Optimal design of hybrid combined cooling, heating and power systems considering the uncertainties of load demands and renewable energy sources

Wang, Jiangjiang; Qi, Xiaoling; Ren, Fukang; Zhang, Guoqing; Wang, Jiahao

doi:10.1016/j.jclepro.2020.125357

Cited by 78 publications

(24 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The energy hub model is represented in Figure 1 35

([], \begin{array}{l} l_{E} ((), T) \\ l_{H} ((), T) \\ l_{C} ((), T) \\ l_{HW} ((), T) \end{array}) goodbreak= c ([], \begin{array}{l} e_{Grid}^{CCHP} ((), T) \\ q_{Sol} ((), T) \\ f_{Gas} ((), T) \\ q_{Geo} ((), T) \end{array}) goodbreak- s ([], \begin{array}{l} e_{E} ((), T) \\ q_{H} ((), T) \\ q_{C} ((), T) \\ q_{HW} ((), T) \end{array}), \forall T \in t

Let, L , E , Q , and F represent weight, power, warmth, and fuel (kW); e , h , c , hw, grid, sol, gasoline, geo constitute home warm water, country wide grid, sun electricity, and geothermal energy. E _ and Q _ constitute interior garage power and batteries warmth strength including water tank (kWh).…”

Section: Modeling Of Cchp Systemmentioning

confidence: 99%

“…For producing power the natural gas, Fgas (kW), drives both GT are utilized and producing heat AC/ H is used. The GT power production, Egt (kW) is shown as,

e_{GT} ((), T) goodbreak= V η_{GT}^{E} f_{Gas} ((), T), \forall T \in t

Recovering waste heat as exhausting gas, Qgt (kW) is expressed as 35 …”

Section: Modeling Of Cchp Systemmentioning

confidence: 99%

“…The hourly power,

e_{PV / t} ((), kW)

, heat,

q_{PV / t} ((), kW)

, produced with CPC photovoltaic/T may be prepared panel area, efficiency of power, thermal and solar irradiance as 35

e_{PV / t} ((), T) goodbreak= η_{PV / t}^{E} i Sol ((), T) a_{PV / t}, \forall T \in t

q_{PV / t} ((), T) goodbreak= η_{PV / t}^{H} i Sol ((), T) a_{PV / t}, \forall T \in t

Based on the CPC PV/T collectors, the regression model of EPV/T and QPV/T is 35

e_{PV / t} ((), T) goodbreak= η_{PV / t}^{E} i Sol ((), T) a_{PV / t}, \forall T \in t

\begin{matrix} lefttrue \\ q_{PV / t} (T) = [- 0.0253 r_{PV / t} + 0.0069 i_{Sol} (T) - 0.0938] \\ C_{PV} / t η_{CPV / t} a_{PV / t}, \forall T \in t, \forall T \in t \end{matrix}

here,

η_{S . PV / t} (() %)

implies power performance of PV/T at standard condition (Isol ¼ 1000 W/m 2 along atmosphere temperature implies 25°C), hc; PV = T (%) implies performance of CPC reflector, RPV = T implies photovoltaic area ratio under the roof of photovoltaic/T.…”

Section: Modeling Of Cchp Systemmentioning

confidence: 99%

“…The thermal energy output, Qh, hp (kW), or cold energy, Qc, hp (kW), of GSHP are demonstrated with electric and geothermal energies basically shown 35

q_{HP} ((), T) goodbreak= F ((),, e_{HP} ((), T), q_{GEO} ((), T)) goodbreak= {cop}_{HP} e_{HP} ((), T), \forall T \in t

where, COP hp implies COP of GSHP. The GSHP regression model through partial load ratio is approximated in soil heat exchanger 100 m size.

\begin{matrix} lefttrue \\ {cop}_{HP} = 2.852525 + 2.868282 pl - 0.017015 Δ t_{SOSI} \\ - 0.037951 pl Δ t_{SOSI}, \forall T \in t \end{matrix}

The GSHP is operating under the mode of heating, the source water leaving temperature ready to 15 C along with 35 to 60 C sump water temperature range about 40 C. Assuming balances of electricity in the force center, the power of GT, photovoltaic energy/T, and the grid are employed to meet the needs.…”

Section: Modeling Of Cchp Systemmentioning

confidence: 99%

“…Stochastic optimization processes are shown in Figure 2, with sun energy and user masses. The uncertainty parameters, solar irradiance and hundreds, are considered to fit regular distribution observed, 35

pdf ((),,, X, μ, σ) goodbreak= \frac{1}{σ \sqrt{2 π}} EXP ((), goodbreak- \frac{{((), X goodbreak- μ)}^{2}}{2 σ^{2}})

…”

Section: Modeling Of Cchp Systemmentioning

confidence: 99%

See 4 more Smart Citations

Multiobjective optimization model for renewable energy sources and load demands uncertainty consideration for optimal design of hybrid combined cooling, heating, and power systems

