Park integrated energy system planning method and device considering source load uncertainty

Abstract

The invention discloses a park integrated energy system planning method and device considering source load uncertainty, and the method comprises the steps: carrying out the modeling of equipment in a park integrated energy system through setting a modeling strategy, and obtaining a park integrated energy system constraint model; constructing a global optimization model, a stage optimization model and a local optimization model; parameters of the system are initialized through the global optimization model, planning stages are divided, and local adjustment points are set; at the starting point of the first planning stage, calling the stage optimization model to obtain a stage planning scheme; calling the local optimization model at a first local adjustment point in the first planning stage to obtain a local planning scheme of the first local adjustment point; obtaining local planning schemes at the local adjustment points in sequence until the first planning stage is finished; and iteratively circulating until all the planning stages finish planning adjustment. According to the method, the influence of multiple uncertain factors is dealt with in a mode of combining dynamic planning, evaluation and adjustment.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system planning and scheduling, and in particular to a method and device for planning a park integrated energy system taking into account source and load uncertainty. Background Art

[0002] Park-level integrated energy system (PIES) is a user-side integrated energy system widely used in industrial parks, schools and other places. In recent years, PIES has attracted extensive attention from academia and industry due to its high energy utilization efficiency.

[0003] At present, reasonable planning is the basis for ensuring the safe, economical and efficient operation of PIES. A large number of scholars have conducted research on the planning of PIES. Some literatures have proposed a PIES capacity allocation model with the goal of minimizing investment costs. However, this single-stage planning method has great limitations and fails to fully consider long-term uncertain factors such as changes in load levels and technological development during the planning period. It may lead to problems such as premature investment in the early stage, equipment redundancy, and insufficient equipment capacity in the later stage, which seriously damages the economic efficiency of the planning scheme. Other literatures have proposed a multi-stage equipment configuration model for PIES, which invests equipment in PIES in stages during the planning period. However, this method is still a static planning method, that is, the equipment investment strategy for each stage is determined at one time at the beginning of the planning period and implemented sequentially during the planning period. However, due to the large changes in uncertainty factors in the long term, if the initial planning scheme is directly implemented without re-optimization or adjustment, it may not be able to adapt to the actual situation, which will have an adverse impact on the economic efficiency and energy supply reliability of PIES. Some literatures have proposed updating the source-load forecast information at the beginning of each stage and formulating a new planning scheme based on the updated information. However, this method has fixed the stage division at the beginning of planning and only updates the plan at the beginning of each stage, ignoring problems such as load surges and sharp fluctuations in energy prices that may occur within the stage. It is unable to respond to rapid changes in the external environment in a timely manner, affecting the flexibility and adaptability of the plan.

[0004] Therefore, how to invent a PIES dynamic programming method to cope with the impact of multiple uncertain factors by combining dynamic programming, evaluation and adjustment has become an urgent problem to be solved. Summary of the invention

[0005] To this end, the present invention provides a park integrated energy system planning method and device that takes into account source and load uncertainty, and copes with the impact of multiple uncertain factors by combining dynamic planning, evaluation and adjustment.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a method for planning a comprehensive energy system for a park considering source and load uncertainty, comprising:

[0007] By setting the modeling strategy, the photovoltaic, cogeneration units, heat pumps, gas boilers, electric energy storage and thermal energy storage equipment in the park's comprehensive energy system are modeled respectively to obtain the park's comprehensive energy system constraint model;

[0008] According to the park comprehensive energy system constraint model and dynamic programming framework, a global optimization model, a stage optimization model and a local optimization model are constructed respectively;

[0009] Initializing the parameters of the park's integrated energy system through the global optimization model, dividing the planning stages and setting local adjustment points;

[0010] At the starting point of the first planning stage, the stage optimization model is called according to the performance requirements of the stage planning scheme; the stage planning scheme of the first planning stage is obtained by solving the stage optimization model; according to the stage planning scheme of the first planning stage, the newly added equipment is put into the park comprehensive energy system;

[0011] At the first local adjustment point of the first planning stage, calling the local optimization model; obtaining a local planning solution for the first local adjustment point by solving the local optimization model; successively solving the local optimization model at the local adjustment points to obtain the local planning solution for the local adjustment points until the first planning stage ends;

[0012] After the planning adjustment is completed in the first planning stage, the planning scheme of the second stage and the local planning scheme of the second stage are obtained in sequence for the second planning stage; and the iterative cycle is performed until the planning adjustment is completed in all planning stages.

[0013] As a preferred scheme for the planning method of the park comprehensive energy system considering the uncertainty of source and load, in the process of modeling the photovoltaic, cogeneration unit, heat pump, gas boiler, electric energy storage and thermal energy storage equipment in the park comprehensive energy system by setting the modeling strategy, the model expression of the cogeneration unit equipment is:

[0014]

[0015]

[0016] In the formula, and are the thermal power and gas consumption of CHP in period t of year y, respectively; P CHP,h and are the upper and lower limits of CHP heating power respectively; hCHP,h and h CHP,e are the heating efficiency and power supply efficiency of CHP respectively;

[0017] The model expression of the heat pump equipment is:

[0018]

[0019] In the formula, and are the heating power and electrical power consumed by HP in period t of year y; COP H,HP is the energy efficiency coefficient of HP electric heating; It is the upper limit of HP heating power;

[0020] The model expression of gas boiler equipment is:

[0021]

[0022] In the formula, and are the heating power and gas consumption of GB in period t of year y, respectively; is the upper limit of heating power of GB; h GB is the heating efficiency of GB;

[0023] The model expression of photovoltaic equipment is:

[0024]

[0025] In the formula, and are the power consumption and output power of PV in period t of year y, respectively;

[0026] The model expressions of electrical energy storage and thermal energy storage equipment are:

[0027]

[0028] S min ≤S y,t ≤S max

[0029]

[0030] In the formula, and are the energy storage power and energy release power in period t of year y respectively; P c,max and P d,max is the maximum energy storage and release power; y,t is a 0-1 variable, which is the charge and discharge state of the energy storage device at time t in year y, z y,t When it is 1, it indicates energy storage state, and when it is 0, it indicates energy release state; S y,tS is the state of charge of the energy storage device at time t in year y; min and S max are the upper and lower limits of the energy storage charge state respectively; h c and h d are energy storage and release efficiency respectively; W ESS is the capacity of the energy storage device; S0 and They are the charge states at the initial and final stages of energy storage device scheduling, respectively.

[0031] As a preferred scheme for a park integrated energy system planning method considering source and load uncertainty, in the process of dividing the park integrated energy system into planning stages and setting local adjustment points in the global optimization model, the expression of the planning stage is:

[0032]

[0033] Where P i is the i-th stage; N P is the number of stages;

[0034] The set expression of local adjustment points in the planning stage is:

[0035]

[0036] Where N i For stage P i The number of local adjustment points; For stage P i The kth local adjustment point within.

[0037] As a preferred scheme for a park integrated energy system planning method considering source and load uncertainty, the objective function of the stage optimization model is to minimize the investment cost, operating cost, maintenance cost and equipment residual value cost from the current stage to the end of the planning time; the objective function expression is:

[0038]

[0039] R y =(1+r) -y

[0040]

[0041] Where C is the total life cycle cost; n s and n y are the stage number and year number corresponding to the current stage respectively; N s and N y are the total number of stages and the total number of years respectively; n p is the year number corresponding to the initial stage p; is the equipment investment cost at the beginning of stage p; and are the operating cost and maintenance cost in year y respectively; R n is the present value coefficient at the end of the planning period; The residual value of the equipment invested from the current time to the end of the plan; R y is the present value coefficient of the yth year; r is the discount rate;

[0042] N = {PV, CHP, HP, GB, ES, HS}; c int,n is the unit capacity investment cost of equipment n; It refers to the investment capacity of equipment n in stage p; is the price at which PIES purchases electricity from the grid at time t; c gas is the natural gas price; c loss Penalty costs for insufficient heating; and They are respectively the power purchased from the grid and the insufficient heating power in period t of year y; and are the gas consumption of CHP and GB in period t of year y, respectively; is the power of equipment n in the yth year and period t; c om,n is the unit power maintenance price of equipment n; Y p Y is the number of years from the beginning of stage p to the end of the integration period; n is the lifetime of device n.

