Multi-region power system capacity configuration determination method considering intra-region and inter-region market joint clearing of capacity market

Abstract

The invention relates to a multi-region power system capacity configuration determination method considering intra-region and inter-region market joint clearing of a capacity market, and the method comprises the steps: constructing a clearing model for intra-region and inter-region electric energy market joint optimization and a capacity market clearing model, and determining the coupling constraint of the intra-region electric energy market and inter-region power purchase, and establishing a joint clearing model to obtain a double-layer optimization model, and solving the joint clearing model by using a Karush-Kuhn-Tucker condition to obtain a joint optimization scheme of power generation installed capacity configuration of each region, cross-region power transmission capacity distribution and system reserve capacity configuration. According to the method, the capacity market, the intra-regional electric energy market and the inter-regional electric energy market are comprehensively considered, so that combined clearing of the electric energy market and the capacity market is realized on the premise of ensuring the stability of the power system, and optimal configuration of resources and coordinated clearing of the electric energy market and the capacity market are realized in the regional market range; and technical support is provided for combined stable operation of a future capacity market and an electric energy market.

Description

Technical Field

[0001] This invention belongs to the field of power system automation technology and relates to a method for determining the capacity configuration of a multi-regional power system that considers joint clearing of the inter-regional market within the capacity market. Background Technology

[0002] From the perspective of power system technology development trends and the technical challenges of high-proportion renewable energy integration, the capacity market and the electricity market jointly shape the power resource allocation mechanism. Highly flexible power regulation resources lack stable technical support policies, while high-proportion renewable energy integration inevitably requires the synchronous development of flexible resources, indicating a close technical link between the electricity market and the capacity market.

[0003] Capacity resource allocation parameters directly affect system output dispatching schemes, thereby altering the technical balance of electrical energy. The technical attributes of the capacity market and the electricity market are inherently consistent; both are developing towards systematization and coordination, with the common goal of improving system operating efficiency and promoting the transformation of the power system towards renewable energy technologies. The technical parameters of the two markets exhibit a positive correlation, reflecting the inherent laws governing system resource allocation.

[0004] The high proportion of renewable energy connected to the grid places higher technical demands on the dynamic regulation capabilities of the power system. The resource allocation mechanism of the capacity market not only enhances the reliability of power supply but also provides stable support for the technological development of flexible resource regulation. The technological coupling of the capacity market and the energy market has multiple system benefits: firstly, it can improve the utilization level of renewable energy while maintaining a high renewable energy absorption rate; secondly, it can strengthen the technological development path of flexible resource regulation, accelerating the clean and low-carbon transformation of the power system; and simultaneously, it ensures the security and reliability of system supply during the adaptation to a high proportion of renewable energy integration, and optimizes the comprehensive system indicators for renewable energy development. Summary of the Invention

[0005] This invention proposes a method for determining the capacity allocation of multi-regional power systems, considering joint clearing of the capacity market within and between regions. Taking joint clearing of the intra-provincial and inter-provincial power markets as an example, the method includes: constructing a joint optimization clearing model for the intra-provincial and inter-provincial power markets, as well as a clearing model for the capacity market; determining the coupling constraints between the intra-provincial power market and inter-provincial power purchases, and establishing a joint clearing model for the power market and the capacity market. For this joint clearing model, a two-level optimization model is proposed and solved using KKT conditions to obtain the clearing results.

[0006] This invention comprehensively considers the capacity market, the regional electricity market, and the inter-regional electricity market, achieving joint clearing of the electricity market and the capacity market while ensuring the stable operation of the power system. With the goal of finding the optimal economic solution, the method achieves optimal resource allocation within the regional market, providing technical support for the future joint and stable operation of the capacity market and the electricity market.

[0007] The technical solution adopted in this invention is as follows:

[0008] A method for determining the capacity allocation of a multi-regional power system that considers joint clearing of capacity markets within and between regions includes the following steps:

[0009] Step 1: Based on the clearing model of the joint optimization of the regional and inter-regional power market and the capacity market clearing model, construct the system performance constraint relationship. According to the technical coupling mechanism of the regional and inter-regional power market and capacity market, form a complete joint clearing model, namely the two-layer optimization model.

[0010] Step 2: Establish the Karush-Kuhn-Tucker conditions for the joint clearing model of the regional and inter-regional power market and capacity market, and based on these conditions, adopt a two-layer algorithm structure suitable for cross-regional power dispatch. Through iterative optimization, obtain the joint optimization scheme for the configuration of power generation capacity in each region, the allocation of cross-regional transmission capacity, and the configuration of system reserve capacity.

