Combined cooling heating and power supply cross-border comprehensive energy system double-layer optimization method based on energy storage power station service

Abstract

The invention discloses a combined cooling heating and power supply cross-border comprehensive energy system double-layer optimization method based on energy storage power station service, which comprises the following steps: applying the energy storage power station service to a cross-border comprehensive energy system with combined heat and power generation to construct a combined cooling heating and power supply cross-border comprehensive energy system based on the energy storage power station service; the method comprises the following steps: establishing a double-layer optimization model constructed by an upper-layer model and a lower-layer model for a combined cooling heating and power supply cross-border comprehensive energy system based on energy storage power station service, and solving the double-layer optimization model; in the solving process, a lower-layer problem is converted into a group of additional constraints of an upper-layer model by constructing a Lagrange function for a lower-layer model and applying a KKT complementary relaxation condition in the lower-layer model; and performing linear conversion on a nonlinear part in a single-layer model formed by the upper-layer model and the additional constraint into mixed integer linear constraint. According to the double-layer optimization model under the service of the shared energy storage power station, the customer cost can be effectively reduced, energy storage resources are saved, and mutual benefits between customers and operators of the energy storage power station are promoted.

Description

Technical Field

[0001] The present invention relates to a double-layer optimization method for a cross-border integrated energy system of combined cooling, heating and power based on energy storage power station services, and belongs to the field of target optimization of cross-border integrated energy systems. Background Art

[0002] Integrated energy systems (IES) are considered an effective solution to the energy crisis. IES achieves multi-energy complementarity, layered energy utilization, and refined development of energy resources. In recent decades, the international community has become increasingly concerned about global warming and has recognized the urgent need to reform the current economic development model. Efforts are being made to transition to a low-carbon, environmentally friendly model, aiming to address the fundamental challenges posed by current economic practices. However, while a single IES can provide local benefits, it often lacks the resource diversity, scalability, and economic efficiency of a cross-border integrated energy system (CBIES). CBIES can leverage a wider resource pool and shared infrastructure to enhance energy security and sustainability. Therefore, CBIES plays a vital role in promoting international energy interconnection, which is characterized by rapid deployment, wide coverage, and support for local renewable energy consumption and multi-energy integration. In addition, the synergy between independent energy systems in different countries can significantly expand energy supply capacity while improving the stability of the power grid. Such cooperation greatly improves the overall reliability of energy supply.

[0003] Recent research on planning and developing CBIES has made significant progress. Reference [1] discusses the significant advantages of cross-border integration of renewable energy systems. It explores various models of global energy trade between regions and evaluates their benefits and challenges. Reference [2] studies cross-border energy trade and renewable energy integration, which are increasingly important for countries to effectively meet energy needs, reduce costs, promote socio-economic stability, and address climate issues. Reference [3] builds a thermodynamic model of the proposed IES based on a field survey of a real China-Myanmar cross-border industrial park and evaluates its performance. Reference [4] adopts a multi-model approach to examine the changes in local energy market output indicators under different scenarios when infrastructure is changed. Reference [5] proposes an innovative approach to achieve such cross-border communities by connecting the distribution system to each country through switchable components, generation, storage or consumption assets. Reference [6] studies a key issue of generation adequacy policy in a multi-market environment: the impact of incorporating foreign generators and interconnectors into national capacity mechanisms. Reference [7] explores factors related to cross-border grid interconnection in the South Asian Association for Regional Cooperation (SAARC) region, including regional barriers, trade potential, technical standardization, and regulatory framework. Reference [8] explores the role of cross-border electricity trade (CBET) in promoting the growth of renewable energy, and uses a non-parametric regression method to demonstrate a significant causal relationship between CBET and the increase in renewable energy generation by addressing key challenges such as intermittency and reliability.

[0004] Energy storage technology enables the rapid storage and release of electricity, allowing users to store excess energy and reduce electricity costs by utilizing the difference between peak and off-peak electricity prices. This approach optimizes the efficiency of energy resource utilization, alleviates power system load fluctuations, and enhances the stability of power supply. In reference [9], a regional integrated energy system was developed that incorporates renewable energy, energy storage, and electricity / heat sharing between power stations. A multi-objective optimization model was developed for the regional integrated energy system, focusing on economic benefits, carbon emission reduction, and system reliability. Reference

[10] proposed a joint planning method based on a two-stage multi-cooperative game for local integrated energy system alliances and shared energy storage suppliers based on various scenarios and uncertainties of electric vehicles. Reference

[11] developed a three-level planning and scheduling model for electric vehicle charging stations to support shared energy storage power stations serving multi-park integrated energy systems. In addition, a profit distribution model based on Shapley value was established to enhance the cooperation and participation enthusiasm among stakeholders.

[0005] Existing research mainly deals with the integration of cross-border renewable energy systems, global energy trade challenges, and the integration of transnational electricity markets. It examines the development of cross-border energy system models and the impact of infrastructure changes on energy markets. However, these literatures optimize and apply energy storage technology, highlighting its core role in cross-border integrated energy systems, but there is limited exploration of how two-level optimization models and shared energy storage services enhance system economy and stability. In addition, current research does not fully consider the key role of energy storage in optimizing scheduling and controlling costs, nor does it systematically analyze the contribution of energy storage to improving resource utilization efficiency.