[0043] As a preferred scheme for a park integrated energy system planning method that takes into account source and load uncertainty, the local optimization model includes an evaluation sub-model and a local adjustment sub-model; in the process of obtaining a local planning scheme through the local optimization model, the operation status of the park integrated energy system is evaluated through the evaluation sub-model to obtain an evaluation strategy; based on the evaluation strategy, if the operation status of the park integrated energy system is good, the original planning scheme will continue to be implemented; if there are problems in the operation of the park integrated energy system, the local adjustment sub-model will be called to obtain an adjusted planning scheme.

[0044] The present invention also provides a park comprehensive energy system planning device considering source-load uncertainty, based on the above park comprehensive energy system planning method considering source-load uncertainty, comprising:

[0045] The park comprehensive energy system modeling module is used to model the photovoltaic, cogeneration units, heat pumps, gas boilers, electric energy storage and thermal energy storage equipment in the park comprehensive energy system by setting modeling strategies to obtain the park comprehensive energy system constraint model;

[0046] An optimization model building module, used to build a global optimization model, a stage optimization model and a local optimization model according to the park comprehensive energy system constraint model and the dynamic programming framework;

[0047] A global optimization model processing module, used to initialize the parameters of the park comprehensive energy system through the global optimization model, divide the planning stages and set local adjustment points;

[0048] The stage optimization model processing module is used to call the stage optimization model at the starting point of the first planning stage according to the performance requirements of the stage planning scheme; obtain the stage planning scheme of the first planning stage by solving the stage optimization model; and put the newly added equipment into the park comprehensive energy system according to the stage planning scheme of the first planning stage;

[0049] A local optimization model processing module is used to call the local optimization model at the first local adjustment point of the first planning stage; obtain the local planning scheme of the first local adjustment point by solving the local optimization model; and successively solve the local optimization model at the local adjustment points to obtain the local planning scheme of the local adjustment points until the first planning stage ends;

[0050] The iterative cycle planning adjustment module is used to obtain the planning scheme of the second stage and the local planning scheme of the second stage in sequence for the second planning stage after the planning adjustment is completed in the first planning stage; iterate and cycle until the planning adjustment is completed in all planning stages.

[0051] As a preferred solution of a park comprehensive energy system planning device considering source and load uncertainty, in the park comprehensive energy system modeling module, in the process of modeling photovoltaic, cogeneration unit, heat pump, gas boiler, electric energy storage and thermal energy storage equipment in the park comprehensive energy system by setting modeling strategies, the model expression of the cogeneration unit equipment is:

[0052]

[0053] In the formula, and are the thermal power and gas consumption of CHP in period t of year y, respectively; P CHP,h and are the upper and lower limits of CHP heating power respectively; h CHP,h and h CHP,e are the heating efficiency and power supply efficiency of CHP respectively;

[0054] The model expression of the heat pump equipment is:

[0055]

[0056] In the formula, and are the heating power and electrical power consumed by HP in period t of year y; COP H,HP is the electric heating efficiency coefficient of HP; It is the upper limit of HP heating power;

[0057] The model expression of gas boiler equipment is:

[0058]

[0059] In the formula, and are the heating power and gas consumption of GB in period t of year y, respectively; is the upper limit of heating power of GB; h GB is the heating efficiency of GB;

[0060] The model expression of photovoltaic equipment is:

[0061]

[0062] In the formula, and are the power consumption and output power of PV in period t of year y, respectively;

[0063] The model expressions of electrical energy storage and thermal energy storage equipment are:

[0064]

[0065] S min ≤S y,t ≤S max

[0066]

[0067] In the formula, and are the energy storage power and energy release power in period t of year y respectively; P c,max and P d,max is the maximum energy storage and release power; y,t is a 0-1 variable, which is the charge and discharge state of the energy storage device at time t in year y, z y,t When it is 1, it indicates energy storage state, and when it is 0, it indicates energy release state; S y,t S is the state of charge of the energy storage device at time t in year y; min and S max are the upper and lower limits of the energy storage charge state respectively; h c and h d are energy storage and release efficiency respectively; W ESS is the capacity of the energy storage device; S0 and STd They are the charge states at the initial and final stages of energy storage device scheduling, respectively.

[0068] As a preferred solution of a park comprehensive energy system planning device considering source and load uncertainty, in the global optimization model processing module, in the process of dividing the park comprehensive energy system into planning stages and setting local adjustment points in the global optimization model, the expression of the planning stage is:

[0069]

[0070] Where P i is the i-th stage; N P is the number of stages;

[0071] The set expression of local adjustment points in the planning stage is:

[0072]

[0073] Where N i For stage P i The number of local adjustment points; For stage P i The kth local adjustment point within.

[0074] As a preferred solution for a park integrated energy system planning device that takes into account source and load uncertainty, in the stage optimization model processing module, the stage optimization model objective function is to minimize the investment cost, operating cost, maintenance cost and equipment residual value cost from the current stage to the end of the planning time; the objective function expression is:

[0075]

[0076] R y =(1+r) -y

[0077]

[0078]

[0079] Where C is the total life cycle cost; n s and n y are the stage number and year number corresponding to the current stage respectively; N s and N y are the total number of stages and the total number of years respectively; n p is the year number corresponding to the initial stage p; is the equipment investment cost at the beginning of stage p; and are the operating cost and maintenance cost in year y respectively; R nis the present value coefficient at the end of the planning period; The residual value of the equipment invested from the current time to the end of the plan; R y is the present value coefficient of the yth year; r is the discount rate;

[0080] N = {PV, CHP, HP, GB, ES, HS}; c int,n is the unit capacity investment cost of equipment n; It refers to the investment capacity of equipment n in stage p; is the price at which PIES purchases electricity from the grid at time t; c gas is the natural gas price; c loss Penalty costs for insufficient heating; and They are respectively the power purchased from the grid and the insufficient heating power in period t of year y; and are the gas consumption of CHP and GB in period t of year y, respectively; is the power of equipment n in the yth year and period t; c om,n is the unit power maintenance price of equipment n; Y p Y is the number of years from the beginning of stage p to the end of the integration period; n is the lifetime of device n.

[0081] As a preferred solution for a park comprehensive energy system planning device that takes into account source and load uncertainty, in the local optimization model processing module, the local optimization model includes an evaluation sub-model and a local adjustment sub-model; in the process of obtaining a local planning scheme through the local optimization model, the operation status of the park comprehensive energy system is evaluated through the evaluation sub-model to obtain an evaluation strategy; based on the evaluation strategy, if the operation status of the park comprehensive energy system is good, the original planning scheme will continue to be implemented; if there are problems in the operation of the park comprehensive energy system, the local adjustment sub-model will be called to obtain an adjusted planning scheme.