Elango¹,

Arumugam²,

Umasankar³

2022

Intl J of Energy Research

View full text Add to dashboard Cite

Summary This article proposes a multiobjective optimization model for renewable energy sources (RESs) and load demands uncertainty consideration for optimal design of hybrid combined cooling, heating, and power systems (CCHP). The hybrid CCHP system contains turbine, photovoltaic/thermal collectors, cooler/heater, supply setup, battery, and tank storage. The proposed hybrid method is the joint implementation of Garra Rufa Fish Optimization (GRFO) and Student Psychology Optimization Algorithm (SPOA); hence, it is named GRFO‐SPOA approach. An energy converters energy‐hub model along storage devices considers the properties of component of off‐design. The uncertainty of solar radiation together with building loads is exhibited at parametric manner and probability distributions. Assuming the uncertainty with system reliability and hybrid CCHP is enhanced to attain the feasible energetic, economic, and environmental benefit utilizing the GRFO‐SPOA method. To predict a new set, the GRFO‐SPOA method utilizes current datasets in the uncertainty modeling. The decision variables involves capacity of gas turbine and photovoltaic/thermal collectors, capacity of battery with water storing tank, and operational ratio of heat pump. The proposed method is implemented in MATLAB/Simulink; its efficiency is analyzed with other existing methods, like GA, SSA, and TSA technique. Once the confidence level of the system diminishes as of 0.99 to 0.50, the hybrid CCHP likened with traditional separate production system saves on average 13.7% of main energy and lessens 80% of acid gas emissions carbonic. The annual value saving rate is decreased because the confidence level of the system decreases and the uncertainty maximizes. The sensitivity analysis of the economist frontiers is executed on key economic parameters; therefore, it is obtained as an outcome of annual value saving rate is very sensitive to the value of fossil fuels, and the value of inversion of the star collectors has a stronger impact than that of turbine. The fist‐order statistical evaluation parameters, like mean, median, and SD, at 100 iterations for proposed technique is 0.61038, 0.5317, and 0.00543. Computation time utilizing 100, 150, 200, 250, and 500 trails of proposed technique is 48.1740 seconds, 51.2133 seconds, 71.0483 seconds, 60.00126 seconds, and 57.80132 seconds.

show abstract

“…The energy hub model is represented in Figure 1 35

([], \begin{array}{l} l_{E} ((), T) \\ l_{H} ((), T) \\ l_{C} ((), T) \\ l_{HW} ((), T) \end{array}) goodbreak= c ([], \begin{array}{l} e_{Grid}^{CCHP} ((), T) \\ q_{Sol} ((), T) \\ f_{Gas} ((), T) \\ q_{Geo} ((), T) \end{array}) goodbreak- s ([], \begin{array}{l} e_{E} ((), T) \\ q_{H} ((), T) \\ q_{C} ((), T) \\ q_{HW} ((), T) \end{array}), \forall T \in t

Section: Modeling Of Cchp Systemmentioning

confidence: 99%

“…For producing power the natural gas, Fgas (kW), drives both GT are utilized and producing heat AC/ H is used. The GT power production, Egt (kW) is shown as,

e_{GT} ((), T) goodbreak= V η_{GT}^{E} f_{Gas} ((), T), \forall T \in t

Recovering waste heat as exhausting gas, Qgt (kW) is expressed as 35 …”

Section: Modeling Of Cchp Systemmentioning

confidence: 99%

“…The hourly power,

e_{PV / t} ((), kW)

, heat,

q_{PV / t} ((), kW)

, produced with CPC photovoltaic/T may be prepared panel area, efficiency of power, thermal and solar irradiance as 35

e_{PV / t} ((), T) goodbreak= η_{PV / t}^{E} i Sol ((), T) a_{PV / t}, \forall T \in t

q_{PV / t} ((), T) goodbreak= η_{PV / t}^{H} i Sol ((), T) a_{PV / t}, \forall T \in t

Based on the CPC PV/T collectors, the regression model of EPV/T and QPV/T is 35

e_{PV / t} ((), T) goodbreak= η_{PV / t}^{E} i Sol ((), T) a_{PV / t}, \forall T \in t

\begin{matrix} lefttrue \\ q_{PV / t} (T) = [- 0.0253 r_{PV / t} + 0.0069 i_{Sol} (T) - 0.0938] \\ C_{PV} / t η_{CPV / t} a_{PV / t}, \forall T \in t, \forall T \in t \end{matrix}

here,

η_{S . PV / t} (() %)

Section: Modeling Of Cchp Systemmentioning

confidence: 99%

“…The thermal energy output, Qh, hp (kW), or cold energy, Qc, hp (kW), of GSHP are demonstrated with electric and geothermal energies basically shown 35

q_{HP} ((), T) goodbreak= F ((),, e_{HP} ((), T), q_{GEO} ((), T)) goodbreak= {cop}_{HP} e_{HP} ((), T), \forall T \in t

where, COP hp implies COP of GSHP. The GSHP regression model through partial load ratio is approximated in soil heat exchanger 100 m size.