[0006] The references mentioned above are as follows:

[0007] [1] Anbumozhi V, Kumar Singh B. Cross-Border Integration of Renewable Energy Systems. Cross-Border Integration of Renewable Energy Systems. 2023.

[0008] [2] Anbumozhi V,Bhupendra KS. Cross-Border Integration of RenewableEnergy Systems: Experiences, Impacts, and Drivers. Taylor&Francis; 2024.

[0009] [3]Lin

[0010] [4][9]Siddiqui S,Vaillancourt K,Bahn O,Victor N,Nichols C,Avraam C,Brown M.Integrated North American energy markets under different futures ofcross-border energy infrastructure.Energy Policy.2020Sep 1;144:111658.

[0011] [5]Stroink A,Diestelmeier L,Hurink JL,Wawer T.Benefits of cross-border citizen energy communities at distribution system level.EnergyStrategy Reviews.2022Mar 1;40:100821.

[0012] [6]Cepeda M.Assessing cross-border integration of capacity mechanismsin coupled electricity markets.Energy Policy.2018Aug 1;119:28-40.

[0013] [7]Ul-Haq A,Hassan MS,Jalal M,Ahmad S,Anjum MA,Khalil IU,WaqarA.Cross-border power trade and grid interconnection in SAARC region:Technicalstandardization and power pool model.IEEEAccess.2019Dec 9;7:178977-9001.

[0014] [8]Jha AP,Mahajan A,Singh SK,Kumar P.Renewable energy proliferationfor sustainable development:Role of cross-border electricity trade.RenewableEnergy.2022Dec1;201:1189-99.

[0015] [9]Jia J,Li H,Wu D,Guo J,Jiang L,Fan Z.Multi-objective optimizationstudy of regional integrated energy systems coupled with renewable energy,energy storage,and inter-station energy sharing.Renewable Energy.2024May 1;225:120328.

[0016]

[10] Chen C,Zhu Y,Zhang T,Li Q,Li Z,Liang H,Liu C,MaY,Lin Z,YangL.Two-stage multiple cooperative games-basedjoint planning for shared energystorage provider and local integrated energy systems.Energy.2023Dec 1;284:129114.

[0017]

[11] Jianwei G,Fangjie G,Yu Y,Haoyu W,Yi Z,Pengcheng L.Configurationoptimization and benefit allocation model of multi-park integrated energysystems considering electric vehicle charging station to assist services ofshared energy storage power station.Journal of Cleaner Production.2022Feb 15;336:130381. Summary of the invention

[0018] The present invention provides a two-layer optimization method for a cross-border integrated energy system of combined heat and power based on energy storage power station services, which is used to establish a two-layer optimization model constructed by an upper model and a lower model for the cross-border integrated energy system of combined heat and power based on energy storage power station services, and solve it to obtain the optimization result.

[0019] The technical solution of the present invention is: a two-layer optimization method for a cross-border integrated energy system of combined heat and power based on energy storage power station services, comprising: applying energy storage power station services to a cross-border integrated energy system with cogeneration to construct a cross-border integrated energy system of combined heat and power based on energy storage power station services; for the cross-border integrated energy system of combined heat and power based on energy storage power station services, a two-layer optimization model constructed by an upper model and a lower model is established and solved; during the solution process, the lower problem is converted into a set of additional constraints of the upper model by constructing a Lagrangian function for the lower model and applying the KKT complementary relaxation conditions in the lower model; the nonlinear part in the single-layer model formed by the upper model and the additional constraints is linearly converted into a mixed integer linear constraint.

[0020] Furthermore, the upper model includes establishing a first objective function and a first constraint condition to minimize the annual operating cost of the energy storage power station as the optimization goal; the first objective function is:

[0021]

[0022] Where C1 represents the annual operating cost of the energy storage power station; W is the number of typical days, C er is the exchange rate, T w is the number of days corresponding to the wth typical day; C inv,w is the average daily investment and maintenance cost of the energy storage power station, C ess,s,w is the cost of purchasing electricity from the IES in each country on a typical day, C ess,b,w is the revenue from selling electricity from each country’s IES on a typical day, C serve,w It is the daily service fee of the energy storage power station;

[0023] The first constraint condition includes:

[0024] Energy storage power station capacity constraints:

[0025]

[0026] In the formula, β is the energy multiplier of the energy storage power station; and They are the maximum charge / discharge power and maximum capacity of the energy storage power station respectively;

[0027] Charging state and charging and discharging power constraints of energy storage power stations:

[0028]

[0029] In the formula, E ess (t) is the energy stored in the energy storage power station during period t, η abs and η relea are the charging and discharging efficiency of the energy storage power station, P ess,abs (t) and P ess,relea (t) are the charging power and discharging power of the energy storage power station in period t, Δt is the scheduling period; E ess (0) is the initial storage energy of the energy storage station, U abs (t) and U relea (t) are binary variables representing the charging and discharging states of the energy storage power station during period t.