[0082] The present invention has the following advantages: by setting a modeling strategy, photovoltaic, cogeneration units, heat pumps, gas boilers, electric energy storage and thermal energy storage equipment in the park comprehensive energy system are modeled respectively to obtain a park comprehensive energy system constraint model; according to the park comprehensive energy system constraint model and the dynamic programming framework, a global optimization model, a stage optimization model and a local optimization model are constructed respectively; the parameters of the park comprehensive energy system are initialized by the global optimization model, and the planning stages are divided and local adjustment points are set; at the starting point of the first planning stage, the stage optimization model is called according to the performance requirements of the stage planning scheme; by solving the stage optimization model, the stage optimization model of the first planning stage is obtained. segment planning scheme; according to the stage planning scheme of the first planning stage, the new equipment is put into the integrated energy system of the park; at the first local adjustment point of the first planning stage, the local optimization model is called; by solving the local optimization model, the local planning scheme of the first local adjustment point is obtained; in turn, at the local adjustment points, the local optimization model is solved to obtain the local planning scheme of the local adjustment points until the end of the first planning stage; after the planning adjustment of the first planning stage is completed, the planning scheme of the second stage and the local planning scheme of the second stage are obtained in turn for the second planning stage; iterative loop is performed until the planning adjustment of all planning stages is completed. The present invention copes with the influence of multiple uncertain factors by combining dynamic planning, evaluation and adjustment. At the same time, the present invention has high flexibility and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0084] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0085] Figure 1 A schematic flow chart of a method for planning a park integrated energy system taking into account source-load uncertainty provided in Example 1 of the present invention;

[0086] Figure 2A schematic diagram of the PIES structure in a method for planning a campus integrated energy system taking into account source-load uncertainty provided in Example 1 of the present invention;

[0087] Figure 3 A schematic diagram of a dynamic planning implementation framework in a park integrated energy system planning method considering source-load uncertainty provided in Example 1 of the present invention;

[0088] Figure 4 A schematic diagram of the electric load, thermal load and photovoltaic output curves on each typical day in a possible embodiment provided in Embodiment 1 of the present invention;

[0089] Figure 5 This is a schematic diagram of the load level of each stage of PIES in a possible embodiment provided in Embodiment 1 of the present invention; wherein (a), (b), and (c) respectively give the predicted electric load level of each stage and the predicted electric load level updated at the local adjustment point (end of the 1st, 4th, and 9th year); (d), (e), and (f) respectively give the predicted value and actual value of the thermal load level;

[0090] Figure 6 This is a schematic diagram of power balance on a typical winter day from the beginning of the first stage to the end of the first year in a possible embodiment provided in Embodiment 1 of the present invention; wherein (a) is the electric power balance; (b) is the thermal power balance;

[0091] Figure 7 This is a schematic diagram of power balance on a typical winter day from the end of the first year to the end of the second year in a possible embodiment provided in Embodiment 1 of the present invention; wherein (a) is the electric power balance; (b) is the thermal power balance;

[0092] Figure 8 A schematic diagram of the planning scheme of three schemes in a possible embodiment provided in Embodiment 1 of the present invention;

[0093] Fig. 9 A schematic diagram of the accumulated investment costs of three schemes in a possible embodiment provided in Embodiment 1 of the present invention;

[0094] Fig.10 This is a schematic diagram of the architecture of a campus integrated energy system planning device that takes into account source-load uncertainty, as provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0095] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0096] Example 1

[0097] See also Figure 1 Embodiment 1 of the present invention provides a method for planning a comprehensive energy system for a park considering uncertainty of source and load, comprising the following steps:

[0098] S1. By setting the modeling strategy, the photovoltaic, cogeneration units, heat pumps, gas boilers, electric energy storage and thermal energy storage equipment in the park's comprehensive energy system are modeled to obtain the park's comprehensive energy system constraint model;

[0099] S2. According to the park comprehensive energy system constraint model and dynamic programming framework, a global optimization model, a stage optimization model and a local optimization model are constructed respectively;

[0100] S3. Initializing the parameters of the park integrated energy system through the global optimization model, dividing the planning stages and setting local adjustment points;

[0101] S4. At the starting point of the first planning stage, according to the performance requirements of the stage planning scheme, calling the stage optimization model; obtaining the stage planning scheme of the first planning stage by solving the stage optimization model; according to the stage planning scheme of the first planning stage, adding new equipment to the park integrated energy system;

[0102] S5. At the first local adjustment point of the first planning stage, calling the local optimization model; obtaining a local planning solution for the first local adjustment point by solving the local optimization model; successively solving the local optimization model at the local adjustment points to obtain a local planning solution for the local adjustment points until the first planning stage ends;

[0103] S6. After the planning adjustment is completed in the first planning stage, the planning scheme of the second stage and the local planning scheme of the second stage are obtained in sequence for the second planning stage; and the cycle is iterated until the planning adjustment is completed in all planning stages.

[0104] In this embodiment, in step S1, by setting a modeling strategy, the photovoltaic, cogeneration units, heat pumps, gas boilers, electric energy storage and thermal energy storage equipment in the park comprehensive energy system are modeled respectively to obtain a constraint model of the park comprehensive energy system;

[0105] Specifically, Figure 2 As shown in the figure, the park's comprehensive energy system includes photovoltaic (PV), combined heat and power unit (CHP), heat pump (HP), gas boiler (GB), as well as electricity storage (ES) and heat storage (HS) devices, and the loads include electrical load and thermal load.

[0106] The park integrated energy system is modeled by setting the modeling strategy. The model expressions of each device are as follows:

[0107] The model expression of the combined heat and power unit equipment is:

[0108]

[0109] In the formula, and are the thermal power and gas consumption of CHP in period t of year y, respectively; P CHP,h and are the upper and lower limits of CHP heating power respectively; h CHP,h and h CHP,e are the heating efficiency and power supply efficiency of CHP respectively;

[0110] The model expression of the heat pump equipment is:

[0111]

[0112] In the formula, and are the heating power and electrical power consumed by HP in period t of year y; COP H,HP is the energy efficiency coefficient of HP electric heating; It is the upper limit of HP heating power;

[0113] The model expression of gas boiler equipment is:

[0114]

[0115] In the formula, and are the heating power and gas consumption of GB in period t of year y, respectively; is the upper limit of heating power of GB; h GB is the heating efficiency of GB;

[0116] The model expression of photovoltaic equipment is:

[0117]

[0118] In the formula, and are the power consumption and output power of PV in period t of year y, respectively;

[0119] The model expressions of electrical energy storage and thermal energy storage equipment are:

[0120]

[0121] S min ≤S y,t ≤S max (11)

[0122]

[0123] In the formula, and are the energy storage power and energy release power in period t of year y respectively; P c,max and P d,max is the maximum energy storage and release power; y,t is a 0-1 variable, which is the charge and discharge state of the energy storage device at time t in year y, z y,t When it is 1, it indicates energy storage state, and when it is 0, it indicates energy release state; S y,t S is the state of charge of the energy storage device at time t in year y; min and S max are the upper and lower limits of the energy storage charge state respectively; h c and h d are energy storage and release efficiency respectively; W ESS is the capacity of the energy storage device; S0 and They are the charge states at the initial and final stages of the energy storage device scheduling. The energy storage mechanism and energy conversion relationship of ES and HS are similar, and both can be described by this model.

[0124] In this embodiment, in step S2, a global optimization model, a stage optimization model and a local optimization model are constructed respectively according to the park integrated energy system constraint model and the dynamic programming framework;

[0125] Among them, Figure 3As shown in the figure, the dynamic programming framework includes a global optimization model (OPT3), a stage optimization model (OPT2) and a local optimization model (OPT1) to obtain, evaluate and update the planning scheme. OPT3 optimizes the planning scheme at the global level and controls the entire optimization process. OPT3 divides the planning time into multiple stages and sets local adjustment points. Then OPT2 and OPT1 are continuously called during the planning process. OPT2 updates the forecast data at the beginning of each stage, such as the changing trend of the load level on a long time scale, and optimizes to obtain a new planning scheme. OPT1 includes an evaluation submodel and a local adjustment submodel, which are called at each local adjustment point. First, the evaluation submodel is called to evaluate the implementation effect of the planning scheme; then, according to the evaluation results of the evaluation submodel, it is determined whether to call the local adjustment submodel; if necessary, the local adjustment submodel is called to adjust the current planning scheme. In addition, the impact of short-term uncertainty on the planning results is also considered in OPT2 and OPT1. Appropriate optimization methods such as stochastic optimization and robust optimization can be selected according to the actual situation or the needs of decision makers. The three optimization models in the dynamic programming framework improve the adaptability of the planning scheme under long-term and short-term uncertainties through collaborative optimization.