[0011] In the above technical solution, further, step 1 involves constructing a clearing model for joint optimization of the regional electricity market and a capacity market clearing model. Through technical constraint analysis, the inter-regional interaction relationships are determined, forming a two-layer optimization model, as detailed below:

[0012] In the upper-level model, the impact of the deviation between new energy output and load demand forecast on system stability and its constraint on inter-regional power dispatch strategy are considered. First, the system performance is evaluated for typical peak load day scenarios, and the inter-regional power transmission parameters, the unit output dispatch index and system reserve capacity configuration requirements are calculated. With the goal of optimizing the system operation index in the region, a technical model is constructed to determine the optimal inter-regional power exchange scheme.

[0013] In the lower-level model, the inter-regional system is guided by improving the efficiency of inter-regional resource utilization. It meets the cross-regional power balance requirements by optimizing the output configuration of the sending-end units of the inter-regional interconnection lines, and generates a set of technical parameter indicators for the inter-regional system.

[0014] Furthermore, the upper-level model aims to minimize the expected market operating costs within the region, i.e., the objective function is:

[0015]

[0016] Where: Ω T Ω G Ω RE Ω B and Ω D These respectively represent the collection of time, conventional generating units, new energy generating units, energy storage equipment, and inter-regional markets; and These are the price quote and output of conventional generator unit i within the region at time t; and These are the price quotes and power outputs of the new energy generating unit w within the region at time t; and The pricing and charging fees for energy storage device b within the region at time t; and The discharge and charging power of energy storage device b in the region at time t; and These represent the clearing price and the volume of electricity purchased in the inter-regional market d during time period t, respectively.

[0017] The specific constraints of the upper-level optimization model are as follows:

[0018]

[0019] In the formula, P t L P represents the total system load within the region at time t. i gen,min and P i gen,max These are the minimum and maximum output values ​​of generator set i under normal conditions, respectively; The switching state of generator set i at time t under normal conditions; RU i and RD i These represent climbing power and descending power, respectively; RST i and RSH i These represent the power output for power-on ramp-up and power output for power-off ramp-up; T i on and T i off These represent the minimum continuous power-on time and the shortest power-off time, respectively. and Let be the minimum and maximum energy capacities of energy storage system i; The energy storage state of energy storage system i at time t; and These represent the charging power and discharging power of energy storage system i at time t, respectively. and These are indicators for charging and discharging status, respectively. Let i be the rated capacity of energy storage system i; and These represent the charging and discharging efficiencies of the energy storage system.

[0020] Furthermore, after inter-regional electricity purchase demand is formed, the inter-regional market will operate under the condition of simultaneously satisfying power system load balance, unit operation constraints, and grid security constraints, and will be optimized and cleared with the objective of minimizing the generation cost of the sending-end units of the inter-regional interconnection lines. That is, the objective function of the lower-level model is:

[0021]

[0022] In the formula, Ω T Ω D and Ω G The set of generator units i in the sending-end region d connected by time and inter-regional communication lines respectively; The clearing volume of generator unit i in the sending region d participating in inter-regional transactions; The inter-regional transmission price for power transmission in the sending region d; Clearing out inter-regional power transactions for generator unit i in sending region d;

[0023] The specific constraints of the lower-level optimization model are as follows:

[0024]

[0025] In the formula, ξ d Transmission loss of the inter-regional tie line connected to the sending region d; The total electrical energy transmitted by the inter-regional tie lines connected to the sending region d; λ t For the dual variables of the constraint; and These are the upper and lower limits of the cleared power from inter-regional transactions for generator unit i in sending region d, respectively. and These represent the upper and lower limits of the cleared electricity traded between regions in the sending region d.

[0026] Furthermore, considering the technological coupling characteristics of the electricity market and the capacity market, an innovative nested two-layer optimization structure is adopted. This structure, based on the two-layer model of the regional electricity market as the upper layer and the inter-regional electricity market as the lower layer, further constructs a coupled optimization architecture for the capacity market and the electricity market. Specifically, the capacity market allocation mechanism is constructed as the uppermost model, and the aforementioned complete two-layer model of inter-regional and regional electricity coordination is placed as a nested structure in the lower layer, forming a three-layer nested optimization architecture. In this architecture, the uppermost capacity market model transmits resource allocation parameters of installed capacity configuration and reserve capacity requirements as key technical variables to the lower-level electricity market two-layer model, while the lower-level electricity market two-layer model feeds back system operation status indicators and technical indicators of capacity demand gaps to the upper layer, realizing deep technological coupling optimization between the capacity market and the electricity market.

