# Optimal dispatch of zero-carbon-emission micro Energy Internet integrated with non-supplementary fired compressed air energy storage system

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## Abstract

To utilize heat and electricity in a clean and integrated manner, a zero-carbon-emission micro Energy Internet (ZCE-MEI) architecture is proposed by incorporating non-supplementary fired compressed air energy storage (NSF-CAES) hub. A typical ZCE-MEI combining power distribution network (PDN) and district heating network (DHN) with NSF-CAES is considered in this paper. NSF-CAES hub is formulated to take the thermal dynamic and pressure behavior into account to enhance dispatch flexibility. A modified DistFlow model is utilized to allow several discrete and continuous reactive power compensators to maintain voltage quality of PDN. Optimal operation of the ZCE-MEI is firstly modeled as a mixed integer nonlinear programming (MINLP). Several transformations and simplifications are taken to convert the problem as a mixed integer linear programming (MILP) which can be effectively solved by CPLEX. A typical test system composed of a NSF-CAES hub, a 33-bus PDN, and an 8-node DHN is adopted to verify the effectiveness of the proposed ZCE-MEI in terms of reducing operation cost and wind curtailment.

## Keywords

Zero-carbon-emission micro Energy Internet Non-supplementary fired compressed air energy storage District heating network Power distribution network DistFlow Mixed integer linear programming## 1 Introduction

The dual-pressure from global energy crisis and environment pollution, has led to the reformation of energy utilization behaviors. Exploiting renewable energies is a world-wide consensus to address energy and environment issues. Renewable energies, such as wind power and solar power, have gained a rapid development both in concentrated and distributed manner in the last decades [1]. However, most available wind and solar power are greatly curtailed in the recent years, especially in Northeast and Northwest China, which prevents the stable development of renewable energy industry [2].

Utilizing multiple energy carriers including electricity, heat, cold, and natural gas in an integrated way is a trend to reduce the waste of wind and solar power. Integrated energy system (IES) is a symbolic system incorporating multiple energy carriers by connecting several energy networks with a few energy hubs (EH) capable of the transmission, conversion, and storage among different energy carriers [3, 4]. Through IES and EH, different energy networks can be co-optimized and managed to improve the utilization ratio of wind and solar power and increase dispatch flexibility of the whole energy supply system [5, 6, 7, 8].

CHP unit is a kind of EH capable of supplying heat and electricity simultaneously, i.e., co-generation. In this respect, CHP is utilized to co-optimize heating network and electrical network to increase the flexibility and reduce curtailed wind and solar power [5, 6, 7]. Unfortunately, CHP needs natural gas backup to generate electricity, which breaks the original intention to release environment issue due to carbon emission caused by burning fossil fuel. Compressed air energy storage (CAES), a promising energy storage technique, also uses natural gas combustion to produce electricity and leads to environment issues similar to CHP. By incorporating thermal energy storage system (TES) into CAES, advanced adiabatic compressed air energy storage system (AA-CAES) and non-supplementary fired compressed air energy storage system (NSF-CAES) are capable of storing the thermal generated during air compression process in an air storage tank, and releasing it to heat the compressed air during electricity generation process [9, 10]. Thus, no gas combustion is needed in such advanced CAES systems. Similar with CHP, NSF-CAES is a class of EH capable of combined cooling, heating, and power generation. Owing to the zero-carbon-emission character, NSF-CAES hub can be adopted to construct a zero-carbon IES. On that basis, a zero-carbon-emission micro Energy Internet (ZCE-MEI) architecture is proposed by developing NSF-CAES as a clean EH to encompass the power distribution network (PDN) and district heating network (DHN) in this paper. The feasibility of using NSF-CAES as a clean energy hub in Energy Internet has been analyzed in [11], while more emphasis has been put on the scheduling of ZCE-MEI in this paper.

