A method for long-term power structure equilibrium under an electricity carbon market that considers external power imports.

Abstract

This invention discloses a long-term equilibrium power structure method under a carbon market considering external electricity imports: A stochastic optimization model is established, and by adjusting the costs of different power types, the initial optimal power structure under different renewable energy ratios is determined with the goal of minimizing total cost. Under the electricity market mechanism, carbon costs and external electricity imports are introduced, and the volatility of load and renewable energy output is considered. The electricity sales revenue and total cost for each power type under different scenarios are calculated to obtain the revenue situation. Considering the revenue and risk situation under different scenarios, the investment decisions of power generators for different power types are determined based on their risk preferences, and the installed capacity is updated until the installed capacity no longer changes. That is, the long-term equilibrium power structure under different initial renewable energy ratios is obtained through a dynamic simulation model.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization analysis, and in particular relates to a long-term equilibrium method for power supply structure under an electricity carbon market that considers external power imports. Background Technology

[0002] As a major carbon emitter, the power sector will be significantly impacted by the "dual carbon" targets and the establishment of a carbon market, prompting a shift in power generation structures towards low-carbon technologies. However, due to differences in geographical environment and technological levels, clean electricity from outside the region will become an important source of power for some areas. The large-scale integration of renewable energy into the grid will simultaneously alter the evolution of power generation structures both within and outside the region. Considering long-term equilibrium methods for power generation structures based on electricity from outside the region will help power grid companies conduct grid planning and dispatch under the "dual carbon" targets, ensuring the reliability and low-carbon level of power supply.

[0003] Therefore, the evolution of the power supply structure is the key to obtaining a long-term balanced power supply structure. In order to explore the power supply structure guided by the spontaneous investment behavior of power generators under different proportions of renewable energy, modeling and simulating the long-term balanced power supply structure under different proportions of renewable energy is of great significance for power grid companies to carry out power grid planning in the process of achieving the "dual carbon" goal.

[0004] In the prior art, the prior art proposed in reference document CN 104573875A is a method for low-carbon power grid optimization planning. This method includes: dividing the power capacity of each region into two categories: local capacity and external transmission capacity; establishing point-to-point direct transmission lines between regions in an equivalent network to clarify the flow of electricity and the transfer of carbon emissions between regions, and measuring carbon emissions from electricity consumption. It establishes decision variables for a low-carbon power grid optimization model, thereby constructing a low-carbon power grid optimization model composed of an objective function and constraints, and converting the constraints in the optimization model into a usable matrix calculation form. A solver is then used to solve the low-carbon power grid optimization model to obtain the low-carbon power grid optimization planning for various power sources and transmission lines in the power system. However, compared to the scheme in the prior art, which obtains a static optimal power structure through centralized optimization considering a series of constraints, this method cannot reflect the dynamic evolution process and suffers from more practical problems such as individual investment behavior of power generators and market information asymmetry. Meanwhile, existing technologies do not discuss what new power structure the spontaneous investment behavior of power generators will guide the centrally optimized power structure to, namely, the power structure with the best economic benefits for the whole society and the power structure most beneficial to power generators. Summary of the Invention

[0005] The technical objective of this invention is to provide a long-term power structure balancing method under an electricity carbon market that considers external electricity imports, so as to achieve optimized planning of a low-carbon power grid.

[0006] To solve the above problems, the technical solution of the present invention is as follows:

[0007] A method for long-term power structure equilibrium under an electricity carbon market that considers external power imports includes the following steps:

[0008] S1: Establish a stochastic optimization model, and determine the initial optimal power structure under different proportions of new energy sources by adjusting the cost of different power types and minimizing the total cost as the optimization objective.

[0009] S2: Combining market mechanisms and considering the volatility of load and renewable energy output, calculate the electricity sales revenue and total cost of each power type under the initial optimal power structure in different scenarios, thereby obtaining the revenue of different power types in different scenarios.