[0027] Furthermore, the capacity market allocation mechanism is a capacity market collaborative allocation mechanism covering three types of resources within the region: conventional power generation units, new energy power generation units, and energy storage power stations. Among them, all three types of power sources can provide capacity support to ensure system sufficiency during peak load periods. Conventional power generation units and energy storage power stations also have the technical characteristics of providing flexible system adjustment capabilities to effectively cope with random fluctuations in load, wind power, and photovoltaic power.

[0028] Furthermore, the optimization objective of the two-layer optimization structure is to optimize system operating indicators. By balancing the technical characteristics of various resources and system operating requirements, it achieves synergistic optimization of resource allocation and system performance, thereby improving the stability and reliability of the power system. The objective function is as follows:

[0029]

[0030] In the formula, P i g , and These represent the winning bid capacities in the capacity market for conventional unit i, new energy unit w, and energy storage power station b, respectively. and These are the bid prices in the capacity market for conventional unit i, new energy unit w, and energy storage power station b, respectively. d represents the winning bid capacity of the l-th segment of the capacity demand curve; l,n The price is the l-th segment of the capacity demand curve; its constraints include:

[0031] 1) The capacity supply and demand balance constraint to ensure system adequacy during peak load periods is:

[0032]

[0033] 2) The capacity constraints for winning bids in multiple types of generating units are:

[0034] Pi g,min ≤P i g ≤P i g,max (19)

[0035]

[0036] 3) The capacity constraints for the winning bid are:

[0037] 0≤P l d ≤P l d,max (twenty two)

[0038] In the formula, P i g,min and P i g,max These are the minimum participating capacity and maximum effective capacity of conventional unit i, respectively; and These are the minimum participating capacity and the maximum effective capacity of the new energy unit w, respectively. and P represents the minimum participating capacity and maximum effective capacity of energy storage device b, respectively; l d,max This represents the maximum capacity of the l-th segment of the capacity demand curve.

[0039] Furthermore, step 2 is detailed as follows:

[0040] For system optimization problems with hierarchical structures, a model transformation technique based on KKT conditions is adopted. Since the decision parameters in the upper-level problem serve as constraints for the lower-level problem, and the lower-level problem has strict convex function characteristics and continuity, the multi-level problem is transformed into a single-level unified solution framework through KKT conditions. The lower-level optimization model is then transformed into an equivalent set of first-order optimality conditions using KKT conditions, which serve as the technical constraints for the upper-level model, forming a complete solution system.

[0041] In the specific implementation process, the KKT conditions include the following key technical elements: the gradient condition reflects the first-order optimality requirement; the complementary relaxation condition ensures the optimality at the constraint boundary; the feasibility condition guarantees the technical effectiveness of the solution; and the nonnegativity condition ensures the sign property of the Lagrange multipliers. Through the integration of technical conditions, a unified solution for multi-level models can be achieved.

[0042] The beneficial effects of this invention are:

[0043] This invention addresses the issue of regional and inter-regional joint clearing of electricity markets within a region, considering capacity markets. It proposes a method for determining the capacity configuration of multi-regional power systems, based on coupled constraints and a two-layer optimization model, involving joint clearing of regional and inter-regional markets within a region. The method outputs optimal installed capacity configurations for different types of generating units (thermal, hydro, wind, and photovoltaic) in each region; transmission capacity allocation schemes for inter-regional transmission lines; and the installed capacity and spatial layout of energy storage facilities. It also assists in confirming system operation parameters such as unit output allocation schemes for different time periods, inter-regional power flow distribution and transmission power, system reserve capacity configuration, and load shedding distribution. Through this joint clearing mechanism, the optimal installed capacity configuration of the multi-regional power system is ultimately obtained under system reliability constraints, providing technical support for power system planning and operation, and ensuring the rational allocation and efficient utilization of resources while guaranteeing power supply security.

[0044] Compared to traditional independent clearing methods in the electricity market, this invention introduces a coupling mechanism between the capacity market and inter-regional electricity purchase, enabling the intra-regional and inter-regional electricity markets to be optimized in a coordinated manner. This significantly improves the overall efficiency of resource allocation and achieves dual optimization of the clearing results of both the electricity market and the capacity market.

