Cave energy utilization system and optimized operation method for karst landform areas

Abstract

The present invention relates to a karst cave energy utilization system and an optimized operation method for karst landform areas, including an energy side as a supply side and a load side as a demand side; the energy side includes: conventional new energy for grid-connected power generation, a solar photovoltaic heat collection module, a karst cave heat storage module, a karst cave thermal power generation module, and a karst cave temperature difference power generation module, wherein the karst cave temperature difference power generation module and the solar thermal heat collection, the karst cave heat storage module, and the karst cave thermal power generation module assist each other to form a karst cave energy generation, storage, and utilization system; the load side includes: electrical load, biogas / gas load, and thermal load; with the help of natural karst landform karst cave energy resources, the large-capacity and large-space carrier of the "karst cave" and the "constant temperature and humidity" climate advantages are fully exploited, the conventional electrochemical energy storage is abandoned to participate in the new energy consumption technology, the protection of the rural natural environment is strengthened, the access of distributed energy in rural areas is promoted, and the problems of rural electricity shortage and green energy use are solved.

Description

Technical Field

[0001] The present invention belongs to the field of energy utilization technology and optimized operation control technology, and relates to a cave energy utilization system and optimized operation method for karst landform areas, and in particular to a comprehensive energy utilization system and optimized control method for rural areas with karst landforms that can effectively utilize natural resources such as caves, wind, and light to achieve efficient and complementary utilization of multiple energy sources. Background Art

[0002] At present, the way of electricity and energy consumption in rural areas is still relatively backward. Most areas are in the terminal power supply system of traditional rural power grids. Due to the increase of power load, power shortage or unstable voltage and power quality often occur. my country's rural low-speed wind and solar energy resources are widely distributed and very rich. With the gradual maturity of new energy power generation technology, some local areas such as rural towns that pay attention to environmental protection have begun to explore and introduce new energy electricity, and try to use various types of new energy to build green energy islands, low-carbon industrial parks, low-carbon new rural areas, green parks and other demonstration projects to reduce dependence on fossil energy.

[0003] However, the high proportion of wind power, photovoltaic and other new energy grid connection will bring many challenges to the rural power system. Its random, fluctuating and intermittent characteristics will cause problems such as safe consumption. Existing new energy consumption complementary technologies usually use energy storage to shift energy time and achieve a certain degree of peak shaving and valley filling. However, if too many electrochemical energy storage stations are introduced in rural areas, the risk of damage to the rural natural environment will increase, which is contrary to the policy of beautiful rural construction. At the same time, it is difficult to meet the requirements for its economic efficiency.

[0004] As a natural geographical unit with a special geological background, karst landform is influenced by its lithological characteristics. The energy resources, soil, hydrology, vegetation and human environment in the region are unique. Since carbonate caves are underground, there is no direct solar radiation and scattered radiation changes in the sky, so the climate of the cave is substantially different from the climate outside the cave. The climate outside the cave belongs to the atmosphere, and the climate of the cave belongs to the lithosphere. The temperature of the lithosphere is less affected by the temperature of the atmosphere. The air temperature and humidity of the cave are stable. The "year-round constant temperature" and "humidity saturation" inside are the biggest characteristics of caves in the southwest region and even in my country. The internal space of the cave is naturally formed, and the interior maintains a constant temperature and humidity all year round. It is an ideal "energy storage body". At the same time, there is a large temperature difference between the outside and inside of the cave in winter and summer, which is also an ideal "natural air conditioner".

[0005] Therefore, how to fully tap the advantages of "caves" with large capacity and large space as carriers and "constant temperature and humidity" climate, build a reasonable multi-type energy complementary utilization system in rural areas with karst landforms, and at the same time formulate efficient optimization and absorption strategies to effectively reduce the impact of random fluctuations such as wind power and light power, and improve the high-quality and safe energy use in rural areas with karst landforms, has become a research that technical personnel in this field urgently need to carry out. Summary of the invention

[0006] In view of this, in order to solve the problems that the prior art has not fully explored the large-capacity and large-space carriers of "caves" and the "constant temperature and humidity" climate advantages, has not constructed a reasonable multi-type energy complementary utilization system in rural areas with karst landforms, and there are power shortages or unstable voltage and power quality in rural areas with karst landforms, the present invention provides a cave energy utilization system and an optimized operation method for karst landform areas. It mainly conducts research on cave energy utilization in areas or rural areas with cave landform characteristics, develops conventional renewable energy sources such as solar power generation, wind power generation, and biomass biogas power generation, focuses on utilizing the characteristics of local caves and cave groups, adds new energy technologies such as cave temperature difference power generation and cave heat storage, highlights the characteristics of "cave energy", and improves high-quality and safe energy use in rural areas with karst landforms.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A karst cave energy utilization system for karst landform areas includes an energy side as a supply side and a load side as a demand side; the energy side includes: conventional new energy sources for grid-connected power generation, solar photovoltaic heat collection modules, karst cave heat storage modules, karst cave thermal power generation modules, and karst cave temperature difference power generation modules, wherein the karst cave temperature difference power generation modules and solar thermal heat collection, karst cave heat storage modules, and karst cave thermal power generation modules assist each other to form a karst cave energy generation, storage, and utilization system; the load side includes: electrical load, biogas / gas load, and thermal load;