[0126] In this embodiment, in step S3, the parameters of the park integrated energy system are initialized through the global optimization model, and the planning stages are divided and local adjustment points are set;

[0127] Specifically, Figure 3 As shown in the figure, the dynamic planning framework of PIES fully considers the actual development of PIES and realizes the optimization, supervision and correction of the whole process, that is, the planning scheme is updated according to the updated load level at each stage, and the implementation of the scheme is supervised and adjusted in time during the stage. In the planning process, the planning scheme is updated by implementing and continuously calling the optimization model to ensure the adaptability of the planning scheme in a changing environment.

[0128] Among them, dynamic planning time division:

[0129] Considering that PIES planning generally involves a long period of time, factors such as load level, equipment unit investment cost, and energy price continue to change during the planning process, and investing all resources and funds at one time may face high financial risks, the construction of PIES is often divided into multiple phases. The planning time can be divided into several stages:

[0130]

[0131] Where P i is the i-th stage; N P is the number of stages.

[0132] Since PIES develops and changes rapidly, even in one stage Pi The external environment may also change significantly, such as a surge in load levels, sudden changes in energy prices, etc. At the same time, the forecast data at the beginning of the stage may deviate greatly from the actual data. Therefore, several local adjustment points can be set according to needs within the stage to adjust the PIES equipment configuration to ensure that it operates in the best state. i The set of internal local adjustment points is expressed as:

[0133]

[0134] Where N i For stage P i The number of local adjustment points; For stage P i The kth local adjustment point within.

[0135] The global optimization model initializes the parameters of the park's integrated energy system, including stage division, local adjustment point selection, etc. Stage division is based on the prediction of future load growth trends to capture long-term uncertain changes. At the same time, local adjustment points are set in each stage to deal with the problems caused by the deviation between actual data and predicted data during operation.

[0136] OPT3 coordinates OPT2 and OPT1 during the dynamic programming process. OPT2 is called at the beginning of each stage and OPT1 is called at each local adjustment point to cope with long-term and short-term uncertainties.

[0137] In this embodiment, in step S4, at the starting point of the first planning stage, the stage optimization model is called according to the performance requirements of the stage planning scheme; the stage planning scheme of the first planning stage is obtained by solving the stage optimization model; according to the stage planning scheme of the first planning stage, the newly added equipment is put into the park comprehensive energy system;

[0138] Specifically, at the starting point of the first stage, according to the planner's performance requirements for the planning scheme and the known information of random variables, the appropriate optimization method is selected, the corresponding stage optimization model (OPT2) is called, the planning scheme of this stage is optimized, and the new equipment is put into PIES according to the planning scheme.

[0139] Among them, OPT2 is called at the beginning of each stage. OPT2 uses the updated uncertain parameter forecast data to optimize all investment variables and operating variables from the current stage to the end of the planning time, but only executes the optimized values ​​of the variables corresponding to the current stage. The rolling optimization mode of OPT2 can handle long-term uncertainties such as changes in load levels. At the same time, for short-term uncertainties involved in operation, such as fluctuations in renewable energy output and uncertainty in load demand, decision makers can choose appropriate optimization methods to solve OPT2 according to actual conditions such as uncertainty prediction accuracy, robustness requirements, and risk preferences, such as deterministic optimization, stochastic optimization, and robust optimization. First, taking OPT2 based on deterministic optimization as an example, the formula of OPT2 is given. Then, OPT2 based on stochastic optimization and OPT2 based on robust optimization are established.

[0140] The objective function of OPT2 is to minimize the investment cost, operating cost, maintenance cost and equipment residual value cost from the current stage to the end of the planning time. The objective function expression is:

[0141]

[0142] R y =(1+r) -y (17)

[0143]

[0144] Where C is the total life cycle cost; n s and n y are the stage number and year number corresponding to the current stage respectively; N s and N y are the total number of stages and the total number of years respectively; n p is the year number corresponding to the initial stage p; is the equipment investment cost at the beginning of stage p; and are the operating cost and maintenance cost in year y respectively; R n is the present value coefficient at the end of the planning period; The residual value of the equipment invested from the current time to the end of the plan; R y is the present value coefficient of the yth year; r is the discount rate; N = {PV, CHP, HP, GB, ES, HS}; c int,n is the unit capacity investment cost of equipment n; It refers to the investment capacity of equipment n in stage p; is the price at which PIES purchases electricity from the grid at time t; c gas is the natural gas price; c loss Penalty costs for insufficient heating; and They are respectively the power purchased from the grid and the insufficient heating power in period t of year y; and are the gas consumption of CHP and GB in period t of year y, respectively; is the power of equipment n in the yth year and period t; c om,n is the unit power maintenance price of equipment n; Y p Y is the number of years from the beginning of stage p to the end of the integration period; n is the lifetime of device n.

[0145] The constraints of OPT2 include equipment operation constraints (1)-(13), and power and gas purchase constraints as shown in (22)-(23):

[0146]

[0147]

[0148] Where P grid,max Indicates the upper limit of PIES electricity purchase.

[0149] The power balance constraints are shown in equations (24)-(25):

[0150]

[0151] In addition, the maximum output of each energy production and conversion equipment cannot exceed its cumulative investment capacity, as shown in formula (26):

[0152]

[0153] In the formula, p y Indicates the stage number corresponding to year y.

[0154] The compact form of OPT2 is:

[0155]

[0156] Where x and y are optimization variables, namely investment variables and operating variables, respectively; c and f are coefficient column vectors corresponding to the objective function; F, G, D, I are coefficient matrices of variables in constraints, and u and h are constant column vectors.

[0157] Furthermore, considering the uncertainty of photovoltaic output, electric load power and thermal load power, a random stage optimization model and a robust stage optimization model are constructed on the basis of the deterministic optimization model.

[0158] In the random stage optimization model, the scenario generation and scenario reduction method is used to describe the short-term uncertainty of random variables, as shown in equation (28). The compact form of OPT2 is shown in equation (29):

[0159]

[0160] Where S is the scene set obtained after reduction; a s is the probability corresponding to scene s; y s is the operational decision variable under scenario s; u s is the column vector of random variables under scenario s; Represents the possible scenarios of random variables; The set of scenarios representing random variables.

[0161] In the robust stage optimization model, the interval model is used to represent the short-term uncertainty of random variables, as shown in formula (30). The compact form of OPT2 is shown in formula (31):

[0162]

[0163] In the formula, max-min finds the worst possible scenario for a random variable; u is the column vector of the random variable; represents the actual power of the random variable in period t in year y; It represents the predicted value of the random variable in the t period of the yth year; It represents the forecast error of the random variable in period t in year y.

[0164] In this embodiment, in step S5, at the first local adjustment point of the first planning stage, the local optimization model is called; by solving the local optimization model, a local planning scheme for the first local adjustment point is obtained; at the local adjustment points, the local optimization model is solved in sequence to obtain a local planning scheme for the local adjustment point, until the first planning stage ends;

[0165] Specifically, the first local adjustment point in the first planning stage OPT3 selects the optimization method according to the actual situation and calls the corresponding local optimization model (OPT1). First, the evaluation sub-model in OPT1 is called to evaluate the implementation of the existing planning scheme and determine whether the local adjustment sub-model needs to be called. If necessary, the local adjustment sub-model is called to obtain a new investment strategy and update the planning scheme; if not, the original planning scheme is continued. At each local adjustment point thereafter, the optimization method is selected and the corresponding OPT1 is called to obtain the corresponding investment strategy until the first stage ends.

