An optimization planning method for distributed power generation in power grid

Abstract

The present invention discloses an optimization planning method for distributed power sources in a power grid, comprising the following steps: constructing an objective function according to the type selection, site selection, and capacity determination of distributed power sources, the site selection and capacity determination of energy storage equipment, and the system network, wherein the objective function includes an objective function for minimizing planning cost and operating cost, an objective function for maximizing utilization rate of new energy, and an objective function for minimizing environmental emissions; setting constraints for the objective function according to the characteristics of various types of operating equipment and flexible loads, and building an optimization model for the maximum absorption capacity of new energy sources taking flexible loads into account according to the objective function and its constraints; obtaining the optimal solution of the objective function for minimizing planning cost and operating cost based on the constraints, and combining the optimization model to obtain an optimal distributed energy planning scheme that fits the actual operation of the power grid. This improves the flexibility, interactive capability, and utilization efficiency of the power grid, and further improves the user's power experience.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid regulation, and in particular to an optimization planning method for distributed power sources in a power grid. Background Art

[0002] With the development of building electrification and the extensive use of smart home appliances, as well as global warming and the frequent occurrence of extreme weather, the load intensity in the power consumption area has gradually increased. For example, the frequent occurrence of extremely high temperatures in summer has caused rivers to dry up and hydropower generation to be greatly reduced. At the same time, the use of air conditioners has greatly increased, and the space in urban areas is limited, making it difficult to expand the power system. In the face of the difficulties caused by extreme weather, the more practical solution is to use power system demand-side management and demand-side response. This method can alleviate the problem of power supply and power expansion in urban areas, but it is difficult to play a role in the power expansion of distribution terminals with a large number of flexible loads, such as users in a community or a building.

[0003] Therefore, it is necessary to explore a solution to the problem of power expansion in modern residential renovation and office buildings and other office spaces. Flexible loads are the focus of current industry research. How to effectively solve the scheduling and regulation of flexible loads to alleviate the contradiction between the supply and demand side of the power grid is also one of the important topics of current research. Building a "flexible power grid" is to meet the optimal access of distributed power sources and diversified loads, and to create a smart and friendly flexible distribution network. It will have four characteristics: high adaptability of source-load access, more flexible distribution network operation, more intelligent secondary upgrade, and full interaction between source, network, load and storage. The flexible regulation capability of flexible loads has changed the original one-way and passive mode of load regulation, and also changed the rigidity and uncertainty of load parameters. In this case, the increase of a large number of flexible loads will bring new difficulties to the grid dispatching, which is already more complicated in the regulation method, so further research and solution are needed. At the same time, the types of flexible loads in the current power grid are increasing. In addition to traditional household appliances such as air conditioners and washing machines, the popularization of electric vehicles and their charging piles will also increase the uncertainty of flexible loads. In the existing technology, there is a lack of more in-depth research on how to provide power grid companies with a technical basis for flexible resource regulation and control, and guide users to participate in the grid demand-side response in an orderly manner; how to strengthen the regulation and utilization of flexible resources on the power demand side, improve the stability and absorption capacity of new energy output in the construction of new power systems, reduce the grid investment caused by promoting the absorption of new energy, and improve the flexibility, interaction and utilization efficiency of the grid; at the same time, a more scientific technical solution needs to be given to ensure that power users change from passive participation in orderly electricity consumption to paid participation in demand response, which will greatly improve users' electricity consumption experience. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for optimizing the planning of distributed power sources in a power grid, thereby improving the user's power consumption experience and the convenience of power consumption.

[0005] In order to solve the above technical problems, a technical solution adopted by the present invention is:

[0006] A method for optimizing distributed power generation in a power grid comprises the following steps:

[0007] According to the selection, site selection, and capacity of distributed power sources, the site selection and capacity of energy storage equipment, and the system network, an objective function is constructed, wherein the objective function includes an objective function of minimizing planning costs and operating costs, an objective function of maximizing new energy utilization, and an objective function of minimizing environmental emissions;

[0008] According to the characteristics of various operating equipment and flexible loads, constraints are set for the objective function, and a new energy maximum absorption capacity optimization model taking flexible loads into account is established according to the objective function and its constraints;

[0009] Based on the optimization model, the optimal solution of the objective function of minimizing the planning cost and the operating cost is obtained to obtain the optimal distributed energy planning scheme that fits the actual power grid operation conditions.

[0010] The beneficial effects of the present invention are as follows: the present invention is based on the actual equipment configuration model, takes the green, safe and economical power supply as the goal, considers the user's temperature perception boundary, industrial production characteristics and green travel characteristics, and combines the power grid operation load control requirements to propose a cascade control model for flexible load resources, reduce user negative perception, improve the precision of power grid control, achieve a smooth reduction of power grid peak load, strengthen the control and utilization of flexible resources on the power demand side, improve the stability and absorption capacity of new energy output in the process of new power system construction, reduce the power grid investment caused by promoting the absorption of new energy, and improve the flexibility, interaction and utilization efficiency of the power grid. At the same time, it guides users to participate in the demand-side response of the power grid in an orderly manner, ensures that power users change from passive participation in orderly power consumption to paid participation in demand response, and greatly improves the user's power experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 The present invention is a flowchart of a method for optimizing distributed power generation in a power grid according to an embodiment of the present invention. DETAILED DESCRIPTION