[0009] The solar photovoltaic heat collection module uses advanced heat collection devices to collect solar radiation heat. Part of the heat is directly used for power generation through the cave thermal power generation module, and the other part of the heat is flexibly stored in the cave thermal storage module to achieve long-term stable output. The cave thermal storage module and cave thermal power generation module with caves as carriers are respectively composed of a thermal storage unit (TESS) and a power conversion unit (PB), and energy is transferred between the various parts through heat transfer media.

[0010] The light field collects and absorbs the direct solar radiation energy through the heliostat array and transfers it to the heat collection device. The solar energy is converted into heat energy and transferred to the heat transfer medium. Part of the heat energy in the heat transfer medium is transported to the PB system to heat the water vapor to drive the motor to generate electricity, realizing heat-to-electricity conversion. The other part of the heat energy is stored in the TESS. When heat supply is needed, the heat is released according to the regulation requirements to generate electricity.

[0011] The cave temperature difference power generation module uses the water with constant temperature in the cave as the heat transfer medium. It introduces the water inside the cave into the TESS through water pumps and pipes, forming a temperature difference, which in turn generates electricity and is connected to the grid for power generation.

[0012] Furthermore, conventional new energy sources include upstream power grids, distributed wind power generators, biomass gas generators, emergency diesel generators, and solar photovoltaic generators.

[0013] Based on the above-mentioned optimization operation method of the karst cave energy utilization system, it includes three links: model prediction, rolling optimization and feedback correction. The core idea is the rolling time domain dynamic prediction. The prediction model (MPC model) is usually established in the form of discretized state space. k is the current moment, Rs(k) is the set reference value, u(k) is the input control variable, y(k) is the output, and d(k) is the disturbance. In the rolling time domain dynamic prediction, there are two time domains: the prediction time domain (p time intervals) and the control time domain (m time intervals), and p≥m;

[0014] The specific steps of the entire optimization operation process are as follows:

[0015] S1. At the current time k, based on the current state and prediction model, according to the historical information {u(kj), y(kj)|j≥1} and the future input {u(k+j-1)|j=1,…,m}, predict the future output of the system {y(k+j)|j=1,…,p} to provide prior information for the optimization model;

[0016] S2. Taking into account the current and future constraints, solve the optimization problem in the control time domain m and obtain the optimal control sequence Δu(k+j) in these time periods;

[0017] S3, actually applying the first value of the calculated optimal control sequence to the control system;

[0018] S4. Resample at time k+1, correct the predicted output based on the model according to the actual output of the system, update the state of the system, and repeat the above steps S1 to S4.

[0019] Furthermore, the model predicts the following specific contents:

[0020] According to the power balance equation of the karst energy utilization system, the power balance equation inside the solar photovoltaic heat collection module, the karst heat storage module, the karst thermal power generation module and the relevant constraints of TESS, the vector x(k) consisting of the output of conventional units, the output of karst temperature difference power generation, the karst TESS storage / release power and the karst TESS heat storage capacity is selected as G (k),P CSP (k),P cha (k),P dis (k), E TESS (k)] T is the state variable; the vector u(k) consisting of the output increment of the conventional unit and the heat release power increment of the cave TESS is selected as follows: G (k),ΔP dis (k)] T is the control variable; the vector d(k) consisting of the predicted power increment of the light intensity, wind power and load demand is selected as d(k) = [ΔP S (k),ΔP W (k),ΔL(k)] T is the disturbance input; select the vector y(k) consisting of the output of conventional units and the output of karst cave temperature difference power generation = [P G (k),P CSP (k)] T is the output variable; the following multi-input and multi-output state space representation prediction model is established:

[0021]

[0022]

[0023] In the formula, is the thermal energy stored in the cave TESS during period t, To prevent the solidification of the heat storage molten salt, the minimum heat storage capacity of the cave TESS is: To prevent the heat storage molten salt from overheating, the maximum heat storage capacity of the cave TESS; and are the initial and final values ​​of the heat storage of the cave TESS in the scheduling cycle. To meet the demand of the next scheduling cycle, the initial and final heat storage of the TESS in a scheduling cycle should be equal; is the heat storage and release state of the cave TESS during period t, which is a 0-1 variable, 1 means that the cave TESS stores heat energy, and 0 means that the cave TESS releases heat energy; and are the maximum heat storage and heat release powers of the cave TESS, respectively; γ is the heat dissipation coefficient of the cave TESS; η cha and η dis They are the heat storage and heat release efficiencies of the cave TESS, respectively.