\begin{matrix} lefttrue \\ {cop}_{HP} = 2.852525 + 2.868282 pl - 0.017015 Δ t_{SOSI} \\ - 0.037951 pl Δ t_{SOSI}, \forall T \in t \end{matrix}

Section: Modeling Of Cchp Systemmentioning

confidence: 99%

pdf ((),,, X, μ, σ) goodbreak= \frac{1}{σ \sqrt{2 π}} EXP ((), goodbreak- \frac{{((), X goodbreak- μ)}^{2}}{2 σ^{2}})

…”

Section: Modeling Of Cchp Systemmentioning

confidence: 99%

See 3 more Smart Citations

Multiobjective optimization model for renewable energy sources and load demands uncertainty consideration for optimal design of hybrid combined cooling, heating, and power systems

Elango¹,

Arumugam²,

Umasankar³

2022

Intl J of Energy Research

View full text Add to dashboard Cite

show abstract

Reduction of greenhouse gas emissions via flexible operation of cross‐sector integrated energy systems under uncertainties in demand, fuel prices, and solar irradiation

Mahalec

2023

Can J Chem Eng

View full text Add to dashboard Cite

This work investigates how the flexible operation of the light industrial plants integrated in a cross-sector energy cluster with community energy systems can achieve further greenhouse gas (GHG) reductions under uncertainties associated with natural gas prices, solar irradiation, as well as heating, cooling, and electricity demand. The optimal flexible operation and design of a cross-sector integrated cluster comprising a bakery plant, a brewery, a confectionery plant, a residential building, and a supermarket under uncertainties are compared to the operation and design of systems without uncertainties. When uncertainties are considered, the overall GHG emissions of the integrated system with steady industrial production rates for all uncertainty scenarios are over 4% higher than the integrated system in the deterministic scenario (a single scenario).Flexible operation of the industrial plants, whereby production rates are varied throughout the day, contributes an additional 3% reduction in GHG emissions under uncertainties, where the GHG emissions are only 1% higher than the deterministic scenario. Additionally, the system with flexible production rates purchases over 14.3% less electricity from the grid and uses over 72.2% less natural gas for operating the backup boiler, which relies less on supplementary energy resources. This shows that optimally designed integrated systems with flexible industry production schedules are resilient to uncertainties in energy demands, daily weather fluctuations, and fuel prices.

show abstract

Operation Performance Analysis of a Novel Renewable Energy‐Driven Multienergy Supply System Based on Wind, Photovoltaic, Concentrating Solar Power, Proton Exchange Membrane Electrolyzers, and Proton Exchange Membrane Fuel Cell

Liu

Zhai

et al. 2023

Energy Tech

View full text Add to dashboard Cite

In pursuing clean energy systems and carbon neutrality, the efficient use of renewable energy is key. This research introduces a novel wind–solar–hydrogen multienergy supply (WSH‐MES) system, powered by renewables, designed to stabilize power output through regulated concentrating solar power (CSP) and proton exchange membrane electrolyzers (PEMEs). An active regulation strategy for thermal and hydrogen storage is developed to counterbalance the volatility of wind and photovoltaic power. Key results reveal that the WSH‐MES system surpasses WP–PV and WP–PV–CSP systems in reliability, reducing power supply loss probability to 2.81%. This improvement is largely due to the controlled CSP and PEME, which effectively mitigates the stochastic fluctuations in wind and solar power generation, thereby ensuring more stable and reliable power output. Moreover, the system achieves an 80.97% primary renewable energy rate and a 0.12 power discard rate. In conclusion, this research comprehensively examines the operational characteristics of the WSH‐MES system, elucidates the practical advantages of the WSH‐MES system in terms of reliability, renewable energy utilization, and adaptability under various conditions. The results offer valuable insights into the design and operation of multienergy supply systems, potentially fostering the large‐scale implementation of renewable energy systems and leading toward a sustainable energy future.

show abstract

Optimal design of hybrid combined cooling, heating and power systems considering the uncertainties of load demands and renewable energy sources

Cited by 78 publications

References 32 publications

Multiobjective optimization model for renewable energy sources and load demands uncertainty consideration for optimal design of hybrid combined cooling, heating, and power systems

Multiobjective optimization model for renewable energy sources and load demands uncertainty consideration for optimal design of hybrid combined cooling, heating, and power systems

Reduction of greenhouse gas emissions via flexible operation of cross‐sector integrated energy systems under uncertainties in demand, fuel prices, and solar irradiation

Operation Performance Analysis of a Novel Renewable Energy‐Driven Multienergy Supply System Based on Wind, Photovoltaic, Concentrating Solar Power, Proton Exchange Membrane Electrolyzers, and Proton Exchange Membrane Fuel Cell

Contact Info

Product

Resources

About