A comprehensive energy planning and design method based on annual production data

Abstract

The present invention discloses a comprehensive energy planning and design method based on full-year production data, establishes an optimization model according to environmental resource information, load information, equipment parameter information, cost parameters and operation parameters; solves the optimization model with the maximum internal rate of return IRR as the goal and obtains the capacity configuration information of the equipment and the operation status information of each period of the year; the present invention can optimize the system based on the full-year production data, that is, including data at different seasons, months and even hourly levels. The planning method based on the full-year production data can better capture the spatiotemporal changes of energy demand and supply, and promote the efficient, green and flexible development of the energy system.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy planning, and in particular relates to an integrated energy planning design method based on annual production data. Background Art

[0002] With the continuous growth of global energy demand and the increasing complexity of energy structure, traditional energy planning methods have gradually exposed many shortcomings, especially in the process of dealing with complex energy production, consumption and environmental constraints. In order to achieve a more efficient, flexible and sustainable energy system, integrated energy planning has gradually become an important direction of modern energy management and system design. Integrated energy planning not only needs to consider the coordinated use of energy such as electricity, heat and gas, but also needs to deal with the balance of energy supply, demand, cost, environmental impact and other levels. The variables and constraints involved are huge and complex.

[0003] Traditional planning methods based on typical days fail to fully consider the peak load changes in the production process and the spatiotemporal coordination between different energy sources. In the design of multi-energy systems, the production and consumption of different energy forms (such as electricity, heat, natural gas, etc.) have large time variability. Ignoring this will lead to inefficient system design and even cause unnecessary environmental pollution and economic costs. Summary of the invention

[0004] The purpose of the present invention is to provide a comprehensive energy planning and design method based on annual production data to solve the problem that the planning method based on typical days fails to fully consider the peak load changes in the production process and the spatiotemporal coordination between different energy sources.

[0005] The present invention adopts the following technical scheme: a comprehensive energy planning and design method based on annual production data, establishing an optimization model according to environmental resource information, load information, equipment parameter information, cost parameters and operation parameters; solving the optimization model with the maximum internal rate of return IRR as the goal and obtaining the capacity configuration information of the equipment and the operation status information of each period of the year;

[0006] The energy balance constraints in the optimization model include:

[0007]

[0008] Where, effPV i is the power generation coefficient of the photovoltaic device at the i-th hour, capPV k is the residual capacity factor of the photovoltaic equipment in the kth year, effWT i is the power generation coefficient of the wind turbine at the i-th hour; yPG ki is the electricity purchase amount at the i-th hour in the k-th year; xPV is the installed capacity of photovoltaic equipment; xWT is the installed capacity of wind turbines; yDGki is the power generation of the diesel engine at the i-th hour in the k-th year; yCHPE ki is the power generation of CHP in the i-th hour of the k-th year; yBES ki is the charge and discharge amount of the energy storage at the i-th hour in the k-th year; lE i is the electricity load in the i-th hour; yEB ki is the heat output of the electric boiler at the i-th hour in the k-th year; effEB is the energy efficiency coefficient of the electric boiler; yEC ki is the cooling capacity of the chiller at the i-th hour in the k-th year; effEC is the energy efficiency coefficient of the chiller.

[0009] Furthermore, the energy balance constraints in the optimization model also include:

[0010]

[0011] In the formula, yPT ki is the purchased heat amount in the i-th hour of the k-th year; xST is the installed capacity of CSP; effST i is the heating coefficient of CSP in the i-th hour; yCHPT ki is the heat output of CHP in the i-th hour of the k-th year; yEB ki is the heat output of the electric boiler at the i-th hour in the k-th year; yGB ki is the heat output of the gas boiler at the i-th hour in the k-th year; yTS ki is the heat storage / release amount of thermal energy in the kth year and the ith hour; lT i is the heat load in the ith hour; yBAC ki is the heat generated by lithium bromide at the i-th hour in the k-th year; effBAC is the energy efficiency coefficient of lithium bromide.

[0012] Furthermore, the energy balance constraints in the optimization model also include:

[0013] yPC ki +yEC ki +yDCE ki +yBAC ki +yCS ki ≥lC i .

[0014] In the formula, yPC ki is the purchased cooling capacity at the i-th hour in the k-th year; yEC ki is the cooling capacity of the chiller at the i-th hour in the k-th year; yDCE ki is the heat output of the direct-fired engine at the i-th hour in the k-th year; yBAC ki is the heat generated by lithium bromide in the kth year and the ith hour; yCS kiis the storage / release capacity of the cold storage system at the i-th hour in the k-th year; lC i is the cooling load in the i-th hour.

