Optimal configuration scheduling method and system for power grid energy storage system with ultrahigh proportion of new energy

Abstract

The invention provides a new energy ultrahigh-proportion power grid energy storage system optimal configuration scheduling method and system, and is applied to the technical field of energy storage system configuration. The method comprises the following steps: constructing a cost model of the energy storage system associated with capacity optimization of the energy storage system by considering the investment cost and the life cycle of the energy storage system; taking the maximization of the comprehensive benefit of a power grid as the target of an outer-layer configuration model, taking the minimization of the operation cost of a thermal power generating unit as the target of an inner-layer operation model, and constructing a double-layer optimization configuration model of the energy storage system based on the cost model of the energy storage system; and solving the double-layer optimization configuration model based on the obtained power generation power predicted value and the load power predicted value of each new energy station in the power grid in the future period to obtain an optimization result. According to the method, the problems that the peak regulation capability of the energy storage system in the power grid system with the ultrahigh new energy proportion is relatively weak, the wind and light abandoning rate is still relatively high, and the cost of the energy storage system participating in power grid dispatching is relatively high are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage system configuration, and in particular to a method and system for optimizing configuration and scheduling of a power grid energy storage system with a very high proportion of new energy. Background Art

[0002] With the continuous changes in the international environment and energy forms, the newly installed capacity of wind power and photovoltaic power is increasing rapidly. However, the output of new energy power generation is intermittent and volatile, which will lead to the economic cost of safe operation of the power grid and the generally high wind and photovoltaic abandonment rate.

[0003] Energy storage systems are highly flexible, have bidirectional power regulation, and have fast response speeds. Currently, energy storage systems are deployed to alleviate the economic costs and high wind and solar power abandonment rates brought about by the rapid development of the above-mentioned new energy sources. However, in power grid systems with a very high proportion of new energy sources, due to the lack of reasonable configuration and complementary coordinated control of energy storage systems, the peak-shaving capacity of energy storage systems is weak, the wind and solar power abandonment rates are still high, and the cost of energy storage systems participating in power grid dispatching is high. Summary of the invention

[0004] In order to overcome the problems that in the power grid system with a very high proportion of renewable energy, the peak-shaving capacity of the energy storage system is weak, the wind and solar power abandonment rates are still high, and the cost of the energy storage system participating in power grid dispatch is high, the present invention provides a method and system for optimizing the configuration and dispatching of the energy storage system in the power grid with a very high proportion of renewable energy.

[0005] On the one hand, the present invention provides a method for optimizing configuration and scheduling of a power grid energy storage system with a high proportion of new energy, comprising:

[0006] Considering the investment cost and life cycle of the energy storage system, a cost model of the energy storage system associated with the capacity optimization of the energy storage system is constructed;

[0007] Taking maximizing the comprehensive benefits of the power grid as the goal of the outer configuration model and minimizing the operating costs of the thermal power units as the goal of the inner operation model, a two-layer optimization configuration model of the energy storage system is constructed based on the cost model of the energy storage system;

[0008] The two-layer optimization configuration model is solved based on the obtained predicted power generation power and load power of each new energy station in the power grid in the future period, and the optimized configuration information of the energy storage system in the future period and the respective operation scheduling plans of the energy storage system, thermal power units and new energy stations under the optimized configuration information are obtained.

[0009] Optionally, the energy storage system is a battery energy storage system, and the cost model of the energy storage system associated with the capacity optimization of the energy storage system is constructed by considering the investment cost and life cycle of the energy storage system, including:

[0010] Determining an equivalent life cycle of the battery energy storage system based on the charge and discharge depth of the battery energy storage system;

[0011] Determine the unit capacity cost and unit power cost based on the equivalent life cycle, investment cost and operation and maintenance cost of the battery energy storage system;

[0012] A cost model of the battery energy storage system is constructed based on the unit capacity cost and the unit power cost.

[0013] Optionally, the cost model of the battery energy storage system is expressed as:

[0014]

[0015] In the formula, C INV is the cost of the battery energy storage system, including investment and operation and maintenance costs, P rate,es,i and S rate,es,i are the rated charge and discharge power and rated charge and discharge capacity of the es-th battery energy storage system at node i, α ESS and β ESS are the annual unit power and capacity costs of each battery energy storage system, N ESS is the number of battery energy storage systems, c ESS and c OM are the unit investment and operation and maintenance costs of each battery energy storage system, T life is the equivalent life cycle of the battery energy storage system, μ is the cost ratio;

[0016] The expression of the equivalent life cycle of the battery energy storage system is:

[0017]

[0018] Where N M is the number of charge and discharge times, D cm is the cycle life of the mth charge and discharge process, DOD m is the charge and discharge depth corresponding to the mth charge and discharge process.