The studies that are suitable for modeling CAES has been available in [12, 13, 14, 15, 16]. CAES system and NSF-CAES system are formulated and implemented to power network dispatch operation in [12, 13] respectively. The optimal scheduling of wind power integrated with CAES in transmission system is studied in [14]. Meanwhile, by considering wind power generation and CAES, a low-carbon-emission micro grid architecture and corresponding thermal-wind-storage joint operation dispatch method are proposed respectively in [15, 16]. Optimal operation strategies of CAES on electricity spot markets with fluctuating prices are reported in [17]. On the other hand, the combined operation of electricity and heating system has been investigated in several literatures as [5, 6, 7, 8, 18, 19]. Optimal operation strategies have been developed in [5] to accommodate wind power. Dispatch problem of combined heat and power, and transmission-constrained unit commitment by co-optimize PDN and DHN are investigated in [6, 7] respectively. Two combined analysis methods have been developed in [8] to analysis the operation of heating and electricity network. Optimal power flow of integrated electrical and heating system is studied in [18]. Besides, coordinated scheduling of energy resources for distributed district heating and cooling systems in an integrated energy grid has been investigated in [19].

Although some existing references are dedicated to explore the operation of CAES and combined operation of integrated electricity and heating systems, most of them establish a simplified efficiency based power block model to formulate CAES, without modeling the pressure and temperature dynamic of CAES. CAES is a natural EH capable of co-generation of cool, heat and power. It is necessary to consider the pressure behaviors and temperature dynamic to enhance dispatch flexibility. On the other hand, with the high penetration of renewable energies, voltage management of PDN is more difficult and important compared with traditional PDN. Thus, voltage, reactive power, and corresponding reactive compensators need to be formulated to maintain reactive power balance and voltage quality in the optimal operation of PDN. Besides, most exist combined heat and electricity systems use CHP as the interface between PDN and DHN, which undoubtedly opposites the requirement of zero-carbon-emission. In this regard, we intend to develop a short-term day-ahead scheduling model for the proposed ZCE-MEI integrated NSF-CAES to reduce wind power curtailment and save system operation cost.

The contribution of this paper mainly includes the following three parts. Firstly, a micro Energy Internet architecture is proposed, NSF-CAES is utilized as a clean EH to achieve zero-carbon-emission. Secondly, detailed dispatch model of NSF-CAES hub is constructed by considering the pressure behaviors and thermal dynamics. DistFlow model of the radial PDN is also formulated by incorporating discrete and continuous reactive power compensators, and on load tap changer (OLTC). Thirdly, operation of the proposed ZCE-MEI is optimized to reduce curtailed wind power and operation cost compared with traditional PDN and DHN.

The rest of this paper is organized as follows. Micro Energy Internet and ZCE-MEI architecture are proposed in Section 2, operation mechanism and detailed dispatch model of NSF-CAES hub are also formulated to incorporate the pressure behaviors and temperature dynamics. In Section 3, optimal operation of ZCE-MEI incorporating PDN and DHN with NSF-CAES hub is modeled to reduce operation cost and wind curtailment. The effectiveness of the proposed architecture and dispatch model is verified through a typical ZCE-MEI composed of a NSF-CAES hub, an 8-node DHN, and a 33-bus PDN in Section 4. Conclusions and further research directions are drawn in Section 5.

## 2 **Zero-carbon emission micro Energy Internet**

### 2.1 Micro Energy Internet

Micro Energy Internet (MEI) is a system composed of distributed energy sources, energy storage units, multi-carrier energy sources, multi-carrier loads, and distribution networks [20]. MEI can be operated independently or connected to public energy networks. Urban and rural community, hospital, industrial park, and school are representatives of MEI. MEI aims at realizing an integrated optimization and dispatch of multiple energy sources to save costs and reduce emissions through the conversion and storage among different energy carriers.