[0166] The local optimization model consists of two sub-models, namely the evaluation sub-model and the local adjustment sub-model. When the local optimization model is called, the evaluation sub-model is first used to evaluate the operation of PIES to obtain the evaluation strategy. If the implementation is good, the original planning scheme will continue to be implemented; if the evaluation finds that there are problems in the operation of PIES, the local adjustment sub-model will be called to obtain the adjusted planning scheme.

[0167] In this embodiment, in the evaluation sub-model, the economic index, energy supply reliability index and renewable energy consumption index are used to quantitatively measure the operation status of PIES from the last local adjustment point to the current local adjustment point.

[0168] At local adjustment point The current corresponding year is The year corresponding to the last local adjustment point is The economic index is shown in (32), equations (33)-(34) are energy supply reliability indicators, and equation (35) is the renewable energy consumption index.

[0169]

[0170] Where, LESR e and LESR h They are the electricity supply shortage rate and the heat supply shortage rate respectively; and They are renewable energy consumption power and production power respectively.

[0171] In this embodiment, the deterministic local adjustment sub-model is taken as an example to introduce the composition of the local adjustment sub-model in detail. First, the forecast data is updated according to the actual operation situation. The optimization goal of the local adjustment sub-model is to minimize the full life cycle cost from this point to the end of the planning, including the investment cost of the adjustment point, the initial investment cost of each subsequent stage, the operating cost, the maintenance cost, and the equipment residual value cost, which is expressed as:

[0172]

[0173] In the formula, C adj represents the investment cost of the new equipment at this local adjustment point; p k is the stage number corresponding to the local adjustment point; y k Indicates the year number corresponding to the local adjustment point; It represents the residual value of the investment equipment from this point to the end of planning.

[0174] Taking equipment n as an example, the equipment capacity and operation constraints in year y are expressed as:

[0175]

[0176] In the formula, represents the capacity of equipment n in year y; represents the newly added investment capacity of the device n of the current local adjustment sub-model; k represents the number of times the local adjustment sub-model is currently called; It represents the newly added investment capacity of equipment n when the local adjustment sub-model is called for the i-th time.

[0177] The constraints of the model include equipment operation constraints, electricity and gas purchase constraints, power balance constraints, and equipment maximum output constraints.

[0178] In this embodiment, in step S6, after the planning adjustment is completed in the first planning stage, the planning scheme of the second stage and the local planning scheme of the second stage are obtained in sequence for the second planning stage; and the iterative loop is performed until the planning adjustment is completed in all planning stages.

[0179] Specifically, after the planning adjustment is completed in the first planning stage, in each subsequent stage, the above-mentioned step S4 of calling OPT2 and step S5 of calling OPT1 are repeated until the dynamic planning process is completed.

[0180] In a possible embodiment, a dynamic programming example of a power / heat / natural gas coupling PIES study in northern China is provided as follows:

[0181] The planning period is set at 15 years, and based on the forecast of future load growth trends, the planning period is divided into three stages, namely 3 years, 5 years and 7 years. And within each stage, the end of each year is set as a local adjustment point. Investment equipment includes PV, CHP, HP, GB, ES, and HS. The electricity price adopts time-of-use electricity price, and the natural gas price is 0.28 yuan / kWh. The net residual value rate of the equipment is 6%, and the discount rate is 8%. The maximum allowable values ​​of LESRe and LESRh are set to 5%. In order to reflect the seasonal differences in the uncertain parameters of load and photovoltaic output, three typical days in summer, winter and transition seasons were selected. Figure 4 As shown, the electric load, thermal load and photovoltaic output curves for each typical day, the dark blue lines represent their predicted values, the shaded area represents the fluctuation range of these random variables, and the upper and lower deviations are set to 20%, and the light blue lines represent the scenarios obtained after scenario generation and reduction.

[0182] like Figure 5 As shown in Figure 2, the predicted load level and actual load level within the planning time are given. During the implementation of the planning scheme, the predicted data of the load level will be continuously updated at the beginning of each stage and at the local adjustment points within each stage. Figure 5(a)-(c) show the forecast load level for each stage and the updated forecast load level at the local adjustment point (end of the 1st, 4th, and 9th year). The local adjustment point updates the forecast data according to the actual load level during the actual operation. For example, Figure 5 In (a), the load level is first predicted at the beginning of the first phase, but when the first local adjustment point (end of year 1) is reached, the actual load level is found to be much higher than the previous prediction. Therefore, the predicted load level is updated at this local adjustment point based on the actual operation results. Figure 5 (d)-(f) give the predicted and actual values ​​of the heat load level, respectively.

[0183] At the beginning of the first stage, OPT2 is called to obtain the planning scheme, including the type and capacity of investment equipment, as shown in Table 1. However, only the investment strategy corresponding to the first stage is executed, and the investment strategies for the second and third stages need to be re-optimized later.

[0184] stage PV / kW ES / kW CHP / kW HP / kW HS / kW GB / kW <![CDATA[P1]]> 365 435 120 145 330 0 <![CDATA[P2]]> 395 155 170 160 355 0 <![CDATA[P3]]> 300 0 140 145 375 0 Cumulative capacity 1060 590 430 450 1060 0

[0185] Table 1: Initial planning scheme for the first phase

[0186] When the first local adjustment point in the first stage (the end of the first year) is reached, the evaluation submodel of OPT1 is first called to evaluate the operation of PIES. The balance of electric power and thermal power on a typical winter day during the first year of operation is as follows: Figure 6 As shown. PIES has sufficient power supply and LESHe is zero. All PV output is consumed by PIES, and the PV consumption rate is 100%. However, due to the large heat load, there is a shortage of heat supply during the period of 6:00-22:00, especially during the peak load period of 7:00-9:00. The shortage of heat supply is very serious, with the maximum shortage of heat supply reaching 240kW, resulting in LESRh reaching 10.73%, indicating that the current PIES heating reliability is low. The operating cost in the first year is 2.42×106 yuan, of which the penalty cost of insufficient heat supply is high, resulting in high operating costs. The shortage of heat supply is due to the rapid growth of heat load, which exceeds the heat load level predicted at the beginning of the first stage. The capacity of heating equipment such as CHP and HP is insufficient and cannot meet the heat load demand.

[0187] Therefore, the current planning scheme needs to be adjusted. The updated planning scheme is obtained by calling the local adjustment sub-model of OPT1, and the adjusted capacity is shown in Table 2. At this time, the capacity of CHP, HP and HS is expanded to improve the heating capacity of PIES and make up for the insufficient heating. At the same time, the capacity of PV and ES is expanded to give PIES a larger power generation capacity to meet the increased electrical load.

[0188] capacity PV / kW ES / kW CHP / kW HP / kW HS / kW Existing capacity 365 435 120 145 330 Adjust capacity 235 210 100 115 285 Cumulative capacity 600 645 220 260 615

[0189] Table 2 Adjustment capacity

[0190] At the second local adjustment point (end of the second year), the evaluation sub-model of OPT1 is called again to evaluate the operation status, such as Figure 7 As shown in the figure, the balance of electric power and thermal power on a typical winter day between two local adjustment points is given. After the planning scheme is updated, the heating capacity of PIES is improved. HP uses low-priced electric energy to produce heat from 0:00 to 6:00, stores the heat energy in HS, and then releases it from 8:00 to 10:00 to meet the higher heat load demand. In another peak load period from 18:00 to 21:00, the heat load demand can be fully met due to the enhanced heating capacity of HP, HS and CHP. At this time, the problem of insufficient heating is solved because the equipment capacity configuration of PIES matches the load level. LESHe and LESRh are both zero. The operating cost is reduced by 1.10×106 yuan compared with the first year. The above evaluation results show that PIES is in good operation and there is no need to adjust the planning scheme.

