Research on the Optimization of Combined Heat and Power Microgrids with Renewable Energy Fan Wu, Qinglai Guo, Hongbin Sun, Zhaoguang Pan The Department of Electrical Engineering State Key Laboratory of Power Systems, Tsinghua University Beijing, China
[email protected] Abstract—With the shortage of traditional fossil energy and environment problem aggravating, renewable energy and CHP or CCHP ,which is considered as a method for the efficient use of energy, have received a great deal of attention. How to improve the utilization rate of renewable energy and achieve maximum economic revenue now seems very important. In this paper, an optimization model of a combined heat and power microgrid containing renewable energy is proposed, as a powerful tool to coordinate different energy forms. Based on the case studies, it is confirmed that the optimization model can reduce the microgrid cost and improve the utilization rate of renewable energy effectively. Keywords-CHP; energy;
Microgrid;
I.
Optimization;
Renewable
INTRODUCTION
With the rapid development of global economy, the shortage of traditional fossil energy is becoming more and more serious[1][2]. Meanwhile, environment pollution has become the focus of world attention[3]. Thus it has been an urgent demand for human to exploit renewable energy and improve energy utilization rate. Microgrids and CHP or CCHP, which are developing quickly in recent years, are considered as good solutions to these problems. CHP is an energy system established on the cascade utilization of energy. It generates electricity and heating, simultaneously through burning natural gas or other fuel. Meanwhile it can provide heating by using waste heat. In this way, CHP has a high energy utilization rate up to 80%[4]. The microgrids supply power to users by renewable energy, energy storage and other distributed generations (DGs)[5]. But because of the uncertainty of renewable energy, the microgrids cannot make full use of renewable energy. Due to CHP, combined heat and power microgrids can coordinate the supply of electricity and heat at the same time in order to reduce the total cost and improve the energy utilization rate. This work was supported by the National High Technology Research Program (2012AA050218), the National Science Fund for Distinguished Young Scholars (51025725), the National Science Fund of China (51361135703) and the Tsinghua University Initiative Scientific Research Program.
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There are some researches about microgrids and CHP or CCHP[6]-[12] so far. Most of them focus on reducing pollution and cost by optimizing the microgrids operation. Several researches have proposed the optimization model for dynamic economic dispatch or the system configuration. But few studies notice the change on the utilization rate of renewable energy after combining electricity and heat. In this paper, the optimization model is proposed and tested to confirm that microgrids combined heat with power can reduce the total cost and improve the utilization rate of renewable energy, which will benefit the sustainable development. II.
MICROGRID MODEL WITH CHP AND RENEWABLE ENERGY
General microgrids mainly include several kinds of distributed generations, energy storages and electrical equipment. The combined heat and power microgrid model with renewable energy is shown in Fig 1: Natural Gas
The Electric System
PCC Electrical Energy Storage
Customer 1 Photovoltaic Power Generation
CHP Electro-thermal Conversion
Wind Power Generation
……Customer 2…… Thermal Energy Storage
The Heating System
Fig 1. The model of combined heat and power microgrid with renewable energy
This combined heat and power microgrid consists of renewable energy, energy storages, energy customers and CHP. In order to make the system more flexible, the electrothermal conversion is added, which is able to convert electricity to heat. Thus the redundant renewable energy could be made full used of and the utilization rate of renewable energy could be improved. Among these components, CHP is the most important one. By burning gas,
CHP can provide electricity, and supply heat using waste heat. Heat and power are combined by CHP and electrothermal conversion. III.
