Optimal Scheduling Approach for a Combined Cooling, Heating and Power Building Microgrid Considering Virtual Storage System Xiaolong Jin, Student Member, IEEE, Xudong Wang, Yunfei Mu, Member, IEEE, Hongjie Jia, Member, IEEE, Xiandong Xu, Member, IEEE, Yan Qi, Xiaodan Yu, Member, IEEE, Fengyu Qi Heat transfer coefficient of wall/ window in the building. Heat transfer coefficient of walUwindow in the building. Outdoor/ Indoor temperature.
Abstract-In this paper, a building based virtual storage system (VSS)
model
is
developed
by
utilizing
the
heat
storage
characteristics of the building. Then, the VSS is integrated to the optimal scheduling model of the building microgrid
(BM)
for
operation cost reduction. The indoor temperature of the building is adjusted within the customer temperature comfort level range to manage the charging/discharging power of the VSS. Finally, two different types of
BM
cases under the summer refrigeration
scenario are carried out to demonstrate the effectiveness of the proposed
optimal
scheduling
approach.
Numerical
studies
II SC
Solar radiation/ Shading coefficient.
Qin
Internal heat gain caused by metabolism and electric appliances in the building. The density, specific heat capacity and volume of the air in the building. Efficiency of the microturbine/ heat recovery system. Thermoelectric ratio of the microturbine.
p,C,V
demonstrate that the proposed optimal scheduling approach can make full use of the potential of the VSS and furthermore contribute to the operation cost reduction of the
BM,
while
guarantee the customer temperature comfort level at the same time.
Coefficient of performance of absorption chiller/ electric chiller. Charging/ discharging efficiency of battery storage system The initial storage power of battery storage system Customer sensitivity coefficient.
Index Terms-Combined cooling, heating and power building
microgrid
(BM);
heat storage characteristics of building; virtual
storage system (VSS); optimal scheduling
NOMENCLATURE
Abbreviations: Building microgrid. BM Virtual storage system. VSS CCHP Combined cooling, heating and power. Parameters and constants: Real-time electricity purchasing! selling Cph/Cse prices. Natural gas price. Cgas Electric load of BM. Pel Electric power generated by photovoltaic/ PPV/PWT wind turbine. This work was financially supported by the National High Technology R&D Program (863) of China (Grant no. 2015AA050403), the project of National Natural Science Foundation of China (Grant Nos. 51307115, 51377117. and 51277128, and the project of "research on forecast and coordination control technique of smart grid park " of State Grid Tianjin Electric Power Company. X. lin. Y. Mu, H. Jia, X. Yu and F. Qi are with Key Laboratory of Smart Grid of Ministry of Education,Tianjin University,Tianjin,300072. P. R. China
[email protected].
[email protected].
[email protected]. (e-mail:
[email protected] and
[email protected]). X. Xu is with School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast BT9 5AH, United Kingdom (e-mail:
[email protected]). X. Wang and Y. Qi are with Tianjin Electric Power Research Institute, Tianjin,300384,China (
[email protected]). Corresponding author: Y. Mu,
[email protected].
y Variables: PeJPgas PMT/PEC
Pbt QAclQEC
Q,1,building / Q:I,bUilding
Electric power exchange with the external grid/ Natural gas purchase. Electric power generated/ consumed by microturbine/ electric chiller. Charging and discharging power of the battery storage system. cooling power generated by the absorption chiller/ electric chiller. Cooling demand of the building with/ without VSS. I.
ITH
the
INTRODUCTION
continuous
development
technologies
in
Wrenewable and distributed power generations, various
local distributed energy systems have been employed in buildings, which provides safe, secure, sustainable and affordable energy consumption solutions for buildings [1]. An integrated energy system interconnecting loads, distributed energy resources and dispatchable distributed generators in buildings can be considered as a building microgrid (BM) [2].
2 The microgrid technologies provide an opportunity for the collaborative optimization and management of various energy systems in BM, which contributes to operation cost reduction [3]. Several studies have been carried out to investigate the optimal scheduling for combined cooling, heating and power (CCHP) microgrid. An optimal scheduling model was designed for a low energy building containing CCHP to minimize the overall cost of electricity and natural gas for the building operation over a time horizon in [3]. A hierarchical energy management system was designed in [4] for the multi-energy CCHP based microgrid. An appliance scheduling scheme for residential building energy management controllers is proposed in [5] by taking advantage of the time-varying retail pricing enabled by the two-way communication infrastructure of the smart grid. A model predictive control (MPC) based strategy using nonlinear programming (NLP) algorithm is proposed to optimize the scheduling of the BM under day-ahead electricity pricing in [6]. The mixed-integer nonlinear programming approach is used to solve their optimal scheduling problem of various energy systems in building integrated with energy generation and thermal energy storage in [7]. The controllable loads at demand side, such as the refrigerators, freezers, air conditioners, water heaters, heat pumps and electric vehicles, can change their normal power consumption patterns for active participating in the auxiliary service of power system due to their fast disconnection, energy storage and controllable characteristics [8]-[12]. For the building, the heating! cooling energy exchange between indoor and outdoor is very slow due to the heat storage characteristics of building. Therefore, heating! refrigeration equipment can be adjusted to start in advance or increase power consumption when the electricity price is low. In the same way, heating! refrigeration equipment can be adjusted to shut in advance or reduce power consumption when the electricity price is high. Since the heating! cooling demand is adjusted within the customer temperature comfort level range, the building is modeled as a virtual storage system (VSS) to participate in the optimal economic scheduling of BM for operation cost reduction, which fulfils the potential of the demand response of the building. In this paper, a building based VSS model is developed utilizing the heat storage characteristics of the building. Then, the VSS model is integrated to the optimal scheduling model of the BM for operation cost reduction. The indoor temperature of the building is adjusted within the customer temperature comfort level range to manage the charging! discharging power of the VSS. The proposed optimal scheduling approach can make full use of the potential of the VSS and furthermore contribute to the operation cost reduction of the BM. II.