[0012] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0013] The embodiment of the present invention provides a method for optimizing the planning of distributed power sources in a power grid. Figure 1 , including the following steps:

[0014] According to the selection, site selection, and capacity of distributed power sources, the site selection and capacity of energy storage equipment, and the system network, an objective function is constructed, wherein the objective function includes an objective function of minimizing planning costs and operating costs, an objective function of maximizing new energy utilization, and an objective function of minimizing environmental emissions;

[0015] According to the characteristics of various operating equipment and flexible loads, constraints are set for the objective function, and a new energy maximum absorption capacity optimization model taking flexible loads into account is established according to the objective function and its constraints;

[0016] Based on the optimization model, the optimal solution of the objective function of minimizing the planning cost and the operating cost is obtained to obtain the optimal distributed energy planning scheme that fits the actual power grid operation conditions.

[0017] From the above description, it can be seen that the beneficial effects of the present invention are: the present invention is constructed based on the actual equipment configuration model, with the goal of green, safe and economical power supply, taking into account the user's temperature perception boundary, industrial production characteristics and green travel characteristics, and combining the power grid operation load control needs, a cascade control model of flexible load resources is proposed to reduce user negative perception, improve the precision of power grid control, and achieve a smooth reduction of power grid peak loads. It strengthens the control and utilization of flexible resources on the power demand side, improves the stability and absorption capacity of new energy output in the process of new power system construction, reduces the power grid investment caused by promoting the absorption of new energy, and improves the flexibility, interaction and utilization efficiency of the power grid. At the same time, it guides users to participate in the demand-side response of the power grid in an orderly manner, ensuring that power users change from passive participation in orderly power consumption to paid participation in demand response, greatly improving the user's power experience.

[0018] Furthermore, the objective function for minimizing the planning cost and the operating cost is:

[0019] minC=C dg +C es +C el +C dah +C rts ;

[0020] In the formula, C represents the total system cost, including planning cost and operating cost, C dg , C es , C el They represent the equipment costs of distributed generation, energy storage, and transmission network, respectively. dah , C rts They represent the day-ahead dispatching cost and the real-time dispatching cost of the distribution network respectively;

[0021]

[0022] Where Pd Represents the capacity of distributed power source d; C d represents the unit capacity cost of distributed generation d; P s Represents the capacity of energy storage device s; C s represents the unit capacity cost of energy storage equipment s; L l Indicates the length of the newly built line L; C l represents the unit length cost of the transmission line; C dah represents the day-ahead scheduling cost, represents the power generation cost of distributed gas-fired units in the day-ahead dispatch plan; represents the cost of purchasing electricity from the main grid in the day-ahead dispatch plan; C rts represents the real-time dispatching cost of the distribution network; represents the real-time dispatching cost of distributed gas generators; represents the real-time dispatch cost of purchasing electricity in the wholesale market; Represents the real-time dispatch cost of demand response resources.

[0023] From the above description, it can be seen that the equipment costs of distributed power sources, energy storage, transmission networks, and scheduling costs should be fully considered, and an objective function for minimizing planning costs and operating costs should be constructed to achieve the economic goal of power supply.

[0024] Furthermore, for the day-ahead scheduling cost, the objective function of minimizing the planning cost and the operating cost is simplified to:

[0025]

[0026] In the formula, represents the power generation cost of distributed gas-fired units in the day-ahead dispatch plan; T represents the length of a typical dispatch day; represents the power of the g-th gas generator unit at time t in the day-ahead dispatch; η gas Indicates the power generation efficiency of distributed gas-fired units; Q gas Indicates the calorific value of natural gas; C gas represents the price of natural gas; represents the day-ahead dispatch cost of demand response resources; Indicates the adjustable load call status of the user corresponding to node i at time t in the day-ahead dispatch; It indicates the fee that needs to be paid to the user for calling the user's available load resources during the day-ahead scheduling phase;

[0027] Based on the constant system state, simplifying the integral part in the above formula, the electricity purchase cost is:

[0028]

[0029] In the formula, represents the cost of purchasing electricity from the main grid in the day-ahead dispatch plan; P grid (t) represents the power injected from the main grid to the connection point of the distribution network at time t; represents the day-ahead electricity price in the wholesale market;

[0030] For real-time scheduling costs, adjusting resources and additional payment costs:

[0031] When the distributed gas-fired units are generating electricity, the corresponding power generation cost is:

[0032]

[0033] In the formula, represents the real-time dispatching cost of distributed gas generators; represents the output of distributed gas generator set g at time t in real-time scheduling;

[0034] When purchasing electricity from the wholesale market, the real-time dispatch cost of the wholesale market is:

[0035]

[0036]

[0037] Where, ΔC grid (t) represents the real-time dispatch cost of electricity purchased in the wholesale market at time t; It represents the power injected by the main grid into the grid connection point of the distribution network system at time t in real-time dispatch; It represents the power injected by the main grid into the grid connection point of the distribution network system at time t in the day-ahead dispatch plan; Indicates the real-time electricity price in the wholesale market; Represents the electricity price in the day-ahead dispatch plan.

[0038] From the above description, it can be seen that the day-ahead scheduling cost and real-time scheduling cost are fully considered, and the objective function of minimizing the planning cost and operating cost is simplified.