[0191] At the subsequent local adjustment points in the first stage, OPT1 is called repeatedly until the last local adjustment point. In the subsequent stages, the planning scheme is dynamically optimized by repeating the above process to call OPT2 and OPT1 until the planning is completed. Table 3 shows the final planning scheme.

[0192] Time equipment PV / kW ES / kW CHP / kW HP / kW HS / kW The first stage 365 435 120 145 330 End of first year 235 210 100 115 285 The beginning of the second stage 235 0 100 70 130 End of fourth year 65 0 35 30 50 The beginning of the third stage 100 0 60 90 290

[0193] Table 3 Final planning scheme

[0194] In order to illustrate the advantages of the present invention and the difference between it and other methods, it is compared with the traditional multi-stage planning and multi-stage rolling planning methods, and three comparison schemes are designed:

[0195] Solution 1: Optimizing the PIES planning solution using the method of the present invention;

[0196] Option 2: Use a multi-stage planning approach to optimize the PIES planning scheme;

[0197] Option 3: Use a multi-stage rolling planning method to optimize the PIES planning scheme.

[0198] Due to the differences in the three planning methods, the optimization process and results are also different. Scheme 1 updates the forecast data and planning scheme at the beginning of each stage, then evaluates the operation status of PIES and adjusts the planning scheme at local adjustment points within the stage. Scheme 2 obtains the planning scheme at the beginning of the first stage at one time. The plan includes the investment strategies of all stages and is implemented in sequence at the beginning of each stage. Scheme 3 updates the forecast data and planning scheme at the beginning of each stage, that is, re-optimizes the investment strategy in each stage, and only implements the investment strategy of the current stage, but does not adjust the planning scheme within the stage.

[0199] The planning schemes corresponding to Scheme 1, Scheme 2 and Scheme 3 are as follows: Figure 8 As shown in Figure 2, the three schemes have the same investment decisions at the beginning of the first stage, because at this time, the three schemes all use the same future load level forecast data to solve OPT2. At the beginning of the subsequent stage, Schemes 1 and 3 update the forecast data and obtain new planning schemes, while Scheme 2 still implements the planning scheme obtained at the beginning of the first stage. In addition, Scheme 1 has a local adjustment point and After evaluating the actual operating conditions, the planning scheme was adjusted to increase the capacity allocation of equipment, while the other two schemes were not adjusted.

[0200] like Fig. 9 As shown in the figure, the cumulative costs of the three schemes are the same. At the beginning of the first stage, the cumulative costs of the three schemes are equal because the investment decisions of the three schemes are the same. Scheme 1 increases the investment in equipment at the first local adjustment point to meet the growing demand for electricity and heat loads, resulting in the highest cumulative cost of Scheme 1 at this time. However, the adjusted capacity configuration of Scheme 1 helps to reduce the PIES operating cost in the subsequent period of the first stage. The operating cost of Scheme 1 from the end of the first year to the end of the second year is 8.97×105 yuan lower than that of Scheme 2 and Scheme 3. At the beginning of the second stage, each scheme invested a new set of equipment in PIES to meet the load growth in the second stage. In this stage, the investment cost and operating cost of Scheme 1 are lower than those of the other two schemes because the scheme adjusted the planning scheme at the first local adjustment point. Since then, the cumulative cost of Scheme 1 has been the lowest among the three schemes. Since Scheme 3 updates the forecast data and re-optimizes the planning scheme at the beginning of each stage, its economic performance is better than Scheme 2. Compared with Scheme 3, Scheme 1 also adjusts the planning scheme within the stage to eliminate the forecast error of long-term uncertain factors, further improving the economy of Scheme 1. However, as Fig. 9 As shown, Scheme 3 is not necessarily the local optimal at every moment. For example, Scheme 1 has the highest cumulative cost in the second year, but it is the global optimal because it dynamically optimizes and adjusts the planning scheme as the planning process proceeds to obtain better overall performance.

[0201] In summary, the present invention models photovoltaic, cogeneration units, heat pumps, gas boilers, electric energy storage and thermal energy storage equipment in the park comprehensive energy system by setting modeling strategies, and obtains the park comprehensive energy system constraint model; according to the park comprehensive energy system constraint model and the dynamic programming framework, a global optimization model, a stage optimization model and a local optimization model are respectively constructed; the parameters of the park comprehensive energy system are initialized through the global optimization model, and the planning stages are divided and local adjustment points are set; at the starting point of the first planning stage, the stage optimization model is called according to the performance requirements of the stage planning scheme; by solving the stage optimization model, the stage performance of the first planning stage is obtained. Planning scheme; according to the stage planning scheme of the first planning stage, the new equipment is put into the integrated energy system of the park; at the first local adjustment point of the first planning stage, the local optimization model is called; by solving the local optimization model, the local planning scheme of the first local adjustment point is obtained; in turn, at the local adjustment points, the local optimization model is solved to obtain the local planning scheme of the local adjustment points until the end of the first planning stage; after the planning adjustment of the first planning stage is completed, the planning scheme of the second stage and the local planning scheme of the second stage are obtained in turn for the second planning stage; iterative loop is performed until the planning adjustment of all planning stages is completed. The present invention copes with the influence of multiple uncertain factors by combining dynamic planning, evaluation and adjustment. At the same time, the present invention has high flexibility and adaptability.

[0202] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0203] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0204] Example 2

[0205] See also Fig.10 Embodiment 2 of the present invention further provides a park integrated energy system planning device considering source-load uncertainty, comprising:

[0206] The park comprehensive energy system modeling module 001 is used to model the photovoltaic, cogeneration units, heat pumps, gas boilers, electric energy storage and thermal energy storage equipment in the park comprehensive energy system by setting modeling strategies to obtain the park comprehensive energy system constraint model;

[0207] The optimization model construction module 002 is used to construct a global optimization model, a stage optimization model and a local optimization model according to the park comprehensive energy system constraint model and the dynamic programming framework;

[0208] The global optimization model processing module 003 is used to initialize the parameters of the park integrated energy system through the global optimization model, divide the planning stages and set local adjustment points;

[0209] The stage optimization model processing module 004 is used to call the stage optimization model at the starting point of the first planning stage according to the performance requirements of the stage planning scheme; obtain the stage planning scheme of the first planning stage by solving the stage optimization model; and put the newly added equipment into the park comprehensive energy system according to the stage planning scheme of the first planning stage;

[0210] The local optimization model processing module 005 is used to call the local optimization model at the first local adjustment point of the first planning stage; obtain the local planning scheme of the first local adjustment point by solving the local optimization model; successively solve the local optimization model at the local adjustment points to obtain the local planning scheme of the local adjustment points until the first planning stage ends;

[0211] The iterative loop planning adjustment module 006 is used to obtain the planning scheme of the second stage and the local planning scheme of the second stage in sequence for the second planning stage after the planning adjustment of the first planning stage is completed; and iterate the loop until the planning adjustment of all planning stages is completed.

