Because of the randomness of wind power and photovoltaic (PV) output of new energy bases, the problem of peak regulation capability and voltage stability of ultra-high voltage direct current (UHVDC) transmission lines, we proposed an optimum allocation method of installed capacity of the solar-thermal power station based on chance constrained programming in this work. Firstly, we established the uncertainty model of wind power and PV based on the chance constrained planning theory. Then we used the K-medoids clustering method to cluster the scenarios considering the actual operation scenarios throughout the year. Secondly, we established the optimal configuration model based on the objective function of the strongest transient voltage stability and the lowest overall cost of operation. Finally, by quantitative analysis of actual wind power and photovoltaic new energy base, this work verified the feasibility of the proposed method. As a result of the simulations, we found that using the optimal configuration method of solar-thermal power stations could ensure an accurate allocation of installed capacity. When the installed capacity of the solar-thermal power station is 1 × 10^{6} kW, the transient voltage recovery index (TVRI) is 0.359, which has a strong voltage support capacity for the system. Based on the results of this work, the optimal configuration of the installed capacity of the solar-thermal power plant can improve peak shaving performance, transient voltage support capability, and new energy consumption while satisfying the Direct Current (DC) outgoing transmission premise.

According to the

At present, there has been much research on the capacity optimization configuration of solar-thermal power stations. In the literature [

The above-mentioned literature configures the capacity for the regulation demand, operation scenario, and demand response, but the current research on the capacity configuration of the solar-thermal power station does not involve the transient voltage support stability of the system under the DC transmission demand.

Given the above problems, this paper establishes the uncertainty model of PV and wind power based on the chance constrained programming theory. Taking the strongest transient voltage stability and the lowest comprehensive operation cost as the objective function. Take a new energy base as an example for simulation analysis. TVRI analyze the transient voltage stability under different solar-thermal power stations’ installed capacities, and according to the peak-to-valley difference and cumulative climbing amount analyzes the peak shaving effect after accessing solar-thermal power station.

Chance constrained programming is a decision-making method that considers that the constraints cannot be fully satisfied in adverse environments. It describes random variables in the form of probability constraints. Based on this, the corresponding confidence level is given to ensure that the probability of the optimal solution meeting the constraints is greater than its given confidence level.

The uncertainty of PV output is mainly studied in the following three aspects: solar irradiance, PV output power and PV output prediction error. The expected value of PV forecast error is about 0, which can be described by normal distribution [

Suppose that the random variable of the output prediction error of PV power station is

Wind power is easily affected by meteorological factors, resulting in power uncertainty. From the literature [

Considering the time series characteristics of wind power and PV output, using the K-medoids algorithm. K-medoids algorithm in this work can be described from

The economic factor is a key role in the planning. Most studies in China and abroad take the minimum comprehensive operation cost of the system as the objective function.

This paper started from the overall benefits of the addition of the solar-thermal power station, which took into account the investment cost, operation cost, and transient stability. Taking solar-thermal power station installed capacity as the decision variable, the maximum transient voltage stability and the minimum comprehensive operation cost as the comprehensive objective function, the comprehensive objective function F in this work can be described from

(1) Maximum objective of TVRI

TVRI is used to characterize the voltage recovery ability of the system after the failure of PV addition. The smaller TVRI is, the stronger the transient voltage recovery stability and the stronger the voltage support ability of the system. Hence,

(2) Minimum objective of comprehensive operating cost

The flexibility of the solar-thermal power station, on one hand, it can reduce the peak regulation pressure of conventional peak regulation units and reduce the peak regulation cost; on the other hand, it can improve the output of wind and PV energy and improve the economy of system operation. The sum of the solar-thermal power station peak regulation cost

The constraints of the model include the operational constraints of the solar-thermal power station, DC transmission line constraints and system operation constraints. The various constraint models are expressed below.

