A two-level optimization method for a cross-border integrated energy system based on energy storage power station services for combined cooling, heating and power generation

Abstract

The present invention discloses a two-layer optimization method for a cross-border integrated energy system with combined cooling, heating 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 with combined cooling, heating and power based on energy storage power station services; establishing a two-layer optimization model constructed by an upper-layer model and a lower-layer model for the cross-border integrated energy system with combined cooling, heating and power based on energy storage power station services, and solving the model; during the solution process, by constructing a Lagrangian function for the lower-layer model and applying the KKT complementary relaxation condition in the lower-layer model, the lower-layer problem is converted into a set of additional constraints for the upper-layer model; and linearly converting the nonlinear part in the single-layer model formed by the upper model and the additional constraints into mixed integer linear constraints. The two-layer optimization model under the shared energy storage power station service proposed by the present invention can effectively reduce customer costs, save energy storage resources, and promote mutual benefit between customers and energy storage power station operators.

Description

Technical Field

[0001] The present invention relates to a double-layer optimization method for a cross-border integrated energy system based on combined cooling, heating and power supply services of an energy storage power station, 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 enable 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 recognized the urgent need to reform the current economic development model. Efforts are underway to transition to a low-carbon, environmentally friendly model to address the fundamental challenges posed by current economic practices. However, while individual IES can provide local benefits, they often lack the resource diversity, scalability, and economic efficiency of cross-border integrated energy systems (CBIES). CBIES can leverage a broader resource pool and shared infrastructure to enhance energy security and sustainability. Therefore, CBIES play a vital role in promoting international energy connectivity, characterized by rapid deployment, widespread coverage, and support for local renewable energy consumption and multi-energy integration. Furthermore, synergy between independent energy systems in different countries can significantly expand energy supply capacity while improving grid stability. This collaboration significantly enhances 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] establishes 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] uses 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 elements, generation, storage or consumption assets. Reference [6] studies a key issue of power 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 cooperation and participation enthusiasm among stakeholders.

[0005] Existing research primarily addresses 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, while this literature optimizes and applies energy storage technology, highlighting its central role in cross-border integrated energy systems, there is limited exploration of how two-tier optimization models and shared energy storage services can enhance system economics and stability. Furthermore, current research does not fully consider the key role of energy storage in optimizing dispatch 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 cooling, heating 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 cooling, heating 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-level optimization method for a cross-border integrated energy system with 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 with combined heat and power based on energy storage power station services; for the cross-border integrated energy system with combined heat and power based on energy storage power station services, establishing a two-level optimization model constructed by an upper-level model and a lower-level model, and solving the model; during the solution process, by constructing a Lagrangian function for the lower-level model and applying the KKT complementary relaxation condition in the lower-level model, the lower-level problem is converted into a set of additional constraints for the upper-level model; and linearly converting the nonlinear part in the single-level model formed by the upper model and the additional constraints into mixed integer linear constraints.

[0020] Furthermore, the upper model includes establishing a first objective function and a first constraint condition with the optimization goal of minimizing the annual operating cost of the energy storage power station; 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 each country's IES on a typical day, C ess,b,w is the revenue from selling electricity from each country’s IES on each 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] Where β is the energy multiplier of the energy storage power station; and are the maximum charge / discharge power and maximum capacity of the energy storage station respectively;

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

[0028]

[0029] Where, E ess (t) is the energy stored in the energy storage station during period t, η abs and η relea are the charging and discharging efficiencies of the energy storage 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 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 each typical day, C ess,s,w is the cost C of purchasing electricity from IES in each country on a typical day 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 during 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 country i in period t on each typical day, P ess,b,w,i (t) The amount of electricity purchased by the i-th country from the energy storage power station during the t-th period of each typical day, P ess,s,w,i (t) The amount of electricity sold by the i-th country to the energy storage power station during the t-th period of 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;

[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 i-th country during each typical day and time period, P heat,w,i (t) is the heat load power of country i during period t on each typical day; Q GB,w,i (t) is the thermal power output of the gas boiler in country i during the period t on each typical day

[0043] Waste heat boiler balance constraints:

[0044]

[0045] Where η HX and η AC are the efficiencies of the heat exchanger and absorption chiller, 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 device output upper and lower limit constraints:

[0050]

[0051] Where, 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] Where, The maximum amount of power each country can purchase from the grid.