[0212] In this embodiment, in the park comprehensive energy system modeling module 001, in the process of modeling the photovoltaic, cogeneration unit, heat pump, gas boiler, electric energy storage and thermal energy storage equipment in the park comprehensive energy system by setting the modeling strategy, the model expression of the cogeneration unit equipment is:

[0213]

[0214] In the formula, and are the thermal power and gas consumption of CHP in period t of year y, respectively; P CHP,h and are the upper and lower limits of CHP heating power respectively; hCHP,h and h CHP,e are the heating efficiency and power supply efficiency of CHP respectively;

[0215] The model expression of the heat pump equipment is:

[0216]

[0217] In the formula, and are the heating power and electrical power consumed by HP in period t of year y; COP H,HP is the energy efficiency coefficient of HP electric heating; It is the upper limit of HP heating power;

[0218] The model expression of gas boiler equipment is:

[0219]

[0220] In the formula, and are the heating power and gas consumption of GB in period t of year y, respectively; is the upper limit of heating power of GB; h GB is the heating efficiency of GB;

[0221] The model expression of photovoltaic equipment is:

[0222]

[0223] In the formula, and are the power consumption and output power of PV in period t of year y, respectively;

[0224] The model expressions of electrical energy storage and thermal energy storage equipment are:

[0225]

[0226] S min ≤S y,t ≤S max

[0227]

[0228] In the formula, and are the energy storage power and energy release power in period t of year y respectively; P c,max and P d,max is the maximum energy storage and release power; y,t is a 0-1 variable, which is the charge and discharge state of the energy storage device at time t in year y, z y,t When it is 1, it indicates energy storage state, and when it is 0, it indicates energy release state; S y,tS is the state of charge of the energy storage device at time t in year y; min and S max are the upper and lower limits of the energy storage charge state respectively; h c and h d are energy storage and release efficiency respectively; W ESS is the capacity of the energy storage device; S0 and They are the charge states at the initial and final stages of energy storage device scheduling, respectively.

[0229] In this embodiment, in the global optimization model processing module 003, in the process of dividing the park comprehensive energy system into planning stages and setting local adjustment points in the global optimization model, the expression of the planning stage is:

[0230]

[0231] Where P i is the i-th stage; N P is the number of stages;

[0232] The set expression of local adjustment points in the planning stage is:

[0233]

[0234] Where N i For stage P i The number of local adjustment points; For stage P i The kth local adjustment point within.

[0235] In this embodiment, in the stage optimization model processing module 004, the stage optimization model objective function is to minimize the investment cost, operating cost, maintenance cost and equipment residual value cost from the current stage to the end of the planning time; the objective function expression is:

[0236]

[0237] R y =(1+r) -y

[0238]

[0239] Where C is the total life cycle cost; n s and n y are the stage number and year number corresponding to the current stage respectively; N s and N y are the total number of stages and the total number of years respectively; n p is the year number corresponding to the initial stage p; is the equipment investment cost at the beginning of stage p; and are the operating cost and maintenance cost in year y respectively; R n is the present value coefficient at the end of the planning period; The residual value of the equipment invested from the current time to the end of the plan; R y is the present value coefficient of the yth year; r is the discount rate; N = {PV, CHP, HP, GB, ES, HS}; c int,n is the unit capacity investment cost of equipment n; It refers to the investment capacity of equipment n in stage p; is the price at which PIES purchases electricity from the grid at time t; c gas is the natural gas price; c loss Penalty costs for insufficient heating; and They are respectively the power purchased from the grid and the insufficient heating power in period t of year y; and are the gas consumption of CHP and GB in period t of year y, respectively; is the power of equipment n in the yth year and period t; c om,n is the unit power maintenance price of equipment n; Y p Y is the number of years from the beginning of stage p to the end of the integration period; n is the lifetime of device n.

[0240] In this embodiment, in the local optimization model processing module 005, the local optimization model includes an evaluation sub-model and a local adjustment sub-model; in the process of obtaining the local planning scheme through the local optimization model, the operation status of the park comprehensive energy system is evaluated through the evaluation sub-model to obtain the evaluation strategy; according to the evaluation strategy, if the operation status of the park comprehensive energy system is good, the original planning scheme will continue to be implemented; if there are problems in the operation of the park comprehensive energy system, the local adjustment sub-model will be called to obtain the adjusted planning scheme.

[0241] It should be noted that the information interaction, execution process and other contents between the modules of the above-mentioned system are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and will not be repeated here.

[0242] Example 3

[0243] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for a method for planning a comprehensive energy system for a campus taking into account source-load uncertainty is stored. The program code includes instructions for executing embodiment 1 or any possible implementation thereof for a method for planning a comprehensive energy system for a campus taking into account source-load uncertainty.

[0244] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0245] Example 4

[0246] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0247] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a method for planning a campus integrated energy system that takes into account source and load uncertainty, which is embodiment 1 or any possible implementation thereof.

[0248] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0249] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium, or transmitted from a computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0250] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing system, they can be concentrated on a single computing system, or distributed on a network composed of multiple computing systems, and optionally, they can be implemented by a program code executable by a computing system, so that they can be stored in a storage system and executed by the computing system, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0251] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for planning a comprehensive energy system for a park considering uncertainty of source and load, characterized in that: include: By setting the modeling strategy, the photovoltaic, cogeneration units, heat pumps, gas boilers, electric energy storage and thermal energy storage equipment in the park's comprehensive energy system are modeled respectively to obtain the park's comprehensive energy system constraint model; According to the park comprehensive energy system constraint model and dynamic programming framework, a global optimization model, a stage optimization model and a local optimization model are constructed respectively; Initializing the parameters of the park's integrated energy system through the global optimization model, dividing the planning stages and setting local adjustment points; At the start point of the first planning stage, calling the stage optimization model according to the performance requirements of the stage planning scheme; Obtaining a stage planning scheme for the first planning stage by solving the stage optimization model; According to the stage planning scheme of the first planning stage, new equipment will be put into the park's integrated energy system; At the first local adjustment point of the first planning stage, calling the local optimization model; obtaining a local planning solution for the first local adjustment point by solving the local optimization model; successively solving the local optimization model at the local adjustment points to obtain the local planning solution for the local adjustment points until the first planning stage ends; After the planning adjustment is completed in the first planning stage, the planning scheme of the second stage and the local planning scheme of the second stage are obtained in sequence for the second planning stage; and the iterative cycle is performed until the planning adjustment is completed in all planning stages.

2. A method for planning a park integrated energy system considering source-load uncertainty according to claim 1, characterized in that: In the process of modeling the photovoltaic, cogeneration units, heat pumps, gas boilers, electric energy storage and thermal energy storage equipment in the park's comprehensive energy system by setting modeling strategies, the model expression of the cogeneration unit equipment is: In the formula, and are the thermal power and gas consumption of CHP in period t of year y, respectively; P CHP,h and are the upper and lower limits of CHP heating power respectively; h CHP,h and h CHP,e are the heating efficiency and power supply efficiency of CHP respectively; The model expression of the heat pump equipment is: In the formula, and are the heating power and electrical power consumed by HP in period t of year y; COP H,HP is the energy efficiency coefficient of HP electric heating; It is the upper limit of HP heating power; The model expression of gas boiler equipment is: In the formula, and are the heating power and gas consumption of GB in period t of year y, respectively; is the upper limit of heating power of GB; h GB is the heating efficiency of GB; The model expression of photovoltaic equipment is: In the formula, and are the power consumption and output power of PV in period t of year y, respectively; The model expressions of electrical energy storage and thermal energy storage equipment are: S min ≤S y,t ≤S max In the formula, and are the energy storage power and energy release power in the t period of year y respectively; P c,max and P d,max is the maximum energy storage and release power; y,t is a 0-1 variable, which is the charge and discharge state of the energy storage device at time t in year y, z y,t When it is 1, it indicates energy storage state, and when it is 0, it indicates energy release state; S y,t S is the state of charge of the energy storage device at time t in year y; min and S max are the upper and lower limits of the energy storage charge state respectively; h c and h d are energy storage and release efficiency respectively; W ESS is the capacity of the energy storage device; S0 and They are the charge states at the initial and final stages of energy storage device scheduling, respectively.

3. A method for planning a park integrated energy system considering source-load uncertainty according to claim 2, characterized in that: In the process of dividing the park comprehensive energy system into planning stages and setting local adjustment points in the global optimization model, the expression of the planning stage is: P I =[P1,P2,...,P i ,...,P NP ] Where P i is the i-th stage; N P is the number of stages; The set expression of local adjustment points in the planning stage is: Where N i For stage P i The number of local adjustment points; For stage P i The kth local adjustment point within.