OPTIMIZATION MODEL OF MICROGRID WITH CHP AND RENEWABLE ENERGY
In order to achieve the goal to improve the utilization rate of renewable energy and reduce the cost, the optimization model is proposed in this paper as follows. A. Renewable energy and loads The renewable energy in this paper mainly contains wind power and photovoltaic power. They both work in Maximum Power Point Tracking (MPPT) mode, and the output can be represented by forecasting curves. The renewable energy generation can be put into operation or stopped to make the optimization strategy more flexible, which is indicated as an integer variable k j . Although the cost of renewable energy is very low, it is described as operating costs C1 and maintenance costs C2 as follows:
(
)
C j ( i ) = C1k j ( i ) × Pj ( i ) + C2 × 1 − k j ( i ) × Pj max (1) The output limit of each renewable energy generator is as follows: Pj min ≤ Pj ( i ) ≤ Pj max (2) Similarly, the electric load and heat load are both considered to be predicted and represented as several curves. B. Energy storages Electric energy storage and thermal energy storage are involved, and they are used to store or release energy to make the optimization strategy more schedulable. The electric energy storage has its technical limit: (3) Pmin ≤ Pdis , char ≤ Pmax
SoC ( i ) = SoC ( i − 1) + (ηc Pchar ( i ) − ηd Pdis ( i ) ) / EEES SoCmin ≤ SoC ≤ SoCmax
(4) (5)
And the thermal energy storage can be expressed as:
0 ≤ H TI ,TO ( i ) ≤ H TI ,TO ,max
LT ( i ) = ηT LT ( i − 1) + ηTI H TI ( i ) − H TO ( i ) 0 ≤ LT ( i ) ≤ LT ,max
(6) (7) (8)
Constraints (3) and (6) have expressed the charging and discharging power limits of energy storages. Constraints (5) and (8) are storage capacity limits. Constraint (7) shows the energy balance of storage, including the efficiency and losses of charging and discharging, where η c η d mean the efficiency of charging and discharging. The cost of energy storage is also considered in the model. It can be described as:
Cstorage ( i ) = C EES × ( Pchar ( i ) + Pdis ( i ) ) + CTES × ( H TI ( i ) + H TO ( i ) )
(9)
C. Electro-thermal conversion In this paper, electro-thermal conversion can be expressed as the equipment translated electricity to heat. It has its technical limit: H EH ( i ) = η EH PEH ( i ) (10)
0 ≤ PEH ( i ) ≤ PEH ,max
(11)
η EH means the conversion efficiency and its cost is: Cconvert ( i ) = CEH PEH ( i )
(12)
D. Combined heat and power system(CHP) CHP can provide electricity by burning gas, and the relationship between CHP’s power output P ( i ) and gas
consumption F ( i ) is as follows:
a f P ( i ) + bf = F ( i )
(13)
a f and b f are the coefficients of the relation. In this paper they are 3.6 and 0.4. For convenience, we consider that the input of gas (kW) and the heat output of CHP have a linear relationship like: H l ( i ) = η f − h Fl ( i ) (14) And the coefficient η f − h in this paper is 0.42. CHP has its output power limit and climbing (falling) rate limit: Pl min ≤ Pl ( i ) ≤ Pl max (15)
− RDl ≤ Pl ( i + 1) − Pl ( i ) ≤ RU l
(16) Constraint (15) shows the output power limit of CHP. The climbing (falling) rate constraint of CHP is given as constraint (16). The cost of CHP and gas is considered in the total costs as well. E. Energy Balance Constrains And the system energy balance constrains include the following: m
nCHP
j =1
l =1
∑ Pj ( i ) +
∑ P ( i ) + Pbuy ( i ) + Pdischar ( i ) l
= Psell ( i ) + Pchar ( i ) + PEH ( i ) + Eload ( i ) nCHP
∑ H (i ) +η l
l =1
EH
PEH ( i ) + ηTO H TO ( i )
≥ Hload ( i ) + H TI ( i )
Pgrid ,min ≤ Pbuy ( i ) , P sell ( i ) ≤ Pgrid ,max
(17)
(18) (19)
Constraint (17) and (18) show the power balance constraints of electricity and heat, respectively. Constraint (19) limits the power transaction between microgrid and the utility grid, and its cost is: C trade ( i ) = Cbuy Pbuy ( i ) − Csell Psell ( i ) (20)
F. Objective Function The objective function is minimizing the total operational cost F in the microgrid with electricity and heat over the 24 hours. The cost contains renewable energy cost C j ( i ) , the energy transaction cost between microgrid
wind turbine generators, two of them are overlapping with each other. And we adopt the combined heat and power microgrid system shown in Fig 1. 35
30
and utility grid Ctrade ( i ) , the conversion cost Cconvert ( i ) , the
25
Power/kW
storage cost C storage ( i ) , the cost of gas C gas and
maintenance cost of CHP CCHP .
20
m
C ( i ) = ∑ C j ( i ) + Ctrade ( i ) + Cconvert ( i ) nCHP
j =1
+ ∑ ( C gas Fl ( i ) + CCHP Pl ( i ) ) + Cstorage ( i )
15
(21) 10
l =1
Electric Load Heat Load 0
5
10
15
20
25
Time/H 24
F = ∑ C (i )
Fig 2. Electric load and heat load
(22)
15
i =1
IV.