SYSTEM STRUCTURE AND MATHEMATICAL MODEL OF THE BUILDING MrCROGRID
A. Structure of Typical Building Microgrid Two different types of BM are shown in the Fig.I. 1) The fIrst type of BM includes wind turbine, photovoltaic panel, battery storage system and electric chiller. The electric
chiller undertakes all the cooling demand of the building. The first type of BM consumes electric power to satisfy cooling demand. 2) The second type of BM includes wind turbine, photovoltaic panel, microturbine, battery storage system and absorption chiller. The microturbine and absorption chiller compose the CCHP, which undertakes all the cooling demand of the building. The second type of BM consumes natural gas to provide cooling demand and generate electric power at the same time. J�x
.
Ballery storage system PhI PWT'-'
External grid....... Wind turbine
--...
I�,v (n) External
PEe
Electric chiller
%
I�l
(,?�£
........
Electric chiller based Building microgrid
P",
grid�
PWT
.-...
PV:.:r�__ .�pp -, v �
�. p.
Battery SIOragC system
Absorplio chiller
1MT X �\1T Microturbinc (b)
CCHP
based Building microgrid
Fig. 1. Schematic diagram of typical BM.
B. System Modeling 1) Microturbine The power generation of the microturbine is shown as Eq. (1). (1)
PMT;=�asX17MT
2) Absorption Chiller The absorption chiller is driven by the recovered heat from the microturbine. The cooling power generated by the absorption chiller is depended on the exhaust heat output from the microturbine, as shown in Eq. (2).
QAC;=17�IEXI'MTXPMTJxCO�C
(2)
3) Electric Chiller The electricity consumption of the electric chiller is determined by the cooling demand and the coefficient of performance, as shown in Eq. (3).
(k; =PEC;XCO PEC
(3)
4) Building based VSS The VSS model is developed by utilizing the building thermal equilibrium equation [13] (shown in Eq. (4».
dE �Q=pxCxVx---1!!.
(4) dr Considering the summer refrigeration scenario, the Eq. (4) is expressed as Eq. (5):
(
k_ xF_ x Too,
- 'fu,) +kmnxFm" X( Tool -'fu,)
+lxF�inxSC+Qn-Q,l=pxCxVx The first term in Eq. (5) (kw•1l x FW'1l x (7;,01
-
ar
;
(5)
d
T.n)
represents the
heat exchanged between exterior walls and outdoor; (TOlle Tin) is the temperature difference between indoor and outdoor; the second term ( kwin X Fwin X (7;,01
-
T.n)
) represents the heat
3 t
exchanged between external window of the building and outdoor; the third term
( I X Fwin X SC
QvSS,( ={2;1,buildingJ - QI,building,( III. A.
(6)
FORMULATJON OF THE OPTIMAL SCHEDULING MODEL
Objective Function
The objective function depicted in Eq. (7) is to minimize the total operation cost for the BM. min � L...
t=l
{(
Wbt
) represents the heat gain
from the solar radiant heat; Qin is the internal heat gain caused by metabolism and electric appliances in the building; QcI is the cooling power generated by the refrigeration equipment. All the above terms determine the indoor heat variations under the summer refrigeration scenario. The VSS model is developed by utilizing the building thermal equilibrium equation. The mathematical relationship between the indoor temperature and cooling power output of refrigeration equipment is obtained based on Eq. (5). Due to the heat storage characteristics of building, the cooling demand (equal to cooling power output of refrigeration equipment) or indoor temperature is adjusted to respond to the optimal scheduling of BM, while guarantee the customer comfort level (takes the indoor temperature regulation range into consideration) at the same time. Therefore, the building is modeled as a VSS to participate in the optimal scheduling of the BM for operation cost reduction. The charging/discharging power of the building based VSS is given by the Eq. (6).
CPh,t +Cse,t p Cph,t -Cse,t p I eX,t I 2 eX,t + 2
+( Pwr,tCwr_om +PpV,tCpV_om
J
< Wbt,t =Wbt(O) - 2lbt/lbt& < Wbt
17 'rx
=
{
(12)
i=l
17ch
lbt,i
lI17dis
Pbt,i
:0::0
(13)
>0
(14)
t=l 6) Indoor temperature constraint:
-
lin