[0039] Furthermore, the objective function for maximizing the utilization rate of new energy is:

[0040]

[0041] In the formula, r represents the utilization rate of new energy, and They represent the upper limits of the output of distributed wind power and distributed photovoltaic power generation in the system at time t, are the total output power of all distributed wind power and photovoltaic power generation in the system at time t.

[0042] From the above description, it can be seen that based on the characteristics of wind power generation and photovoltaic power generation, an objective function for maximizing the utilization rate of new energy is constructed to achieve the goal of safe power supply.

[0043] Furthermore, the environmental emission minimization objective function is:

[0044]

[0045] In the formula, C env represents the emission cost of distributed gas turbines, ω NOx ,ω SO2 ,ω DUST They represent the emission coefficients of nitrogen oxides, sulfur dioxide and smoke of the gas generator set respectively. Represents the output power of the gas unit in real-time scheduling at time t.

[0046] From the above description, it can be seen that the present invention takes into account the impact of three types of pollutants: nitrogen oxides, sulfur dioxide and smoke, constructs an environmental emission minimization objective function, and achieves the green goal of power supply.

[0047] Furthermore, the constraint condition includes a bearing capacity constraint, and the bearing capacity constraint is specifically:

[0048]

[0049] Where P d represents the capacity of distributed generation d, D t represents the total system power demand at time t in the day-ahead load forecast, D i,t It represents the power demand of node i at time t in the day-ahead load forecast.

[0050] From the above description, it can be seen that a constraint model is established for the distribution network's carrying capacity for distributed generation, so that the capacity of distributed generation in the system is not higher than the typical daily maximum load of the system, which is used to further restrict the optimization results.

[0051] Furthermore, the constraint condition includes a system steady-state operation constraint, and the system steady-state operation constraint is specifically:

[0052] Calculate the power balance constraint:

[0053]

[0054] In the formula, They represent the power injected by the distributed gas units, distributed wind power, distributed photovoltaic, energy storage and main grid in the system at time t in the day-ahead dispatch. They represent the injected power of the i-th node at time t in the day-ahead scheduling, represents the line loss of the ith node at time t in the day-ahead scheduling, represents the output power of the available load resources of the i-th node user at time t in the day-ahead scheduling, They represent the power injected by distributed gas units, distributed wind power, distributed photovoltaic, energy storage and main grid in the system at time t in real-time scheduling. They represent the injected power of the i-th node at time t in real-time scheduling, represents the line loss of the i-th node at time t in real-time scheduling, represents the output power of the load resources available to the user at the ith node at time t in real-time scheduling, represents the output power of the load resources available to the user at time t in the day-ahead scheduling and real-time scheduling of node i, represents the power output of each user's load resource to node i at time t in day-ahead scheduling and real-time scheduling, μ i,t is the weight coefficient of the i-th node at time t, with a value between 0 and 1;

[0055] Calculate the node voltage range:

[0056]

[0057] In the formula, U, They represent the lower and upper limits of the voltage at node i, respectively. represents the voltage of node i at time t in day-ahead scheduling and real-time scheduling;

[0058] Calculate the branch current limit:

[0059]

[0060] In the formula, I l represents the upper limit of the current in branch l, It represents the current of branch 1 at time t in day-ahead scheduling or real-time scheduling.

[0061] From the above description, it can be seen that a constraint condition model is established for power balance so that the output and load power of the system are balanced at any time in the day-ahead scheduling stage and the real-time scheduling stage, which is used to further restrict the optimization results.

[0062] Furthermore, the constraint condition includes a distributed power output constraint, and the distributed power output constraint is specifically:

[0063] Calculate the distributed gas turbine output constraints:

[0064]

[0065] In the formula, P gas and Respectively represent the gas engine output power P gasThe minimum and maximum outputs are determined by the unit parameters; represents the output power of the gas generator set at time t in day-ahead scheduling and real-time scheduling; v down and ν up are the maximum downward and upward climbing rates of the unit respectively;

[0066] Calculate the output constraints of distributed photovoltaic power generation:

[0067]

[0068] In the formula, It represents the upper limit of the output of distributed photovoltaic units PV at time t in day-ahead scheduling and real-time scheduling; represents the output of distributed photovoltaic units PV at time t in day-ahead scheduling and real-time scheduling; L dah ,rts (t) represents the local light intensity at time t in day-ahead scheduling and real-time scheduling; M PV Represents the light receiving area of ​​the distributed photovoltaic generator PV; θ PV Indicates the power generation efficiency of the photovoltaic unit PV;

[0069] Calculate the distributed wind power output constraints:

[0070]

[0071]

[0072] In the formula, represents the power of distributed wind power at time t in the day-ahead dispatching and real-time dispatching stages, wind is the number of the wind turbine; v is the upper limit of the distributed wind power at the corresponding time; dah,rts (t) represents the wind speed at time t during the day-ahead dispatch and real-time dispatch phases; v in 、v out 、v rate Respectively represent the cut-in wind speed, cut-off wind speed and rated wind speed of the fan; Indicates the rated power of the fan wind.

[0073] From the above description, it can be seen that for the distributed gas turbine output, distributed photovoltaic power generation output and distributed wind power output, respective constraint condition models are established to further restrict the optimization results.