4. A method for planning a park integrated energy system considering source-load uncertainty according to claim 3, characterized in that: The objective function of the stage optimization model is to minimize the investment cost, operating cost, maintenance cost and equipment residual value cost from the current stage to the end of the planning time; the objective function expression is: R y ＝(1+r) -y Where C is the life cycle cost; n s and n y are the stage number and year number corresponding to the current stage respectively; N s and N y are the total number of stages and the total number of years respectively; n p is the year number corresponding to the initial stage p; is the equipment investment cost at the beginning of stage p; and are the operating cost and maintenance cost in year y respectively; R n is the present value coefficient at the end of the planning period; The residual value of the equipment invested from the current time to the end of the plan; R y is the present value coefficient of the yth year; r is the discount rate; N = {PV, CHP, HP, GB, ES, HS}; c int,n is the unit capacity investment cost of equipment n; It refers to the investment capacity of equipment n in stage p; is the price at which PIES purchases electricity from the grid at time t; c gas is the natural gas price; c loss Penalty costs for insufficient heating; and They are respectively the power purchased from the grid and the insufficient heating power in period t of year y; and are the gas consumption of CHP and GB in period t of year y, respectively; is the power of equipment n in the yth year and period t; c om,n is the unit power maintenance price of equipment n; Y p Y is the number of years from the beginning of stage p to the end of the integration period; n is the lifetime of device n.

5. A method for planning a comprehensive energy system for a park considering source-load uncertainty according to claim 4, characterized in that: The local optimization model includes an evaluation sub-model and a local adjustment sub-model; in the process of obtaining the local planning scheme through the local optimization model, the operation status of the park comprehensive energy system is evaluated through the evaluation sub-model to obtain an evaluation strategy; according to the evaluation strategy, if the operation status of the park comprehensive energy system is good, the original planning scheme will continue to be implemented; if there are problems in the operation of the park comprehensive energy system, the local adjustment sub-model will be called to obtain the adjusted planning scheme.

6. A device for planning a comprehensive energy system for a park considering uncertainty of source and load, adopting a method for planning a comprehensive energy system for a park considering uncertainty of source and load as described in any one of claims 1 to 5, characterized in that: include: The park comprehensive energy system modeling module is used to model the photovoltaic, cogeneration units, heat pumps, gas boilers, electric energy storage and thermal energy storage equipment in the park comprehensive energy system by setting modeling strategies to obtain the park comprehensive energy system constraint model; An optimization model building module, used to build a global optimization model, a stage optimization model and a local optimization model according to the park comprehensive energy system constraint model and the dynamic programming framework; A global optimization model processing module, used to initialize the parameters of the park comprehensive energy system through the global optimization model, divide the planning stages and set local adjustment points; A stage optimization model processing module is used to call the stage optimization model at the starting point of the first planning stage according to the performance requirements of the stage planning scheme; Obtaining a stage planning scheme for the first planning stage by solving the stage optimization model; According to the stage planning scheme of the first planning stage, new equipment will be put into the park's integrated energy system; A local optimization model processing module is used to call the local optimization model at the first local adjustment point of the first planning stage; obtain the local planning scheme of the first local adjustment point by solving the local optimization model; and successively solve the local optimization model at the local adjustment points to obtain the local planning scheme of the local adjustment points until the first planning stage ends; The iterative cycle planning adjustment module is used to obtain the planning scheme of the second stage and the local planning scheme of the second stage in sequence for the second planning stage after the planning adjustment is completed in the first planning stage; iterate and cycle until the planning adjustment is completed in all planning stages.

7. A park integrated energy system planning device considering source and load uncertainty according to claim 6, characterized in that: In the park comprehensive energy system modeling module, in the process of modeling the photovoltaic, cogeneration unit, heat pump, gas boiler, electric energy storage and thermal energy storage equipment in the park comprehensive energy system by setting the modeling strategy, the model expression of the cogeneration unit equipment is: In the formula, and are the thermal power and gas consumption of CHP in period t of year y, respectively; P CHP,h and P CHP,h are the upper and lower limits of CHP heating power respectively; h CHP,h and h CHP,e are the heating efficiency and power supply efficiency of CHP respectively; The model expression of the heat pump equipment is: In the formula, and are the heating power and electrical power consumed by HP in period t of year y; COP H,HP is the electric heating efficiency coefficient of HP; It is the upper limit of HP heating power; The model expression of gas boiler equipment is: In the formula, and are the heating power and gas consumption of GB in period t of year y, respectively; is the upper limit of heating power of GB; h GB is the heating efficiency of GB; The model expression of photovoltaic equipment is: In the formula, and are the power consumption and output power of PV in period t of year y, respectively; The model expressions of electrical energy storage and thermal energy storage equipment are: S min ≤S y,t ≤S max In the formula, and are the energy storage power and energy release power in period t of year y respectively; P c,max and P d,max is the maximum energy storage and release power; y,t is a 0-1 variable, which is the charge and discharge state of the energy storage device at time t in year y, z y,t When it is 1, it indicates energy storage state, and when it is 0, it indicates energy release state; S y,t S is the state of charge of the energy storage device at time t in year y; min and S max are the upper and lower limits of the energy storage charge state respectively; h c and h d are energy storage and release efficiency respectively; W ESS is the capacity of the energy storage device; S0 and They are the charge states at the initial and final stages of energy storage device scheduling, respectively.

8. A park integrated energy system planning device considering source and load uncertainty according to claim 7, characterized in that: In the global optimization model processing module, in the process of dividing the park comprehensive energy system into planning stages and setting local adjustment points in the global optimization model, the expression of the planning stage is: Where P i is the i-th stage; N P is the number of stages; The set expression of local adjustment points in the planning stage is: Where N i For stage P i The number of local adjustment points; For stage P i The kth local adjustment point within.

9. A park integrated energy system planning device considering source and load uncertainty according to claim 8, characterized in that: In the stage optimization model processing module, the objective function of the stage optimization model is to minimize the investment cost, operating cost, maintenance cost and equipment residual value cost from the current stage to the end of the planning time; the objective function expression is: R y ＝(1+r) -y Where C is the total life cycle cost; n s and n y are the stage number and year number corresponding to the current stage respectively; N s and N y are the total number of stages and the total number of years respectively; n p is the year number corresponding to the initial stage p; is the equipment investment cost at the beginning of stage p; and are the operating cost and maintenance cost in year y respectively; R n is the present value coefficient at the end of the planning period; The residual value of the equipment invested from the current time to the end of the plan; R y is the present value coefficient of the yth year; r is the discount rate; N = {PV, CHP, HP, GB, ES, HS}; c int,n is the unit capacity investment cost of equipment n; It refers to the investment capacity of equipment n in stage p; is the price at which PIES purchases electricity from the grid at time t; c gas is the natural gas price; c loss Penalty costs for insufficient heating; and They are respectively the power purchased from the grid and the insufficient heating power in period t of year y; and are the gas consumption of CHP and GB in period t of year y, respectively; is the power of equipment n in the yth year and period t; c om,n is the unit power maintenance price of equipment n; Y p Y is the number of years from the beginning of stage p to the end of the integration period; n is the lifetime of device n.

10. A park integrated energy system planning device considering source and load uncertainty according to claim 9, characterized in that: In the local optimization model processing module, the local optimization model includes an evaluation sub-model and a local adjustment sub-model; in the process of obtaining the local planning scheme through the local optimization model, the operation status of the park comprehensive energy system is evaluated through the evaluation sub-model to obtain the evaluation strategy; according to the evaluation strategy, if the operation status of the park comprehensive energy system is good, the original planning scheme will continue to be implemented; if there are problems in the operation of the park comprehensive energy system, the local adjustment sub-model will be called to obtain the adjusted planning scheme.

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