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This paper proposes a hybrid energy storage system model adapted to industrial enterprises. The operation of the hybrid energy storage system is optimized during the electricity supply in several scenarios. A bipolar second-order RC battery model, which can accurately respond to the end voltage, (State of charge) SOC, ageing mechanism and other characteristics of the battery, is established. The batteries and the supercapacitor consist of a hybrid energy storage system. The system operation cost and the battery cycle life are investigated. This paper realizes energy scheduling through load prediction technology. The proposed energy scheduling strategy plans the operation of the hybrid energy storage system and reduces the frequency of the battery's charging and discharging. The results show that the proposed prediction model keeps the hybrid energy storage model's overall electric load prediction accuracy up to 97.12%–98.89%. Combining the load prediction technique with the optimal scheduling strategy, the decay of lithium battery capacity of 120kwh to 96.16kwh is better than the decay of battery capacity of 120kwh to 87.32kwh under no scheduling strategy set. The total economic cost per quarter is reduced by $20,000-$35,000.

As the electricity demand of the human’s activities continues to increase, the power grid operation is facing a constantly rising pressure. The capacity configuration of energy storage systems has recently been a popular research issue especially relating to the renewable energy system research. A unqualified capacity configuration leads insufficient power supply to the power grid. Therefore, this paper proposes a capacity allocation of energy storage power plant based on power load predicting technology, combining the grey predicting model, improved BP neural network predicting model and multiple linear Regression predicting models to establish a grey regression neural network predicting model to predict the future power load; constructing a photovoltaic (PV) energy storage power plant capacity configuration model based on the predicting results, and solving the model using multi-objective snake optimization algorithm. The optimal value of PV energy storage plant capacity configuration is obtained using a multi-objective snake optimization algorithm. The results show that the grey regression neural network prediction model proposed in this paper has reliable prediction results and a high accuracy rate, and the correlation coefficient is as high as 99.7% in the regression analysis; the energy storage power plant can share about 150 power loads in the grid during the peak electricity consumption period, which effectively reduces the pressure on the grid operation.

A new structure combined cooling, heating and power (CCHP) system integrating green power applying a control strategy based on a improved whale optimization algorithm is proposed in this paper. This paper fully considers the high energy demand and a large amount of waste heat in chemical enterprises. By absorbing chemical waste heating (mainly from chemical wastewater), coupling wind and photovoltaic equipment, adding cooling, heating, and electricity storage equipment, combined with an energy scheduling strategy, and an efficient CCHP energy system for the target enterprise is realized. The improved whale optimization algorithm is used to optimize cost management. The results show that after the optimization scheduling with the improved whale optimization algorithm, the CCHP system meets the energy demand with higher efficiency at a lower cost. In winter and summer, heat pump recycling chemical wastewater provides 15.4%–57.9% and 11.5%–62.9% of the heating load of the heating network. Meanwhile, the lithium bromide refrigerator in the cooling-net equipment provides an average cooling load of 6.25–58.4% and 7.01–16.64%.​ Finally, the improved whale optimization algorithm reduces the total cost by 2.4 % and 2.17 %.

The CCHP system is a reasonable and effective method to improve the current situation of energy use. Capacity allocation is of great significance in improving the performance of the CCHP system. Due to the particularity of chemical enterprises’ production process, the demand for cooling, heating, and power load is also relatively particular, which makes the dynamic loads challenging to be satisfied. Because of the above problems, the structure of the typical CCHP system is improved, embodied in the collocation of multi-stage lithium bromide chiller, and the use of various energy storage devices. Based on the improved ant lion intelligent optimization (ALO) algorithm, the comprehensive evaluation index coupled with energy benefit, economic benefit, and environmental benefit, is taken as the objective function, and the equipment capacity configuration of the CCHP system for chemical enterprises is studied. Considering winter, summer, and transition seasons, the results show that the system is better than the typical CCHP system. The annual cost savings of the new structural system are up to 13%, and the carbon dioxide emissions of the new structural system are reduced by up to 36.39%. The primary energy utilization rate of the new structure system is increased by 18%, and the comprehensive evaluation index also performs better. The optimal index can reach 0.814.