(1) The internal power balance constraint of solar-thermal power station can be expressed from

(2) The constraint of thermal conduction working substance segment can be expressed from

(3) The capacity constraint of the thermal storage subsystem can be expressed from

(4) The constraint of power cycle subsystem can be expressed from

(1) The power operation area constraint can be expressed from

(2) The DC outgoing capacity constraint can be expressed from

(3) The upper and lower limits of power transmission adjustment constraint can be expressed from

(1) The system power balance constraint is as follows:

(2) The spinning reserve constraints as given by

(3) The upper and lower limit constraint of solar-thermal power station output power as shown in

(4) The operation constraint of PV power station and wind farm as given by

To analyse the transient voltage stability of the system under different solar-thermal power station installed capacities, the TVRI index is used to analyse the transient voltage stability of the system under the three-phase grounding short-circuit fault [

Using the peak-to-valley difference and cumulative climbing amount analyze the peak shaving effect after accessing the solar-thermal power station [

The solar-thermal power station installed capacity optimal allocation model proposed in this paper is a nonlinear programming problem. As shown in

After converting the chance constraint condition into the equivalent form and linearization, the CPLEX solver is called in the YALMIP to solve the model. The specific steps of model solving are as follows:

Firstly, input the original data (wind power and PV output forecast data, basic parameters of the arithmetic system and mathematical model of the solar-thermal power station, etc.).

Uncertainty modeling by probability distribution density function of the prediction errors of wind power and PV to obtain the set of scenarios considering the uncertainty of wind power and PV output.

Then the 365 scenario sets considering wind power and PV uncertainty are effectively scenario clustered by the K-medoids clustering algorithm to obtain 4 typical scenarios.

Finally, a multi-objective planning model is established in the MATLAB-YALMIP development environment with the maximum transient voltage stability and the minimum integrated operating cost as the integrated objective function, and the nonlinear planning model is transformed into a mixed integer linear planning problem (MILP), and the CPLEX solver is invoked to solve the planning model.

Take the improvement calculation of a new energy base in Gansu Province as an example. As of July 2021, the base has a design ±800 kV DC transmission capacity of 8 × 10^{6} kW, a design annual power transmission of 4 × 10^{10} kWh, and a total installed capacity of 1.33 × 10^{7} kW for wind and PV, including 1.045 × 10^{5} kW for wind power and 2.85 × 10^{4} kW for PV power. The solar-thermal power data comes from SAM software. The main parameters of 100 MW solar-thermal power station are shown in

Parameter | Unit | Value |
---|---|---|

Thermoelectric conversion efficiency |
% | 40 |

Optical-thermal conversion efficiency |
% | 50 |

Light field area |
m^{2} |
1.50 × 10^{6} |

Maximum output of solar-thermal power station |
MW | 100 |

Minimum output of solar-thermal power station |
MW | 10 |

Operation life of solar-thermal power station | n/year | 20 |

The parameter settings in this paper are as follows:

The peak-shaving cost coefficients ^{5} yuan/FLH. Since for most solar-thermal power stations, thermal storage capacity configurations are within 10 FLHs, this paper sets the thermal storage capacity configuration as 10 FLHs.

The initial 365 scene sets are clustered by K-medoids and reduced to 4 typical scenes. The clustering curve of wind power and PV output is shown in

According to the configuration of the solar-thermal power station; its installed capacity is as shown in

Parameter | Unit | Value |
---|---|---|

Total installed capacity | 10^{4} kW |
100 |

Single seat installed capacity | MW | 100 |

Quantity | 10 | |

DC outgoing capacity | 10^{4} kW |
800 |

To evaluate the impact of the solar-thermal power station, the operation characteristics of the base are as shown in ^{5} to 1.75 × 10^{6} kW, the curve of TVRI and comprehensive operation cost vary as shown in

It can be seen that with the increase of installed capacity, TVRI gradually decreases and then tends to be flat, indicating that the addition of the solar-thermal power station can improve the voltage stability of the base. The comprehensive operation cost increases gradually with the increase of installed capacity. This is because the larger the installed capacity of the solar-thermal power station, the higher the investment cost, and the lower the operation cost and peak regulation cost. When the installed capacity exceeds 1 × 10^{6} kW, the investment cost is too high, so the comprehensive cost increases significantly. In the double objective optimization model with the strongest transient voltage stability and the lowest comprehensive operation cost, when the installed capacity is 1 × 10^{6} kW, the TVRI is 0.359. Compared with the non-addition of the solar-thermal power station, the transient voltage stability recovery ability is greatly enhanced, and the comprehensive operation cost is 2.276 × 10^{10} yuan. At this time, it is the optimal solution, which can not only improve the transient stability of the system but also reduces the operation cost.