[0010] S3: Based on the revenue calculated in step S2, consider the revenue and risk under different scenarios, determine the investment decisions for different power types and update their installed capacity according to the risk preferences of power generators, that is, change the initial optimal power structure; repeat steps S2 to S3 until all power types no longer update their installed capacity, that is, obtain a long-term balanced power structure.

[0011] Step S1 further includes step S1.1, which is as follows:

[0012] A parameter extraction function for incoming calls from outside the area is constructed, and the transmission cost of incoming calls from outside the area is calculated using the following formula:

[0013]

[0014] Among them, VC g,trans For the annual transmission cost of generator sets outside the region, π trans P is the cost of transmitting a unit of electricity outside the region. g,t The total amount of electricity transmitted to this region annually by generators outside the region at time t, where g represents different power source types.

[0015] Step S1 further includes step S1.2, which is as follows:

[0016] By coupling the carbon market with the electricity market, carbon prices are reflected in the generation costs of different power sources as carbon costs. The carbon costs of thermal power units are then calculated using the following formula:

[0017]

[0018] Among them, VC g,carbon For the annual carbon emission cost of generator set g, η g Let π be the carbon emission factor of power source type g. carbon Let P be the carbon price, η be the paid ratio, and P be the carbon price.g,t Let g be the amount of electricity generated by generator set g at time t.

[0019] Step S1 further includes step S1.3, which is as follows:

[0020] A stochastic optimization model is adopted to consider the possibilities of multiple scenarios. By adjusting the cost of different power types, the initial optimal power structure is obtained through centralized optimization.

[0021] The objective function of the stochastic optimization model is as follows:

[0022]

[0023] Where, ξ D and ξ R Let be the sets of fluctuations for load and renewable energy power, respectively, and d and r be arbitrary selected scenarios, min∑ g∈G C g ×FC g C represents the total investment cost of the generator set. g For the installed capacity of generator set g, FC g The annualized investment cost per unit capacity of the generator set is g. P represents the average total operating cost of the generator set across all scenarios. dr,g,t Let VC be the output of generator set g at time t in scenario dr. dr,g The operating cost of generator unit g includes fuel cost and variable maintenance cost. For thermal power units, carbon cost needs to be added separately, and for external units, transmission cost needs to be added separately. VOLL is the average cost of loadout across all scenarios, where VOLL is the user's loadout value. dr,t This represents the load loss at time t in scenario dr.

[0024] The power conservation constraints for the stochastic optimization model are as follows:

[0025]

[0026] Among them, L dr,t The load at each moment;

[0027] The upper and lower limits of the generator set's output are constrained as follows:

[0028]

[0029] Among them, A dr,g,t Let g be the availability rate of generator set g at time t under scenario dr;

[0030] The upper and lower limits of the load shedding are as follows:

[0031]

[0032] Step S2 further includes step S2.1, which is as follows:

[0033] The electricity sales revenue for each power source type in the electricity carbon market is calculated as follows:

[0034]

[0035] Where, λ dr,t Let t be the clearing price at time t.

[0036] Step S2 further includes step S2.2, which is as follows:

[0037] Based on the electricity sales revenue minus costs, the revenue of different power sources in different scenarios is obtained, as detailed below:

[0038]

[0039] Where, N dr,g Let F be the profit of generator set g in scenario dr. dr,g The cost of generator set g;

[0040] In addition, F dr,g This includes fixed costs and operating costs. Operating costs are categorized according to different generator set types and geographical locations, specifically as follows:

[0041]

[0042]

[0043] Step S3 further includes step S3.1, which is as follows:

[0044] Constructing an investment decision deterministic function requires considering the average return and return risk across all scenarios. The result of this function will then serve as the basis for investment decisions for different generator sets, as detailed below:

[0045]

[0046] Where, mean(F) dr,g Let be the average return of generator set g across all scenarios, std(F) dr,g Let ) be the standard deviation of the revenue of generator set g across all scenarios, and a g Risk weights.