[0024] Furthermore, the specific contents of rolling optimization are as follows:

[0025] The predicted values ​​of wind power, light intensity and load demand in the rolling optimization scheduling model are continuously updated as the prediction domain is moved forward and compressed, and the model is continuously solved online until the scheduling plan within the scheduling cycle is executed;

[0026] The objective function of the rolling optimization scheduling model is:

[0027]

[0028]

[0029]

[0030] In the formula, t s The starting period of rolling optimization scheduling is continuously updated and moved forward; F g is the fuel cost of conventional units, F qt is the start-up and shutdown cost of the conventional unit and the karst temperature difference power generation module unit; t, i, j are the time period number, conventional unit number, and karst temperature difference power generation module unit number respectively; N T 、N G 、N CSP They are the number of dispatching periods, the number of conventional units, and the number of cave temperature difference power generation module units; is the output of conventional unit i during period t; a i 、b i 、c i are the cost coefficients of conventional unit i; S i , Q j are the start-up and shutdown costs of conventional unit i and cave temperature difference power generation module unit j respectively; u i,t 、x j,t They are the start and stop states of conventional unit i and cave temperature difference power generation module unit j in time period t, both of which are 0-1 variables.

[0031] Furthermore, the rolling optimization scheduling constraints include: system power balance constraints, conventional unit and cave temperature difference power generation operation constraints, solar thermal concentration, cave heat storage, cave thermal power generation module power balance constraints and operation constraints.

[0032] Furthermore, the real-time dynamic adjustment based on model predictive control is as follows: according to the values ​​of light intensity, wind power and load demand within Δt', by rolling iteration of this state space prediction model, forward prediction N steps, the vector Y consisting of the expected output values ​​of the conventional unit output and the cave temperature difference power generation module unit output within the prediction time domain Δt is obtained f :

[0033]

[0034] In Δt', the vector R consisting of the output plan value of the conventional unit and the output plan value of the cave temperature difference power generation module unit in the period Δt obtained by solving the rolling optimization phase corresponding to the dynamic adjustment time t' is f In order to track the control target, the output value Y of the conventional unit output and the unit output of the cave temperature difference power generation module in the prediction time domain Δt is used. f and the rolling optimization output plan value R f The goal is to minimize the error between

[0035] Real-time dynamic adjustment of model objective function:

[0036] minJ=(R f -Y f ) T W err (R f -Y f )+U T Q u U (7)

[0037] In the formula, is the weight coefficient matrix of the conventional unit output tracking error and the cave temperature difference power generation module unit output tracking error; Q u is the weight coefficient matrix of the control variables;

[0038] Dynamically adjust model constraints in real time:

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046] Where t' is the time corresponding to the interval Δt'; t * It is the moment of the rolling optimization phase corresponding to the dynamic adjustment moment t'.

[0047] Furthermore, a multi-time-scale coordinated optimization dispatching control process combining MPC's intraday large-scale rolling optimization with real-time small-scale dynamic adjustment is proposed. The specific contents are as follows: before the start of each period of dynamic adjustment, the system status is updated in real time to obtain real-time information on wind power, light intensity and load demand, and then small-scale optimization dispatching is performed until all periods are completed. The latest system status information is collected and passed to the rolling optimization layer for large-time-scale economic dispatching, and the solution is continuously rolled forward.

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

[0049] 1. The karst cave energy utilization system disclosed in the present invention is designed to construct a multi-type energy complementary utilization system in karst rural areas. In addition to wind, solar and biomass energy generation, the energy side adds a solar photovoltaic heat collection module. With the help of advanced heat collection devices, the collected solar radiation heat is directly generated through the karst thermal power generation module and the karst temperature difference power generation module, and part of the heat is flexibly stored in the karst heat storage module to achieve long-term stable output. At the same time, an optimized operation consumption method is formulated, and a model predictive control karst energy utilization system optimization operation framework is established to effectively reduce the impact of random fluctuation characteristics such as wind power and light power, and improve the high-quality and safe energy use in karst rural areas. With the help of natural karst cave energy resources, the large-capacity and large-space carrier of "caves" and the "constant temperature and humidity" climate advantages are fully explored, and conventional electrochemical energy storage is abandoned to participate in new energy consumption technology, strengthen the protection of rural natural environment, promote rural distributed energy access, and solve the problems of rural electricity shortage and green energy use.

[0050] 2. The karst cave energy utilization system disclosed in the present invention fully exploits the advantages of the large-capacity and large-space carrier of "caves" and the "constant temperature and humidity" climate, and establishes a multi-source joint energy consumption system that utilizes the temperature difference power generation and heat storage power generation of caves. On this basis, a multi-time scale coordinated optimization operation control method is proposed. During the rolling optimization, a closed-loop control is formed through the real-time information feedback correction link of the state quantity, and the real-time tracking of the optimization target is realized. While further improving the economy of the system, it can avoid the occurrence of serious deviations such as over-control and under-control of scheduling decisions caused by the accumulation of power prediction errors and the scheduling control process, thereby ensuring the safe and reliable absorption of green energy by the system operation.