[0074] Furthermore, the constraint condition includes energy storage operation constraint, and the energy storage operation constraint is specifically:

[0075]

[0076] In the formula, Es dah,rts(t) represents the remaining energy of energy storage at time t in the day-ahead dispatch and real-time dispatch stages; are the charging and discharging power of energy storage at time t in the day-ahead dispatch and real-time dispatch phases respectively; η ch and η dsc They are the charging efficiency and discharging efficiency of the energy storage device; and is a 0-1 variable; ε is the self-discharge rate (loss) of the stored energy, with a value between 0-100%;

[0077] The charging and discharging power of the energy storage system meets the following conditions:

[0078]

[0079] In the formula, They are the maximum charging power and maximum discharging power of energy storage at time t in the day-ahead dispatch and real-time dispatch phases;

[0080] The energy storage system capacity meets the following conditions:

[0081]

[0082] In the formula, Es dah,rts (t) represents the amount of energy stored at time t, Es dah,rts (0) represents the initial capacity of energy storage, Indicates the upper limit of energy storage capacity.

[0083] From the above description, it can be seen that limiting the charging and discharging power of the energy storage system and the system power range ensures the optimized operation of the energy storage in the three operating states of charging, discharging and stability.

[0084] Furthermore, the constraint condition includes a demand response constraint, and the demand response constraint is specifically:

[0085] The dummy variables satisfy the following conditions:

[0086]

[0087] In the formula, and is a variable in [0,1], which represents the load resource scheduling status of node i at day-ahead and real-time scheduling stage t respectively.

[0088] From the above description, it can be seen that a constraint condition model is established for demand response, requiring that a load node be powered off at most once a day, which is used to further restrict the optimization results.

[0089] The above-mentioned optimization planning method of a distributed power source in a power grid of the present invention can improve the flexibility, interaction capability and utilization efficiency of the power grid, and improve the user's power consumption experience. The following is an explanation through specific implementation methods:

[0090] Embodiment 1

[0091] A method for optimizing distributed power generation in a power grid comprises the following steps:

[0092] The first step is to construct the objective function based on the selection, site selection, and capacity of distributed power sources, the site selection and capacity of energy storage equipment, and the system network. The objective function includes the objective function of minimizing planning cost and operating cost, the objective function of maximizing new energy utilization, and the objective function of minimizing environmental emissions.

[0093] Specifically, the objective function for minimizing planning cost and operating cost is:

[0094] minC=C dg +C es +C el +C dah +C rts ;

[0095] In the formula, C represents the total system cost, including planning cost and operating cost, C dg , C es , C el They represent the equipment costs of distributed generation, energy storage, and transmission network, respectively. dah , C rts They represent the day-ahead dispatching cost and the real-time dispatching cost of the distribution network respectively;

[0096]

[0097] Where P d Represents the capacity of distributed power source d; C d represents the unit capacity cost of distributed generation d; P s Represents the capacity of energy storage device s; C s represents the unit capacity cost of energy storage equipment s; L l Indicates the length of the newly built line L; C l represents the unit length cost of the transmission line; C dah represents the day-ahead scheduling cost, represents the power generation cost of distributed gas-fired units in the day-ahead dispatch plan; represents the cost of purchasing electricity from the main grid in the day-ahead dispatch plan; C rts represents the real-time dispatching cost of the distribution network; represents the real-time dispatching cost of distributed gas generators; represents the real-time dispatch cost of purchasing electricity in the wholesale market; Represents the real-time dispatch cost of demand response resources.

[0098] Day-ahead dispatch arranges the output of the unit based on the forecast values ​​of wind speed, sunlight and load, and determines the strategy of purchasing electricity from the wholesale market and the strategy of shedding load in response to demand. Therefore, the day-ahead dispatch cost refers to the power generation cost of the unit in the day-ahead dispatch plan, the cost of calling demand response resources in the day-ahead stage, and the cost of purchasing electricity from the main grid. In the model constructed by this patent, since distributed wind power and photovoltaic power generation rely on natural resources to produce electricity and do not require fuel consumption, their power generation costs are very small and can be ignored. Similarly, the dispatch cost of energy storage is ignored. Therefore, the day-ahead dispatch cost of the system is equal to the sum of the power generation cost of the distributed gas-fired units and the cost of the load that can be called on the day-ahead.

[0099] For the day-ahead scheduling cost, the objective function of minimizing the planning cost and operating cost is simplified to:

[0100]

[0101] In the formula, represents the power generation cost of distributed gas-fired units in the day-ahead dispatch plan; T represents the length of a typical dispatch day; represents the power of the g-th gas generator unit at time t in the day-ahead dispatch; η gas Indicates the power generation efficiency of distributed gas-fired units; Q gas Indicates the calorific value of natural gas; C gas represents the price of natural gas; represents the day-ahead dispatch cost of demand response resources; Indicates the adjustable load call status of the user corresponding to node i at time t in the day-ahead dispatch; It indicates the fee that needs to be paid to the user for calling the user's available load resources during the day-ahead scheduling phase;

[0102] Preferably, in the actual calculation process, the time length covered by the calculation of a single optimization section in this patent is 1 hour, that is, in the 24 hours of a typical scheduling day, it is assumed that the system state is constant in each hour. Therefore, the integral part in the above formula can be simplified as follows:

[0103]

[0104] Among them, the unit of 3600 on the right side of the above formula is seconds, and obviously the units on both sides of the equal sign are kJ.