Except for MEI, a few solutions including micro gird (MG), virtual power plant (VPP) have been proposed to handle the energy supply issues. MG is a system consists of at least one clean energy generation unit and energy storage unit, mainly supplying personal power load demand in specific geographical areas [21]. MG connected to the PDN could operate in an isolated mode or a grid connection mode [21]. VPP is a system composed of several distributed generation units, can usually be regarded as a traditional power plant. VPP puts more emphasis on the comprehensive generation and trading characters of the whole virtual plant, and usually used in the electricity market [22]. MG and VPP only focus on the power supply without considering other energy forms such as thermal energy considered in CHP. CHP can supply thermal and power energy simultaneously, which could be viewed as a generation unit in MEI. Besides, MEI could accommodate the flow distribution of both power and other energy carriers. Undoubtedly, MG is the basis of the MEI, which puts more emphasis on the coordinated management and operation of multiple energy carriers.

### 2.2 NSF-CAES hub

### 2.3 NSF-CAES formulation

- 1)
The air is considered as an ideal one, and meets ideal gas equation.

- 2)
Air storage tank adopts the isothermal model, i.e., temperature of stored air equals to that of ambient [24].

- 3)
Air storage tank employs the constant volume model, i.e., volume of air storage tank keeps no change [24].

- 4)
Compressor and turbine use the adiabatic model.

- 5)
Heat loss of thermal energy storage tank is neglected.

- 6)
Power consumption of circulating pump is neglected.

- 7)
Pressure loss of high pressure air and water through the heat exchanger is neglected.

#### 2.3.1 Compressor

*r*

^{th}stage compressor during charging satisfies:

*r*

^{th}stage compressor at time

*t*; \(\eta_{j,r}^{c}\) is the adiabatic efficiency of

*r*

^{th}stage compressor;

*κ*is the adiabatic exponent of air;

*R*

_{ g }is the gas constant; \(qm_{j,t}^{c}\) is the mass flow rate of air of compressor

*j*at time

*t*; \(\tau_{j,r,t}^{c,in}\) is the inlet temperature of

*r*

^{th}stage compressor at time period

*t*; \(pr_{j,r,t}^{c,in}\) and \(pr_{j,r,t}^{c,out}\) are the inlet and outlet air pressure of

*r*

^{th}stage compressor at time period

*t*.

*r*

^{th}stage compressor.

*n*

_{ c }is the number of compressor stage; \(A_{j,t}^{c}\) is the total power consumption of compressor.

*j.*

*r*

^{th}stage compressor is given by [10, 20, 23]

*r*

^{th}stage compressor at time period

*t*.

*r*

^{th}stage compressor at time period

*t*;

*pr*

^{ am }is the ambient pressure; \(pr_{j,r}^{c,in,l}\) and \(pr_{j,r}^{c,in,u}\) are lower and upper bound of inlet air pressure of

*r*

^{th}stage compressor; \(pr_{j,r,t}^{c,out}\) and \(pr_{{j,n_{c} ,t}}^{c,out}\) are the outlet air pressure of

*r*

^{th}stage compressor; \(pr_{j,r}^{c,out,l}\) and \(pr_{j,r}^{c,out,u}\) are the lower and upper bound of outlet air pressure of

*r*

^{th}stage compressor;

*β*

_{ j,r }is the compression ratio of

*r*

^{th}stage compressor.

Equation (6) denotes the inlet air pressure of 1-stage compressor. Equations (7) and (8) depict the bound of inlet and outlet air pressure of *r* ^{th} stage compressor. The relationship between inlet air pressure of (*r* + 1)^{th} stage and outlet air pressure of *r* ^{th} stage is given by (9). Equation (10) describes the link between outlet air pressure of the last stage and air pressure of air storage tank. Correlations of inlet air pressure and outlet air pressure of *r* ^{th} stage compressor are shown in (10) and (12).

#### 2.3.2 Turbine

To some extent, the operation mechanism of turbine can be regarded as an inverse process of compressor. Thus, the formulations for turbine can be easily inferred from that of compressor.