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

[0056]

[0057] Where, 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 i-th 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, are the Lagrange multipliers corresponding to the inequality constraints.

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

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

[0060] This paper establishes a cross-border integrated energy system architecture for combined cooling, heating, and power (CCHP) based on energy storage power station services. Based on this architecture, a two-tier optimization model is used to develop an energy storage decision-making framework, which considers the interests of both energy storage station operators and CBIES. Furthermore, to manage exchange rate fluctuations and uncertainties in cross-border energy transactions, this paper employs an ARIMA-GARCH model. Finally, this paper demonstrates the rationality and effectiveness of the proposed two-tier optimization model through case studies. Practice demonstrates that the proposed two-tier optimization model for shared energy storage power station services can effectively reduce customer costs, conserve energy storage resources, and promote mutual benefit between customers and energy storage 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 two-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, as well as the estimated power generation of photovoltaic and wind power, 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, as well as the estimated power generation of photovoltaic power generation, are shown in Figure 1. (a) Spring; (b) Summer; (c) Autumn; (d) Winter.

[0066] Figure 6 Figure 1 is a schematic diagram of the power curves of cooling, heating, and electricity loads in Country C, as well as the expected power generation of photovoltaic and wind power generation; (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 batteries in the shared energy storage power station;

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

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

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

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

[0073] Figure 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] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to 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, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other in any way.

[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 includes 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 in the figure, the IES of the countries formed a cross-border integrated energy system (CBIES) for combined cooling, heating and power. The CBIES mainly includes energy supply equipment, energy conversion equipment, energy storage equipment and cooling, heating 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. This invention uses the ARIMA-GARCH model to forecast exchange rates. The ARIMA model can model the conditional mean of exchange rate series, capturing trends and seasonal patterns, while the GARCH model addresses the variability of volatility, a common characteristic of financial data such as exchange rates. This combination not only allows for the prediction of 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 decision-making in cross-border financial activities.

[0078] The ARIMA model is used to analyze and forecast time series data; the model is set as 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 the time series; δ t is the error term.

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

[0082]

[0083] Where, 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] Where, 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] Where 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 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, respectively.

[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-level model first makes a decision and passes the value of its decision variable to the lower-level model. Based on the upper-level decision, the lower-level model defines the feasible region and optimizes its objective function to obtain the optimal value. The results of the lower level are then fed back to the upper level. Through this iterative process, the overall optimal solution and its corresponding optimal value are determined. The upper-level problem depends on the optimal solution of the lower-level problem, and the lower-level solution is in turn affected by the upper-level decision variables. The significance of two-level programming lies in its ability to consider the interests of both levels simultaneously, ensuring that the lower level adheres to the decisions made by the upper level. However, under the constraints set by the upper-level decision, the lower level retains a certain degree of autonomy.

[0094] S3.1. The upper model solves the problem of optimizing the annual operating costs of a 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 charge and discharge 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 typical day number, 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 each country's IES on a typical day, C ess,b,w is the revenue from selling electricity from each country’s IES on each 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 charge / discharge 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 price of electricity sold by CBIES to the energy storage power station in period t, and P ess,s,w,i (t) The amount of electricity sold by country i to the energy storage power station during the tth period of each typical day, where Δt is the dispatch period.

[0104] (3) Revenue from electricity sold 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 country i 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] Where, E ess (t) is the energy stored in the energy storage station during period t, η abs and η relea are the charging and discharging efficiencies of the energy storage station, P ess,abs (t) and P ess,relea(t) are the charging power and discharging power of the energy storage station during 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 station during period t, where 0 or 1 represents the status.

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

[0118]

[0119] Where C grid,w 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 country i on each typical day in period t.

[0123] (2) Gas costs

[0124]

[0125] Where 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 country i during period t on each typical day, 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, 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) Cooling 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 period t on each typical day; 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 chiller, γ 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 station represents the charging and discharging power of the energy storage station.

