efficient writing of codes. Comparative simulation case studies were conducted on three test-bed MGs to evaluate the optimization results and computation times of the compared optimization solvers.

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Hybrid Energy Management System for Operation of Wind Farm System Considering Grid-Code Constraints

Energies ◽

10.3390/en12244672 ◽

2019 ◽

Vol 12 (24) ◽

pp. 4672 ◽

Cited By ~ 2

Author(s):

Van-Hai Bui ◽

Akhtar Hussain ◽

Woon-Gyu Lee ◽

Hak-Man Kim

Keyword(s):

Energy Management ◽

Management System ◽

Wind Farm ◽

Mixed Integer ◽

Energy Management System ◽

Optimization Strategy ◽

Current Output ◽

Hybrid Energy ◽

Stage 1 ◽

Optimal Set

In this paper, a hybrid energy management system is developed to optimize the operation of a wind farm (WF) by combining centralized and decentralized approaches. A two-stage optimization strategy, including distributed information sharing (stage 1); and centralized optimization (stage 2) is proposed to find out the optimal set-points of wind turbine generators (WTGs) considering grid-code constraints. In stage 1, cluster energy management systems (CEMSs) and transmission system operator (TSO) interact with their neighboring agents to share information using diffusion strategy and then determine the mismatch power amount between the current output power of WF and the required power from TSO. This amount of mismatch power is optimally allocated to all clusters through the CEMSs. In stage 2, a mixed-integer linear programming (MILP)-based optimization model is developed for each CEMS to find out the optimal set-points of WTGs in the corresponding cluster. The CEMSs are responsible for ensuring the operation of WF in accordance with the requirements of TSO (i.e., grid-code constraints) and also minimizing the power deviation for the set-points of WTGs in each cluster. The minimization of power deviation helps to reduce the internal power fluctuations inside each cluster. Finally, to evaluate the effectiveness of the proposed method, several case studies are analyzed in the simulations section for operation of a WF with 20 WTGs in four different clusters.

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