[0047] Step S3 further includes step S3.2, which is as follows:

[0048] Update the installed capacity based on the investment decision in step S3.1, and determine whether to update the installed capacity again, as follows:

[0049]

[0050] Among them, C g,i+1 and C g,i Let be the installed capacity at the (i+1)th and ith iterations, respectively, and β be the proportion of user investment. The update of the installed capacity depends on the revenue. With fixed cost FC g The ratio of RFR to risk-free rate.

[0051] Because of the above technical solutions, this invention has the following advantages and positive effects compared with the prior art:

[0052] This invention establishes a model that considers electricity from outside the region under the carbon market, abstracting specific carbon market rules and the characteristics of electricity from outside the region into a specific mathematical model, providing a scenario setting for the long-term equilibrium model;

[0053] A stochastic optimization model was established to determine the power supply structure with different proportions of renewable energy. Due to the uncertainty of load and renewable energy output, a stochastic optimization model considering multiple scenarios was established. By adjusting the fixed cost of renewable energy, the optimal power supply structure that meets the requirements of different proportions of renewable energy under the overall planning is obtained with the goal of minimizing the total cost. This can be used as the initial value for the long-term equilibrium model.

[0054] A long-term equilibrium calculation method for power structure considering risks was designed. This method takes into account the revenue and risk of power generators under different scenarios and their risk preferences to determine their investment decisions for the power generation capacity of different power types. Finally, a long-term equilibrium power structure is obtained through a dynamic simulation model. Attached Figure Description

[0055] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention.

[0056] Figure 1 This is a flowchart of a long-term power structure balancing method under an electricity carbon market that considers external power supply according to the present invention.

[0057] Figure 2 This is a schematic diagram of the power supply from outside the designated area according to the present invention. Detailed Implementation

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without any creative effort.

[0059] To keep the drawings concise, only the parts relevant to the invention are shown schematically in each figure, and they do not represent the actual structure of the product. Furthermore, for ease of understanding, in some figures, only one of components with the same structure or function is shown schematically, or only one is labeled. In this document, "one" can mean not only "only one" but also "more than one".

[0060] The following detailed description, in conjunction with the accompanying drawings and specific embodiments, provides a method for long-term power structure balancing under an electricity-carbon market considering external power imports, as proposed in this invention. The advantages and features of this invention will become clearer from the following description and claims.

[0061] Example

[0062] Power structure planning under the carbon electricity market is a current hot research area. Traditional power structure planning considering external power imports is based on static planning problems with centralized optimization, lacking research on the dynamic investment process of power generators in different types of power sources within and outside the region, i.e., studying the dynamic evolution path of power structure under different policies and scenarios. Therefore, this embodiment considers the impact of external power imports under the carbon electricity market, and, combined with the expected returns and risks of power generators, establishes models for power generators' investments in different types of power sources, forming a long-term equilibrium calculation model for the power structure.

[0063] Furthermore, current research on power structure planning based on the dynamic investment of power generators does not consider the impact of different renewable energy ratios. Different renewable energy ratios will change the investment decisions of power generators, thus affecting the initially planned power structure. Therefore, this embodiment obtains a power structure with different renewable energy ratios through stochastic optimization. This power structure serves as the initial power structure (considered as a power structure guided by different policy expectations). Then, based on the investment of power generators, a long-term equilibrium power structure is obtained through iterative analysis. This allows for the analysis of changes in the initially set renewable energy ratio target under market-based mechanisms.

[0064] This embodiment will now be described in detail, see below. Figure 1 and Figure 2 This embodiment provides a method for long-term power structure balancing under an electricity carbon market that considers external power imports, including the following steps:

[0065] First, in step S1, a model (stochastic optimization model) considering electricity from outside the region was established under the electricity carbon market. By adjusting the costs of different power sources, the optimal power structure under different power source proportions was determined based on the stochastic optimization model with the goal of minimizing total cost.