[0045] Compared with existing research, the method of this invention takes into account the synergistic effects of both the capacity market and the electricity market. The resulting joint clearing result can achieve stable operation of the power system, ensuring the rationality of the system's installed capacity configuration and the sufficiency of power supply. Simultaneously, this invention guarantees the technical reliability and optimized capacity configuration of multi-regional power system operation. Furthermore, the proposed joint clearing method achieves a good balance between solution accuracy and operability in practical engineering applications, providing an effective technical means for power system planning and operation. Attached Figure Description

[0046] Figure 1 It is the overall framework structure of the capacity market.

[0047] Figure 2 It is a market investment simulation process based on system dynamics.

[0048] Figure 3 This refers to the market clearing situation.

[0049] Figure 4 This refers to the trading situation in the electricity market.

[0050] Figure 5 It is the incentive effect of the capacity market compensation mechanism on diversified resources.

[0051] Figure 6 This refers to the impact of the capacity market compensation mechanism on the system's power shortage.

[0052] Figure 7 This refers to the impact of the capacity market compensation mechanism on system costs. Detailed Implementation

[0053] According to a specific embodiment of the present invention, using a province as the region, this example describes a method for determining the capacity configuration of a multi-regional power system considering joint clearing of the capacity market within and between regions. The method includes: constructing a joint optimization clearing model for the intra-provincial and inter-provincial electricity markets, as well as a capacity market clearing model; determining the coupling constraints between the intra-provincial and inter-provincial electricity purchase markets, and establishing a joint clearing model for the electricity market and the capacity market. For this joint clearing model, a two-level optimization model is proposed and solved using KKT conditions to obtain the clearing results, resulting in a joint optimization scheme for the configuration of installed power generation capacity in each region, the allocation of inter-regional transmission capacity, and the configuration of system reserve capacity.

[0054] This invention comprehensively considers the capacity market, intra-provincial electricity market, and inter-provincial electricity market, achieving joint clearing of the electricity market and capacity market while ensuring the stable operation of the power system. With the optimal economic solution as the objective, the method achieves optimal resource allocation within the regional market, providing technical support for the future joint and stable operation of the capacity market and electricity market. The method specifically includes the following steps:

[0055] Step 1: Based on the joint optimization configuration model and capacity configuration model of the provincial and inter-provincial power energy systems, construct the system performance constraint relationship, establish the technical coupling mechanism between the provincial and inter-provincial power energy systems and capacity configuration, and form a complete joint optimization configuration technical solution;

[0056] Step 2: Establish the Karush-Kuhn-Tucker conditions for the joint optimization model of power systems and capacity configuration within and between provinces, and design a two-layer algorithm structure suitable for cross-regional power dispatch based on these conditions. Solve the joint dispatch scheme of power systems and capacity configuration within and between provinces through iterative optimization, and output the system operation parameters and resource allocation results.

[0057] Step 1 involves constructing a joint optimization configuration model and a capacity configuration model for intra-provincial and inter-provincial power systems, and determining the inter-regional interaction relationships through technical constraint analysis, as detailed below:

[0058] In the upper-level model, the impact of the deviation between renewable energy output and load demand forecasting on system stability and its constraint on inter-provincial power dispatching strategies are considered. First, system performance is evaluated for typical peak load days, calculating inter-provincial power transmission capacity configuration parameters, intra-provincial unit output allocation indicators, and system reserve capacity configuration requirements. A technical model is constructed with the goal of optimizing intra-provincial system installed capacity configuration to determine the optimal inter-provincial power exchange capacity scheme. In the lower-level model, the inter-provincial system is guided by improving inter-regional resource allocation efficiency. It meets cross-regional power balance requirements by optimizing the installed capacity configuration of sending-end units along inter-provincial interconnection lines, generating a set of technical parameter indicators for the inter-provincial system.

[0059] The upper-level model aims to minimize the expected operating costs of the provincial market, i.e., the objective function is:

[0060]

[0061] Where: Ω T Ω G Ω RE Ω B and Ω D These respectively represent the collection of time, conventional generating units, new energy generating units, energy storage equipment, and inter-provincial markets; and The prices and outputs of conventional generator unit i within the province at time t are respectively: and The figures are the price and output of the new energy generating units w within the province at time t; and The quotation for discharging and the payment for charging of energy storage device b within the province at time t; and The discharge and charging power of energy storage device b within the province at time t; and These represent the clearing price and the volume of electricity purchased in the inter-provincial market during time period t, respectively.