[0105] Based on the constant system state, the integral part of the objective function of minimizing planning cost and operating cost is simplified again, and the electricity purchase cost is:

[0106]

[0107] In the formula, represents the cost of purchasing electricity from the main grid in the day-ahead dispatch plan; P grid (t) represents the power injected from the main grid to the connection point of the distribution network at time t; represents the day-ahead electricity price in the wholesale market;

[0108] Real-time scheduling is subject to the constraints of the external environment and the actual value of user load, and adjusts the day-ahead scheduling plan and resources. The unit output and transaction plan obtained by real-time scheduling represent the actual operation and transaction status of the system. There are two factors affecting real-time scheduling: one is that the supply side has deviations from the predicted value due to one or both of the wind speed or light intensity, which leads to deviations in the output of the corresponding distributed renewable power source. The other is that the user power on the demand side deviates from the predicted value. In order to cope with the above deviations, resources need to be adjusted and additional costs need to be paid.

[0109] For real-time scheduling costs, adjusting resources and additional payment costs:

[0110] When the distributed gas-fired units are generating electricity, the corresponding power generation cost is:

[0111]

[0112] In the formula, represents the real-time dispatching cost of distributed gas generators; Represents the output of the distributed gas generator g at time t in real-time scheduling. Obviously, when the output of the gas generator obtained by real-time scheduling is greater than the output of the day-ahead scheduling, the real-time scheduling cost is positive, otherwise it is negative.

[0113] According to the existing multi-level settlement market framework in China, in real-time market transactions, if the electricity consumption is less than the day-ahead electricity consumption, the difference will not be settled. However, if the real-time electricity consumption is greater than the day-ahead electricity consumption, the difference will no longer be subject to the day-ahead electricity price, but the real-time electricity price. Obviously, the real-time electricity price is higher than the day-ahead electricity price.

[0114] When purchasing electricity from the wholesale market, the real-time dispatch cost of the wholesale market is:

[0115]

[0116] Where, ΔC grid (t) represents the real-time dispatch cost of electricity purchased in the wholesale market at time t; It represents the power injected by the main grid into the grid connection point of the distribution network system at time t in real-time dispatch; It represents the power injected by the main grid into the grid connection point of the distribution network system at time t in the day-ahead dispatch plan; Indicates the real-time electricity price in the wholesale market; Represents the electricity price in the day-ahead dispatch plan.

[0117] Specifically, the objective function for maximizing the utilization rate of new energy is:

[0118]

[0119] In the formula, r represents the utilization rate of new energy, and They represent the upper limits of the output of distributed wind power and distributed photovoltaic power generation in the system at time t, are the total output power of all distributed wind power and photovoltaic power generation in the system at time t.

[0120] Distributed gas turbines that burn fossil fuels will emit pollutants into the atmosphere, so the impact of three types of pollutants, nitrogen oxides, sulfur dioxide and smoke, is considered in the environmental emission minimization objective function of the present invention.

[0121] Specifically, the objective function for minimizing environmental emissions is:

[0122]

[0123] In the formula, C env represents the emission cost of distributed gas turbines, ω NOx ,ω SO2 ,ω DUST They represent the emission coefficients of nitrogen oxides, sulfur dioxide and smoke of the gas generator set respectively. Represents the output power of the gas unit in real-time scheduling at time t.

[0124] The second step is to set constraints for the objective function according to the characteristics of various operating equipment and flexible loads, and build an optimization model for the maximum absorption capacity of new energy taking into account flexible loads based on the objective function and its constraints;

[0125] The constraints include carrying capacity constraints, system steady-state operation constraints, distributed generation output constraints, energy storage operation constraints, and demand response constraints.

[0126] 1) Bearing capacity constraints are as follows:

[0127]

[0128] Where P d represents the capacity of distributed generation d, D t represents the total system power demand at time t in the day-ahead load forecast, D i,t It represents the power demand of node i at time t in the day-ahead load forecast.

[0129] Considering the distribution network's carrying capacity for distributed generation, it is required here that the capacity of distributed generation in the system should not be higher than the typical daily maximum load of the system.

[0130] 2) The specific constraints of the system steady-state operation are:

[0131] Calculate the power balance constraint:

[0132]

[0133] In the formula, They represent the power injected by the distributed gas units, distributed wind power, distributed photovoltaic, energy storage and main grid in the system at time t in the day-ahead dispatch. They represent the injected power of the i-th node at time t in the day-ahead scheduling, represents the line loss of the ith node at time t in the day-ahead scheduling, represents the output power of the available load resources of the i-th node user at time t in the day-ahead scheduling, They represent the power injected by distributed gas units, distributed wind power, distributed photovoltaic, energy storage and main grid in the system at time t in real-time scheduling. They represent the injected power of the i-th node at time t in real-time scheduling, represents the line loss of the i-th node at time t in real-time scheduling, represents the output power of the load resources available to the user at the ith node at time t in real-time scheduling, represents the output power of the load resources available to the user at time t in the day-ahead scheduling and real-time scheduling of node i, represents the power output of each user's load resource to node i at time t in day-ahead scheduling and real-time scheduling, μ i,t is the weight coefficient of the i-th node at time t, with a value between 0 and 1;

[0134] Calculate the node voltage range:

[0135] In the formula, U , They represent the lower and upper limits of the voltage at node i, respectively. represents the voltage of node i at time t in day-ahead scheduling and real-time scheduling; in this embodiment, it is assumed that the upper and lower limits of the voltage of all nodes are the same.