[0030] Furthermore, the lower model includes a second objective function and a second constraint condition, and the second objective function is:

[0031]

[0032] Where C2 represents the annual operating cost of the cross-border integrated energy system for combined cooling, heating and power generation; W is the number of typical days, C er is the exchange rate, T w is the number of days corresponding to the wth typical day; C grid,w is the cost of purchasing electricity from the grid for each typical day, C fuel,w is the natural gas cost for gas turbines and gas boilers on a typical day, C CO2,w is the tiered carbon trading cost, C inv,w is the average daily investment and maintenance cost of the energy storage power station, C ess,b,w is the revenue from selling electricity from each country’s IES on a typical day, C ess,s,w is the cost of purchasing electricity from the IES in each country on a typical day, C serve,w It is the daily service fee of the energy storage power station;

[0033] The second constraint condition includes:

[0034] Power balance constraints:

[0035] P GT,w,i (t)+P WT,w,i (t)+P PV,w,i (t)+P grid,w,i (t)+P ess,b,w,i (t)-P ess,s,w,i (t)-P EC,w,i (t)-P load,w,i (t)=0,λ1

[0036] Where P GT,w,i (t) is the gas turbine output power of country i in period t on each typical day, P WT,w,i (t) and P PV,w,i (t) are the wind power and photovoltaic power generation of the ith country in the tth period on each typical day, P ess,b,w,i (t) The amount of electricity purchased by the ith country from the energy storage power station at the tth period of each typical day, P ess,s,w,i (t) The amount of electricity sold by the ith country to the energy storage power station at the tth time period on each typical day, P EC,w,i (t) is the power consumption of refrigerators in country i during each typical day and time period t, P load,w,i (t) is the power load power of country i during time period t on each typical day;

[0037] Cold power balance constraints:

[0038] P EC,w,i (t)·η EC +Q AC,w,i (t)-P cool,w,i (t)=0,λ2

[0039] Where η EC is the efficiency of the electric refrigerator, Q AC,w,i (t) is the cooling power output of the absorption chiller for country i at time t on each typical day, P cool,w,i (t) is the cooling load power of country i during period t on each typical day;

[0040] Thermal power balance constraints:

[0041] Q GB,w,i (t)+P HX,w,i (t)-P heat,w,i (t)=0,λ3

[0042] Where P HX,w,i (t) is the heating power output of the heat exchanger in the ith country during each typical day and time period t, P heat,w,i (t) is the heat load power of country i during each typical day and period t; Q GB,w,i (t) is the thermal power output of the gas boiler in country i during period t on each typical day

[0043] Waste heat boiler balance constraints:

[0044]

[0045] Where η HX and η AC are the heat exchanger and absorption chiller efficiencies, γ GT is the thermal power ratio of the gas turbine; ηWH is the waste heat boiler efficiency;

[0046] Energy storage power station charging and discharging power balance constraints:

[0047]

[0048] Where P ess,abs (t) and P ess,relea (t) are the charging power and discharging power of the energy storage power station during period t;

[0049] CBIES equipment output upper and lower limit constraints:

[0050]

[0051] In the formula, and are the minimum and maximum output power of the gas turbine, and are the minimum and maximum output powers of the absorption chiller, and are the minimum and maximum output powers of the electric refrigerator, and are the minimum and maximum output power of the gas boiler, and are the minimum and maximum output powers of the heat exchanger, respectively;

[0052] Constraints on purchasing electricity from the grid:

[0053]

[0054] In the formula, The maximum amount of power each country can buy from the grid.

[0055] Power exchange constraints between CBIES and energy storage power stations:

[0056]

[0057] In the formula, is the maximum exchange power between each country and the energy storage power station, U buy,w,i (t) and U sale,w,i (t) are the purchase and sale status indicators between the ith country and the energy storage power station on each typical day; λ1,λ2,λ3,λ4,λ5 are the Lagrange multipliers corresponding to the equality constraints, is the Lagrange multiplier corresponding to the inequality constraint.

[0058] Furthermore, the exchange rate is predicted using an ARIMA-GARCH model.

[0059] The beneficial effects of the present invention are:

[0060] The present invention establishes a cross-border integrated energy system architecture for combined cooling, heating and power based on energy storage power station services. Based on this architecture, a two-layer optimization model is used to develop an energy storage decision framework, which takes into account the interests of energy storage power station operators and CBIES; further, in order to manage exchange rate fluctuations and uncertainties in cross-border energy transactions, the present invention adopts the ARIMA-GARCH model. Finally, the present invention demonstrates the rationality and effectiveness of the proposed two-layer optimization model through case studies. Practice has proved that the proposed two-layer optimization model under the shared energy storage power station service can effectively reduce customer costs, save energy storage resources, and promote mutual benefit between customers and energy storage power station operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the cross-border integrated energy system architecture;

[0062] Figure 2 It is a schematic diagram of the double-layer optimization solution process;

[0063] Figure 3 It is the exchange rate forecast chart;

[0064] Figure 4 The power curves of cooling, heating and electricity loads in country A and the estimated power generation of photovoltaic and wind power generation are shown in Figure 1. (a) Spring; (b) Summer; (c) Autumn; (d) Winter;

[0065] Figure 5 The power curves of cooling, heating and electricity loads in Country B and the estimated power generation of photovoltaic power generation are shown in Figure 1. (a) Spring; (b) Summer; (c) Autumn; (d) Winter;

