Low-carbon collaborative dual-layer optimization for energy station considering joint electricity and heat demand response

Abstract

In the park-level integrated energy system (PIES) trading market involving various heterogeneous energy sources, the traditional vertically integrated market trading structure struggles to reveal the interactions and collaborative relationships between energy stations and users, posing challenges to the economic and low-carbon operation of the system. To address this issue, a dual-layer optimization strategy for energy station-user, taking into account the demand response for electricity and thermal, is proposed in this paper. The upper layer, represented by energy stations, makes decisions on variables such as the electricity and heat prices sold to users, as well as the output plans of energy supply equipment and the operational status of battery energy storage. The lower layer, comprising users, determines their own electricity and heat demand through demand response. Subsequently, a combination of differential evolution and quadratic programming (DE-QP) is employed to solve the interactive strategies between energy stations and users. The simulation results indicate that, compared to the traditional vertically integrated structure, the strategy proposed in this paper increases the revenue of energy stations and the consumer surplus of users by 5.09% and 2.46%, respectively.

Graphical abstract

Keywords

demand response / dual-layer optimization / energy station / integrated energy system

References

[1] Wang S X, Wang S M, Zhao Q Y. . Optimal dispatch of integrated energy station considering carbon capture and hydrogen demand. Energy, 2023, 269: 126981

[2] Liu T M, Zhang W, Jia Y B. . Optimal operation of integrated energy system including power thermal and gas subsystems. Frontiers in Energy, 2022, 16(1): 105–120

[3] Hua H C, Qin Y C, He Z C. . Energy sharing and frequency regulation in energy Internet via mixed H₂/H_∞ control with Markovian jump. CSEE Journal of Power and Energy Systems, 2021, 7(6): 1302–1311

[4] Hua H C, Shi J B, Chen X Y. . Carbon emission flow based energy routing strategy in Energy Internet.. IEEE Transactions on Industrial Informatics, 2023, 20(3): 3974–3985

[5] Qin Z M, Liu D, Hua H C. . Privacy preserving load control of residential microgrid via deep reinforcement learning. IEEE Transactions on Smart Grid, 2021, 12(5): 4079–4089

[6] Jiang T, Dong X R, Zhang R F. . Active-reactive power scheduling of integrated electricity-gas network with multi-microgrids. Frontiers in Energy, 2023, 17(2): 251–265

[7] Zhang Y M, Jie P F, Liu C H. . Optimizing environmental insulation thickness of buildings with CHP-based district heating system based on amount of energy and energy grade. Frontiers in Energy, 2022, 16(4): 613–628

[8] Rzetelska D, Combrinck M. Fuel poverty and low carbon emissions: A comparative study of the feasibility of the hybrid renewable energy systems incorporating combined heat and power technology. Frontiers in Energy, 2022, 16(2): 336–356

[9] Dolatabadi A, Abdeltawab H, Mohamed Y A R. A novel model-free deep reinforcement learning framework for energy management of a PV integrated energy hub. IEEE Transactions on Power Systems, 2023, 38(5): 4840–4852

[10] Zhang X, Karady G G, Piratla K R. . Network capacity assessment of combined heat and power-based distributed generation in urban energy infrastructures. IEEE Transactions on Smart Grid, 2013, 4(4): 2131–2138

[11] HuaH C, Wu X C, ChenX Y, et al. Carbon reduction oriented regional integrated energy system optimization via cloud-edge cooperative framework. CSEE Journal of Power and Energy Systems, 2023

[12] Gan L, Yang T Y, Wang B. . Three-stage coordinated operation of steel plant-based multi-energy microgrids considering carbon reduction. Energy, 2023, 278: 127639

[13] Tabebordbar A, Rastegar M. Reliability assessment of integrated electricity and natural gas transmission systems in presence of power-to-gas and combined heat and power technologies. CSEE Journal of Power and Energy Systems, 2023, 9(3): 1193–1202

[14] Chen F, Wu C C. A novel methodology for forecasting gas supply reliability of natural gas pipeline systems. Frontiers in Energy, 2020, 14(2): 213–223

[15] Li C B, Yang H Y, Shahidehpour M. . Optimal planning of islanded integrated energy system with solar–biogas energy supply. IEEE Transactions on Sustainable Energy, 2020, 11(4): 2437–2448

[16] Zhou B, Xu D, Li C B. . Optimal scheduling of biogas–solar–wind renewable portfolio for multicarrier energy supplies. IEEE Transactions on Power Systems, 2018, 33(6): 6229–6239