[0062] The specific constraints of the upper-level optimization model are as follows:

[0063]

[0064]

[0065] In the formula, P t L P represents the total system load within the province at time t. i gen,min and P i gen,max These are the minimum and maximum output values ​​of generator set i under normal conditions, respectively; The switching state of generator set i at time t under normal conditions; RU i and RD i These represent climbing power and descending power, respectively; RST i and RSH i These represent the power output for power-on ramp-up and power output for power-off ramp-up; T i on and T i off These represent the minimum continuous power-on time and the shortest power-off time, respectively. and Let be the minimum and maximum energy capacities of energy storage system i; The energy storage state of energy storage system i at time t; and These represent the charging power and discharging power of energy storage system i at time t, respectively. and These are indicators for charging and discharging status, respectively. Let i be the rated capacity of energy storage system i; and These represent the charging and discharging efficiencies of the energy storage system.

[0066] After inter-provincial power purchase demand is formed by inter-provincial traders, the inter-provincial market will operate under the condition of simultaneously satisfying power system load balance, unit operation constraints, and grid security constraints, and will perform optimal clearing with the objective of minimizing the generation cost of the sending-end units of the inter-provincial interconnection lines. Therefore, the inter-provincial market model is actually an economic dispatch model considering security constraints. The objective function of the lower-level model is:

[0067]

[0068] In the formula, Ω T Ω D and Ω G They are the sets of sending-end provinces d and generator sets i in sending-end provinces d, which are connected by time and inter-provincial connecting lines, respectively. The clearing volume of generator unit i in the sending province d participating in inter-provincial transactions; The inter-provincial transmission price for the sending province d; Clearing out of inter-provincial transactions for generating unit i in sending province d.

[0069] The specific constraints of the lower-level optimization model are as follows:

[0070]

[0071] In the formula, ξ d Transmission loss of the inter-provincial link line connected to the sending province d; The total electrical energy transmitted by the inter-provincial tie line connected to the sending province (d); λ t For the dual variable of constraint (Equation 36); and These represent the upper and lower limits of the cleared inter-provincial traded electricity of generator unit i in sending province d, respectively. and These represent the upper and lower limits of the cleared electricity traded between provinces d, respectively.

[0072] To address the technological coupling characteristics of the electricity market and the capacity market, this invention designs an innovative nested two-layer optimization structure. The capacity market allocation mechanism forms the upper layer model, while the inter-provincial and intra-provincial electricity coordination mechanism forms the lower layer. In this architecture, the upper layer model transmits resource allocation parameters as key technical variables to the lower layer, while the lower layer model feeds back system operating status indicators to the upper layer.

[0073] The technical solution of this invention covers a capacity market collaborative allocation mechanism for three types of resources within the province: conventional power generation units, new energy power plants, and energy storage power plants. All three types of power sources can provide capacity support to ensure system adequacy during peak load periods. Conventional power generation units (such as thermal power units) and energy storage power plants also possess the technical characteristics to provide flexible system adjustment capabilities, effectively coping with random fluctuations in load, wind power, and photovoltaic power. This solution aims to improve the overall operational performance of the system, achieving stable and efficient operation of the power system through optimized resource allocation.

[0074] The optimization objective of this technical solution is to optimize system operating indicators. By balancing the technical characteristics of various resources and system operating requirements, it achieves synergistic optimization of resource allocation and system performance, thereby improving the stability and reliability of the power system. The objective function is as follows:

[0075]

[0076] In the formula, P i g , and These represent the winning bid capacities in the capacity market for conventional unit i, new energy unit w, and energy storage power station b, respectively. and These are the bid prices in the capacity market for conventional unit i, new energy unit w, and energy storage power station b, respectively. d represents the winning bid capacity of the l-th segment of the capacity demand curve; l,n Let be the price of the l-th segment of the capacity demand curve. Its constraints include the following:

[0077] 1) The capacity supply and demand balance constraint to ensure system adequacy during peak load periods is:

[0078]

[0079] 2) The capacity constraints for winning bids in multiple types of generating units are:

[0080] P i g,min ≤P i g ≤P i g,max (41)

[0081]

[0082] 3) The capacity constraints for the winning bid are:

[0083] 0≤P l d ≤P l d,max (44)

[0084] In the formula, P i g,min and P i g,max These are the minimum participating capacity and maximum effective capacity of conventional unit i, respectively; and These are the minimum participating capacity and the maximum effective capacity of the new energy unit w, respectively. and P represents the minimum participating capacity and maximum effective capacity of energy storage device b, respectively; l d,max This represents the maximum capacity of the l-th segment of the capacity demand curve;