*j*, power generated by

*s*

^{th}stage turbine can be calculated by

*s*

^{th}stage turbine at time

*t*; \(\eta_{j,s}^{g}\) is the adiabatic efficiency of

*s*

^{th}stage turbine; \(qm_{j,t}^{g}\) is the mass flow rate of air of turbine

*j*; \(\tau_{j,s,t}^{g,in}\) is the inlet temperature of

*s*

^{th}turbine; \(pr_{j,s,t}^{g,in}\) and \(pr_{j,s,t}^{g,out}\) are the inlet and outlet air pressure of

*s*

^{th}stage turbine.

*s*

^{th}stage turbine.

*n*

_{ e }is the number of turbine stage; \(A_{j,t}^{g}\) is the total power generation of NSF-CAES turbine.

*j.*

*s*

^{th}stage turbine at each time period

*t*can be formulated as

*s*

^{th}stage turbine at time period

*t*.

*s*

^{th}stage turbine at time period

*t*;

*pr*

^{ st }is the pressure of air storage tank; \(pr_{j,s}^{g,in,l}\) and \(pr_{j,s}^{c,in,u}\) are lower and upper bound of inlet air pressure of

*s*

^{th}stage turbine; \(pr_{j,s,t}^{g,out}\) and \(pr_{{j,n_{e} ,t}}^{g,out}\) are outlet air pressure of

*s*

^{th}and

*n*

_{ e }stage turbine; \(pr_{j,s}^{g,out,l}\) and \(pr_{j,s}^{g,out,u}\) are lower and upper bound of outlet air pressure of

*s*

^{th}stage turbine;

*γ*

_{ j,s }is the expansion ratio of

*s*

^{th}stage turbine.

The meanings of (18)–(24) can be concluded according to (6)–(12).

#### 2.3.3 Air storage tank

*t*+ 1 can be calculated by

*t*;

*V*is the volume of air storage tank; \(\tau_{t}^{st}\) is the temperature of air storage tank.

Equation (25) is a measure for the state of charge (SOC) of NSF-CAES hub.

#### 2.3.4 Regenerative system

*r*

^{th}stage compressor at time period

*t*during charging can be depicted by [20, 23, 25]:

*r*

^{th}stage cooler;

*c*

_{ a }is the constant pressure specific heat of air.

*s*

^{th}stage turbine during discharging can be calculated by

*s*

^{th}stage heater.

*t*; \(H_{j}^{st,l}\) and \(H_{j}^{st,u}\) are the lower and upper bound of heat can be stored in the heat regeneration system.

Noting that, heat power recycled by the heat exchanger with water as cooling medium as in Fig. 2, i.e., 1-stage water cooling and 2-stage water cooling, is ignored in this part. The modeling process is similar to that of heat exchanger with conduction oil.

## 3 **Operation of zero-carbon-emission micro Energy Internet**

It is worth mentioning that wind power is considered as a class of scheduled generation source, i.e., allowing curtailment during the operation of ZCE-MEI in this paper. The errors between day-ahead forecasting and the real-time situation are neglected. Besides, wind power is regarded as free so that it will be consumed as much as possible [26].

### 3.1 Objective function

*b*

_{ t }and

*θ*

_{ t }are the electricity price and power bought from grid at time period

*t*; \(d_{j,t}^{hp}\) is the power demand of heat pump in DHN;

*n*

_{ hp }is the number of equipped heat pump.

The first term in (35) is the cost of electricity bought from gird for PDN while the second term is the electricity cost for heat pump in DHN. Objective function (35) is subjected to the following constraints.

### 3.2 Constraints

- 1)
Heat pump and circulating water pump

*i*can be calculated by:

*t*;

*c*

_{ w }is the constant pressure specific heat of recycle water; \(m_{i,t}^{g}\) is the mass flow rate of recycle water at node

*i*; \(\tau_{i,t}^{S}\) and \(\tau_{i,t}^{R}\) are the temperature of supply water system and return water system at node

*i*.

*i*.