[17] Yang H Y, Li C B, Shahidehpour M. . Multistage expansion planning of integrated biogas and electric power delivery system considering the regional availability of biomass. IEEE Transactions on Sustainable Energy, 2021, 12(2): 920–930

[18] Xu D, Zhou B, Chan K W. . Distributed multienergy coordination of multimicrogrids with biogas–solar–wind renewables. IEEE Transactions on Industrial Informatics, 2019, 15(6): 3254–3266

[19] Luo Y, Zeng B, Zhang W. . Coordinative planning of public transport electrification, RESs and energy networks for decarbonization of urban multi-energy systems: A government-market dual-driven framework. IEEE Transactions on Sustainable Energy, 2024, 15(1): 538–555

[20] Li C, Yan Z, Yao Y. . Coordinated low-carbon dispatching on source-demand side for integrated electricity-gas system based on integrated demand response exchange. IEEE Transactions on Power Systems, 2024, 39(1): 1287–1303

[21] Aslam W, Andrianesis P, Caramanis M. Optimal HVAC energy and regulation reserve scheduling in power markets. IEEE Transactions on Sustainable Energy, 2024, 15(1): 201–214

[22] Jung J, Song C, Lee D. . Optimal energy procurement with long-term photovoltaic energy contracts considering generation uncertainty: A two-dimensional auction approach. Applied Energy, 2024, 356: 122383

[23] Faraji H, Hemmati R. Centralized multifunctional control of integrated electricity–gas network in district energy system considering resilience. IEEE Transactions on Industrial Informatics, 2024, 20(1): 649–658

[24] Gourisetti S N G, Sebastian-Cardenas D J, Bhattarai B. . Blockchain smart contract reference framework and program logic architecture for transactive energy systems. Applied Energy, 2021, 304: 117860

[25] Yu K, Cen Z G, Chen X Y. . Coordinated planning and operation of inter seasonal heat storage and P2G devices integrated to urban multi-energy system. Energy Reports, 2023, 9: 4617–4634

[26] Song M, Sun W. Applications of thermostatically controlled loads for demand response with the proliferation of variable renewable energy. Frontiers in Energy, 2022, 16(1): 64–73

[27] Wang J D, Chen B Y, Li P. . Distributionally robust optimization of home energy management system based on receding horizon optimization. Frontiers in Energy, 2020, 14(2): 254–266

[28] Xu S S, Shen J, Hua H C. . Trading portfolio strategy optimization via mean-variance model considering multiple energy derivatives. Processes, 2023, 11(2): 532

[29] Wang T H, Hua H C, Shi T Y. . A bi-level dispatch optimization of multi-microgrid considering green electricity consumption willingness under renewable portfolio standard policy. Applied Energy, 2024, 356: 122428

[30] Liu D, Qin Z M, Hua H C. . Incremental incentive mechanism design for diversified consumers in demand response. Applied Energy, 2023, 329: 120240

[31] Hua H C, Chen X Y, Gan L. . Demand-side joint electricity and carbon trading mechanism. IEEE Transactions on Industrial Cyber-Physical Systems, 2024, 2: 14–25

[32] Wang R, Wen X, Wang X. . Low carbon optimal operation of integrated energy system based on carbon capture technology, LCA carbon emissions and ladder-type carbon trading. Applied Energy, 2022, 311: 118664

[33] Hua H C, Gashi B, Zhang M. Robust risk-sensitive control. International Journal of Robust and Nonlinear Control, 2023, 33(10): 5484–5509

[34] Wang T H, Liang H, He B. . A novel consensus-based optimal control strategy for multi-microgrid systems with battery degradation consideration. CSEE Journal of Power and Energy Systems, 2022, 1–13

[35] Hua H C, Qin Z M, Dong N Q. . Data-driven dynamical control for bottom-up energy Internet system. IEEE Transactions on Sustainable Energy, 2022, 13(1): 315–327

[36] Hua H C, Qin Y C, Hao C T. . Stochastic optimal control for energy Internet: A bottom-up energy management approach. IEEE Transactions on Industrial Informatics, 2019, 15(3): 1788–1797

[37] Wang Y, Wang Y, Huang Y. . Optimal scheduling of the regional integrated energy system considering economy and environment. IEEE Transactions on Sustainable Energy, 2019, 10(4): 1939–1949

RIGHTS & PERMISSIONS

Higher Education Press 2024