Optimal dispatch of combined heat and power units based on particle swarm optimization with genetic algorithm
Source abstract
Abstract
The main purpose of this paper is to optimize the consumption of renewable energy and minimize the total cost of system operation, and a combined heat and power unit is added to the wind, water, and water combined dispatching model to solve typical daily dispatch problems in different seasons. Initially, a combined wind–solar–water-fired optimal scheduling model containing cogeneration units and renewable energy preferential consumption is constructed, and the model is solved using particle swarm optimization with the genetic algorithm. The simulation analysis of typical electrical load and heat load during summer and winter in a certain area is implemented as an example to verify the feasibility of the model and algorithm. Compared with the traditional vertical integrated electric-heated coordinated dispatch, the proposed method reflects the willingness of the combined heat and power to actively participate in the paid peak regulation of the power grid under economic incentives.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Integrated Energy Systems Optimization · Electric Power System Optimization · Microgrid Control and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Electrical Engineering & New Energy, China Three Gorges University 1 , Yichang 443000, China
Hubei Province Collaborative Innovation Center for New Energy Microgrid 2 , Yichang 443000, China
Funding
National Innovation Talent Promotion Program
References (40)
Show source-provided references
1. A hybrid wind power forecasting approach based on Bayesian model averaging and ensemble learning10.1016/j.renene.2019.07.166 ↗
2. Very short-term wind power density forecasting through artificial neural networks for microgrid control10.1016/j.renene.2019.07.067 ↗
3. Conditional aggregated probabilistic wind power forecasting based on spatio-temporal correlation10.1016/j.apenergy.2019.113842 ↗
4. Robust unit commitment with wind power and pumped storage hydro10.1109/tpwrs.2011.2169817 ↗
5. Short-term forecast of generation of electric energy in photovoltaic systems10.1016/j.rser.2017.07.032 ↗
6. Consideration of reliability and economy to Capacity Configuration of energy storage system: Case Study of a large scale wind power plant in the Northwest China10.1016/j.egypro.2018.09.073 ↗
7. Research on application of energy storage technology in renewable energy source generation10.4028/www.scientific.net/amr.1070-1072.418 ↗
8. Battery energy storage sizing based on a model predictive control strategy with operational constraints to smooth the wind power10.1016/j.ijepes.2019.105471 ↗
9. The role of wind power and solar PV in reducing risks in the Brazilian hydro-thermal power system10.1016/j.energy.2016.03.059 ↗
10. Improved multi-objective model and analysis of the coordinated operation of a hydro-wind-photovoltaic system10.1016/j.energy.2017.06.047 ↗
11. Complementarity maps of wind and solar energy resources for Rio Grande do Sul, Brazil10.4236/epe.2017.99034 ↗
12. Discussions on the architecture and operation mode of future power grids10.3390/en4071025 ↗
13. A modified bacteria foraging based optimal power flow framework for hydro-thermal-wind generation system in the presence of STATCOM10.1016/j.energy.2017.02.090 ↗
14. Multi-objective optimization for integrated hydro–photovoltaic power system10.1016/j.apenergy.2015.09.018 ↗
15. Optimization model of an efficient collaborative power dispatching system for carbon emissions trading in China10.3390/en10091405 ↗
16. Feasibility study of small hydro/PV/wind hybrid system for off-grid rural electrification in Ethiopia10.1016/j.apenergy.2011.11.059 ↗
17. A feasibility study of a stand-alone hybrid solar–wind–battery system for a remote island10.1016/j.apenergy.2014.01.090 ↗
18. Pumped storage-based standalone photovoltaic power generation system: Modeling and techno-economic optimization10.1016/j.apenergy.2014.06.005 ↗
19. Comparison of integration solutions for wind power in the Netherlands10.1049/iet-rpg.2008.0080 ↗
20. A curtailed wind power accommodation strategy based on wind-hydrogen-heat-storage integrated energy network
21. A model for optimal scheduling of hydro thermal systems including pumped-storage and wind power10.1049/iet-gtd.2012.0639 ↗
22. Peak-load regulation strategy of power grid with cogeneration units
23. Economic valuation of heat pumps and electric boilers in the Danish energy system10.1016/j.apenergy.2015.08.115 ↗
24. Optimal operation strategy considering wind power accommodation in heating district
25. Research on accommodation scheme of curtailed wind power based on peak-shaving electric boiler in secondary heat supply network10.13335/j.1000-3673.pst.2015.11.041 ↗
26. Impacts of wind power on thermal generation unit commitment and dispatch10.1109/tec.2006.889616 ↗
27. Research on the wind power accommodation based on peak shaving by using heat storage electric boiler
28. Multi-objectives combined electric heating dispatch model of wind power accommodation with heat storage device
29. Coordinated wind power accommodating dispatch model based on electric boiler and CHP with thermal energy storage10.13334/j.0258-8013.pcsee.152406 ↗
30. Particle swarm optimization
31. Semi-definite programming-based method for security-constrained unit commitment with operational and optimal power flow constraints10.1049/iet-gtd:20070516 ↗
32. Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning10.1109/tii.2012.2198665 ↗
33. Double global optimum genetic algorithm–particle swarm optimization-based welding robot path planning10.1080/0305215x.2015.1005084 ↗
34. A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks10.1016/j.apm.2014.12.016 ↗
35. A hybrid of genetic algorithm and particle swarm optimization for recurrent network design10.1109/tsmcb.2003.818557 ↗
36. A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems10.1016/j.ijepes.2011.08.023 ↗
37. A combination of genetic algorithm and particle swarm optimization for optimal distributed generation location and sizing in distribution systems with fuzzy optimal theory10.1080/15435075.2011.625590 ↗
38. Closure to discussion on “Economic load dispatch—A comparative study on heuristic optimization techniques with an improved coordinated aggregation-based PSO”10.1109/tpwrs.2009.2037534 ↗
39. Economic dispatch with CHP and wind power using probabilistic sequence theory and hybrid heuristic algorithm10.1063/1.4976144 ↗
40. Combined heat and power economic dispatch problem using advanced modified particle swarm optimization10.1063/1.5048833 ↗