*i*satisfies [18]:

*i*at time period

*t*; \(d_{i,t}^{cp}\) is the power demand of circulating water pump at node

*i*;

*ρ*is the density of recycle water; \(\eta_{i}^{cp}\) is the efficiency of circulating water pump.

*i.*

- 2)
Heat load

*i*of DHN in ZCE-MEI can be calculated by [8, 25]:

*i*at time period

*t*; \(h_{i,t}^{d}\) is heat load demand.

*i*, i.e.,

*i*is constrained by:

- 3)
Heat network

*i*∊

*H*(

*N*), the following equations are satisfied:

*F*(

*i*) and

*T*(

*i*) are the set of pipes with node

*i*as ‘from’ or ‘to’ node; \(m_{b,t}^{S}\) and \(m_{b,t}^{R}\) are the mass flow rate of recycle water of supply and return water system of pipe

*b*at time

*t*; \(m_{i,t}^{g}\) and \(m_{i,t}^{d}\) are the mass flow rate of recycle water of heat generation unit and heat load at node

*i.*

*i*∊

*H*(

*N*) and temperature of pipe

*b*∊

*H*(

*P*) can be depicted as:

*b*of supply system and return system at time

*t.*

*b*of supply system and return system at time

*t.*

*b*of supply network and return network should be limited according the physical character of pipe, i.e.,

*b.*

*b*can be illustrated as [18, 25]:

*μ*

_{ b }is the pressure loss coefficient of pipe.

*b*is an exponential function of inlet temperature of pipe

*b*.

*λ*

_{ b }is temperature loss coefficient of pipe;

*L*

_{ b }is the length of pipe

*b*; \(\tau_{t}^{am}\) is the ambient temperature at time period

*t*.

- 4)
Power distribution network

*P*

_{ ij }and

*Q*

_{ ij }are the active power and reactive power flow of line

*l*(

*i,j*); \(p_{j}^{g}\) and \(q_{j}^{g}\) are the active power and reactive power generation of generation unit at bus

*j*; \(p_{j}^{d}\) and \(q_{j}^{d}\) are the active power and reactive power demand of load at bus

*j*;

*i*

_{ ij }is square of current through line

*l*(

*i,j*), while \(i_{ij}^{u}\) is the upper bound;

*r*

_{ ij }and

*x*

_{ ij }are the resistance and reactance of line

*l*(

*i,j*), while

*z*

_{ ij }is the impedance of line

*l*(

*i,j*);

*U*

_{ i }is the square of voltage amplitude of bus

*i*;

*V*

_{ sl }is the voltage of slack bus; \(p_{i}^{l}\) and \(p_{i}^{u}\) are the upper and lower bound of active power output of generation unit; \(q_{i}^{l}\) and \(q_{i}^{u}\) are the upper and lower bound of reactive power output of generation unit.

Equations (53) and (54) model the active power and reactive power balance of PDN, respectively. Voltage drops along the distribution line *l*(*i,j*) is depicted in (55). Equation (56) describes the connection between power, square of voltage, and square of current. The limits of square of current, square of voltage, active power, and reactive power are respectively shown in (57)–(59).

*j*at time period

*t*; \(A_{j,t}^{c}\) is the power demand of NSF-CAES hub;

*C*

_{ j,t }is the value of shunt capacitors/reactors;

*Q*

_{ cj,t }is the supplemented reactive power of continuous compensator;

*K*

_{ ij,t }is the tap ratio of OLTC on line

*l*(

*i,j*); \(W_{i}^{g,l}\) and \(W_{i}^{g,u}\) are the lower and upper bound of available wind power output.

Active power distribution of line *l*(*i,j*) is formulated as (65). Equations (66) and (67) depict the reactive power distribution for line *l*(*i,j*) with and without reactive power compensator equipped on bus *j*, respectively. Equations (68) and (69) denote the voltage of bus *j* for line *l*(*i,j*) with or without OLTC. (70) is similar to (58). The available wind power is constrained by (71).