[0136] Calculate the branch current limit:

[0137]

[0138] In the formula, I l represents the upper limit of the current in branch l, It represents the current of branch 1 at time t in day-ahead scheduling or real-time scheduling. Affected by the line specifications, the upper limits of the currents of different branches in the system vary.

[0139] The steady-state operation constraint requires that the system output and load power remain balanced at any time during the day-ahead dispatching phase and the real-time dispatching phase.

[0140] 3) The specific output constraints of distributed power generation are:

[0141] Calculate the distributed gas turbine output constraints:

[0142]

[0143] In the formula, P gas and Respectively represent the gas engine output power P gas The minimum and maximum outputs are determined by the unit parameters; represents the output power of the gas generator set at time t in day-ahead scheduling and real-time scheduling; v down and v up are the maximum downward and upward climbing rates of the unit respectively; that is, whether it is the output power of the gas unit at time t in the day-ahead scheduling or the output power of the gas unit at time t in the real-time scheduling, it needs to be greater than or equal to P gas , and is less than or equal to

[0144] In this embodiment, it is required that the output of the distributed gas turbine should be within its upper and lower limits at any time, and the unit climbing constraint should also be met, that is, there is a maximum upper limit on the output change within a unit time interval.

[0145] Calculate the output constraints of distributed photovoltaic power generation:

[0146]

[0147] In the formula, It represents the upper limit of the output of distributed photovoltaic units PV at time t in day-ahead scheduling and real-time scheduling; represents the output of distributed photovoltaic units PV at time t in day-ahead scheduling and real-time scheduling; L dah ,rts (t) represents the local light intensity at time t in day-ahead scheduling and real-time scheduling; M PV Represents the light receiving area of ​​the distributed photovoltaic generator PV; θ PV Indicates the power generation efficiency of the photovoltaic unit PV;

[0148] Under full grid access conditions, the output of distributed photovoltaic power generation is a function of the local light intensity. The maximum output of photovoltaic power generation at any time is a function of the local light intensity.

[0149] Calculate the distributed wind power output constraints:

[0150]

[0151]

[0152] In the formula, It represents the power of distributed wind power at time t in the day-ahead dispatching and real-time dispatching stages, and wind is the number of the wind turbine; is the upper limit of distributed wind power at the corresponding moment; v dah,rts (t) represents the wind speed at time t during the day-ahead dispatch and real-time dispatch phases; v in 、v out 、v rate Respectively represent the cut-in wind speed, cut-off wind speed and rated wind speed of the fan; Indicates the rated power of the fan wind.

[0153] Similar to photovoltaic power generation, the distributed wind power output constraint is described as follows: the maximum value of wind power output at any time is a function of the local wind speed.

[0154] 4) The specific constraints of energy storage operation are:

[0155]

[0156] In the formula, Es dah,rts (t) represents the remaining energy of energy storage at time t in the day-ahead dispatch and real-time dispatch stages; are the charging and discharging power of energy storage at time t in the day-ahead dispatch and real-time dispatch phases respectively; η ch and η dsc They are the charging efficiency and discharging efficiency of the energy storage device; and It is a 0-1 variable, ensuring that the energy storage must be in one of the three states of charging, discharging, and stability at the same time; ε is the self-discharge rate (loss) of the energy storage, and the value is between 0-100%;

[0157] Energy storage includes three operating states: charging, discharging and stable.

[0158] The charging and discharging power of the energy storage system meets the following conditions:

[0159]

[0160] In the formula, They are the maximum charging power and maximum discharging power of energy storage at time t in the day-ahead dispatch and real-time dispatch phases;

[0161] The energy storage system capacity meets the following conditions:

[0162]

[0163] In the formula, Es dah,rts(t) represents the amount of energy stored at time t, Es dah,rts (0) represents the initial capacity of energy storage, Indicates the upper limit of energy storage capacity.

[0164] In this embodiment, the remaining power of the energy storage at any time cannot be 0, nor can it exceed the upper limit. In addition, to ensure that the energy storage can be charged or discharged at t=0, the initial power of the energy storage is set to 50% of its upper capacity limit.

[0165] 5) The specific demand response constraints are:

[0166] The dummy variables satisfy the following conditions:

[0167]

[0168] In the formula, and is a variable in [0,1], which represents the load resource scheduling status of node i at day-ahead and real-time scheduling stage t respectively.

[0169] In this embodiment, since there is no requirement for equal load shifting for the available load, the demand response constraint is relatively simple, requiring that a load node be shut down at most once a day.

[0170] The third step is to obtain the optimal solution of the objective function of minimizing the planning cost and operating cost based on the constraints, and combine the optimization model to obtain the optimal distributed energy planning scheme that fits the actual grid operation. The best optimization effect is achieved when the three objective functions are met at the same time.

[0171] Due to the influence of various cost parameters, power generation and other factors in the optimization objective function, such as fluctuations in energy costs (oil and natural gas prices), and changes in power generation caused by equipment updates and maintenance, in order to achieve the optimal solution of the objective function while meeting the constraints in the present invention, it is necessary to repeatedly adjust the output power of the gas generator set, the energy storage power level and the electricity purchase cost of the wholesale market, that is, repeatedly adjust the increment / increase of each parameter in the optimization function based on the initial value, and then re-judge whether the adjusted parameters meet the constraints and the optimal solution of the objective function at the same time, so that in each iterative calculation, a planning configuration scheme that is closer to the optimal solution of the system is obtained.