[0066] Step S1 can be further divided into S1.1, S1.2, and S1.3. Specifically, in step S1.1, a parameter extraction function for imported power from outside the region is constructed as a mathematical model for imported power from outside the region. Due to the low-carbon transformation of the energy structure, the proportion of new energy power generation will gradually increase. However, new energy power generation is highly dependent on objective factors such as geographical location and climate (e.g., hydropower, wind power). In addition, power generators in the region usually consider investing outside the region. In addition to fixed costs and operating costs, generator sets used to provide imported power from outside the region also need to bear the additional costs of cross-regional power transmission, i.e., transmission costs, compared to generator sets within the region. A schematic diagram is shown below. Figure 2 As shown, region A is the local region, and region B is the region providing external power. Therefore, a function is constructed here to calculate the transmission cost of external power, and the calculation formula is as follows:

[0067]

[0068] Among them, VC g,trans For the annual transmission cost of generator sets outside the region, π trans P is the cost of transmitting a unit of electricity outside the region. g,t The total amount of electricity transmitted to this region annually by generators outside the region at time t, where g represents different power source types.

[0069] Next, in step S1.2, the carbon market is coupled with the electricity market, meaning the carbon price is reflected in the power generation cost of the generating units as a carbon cost. The emergence of a carbon price will indirectly increase the variable costs of traditional thermal power generating units, thereby raising electricity prices. While the costs of traditional generating units increase, the profits of renewable energy generating units further increase. This will change the power generators' strategies for different power source types, spontaneously promoting the transformation of the power structure towards cleaner and lower-carbon power. Therefore, the carbon cost of thermal power sources is calculated in this way, i.e., a carbon cost generation function is constructed. The formula for calculating the function is as follows:

[0070]

[0071] Among them, VC g,carbon For the annual carbon emission cost of generator set g, η g π is the carbon emission factor for power source type g, i.e., the amount of carbon dioxide emitted per unit of electricity produced. carbon Let P be the carbon price, σ be the paid ratio, and as σ increases, the carbon cost faced by the generator set increases. When σ = 0, it means that the coupling of the carbon market has not been considered.g,t Let g be the amount of electricity generated by generator set g at time t.

[0072] Next, in step S1.3, by adjusting the cost parameters of the generator sets, an optimal power structure with different proportions of renewable energy is generated as the initial power structure. Considering the uncertainties in load and renewable energy output, this embodiment uses a stochastic optimization model to account for multiple scenarios, and then obtains the initial power structure through centralized optimization. The objective function of the stochastic optimization model is as follows:

[0073]

[0074] Because the uncertainties of load and renewable energy output are taken into account, there are multiple scenarios in each iteration, where ξ D and ξ R Let be the sets of fluctuations in load and renewable energy power, respectively, and let d and r be arbitrary selected scenarios, mib∑ g∈G C g ×FC g C represents the total investment cost of the generator set. g For the installed capacity of generator set g, FC g The annualized investment cost per unit capacity of the generator set is g. P represents the average total operating cost of the generator set across all scenarios. dr,g,t Let VC be the output of generator set g at time t in scenario dr. dr,g The operating cost of generator unit g consists of fuel cost and variable maintenance cost. If it is a thermal power unit, carbon cost needs to be added in addition; if it is an external unit, transmission cost needs to be added in addition. VOLL is the average cost of loadout across all scenarios, where VOLL is the user's loadout value. dr,t This represents the load loss at time t in scenario dr.

[0075] The power conservation constraints of the above stochastic optimization model are as follows:

[0076]

[0077] Among them, L dr,t The load at each moment.

[0078] The upper and lower limits of the generator set's output are constrained as follows:

[0079]

[0080] Among them, A dr,g,tLet g be the availability rate of generator set g at time t under scenario dr, which is the maximum power generation capacity of the generator set. The availability rate of traditional generator sets is a constant, while the availability rate of new energy generator sets varies with the scenario and time.