Wind 1 Wind 2 Wind 3 PV 1 PV 2
CASE STUDIES
In this section, case studies are carried out to test the proposed optimization model. It is compared to a base case which optimizes electricity and heat separately, using the same renewable energy output, electric load and heat load. Because of the limitation of space, we mainly discuss a typical case suitable for winter as follows. A. Case Configuration As mentioned above, the renewable energy and loads can be expressed as several forecasting curves given by Fig.2 and Fig.3. In Fig 3, red lines show the output of two PV power generations, and blue lines show the output of three
Power/kW
10
5
0
0
5
10
15
20
Time/H
Fig 3. The output of renewable energy
B. Simulation and Analysis
40 Electric Load Renewable Energy Output power of CHP Charging of EES Discharging of EES Transaction with utility grid Electro-thermal conversion
30
High electric load
Power/kW
20
10
Lack of renewable energy
Discharging of EES
0
-10
Electro-thermal conversion
Charging of EES -20
0
5
10
15 Time/H
Fig 4. The optimization result of electric system
20
25
25
35
Heat Load Heat output of CHP Charging of TES Discharging of TES Electro-thermal conversion
High thermal load 30
25
High thermal load
Power/kW
20
15
10
Electro-thermal conversion
5
0
Charging and discharging of TES -5
0
5
10
15
20
25
Time/H
Fig 5. The optimization result of thermal system
This optimization model proposed is a Mixed Integer Linear Programming (MILP), and it is solved in Matlab with IBM ILOG CPLEX 12.5. Fig 4 and Fig 5 show the optimization results of electric system and thermal system. From Fig 4 we can find that in winter case due to the fluctuation of renewable energy, the electric energy storage charges when electric load is low or renewable energy is sufficient, and discharges when electric load increases or renewable energy lacks. For example at the beginning of the day, the EES charges because of the high renewable energy output and low electric load. At about 8 o’clock, the output of renewable energy reaches its minimum, the EES starts to discharge. Also at about 20, the electric load gets to the maximum and the renewable energy and output power of CHP are not enough, EES discharges to meet the deficiency. Fig 5 shows the optimization results of thermal system. It shows that because of the high demand of heating, the heat output of CHP is not enough, thus the electro-thermal conversion has a certain operating power to meet the deficiency. Meanwhile the thermal energy storage has adjusted the time distribution of heat. According to Fig 4 and Fig 5, we can see that the heat output of CHP does not fully supply the heat load. And in many periods, the output power of CHP is reduced even the heat energy is insufficient. This is because in those periods electric energy is abundant, the electro-thermal conversion could convert redundant electric energy to heat in order to reduce the total cost. Thus the surplus renewable energy is used to improve the utilization rate. Compared to the results of the base case which optimizes electricity and heat separately, Table 1 is formed as follows:
Table 1. Simulation results contrast
Separating electric and heat Combining electric with heat Cost saving
Total cost/ RMB 321.1450(electricity)+350.5714(heat) =671.7164 573.0947 14.6820%
From Table 1, it is confirmed that microgrids combined with heat by using CHP and electro-thermal conversion can reduce the total cost effectively, 14.8620% in this case. At the same time, the close coupling between electricity and heat (CHP and electro-thermal conversion), actually improve the utilization rate of renewable energy, which is one reason for lower cost. In this case, the utilization of renewable energy is shown in Fig 6 and Table 2. When heat and power is optimized together, more renewable energy is used, as well as less cost and emission. From the table, we can find that the effect is obvious. Table 2 The comparison of renewable energy utilization The increase of The utilization of renewable energy renewable energy utilization /kWh /kWh Separating electric and 204.2500 heat 107.6500 Combining electric with 311.9000 heat
25
The utilization of renewable energy/kW
Separating Electric and Heat Combining Electric and Heat 20
15
10
5
0
0
5
10
15
20
25
Time/H
Fig 6. The utilizations of renewable energy in two models
V. CONCLUSIONS This paper studies the optimization of combined heat and power microgrids with renewable energy, which contains CHPs, conversion equipments, DGs and energy storages. The combined optimization model is proposed as a MILP to minimize the total cost with several technical constraints. Case studies are carried out, and is compared with a base case which optimizes electricity and heat separately. The results confirm that the optimization model of combined heat and power microgrids can reduce the total cost effectively and improve the utilization rate of renewable energy obviously. The combination and coordination of heat and power is suggested in microgrids to improve renewable energy utilization rate and reduce cost. Future work involves more complicated network model, uncertainty and users’ flexibility. REFERENCES [1] [2] [3]
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