Mixed-Integer Linear Programming for Decentralized Multi-Carrier Optimal Energy Management of a Micro-Grid
Source abstract
Abstract
Increasing the load demand and penetration of renewable energy sources (RESs) poses real challenges for optimal energy management of distribution networks. Moreover, considering multi-carrier energy systems has increased the efficiency of systems, and provides an opportunity for using the advantages of RESs. In this regard, we adopted a new framework based on the new challenges in the multi-carrier energy micro-grid (MEMG). In the proposed method, a comprehensive MEMG was modeled that benefits from a large assortment of distributed energy resources (DERs), such as micro-turbines, fuel cells, wind turbines, and energy storage. Considering many DERs is necessary, because these resources could cover one another’s disadvantages, which have a great impact on the total cost of the MEMG and decrease the emission impacts of fossil-fuel-based units. Furthermore, waste power plants, inverters, rectifiers, and emission constraints are considered in the proposed method for modeling a practical MEMG. Additionally, for modeling the uncertainty of stochastic parameters, a model based on a multilayer neural network was used in this paper. The results of this study indicate that using a decentralized model, along with stochastic methods for predicting uncertainty, can reduce operational costs in micro-grids and computational complexity compared with optimal centralized programming methods. Finally, the equations and results obtained from the proposed method were evaluated by experiments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Microgrid Control and Optimization · Hybrid Renewable Energy Systems · Electric Vehicles and Infrastructure
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran 1631714191, Iran
Cross Labs, Cross-Compass Ltd., Tokyo 104-0045, Japan
Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
References (48)
Show source-provided references
1. Optimal design of installation capacity and operation strategy for distributed energy system10.1016/j.applthermaleng.2017.07.011 ↗
2. Robust multi-objective control of hybrid renewable microgeneration systems with energy storage10.1016/j.applthermaleng.2016.09.070 ↗
3. A multi-agent system for optimal sizing of a cooperative self-sustainable multi-carrier microgrid10.1016/j.scs.2018.01.016 ↗
4. Using a multi-method approach for decision-making about a sustainable local distributed energy system: A case study from Finland10.1016/j.jclepro.2016.07.173 ↗
5. Optimization and performance analysis of solar hybrid CCHP systems under different operation strategies10.1016/j.applthermaleng.2018.01.046 ↗
6. A two-stage optimization and control for CCHP microgrid energy management10.1016/j.applthermaleng.2017.05.188 ↗
7. Recent techniques to model uncertainties in power generation from renewable energy sources and loads in microgrids–A review10.1016/j.rser.2016.12.063 ↗
8. A multi-period unit commitment problem under a new hybrid uncertainty set for a renewable energy source10.1016/j.renene.2016.05.095 ↗
9. Simulation and optimization of stand-alone hybrid renewable energy systems10.1016/j.rser.2009.01.010 ↗
10. Electric vehicle charging in an office building microgrid with distributed energy resources10.1109/TSTE.2014.2314754 ↗
11. Lasseter, R., Akhil, A., Marnay, C., Stephens, J., Dagle, J., Guttromsom, R., Meliopoulous, A.S., Yinger, R., and Eto, J. (2002). Integration of distributed energy resources. The CERTS Microgrid Concept, Lawrence Berkeley National Lab.(LBNL). No. LBNL-50829.10.2172/799644 ↗
12. Optimal economic dispatch of FC-CHP based heat and power micro-grids10.1016/j.applthermaleng.2016.12.016 ↗
13. Synthesis of an intelligent rural village microgrid control strategy based on smartgrid multi-agent modelling and transactive energy management principles10.1016/j.energy.2018.01.056 ↗
14. Stochastic networked microgrid energy management with correlated wind generators10.1109/TPWRS.2017.2650683 ↗
15. Multi-agent microgrid energy management based on deep learning forecaster10.1016/j.energy.2019.115873 ↗
16. Optimal economic and environment operation of micro-grid power systems10.1016/j.enconman.2016.05.074 ↗
17. Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational search algorithm10.1016/j.energy.2012.03.064 ↗