[0066] Figure 6 The power curves of cooling, heating and electricity loads in Country C and the estimated power generation of photovoltaic and wind power generation are shown in Figure 1. (a) Spring; (b) Summer; (c) Autumn; (d) Winter;

[0067] Figure 7 It is the time-of-use electricity price;

[0068] Figure 8 It is the result of optimizing the charging and discharging behavior of the batteries in the shared energy storage power station;

[0069] Fig. 9 It is the electricity trading between countries and energy storage power stations in summer and winter; (a) summer; (b) winter;

[0070] Fig.10 is the output power of each device in country A in summer and winter; (a) summer; (b) winter;

[0071] Fig.11 is the output power of each device in country B in summer and winter; (a) summer; (b) winter;

[0072] Fig.12 is the output power of each device in country C in summer and winter; (a) summer; (b) winter;

[0073] Fig.13 It is the dependence of the cross-border integrated energy system on changes in the exchange rate between the US dollar and the RMB. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily without conflict.

[0075] Example 1: Figure 1-13 As shown, a two-layer optimization method for a cross-border integrated energy system of combined cooling, heating and power based on energy storage power station services, the method comprises the following steps:

[0076] S1. Establish a cross-border integrated energy system architecture for combined cooling, heating and power based on energy storage power station services; the cross-border integrated energy system for combined cooling, heating and power based on energy storage power station services includes IES and shared energy storage power stations in multiple countries; the specific structure is as follows Figure 1 As shown, the IES of the countries form a cross-border integrated energy system (CBIES) for combined cooling, heating and power. CBIES mainly includes energy supply equipment, energy conversion equipment, energy storage equipment and cold, heat and electricity loads; among them, energy supply: wind and solar renewable energy power generation, power grid and natural gas grid; energy conversion: gas turbines, gas boilers, waste heat boilers, absorption chillers, electric chillers and heat exchangers.

[0077] S2. The present invention uses the ARIMA-GARCH model to predict exchange rates. The ARIMA model can model the conditional mean of the exchange rate series, capturing trends and seasonal patterns, while the GARCH model handles the variability of volatility, which is a common feature of financial data such as exchange rates. This combination not only predicts future exchange rates, but also makes it a powerful tool for forecasting in environments with significant economic and market fluctuations, thereby supporting better financial and strategic decisions in cross-border financial activities.

[0078] The ARIMA model is used to analyze and forecast time series data; the model is set to ARIMA(p,d,q):

[0079]

[0080] Where p is the number of autoregressive terms; d is the number of non-seasonal differences required for stationarity; q is the number of lagged forecast errors in the forecast equation; Φ k is the parameter of the autoregressive part; Ω h is the parameter of the moving average part; L is the lag operator; X t is a time series; δ t is the error term.

[0081] The GARCH model is often used to model financial time series that exhibit volatility clustering and is defined as follows:

[0082]

[0083] In the formula, is the conditional variance; α0 is a constant; α k is the coefficient of the autoregressive conditional heteroskedasticity part of the model; β h are the coefficients of the GARCH part of the model; m and s are the orders of the autoregressive conditional heteroskedasticity and GARCH components, respectively.

[0084] Therefore, the exchange rate forecast is as follows:

[0085]

[0086] In the formula, the exchange rate forecast C er represents the exchange rate forecast at time t+1; in addition, represents the variance of the GARCH model at time t+1; z is the quantile of the normal distribution.

[0087] The change of total cost after the alliance with the exchange rate can be expressed as:

[0088] C alliance (X) = C alliance,0 +k alliance (X-X0) (4)

[0089] In the formula, C alliance (X) and C alliance,0 are the total costs calculated at the current exchange rate and the benchmark exchange rate respectively; k alliance is the coefficient of cost sensitivity to exchange rate changes; X and X0 are the current exchange rate and the benchmark exchange rate respectively.

[0090] The change in the exchange rate can be described by the following formula, which illustrates the dependence on the US dollar and the RMB:

[0091]

[0092] Where D USD (X) and D CNY (X) are the interdependence of the exchange rate fluctuations of the U.S. dollar and the RMB.

[0093] S3. The two-level optimization constructed by the present invention is an optimization problem involving two hierarchical systems, each of which has its own objective function and constraints. The upper model first makes a decision and passes the value of its decision variable to the lower model. Based on the upper decision, the lower model defines the feasible area and optimizes its objective function to obtain the optimal value. Then the result of the lower layer is fed back to the upper layer. Through this iterative process, the overall optimal solution and its corresponding optimal value are determined. The upper problem depends on the optimal solution of the lower problem, and the lower solution is affected by the upper decision variables. The significance of two-level programming is that it can consider the interests of both layers at the same time and ensure that the lower layer adheres to the decisions made by the upper layer. However, under the constraints set by the upper decision, the lower layer retains a certain degree of autonomy.

[0094] S3.1, The upper model solves the optimization problem of the annual operating cost of the shared energy storage power station during the planning period. The decision variables include the capacity allocation of the energy storage power station and its maximum charging and discharging power.