[0015] Furthermore, the cost parameters include: the investment cost coefficient of the photovoltaic equipment corresponding to the kth year The investment cost coefficient of the fan in the kth year The investment cost coefficient of diesel engine in year k The investment cost coefficient of cogeneration in year k The investment cost coefficient of the electric energy storage system in year k The investment cost coefficient of CSP in year k The investment cost coefficient of the electric boiler in the kth year The investment cost coefficient of the gas boiler in the kth year The investment cost coefficient of the thermal energy storage system in year k The investment cost coefficient of the chiller in year k The investment cost coefficient of the direct-fired engine in the kth year The investment cost coefficient of lithium bromide in year k The investment cost coefficient of the cold storage system in year k

[0016] Furthermore, the operating parameters include: the maintenance cost coefficient of the photovoltaic equipment corresponding to the kth year The maintenance cost coefficient of the fan in the kth year Maintenance cost coefficient of diesel engine in year k The maintenance cost coefficient of CHP in year k Maintenance cost coefficient of the electric energy storage system corresponding to the kth year Maintenance cost coefficient of CSP heating corresponding to the kth year The maintenance cost coefficient of the electric boiler in the kth year Maintenance cost coefficient of gas boiler corresponding to the kth year Maintenance cost coefficient of thermal energy storage system corresponding to the kth year The maintenance cost coefficient of the chiller in the kth year Maintenance cost coefficient of direct-fired engine in year k Maintenance cost coefficient of lithium bromide corresponding to the kth year Maintenance cost coefficient of the cold storage system corresponding to the kth year

[0017] Furthermore, the environmental resource information includes: the power generation coefficient effPV of the photovoltaic device at the i-th hour i , the wind turbine power generation coefficient effWT at the i-th houri , the heating coefficient effST of solar thermal heating at the i-th hour i .

[0018] Furthermore, the load information includes: the power load lE of the ith hour i , heat load lT in the i-th hour i , cooling load lC in the i-th hour i .

[0019] Furthermore, the equipment parameter information includes: the remaining capacity coefficient capPV of the photovoltaic equipment in the kth year k , energy efficiency coefficient effEB of electric boiler, energy efficiency coefficient effEC of chiller, energy efficiency coefficient effBAC of lithium bromide, energy conversion coefficient effDCE of direct-fired engine, energy conversion coefficient effDG of diesel engine, energy conversion coefficient effCHP of cogeneration, energy conversion coefficient effGB of gas boiler, maximum heat-to-electricity ratio CHPrate of cogeneration max 、Minimum heat-to-power ratio CHPrate of cogeneration min , the minimum load rate of combined heat and power CHPloadrate min 、Charging efficiency of electric energy storage system effBES c 、Discharge efficiency of electric energy storage system effBES d , heat release efficiency effTS of thermal energy storage system d , the thermal storage efficiency effTS of the thermal energy storage system c , Cooling efficiency of the cold storage system effCS d , the cold storage efficiency effCS of the cold storage system c , Cold storage ratio of cold storage system Cooling rate of cold storage system Thermal storage rate of thermal energy storage system Heat release rate of thermal energy storage system Charging rate of electric energy storage system Discharge rate of electric energy storage system

[0020] Furthermore, solving the optimization model and obtaining the equipment capacity configuration information and the operating status information for each period of the year include:

[0021]

[0022]

[0023] Furthermore, the equipment output constraint condition in the optimization model is:

[0024] Yj Y ki ≤xDG;

[0025] yCHPE ki +yCHPT ki ≤xCHP;

[0026] yCHPT ki / yCHPE ki ≤CHPrate max ;

[0027] yCHPT ki / yCHPE ki ≥CHPrate min ;yCHPE ki +yCHPT ki ≥CHPloadrate min ×xCHP;

[0028] yBES ki ≤zBES ki ;

[0029] -yBES ki ≤xBES-zBES ki ;

[0030] zBES k,i+1 =zBES ki -yBES ki ;

[0031]

[0032]

[0033] yEB ki ≤xEB;

[0034] yGB ki ≤xGB;

[0035] yTS ki ≤zTS ki ;

[0036] -yTS ki ≤xTS-zTS ki ;

