Incorporating deep learning of load predictions to enhance the optimal active energy management of combined cooling, heating and power system

Abstract The energy management of combined cooling, heating and power (CCHP) system is essential for simultaneously improving its energy and economic performances. However, the conventional operation strategies are mainly logical control, which passively adapts to users’ demands. This paper proposes an optimal economic energy dispatch model of the CCHP system incorporating deep learning of load predictions to fulfill active control strategy in dynamic programming. A cross linear optimization method with a half update strategy of component efficiencies is developed to solve and calculate the variable component efficiencies in the CCHP system. Compared to the genetic algorithm, the proposed method achieves better results and the convergence time is reduced by 93%. The model predictive control of the CCHP system in load predictions of an artificial neural network is combined to the dynamic programming to realize the active energy dispatch strategy. The effects of short-term prediction and long-term prediction on forecast performances and operation costs are discussed. The case study demonstrates that the ideal prediction horizon of 8 h is recommended to fully realize the active functions of energy storage devices in the CCHP system. The proposed active strategy with model predictive control reduces the operational cost by 3.66% compared to the passive control strategy.

[1]  Y. Kwak,et al.  Model predictive control of building energy systems with thermal energy storage in response to occupancy variations and time-variant electricity prices , 2020 .

[2]  Jian Qi Wang,et al.  LSTM based long-term energy consumption prediction with periodicity , 2020 .

[3]  Dongbo Zhao,et al.  Improving operational flexibility of integrated energy system with uncertain renewable generations considering thermal inertia of buildings , 2020, Energy Conversion and Management.

[4]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[5]  Zhe Tian,et al.  Optimization and extraction of an operation strategy for the distributed energy system of a research station in Antarctica , 2020 .

[6]  Amjad Anvari-Moghaddam,et al.  Application of CCHPs in a centralized domestic heating, cooling and power network—Thermodynamic and economic implications , 2020 .

[7]  Lorenzo Bartolucci,et al.  Hybrid renewable energy systems: Influence of short term forecasting on model predictive control performance , 2019, Energy.

[8]  Yi Chen,et al.  Multi-objective optimization of combined cooling, heating and power system integrated with solar and geothermal energies , 2019, Energy Conversion and Management.

[9]  Jiangjiang Wang,et al.  Optimal design of hybrid combined cooling, heating and power systems considering the uncertainties of load demands and renewable energy sources , 2021 .

[10]  M. Chahartaghi,et al.  Energy, exergy, and economic evaluations of a CCHP system by using the internal combustion engines and gas turbine as prime movers , 2018, Energy Conversion and Management.

[11]  Jiang Du,et al.  Model predictive control of commercial buildings in demand response programs in the presence of thermal storage , 2019, Journal of Cleaner Production.

[12]  Lin Shi,et al.  Performance assessment of CCHP systems with different cooling supply modes and operation strategies , 2019, Energy Conversion and Management.

[13]  M. Tazerout,et al.  Multi-objective optimization of CCHP system with hybrid chiller under new electric load following operation strategy , 2020 .

[14]  A. Gracia,et al.  Model predictive control applied to a heating system with PV panels and thermal energy storage , 2020 .

[15]  Yuekuan Zhou,et al.  Machine-learning based hybrid demand-side controller for high-rise office buildings with high energy flexibilities , 2020 .

[16]  Chenghui Zhang,et al.  Stochastic dynamic solution for off-design operation optimization of combined cooling, heating, and power systems with energy storage , 2019 .

[17]  Ran Tian,et al.  An improved operation strategy for CCHP system based on high-speed railways station case study , 2020 .

[18]  M. Chahartaghi,et al.  Technical and economic analyses of a combined cooling, heating and power system based on a hybrid microturbine (solar-gas) for a residential building , 2020 .

[19]  Jussara Farias Fardin,et al.  Management of an island and grid-connected microgrid using hybrid economic model predictive control with weather data , 2020 .

[20]  Mohammad Rizwan,et al.  Energy management supporting high penetration of solar photovoltaic generation for smart grid using solar forecasts and pumped hydro storage system , 2018 .

[21]  Nathan G. Johnson,et al.  Model predictive control of microgrids for real-time ancillary service market participation , 2020 .

[22]  Jack Brouwer,et al.  Neural-network-based optimization for economic dispatch of combined heat and power systems , 2020, Applied Energy.

[23]  Shiwei Yu,et al.  Optimization and evaluation of CCHP systems considering incentive policies under different operation strategies , 2018, Energy.

[24]  Yue Xiang,et al.  Cost-benefit analysis of integrated energy system planning considering demand response , 2020, Energy.

[25]  Jiangjiang Wang,et al.  Life cycle assessment (LCA) optimization of solar-assisted hybrid CCHP system , 2015 .

[26]  David Zumoffen,et al.  Integration of sizing and energy management based on economic predictive control for standalone hybrid renewable energy systems , 2019, Renewable Energy.

[27]  Meng Li,et al.  Energy, exergy, exergoeconomic and environmental (4E) analysis of a distributed generation solar-assisted CCHP (combined cooling, heating and power) gas turbine system , 2019, Energy.

[28]  G. Shu,et al.  Operation performance comparison of CCHP systems with cascade waste heat recovery systems by simulation and operation optimisation , 2020 .

[29]  Guoqiang Zhang,et al.  An off-design model to optimize CCHP-GSHP system considering carbon tax , 2019, Energy Conversion and Management.

[30]  Mary Ann Piette,et al.  Building thermal load prediction through shallow machine learning and deep learning , 2020, Applied Energy.

[31]  Tao Liu,et al.  Multi-objective optimization of a solar hybrid CCHP system based on different operation modes , 2020 .

[32]  Jiangjiang Wang,et al.  Hybrid solar-assisted combined cooling, heating, and power systems: A review , 2020 .

[33]  Xin Yang,et al.  Application of hybrid model based on double decomposition, error correction and deep learning in short-term wind speed prediction , 2020 .

[34]  Jianmin Hou,et al.  Distributed energy systems: Multi-objective optimization and evaluation under different operational strategies , 2021 .