[0095] The goal of the upper-level optimization is to minimize the annual operating cost of the energy storage power station:

[0096]

[0097] Where W is the number of typical days, C er is the exchange rate, T w is the number of days corresponding to the wth typical day; C inv,w is the average daily investment and maintenance cost of the energy storage power station, C ess,s,w is the cost of purchasing electricity from the IES in each country on a typical day, C ess,b,w is the revenue from selling electricity from each country’s IES on a typical day, C serve,w It is the daily service fee of the energy storage power station.

[0098] (1) Average daily investment and maintenance costs of energy storage power stations

[0099]

[0100] Where η P and η S are the electricity cost and capacity cost of the energy storage power station respectively. and are the maximum charging / discharging power and maximum capacity of the energy storage station, Ts is the expected operating days of the energy storage power station, M ess Daily maintenance cost.

[0101] (2) The cost of purchasing electricity from various energy systems IES on a typical day:

[0102]

[0103] Where N is the number of countries, N T is the dispatch period, δ(t) is the electricity price that CBIES sells to the energy storage power station in period t, and P ess,s,w,i (t) The amount of electricity sold by the ith country to the energy storage power station during the tth period of each typical day, and Δt is the dispatch period.

[0104] (3) Revenue from the sale of electricity to the cross-border integrated energy system CBIES on each typical day:

[0105]

[0106] Where λ(t) is the price of electricity purchased by CBIES from the energy storage power station during period t, P ess,b,w,i (t) The amount of electricity purchased by the ith country from the energy storage power station during the tth period of each typical day.

[0107] (4) Service fees charged by the energy storage power station to CBIES on each typical day:

[0108]

[0109] Where θ(t) is the service fee rate paid by CBIES to the energy storage power station during period t, in units of RMB / kWh.

[0110] The upper model constraints are as follows:

[0111] (1) Energy storage power station capacity constraints

[0112]

[0113] Where β is the energy multiplier of the energy storage power station.

[0114] (2) Charging status and charging and discharging power constraints of energy storage power stations

[0115]

[0116] In the formula, E ess (t) is the energy stored in the energy storage power station during period t, η abs and η relea are the charging and discharging efficiency of the energy storage power station, P ess,abs (t) and P ess,relea(t) are the charging power and discharging power of the energy storage power station in period t, E ess (0) is the initial storage energy of the energy storage station, U abs (t) and U relea (t) are binary variables representing the charging and discharging status of the energy storage power station during period t, where 0 or 1 represents the status.

[0117] S3.2, the lower model is responsible for solving the optimal operation of the cross-border integrated energy system of combined cooling, heating and power. The goal of the lower model is to minimize the annual operating cost of the cross-border integrated energy system of combined cooling, heating and power based on the services provided by the energy storage power station:

[0118]

[0119] In the formula, C grid,w is the cost of purchasing electricity from the grid for each typical day, C fuel,w is the natural gas cost for gas turbines and gas boilers on a typical day, C CO2,w It is the tiered carbon trading cost.

[0120] (1) Cost of purchasing electricity from the grid

[0121]

[0122] Where τ(t) is the electricity purchase price in period t, P grid,w,i (t) is the amount of electricity purchased from the grid by the ith country on each typical day in period t.

[0123] (2) Gas cost

[0124]

[0125] In the formula, c gas is the natural gas price, P GT,w,i (t) and Q GB,w,i (t) are the gas turbine output power and gas boiler output power of the ith country in each typical day and period t, respectively, η GT and η GB are the efficiencies of the gas turbine and gas boiler, L NG is the calorific value of natural gas.

[0126] (3) Tiered carbon trading costs

[0127]

[0128] In the formula, the tiered carbon trading cost is defined as The parameters include the basic price of carbon trading λ, the length of the carbon emission interval l, the price growth multiplier 1+α, and the carbon trading volume participating in CBIES.

[0129] The lower model constraints are as follows:

[0130] (1) Power balance constraints

[0131] P GT,w,i (t)+P WT,w,i (t)+P PV,w,i (t)+P grid,w,i (t)+P ess,b,w,i (t)-P ess,s,w,i (t)-P EC,w,i (t)-P load,w,i (t)=0,λ1(17)

[0132] Where P WT,w,i (t) and P PV,w,i (t) are the wind power and photovoltaic power generation of country i in period t on each typical day, P EC,w,i (t) is the power consumption of refrigerators in country i during each typical day and time period t, P load,w,i (t) is the power load power of country i during period t on each typical day.

[0133] (2) Cold power balance constraint

[0134] P EC,w,i (t)·η EC +Q AC,w,i (t)-P cool,w,i (t)=0,λ2 (18)

[0135] Where η EC is the efficiency of the electric refrigerator, Q AC,w,i (t) is the cooling power output of the absorption chiller for country i at time t on each typical day, P cool,w,i (t) is the cooling load power of country i during period t on each typical day.

[0136] (3) Thermal power balance constraints

[0137] Q GB,w,i (t)+P HX,w,i (t)-P heat,w,i (t)=0,λ3 (19)

[0138] Where P HX,w,i (t) is the heating power output of the heat exchanger in country i during each typical day and time period t, P heat,w,i (t) is the heat load power of country i during each typical day and period t; Q GB,w,i (t) is the thermal power output of the gas boiler in country i during time period t on each typical day.

