Customised Multi-Energy Pricing: Model and Solutions
With the increasing interdependence among energies (e.g., electricity, natural gas and heat) and the development of a decentralised energy system, a novel retail pricing scheme in the multi-energy market is demanded. Therefore, the problem of designing a customised multi-energy pricing scheme for en...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/4/2080 |
| _version_ | 1811153809759535104 |
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| author | Qiuyi Hong Fanlin Meng Jian Liu |
| author_facet | Qiuyi Hong Fanlin Meng Jian Liu |
| author_sort | Qiuyi Hong |
| collection | DOAJ |
| description | With the increasing interdependence among energies (e.g., electricity, natural gas and heat) and the development of a decentralised energy system, a novel retail pricing scheme in the multi-energy market is demanded. Therefore, the problem of designing a customised multi-energy pricing scheme for energy retailers is investigated in this paper. In particular, the proposed pricing scheme is formulated as a bilevel optimisation problem. At the upper level, the energy retailer (leader) aims to maximise its profit. Microgrids (followers) equipped with energy converters, storage, renewable energy sources (RES) and demand response (DR) programs are located at the lower level and minimise their operational costs. Three hybrid algorithms combining metaheuristic algorithms (i.e., particle swarm optimisation (PSO), genetic algorithm (GA) and simulated annealing (SA)) with the mixed-integer linear program (MILP) are developed to solve the proposed bilevel problem. Numerical results verify the feasibility and effectiveness of the proposed model and solution algorithms. We find that GA outperforms other solution algorithms to obtain a higher retailer’s profit through comparison. In addition, the proposed customised pricing scheme could benefit the retailer’s profitability and net profit margin compared to the widely adopted uniform pricing scheme due to the reduction in the overall energy purchasing costs in the wholesale markets. Lastly, the negative correlations between the rated capacity and power of the energy storage and both retailer’s profit and the microgrid’s operational cost are illustrated. |
| first_indexed | 2024-03-11T08:51:58Z |
| format | Article |
| id | doaj.art-90183796a1aa4868b869974097922737 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T08:51:58Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-90183796a1aa4868b8699740979227372023-11-16T20:21:52ZengMDPI AGEnergies1996-10732023-02-01164208010.3390/en16042080Customised Multi-Energy Pricing: Model and SolutionsQiuyi Hong0Fanlin Meng1Jian Liu2Department of Mathematical Sciences, University of Essex, Colchester CO4 3SQ, UKAlliance Manchester Business School, University of Manchester, Manchester M15 6PB, UKDepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USAWith the increasing interdependence among energies (e.g., electricity, natural gas and heat) and the development of a decentralised energy system, a novel retail pricing scheme in the multi-energy market is demanded. Therefore, the problem of designing a customised multi-energy pricing scheme for energy retailers is investigated in this paper. In particular, the proposed pricing scheme is formulated as a bilevel optimisation problem. At the upper level, the energy retailer (leader) aims to maximise its profit. Microgrids (followers) equipped with energy converters, storage, renewable energy sources (RES) and demand response (DR) programs are located at the lower level and minimise their operational costs. Three hybrid algorithms combining metaheuristic algorithms (i.e., particle swarm optimisation (PSO), genetic algorithm (GA) and simulated annealing (SA)) with the mixed-integer linear program (MILP) are developed to solve the proposed bilevel problem. Numerical results verify the feasibility and effectiveness of the proposed model and solution algorithms. We find that GA outperforms other solution algorithms to obtain a higher retailer’s profit through comparison. In addition, the proposed customised pricing scheme could benefit the retailer’s profitability and net profit margin compared to the widely adopted uniform pricing scheme due to the reduction in the overall energy purchasing costs in the wholesale markets. Lastly, the negative correlations between the rated capacity and power of the energy storage and both retailer’s profit and the microgrid’s operational cost are illustrated.https://www.mdpi.com/1996-1073/16/4/2080customised pricing schememulti-energy marketbilevel optimisation modelmetaheuristic algorithms |
| spellingShingle | Qiuyi Hong Fanlin Meng Jian Liu Customised Multi-Energy Pricing: Model and Solutions Energies customised pricing scheme multi-energy market bilevel optimisation model metaheuristic algorithms |
| title | Customised Multi-Energy Pricing: Model and Solutions |
| title_full | Customised Multi-Energy Pricing: Model and Solutions |
| title_fullStr | Customised Multi-Energy Pricing: Model and Solutions |
| title_full_unstemmed | Customised Multi-Energy Pricing: Model and Solutions |
| title_short | Customised Multi-Energy Pricing: Model and Solutions |
| title_sort | customised multi energy pricing model and solutions |
| topic | customised pricing scheme multi-energy market bilevel optimisation model metaheuristic algorithms |
| url | https://www.mdpi.com/1996-1073/16/4/2080 |
| work_keys_str_mv | AT qiuyihong customisedmultienergypricingmodelandsolutions AT fanlinmeng customisedmultienergypricingmodelandsolutions AT jianliu customisedmultienergypricingmodelandsolutions |