[0019] Optionally, the outer configuration model is targeted at maximizing the comprehensive benefits of the power grid, the inner operation model is targeted at minimizing the operating costs of thermal power units, and a two-layer optimization configuration model of the energy storage system is constructed based on the cost model of the energy storage system, including:

[0020] Determine the objective function of the outer configuration model based on the net income of the energy storage system, the income from reducing wind and solar power abandonment, the operating income of the thermal power units and the income from reducing active power losses in the power grid; the net income of the energy storage system is determined based on the cost model of the energy storage system;

[0021] Taking power grid flow constraint and energy storage system capacity constraint as constraint conditions of the outer configuration model;

[0022] Minimizing the fuel cost and the start-up and shutdown cost of the thermal power unit is used as the objective function of the inner layer operation model;

[0023] The constraints of the inner layer operation model are power balance constraints of the power grid, output constraints of wind and solar power units, constraints of thermal power units and constraints of energy storage systems;

[0024] The two-layer optimization configuration model is constructed based on the objective function and constraint conditions of the outer configuration model and the objective function and constraint conditions of the inner operation model.

[0025] Optionally, the objective function of the outer configuration model is expressed as:

[0026] max C PS =C ESS +C REG +C TU +C N

[0027] Among them, C PS represents the comprehensive benefit of the power grid, C ESS represents the net benefit of the energy storage system, C REG represents the benefits of reducing wind and solar power abandonment, C TU represents the operating income of thermal power units, C N Represents the benefits of reducing active power losses in the power grid;

[0028] The net benefit of the energy storage system is expressed as:

[0029]

[0030] In the formula, σ TOU and σ pa are the time-of-use electricity price and the compensation price for peak load regulation service provided by the energy storage system, N WT is the number of wind farms, N PV is the number of photovoltaic stations, N ESS is the number of battery energy storage systems, P es,t is the charging or discharging power of the es-th energy storage system at time t, ΔP wc,t is the reduction in abandoned power of the wcth wind farm after the energy storage system is configured at time t, ΔP pv,t is the reduction in abandoned power of the pvth photovoltaic station after the energy storage system is configured at time t, Δt is the optimization step length, and T is the optimization period;

[0031] The expression for the benefit of reducing wind and solar power abandonment is:

[0032]

[0033] In the formula, σ REG the price of electricity generated by renewable energy;

[0034] The expression of the operating income of the thermal power unit is:

[0035] C TU =ΔC F +ΔC ON

[0036]

[0037] Where, ΔC F and ΔC ON are the reduction in fuel and start-up and shutdown costs of thermal power units after configuring the energy storage system, C F and C ON are the fuel cost and start-up and shutdown cost of thermal power units, N F is the number of thermal power units, s f,t is the start / stop status of the fth thermal power unit at time t, s f,t-1 is the start / stop status of the fth thermal power unit at time t-1, C f,t is the start-up and shutdown cost of the fth thermal power unit at time t, a f 、b f and c f are the fuel cost coefficients of thermal power units, P f,t is the output of the fth thermal power unit at time t;

[0038] The expression of the benefit of reducing the active power loss of the power grid is:

[0039]

[0040] In the formula, ΔP loss,t is the reduction in active network loss of the power grid after the energy storage system is configured at time t;

[0041] The expression of the power grid flow constraint is:

[0042]

[0043] Where i and j are node indices respectively, P fi and Q fi are the active power and reactive power of the thermal power unit at node i, respectively, P wi and Q wi are the active power and reactive power of the wind farm at node i, respectively, P pvi and Q pvi are the active power and reactive power of the photovoltaic station at node i, P li and Qli are the active power and reactive power of the load at node i, respectively, P esi is the charging and discharging power of the energy storage system at node i, P wci and P pvci are the abandoned wind power and abandoned optical power at node i, G ij and B ij are the conductance and susceptance between nodes i and j, respectively, δ ij is the phase angle between nodes i and j, U i and U j are the voltages of node i and node j respectively, and N is the number of nodes;

[0044] The expression of the energy storage system capacity constraint is:

[0045]

[0046] In the formula, S es,t is the charge and discharge capacity of the energy storage system at time t, P es,max and S es,max They are the maximum charging and discharging power and maximum charging and discharging capacity of the energy storage system respectively.