[0139] (4) Waste heat boiler balance constraints

[0140]

[0141] Where η HX and η AC are the efficiencies of the heat exchanger and absorption refrigerator, respectively, GT is the thermal power ratio of the gas turbine; η WH is the waste heat boiler efficiency.

[0142] (5) Energy storage power station charging and discharging power balance constraints

[0143] The sum of the power exchanged between each IES and the energy storage power station represents the charging and discharging power of the energy storage power station.

[0144]

[0145] (6) CBIES equipment output upper and lower limit constraints

[0146]

[0147] In the formula, and are the minimum and maximum output power of the gas turbine, and are the minimum and maximum output powers of the absorption chiller, and are the minimum and maximum output powers of the electric refrigerator, and are the minimum and maximum output power of the gas boiler, and are the minimum and maximum output powers of the heat exchanger respectively.

[0148] (7) Constraints on purchasing electricity from the grid

[0149]

[0150] In the formula, The maximum amount of power each country can buy from the grid.

[0151] (8) Power exchange constraints between CBIES and energy storage power stations

[0152]

[0153] In the formula, is the maximum exchange power between each country and the energy storage power station, U buy,w,i (t) and U sale,w,i(t) are the purchase and sale status indicators between the ith country and the energy storage power station on each typical day; λ1,λ2,λ3,λ4,λ5 are the Lagrange multipliers corresponding to the equality constraints, is the Lagrange multiplier corresponding to the inequality constraint.

[0154] The solution process is as follows Figure 2 As shown. In the two-layer model constructed by the present invention, the upper model includes integer and continuous variables, as well as nonlinear constraints, and the lower model is a mixed integer linear programming (MILP) problem. Due to the coupling between the upper and lower models, direct solution is challenging. By constructing a Lagrangian function for the lower model and applying the KKT complementary relaxation conditions in the lower model, the lower problem is converted into a set of additional constraints for the upper model; a single-layer model is formed for the upper model and the additional constraints, and in order to linearize the nonlinear terms in this single-layer model, the Big-M method is applied to convert it into a single-level MILP problem.

[0155] In order to verify the effectiveness of the present invention, the model is solved using the Cplex solver of MATLAB. The following case analysis is given as follows:

[0156] The cross-border CCHP energy system in the present invention includes three countries: Country A, Country B and Country C. Users in each country are directly connected to the shared energy storage power station, and there is no direct connection between users in each country. Each typical day is 90 days, and the scheduling cycle of each typical day is 24 hours. The relevant parameters of the equipment are shown in Table 1. The full-year exchange rate forecast data is as follows Figure 3 shown.

[0157] Table 1 Equipment related parameters

[0158]

[0159] Figure 4-6 The maximum predicted PV and wind power output data for typical days in the four seasons for each country are shown, as well as the predicted cooling, heating, and electricity loads. Country A operates as a multi-power IES, while Country C operates as a power-deficient IES. PV power generation in countries A and B is most prominent in the summer, with peak loads concentrated in the summer and winter, significantly affected by cooling and heating demand. In contrast, wind power generation in country C shows stable output in all seasons, with a significant increase in load in winter, indicating a high reliance on wind resources. The energy systems of the three countries show complementary characteristics in terms of renewable energy output and load distribution. By leveraging cross-border energy coordination and storage management, the overall operating efficiency and stability of the system can be significantly improved.

[0160] Figure 7It is a time-of-use electricity price, indicating that energy storage power stations provide a cost-effective option for purchasing electricity at different times of the day. During peak hours (08:00-12:00, 17:00-21:00), the grid electricity price is 1.36 yuan / kWh, and the energy storage power station price is slightly lower, 1.15 yuan / kWh, with obvious price advantages. During mid-peak hours (12:00-17:00 and 21:00-24:00), the energy storage power station price is 0.75 yuan / kWh, which is also lower than the grid electricity price of 0.82 yuan / kWh. During the trough period (00:00-08:00), the energy storage power station price is 0.40 yuan / kWh, slightly higher than the grid electricity price of 0.37 yuan / kWh, but still at a low level. The electricity sales price of the energy storage power station has always remained stable at a low level, at 0.95 yuan / kWh during peak hours, 0.55 yuan / kWh during mid-peak hours, and 0.20 yuan / kWh during trough hours.

[0161] The optimization results of the battery charging and discharging behavior of the shared energy storage power station are as follows: Figure 8 As shown. Positive power indicates discharge, and negative power indicates charging. The operating costs of the cross-border combined cooling, heating and power IES in four seasons, as well as the operating costs of IES in various countries are shown in Table 2-5. When the cross-border combined cooling, heating and power integrated energy system operates independently without energy storage, the annual operating cost is 2.786534 billion yuan (total cost in four seasons). After the cross-border combined cooling, heating and power integrated energy system participates in the shared energy storage power station service, its annual operating cost is reduced to 1.253774 billion yuan (total cost in four seasons), which is 55.01% lower than that of independent operation.

