Long-term economic planning of combined cooling heating and power systems considering energy storage and demand response

Liu, Zuming; Zhao, Yingru; Wang, Xiaonan

doi:10.1016/j.apenergy.2020.115819

Cited by 57 publications

(12 citation statements)

References 43 publications

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“…However, investment options that alter the existing flexibility, e.g., product storage extension for a flexible production process, or electrification measures, should also be considered when assessing the flexibility potential of L-MESs. E.g., Liu et al [27] found synergistic effects when energy storage and DR of cooling, heating, and power were combined.…”

Section: Dr and Investmentsmentioning

confidence: 99%

Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems

et al. 2022

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A major barrier to investments in clean and future-proof energy technologies of local multi-energy systems (L-MESs) is the lack of knowledge about their impacts on profitability and carbon footprints due to their complex techno-economic interactions. To reduce this problem, decision support tools should integrate various forms of decarbonization measures. This paper proposes the Demand Response Analysis Framework (DRAF), a new open-source Python decision support tool that integrally optimizes the design and operation of energy technologies considering demand-side flexibility, electrification, and renewable energy sources. It quantifies decarbonization and cost reduction potential using multi-objective mixed-integer linear programming and provides decision-makers of L-MESs with optimal scenarios regarding costs, emissions, or Pareto efficiency. DRAF supports all steps of the energy system optimization process from time series analysis to interactive plotting and data export. It comes with several component templates that allow a quick start without limiting the modeling possibilities thanks to a generic model generator. Other key features are the access and preparation of time series, such as dynamic carbon emission factors or wholesale electricity prices; and the generation, handling, and parallel computing of scenarios. We demonstrate DRAF’s capabilities through three case studies on (1) the DR of industrial production processes, (2) the design optimization of battery and photovoltaic systems, and (3) the design optimization and DR of distributed thermal energy resources.

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Section: Dr and Investmentsmentioning

confidence: 99%

Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems

et al. 2022

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“…The maximum charging/discharging rate of the units is presented in (9) and (10). The binary variables u and v show the charging/discharging status of the battery.…”

Section: Limitation and Constraintsmentioning

confidence: 99%

“…The model performance is demonstrated on a remotely islanded solar‐diesel‐battery grid. In [10], DRP is joint with battery storage in a combined heat and power system which could mitigate the operation cost by 6.5% and 11.7%, respectively. Moreover, it is shown that the combined model could minimize the energy losses and costs, simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Effective demand response and GANs for optimal constraint unit commitment in solar‐tidal based microgrids

Mobtahej

Esapour

Tajalli

et al. 2021

IET Renewable Power Gen

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A new approach for optimal demand response program (DRP) in the microgrid considering the high penetration of the solar energy and tidal units as significant and popular renewable sources in the system is proposed here. The proposed method makes use of a multi-objective problem (MOP) to not only minimize the total operation cost of the scheduling problem but also mitigate the high risk of the interruption in power delivery due to the components failure rate and long repairing rates. Considering the high complexity and nonlinearity of the formulation, a novel heuristic method based on the firefly algorithm is introduced to solve the problem without any assumption or killing the accuracy. In addition, a dynamic three-phase correction (DPC) formulation is proposed which can help to increase the global search characteristics of the method when boosting the convergence capability of the model. Due to the hard predictability nature of the solar irradiance, a deep learning model based on generative adversarial networks (GAN) is presented to predict the output power of the solar and tidal units properly. The high performance and feasibility of the proposed multi-layer problem are assessed on an IEEE test system.

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“…Terlouw, Tom et al applied electricity storage and heat storage to supply domestic hot water and space heating for home energy system and community energy system. For combined cooling, heating, and power systems, Liu et al confirmed the influence of energy storage and demand response [22]. In addition, hydrogen is increasingly being used as a form of energy storage.…”

Section: Introductionmentioning

confidence: 99%

“…For combined cooling, heating, and power systems, Liu et al. confirmed the influence of energy storage and demand response [22]. In addition, hydrogen is increasingly being used as a form of energy storage.…”

Section: Introductionmentioning

confidence: 99%

Coordination optimization of hydrogen‐based multi‐energy system with multiple storages for industrial park

Liu

Gao

et al. 2022

IET Generation Trans & Dist

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Supply-demand coordination optimization of hydrogen-based multi-energy system provides an effective way to improve the overall energy utilization efficiency and mitigate the challenges of energy and environmental crisis for industrial park. However, the uncertainties of energy supply and demand and the time coupling caused by storage system bring great challenges for energy efficiency and feasibility of strategy. To address the supply-demand coordination problem, a scenario-based operation optimization model of hydrogen-based multi-energy system is formulated. In this model, grey hydrogen, green hydrogen, and renewable energy are coordinated. Furthermore, in order to formulate the hard constraints of the gas storage, the robust constraints with adjustable robust parameters are added to make a trade-off between feasibility and economy of operation strategy. The robust constraints are non-linear, time-coupled, dynamic and infinite dimensional and bring high computational complexity. Thus, the analytical solutions of the worst case are developed under the polyhedral budget constraints to transform the model into a mixed integer linear programming which can be efficiently solved. Finally, numerical test based on a real case is analysed and the results show that supply-demand coordination can reduce the energy cost about 11.29% and energy storage system can significantly improve the flexibility of system.

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Long-term economic planning of combined cooling heating and power systems considering energy storage and demand response

Cited by 57 publications

References 43 publications

Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems

Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems

Effective demand response and GANs for optimal constraint unit commitment in solar‐tidal based microgrids

Coordination optimization of hydrogen‐based multi‐energy system with multiple storages for industrial park

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