[0047] Optionally, the energy storage system constraints include a charge and discharge state constraint of the energy storage system, a power constraint of the energy storage system, a capacity constraint of the energy storage system, and a charge state constraint of the energy storage system.

[0048] Optionally, the objective function expression of the inner layer operation model is:

[0049] minC G =C F +C ON ;

[0050] In the formula, C G is the total operating cost of the thermal power unit, C F and C ON They are the fuel cost and start-up and shutdown cost of thermal power units;

[0051] The expression of the power grid power balance constraint is:

[0052]

[0053] Where P f,t is the output of the fth thermal power unit at time t, P w,t and P wc,t are the output power and abandoned wind power of the w-th wind farm at time t, respectively, pv,t and P pvc,t are the output power and abandoned power of the pvth photovoltaic station at time t, P es,tis the charging and discharging power of the es-th energy storage system at time t, ∑P l,t is the total load power of the power grid at time t; N F is the number of thermal power units, N WT is the number of wind farms, N PV is the number of photovoltaic stations, N ESS is the number of battery energy storage systems;

[0054] The expression of the charge and discharge state constraint of the energy storage system is:

[0055] s c,t +s dis,t ≤1;

[0056] In the formula, s c,t represents the charging state of the energy storage system at time t, s dis,t represents the discharge state of the energy storage system at time t. When the energy storage system is in the charging state at time t, s c,t =1,s dis,t =0; when the energy storage system is in the discharge state at time t, s c,t =0,s dis,t =1;

[0057] The power constraint expression of the energy storage system is:

[0058] -P es,rate ≤P es,t ≤P es,rate ;

[0059] Where P es,t is the charging and discharging power of the es-th energy storage system at time t, P es,rate is the rated charge and discharge power of the esth energy storage system;

[0060] The expression of the capacity constraint of the energy storage system is:

[0061]

[0062] In the formula, S es,t is the charge and discharge capacity of the es-th energy storage system at time t, S es,rate is the rated charge and discharge capacity of the es-th energy storage system, S es,t-1 is the charge and discharge capacity of the es-th energy storage system at time t-1, and Δt is the optimization step length;

[0063] The expression of the state of charge constraint of the energy storage system is:

[0064] S es,t＝1 =S es,t＝T ;

[0065] In the formula, S es,t＝1represents the charge and discharge capacity of the esth energy storage system at the beginning of the current optimization cycle, S es,t＝T It represents the charge and discharge capacity of the esth energy storage system at the end of the current optimization cycle, and T is the optimization cycle.

[0066] Optionally, the two-layer optimization configuration model is solved based on the obtained generation power forecast value and load power forecast value of each new energy station in the power grid in the future period, and the optimization configuration information of the energy storage system in the future period and the operation scheduling plan of the energy storage system, thermal power unit and new energy station under the optimization configuration information are obtained, including:

[0067] The improved beetle whisker search algorithm is used to solve the double-layer optimization configuration model, with the location and capacity of the energy storage system as the beetle's position, the comprehensive benefits of the power grid as the fitness function, and the outer configuration model is optimized based on the predicted power generation value and load power prediction value of each new energy station in the future period, to obtain the location and capacity of the energy storage system optimized in the current round;

[0068] The location and capacity of the energy storage system optimized in the current round are transferred to the inner operation model, and the solver is called to solve the operation scheduling plans of the energy storage system, thermal power units and new energy stations in the current round;

[0069] Transmitting the respective operation scheduling plans of the current round of energy storage systems, thermal power units and new energy stations to the outer configuration model for the next round of iterative calculations;

[0070] In each round of optimization process, the beetle position is updated based on chaotic perturbations.

[0071] Optionally, the optimized configuration information includes an optimized position and an optimized capacity of the energy storage system, and the operation scheduling plan of the new energy station includes optimized abandoned wind and solar power. After obtaining the optimized configuration information of the energy storage system in the future period and the respective operation scheduling plans of the energy storage system, the thermal power unit and the new energy station under the optimized configuration information, the method further includes:

[0072] Configure the energy storage system for the future period based on the optimized position and optimized capacity of the energy storage system;

[0073] Performing charging and discharging scheduling of the energy storage system configured in the corresponding future time period based on the operation scheduling plan of the energy storage system;

[0074] Based on the operation scheduling plan of the thermal power unit, the start / stop state and output of each thermal power unit in the corresponding future period are controlled;

[0075] Based on the optimized abandoned wind and solar power, operation scheduling of wind farms and photovoltaic farms in corresponding future time periods is performed.