18. Optimal operation of hybrid microgrids for enhancing resiliency considering feasible islanding and survivability10.1049/iet-rpg.2016.0820 ↗
19. Scenario-based stochastic operation management of microgrid including wind, photovoltaic, micro-turbine, fuel cell and energy storage devices10.1016/j.ijepes.2013.08.004 ↗
20. Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty10.1016/j.apenergy.2017.04.071 ↗
21. A mathematical model for the optimal operation of the University of Genoa Smart Polygeneration Microgrid: Evaluation of technical, economic and environmental performance indicators10.1016/j.energy.2013.10.039 ↗
22. Bracco, S., Brignone, M., Delfino, F., Pampararo, F., Rossi, M., Ferro, G., and Robba, M. (2018). An optimization model for polygeneration microgrids with renewables, electrical and thermal storage: Application to the savona campus. 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), IEEE.10.1109/EEEIC.2018.8493965 ↗
23. An operational optimization method for a complex polygeneration plant based on real-time measurements10.1016/j.enconman.2018.05.076 ↗
24. The choice of operating strategy for a complex polygeneration system: A case study for a residential building in Italy10.1016/j.enconman.2018.02.066 ↗
25. A goal programming methodology for multiobjective optimization of distributed energy hubs operation10.1016/j.applthermaleng.2013.10.031 ↗
26. Multiobjective optimization of multi-carrier energy system using a combination of ANFIS and genetic algorithms10.1109/TSG.2016.2609740 ↗
27. A multi-agent decentralized energy management system based on distributed intelligence for the design and control of autonomous polygeneration microgrids10.1016/j.enconman.2015.06.021 ↗
28. Karavas, C.S., Arvanitis, K., and Papadakis, G. (2017). A game theory approach to multi-agent decentralized energy management of autonomous polygeneration microgrids. Energies, 10.10.3390/en10111756 ↗
29. Optimisation of multicarrier microgrid layout using selected metaheuristics10.1016/j.ijepes.2018.01.022 ↗
30. A scenario-based optimization of Smart Energy Hub operation in a stochastic environment using conditional-value-at-risk10.1016/j.scs.2018.01.045 ↗
31. A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response10.1016/j.apenergy.2018.05.113 ↗
32. An optimal power allocation scheme of microgrid using grey wolf optimizer10.1109/ACCESS.2019.2942352 ↗
33. A multi-agent system approach for optimal microgrid expansion planning under uncertainty10.1016/j.ijepes.2019.01.044 ↗
34. Decentralized multi-agent based energy management of microgrid using reinforcement learning10.1016/j.ijepes.2020.106211 ↗
35. Game theoretical analysis on capacity configuration for microgrid based on multi-agent system10.1016/j.ijepes.2020.106485 ↗
36. A Novel Cross-Case Electric Vehicle Demand Modeling Based on 3D Convolutional Generative Adversarial Networks10.1109/TPWRS.2021.3100994 ↗
37. Deep Learning-Based Forecasting Approach in Smart Grids With Microclustering and Bidirectional LSTM Network10.1109/TIE.2020.3009604 ↗
38. Optimal bidding strategy of a virtual power plant in day-ahead energy and frequency regulation markets: A deep learning-based approach10.1016/j.ijepes.2020.106646 ↗
39. Charging demand of plug-in electric vehicles: Forecasting travel behavior based on a novel rough artificial neural network approach10.1016/j.jclepro.2019.04.345 ↗
40. Spatio-temporal graph deep neural network for short-term wind speed forecasting10.1109/TSTE.2018.2844102 ↗
41. Optimal participation of a virtual power plant in electricity market considering renewable energy: A deep learning-based approach10.1016/j.segan.2021.100448 ↗
42. A new multi-objective competitive open vehicle routing problem solved by particle swarm optimization10.1007/s11067-011-9169-4 ↗
43. Converting the 0–1 polynomial programming problem to a 0–1 linear program10.1287/opre.22.1.180 ↗
44. Soroudi, A. (2017). Power System Optimization Modeling in GAMS, Springer.10.1007/978-3-319-62350-4 ↗
45. (2021, May 20). Power Data. Available online: http://www.ieso.ca/power-data.
46. (2021, May 20). Renewable Energy Policy Network for the 21st Century. Available online: http://climate.weather.gc.ca/historicaldata/searchhistoricdatae.html.
47. Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source10.1016/j.energy.2011.09.017 ↗
48. The performance of a grid-tied microgrid with hydrogen storage and a hydrogen fuel cell stack10.1016/j.enconman.2014.07.045 ↗