[0081] The upper and lower limits of the load shedding are as follows:

[0082]

[0083] After step S1, proceed to step S2. In conjunction with market mechanisms, it is necessary to consider the volatility of load and renewable energy output, calculate the electricity sales revenue and total cost of each power type under the optimal power structure in different scenarios, and thus obtain the revenue situation of different power types in different scenarios.

[0084] In step S2.1, a revenue calculation function for selling electricity in the electricity carbon market is constructed for various types of power generation units. The calculation formula is as follows:

[0085]

[0086] Where, λ dr,t Let t be the clearing price at time t, at which the generator sets sell electricity.

[0087] Moving on to step S2.2, to obtain the revenue situation, costs are deducted from the data from step S2.1 to obtain the revenue situation of different power supply types in different scenarios, as detailed below:

[0088]

[0089] Where, N dr,g Let F be the profit of generator set g in scenario dr. dr,g The cost of generator set g; in addition, F dr,g This includes fixed costs and operating costs. Operating costs are categorized according to different generator set types and transmission costs, specifically as follows:

[0090]

[0091]

[0092] Finally, proceed to step S3. Based on the revenue calculated in step S2, consider the revenue and risk under different scenarios, determine the investment decisions for different power types and update their installed capacity according to the risk preferences of power generators, that is, change the optimal power structure; repeat steps S2 to S3 until all power types no longer update their installed capacity, indicating that a long-term balanced power structure has been obtained.

[0093] Specifically, step S3 further includes step S3.1, constructing an investment decision determination function, which needs to consider the average return and return risk under all scenarios, and using the result of the investment decision determination function as the basis for investment decisions for different generator sets, as follows:

[0094]

[0095] Where, mean(F) dr,g ) represents the average revenue of generator set g across all scenarios, and this value represents the expected revenue of the generator owner; std(F dr,g The standard deviation of the generator set g's revenue across all scenarios represents the generator operator's revenue risk. The larger this value, the greater the risk for the generator operator to obtain the expected revenue, and the lower the user's utility; therefore, it is a minus sign. g The risk weight depends on the risk appetite of different generators.

[0096] Proceed to step S3.2, update the installed capacity based on the investment decision in step S3.1, and determine whether to update the installed capacity further, as follows:

[0097]

[0098] Among them, C g,i+1 and C g,i Let be the installed capacity at the (i+1)th and ith iterations, respectively, and β be the proportion of user investment. The update of the installed capacity depends on the revenue. With fixed cost FC g The ratio of RFR to the expansion and decommissioning of power generation capacity corresponds to the expansion and decommissioning of power generation capacity. RFR is the risk-free rate of return.

[0099] The updated power structure is then fed back into module S2 for the next iteration. If the profit margin of the power generators' investment is less than the risk-free rate of return, they will cease investment. When all power generators have stopped building capacity or have withdrawn from the market, the resulting power structure represents the long-term equilibrium. Comparing this with the centrally optimized power structure allows for analysis of how to adjust relevant settings to achieve the set renewable energy targets.

[0100] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments. Even if various changes are made to the present invention, if these changes fall within the scope of the claims of the present invention and their equivalents, they shall still fall within the protection scope of the present invention.