[0172] The present invention is based on the construction of an actual equipment configuration model, with the goal of green, safe and economical power supply. Based on the selection, site selection and sizing of distributed power sources, the site selection and sizing of energy storage equipment and the expansion of the system network, an optimization objective function for minimizing planning costs and operating costs is set, and cost calculation models for various types of distributed power supply units are established, in order to obtain the optimal solution that is close to the actual power grid operation conditions.

[0173] In summary, the present invention provides an optimization planning method for distributed power sources in a power grid. By considering the user's temperature sensitivity boundary, industrial production characteristics and green travel characteristics, and combining the load control requirements of power grid operation, a cascade control model of flexible load resources is proposed, thereby improving the flexibility, interaction capability and utilization efficiency of the power grid, and further improving the user's electricity experience.

[0174] It should be noted that, for the convenience of description, the aforementioned method embodiments are all described as a series of action combinations, but those skilled in the art should be aware that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0175] In the above embodiment, the objective functions of minimizing planning cost and operating cost are mainly satisfied, and the optimal effect is obtained when the above three objective functions are satisfied at the same time. Obviously, it should be known that satisfying any one or two of the objective functions can also achieve partial optimization effect on energy planning, so it is also within the patent scope of the present invention.

[0176] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An optimization planning method for distributed power generation in power grid, It is characterized in that The following steps are involved: According to the selection, site selection, and capacity of distributed power sources, the site selection and capacity selection of energy storage equipment, and the system network, the objective function is constructed. The objective function includes the objective function of minimizing planning cost and operating cost, the objective function of maximizing the utilization rate of new energy, and the objective function of minimizing environmental emissions; According to the characteristics of various operating equipment and flexible loads, set constraints for the objective function, and build an optimization model for the maximum absorption capacity of new energy taking into account flexible loads based on the objective function and its constraints; Based on the constraints, the optimal solution of the objective function of minimizing planning cost and operating cost is obtained, and the optimization model is combined to obtain the optimal distributed energy planning scheme that fits the actual grid operation conditions; The objective function for minimizing planning cost and operating cost is: minC=C dg +C es +C el +C dah +C rts ; In the formula, C represents the total system cost, including planning cost and operating cost, C dg , C es , C el They represent the equipment costs of distributed generation, energy storage, and transmission network, respectively. dah , C rts They represent the day-ahead dispatching cost and the real-time dispatching cost of the distribution network respectively; Where P d represents the capacity of distributed power source d; c d represents the unit capacity cost of distributed generation d; P s represents the capacity of energy storage device s; c s represents the unit capacity cost of energy storage equipment s; L l Indicates the length of the newly built line L; c l represents the unit length cost of the transmission line; C dah represents the day-ahead scheduling cost, represents the power generation cost of distributed gas-fired units in the day-ahead dispatch plan; represents the cost of purchasing electricity from the main grid in the day-ahead dispatch plan; C rts represents the real-time dispatching cost of the distribution network; represents the real-time dispatching cost of distributed gas generators; represents the real-time dispatch cost of purchasing electricity in the wholesale market; represents the real-time dispatch cost of demand response resources; For the day-ahead scheduling cost, the objective function of minimizing the planning cost and operating cost is simplified to: In the formula, represents the power generation cost of distributed gas-fired units in the day-ahead dispatch plan; T represents the length of a typical dispatch day; represents the power of the g-th gas generator unit at time t in the day-ahead dispatch; η gas represents the power generation efficiency of distributed gas generator sets; Q gas Indicates the calorific value of natural gas; C gas represents the price of natural gas; represents the day-ahead dispatch cost of demand response resources; Indicates the adjustable load call status of the user corresponding to node i at time t in the day-ahead dispatch; It indicates the fee that needs to be paid to the user for calling the user's available load resources during the day-ahead scheduling phase; Based on the constant system state, simplifying the integral part in the above formula, the electricity purchase cost is: In the formula, represents the cost of purchasing electricity from the main grid in the day-ahead dispatch plan; P grid (t) represents the power injected from the main grid to the connection point of the distribution network at time t; represents the day-ahead electricity price in the wholesale market; For real-time scheduling costs, adjusting resources and additional payment costs: When the distributed gas-fired units are generating electricity, the corresponding power generation cost is: In the formula, represents the real-time dispatching cost of distributed gas generators; represents the output of distributed gas generator set g at time t in real-time scheduling; When purchasing electricity from the wholesale market, the real-time dispatch cost of the wholesale market is: Where, ΔC grid (t) represents the real-time dispatch cost of electricity purchased in the wholesale market at time t; It represents the power injected by the main grid into the grid connection point of the distribution network system at time t in real-time dispatch; It represents the power injected by the main grid into the grid connection point of the distribution network system at time t in the day-ahead dispatch plan; Indicates the real-time electricity price in the wholesale market; Represents the electricity price in the day-ahead dispatch plan.