[0037] zTS k,i+1 =zTS ki -yTS ki ;

[0038]

[0039]

[0040] yECki ≤xEC;

[0041] yDCE ki ≤xDCE;

[0042] YBAC ki ≤xBAC;

[0043] yC ki ≤zCS ki ;

[0044] -yCS ki ≤xCS-zCS ki ;

[0045] ZGar k,i+1 =zCS ki -yCS ki ;

[0046]

[0047]

[0048] The beneficial effects of the present invention are:

[0049] The present invention can optimize the system based on the production data of the whole year, that is, including data of different seasons, months, and even hours. The planning method based on the production data of the whole year can better capture the spatiotemporal changes of energy demand and supply, and promote the efficient, green, and flexible development of the energy system.

[0050] The present invention optimizes the design of energy planning by making full use of known annual load data and environmental resource data, so as to achieve efficient configuration of various equipment in the integrated energy system and reasonable scheduling of resources; specifically, the present invention is committed to rationally designing energy supply structure and management plan through in-depth analysis and optimization of known data, reducing energy procurement and use costs, improving capital utilization efficiency, and thus improving the internal rate of return of enterprise investment projects;

[0051] The present invention is based on the energy planning solution of 8760 hours of environmental data throughout the year. By accurately analyzing and optimizing the configuration and use of energy in the production process throughout the year, it can help enterprises optimize energy use, reduce ineffective investment and operating costs, and improve the overall capital return rate of the project, thereby effectively improving the internal rate of return (IRR) of funds.

[0052] The present invention realizes autonomous decision-making on the operating status of all equipment, so that a more accurate operating cost calculation method can be obtained in the planning stage. With the help of the mathematical rigor of the mixed integer programming model and the powerful computing power of the commercial solver, the present invention provides enterprises with a more accurate energy planning solution, reduces the deviation of human decision-making, and improves the scientificity, accuracy and foresight of decision-making. DETAILED DESCRIPTION

[0053] The present invention is described in detail below in conjunction with specific implementation modes.

[0054] The present invention discloses a comprehensive energy planning and design method based on annual production data, establishing an optimization model according to environmental resource information, load information, equipment parameter information, cost parameters and operation parameters; solving the optimization model with the maximum internal rate of return IRR as the goal and obtaining the capacity configuration information of the equipment and the operation status information of each period of the year.

[0055] Cost parameters include: investment cost coefficient of photovoltaic equipment corresponding to the kth year The investment cost coefficient of the fan in the kth year The investment cost coefficient of diesel engine in year k The investment cost coefficient of cogeneration in year k The investment cost coefficient of the electric energy storage system in year k The investment cost coefficient of CSP in year k The investment cost coefficient of the electric boiler in the kth year The investment cost coefficient of the gas boiler in the kth year The investment cost coefficient of the thermal energy storage system in year k The investment cost coefficient of the chiller in year k The investment cost coefficient of the direct-fired engine in the kth year The investment cost coefficient of lithium bromide in year k The investment cost coefficient of the cold storage system in year k

[0056] Operational parameters include: maintenance cost coefficient of photovoltaic equipment corresponding to the kth year The maintenance cost coefficient of the fan in the kth year Maintenance cost coefficient of diesel engine in year k The maintenance cost coefficient of CHP in year k Maintenance cost coefficient of the electric energy storage system corresponding to the kth year Maintenance cost coefficient of CSP heating corresponding to the kth year The maintenance cost coefficient of the electric boiler in the kth year Maintenance cost coefficient of gas boiler corresponding to the kth year Maintenance cost coefficient of thermal energy storage system corresponding to the kth year The maintenance cost coefficient of the chiller in the kth year Maintenance cost coefficient of direct-fired engine in year k Maintenance cost coefficient of lithium bromide corresponding to the kth year Maintenance cost coefficient of the cold storage system corresponding to the kth year

[0057] Environmental resource information includes: photovoltaic equipment power generation coefficient effPV at the i-th hour i , the wind turbine power generation coefficient effWT at the i-th hour i , the heating coefficient effST of solar thermal heating at the i-th hour i .

[0058] Load information includes: electricity load lE in the i-th hour i , heat load lT in the i-th hour i , cooling load lC in the i-th hour i .