To study the impact of the solar-thermal power station on the DC power transmission of the base, ^{6} kW solar-thermal power station in a typical scenario.

It can be seen that wind power and PV power are still the main output power sources in the base, and the addition of solar-thermal power can better meet the DC transmission demand. From 0:00 to 9:00, the irradiance is low and the wind power fluctuates greatly. At this time, the solar-thermal power stations use the stored heat to generate electricity. From 10:00 to 18:00, when PV is in the period of large power generation, the solar-thermal power station will store part of the heat and generate electricity. At this time, the solar-thermal power station undertakes the task of peak regulation. From 19:00 to 24:00, less PV power is generated, and the solar-thermal power station uses the stored thermal energy to generate power, to meet the demand of DC transmission and stabilize the fluctuation of wind power. The time from 7:00 to 12:00 and from 18:00 to 19:00 are in the peak period of export. The configuration scheme in this paper can better satisfy DC export demands at 19:00 if there are no solar-thermal power stations.

According to the fixed installed capacity of wind power and PV, we obtained the variation curves of DC export limit and new energy consumption under the different installed capacities of the solar-thermal power stations as shown in

It is can be seen in ^{6} kW, the new energy consumption increases by 7.12% compared with that without solar-thermal addition, and the DC transmission limit increases by 23.75%.

TVRI was also analysed under different solar-thermal power stations installed capacity as shown in

Solar-thermal installed capacity/× 10^{4} kW |
TVRI |
---|---|

25 | 0.368 |

50 | 0.365 |

75 | 0.361 |

100 | 0.359 |

125 | 0.354 |

150 | 0.350 |

175 | 0.350 |

It can be seen from

According to

Installed capacity^{4} kW |
^{4} kW |
Relative quantity of |
^{4} kW |
Relative quantity of |
---|---|---|---|---|

0 | 711 | 0 | 1279 | 0 |

25 | 687 | −3.37553 | 1125 | −12.0407 |

50 | 645 | −9.2827 | 979 | −23.4558 |

75 | 565 | −20.5345 | 887 | −30.6489 |

100 | 498 | −29.9578 | 796 | −37.7639 |

125 | 400 | −43.7412 | 722 | −43.5496 |

150 | 305 | −57.1027 | 598 | −53.2447 |

175 | 277 | −61.0408 | 548 | −57.154 |

To sum up, when 1 × 10^{6} kW solar-thermal power stations are configured, the comprehensive operation cost is 2.276 × 10 ^{10} yuan, TVRI is 0.359, and it can reduce the peak-to-valley difference by 29.96%, increase the DC output power by 23.75%, and increase the consumption of new energy by 7.12%. Therefore, the configuration method in this paper is reasonable.

To study the optimal allocation of the installed capacity of the solar-thermal power station in the new energy base, based on the chance constrained programming theory, this paper comprehensively considers the DC transmission demand, transient voltage stability, and peak shaving cost, proposes the optimal allocation method of the installed capacity of the solar-thermal power station, quantitatively analyzes the operating characteristics of the solar-thermal power station, and simulates and verifies it with an example of the actual new energy base, and draws the following conclusions:

Reasonable configuration of the solar-thermal power station installed capacity can improve the transient voltage support capacity, and improve the new energy consumption rate and DC transmission limit on the premise of meeting the DC transmission demand.

The introduction of the solar-thermal power station can improve the flexibility of the base, and improve the peak shaving capacity of traditional units by undertaking peak shaving tasks.

This research was funded by

The authors declare that they have no conflicts of interest to report regarding the present article.