Claims

1. A method for long-term equilibrium of power structure in electricity-carbon market considering external electricity, characterized in that, Includes the following steps: S1: Establish a stochastic optimization model, and determine the initial optimal power structure under different proportions of new energy sources by adjusting the cost of different power types and minimizing the total cost as the optimization objective. S2: Combining market mechanisms and considering the volatility of load and renewable energy output, calculate the electricity sales revenue and total cost of each power type under the initial optimal power structure in different scenarios, thereby obtaining the revenue of different power types in different scenarios. S3: Based on the revenue calculated in step S2, consider the revenue and risk under different scenarios, determine the investment decisions for different power types and update their installed capacity according to the risk preferences of power generators, that is, change the initial optimal power structure; repeat steps S2 to S3 until all power types no longer update their installed capacity, that is, obtain a long-term balanced power structure. Step S1 further includes the following steps: Step S1.1: A parameter extraction function for incoming calls from outside the area is constructed, and the transmission cost of incoming calls from outside the area is calculated using the following formula: in, Selecting the power supply type The annual transmission cost for the portion of the generator set located outside the designated area. The cost of transmitting a unit of electricity outside the region. Selecting the power supply type For the generator set outside the area, every year The total amount of electricity transmitted to this region in real time. Index for power type; Step S1.2: By coupling the carbon market with the electricity market, carbon prices are reflected in the generation costs of different power sources as carbon costs. The carbon costs of thermal power units are then calculated using the following formula: wherein, is the selected power type is the annual carbon emission cost for the generator set, is the selected power type is the carbon emission coefficient for the generator set, is the carbon price, is the compensated proportion, is the selected power type is the is the power generation at the moment; Step S1.3: A stochastic optimization model is adopted to consider the possibilities of multiple scenarios. By adjusting the cost of different power types, the initial optimal power structure is obtained through centralized optimization. The objective function of the stochastic optimization model is as follows: wherein, and are the fluctuation sets of load and new energy power supply respectively, is an arbitrary scenario selected, is the total investment cost of the generator set, is the selected power supply type is the installed capacity of the generator set, is the selected power supply type is the annualized investment cost per unit capacity of the generator set; is the average value of the total operating cost of the generator set output under all scenarios, is the output of the generator set of the power supply type at the moment under the scenario is the operating cost of the generator set, the operating cost including fuel cost and variable operation and maintenance cost, and the thermal power generator set additionally needs to add carbon cost, and the external generator set additionally needs to add transmission cost, is the average value of the loss load cost under all scenarios, is the loss load value of the user, is the loss load amount at the moment under the scenario ​​​​​ The power conservation constraints of the stochastic optimization model are as follows: wherein, is the load amount for each time; The upper and lower limits of the generator set's output are constrained as follows: wherein, is selected power source type for the scenario of a genset down availability at the instant The upper and lower limits of the load shedding are as follows: 。 2.The method of claim 1, wherein, Step S2 further includes step S2.1, which is as follows: The electricity sales revenue for each power source type in the electricity carbon market is calculated as follows: wherein is the clearing price at the time. 3.The method of claim 2, wherein, Step S2 further includes step S2.2, which specifically includes: Based on electricity sales revenue minus costs, the revenue of different power sources in different scenarios is obtained, as detailed below: wherein, is the cost of the selected power type for the scenario of a genset , and is the cost of the selected power type for the scenario of a genset. In addition, The fixed cost and the operation cost are included, and the operation cost is classified according to different generator set types and geographical locations, specifically: 。 4. The method for long-run equilibrium of power structure in electricity-carbon market considering external electricity according to claim 3, characterized in that, Step S3 further includes step S3.1, which is as follows: Constructing an investment decision deterministic function requires considering the average return and return risk across all scenarios. The result of this function will then serve as the basis for investment decisions for different generator sets, as detailed below: wherein, is the selected power type is the mean of the revenues for all scenarios of the generator set, is the selected power type is the standard deviation of the revenues for all scenarios of the generator set, is the risk weight.

5. The method for long-run equilibrium of power structure in electricity-carbon market considering external electricity according to claim 4, characterized in that, Step S3 further includes step S3.2, which is as follows: The installed capacity is updated based on the investment decision in step S3.1, and it is determined whether to update the installed capacity again, as follows: where and are the first and the installed capacity at the time of the i-th iteration, is the ratio of the user's investment, the update of the installed capacity depending on the revenue to the fixed costs , and is the risk-free rate of return.

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