[0059] Equipment parameter information includes: the remaining capacity coefficient capPV of the photovoltaic equipment in the kth year k , energy efficiency coefficient effEB of electric boiler, energy efficiency coefficient effEC of chiller, energy efficiency coefficient effBAC of lithium bromide, energy conversion coefficient effDCE of direct-fired engine, energy conversion coefficient effDG of diesel engine, energy conversion coefficient effCHP of cogeneration, energy conversion coefficient effGB of gas boiler, maximum heat-to-electricity ratio CHPrate of cogeneration max 、Minimum heat-to-power ratio CHPrate of cogeneration min , the minimum load rate of combined heat and power CHPloadrate min 、Charging efficiency of electric energy storage system effBES c 、Discharge efficiency of electric energy storage system effBES d , heat release efficiency effTS of thermal energy storage system d , the thermal storage efficiency effTS of the thermal energy storage system c , Cooling efficiency of the cold storage system effCS d , the cold storage efficiency effCS of the cold storage system c , Cold storage ratio of cold storage system Cooling rate of cold storage system Thermal storage rate of thermal energy storage system Heat release rate of thermal energy storage system Charging rate of electric energy storage system Discharge rate of electric energy storage system

[0060] Solve the optimization model and obtain the equipment capacity configuration information and operation status information for each period of the year, including:

[0061]

[0062]

[0063] The energy balance constraints in the optimization model are:

[0064]

[0065] Where, effPV i is the power generation coefficient of the photovoltaic device at the i-th hour, capPV k is the residual capacity factor of the photovoltaic equipment in the kth year, effWT i is the power generation coefficient of the wind turbine at the i-th hour;

[0066]

[0067] Where, effST i is the heating coefficient of CSP heating at the i-th hour;

[0068] yPC ki +yEC ki +yDCE ki +yBAC ki +yCS ki ≥lC i .

[0069] The equipment output constraints in the optimization model are:

[0070] Yj Y ki ≤xDG;

[0071] YCHPE ki +yCHPT ki ≤xCHP;

[0072] Y ki / yCHPE ki ≤CHPrate max ;

[0073] Y ki / yCHPE ki ≥CHPrate min ;yCHPE ki +yCHPT ki ≥CHPloadrate min ×xCHP;

[0074] yBES ki ≤zBES ki ;

[0075] -yBES ki ≤xBES-zBES ki ;

[0076] zBES k,i+1 =zBES ki -yBES ki ;

[0077]

[0078]

[0079] yEB ki ≤xEB;

[0080] yGB ki ≤xGB;

[0081] yTS ki ≤zTS ki ;

[0082] -yTS ki ≤xTS-zTS ki ;

[0083] zTS k,i+1 =zTS ki -yTS ki ;

[0084]

[0085]

[0086] yEC ki ≤xEC;

[0087] yDCE ki ≤xDCE;

[0088] yBAC ki ≤xBAC;

[0089] yCS ki ≤zCS ki ;

[0090] -yCS ki ≤xCS-zCS ki ;

[0091] zCS k,i+1 =zCS ki -yCS ki ;

[0092]

[0093]

[0094] Cost calculation:

[0095] Investment cost in year k:

[0096]

[0097] Maintenance cost in year k:

[0098]

[0099] The calculation method of electricity cost in the kth year is:

[0100]

[0101] Among them, pe i is the electricity purchase cost coefficient for the i-th hour.

[0102] The calculation method of gas cost in the kth year is:

[0103]

[0104] Where pgas i is the gas purchase cost coefficient for the i-th hour.

[0105] The calculation method of fuel cost in year k is:

[0106]

[0107] Among them, pd i is the oil purchasing cost coefficient at the i-th hour.

[0108] The calculation method of income is:

[0109]

[0110] Among them, se i 、st i ,sc i are the selling prices of electricity, heating and cooling in the i-th hour respectively.

[0111] The cash flow calculation method is:

[0112]

[0113] cash=[cash1,cash2,...,cash k ,...,cash n]

[0114] Where n is the investment period of the project; is the investment cost in the kth year; is the maintenance cost in the kth year; is the electricity cost in the kth year; is the gas cost in the kth year; is the fuel cost in the kth year.

[0115] Calculation of internal rate of return of funds:

[0116] IRR=f IRR (cash)

[0117] Among them, f IRR The formula for solving the internal rate of return of funds is

[0118] After obtaining the above solution model, in order to further use the solver for solving, it is necessary to linearize the constraints of the energy storage part in the model.