[0085] In step 2, the Karush-Kuhn-Tucker (KKT) optimality conditions for the joint clearing model of the intra-provincial and inter-provincial electricity and capacity markets are constructed. Based on these conditions, a hierarchical algorithm structure for cross-regional resource coordination is designed to achieve a technical solution for multi-level power system resource optimization allocation, as detailed below:

[0086] For system optimization problems with hierarchical structures, this invention employs a model transformation technique based on KKT conditions. Since the decision parameters in the upper-level problem serve as constraints for the lower-level problem, and the lower-level problem exhibits strict convex function characteristics and continuity, the multi-level problem can be transformed into a single-level unified solution framework using KKT conditions. This method utilizes KKT conditions to convert the lower-level optimization model into an equivalent set of first-order optimality conditions, which serve as the technical constraints for the upper-level model, forming a complete solution system.

[0087] In its specific implementation, the KKT conditions include the following key technical elements: the gradient condition reflects the first-order optimality requirement; the complementary relaxation condition ensures optimality at the constraint boundary; the feasibility condition guarantees the technical effectiveness of the solution; and the nonnegativity condition ensures the sign property of the Lagrange multipliers. Through the integration of these technical conditions, a unified solution for multi-level models is achieved, effectively avoiding the computational instability and convergence problems that may arise from traditional iterative methods.

[0088] This hierarchical algorithm design based on KKT conditions has the technical advantages of high computational efficiency and strong solution stability. It is particularly suitable for multi-market coordination optimization problems in large-scale power systems, and can achieve efficient allocation of system resources and accurate calculation of operating parameters.

[0089] 1) Lagrange function:

[0090]

[0091] In the formula, f(x) is the objective function of the lower-level problem; H(x) and G(x) are the equality constraints and inequality constraints of the lower-level problem, respectively; λ and μ are the Lagrange coefficients.

[0092] 2) Lagrange stationarity constraint

[0093]

[0094] 3) Complementary relaxation conditions

[0095]

[0096] Therefore, the two-level decision-making model described above can be transformed into a single-level optimization model, that is:

[0097]

[0098] In the formula, and are the dual variables of inequalities (37) and (38), respectively, where the symbol ⊥ indicates that the product of the two expressions is 0.

[0099] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0100] This invention proposes a joint market clearing method between regions within a region that considers capacity market transactions. To verify the proposed method, an example analysis is conducted using a specific region. The power supply structure of this region is shown in Table 1.

[0101] Table 1 Power Supply Structure and Installed Capacity

[0102]

[0103] Firstly, based on such Figure 1 The capacity market clearing simulation shown transmits the parameters of the winning bid capacity to the lower-level electricity market. Afterwards, through the clearing simulation of the inter-provincial and intra-provincial electricity markets, the operating revenue of the winning bid capacity is calculated and transmitted to the upper level. From the investor's perspective, the incentive and inhibitory effects of investment signals are as follows: Figure 2 As shown.

[0104] Figure 3 The data shows the clearing of the capacity market, including the winning capacity and bid prices from various resources. Figure 4 This represents the power output of the provincial electricity market on a typical day. Figure 5 The figure illustrates the incentive effects of multiple resources with and without compensation. With capacity market compensation, the system's sufficiency fluctuates within a normal range. For thermal power units G, this suppresses excessive expansion; for photovoltaic units S, it maintains stable incentives, suppressing growth when potential oversupply is anticipated. Without capacity market compensation, the system's sufficiency initially declines, then gradually recovers, exhibiting a clear lag characteristic, and eventually leading to oversupply. For thermal power units G, in the third and fourth years, due to insufficient system sufficiency, the returns on flexible resources are higher, thus increasing thermal power investment; for photovoltaic units S, they consistently receive positive incentives. Figure 6 Regarding the impact of the capacity market compensation mechanism on system power shortages, without compensation, the system will face severe load shortages five years later due to rapid load growth and insufficient investment in generating units in the early stages. With compensation, although load shedding begins to occur five years later, the load gap is smaller thanks to the capacity adequacy guarantee mechanism. Furthermore, the system can respond promptly to challenges by issuing incentive signals to encourage the construction of new generating units, thereby effectively resolving the load shedding problem after a certain period. Figure 7 Regarding the impact of the capacity market compensation mechanism on system costs, for intra-provincial electricity purchase costs: as the load increases, the electricity purchase cost gradually rises; for inter-provincial electricity purchase costs: due to the capacity market mechanism, there are sufficient generating units within the province, so the growth of inter-provincial electricity purchase costs is relatively slow; for capacity market costs: in the initial stage, the total system cost is higher when there is compensation than when there is no compensation, but as time goes by, the total system cost is expected to decrease.