[0162] Table 2 Operating costs for each country and CBIES in spring

[0163]

[0164] Table 3 Summer operating costs for each country and CBIES

[0165]

[0166] Table 4 Operating costs per country and CBIES in autumn

[0167]

[0168] Table 5 Winter operating costs for each country and CBIES

[0169]

[0170] By utilizing the energy storage services of shared energy storage power stations, the cross-border CCHP integrated energy system can take advantage of the differences and complementarities between the energy systems of different countries at the same time and the electricity consumption patterns of the same IES at different times. The surplus power of a multi-power IES can be supplied to the power-deficient IES through a shared energy storage power station, and the power generated by the same IES during the surplus period can be stored for use during the power shortage period. This approach reduces the need for each IES to purchase electricity or natural gas from the grid, greatly reducing the annual operating costs of the cross-border CCHP integrated energy system.

[0171] Due to the large variability in summer and winter, the analysis focuses on typical days in these two seasons, e.g. Fig. 9 As shown. In the summer, country A mainly sells electricity to energy storage power stations from 1 to 8 o'clock, with a peak of 5000 kW; outside these times, it becomes a net buyer, with a maximum intake of 2003.78 kW. In contrast, country B has a peak purchase of electricity at 4-5 o'clock, reaching 3353.21 kW. On the other hand, country C always sells electricity between 2-8 o'clock, with a peak of 4375.58 kW. As the season turns into winter, country A's electricity sales decrease, while countries B and C maintain strong sales behavior. It is worth noting that at 8 o'clock, country B's electricity sales surged to 4488.8 kW, while country C's electricity sales peaked at 3979.86 kW at 7 o'clock. These figures illustrate how countries adjust transactions with energy storage power stations according to seasonal changes in electricity demand and capacity, optimize grid operation, and improve energy efficiency. This seasonal pattern of electricity trading is crucial to the effective management and operation of energy storage power stations, helping to balance grid loads and improve energy efficiency.

[0172] Figure 10-12 The output power of each device in summer and winter in countries A, B, and C is shown. Gas turbines and refrigeration equipment have significant output peaks in the early morning in summer. For example, in summer, the output of gas turbines in country B reaches a maximum of more than 180kW between 6 and 7 am, indicating a sharp increase in cooling demand. Similarly, in country C, the output power of gas turbines exceeds 1200kW between 6 and 8 am, corresponding to the high power consumption of the refrigeration system. In winter, the output of gas turbines in country A reaches about 469kW between 6-8 am, while the output of heating equipment also rises significantly during this period, reaching a peak of 360.22kW, reflecting the increase in heating demand. In addition, in country B, the output of gas turbines in winter exceeds 2000kW, further highlighting the peak of heating demand. Overall, energy demand in summer and winter shows obvious differences, and the output peak is concentrated in the early morning hours. These demand peaks provide energy storage systems with opportunities to optimize energy efficiency, that is, storing energy during low demand periods and releasing energy during high demand periods.

[0173] like Fig.13As shown in Figure 2, as the exchange rate rises, the system's reliance shifts from the US dollar to the RMB, which is a clear shift. This shift suggests that people prefer to use the RMB in a high exchange rate environment, which may stem from the increased cost efficiency of using it to settle international energy transactions when the RMB appreciates relative to the US dollar. This observation highlights the significant impact of exchange rate fluctuations on the choice of transaction currency in the cross-border energy system.

[0174] According to the second aspect of an embodiment of the present invention, a two-layer optimization system for a cross-border integrated energy system of combined heat and power based on energy storage power station services is provided, comprising: a first module for applying energy storage power station services to a cross-border integrated energy system with combined heat and power to construct a cross-border integrated energy system of combined heat and power based on energy storage power station services; a second module for establishing a two-layer optimization model constructed by an upper model and a lower model for a cross-border integrated energy system of combined heat and power based on energy storage power station services, and solving the same; in the solution process, by constructing a Lagrangian function for the lower model and applying the KKT complementary relaxation condition in the lower model, the lower problem is converted into a set of additional constraints of the upper model; the nonlinear part in the single-layer model formed by the upper model and the additional constraints is linearly converted into a mixed integer linear constraint. The term "module" as used above may be a combination of software and / or hardware that implements a predetermined function. Although the system described in the above embodiment is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived. For parts not described in detail in each module, please refer to the relevant description of this embodiment.

[0175] The specific implementation modes of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above implementation modes, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. A two-layer optimization method for a cross-border integrated energy system of combined cooling, heating and power based on energy storage power station services, characterized in that: include: Apply energy storage power station services to cross-border integrated energy systems with combined heat and power to build a cross-border integrated energy system with combined cooling, heating and power based on energy storage power station services; For the cross-border integrated energy system of combined cooling, heating and power based on energy storage power station services, a two-level optimization model constructed by an upper model and a lower model is established and solved; during the solution process, the lower-level problem is converted into a set of additional constraints for the upper-level model by constructing a Lagrangian function for the lower-level model and applying the KKT complementary relaxation conditions in the lower-level model; the nonlinear part in the single-level model formed by the upper model and the additional constraints is linearly converted into mixed integer linear constraints.