[0119] Linearization of electrical energy storage systems:

[0120]

[0121]

[0122]

[0123]

[0124] in, is the charge amount of the energy storage system at the i-th hour in the k-th year, is the discharge capacity of the energy storage system at the i-th hour in the k-th year; M is a larger value introduced, bBES ki is a binary variable, It is the actual amount of electricity that the energy storage system can handle externally.

[0125] Thermal energy storage system linearization:

[0126]

[0127]

[0128]

[0129]

[0130] in, is the heat storage capacity of the thermal energy storage system at the i-th hour in the k-th year, is the heat released by the thermal energy storage system at the i-th hour in the k-th year; M is a larger value introduced; bTS ki is a binary variable, It is the actual heat throughput of the thermal energy storage system.

[0131] Cold storage system linearization:

[0132]

[0133]

[0134]

[0135]

[0136] in, is the cold storage capacity of the cold storage system at the i-th hour in the k-th year, bCS is the cooling capacity of the cold storage system at the i-th hour in the k-th year; ki is a binary variable, It is the actual cooling capacity of the cold storage system.

[0137] Finally, the commercial solver copt was used to solve the above model. The optimal energy type configuration capacity, economic indicators of project investment, and production and operation status of each equipment at different times of the year were obtained for the corresponding scenario, providing a reference for the design of the integrated energy system.

Claims

1. A comprehensive energy planning and design method based on annual production data, characterized in that: Establish an optimization model based on environmental resource information, load information, equipment parameter information, cost parameters and operation parameters; solve the optimization model with the maximum internal rate of return IRR as the goal and obtain the equipment capacity configuration information and operation status information at each time period throughout the year; The energy balance constraints in the optimization model include: Where, effPV i is the power generation coefficient of the photovoltaic device at the i-th hour, capPV k is the residual capacity factor of the photovoltaic equipment in the kth year, effWT i is the power generation coefficient of the wind turbine at the i-th hour; yPG ki is the electricity purchase amount at the i-th hour in the k-th year; xPV is the installed capacity of photovoltaic equipment; xWT is the installed capacity of wind turbines; yDG ki is the power generation of the diesel engine at the i-th hour in the k-th year; yCHPE ki is the power generation of CHP in the i-th hour of the k-th year; yBES ki is the charge and discharge amount of the energy storage at the i-th hour in the k-th year; lE i is the electricity load in the i-th hour; yEB ki is the heat output of the electric boiler at the i-th hour in the k-th year; effEB is the energy efficiency coefficient of the electric boiler; yEC ki is the cooling capacity of the chiller at the i-th hour in the k-th year; effEC is the energy efficiency coefficient of the chiller.

2. The comprehensive energy planning and design method based on annual production data according to claim 1 is characterized in that: The energy balance constraints in the optimization model also include: In the formula, yPT ki is the purchased heat amount in the i-th hour of the k-th year; xST is the installed capacity of CSP; effST i is the heating coefficient of CSP in the i-th hour; yCHPT ki is the heat output of CHP in the i-th hour of the k-th year; yEB ki is the heat output of the electric boiler at the i-th hour in the k-th year; yGB ki is the heat output of the gas boiler at the i-th hour in the k-th year; yTS ki is the heat storage / release amount of thermal energy in the kth year and the ith hour; lT i is the heat load in the ith hour; yBAC ki is the heat generated by lithium bromide at the i-th hour in the k-th year; effBAC is the energy efficiency coefficient of lithium bromide.

3. The comprehensive energy planning and design method based on annual production data according to claim 1 is characterized in that: The energy balance constraints in the optimization model also include: yPC ki +yEC ki +yDCE ki +yBAC ki +yCS ki ≥lC i 。 In the formula, yPC ki is the purchased cooling capacity at the i-th hour in the k-th year; yEC ki is the cooling capacity of the chiller at the i-th hour in the k-th year; yDCE ki is the heat output of the direct-fired engine at the i-th hour in the k-th year; yBAC ki is the heat generated by lithium bromide in the kth year and the ith hour; yCS ki is the storage / release capacity of the cold storage system at the i-th hour in the k-th year; lC i is the cooling load in the i-th hour.

