Tamale Technical University, Ghana
In the face of the current global shortage of fossil energy, the greenhouse effect seriously affects the development of the world economy and people's health, countries are actively seeking new energy(NE) to replace fossil energy, in order to achieve sustainable economic development and curb the continuous warming of the global climate, which will dominate the future development. At the same time, the breakthrough of modern technology in comprehensive energy technology makes the urban power grid gradually change to electricity-based, and includes water, heat, gas and other multi-energy supply forms. The increase in the installed scale of renewable energy(RE) has brought many problems to the operation and scheduling of the power system(PS). The NEPS(NEPS) actively and fully accepts RE, and its power supply side contains a large number of intermittent and random fluctuations of RE power. The existing optimal dispatch strategies for PSs including RE that consider emissions do not fully consider source-load interaction(SLI), and ignore the deep peak regulation of units and the joint dispatch of user loads under the new situation. To this end, this paper analyzes the user-side characteristics of wind, solar and thermal power generation(PG) units through the multi-objective optimization(MOO) model of the cost of the NEPS, and simulates five dispatching scenarios on the source-load interactive platform. The PS is optimally dispatched to reduce the economic cost of the unit.
New Energy Power System, Source-Load Interaction, Multi-Objective Optimization, Dispatch Model
Hooman Asghari. Key Problems of Source-Load Interaction in New Energy Power System Based on Multi-Objective Optimization. Academic Journal of Energy (2022), Vol. 3, Issue 3: 50-57. https://doi.org/10.38007/RE.2022.030306.
 Roberts J J , Cassula A M , Silveira J L , et al. Robust MOO of a renewable based hybrid PS. Applied Energy, 2018, 223(August):52–68. https://doi.org/10.1016/j.apenergy.2018.04.032
 Arabkoohsar A , Chen S , Yang Y , et al. MOO of a combined cooling, heating, and PS with subcooled compressed air ES considering off-design characteristics. Applied Thermal Engineering, 2021, 187(5–6):116562.
 Pereira K , Pereira B R , Contreras J , et al. A MOO Technique to Develop Protection Systems of Distribution Networks with Distributed Generation. IEEE Transactions on PSs, 2018, PP(6):1-1. https://doi.org/10.1109/TPWRS.2018.2842648
 Abdelkader A , Rabeh A , Ali D M , et al. Multi-objective genetic algorithm based sizing optimization of a stand-alone wind/PV power supply system with enhanced battery/supercapacitor hybrid ES. Energy, 2018, 163(NOV.15):351-363.
 Salkuti S R . Optimal power flow using multi-objective glowworm swarm optimization algorithm in a wind energy integrated PS. International Journal of Green Energy, 2019, 16(15):1-15.
 Siahroodi H J , Mojallali H , Mohtavipour S S . A new stochastic multi-objective framework for the reactive power market considering plug-in electric vehicles using a novel metaheuristic approach. Neural Computing and Applications, 2022, 34(14):11937-11975. https://doi.org/10.1007/s00521-022-07081-z
 Lawhorn D , Rallabandi V , Dan M I . MOO for Aircraft PSs using a Network Graph Representation. IEEE Transactions on Transportation Electrification, 2021, PP(99):1-1.
 Naserabad S N , Mehrpanahi A , Ahmadi G . MOO of HRSG Configurations on the Steam Power Plant Repowering Specifications. Energy, 2018, 159(sep.15):277-293.
 Hong T , Kim J , Lee M . A MOO model for determining the building design and occupant behaviors based on energy, economic, and environmental performance. Energy, 2019, 174(MAY 1):823-834. https://doi.org/10.1016/j.energy.2019.02.035
 Khanmohammadi S , Shahsavar A . Energy analysis and MOO of a novel exhaust air heat recovery system consisting of an air-based building integrated photovoltaic/thermal system and a thermal wheel. Energy Conversion and Management, 2018, 172(SEP.):595-610.
 Behzadi A , Habibollahzade A , Ahmadi P , et al. Multi-objective design optimization of a solar based system for electricity, cooling, and hydrogen production. Energy, 2019, 169(FEB.15):696-709.
 Mazzeo D , Oliveti G , Baglivo C , et al. Energy reliability-constrained method for the MOO of a photovoltaic-wind hybrid system with battery storage. Energy, 2018, 156(AUG.1):688-708. https://doi.org/10.1016/j.energy.2018.04.062
 A M B , B A E , C F E , et al. Systematic analysis and MOO of an integrated power and freshwater production cycle. International Journal of Hydrogen Energy, 2022, 47( 43):18831-18856.
 Babamiri O , Marofi S . A multi-objective simulation–optimization approach for water resource planning of reservoir–river systems based on a coupled quantity–quality model. Environmental Earth Sciences, 2021, 80(11):1-19. https://doi.org/10.1007/s12665-021-09681-9
 Kushal T , Lai K , Illindala M S . Risk-Based Mitigation of Load Curtailment Cyber Attack Using Intelligent Agents in a Shipboard PS. IEEE Transactions on Smart Grid, 2019, 10(5):4741-4750.
 Shabani M J , Moghaddas-Tafreshi S M . Fully-decentralized coordination for simultaneous hydrogen, power, and heat interaction in a multi-carrier-energy system considering private ownership. Electric PSs research, 2020, 180(Mar.):106099.1-106099.15. https://doi.org/10.1016/j.epsr.2019.106099
 Fardanesh B , Shapiro A , Saglimbene P , et al. A Digital Transformation at New York Power Authority: Using Innovative Technologies to Create a More Efficient PS. IEEE Power and Energy Magazine, 2020, 18(2):22-30. https://doi.org/10.1109/MPE.2019.2959051
 Philpott A , Read G , Batstone S , et al. The New Zealand Electricity Market: Challenges of a RE System. IEEE Power and Energy Magazine, 2019, 17(1):43-52.
 Onuka S , Umemura A , Takahashi R , et al. Frequency Control of PS with Renewable Power Sources by HVDC Interconnection Line and Battery Considering Energy Balancing. Journal of Power and Energy Engineering, 2020, 08(4):11-24. https://doi.org/10.4236/jpee.2020.84002