Dispatch Model for CHP With Pipeline and Building Thermal Energy Storage Considering Heat Transfer Process

Dai, Yong; Lei, Chen; Min, Yong; Chen, Qun; Hao, Jianli; Hu, Kang; Xu, Fei

doi:10.1109/tste.2018.2829536

Cited by 136 publications

(59 citation statements)

References 33 publications

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“…C Q is the unit-cost of wind curtailment. P ∆Wind,t is the wind curtailment at time t. Summing up Equations (1), (10), (13), (15), (18), (20), (23), the objective function is expressed as follows:…”

Section: Objective Functionmentioning

confidence: 99%

“…In this dispatch model, the temperature dynamics of the district heating network were considered as energy storage to manage the variability of wind energy. In Reference [13], Dai et al proposed a detailed model for the district heating network, building envelopes, and TES, and taken heat transfer constraints into account. In Reference [14], the conventional thermal units, wind turbines, and CHP with TES were included in an integrated electrical-thermal dispatch model.…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, the values of the parameters in Table 1 and the environmental cost parameters are substituted into Equations (1), (10), (13), (15), (18), (20), and (23). Then, these formulas are summed as the objective function.…”

mentioning

confidence: 99%

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Optimal Dispatch Model Considering Environmental Cost Based on Combined Heat and Power with Thermal Energy Storage and Demand Response

Wang

et al. 2019

Energies

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In order to reduce the pollution caused by coal-fired generating units during the heating season, and promote the wind power accommodation, an electrical and thermal system dispatch model based on combined heat and power (CHP) with thermal energy storage (TES) and demand response (DR) is proposed. In this model, the emission cost of CO2, SO2, NOx, and the operation cost of desulfurization and denitrification units is considered as environmental cost, which will increase the proportion of the fuel cost in an economic dispatch model. Meanwhile, the fuel cost of generating units, the operation cost and investment cost of thermal energy storage and electrical energy storage, the incentive cost of DR, and the cost of wind curtailment are comprehensively considered in this dispatch model. Then, on the promise of satisfying the load demand, taking the minimum total cost as an objective function, the power of each unit is optimized by a genetic algorithm. Compared with the traditional dispatch model, in which the environmental cost is not considered, the numerical results show that the daily average emissions CO2, SO2, NOx, are decreased by 14,354.35 kg, 55.5 kg, and 47.15 kg, respectively, and the wind power accommodation is increased by an average of 6.56% in a week.

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Section: Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Dispatch Model Considering Environmental Cost Based on Combined Heat and Power with Thermal Energy Storage and Demand Response

Wang

et al. 2019

Energies

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“…m tt T supply,max − T supply,min tt (11) However, this formulation is only an upper bound of the maximum of the heat energy stored within the grid as only the last and not all consumers are feed with delay τ max (Figure 1b). Hence, the presented Equations (8)-(11) leads to an outer approximation of the storage capabilities of a heating grid.…”

Section: Outer Approximation Of Heating Grid Dynamics Using State-of-mentioning

confidence: 99%

“…Li et al considers the building thermal inertia in addition to pipeline dynamics with fixed mass flows [10]. Dai et al solve the same problem as Li considering pipeline dynamics with fixed mass flows and building thermal inertia in an iterative optimization scheme [11]. As well considering thermal inertia of buildings and heating grid dynamics [12] proposes an optimization model with fixed transport delays calculated based on design flows which allows to use off-the-shelf solvers.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Combined Heat and Power Generation Units Using the Thermal Inertia of the Connected District Heating Grid as Energy Storage

Merkert¹,

Haime²,

Hohmann

2019

Energies

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A better integration across sectors is an essential element of 4th generation district heating and smart energy systems allowing to react to volatile renewable energy generation. This sector coupling enables to use more cost-efficient storage as storage prices differ for different forms of energy. Thermal energy for example can be stored in comparably cheap storage tanks. Besides such dedicated storage, the thermal inertia of a heating grid can be used as thermal storage as well. In this paper, a classic unit commitment optimization for scheduling of combined heat and power units not considering grid dynamics is extended to cover thermal dynamics of heating grids. First an outer approximation of the grid storage capabilities is developed. Second, a very efficient formulation for the storage dynamics of a heating grid is introduced and its capabilities are shown in a motivating case study. In this study additional savings of several thousand Euros per day are achieved using the thermal inertia of a heating grid as storage.

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Sizing of storage‐based renewable off‐grid system according to supply of electrical and thermal energies considering unscented transformation‐based stochastic optimization: A case study

Bahmani,

Kiani,

Nejatian

et al. 2024

Optim Control Appl Methods

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The paper presents the planning (sizing) of a hybrid islanded system containing only renewable sources including wind turbines, photovoltaics, and bio‐waste energy units for the simultaneous supply of electrical and thermal energy. The mentioned renewable sources are used to supply electrical energy. The bio‐waste unit (BEU) is equipped with combined electrical and thermal technology. It is used along with heat pumps to supply thermal energy. Electrical and thermal storage are employed to make the renewable power output as close as possible to the demand level. Electric storage can be either stationary (e.g., battery) or mobile (e.g., electric vehicles), but thermal storage is as stationary type of storage. In the following, the proposed scheme minimizes construction and maintenance costs imposed by power sources, storage devices, and power electronic converters, and expected storage degradation cost. This plan is bound to the model of operation of sources, storage devices, and power electronic converters. In this model, renewables are the main source of power to supply consumers, where storage is adopted to increase the generation level to make it as close as possible to the demand curve. The uncertain parameters are imposed by the load, renewable phenomena, and mobile storage parameters. To model these uncertainties, stochastic optimization based on unscented transformation is used. Finally, the findings of the paper demonstrate that the suggested scheme succeeds in the economic planning of the system with a simultaneous supply of electrical and thermal energy. The BEU equipped with a combined electricity and heat system along with thermal storage and a heat pump to supply thermal load besides providing electric energy can reduce the planning cost by 2.9% compared with the case with only electrical energy. Also, the mobile storage presence in the hybrid system can reduce the number of stationary storage devices, which alone results in a 7.7% decrease in the planning cost of the hybrid system.

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Dispatch Model for CHP With Pipeline and Building Thermal Energy Storage Considering Heat Transfer Process

Cited by 136 publications

References 33 publications

Optimal Dispatch Model Considering Environmental Cost Based on Combined Heat and Power with Thermal Energy Storage and Demand Response

Optimal Dispatch Model Considering Environmental Cost Based on Combined Heat and Power with Thermal Energy Storage and Demand Response

Optimal Scheduling of Combined Heat and Power Generation Units Using the Thermal Inertia of the Connected District Heating Grid as Energy Storage

Sizing of storage‐based renewable off‐grid system according to supply of electrical and thermal energies considering unscented transformation‐based stochastic optimization: A case study

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