[0051] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0053] Figure 1 (a) is a schematic diagram of a karst cave energy utilization system for karst landform areas according to the present invention;

[0054] Figure 1 (b) is a basic principle diagram of the karst cave energy utilization system for karst landform areas of the present invention;

[0055] Figure 2 This is a basic principle diagram of the model predictive control that participates in the coordinated optimization scheduling of the karst cave energy utilization system of the present invention.

[0056] Figure 3 This is a flow chart of multi-time scale coordinated optimization scheduling of a cave energy utilization system based on model predictive control in the present invention.

[0057] Figure 4 It is the 24-hour forecast, rolling forecast and real-time curve of load demand, wind power and light intensity in the present invention.

[0058] Figure 5 It is the incremental difference diagram and change curve value of different types of energy output obtained by the rolling optimization scheduling model in the present invention. DETAILED DESCRIPTION

[0059] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0060] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0061] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0062] like Figure 1 The karst cave energy utilization system shown in (a) includes an energy side as a supply side and a load side as a demand side; the energy side includes: an upper power grid, a distributed wind power generator set, a biomass biogas generator set, an emergency diesel generator set, a solar photovoltaic generator set, a solar photovoltaic thermal collection module, a karst cave heat storage module, a karst cave thermal power generation module, and a karst cave temperature difference power generation module, wherein the upper power grid, the distributed wind power generator set, the biomass biogas generator set, the emergency diesel generator set, and the solar photovoltaic generator set are conventional new energy sources for grid-connected power generation; the load side includes: electric load, biogas / gas load, and thermal load;

[0063] In addition to the conventional wind, solar and biomass grid-connected power generation in the cave energy utilization system architecture, the solar photovoltaic heat collection module uses advanced heat collection devices to collect solar radiation heat. Part of the heat is directly used for power generation through the cave thermal power generation module, and the other part of the heat is flexibly stored in the cave heat storage module to achieve long-term stable output. The cave heat storage module and cave thermal power generation module with caves as carriers are respectively composed of a thermal energy storage unit (TESS) and a power conversion unit (PB), and energy is transferred between each part through heat transfer media.

[0064] The light field collects and absorbs the direct solar radiation energy through the heliostat array and transfers it to the heat collection device. The solar energy is converted into heat energy and transferred to the heat transfer medium. Part of the heat energy in the heat transfer medium is transported to the PB system to heat the water vapor to drive the motor to generate electricity, realizing heat-to-electricity conversion. The other part of the heat energy is stored in the TESS. When heat supply is needed, the heat is released according to the regulation requirements to generate electricity.

[0065] The cave temperature difference power generation module uses the water with a relatively constant temperature in the cave as the heat transfer medium, and introduces the water inside the cave into the heat storage unit through a water pump and a pipe, thereby forming a temperature difference, and then generating electricity and connecting to the grid for power generation;

[0066] The cave temperature difference power generation module, solar photovoltaic heat collection module, cave heat storage module and cave thermal power generation module assist each other to form a cave energy generation and storage system. The basic structural principle is as follows: Figure 1 (b) as shown.

[0067] Aiming at the above-mentioned karst cave energy utilization system, an optimization operation method of the karst cave energy utilization system based on model predictive control is proposed. The optimization operation method specifically includes three links: model prediction, rolling optimization and feedback correction. The core idea is the rolling time domain dynamic prediction. The MPC prediction model is usually established in the form of discretized state space. Its basic principle is as follows Figure 2 As shown. k is the current moment, Rs(k) is the set reference value, u(k) is the input control variable, y(k) is the output, and d(k) is the disturbance. In the rolling horizon dynamic prediction, there are two horizons: the prediction horizon (p time intervals) and the control horizon (m time intervals), and p≥m.

[0068] The main steps of the entire optimization control process are as follows:

[0069] 1) At the current time k, based on the current state and prediction model, according to the historical information {u(kj), y(kj)|j≥1} and the future input {u(k+j-1)|j=1,…,m}, the future output of the system {y(k+j)|j=1,…,p} is predicted to provide prior information for the optimization model;

[0070] 2) Taking into account the current and future constraints, solve the optimization problem in the control time domain m and obtain the optimal control sequence Δu(k+j) in these time periods;

[0071] 3) actually applying the first value of the calculated optimal control sequence to the control system;

[0072] 4) Resample at time k+1, correct the model-based predicted output according to the actual system output, update the system state, and repeat the above steps.