2. The method for optimizing distributed power generation in a power grid according to claim 1, It is characterized in that The objective function for maximizing the utilization rate of new energy is: In the formula, r represents the utilization rate of new energy, and They represent the upper limits of the output of distributed wind power and distributed photovoltaic power generation in the system at time t, are the total output power of all distributed wind power and photovoltaic power generation in the system at time t.

3. The method for optimizing distributed power generation in a power grid according to claim 1, It is characterized in that The environmental emission minimization objective function is: In the formula, C env represents the emission cost of distributed gas turbines, ω NOx ,ω SO2 ,ω DUST They represent the emission coefficients of nitrogen oxides, sulfur dioxide and smoke of the gas generator set respectively. Represents the output power of the gas unit in real-time scheduling at time t.

4. The method for optimizing distributed power generation in a power grid according to claim 1, It is characterized in that The constraint condition includes a bearing capacity constraint, and the bearing capacity constraint is specifically: Where P d represents the capacity of distributed generation d, D t represents the total system power demand at time t in the day-ahead load forecast, D i,t It represents the power demand of node i at time t in the day-ahead load forecast.

5. The method for optimizing distributed power generation in a power grid according to claim 1, It is characterized in that The constraint conditions include system steady-state operation constraints, which are specifically: Calculate the power balance constraint: In the formula, They represent the power injected by the distributed gas units, distributed wind power, distributed photovoltaic, energy storage and main grid in the system at time t in the day-ahead dispatch. They represent the injected power of the i-th node at time t in the day-ahead scheduling, represents the line loss of the ith node at time t in the day-ahead scheduling, represents the output power of the available load resources of the i-th node user at time t in the day-ahead scheduling, They represent the power injected by distributed gas units, distributed wind power, distributed photovoltaic, energy storage and main grid in the system at time t in real-time scheduling. They represent the injected power of the i-th node at time t in real-time scheduling, represents the line loss of the ith node at time t in real-time scheduling, represents the output power of the load resources available to the ith node user at time t in real-time scheduling, represents the output power of the load resources available to the user at time t in the day-ahead scheduling and real-time scheduling of node i, represents the power output of each user's load resource to node i at time t in day-ahead scheduling and real-time scheduling, μ i,t is the weight coefficient of the i-th node at time t, with a value between 0 and 1; Calculate the node voltage range: In the formula, U, They represent the lower and upper limits of the voltage at node i, respectively. represents the voltage of node i at time t in day-ahead scheduling and real-time scheduling; Calculate the branch current limit: In the formula, represents the upper limit of the current in branch l, It represents the current of branch l at time t in day-ahead scheduling or real-time scheduling.

6. The method for optimizing distributed power generation in a power grid according to claim 1, It is characterized in that The constraint condition includes a distributed power output constraint, and the distributed power output constraint is specifically: Calculate the distributed gas turbine output constraints: In the formula, P gas and Respectively represent the gas engine output power P gas The minimum and maximum outputs are determined by the unit parameters; represents the output power of the gas generator set at time t in day-ahead scheduling and real-time scheduling; v down and ν up are the maximum downward and upward climbing rates of the unit respectively; Calculate the output constraints of distributed photovoltaic power generation: In the formula, It represents the upper limit of the output of distributed photovoltaic units PV at time t in day-ahead scheduling and real-time scheduling; represents the output of distributed photovoltaic units PV at time t in day-ahead scheduling and real-time scheduling; L dah,rts (t) represents the local light intensity at time t in day-ahead scheduling and real-time scheduling; M PV Represents the light receiving area of ​​the distributed photovoltaic generator PV; θ PV Indicates the power generation efficiency of the photovoltaic unit PV; Calculate the distributed wind power output constraints: In the formula, It represents the power of distributed wind power at time t in the day-ahead dispatching and real-time dispatching stages, and wind is the number of the wind turbine; is the upper limit of distributed wind power at the corresponding moment; dah,rts (t) represents the wind speed at time t during the day-ahead dispatch and real-time dispatch stages; υ in ,υ out ,υ rate Respectively represent the cut-in wind speed, cut-off wind speed and rated wind speed of the fan; Indicates the rated power of the fan wind.

7. The method for optimizing distributed power generation in a power grid according to claim 6, It is characterized in that The constraint conditions include energy storage operation constraints, which are specifically: In the formula, Es dah,rts (t) represents the amount of energy stored at time t, It represents the remaining energy of energy storage at time t in the real-time scheduling stage; They are the charging and discharging power of energy storage at time t in the day-ahead dispatch and real-time dispatch phases respectively; η ch and η dsc They are the charging efficiency and discharging efficiency of the energy storage device; and is a 0-1 variable; ε is the self-discharge rate of energy storage, with a value between 0-100%; The charging and discharging power of the energy storage system meets the following conditions: In the formula, They are the maximum charging power and maximum discharging power of energy storage at time t in the day-ahead dispatch and real-time dispatch phases; The energy storage system capacity meets the following conditions: In the formula, Es dah,rts (t) represents the amount of energy stored at time t, Es dah,rts (0) represents the initial capacity of energy storage, Indicates the upper limit of energy storage capacity.

8. The method for optimizing distributed power generation in a power grid according to claim 1, It is characterized in that The constraint condition includes a demand response constraint, and the demand response constraint is specifically: The dummy variables satisfy the following conditions: In the formula, and is a variable with a value of [0,1], which represents the load resource scheduling status of node i at the day-ahead and real-time scheduling stages t respectively.

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