With the proposal of carbon neutrality, how to improve the proportion of clean energy in energy consumption and reduce carbon dioxide emissions has become the important challenge for the traditional energy industry. Based on the idea of multi-energy complementarity, a typical integrated energy system consisting of electric system and gas system is constructed based on the application of power to gas (P2G) technology and gas turbine in this paper. Furthermore, a multi-objective optimization model with economic improvement, carbon emission reduction and peak-load shifting as objectives is proposed, and solved by BSO algorithm. Finally, a typical power-gas coupling system is selected as an example to verify the effectiveness of the model. The results showed that the proposed multi-objective optimization model based on BSO algorithm can better play the complementary characteristics of the electric and gas system, and significantly improve the comprehensive benefits of system operation.

At present, China is undergoing profound energy reform [

The core problem of integrated energy system development lies in system operation optimization [

In view of this situation, basis on the electric power network and natural gas network operation characteristic, this paper puts forward a kind of electricity-gas coupling integrated energy system multi-objective optimization scheduling model which considering economic benefit, environmental benefit and peak-load shifting benefit, and provides theoretical support for the comprehensive benefit improvement of integrated energy system.

As shown in

(1) Economic objective

The economic objective of the multi-objective optimal scheduling of the electrical coupling system is to minimize the operating cost of the system [

(2) Carbon emission objective

The carbon emission of the power-gas coupling system in the whole operation cycle is the difference between the sum of CO_{2} emitted by coal-fired units and gas turbines and CO_{2} consumed by power-gas conversion.

_{2} consumed per unit gas output, which is 1.8 kg/m^{3} in this paper.

(3) Peak-load shifting objective

The goal of multi-objective optimal scheduling of power-gas coupling system is to minimize net power load variance.

The constraints of multi-objective optimal scheduling of power-gas coupling system include power subsystem constraints, natural gas subsystem constraints and coupling constraints of their networks [

Power network constraints include node power balance constraints, unit active power output constraints, unit climbing constraints, node voltage constraints and branch capacity constraints, as follows:

Like the power network, the natural gas network can also be regarded as composed of source, network, load, storage, etc. [

(1) Gas source

The gas source is similar to a generator, injecting natural gas into the natural gas network, and each gas source must meet the output flow limit.

(2) Pipelines

The transmission flow in natural gas pipeline is mainly related to the pressure of the nodes on both sides of the pipeline and the transmission characteristics of the pipeline [

(3) Pressure station

Due to friction resistance in natural gas pipelines, part of the energy will be lost in the transmission process, resulting in pressure drop. Therefore, a certain number of pressure stations should be installed in the natural gas network to ensure reliable transmission of natural gas.

Pressure stations mainly use compressors to increase the pressure. The compressors consume natural gas, and their consumption is related to the natural gas flow through the compressors and the compression ratio:

(4) Storage tank

When the natural gas load fluctuates greatly or the natural gas network breaks down, the gas storage tank can be used as an alternative gas source to ensure the reliable supply of natural gas [

(5) Flow balance

Similar to node power balance constraint in power network, natural gas network also needs to meet node flow balance constraint:

Coupling constraints between power network and natural gas network include P2G constraint and gas turbine constraint.

(1) P2G

P2G constraint including power transformation equation and maximum output gas flow restrictions.

^{3};

(2) Gas turbine

Gas turbine constraint also include power conversion equations and output active power limits [

BSO (Brain Storm Optimization) is a swarm intelligence optimization algorithm, compared with other algorithms, such as genetic algorithm, PSO algorithm, etc., BSO algorithm can use one or two “old” individuals or groups to generate new individuals, new groups, and thus out of the original search range, and effectively avoid falling into the local optimum. The core idea is to simulate the crowd to propose a large number of solutions to problems. Each solution is regarded as a feasible solution, and the process of obtaining the optimal solution through discussion and integration. It has the advantages of good stability, high accuracy, and fast convergence [

(1) Initial solution population is generated

Obtain m pieces of historical data of a typical intraday electrical coupling system as the initial feasible solution of the optimization problem. Perform clustering operations on m initial feasible solutions to obtain n clusters of initial solution populations, and calculate the fitness function value of each initial feasible solution. The clusters containing at least one non-dominated solution are marked as elite clusters, and the clusters without non-dominated solutions are marked as ordinary clusters.

(2) Generation of new solutions

In BSO algorithm, new solutions are generated in the following ways:

(1) Select a random elite cluster and generate a random number

(2) Generate a random number

(3) Select an elite cluster and a common cluster, and generate random number

(4) Calculate the fitness function value of the new individual and compare it with the original solution, and keep the individual with good fitness function value as the new solution.