[0073] The following is an explanation of the contents involved in the proposed method for optimizing the operation of the karst cave energy utilization system:

[0074] 1. Theoretical prediction model of cave energy utilization system

[0075] The optimization process of the MPC prediction model is not performed offline once, but repeatedly and online. Its optimization goal also changes over time, that is, at each moment, a local optimization goal based on that moment is proposed, rather than an unchanging global optimization goal. It is necessary to establish a prediction model for the karst energy utilization system. According to the power balance equation of the karst energy utilization system, the power balance equation inside the solar photovoltaic heat collection module, the karst heat storage module, the karst thermal power generation module, and the relevant constraints of TESS, the vector x(k) consisting of the output of conventional units (wind power, photovoltaic, biogas, etc., hereinafter referred to as abbreviations), the output of karst temperature difference power generation, the storage / release power of karst TESS, and the heat storage capacity of karst TESS is selected. G (k),P CSP (k),P cha (k),P dis (k), E TESS (k)] T is the state variable; the vector u(k) consisting of the output increment of the conventional unit and the heat release power increment of the TESS is selected as [ΔP G (k), ΔP dis (k)] T is the control variable; the vector d(k) consisting of the predicted power increment of the light intensity, wind power and load demand is selected as d(k) = [ΔP S (k),ΔP W (k),ΔL(k)] T is the disturbance input; select the vector y(k) consisting of the output of conventional units and the output of karst cave temperature difference power generation = [P G (k),P CSP (k)] T is the output variable. At this time, the following multi-input and multi-output state space representation prediction model can be established:

[0076]

[0077]

[0078] In the formula, is the thermal energy stored in the TESS during period t, To prevent the solidification of the heat storage molten salt, the minimum heat storage capacity of TESS is: The maximum heat storage capacity of the TESS to prevent overheating of the heat storage molten salt; and are the initial and final values ​​of the TESS heat storage in the scheduling cycle. To meet the needs of the next scheduling cycle, the initial and final heat storage of TESS in a scheduling cycle should be equal; is the heat storage and release state of TESS during period t, which is a 0-1 variable, 1 means TESS stores heat energy, and 0 means TESS releases heat energy; and are the maximum heat storage and heat release powers of TESS respectively; γ is the heat dissipation coefficient of TESS; η cha and η dis are the heat storage and release efficiencies of TESS respectively.

[0079] From equations (1) and (2), we can see that according to the predicted values ​​of light intensity, wind power and load demand, by rolling iteration of this state space prediction model in the prediction time domain and predicting forward p steps, we can obtain the vector Y consisting of the predicted output values ​​of the conventional unit output and the cave temperature difference power generation output in the prediction time domain pΔt: f :

[0080] 2. Rolling optimization control based on model predictive control

[0081] In the rolling optimization stage, the dispatch center first updates the data of wind power, light intensity and load demand in the forecast domain based on historical information, the latest weather information, etc., and then generates a large-scale dispatch plan for the system in the forecast domain based on the system status information and the predicted values ​​of wind power, light intensity and load demand in the forecast domain, including the start and stop status and output of conventional units, the start and stop status and output of karst temperature difference power generation, the storage / release status and power of karst TESS, etc. However, only the dispatch plan in the first time interval is actually executed. At time t0, using the forecast domain (t0 to t0+N T The prediction information of wind power, light intensity and load demand for the next 24 hours (Δt) is used to optimize the solution to obtain the scheduling plan within the prediction domain, but only the plan within the first time interval is actually executed. In the next scheduling period, the prediction domain moves forward by one time interval, and the prediction value and system status within the prediction domain are updated to obtain the scheduling plan within the prediction domain at this time. Only the plan within the first time interval is actually executed. Repeating this rolling process continuously compresses the prediction domain, and the control domain continuously moves backward until the scheduling plan for all time periods within the scheduling cycle is generated and executed.

[0082] The rolling optimization scheduling constraints include: system power balance constraints, operation constraints of conventional units and cave temperature difference power generation, solar thermal concentration, cave heat storage, cave thermal power generation module power balance constraints and operation constraints, etc. However, unlike the deterministic scheduling model, which uses the day-ahead forecast values ​​of wind power, light intensity and load demand to solve the model offline once to obtain the day-ahead scheduling plan, the forecast values ​​of wind power, light intensity and load demand in the rolling optimization scheduling model are continuously updated with the forward compression of the prediction domain, and the model is continuously solved online until the scheduling plan within the scheduling cycle is executed.

[0083] Therefore, the objective function of the rolling optimization scheduling model is rewritten based on the deterministic scheduling model as follows:

[0084] min F R =F g +F qt (3)

[0085]

[0086]

[0087] Where, t s The starting period of rolling optimization scheduling is continuously updated and moved forward; F g is the fuel cost of conventional units, F qt is the start-up and shutdown cost of the conventional unit and the karst temperature difference power generation module unit; t, i, j are the time period number, conventional unit number, and karst temperature difference power generation module unit number respectively; N T 、N G 、N CSP They are the number of dispatching periods, the number of conventional units, and the number of cave temperature difference power generation module units; is the output of conventional unit i during period t; a i , b i 、c i are the cost coefficients of conventional unit i; S i , Q j are the start-up and shutdown costs of conventional unit i and cave temperature difference power generation module unit j respectively; u i,t 、x j,t They are the start and stop states of conventional unit i and cave temperature difference power generation module unit j in time period t, both of which are 0-1 variables.