[0076] On the other hand, the present invention also provides a system for optimizing configuration and dispatching of a power grid energy storage system with a high proportion of new energy, comprising:

[0077] A cost model building module, used to build a cost model of the energy storage system associated with capacity optimization of the energy storage system by considering the investment cost and life cycle of the energy storage system;

[0078] An optimization model construction module is used to construct a double-layer optimization configuration model of the energy storage system based on the cost model of the energy storage system, taking maximizing the comprehensive benefits of the power grid as the goal of the outer configuration model and minimizing the operating cost of the thermal power unit as the goal of the inner operation model;

[0079] The optimization solution module is used to solve the double-layer optimization configuration model based on the obtained power generation power forecast value and load power forecast value of each new energy station in the power grid in the future period, and obtain the optimization configuration information of the energy storage system in the future period and the respective operation scheduling plans of the energy storage system, thermal power units and new energy stations under the optimization configuration information.

[0080] On the other hand, the present invention also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0081] The memory is used to store one or more programs;

[0082] When the one or more programs are executed by the at least one processor, any one of the above-mentioned methods for optimizing configuration and scheduling of a power grid energy storage system with a very high proportion of new energy is implemented.

[0083] On the other hand, the present invention also provides a readable storage medium having an execution program stored thereon, which, when executed, implements any of the above-mentioned methods for optimizing configuration and scheduling of a power grid energy storage system with a very high proportion of new energy.

[0084] Compared with the prior art, the present invention has the following beneficial effects:

[0085] The present invention provides a method and system for optimizing configuration and scheduling of an energy storage system in a power grid with a very high proportion of new energy. On the one hand, a cost model of the energy storage system associated with the capacity optimization of the energy storage system is constructed by considering the investment cost and life cycle of the energy storage system; based on the cost model of the energy storage system, a double-layer optimization configuration model of the energy storage system is constructed, which can combine the life cycle and investment cost of the energy storage system with its capacity optimization, thereby improving the rationality and economy of the optimization result of the energy storage system; on the other hand, a double-layer optimization configuration model of the energy storage system is constructed with the maximization of the comprehensive benefits of the power grid as the goal of the outer configuration model and the minimization of the operating costs of thermal power units as the goal of the inner operating model, and the configuration of the energy storage system is associated with the benefits of the power grid, so as to maximize the benefits of the power grid from the configuration level of the energy storage system and improve the peak-shaving capacity and benefits of the energy storage system; the minimization of the operating costs of thermal power units is used to guide the reduction of the system's abandoned wind and solar power, and on the basis of ensuring the safe and economical operation of the power grid under the background of electricity marketization, the rapid two-way regulation capability of the energy storage system is used to improve the level of new energy consumption and the peak-shaving level of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 A schematic flow chart of a method for optimizing configuration and scheduling of a power grid energy storage system with a high proportion of new energy sources according to the present invention;

[0087] Figure 2 It is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0088] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings.

[0089] Embodiment 1:

[0090] The present invention provides a method for optimizing the configuration and scheduling of a power grid energy storage system with a high proportion of new energy, as shown in the schematic diagram Figure 1 As shown, including:

[0091] Step S110, constructing a cost model of the energy storage system associated with capacity optimization of the energy storage system by considering the investment cost and life cycle of the energy storage system;

[0092] Step S120, taking maximizing the comprehensive benefits of the power grid as the goal of the outer configuration model, taking minimizing the operating cost of the thermal power unit as the goal of the inner operation model, and based on the cost model of the energy storage system, constructing a double-layer optimization configuration model of the energy storage system;

[0093] Step S130, based on the obtained predicted power generation and load power values ​​of each new energy station in the power grid in the future time period, the two-layer optimization configuration model is solved to obtain the optimized configuration information of the energy storage system in the future time period and the respective operation scheduling plans of the energy storage system, thermal power units and new energy stations under the optimized configuration information.