2. The double-layer optimization method for a cross-border integrated energy system for combined cooling, heating and power based on energy storage power station services according to claim 1 is characterized in that: The upper model includes establishing a first objective function and a first constraint condition to minimize the annual operating cost of the energy storage power station as the optimization goal; the first objective function is: Where C1 represents the annual operating cost of the energy storage power station; W is the number of typical days, C er is the exchange rate, T w is the number of days corresponding to the wth typical day; C inv,w is the average daily investment and maintenance cost of the energy storage power station, C ess,s,w is the cost of purchasing electricity from the IES in each country on a typical day, C ess,b,w is the revenue from selling electricity from each country’s IES on a typical day, C serve,w It is the daily service fee of the energy storage power station; The first constraint condition includes: Energy storage power station capacity constraints: In the formula, β is the energy multiplier of the energy storage power station; and They are the maximum charge / discharge power and maximum capacity of the energy storage power station respectively; Charging state and charging and discharging power constraints of energy storage power stations: In the formula, E ess (t) is the energy stored in the energy storage power station during period t, η abs and η relea are the charging and discharging efficiency of the energy storage power station, P ess,abs (t) and P ess,relea (t) are the charging power and discharging power of the energy storage power station in period t, Δt is the scheduling period; E ess (0) is the initial storage energy of the energy storage station, U abs (t) and U relea (t) are binary variables representing the charging and discharging states of the energy storage power station during period t.

3. The double-layer optimization method for a cross-border integrated energy system for combined cooling, heating and power based on energy storage power station services according to claim 1 is characterized in that: The lower model includes a second objective function and a second constraint condition, wherein the second objective function is: Where C2 represents the annual operating cost of the cross-border integrated energy system for combined cooling, heating and power generation; W is the number of typical days, C er is the exchange rate, T w is the number of days corresponding to the wth typical day; C grid,w is the cost of purchasing electricity from the grid for each typical day, C fuel,w is the natural gas cost for gas turbines and gas boilers on a typical day, is the tiered carbon trading cost, C inv,w is the average daily investment and maintenance cost of the energy storage power station, C ess,b,w is the revenue from selling electricity from each country’s IES on a typical day, C ess,s,w is the cost of purchasing electricity from the IES in each country on a typical day, C serve,w It is the daily service fee of the energy storage power station; The second constraint condition includes: Power balance constraints: P GT,w,i (t)+P WT,w,i (t)+P PV,w,i (t)+P grid,w,i (t)+P ess,b,w,i (t)-P ess,s,w,i (t)-P EC,w,i (t)-P load,w,i (t)=0,λ1 Where P GT,w,i (t) is the gas turbine output power of country i in period t on each typical day, P WT,w,i (t) and P PV,w,i (t) are the wind power and photovoltaic power generation of the ith country in the tth period on each typical day, P ess,b,w,i (t) The amount of electricity purchased by the ith country from the energy storage power station at the tth period of each typical day, P ess,s,w,i (t) The amount of electricity sold by the ith country to the energy storage power station at the tth time period on each typical day, P EC,w,i (t) is the power consumption of refrigerators in country i during each typical day and time period t, P load,w,i (t) is the power load power of country i during time period t on each typical day; Cold power balance constraints: P EC,w,i (t)·η EC +Q AC,w,i (t)-P cool,w,i (t)=0,λ2 Where η EC is the efficiency of the electric refrigerator, Q AC,w,i (t) is the cooling power output of the absorption chiller for country i at time t on each typical day, P cool,w,i (t) is the cooling load power of country i during period t on each typical day; Thermal power balance constraints: Q GB,w,i (t)+P HX,w,i (t)-P heat,w,i (t)=0,λ3 Where P HX,w,i (t) is the heating power output of the heat exchanger in the ith country during each typical day and time period t, P heat,w,i (t) is the heat load power of country i during each typical day and period t; Q GB,w,i (t) is the thermal power output of the gas boiler in country i during period t on each typical day Waste heat boiler balance constraints: Where η HX and η AC are the heat exchanger and absorption chiller efficiencies, γ GT is the thermal power ratio of the gas turbine; η WH is the waste heat boiler efficiency; Energy storage power station charging and discharging power balance constraints: Where P ess,abs (t) and P ess,relea (t) are the charging power and discharging power of the energy storage power station during period t; CBIES equipment output upper and lower limit constraints: In the formula, and are the minimum and maximum output power of the gas turbine, and are the minimum and maximum output powers of the absorption chiller, and are the minimum and maximum output powers of the electric refrigerator, and are the minimum and maximum output power of the gas boiler, and are the minimum and maximum output powers of the heat exchanger, respectively; Constraints on purchasing electricity from the grid: In the formula, is the maximum power each country can purchase from the grid; Power exchange constraints between CBIES and energy storage power stations: In the formula, is the maximum exchange power between each country and the energy storage power station, U buy,w,i (t) and U sale,w,i (t) are the purchase and sale status indicators between the ith country and the energy storage power station on each typical day; λ1,λ2,λ3,λ4,λ5 are the Lagrange multipliers corresponding to the equality constraints, is the Lagrange multiplier corresponding to the inequality constraint.

4. The double-layer optimization method for a cross-border integrated energy system for combined cooling, heating and power based on energy storage power station services according to claim 2 or 3, characterized in that: The exchange rate is predicted using the ARIMA-GARCH model.

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