4. The comprehensive energy planning and design method based on annual production data according to claim 1 is characterized in that: The cost parameters include: the investment cost coefficient of the photovoltaic equipment corresponding to the kth year The investment cost coefficient of the fan in the kth year The investment cost coefficient of diesel engine in year k The investment cost coefficient of cogeneration in year k The investment cost coefficient of the electric energy storage system in year k The investment cost coefficient of CSP in year k The investment cost coefficient of the electric boiler in the kth year The investment cost coefficient of the gas boiler in the kth year The investment cost coefficient of the thermal energy storage system in year k The investment cost coefficient of the chiller in year k The investment cost coefficient of the direct-fired engine in the kth year The investment cost coefficient of lithium bromide in year k The investment cost coefficient of the cold storage system in year k 5. The comprehensive energy planning and design method based on annual production data according to claim 1 is characterized in that: The operating parameters include: the maintenance cost coefficient of the photovoltaic equipment corresponding to the kth year The maintenance cost coefficient of the fan in the kth year Maintenance cost coefficient of diesel engine in year k The maintenance cost coefficient of CHP in year k Maintenance cost coefficient of the electric energy storage system corresponding to the kth year Maintenance cost coefficient of CSP heating corresponding to the kth year The maintenance cost coefficient of the electric boiler in the kth year Maintenance cost coefficient of gas boiler corresponding to the kth year Maintenance cost coefficient of thermal energy storage system corresponding to the kth year The maintenance cost coefficient of the chiller in the kth year Maintenance cost coefficient of direct-fired engine in year k Maintenance cost coefficient of lithium bromide corresponding to the kth year Maintenance cost coefficient of the cold storage system corresponding to the kth year 6. The comprehensive energy planning and design method based on annual production data according to claim 1 is characterized in that: The environmental resource information includes: the power generation coefficient effPV of the photovoltaic equipment at the i-th hour i , the wind turbine power generation coefficient effWT at the i-th hour i , the heating coefficient effST of solar thermal heating at the i-th hour i .

7. The comprehensive energy planning and design method based on annual production data according to claim 1 is characterized in that: The load information includes: the power load lE of the i-th hour i , heat load lT in the i-th hour i , cooling load lC in the i-th hour i .

8. The comprehensive energy planning and design method based on annual production data according to claim 1 is characterized in that: The equipment parameter information includes: the remaining capacity coefficient capPV of the photovoltaic equipment in the kth year k , energy efficiency coefficient effEB of electric boiler, energy efficiency coefficient effEC of chiller, energy efficiency coefficient effBAC of lithium bromide, energy conversion coefficient effDCE of direct-fired engine, energy conversion coefficient effDG of diesel engine, energy conversion coefficient effCHP of cogeneration, energy conversion coefficient effGB of gas boiler, maximum heat-to-electricity ratio CHPrate of cogeneration max 、Minimum heat-to-power ratio CHPrate of cogeneration min , the minimum load rate of combined heat and power CHPloadrate min 、Charging efficiency of electric energy storage system effBES c 、Discharge efficiency of electric energy storage system effBES d , heat release efficiency effTS of thermal energy storage system d , the thermal storage efficiency effTS of the thermal energy storage system c , Cooling efficiency of the cold storage system effCS d , the cold storage efficiency effCS of the cold storage system c , Cold storage ratio of cold storage system Cooling rate of cold storage system Thermal storage rate of thermal energy storage system Heat release rate of thermal energy storage system Charging rate of electric energy storage system Discharge rate of electric energy storage system 9. The comprehensive energy planning and design method based on annual production data according to claim 1 is characterized in that: Solve the optimization model and obtain the equipment capacity configuration information and operation status information for each period of the year, including:

10. The comprehensive energy planning and design method based on annual production data according to claim 9 is characterized in that: The equipment output constraint conditions in the optimization model are: yDG ki ≤xDG; yCHPE ki +yCHPT ki ≤xCHP; yCHPT ki / yCHPE ki ≤CHPrate max ; yCHPT ki / yCHPE ki ≥CHPrate min ;yCHPE ki +yCHPT ki ≥CHPloadrate min ×xCHP; yBES ki ≤zBES ki ; -yBES ki ≤xBES-zBES ki ; zBES k,i+1 =zBES ki -yBES ki ; EB ki ≤xEB; the GB ki ≤xGB; yTS ki ≤zTS ki ; -yTS ki ≤xTS-zTS ki ; zTS k,i+1 =zTS ki -yTS ki ; EC ki ≤xEC; yDCE ki ≤xDCE; yBAC ki ≤xBAC; yCS ki ≤zCS ki ; -yCS ki ≤xCS-zCS ki ; z CS k,i+1 =zCS ki -yCS ki ;

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