[0088] 3. Real-time dynamic adjustment based on model predictive control

[0089] There will still be prediction errors in the rolling time domain dynamic prediction. In order to improve the economy and safety of the scheduling plan, in each scheduling interval of the intraday rolling optimization scheduling, a smaller-scale dynamic adjustment is set on the basis of the daily rolling optimization scheduling plan. The control domain of the rolling optimization scheduling, that is, the actual execution time interval Δt of the plan, is evenly divided into N hourly scale intervals Δt'. In each small time scale interval Δt', the rolling optimization scheduling plan is adjusted according to the real-time updated status information of the system and the real-time information of wind power, light intensity and load demand. Repeat this step until the N hourly scale intervals are adjusted, and then return to the rolling optimization scheduling layer to perform optimal scheduling for the next scheduling period.

[0090] Dynamic adjustment is to cope with the changes in wind power, light intensity and load demand within each dispatch interval Δt of rolling optimization dispatching. Δt is evenly divided into N small time intervals Δt', and the rolling dispatching plan is tracked within Δt' based on the rolling dispatching plan, wind and solar load real-time information and system real-time status information. Since the time interval Δt' is small, in order to avoid the frequent start and stop of the unit causing a reduction in the continuity of system operation, the start and stop status of the unit will not be adjusted at this time, and only the output of the controllable unit will be corrected.

[0091] From equations (1) and (2), we can see that according to the values ​​of light intensity, wind power and load demand within Δt', by rolling iteration of this state space prediction model and predicting forward N steps, we can obtain the vector Y consisting of the expected output values ​​of conventional unit output and cave temperature difference power generation output within the prediction time domain Δt f :

[0092]

[0093] Therefore, within Δt', the vector R consisting of the planned output value of the conventional unit and the planned output value of the cave temperature difference power generation in the period Δt obtained by solving the rolling optimization phase corresponding to the dynamic adjustment time t' is f In order to track the control target, the output value Y of the conventional unit and the temperature difference power generation output of the cave in the prediction time domain Δt is used. f and the rolling optimization output plan value R f The goal is to minimize the error between them. The objective function is as follows:

[0094] min J=(R f -Y f ) T W err (R f -Y f )+U T Q u U (7)

[0095] In the formula, is the weight coefficient matrix of conventional unit output tracking error and cave temperature difference power generation output tracking error; Q u is the weight coefficient matrix of the control variables.

[0096] The constraints include:

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104] Where t' is the time corresponding to the interval Δt'; t * It is the moment of the rolling optimization phase corresponding to the dynamic adjustment moment t'.

[0105] 4. Multi-time scale coordination and optimization of scheduling process

[0106] The multi-time scale coordinated optimization scheduling control process based on MPC combining intraday large-scale rolling optimization with real-time small-scale dynamic adjustment is as follows: Figure 3 Before the start of each period of dynamic adjustment, the system status is updated in real time to obtain real-time information on wind power, light intensity and load demand, and then small-scale optimization scheduling is performed until all periods are completed. The latest system status information is collected and passed to the rolling optimization layer for large-scale economic scheduling, and the solution is continuously rolled forward.

[0107] In order to verify the availability and stability of the method of the present invention, this embodiment is described with the following data as an example:

[0108] The correctness and effectiveness of the proposed optimization operation method are verified based on the improved IEEE-39 node energy system. The system includes 10 biogas generators, a 100kw cave temperature difference power generation module and a 400kw small wind turbine. The total installed capacity of the biogas unit is 1662kw. The day-ahead 24h forecast, rolling forecast and real-time curves of load demand, wind power and light intensity are shown in Figure 2. Figure 4 As shown in the figure, the time interval Δt of the rolling optimization scheduling phase is set to 1h, that is, 24 rolling optimizations are performed within the day; the time interval Δt' of the dynamic adjustment phase is set to 15min, that is, 4 dynamic adjustments are performed in each rolling optimization scheduling period.

[0109] By constructing two different scenarios for comparative analysis, the rationality of the proposed optimization operation method is demonstrated:

[0110] ① Single time scale scheduling: only rolling optimization scheduling is performed without real-time dynamic adjustment to verify the effectiveness of the rolling optimization scheduling model;

[0111] ② Multi-time scale coordinated optimization scheduling (the method of the present invention).