Among them, the mutation operation is achieved by adding random values to selected individuals, and the mutation operation is as follows:

(3) Solution generation of multi-objective optimization problem

Repeat

This article takes the modified IEEE39-node electric power network and 20-node natural gas network interconnection electrical coupling system as the object, takes one day as the analysis period and 1 h as the scheduling period, to analyze and test the effectiveness of the model proposed in this article. The schematic diagram of the electrical coupling system is shown in

The load of each node of the power sub-network becomes 80% of the IEEE 39-node power network standard test network, units G1, G7 and G8 are gas turbines, connected to load nodes 4, 10 and 12 of the natural gas network, units G4 and G5 are wind farms with a rated power of 600 MW, both wind farms are equipped with P2G equipment and are connected to natural gas network gas storage tanks S3 and S4. The wind farm abandonment penalty coefficient is both US$ 50(MW·h). The remaining units in the power network are coal-fired units. The parameters of gas turbine and coal-fired units are shown in

Gas turbine | Consumption |
Carbon emission factor | Output constraint | |||||
---|---|---|---|---|---|---|---|---|

h_{2} |
h_{1} |
h_{0} |
^{2}MW |
^{2}MW |
||||

G_{1} |
0.1569 | 5.4658 | 0 | 0.0117 | 0.3700 | 0 | 2.0800 | 5.2000 |

G_{7} |
0.1450 | 5.2581 | 0 | 0.0121 | 0.3650 | 0 | 1.1600 | 2.9000 |

G_{8} |
0.1501 | 5.1131 | 0 | 0.0102 | 0.3750 | 0 | 1.1280 | 2.8200 |

Note: The units of h_{2}, h_{1} and h_{0} are m^{3}/[s·(100MW)^{2}], m^{3}/[s·(100MW)], m^{3}/s, respectively; the units of ^{2}, 100t/(100MW·h), 100t, respectively.

Coal-fired unit | Cost factor | Carbon emission factor | Output constraint | |||||
---|---|---|---|---|---|---|---|---|

a | b | c | ^{2}MW |
^{2}MW |
||||

G_{2} |
0.7003 | 26.9875 | 0 | 0.0389 | 1.2333 | 0 | 3.5530 | 6.4600 |

G_{3} |
0.6827 | 22.9781 | 0 | 0.0260 | 0.9660 | 0 | 3.9875 | 7.2500 |

G_{6} |
0.4255 | 25.0002 | 0 | 0.0489 | 1.2319 | 0 | 3.7785 | 6.8700 |

G_{9} |
0.4580 | 26.0987 | 0 | 0.0282 | 1.0608 | 0 | 4.7575 | 8.6500 |

G_{10} |
0.8909 | 26.1767 | 0 | 0.0273 | 1.2372 | 0 | 6.0500 | 11.0000 |

Note: The units of a, b, and c are 10^{2} USD/(100MW·h)^{2}, 10^{2} USD/(100MW·h), 10^{2} USD, respectively.

Gas source | ^{3}·h^{−1}) |
^{3}·h^{−1}) |
g/(10^{6}USD·Mm^{−3}) |
---|---|---|---|

N1 | 0.0370 | 0.2898 | 0.25 |

N2 | 0.0848 | 0.5503 | 0.25 |

Gas tank | ^{3} |
^{3} |
^{3}·h^{−1}) |
^{3}·h^{−1}) |
^{3}·h^{−1}) |
---|---|---|---|---|---|

S_{1} |
0.420 | 2.520 | 0.210 | 0.420 | / |

S_{2} |
0.240 | 1.440 | 0.120 | 0.240 | / |

S_{3} |
0.060 | 0.360 | 0.030 | 0.060 | 0.0120 |

S_{4} |
0.048 | 0.288 | 0.024 | 0.048 | 0.0096 |

In the dispatch day, the available wind farm power, electrical load and gas load of the system are shown in

In order to fully illustrate the superiority of the multi-objective optimal scheduling of the electric-to-gas-electric coupling system in this article, two scenarios are designed for the calculation example: one is a single-objective optimization scenario that targets economy, and the other is a three-objective optimization scenario that targets economy, carbon emissions, and the comprehensive benefits of peak shaving and valley filling. In order to make full use of wind power to reduce operating costs and carbon emissions, the output of wind farms under Scenario 2 of this article is consistent with Scenario 1.

Make the weights of each target in Scenario 2 equal, the wind farm output corresponding to the optimal solution of the single target in Scenario 1 and the compromise solution of multiple targets in Scenario 2, gas turbine output. The injection power of electric-to-gas and the net electric load are shown in

It can be seen from

Based on considering the operation characteristics and constraints of power system and natural gas system, this paper proposes a multi-objective optimization model of electric coupling integrated energy system, which considers economic benefits, environmental benefits, and peak-cutting and valley filling utility, based on electricity to gas technology and gas turbine technology and uses BSO algorithm to solve it. In this paper, the effectiveness of the model is further verified by an example, the results show that compared with the traditional scheduling optimization based on economy, the proposed model can give better play to the comprehensive benefits of the electric-coupling integrated energy system.