Claims

1. A method for determining capacity configuration of a multi-regional power system considering joint clearing of intra-regional and inter-regional markets with capacity market, characterized in that, The method comprises the following steps: Step 1: constructing system performance constraint relationship based on the interregional and regional electricity market joint optimization clearing model and the capacity market clearing model, and forming a complete joint clearing model, i.e. a double-layer optimization model, according to the technical coupling mechanism of the interregional and regional electricity market and the capacity market; Step 2: establishing the Karush-Kuhn-Tucker condition of the interregional and regional electricity market and the capacity market joint clearing model, and according to the condition, adopting a double-layer algorithm structure suitable for cross-regional power dispatching, and obtaining the joint optimization scheme of the regional power plant capacity configuration, cross-regional power transmission capacity allocation and system reserve capacity configuration through iterative optimization.

2. The method according to claim 1, wherein the step 1 comprises: constructing the interregional and regional electricity market joint optimization clearing model and the capacity market clearing model, determining the interaction relationship between regions through technical constraint analysis, and forming a double-layer optimization model, and the specific steps are as follows: In the upper model, the influence of new energy output and load demand prediction deviation on system stability and its constraint effect on interregional power dispatching strategy are considered; firstly, system performance is evaluated for a typical peak load day scenario, interregional power transmission parameters, regional unit output dispatching index and system reserve capacity configuration requirements are calculated, and a technical model is constructed to optimize regional system operation index as the target, so as to determine the optimal interregional power exchange scheme; In the lower model, the interregional system is oriented to improve the utilization efficiency of regional resources, and the interregional tie-line sending-end unit output configuration is optimized to meet the cross-regional power balance requirement, and the technical parameter index set of the interregional system is generated. The upper model takes the minimization of regional market operation cost expectation as the target, i.e. the objective function is:

3. The method for determining the capacity configuration of a multi-regional power system considering the joint clearing of the intraregional and interregional markets with capacity consideration according to claim 2, characterized in that: The constraint conditions of the upper optimization model are as follows: Ω T Ω G Ω RE Ω B Ω D represent the set of time, conventional units, new energy units, energy storage devices and inter-regional market respectively; and are the bid and output of conventional generating units i in the region at time t; and are the bid and output of new energy units w in the region at time t; and are the discharge bid and charging payment of energy storage devices b in the region at time t; and are the discharge and charging electricity quantity of energy storage devices b in the region at time t; and represent the clearing price and trading electricity purchase quantity of inter-regional market d at time period t; After the interregional power purchase demand is formed, the interregional market will be operated under the conditions of meeting the power system load balance, unit operation constraint and power grid safety constraint, and the interregional tie-line sending-end unit power generation cost is minimized as the target for optimization clearing, i.e. the lower model objective function is: In the formula, P t L P is the total system load in the region at time t; i gen,min and P i gen,max Pmin(i) and Pmax(i) are the minimum and maximum power output of the generator set i in normal state, respectively; S(i, t) is the switch state of the generator set i at time t in normal state. RU i and RD i represent the up and down ramping power, respectively; RST i and RSH i represent the start-up ramp-up power and the shut-down ramp-down power, respectively; T i on and T i off represent the minimum continuous on-time and the minimum off-time, respectively; and are the minimum and maximum energy capacity of the energy storage system i; is the energy storage state of the energy storage system i at time t; and are the charge and discharge power of the energy storage system i at time t, respectively; and are the charge and discharge state indications, respectively; is the rated capacity of the energy storage system i; and are the charge and discharge efficiencies of the energy storage system, respectively.

4. The method for determining the capacity configuration of a multi-regional power system considering the joint clearing of the intraregional and interregional markets with capacity consideration according to claim 2, characterized in that: The constraint conditions of the lower optimization model are as follows: where Ω T , Ω D , and Ω G are the sets of generator units i in the sending area d connected to the inter-area tie-line, respectively; is the dispatch quantity of generator unit i in the sending area d participating in the inter-area transaction; is the inter-area transmission price of the sending area d; is the dispatched energy of generator unit i in the sending area d participating in the inter-area transaction. ​ wherein ξ d is the transmission loss of the inter-area tie-line connected to the sending area d; is the total energy transmitted by the inter-area tie-line connected to the sending area d; λ t is the dual variable of the constraint; and are the upper and lower limits of the inter-area trading out-clearing energy of the generator unit i in the sending area d, respectively; and are the upper and lower limits of the inter-area trading out-clearing energy of the sending area d, respectively.