[0112] like Figure 5As shown. Compared with the day-ahead dispatch, the rolling forecast of wind power, light intensity and load demand has lower error than the day-ahead forecast, and the rolling optimization is a rolling calculation and solution as the dispatch period progresses. The dispatch cost comparison of rolling optimization dispatch and day-ahead dispatch is shown in Table 1 and Table 2. From the comparison of the results in the table, it can be found that under the same constraints, rolling optimization dispatch reduces the start and stop of the unit, the dispatch cost is more economical, and the dispatch plan is more reasonable.

[0113] Table 1 Comparison of day-ahead and rolling optimization scheduling results

[0114]

[0115] Table 2 Comparison of rolling optimization scheduling results with and without dynamic adjustment

[0116]

[0117] By comparing the two strategies, it can be found that the strategy proposed in the present invention can predictably utilize the cave heat storage system to coordinate the power balance between wind power, photovoltaic power and load in the scheduling decision, thereby improving the economic efficiency of the overall system operation; at the same time, the MPC-based cave energy utilization system model ensures the reliability of scheduling decisions through rolling optimization, avoiding the occurrence of serious deviations such as over-control and under-control in scheduling decisions caused by the accumulation of power prediction errors and the scheduling control process, thereby ensuring the safety of system operation.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. The cave energy utilization system for karst landform areas is characterized by: It includes the energy side as the supply side and the load side as the demand side; the energy side includes: conventional new energy sources for grid-connected power generation, solar photovoltaic heat collection modules, cave heat storage modules, cave thermal power generation modules, and cave temperature difference power generation modules, among which the cave temperature difference power generation modules and solar thermal heat collection, cave heat storage modules, and cave thermal power generation modules assist each other to form a cave energy generation, storage and utilization system; the load side includes: electrical load, biogas / gas load, and thermal load; The solar photovoltaic heat collection module uses advanced heat collection devices to collect solar radiation heat. Part of the heat is directly used for power generation through the cave thermal power generation module, and the other part of the heat is flexibly stored in the cave thermal storage module to achieve long-term stable output. The cave thermal storage module and cave thermal power generation module with caves as carriers are respectively composed of a thermal storage unit TESS and a power conversion unit PB, and energy is transferred between the various parts through heat transfer media. The light field collects and absorbs the direct solar radiation energy through the heliostat array and transfers it to the heat collection device. The solar energy is converted into heat energy and transferred to the heat transfer medium. Part of the heat energy in the heat transfer medium is transported to the PB system to heat the water vapor to drive the motor to generate electricity, realizing heat-to-electricity conversion. The other part of the heat energy is stored in the TESS. When heat supply is needed, the heat is released according to the regulation requirements to generate electricity. The cave temperature difference power generation module uses the water with constant temperature in the cave as the heat transfer medium. It introduces the water inside the cave into the TESS through water pumps and pipes, forming a temperature difference, which in turn generates electricity and is connected to the grid for power generation.

2. The karst cave energy utilization system for karst landform areas as claimed in claim 1, characterized in that: Conventional new energy sources include upstream power grids, distributed wind power generators, biomass gas generators, emergency diesel generators, and solar photovoltaic generators.

3. The method for optimizing the operation of the karst cave energy utilization system in karst landform areas according to claim 1 or 2, characterized in that: It includes three links: model prediction, rolling optimization and feedback correction. The core idea is rolling time domain dynamic prediction. The prediction model is usually established in the form of discretized state space, that is, MPC model. k is the current moment, Rs(k) is the set reference value, u(k) is the input control variable, y(k) is the output, and d(k) is the disturbance. In the rolling time domain dynamic prediction, there are two time domains: prediction time domain and control time domain. The prediction time domain includes p time intervals, the control time domain includes m time intervals, and p≥m. The specific steps of the entire optimization operation process are as follows: S1. At the current time k, based on the current state and prediction model, according to the historical information {u(kj), y(kj)|j≥1} and the future input {u(k+j-1)|j=1,…,m}, predict the future output of the system {y(k+j)|j=1,…,p} to provide prior information for the optimization model; S2. Taking into account the current and future constraints, solve the optimization problem in the control time domain m and obtain the optimal control sequence Δu(k+j) in these time periods; S3, actually applying the first value of the calculated optimal control sequence to the control system; S4. Resample at time k+1, correct the predicted output based on the model according to the actual output of the system, update the state of the system, and repeat the above steps S1 to S4.