### 3.3 Model simplifications

- 1)
Linearization of NSF-CAES hub

*pr*, temperature

*τ*, and mass flow rate

*qm*are the adjustable variables of a NSF-CAES hub. It is difficult to adjusting them simultaneously owing to the complexity of hydraulic–thermal dynamics. Fortunately, a practical NSF-CAES hub often operates at a constant pressure and constant temperature (CP–CT) mode [10, 23]. Thus, (1) and (13) reduces to

Similarly, (5) and (17) reduce to

*U*

_{ j,t }

*C*

_{ j,t }in (66) is a nonlinear one, to linearize this term, the discrete variable

*C*can be formulated as [31, 32, 33],

*E*

_{ D }is set of buses for shunt capacitors/reactors;

*s*

_{ j }is the step size of shunt capacitors/reactors; \(C_{j}^{l}\) and \(C_{j}^{u}\) are the lower and upper bound of shunt capacitors/reactors;

*v*

_{ j }is a integer which can be decided by:

*U*

_{ j,t }

*C*

_{ j,t }in (66) is transformed to

*M*is a big number.

*l*(

*i,j*), the term of left side of (68) can be expanded as:

*K*

_{ ij,1},

*K*

_{ ij,2},…,\(K_{{ij,n_{ij} }}^{{}}\) are the available value of tap of OLTC;

*n*

_{ ij }is the number of available OLTC tap value. In this regard, (68) can be linearized as [31, 32, 33]:

*h*

_{ j,k,t }are dummy variable;

*b*

_{ ij,1,t }and \(b_{{ij,n_{ij} ,t}}\) are binary variables, with \(\sum\limits_{k = 1}^{{n_{ij} }} {b_{j,k,t} } = 1.\)

- 3)
Linearization of DHN

As for DHN, there exists four different operating strategies, including constant flow and constant supply temperature (CF–CT), constant flow and variable supply temperature (CF–VT), variable flow and constant supply temperature (VF–CT), variable flow and variable supply temperature (VF–VT) [25, 34]. Under the VF–VT and VF–CT modes, the temperature dynamics (49) and (50) are nonlinear, which made the dispatch problem difficult to be solved. Fortunately, in an actual DHN, CF–VT has more flexibility than CF–CT, and CF–VT yields a linear DHN model.

For simplicity, the widely used CF–VT mode is implemented in this paper, thus, (43), (44), (49) and (50) are unnecessary.

Through above transformations and simplifications, the original MINLP (72) is reduced to a MILP problem, which can be effectively solved by commercial solvers.

## 4 **Case study**

### 4.1 System settings

*matpower*data. Heat load ratio, and mass flow rate of each node are shown in Table 1.

Heat load parameters of the test ZCE-MEI

Node no. | Heat load ratio | Mass flow rate (kg/s) |
---|---|---|

1 | 0 | 10 |

2 | 0 | 0 |

3 | 0 | 0 |

4 | 0 | 0 |

5 | 0.2 | 2 |

6 | 0.2 | 2 |

7 | 0.2 | 2 |

8 | 0.4 | 4 |

To maintain voltage quality of PDN in the designed ZCE-MEI, several kinds of reactive power compensator are equipped at key buses of PDN. Several OLTCs with a minimum tap changer 0.95, maximum tap changer 1.05, and tap step 0.01 are equipped on Line #1, #18, #22, #25 respectively. Meanwhile, shunt capacitors/reactors with a minimum value 0 and maximum value 0.2, and step 0.05 are located on Bus #5, #10, #13, #17, #20, #23, #30 respectively. Besides, SVG are equipped on Bus #4, #9, #14 to provide continuous reactive power.

### 4.2 Simulation of NSF-CAES

^{3}.