5. The method for capacity configuration determination of multi-regional power system considering capacity market of inter-regional market clearing of regional market combination according to claim 1, characterized in that: In view of the technical coupling characteristics of the electricity market and the capacity market, an innovative nested double-layer optimization structure is adopted, which is based on the double-layer model of the regional electricity market as the upper layer and the inter-regional electricity market as the lower layer, and further constructs the coupling optimization architecture of the capacity market and the electricity market. Specifically, the capacity market configuration mechanism is constructed as the uppermost layer model, and the complete double-layer model of the inter-regional and intra-regional electricity coordination is placed as a nested structure in the lower layer, forming a three-layer nested optimization architecture. In this architecture, the uppermost layer capacity market model transmits the installed capacity configuration and the standby capacity requirement resource configuration parameters to the lower layer electricity market double-layer model as key technical variables, and the lower layer electricity market double-layer model feeds back the system operation state index and the capacity demand gap technical index to the upper layer, realizing the deep technical coupling optimization of the capacity market and the electricity market.

6. The method for determining the capacity configuration of a multi-regional power system considering the joint clearing of the intraregional and interregional markets with capacity consideration according to claim 5, characterized in that: The capacity market configuration mechanism is a capacity market collaborative configuration mechanism covering three types of resources, namely, conventional output generating units, new energy power stations and energy storage power stations. The three types of power sources can provide capacity support to ensure system adequacy during load peak periods, and the conventional output generating units and the energy storage power stations also have the technical characteristics of providing system flexible regulation capability to effectively respond to the random fluctuations of load, wind power and photovoltaic power.

7. The method for determining the capacity configuration of a multi-regional power system considering the joint clearing of the intraregional and interregional markets with capacity consideration according to claim 5, characterized in that: The optimization objective of the double-layer optimization structure is the optimization of system operation index, which realizes the collaborative optimization of resource configuration and system performance by balancing the technical characteristics of various types of resources and system operation requirements, improves the stability and reliability indexes of the power system, and the objective function is as follows: In the formula, P i g , and are the winning capacities of the conventional units i, new energy units w and energy storage power stations b in the capacity market, respectively; and are the declared prices of the conventional units i, new energy units w and energy storage power stations b in the capacity market, respectively; is the winning capacity of the lth segment of the capacity demand curve. d l,n Price for the first segment of the capacity demand curve; The constraint conditions include: 1) The capacity supply and demand balance constraint for ensuring system adequacy during load peak period is: 2) The winning capacity constraint in multiple types of generating units is: P i g,min ≤P i g ≤P i g,max 3) The winning capacity constraint is: 0 < P l d ≤ P l d,max In the formula, P i g,min and P i g,max respectively, the minimum participation capacity and the maximum effective capacity of the conventional unit i; and respectively, the minimum participation capacity and the maximum effective capacity of the new energy unit w; and respectively, the minimum participation capacity and the maximum effective capacity of the energy storage device b;P l d,max is the maximum capacity of the lth segment of the capacity demand curve.

8. The multi-regional power system capacity configuration determination method considering the regional inter-regional market joint clearing of the capacity market according to claim 1, characterized in that: Step 2 is specifically as follows: For the system optimization problem with hierarchical structure, the model conversion technology path based on KKT condition is adopted. Since the decision parameters in the upper layer problem are used as the constraint conditions of the lower layer problem, and the lower layer problem has strict convex function characteristics and continuity, the multi-level problem is converted into a single-layer unified solution framework through KKT condition; the KKT condition is used to convert the lower layer optimization model into an equivalent first-order optimality condition set, which is constructed as the technical constraint of the upper layer model, forming a complete solution system; In the specific implementation process, the KKT condition contains the following key technical elements: the gradient condition reflects the first-order optimality requirement; the complementary relaxation condition ensures the optimality at the constraint boundary; the feasibility condition guarantees the technical effectiveness of the solution; and the non-negativity condition ensures the sign characteristic of the Lagrange multiplier. Through the integration of technical conditions, the unified solution of the multi-level model is realized.

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