Multi-Scenario Physical Energy Storage Planning of Integrated Energy Systems Considering Dynamic Characteristics of Heating Network and Demand Response

Xin Deng,Qiang Gao,Zhenzhi Lin,Yuge Chen,Yixin Huang,Wenhao Hou,Li Yang,Xin Chen,Changming Chen

doi:10.3389/fenrg.2021.783588

Abstract

The configuration of energy storage in the integrated energy system (IES) can effectively improve the consumption rate of renewable energy and the flexibility of system operation. Due to the high cost and long cycle of the physical energy storage construction, the configuration of energy storage is limited. The dynamic characteristics of the heating network and the demand-side response (DR) can realize the space-time transfer of energy. Although there is no actual energy storage equipment construction, it plays a similar role to physical energy storage and can be considered as virtual energy storage in IES planning. In this paper, a multi-scenario physical energy storage planning model of IES considering the dynamic characteristics of the heating network and DR is proposed. To make full use of the energy storage potential of the proposed model, the virtual energy storage features of the dynamic heating characteristics of the heating network and DR are analyzed at first. Next, aiming at the uncertainty of wind turbine (WT) and photovoltaic (PV) output, the scenario analysis method is used to describe the wind and photovoltaic power output with different probabilities. Finally, an electrothermal IES with an IEEE 33-node network and a 26-node heating network serves as an example to verify the effectiveness of the proposed model. The case study shows that the proposed model effectively reduces the physical energy storage configuration and achieves the economic trade-off between the investment cost and the operation cost.

Highlights

With the increasing concerns on energy consumption and environmental protection, how to improve energy efficiency is becoming one of the most critical and pressing issues around the globe (Aluisio et al, 2017)
Where PCn,Hs,iP,t, PCn,Hs,iP,t,0, HCn,Hs,iP,t, Ppn,us,ri,t, PPn,Vs,im,t ax, PPn,Vs,i,t, PWn,sT,i,mt ax, and PWn,sT,i,t are the power output of combined heat and power units (CHPs) units, the planning electricity output, the thermal output, the power purchasing, the maximum output of photovoltaic power, the actual output of photovoltaic power, the maximum output of wind power, and the actual output of wind power, respectively; cpen is the punitive price for temporary adjustment of unit output during actual operation, which reflects the degree of CHP units to output as planned; cpt urn is the unit electricity purchasing price of time t; cPV and cWT are the unit costs of wind and photovoltaic abandonment, respectively;a0 ∼ a5 are the coal fee coefficients of CHP units; ps is the probability of typical wind and photovoltaic scenario s
2) The response characteristics of cooling, heating, and power demand in different regions are different, and the load has the characteristics of space-time complementarity, which can be further studied around the coordination and optimization between regions

Summary

INTRODUCTION

With the increasing concerns on energy consumption and environmental protection, how to improve energy efficiency is becoming one of the most critical and pressing issues around the globe (Aluisio et al, 2017). A multi-scenario physical energy storage planning model of IES considering the dynamic characteristics of heating networks and DR is proposed. The water temperature characteristics refer to the coupling relationship between time and the inlet and outlet temperatures of hot water in the same pipe and are the key to describe the virtual energy storage of the heat-supply pipeline network (Chen et al, 2021). MULTI-SCENARIO PLANNING MODEL OF THE INTEGRATED ENERGY SYSTEM CONSIDERING DIVERSE VIRTUAL ENERGY STORAGE where PCn,Hs,iP,t, PCn,Hs,iP,t,0, HCn,Hs,iP,t, Ppn,us,ri,t, PPn,Vs,im,t ax, PPn,Vs,i,t, PWn,sT,i,mt ax, and PWn,sT,i,t are the power output of CHP units, the planning electricity output, the thermal output, the power purchasing, the maximum output of photovoltaic power, the actual output of photovoltaic power, the maximum output of wind power, and the actual output of wind power, respectively; cpen is the punitive price for temporary adjustment of unit output during actual operation, which reflects the degree of CHP units to output as planned; cpt urn is the unit electricity purchasing price of time t; cPV and cWT are the unit costs of wind and photovoltaic abandonment, respectively;a0 ∼ a5 are the coal fee coefficients of CHP units; ps is the probability of typical wind and photovoltaic scenario s

Objective

Findings

CONCLUSION