4. The method for optimizing the operation of the karst cave energy utilization system in karst landform areas according to claim 3, characterized in that: The specific contents of the model prediction are as follows: According to the power balance equation of the karst energy utilization system, the power balance equation inside the solar photovoltaic heat collection module, the karst heat storage module, the karst thermal power generation module and the relevant constraints of TESS, the vector x(k) consisting of the output of conventional units, the output of karst temperature difference power generation, the karst TESS storage / release power and the karst TESS heat storage capacity is selected as G (k),P CSP (k),P cha (k),P dis (k),E TESS (k)] T is the state variable; the vector u(k) consisting of the output increment of the conventional unit and the heat release power increment of the cave TESS is selected as follows: G (k),ΔP dis (k)] T is the control variable; the vector d(k) consisting of the predicted power increment of the light intensity, wind power and load demand is selected as d(k) = [ΔP S (k),ΔP W (k),ΔL(k)] T is the disturbance input; select the vector y(k) consisting of the output of conventional units and the output of karst cave temperature difference power generation = [P G (k),P CSP (k)] T is the output variable; the following multi-input and multi-output state space representation prediction model is established: In the formula, is the thermal energy stored in the cave TESS during period t, To prevent the solidification of the heat storage molten salt, the minimum heat storage capacity of the cave TESS is: To prevent the heat storage molten salt from overheating, the maximum heat storage capacity of the cave TESS; and are the initial and final values ​​of the heat storage of the cave TESS in the scheduling cycle. To meet the demand of the next scheduling cycle, the initial and final heat storage of the TESS in a scheduling cycle should be equal; is the heat storage and release state of the cave TESS during period t, which is a 0-1 variable, 1 means that the cave TESS stores heat energy, and 0 means that the cave TESS releases heat energy; and are the maximum heat storage and heat release powers of the cave TESS, respectively; γ is the heat dissipation coefficient of the cave TESS; η cha and η dis They are the heat storage and heat release efficiency of cave TESS respectively.

5. The method for optimizing the operation of the karst cave energy utilization system in karst landform areas as claimed in claim 4, characterized in that: The specific contents of rolling optimization are as follows: The predicted values ​​of wind power, light intensity and load demand in the rolling optimization scheduling model are continuously updated as the prediction domain is moved forward and compressed, and the model is continuously solved online until the scheduling plan within the scheduling cycle is executed; The objective function of the rolling optimization scheduling model is: minF R =F g +F qt (3) Where, t s The starting period of rolling optimization scheduling is continuously updated and moved forward; F g is the fuel cost of conventional units, F qt is the start-up and shutdown cost of the conventional unit and the karst temperature difference power generation module unit; t, i, j are the time period number, conventional unit number, and karst temperature difference power generation module unit number respectively; N T 、N G 、N CSP They are the number of dispatching periods, the number of conventional units, and the number of cave temperature difference power generation module units; is the output of conventional unit i during period t; a i 、b i 、c i are the cost coefficients of conventional unit i; S i , Q j are the start-up and shutdown costs of conventional unit i and cave temperature difference power generation module unit j respectively; u i,t 、x j,t They are the start and stop states of conventional unit i and cave temperature difference power generation module unit j in time period t, both of which are 0-1 variables.

6. The method for optimizing the operation of the karst cave energy utilization system in karst landform areas according to claim 5, characterized in that: The rolling optimization scheduling constraints include: system power balance constraints, operation constraints of conventional units and cave temperature difference power generation, solar thermal concentration, cave heat storage, and cave thermal power generation module power balance constraints and operation constraints.

7. The method for optimizing the operation of the karst cave energy utilization system in karst landform areas according to claim 6, characterized in that: The real-time dynamic adjustment based on model predictive control is as follows: According to the values ​​of light intensity, wind power and load demand within Δt', the state space prediction model is rolled and iterated, and the prediction is made N steps forward to obtain the vector Y consisting of the expected output values ​​of the conventional unit output and the cave temperature difference power generation module unit output within the prediction time domain Δt f : In Δt', the vector R consisting of the output plan value of the conventional unit and the output plan value of the cave temperature difference power generation module unit in the period Δt obtained by solving the rolling optimization phase corresponding to the dynamic adjustment time t' is f In order to track the control target, the output value Y of the conventional unit output and the unit output of the cave temperature difference power generation module in the prediction time domain Δt is used. f and the rolling optimization output plan value R f The goal is to minimize the error between Real-time dynamic adjustment of model objective function: minJ=(R f -Y f ) T W err (R f -Y f )+U T Q u U (7) In the formula, is the weight coefficient matrix of the conventional unit output tracking error and the cave temperature difference power generation module unit output tracking error; Q u is the weight coefficient matrix of the control variables; Dynamically adjust model constraints in real time: Where t' is the time corresponding to the interval Δt'; t * It is the moment of the rolling optimization phase corresponding to the dynamic adjustment moment t'.

8. The method for optimizing the operation of the karst cave energy utilization system in karst landform areas according to claim 7, characterized in that: The multi-time-scale coordinated optimization dispatching control process based on MPC combines intraday large-scale rolling optimization with real-time small-scale dynamic adjustment. The specific contents are as follows: before the start of each period of dynamic adjustment, the system status is updated in real time to obtain real-time information on wind power, light intensity and load demand, and then small-scale optimization dispatching is carried out until all periods are completed. The latest system status information is collected and passed to the rolling optimization layer for large-time-scale economic dispatch, and the solution is continuously rolled forward.

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