Abstract

With the rapid growth of energy consumption, how to utilize energy in an efficient and cheap way becomes an intensive problem. This paper proposes an optimal operation strategy to reduce system fuel costs and increase system stability by independently considering cooling loads and adjusting CHP heat to power ratio seasonally. In this paper, a mathematical model of CHP operation is introduced to reveal the relationship between the supplementary volume of diesel oil and CHP heat to power ratio. Meanwhile, by analyzing the influence of seasonal factor on energy consumption, CHP heat to power ratio is optimized seasonally. Then, by independently considering the impacts of the cooling loads on system operation, the particle swarm optimization (PSO) algorithm is used to optimize the operation strategy of each device. Finally, this paper validates the positive effects of storage devices on improving system economy and stability under the premise of the time-of-use gas price. Results show that system fuel costs can be reduced by 5.2% if the seasonal factor is considered. Additionally, by optimizing the operation strategy, the peak valley gap of electrical loads in summer reduces by 40.7%. Moreover, the proposed strategy successfully utilizes storage capacity to shift loads and respond to gas price.

Highlights

  • The rapid technical improvement of renewable generation and diversified energy consumption patterns have promoted the integration of energy systems; at the same time, the clearer concepts of Energy Hub and integrated energy system have further accelerated the pace of energy reform [1,2]

  • The results showed that the average daily fuel costs could be reduced by 5.2% if the CHP heat to power ratio was optimized seasonally

  • Taking the seasonal factor into consideration when optimizing the CHP heat to power ratio, the average system fuel costs can be reduced by 5.2% and the CHP heat to power ratio will match up to the system heat to power ratio

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Summary

Introduction

The rapid technical improvement of renewable generation and diversified energy consumption patterns have promoted the integration of energy systems; at the same time, the clearer concepts of Energy Hub and integrated energy system have further accelerated the pace of energy reform [1,2]. The MILP method was proposed in Reference [12] to assess and compare the technical and economic feasibility of domestic energy storage and community energy storage so as to improve the self-consumption of photovoltaic generation and reduce the imbalance between supply and demands. Under battery–exchange operating mode, the probabilistic model of available energy in batteries of a vehicle-to-grid station was proposed in Reference [14] to enhance the reliability performance of distribution systems These studies have made great contributions to reducing energy consumption and improving system reliability; all of these papers ignore the installation costs of energy storage systems and treat the gas price as a constant. (2) Compared to the existing literature, system cooling loads were independently consideredResults rather showed that as thea peak–valley gaporofelectrical the power system could be significantly especially than treated part of thermal loads when optimizing a systemreduced, operation

Results in summer
Energy
Different Energy Carriers’ Output Power Modeling and Operation Constraints
CHP Output Power and Operation Constraints Modeling
Diesel Generator Output Power and Operation Constraints Modeling
Gas Boiler Output Power and Operation Constraints Modeling
Ice Storage System Output Power and Operation Constraints Modeling
Hot Water Tank Output Power and Operation Constraints Modeling
Objective Function
Optimization Algorithm
Case Study
MW and This its rated electrical efficiency was
MWtank and is 4 relatively
Seasonal
Optimization Results of CHP Heat to Power Ratio
Simulation results of the between
The Influence of Cooling Loads on System Operation
Optimal output two storagedevices devices and and average load curve of the
3.14 MW anddiesel
System
10. Optimal
Conclusions

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