[0144]

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

[0146]

[0147] Where, 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] Where, The maximum amount of power each country can purchase from the grid.

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

[0152]

[0153] Where, 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 i-th 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, are the Lagrange multipliers corresponding to the inequality constraints.

[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. 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 combined cooling, heating and power (CCHP) energy system in this 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 a typical day in each of the four seasons are shown, along with 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 generation in countries A and B is most prominent in the summer, with peak loads concentrated in the summer and winter, significantly influenced by cooling and heating demand. In contrast, wind power generation in country C exhibits stable output across all seasons, with a significant increase in load in the winter, indicating a high reliance on wind resources. The energy systems of the three countries exhibit complementary characteristics in terms of renewable energy output and load distribution. By leveraging cross-border energy coordination and storage management, the overall operational efficiency and stability of the systems can be significantly improved.

[0160] Figure 7This 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 and 17:00-21:00), the grid electricity price is 1.36 yuan / kWh, while the energy storage power station price is slightly lower at 1.15 yuan / kWh, offering a significant price advantage. During mid-peak hours (12:00-17:00 and 21:00-24:00), the energy storage power station price is 0.75 yuan / kWh, also lower than the grid price of 0.82 yuan / kWh. During off-peak hours (00:00-08:00), the energy storage power station price is 0.40 yuan / kWh, slightly higher than the grid price of 0.37 yuan / kWh, but still at a relatively low level. The electricity sales price of the energy storage power station has always remained low and stable, at 0.95 yuan / kWh during peak hours, 0.55 yuan / kWh during mid-peak hours, and 0.20 yuan / kWh during off-peak hours.

[0161] The optimization results of the battery charging and discharging behavior of the shared energy storage power station are as follows: Figure 8 Positive power indicates discharge, and negative power indicates charge. The operating costs of a cross-border CHP system for four seasons, as well as the operating costs of IES in different countries, are shown in Table 2-5. When operating independently, the CHP cross-border integrated energy system, excluding energy storage, has an annual operating cost of 2.786534 billion yuan (total cost over four seasons). However, when the CHP cross-border integrated energy system incorporates shared energy storage power station services, its annual operating cost is reduced to 1.253774 billion yuan (total cost over four seasons), a 55.01% reduction compared to standalone 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 for each country and CBIES in autumn

[0167]

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

[0169]

[0170] Leveraging the energy storage services of shared energy storage plants, cross-border CCHP integrated energy systems can capitalize on the differences and complementarities between energy systems in different countries and electricity consumption patterns within the same IES at different times. The surplus power of a multi-power IES can be supplied to a power-deficient IES via a shared energy storage plant. The surplus power generated by the same IES during this period can then be stored for use during power outages. This approach reduces the need for each IES to purchase electricity or natural gas from the grid, significantly 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 the typical days in these two seasons, e.g. Figure 9 As shown in the figure, during the summer, Country A primarily sells electricity to energy storage plants between 1:00 and 8:00 a.m., with a peak of 5,000 kW. Outside these hours, it becomes a net buyer, with a maximum purchase of 2,003.78 kW. Conversely, Country B experiences peak purchases between 4:00 and 5:00 a.m., reaching 3,353.21 kW. Country C, on the other hand, consistently sells electricity between 2:00 and 8:00 a.m., with a peak of 4,375.58 kW. As the seasons shift into winter, Country A's electricity sales decrease, while Countries B and C maintain strong sales. Notably, Country B's electricity sales surge to 4,488.8 kW at 8:00 a.m., while Country C's peak sales at 7:00 a.m. reach 3,979.86 kW. These figures illustrate how countries adjust their transactions with energy storage plants based on seasonal variations in electricity demand and capacity, optimizing grid operations and improving energy efficiency. This seasonal pattern in electricity trading is crucial for the effective management and operation of energy storage plants, helping to balance grid loads and improve energy efficiency.