Rated parameters of compressor

Compressor | | | | | | |
---|---|---|---|---|---|---|

1-stage | 0.1 | 1.15 | 15 | 375 | 250 | 0.85 |

2-stage | 1.15 | 9 | 40 | 366 | 250 | 0.81 |

Rated parameters of turbine

Turbine | | | | | | |
---|---|---|---|---|---|---|

1-stage | 8.4 | 0.94 | 280 | 60 | 500 | 0.82 |

2-stage | 0.94 | 1 | 280 | 60 | 500 | 0.82 |

*η*

_{ e }= 1.46/2.8 = 52.14%, the round trip energy efficiency

*η*

_{ CAES }= (1.46 + 0.4193)/1.3867 = 67.12%. These efficiencies are acceptable for commercial applications to realize return of investment at commercial scale.

### 4.3 Simulation results

- 1)
Operation of NSF-CAES hub

*t*= 1 to

*t*= 6, to charge the air into air storage tank to store the energy in two forms, i.e., thermal energy in TES and molecular potential energy in air storage tank. The molecular potential energy can be used to drive turbine to generate electricity during on-peak periods, such as from

*t*= 13 to

*t*= 14, under the condition of consuming certain thermal energy stored in TES. Although most heat load are supplied by heat pump in the DHN, some of the thermal energy stored in TES during energy charging process can be directly used to supply heat power for heat load to save cost, such as at time periods

*t*= 10,

*t*= 16. Similar conclusions can be drawn by analyzing Fig. 9, Fig. 10, and Fig. 11 simultaneously.

- 2)
Operation cost and wind curtailment

Operation costs under different operation modes

Mode | PDN (×10 | DHN (×10 | SUM (×10 |
---|---|---|---|

MEI | 3.1950 | 3.1208 | 6.3163 |

Single | 3.3601 | 3.1729 | 6.5330 |

*t*= 8 to

*t*= 22, all the available wind power has been utilized to supply the power load demand.

- 3)
Optimal power flow

*t*= 5) and off-peak heat load period (

*t*= 15) are shown in Fig. 14.

It is shown in Fig. 14 that the different heat load at on-peak period (*t* = 15) and off-peak period (*t* = 5) can be supplied by adjusting the return water temperature. Under the condition of the same supply water temperature, the smaller the return water is, the larger the heat power can be supplied. Besides, the temperature of outlet of supply and return water system usually lower than that of inlet of supply and return water system which is similar to the common sense.

*t*= 11) and off-peak time period (

*t*= 5) are depicted in Fig. 15. Voltage of each bus during off-peak power load and on-peak power load and corresponding states of OLTCs are shown in Fig. 16 and Fig. 17.

We can learn from above figures that node voltage and reactive power balance are maintained and supplied in the required limits by adjusting the OLTC and shunt capacitors/reactors at on-peak and off-peak time periods.

## 5 **Conclusion**

This paper proposes a zero-carbon-emission micro Energy Internet architecture to utilize power and heat in an integrated manner. NSF-CAES hub is a clean energy hub accommodating DHN and PDN. A detailed NSF-CAES dispatch model is constructed to consider the pressure behaviors and temperature dynamics. To maintain the voltage quality of PDN in the proposed ZCE-MEI, an improved linearized DistFlow model is utilized to take OLTC and capacitor and reactor shunt compensator into account. Optimal operation of the ZCE-MEI is realized in terms of reducing operation cost. A typical 1 MW NSF-CAES hub is designed to show the effectiveness of the proposed NSF-CAES formulation. Simulation results verified the advantages of the proposed ZCE-MEI in reducing operation cost and wind curtailment.

Noting that, we assume there is no uncertainty in the system parameter, thus the method cannot adapt to situations with parameter uncertainties which is more common in actual system. Our further work will focus on the optimal dispatch of MEI with uncertainty, the robust optimization and stochastic optimization methods would be adopted to handle this kind of problem.

## Notes

**Acknowledgment**

This work was supported in part by the National Natural Science Foundation of China (No. 51321005, No. 51377092, No. 51577163), and Opening Foundation of the Qinghai Province Key Laboratory of Photovoltaic Power Generation and Grid-connected Technology.

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