[0172] Figure 10-12 The output power of various devices in countries A, B, and C during summer and winter is shown. Gas turbine and refrigeration equipment experience significant output peaks in the early morning hours during summer. For example, in country B, gas turbine output peaks between 6:00 AM and 7:00 AM, exceeding 180 kW, indicating a sharp increase in cooling demand. Similarly, in country C, gas turbine output exceeds 1200 kW between 6:00 AM and 8:00 AM, corresponding to high power consumption in the refrigeration system. In winter, gas turbine output in country A reaches approximately 469 kW between 6:00 AM and 8:00 AM, while heating equipment output also rises significantly during this period, peaking at 360.22 kW, reflecting increased heating demand. Furthermore, in country B, gas turbine output exceeds 2000 kW during winter, further highlighting peak heating demand. Overall, energy demand differs significantly between summer and winter, with peak output concentrated in the early morning hours. These demand peaks provide opportunities for energy storage systems to optimize energy efficiency by storing energy during low-demand periods and releasing it during high-demand periods.

[0173] like Figure 13As shown, as the exchange rate rises, the system's reliance shifts from the US dollar to the RMB, a clear shift. This shift suggests a greater preference for using the RMB in a high exchange rate environment, likely due to 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 cross-border energy systems.

[0174] According to a second aspect of an embodiment of the present invention, a two-level optimization system for a cross-border integrated energy system with combined cooling, heating, and power (CCHP) based on energy storage power station services is provided. The system comprises: a first module for applying the energy storage power station service to a cross-border integrated energy system with combined heat and power to construct a cross-border integrated energy system with combined cooling, heating, and power (CCHP) based on the energy storage power station service; a second module for establishing and solving a two-level optimization model for the cross-border integrated energy system with combined cooling, heating, and power (CCHP) based on the energy storage power station service; 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 condition in the lower-level model; and linearly converting the nonlinear parts of the single-level model formed by the upper model and the additional constraints into mixed-integer linear constraints. As used above, the term "module" may refer to 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, implementation in hardware, or a combination of software and hardware, is also possible and contemplated. For portions not described in detail in each module, please refer to the relevant description of this embodiment.

[0175] The specific embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in this field without departing from the scope of the present invention.

Claims

1. A two-layer optimization method for a cross-border integrated energy system based on energy storage power station services for combined cooling, heating and power generation, 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 a cross-border integrated energy system with combined cooling, heating, and power (CCHP) services provided by energy storage power stations, a two-level optimization model consisting of an upper-level model and a lower-level model is established and solved. During the solution, the lower-level problem is transformed 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 condition in the lower-level model. The nonlinear components of the single-level model formed by the upper-level model and the additional constraints are linearly converted into mixed-integer linear constraints. The upper model includes establishing a first objective function and a first constraint condition with the optimization goal of minimizing the annual operating cost of the energy storage power station; 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 each country's IES on a typical day, C ess,b,w is the revenue from selling electricity from each country’s IES on each 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: Where β is the energy multiplier of the energy storage power station; and are the maximum charge / discharge power and maximum capacity of the energy storage station respectively; Charging status and charging and discharging power constraints of energy storage power stations: Where, E ess (t) is the energy stored in the energy storage station during period t, η abs and η relea are the charging and discharging efficiencies of the energy storage station, P ess,abs (t) and P ess,relea (t) are the charging power and discharging power of the energy storage station during 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.

2. The dual-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 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 each typical day, C ess,s,w is the cost C of purchasing electricity from IES in each country on a typical day 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 during 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 country i in period t on each typical day, P ess,b,w,i (t) The amount of electricity purchased by the i-th country from the energy storage power station during the t-th period of each typical day, P ess,s,w,i (t) The amount of electricity sold by the i-th country to the energy storage power station during the t-th period of 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; 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 i-th country during each typical day and time period, P heat,w,i (t) is the heat load power of country i during period t on each typical day; Q GB,w,i (t) is the thermal power output of the gas boiler in country i during the period t on each typical day Waste heat boiler balance constraints: Where η HX and η AC are the efficiencies of the heat exchanger and absorption chiller, 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 device output upper and lower limit constraints: Where, 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: Where, is the maximum power each country can purchase from the grid; Power exchange constraints between CBIES and energy storage power stations: Where, 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 i-th 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, are the Lagrange multipliers corresponding to the inequality constraints.

3. The dual-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 exchange rate is predicted using the ARIMA-GARCH model.

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