[0094] In this example implementation, the present invention is directed to a "wind-solar-thermal-storage" power grid, wherein the energy storage system may be a battery energy storage system, the new energy may include wind power generation and photovoltaic power generation, and the new energy stations include wind power stations and photovoltaic stations. The investment cost of the energy storage system includes the capacity cost and power cost of the energy storage system. The life cycle of the energy storage system refers to the life of the energy storage system. A cost model associated with the capacity optimization of the energy storage system is constructed from the two aspects of life and cost to improve the economy and rationality of the optimization results based on this cost model. A multi-objective two-layer optimization configuration model is established. The goal of the outer configuration model is to maximize the comprehensive benefits of the power grid system, and the optimization variables are the location and capacity of the energy storage system; the goal of the inner operation model is to minimize the operating cost of the thermal power unit, and the optimization variables may include the operation scheduling plan of the energy storage system, the thermal power unit and the new energy unit. For example, it may include the charging and discharging scheduling strategy of the energy storage system, the start and stop status and output of the thermal power unit, and the abandoned wind and solar power. The configuration of the energy storage system is linked to the benefits of the power grid, the benefits of the power grid are maximized from the configuration level of the energy storage system, and the peak-shaving capacity and benefits of the energy storage system are improved. The operating costs of thermal power units are minimized to guide the reduction of the system's wind and solar power curtailment. On the basis of ensuring the safe and economical operation of the power grid in the context of electricity marketization, the level of new energy consumption and power grid peak-shaving is improved through the rapid two-way adjustment capabilities of the energy storage system.

[0095] In some embodiments, basic operation data of the power grid, basic data of the energy storage system, and predicted power generation values ​​and load power values ​​of each new energy station in the power grid in future time periods can also be obtained.

[0096] In this example implementation, the basic operation data of the power grid may include basic power grid data, grid-connected new energy station data and thermal power unit operation data. The basic power grid data may include node admittance and phase angle, etc.; the grid-connected new energy station data may include the electricity price, location and power forecast value of each wind and solar station, and the upper and lower limits of the power generation of each wind and solar station; the thermal power unit operation data may include the upper and lower limits of the output power of each thermal power unit, climbing ability, upper and lower limits of rotating standby, minimum start and stop time, etc. The basic data of the energy storage system may include equivalent service life, unit investment cost, unit operation and maintenance cost, upper and lower limits of power and capacity, etc. The power generation power forecast value and load power forecast value of each new energy station in the power grid in the future period can be obtained by predicting the historical power data and historical load data of each new energy station through a neural network model. The future period can be a period of time after the current moment, such as the next day.

[0097] In an example implementation, the energy storage system is a battery energy storage system, and the step S110 of considering the investment cost and life cycle of the energy storage system to construct a cost model of the energy storage system associated with the capacity optimization of the energy storage system includes:

[0098] Determining an equivalent life cycle of the battery energy storage system based on the charge and discharge depth of the battery energy storage system;

[0099] Determine the unit capacity cost and unit power cost based on the equivalent life cycle, investment cost and operation and maintenance cost of the battery energy storage system;

[0100] A cost model of the battery energy storage system is constructed based on the unit capacity cost and the unit power cost.

[0101] In this example implementation, the two key parameters for capacity optimization of the battery energy storage system are investment cost and life cycle. The life cycle of the battery energy storage system is determined by the depth of the charging and discharging process. The investment cost of the battery energy storage system includes the capacity cost and power cost of the energy storage system. The mathematical description of the cost model of the battery energy storage system is:

[0102]

[0103] In the formula, C INV is the cost of the battery energy storage system, including investment and operation and maintenance costs, P rate,es,i and S rate,es,i are the rated charge and discharge power and rated charge and discharge capacity of the es-th battery energy storage system at node i, α ESS and β ESS are the annual unit power and capacity costs of each battery energy storage system, N ESS is the number of battery energy storage systems, c ESS and cOM are the unit investment and operation and maintenance costs of each battery energy storage system, T life is the equivalent life cycle of the battery energy storage system, and μ is the cost ratio.

[0104] The length of the battery energy storage system's life cycle will directly affect the annual discounted investment cost of the battery energy storage system. The battery energy storage system's life cycle is determined by the depth of the charging and discharging process. Therefore, the equivalent life cycle of the battery energy storage system can be described as follows:

[0105]

[0106] Where N M is the number of charge and discharge times, D cm is the cycle life of the mth charge and discharge process (times), DOD m is the charge and discharge depth corresponding to the mth charge and discharge process.

[0107] It can be seen from the above formula that by incorporating the impact of the battery energy storage system's charging and discharging strategy on its life cycle into the capacity optimization model and then solving the operation guidance plan, a more reasonable battery energy storage system optimization configuration plan that is consistent with the actual situation of the battery energy storage system can be obtained.

[0108] In an exemplary implementation, in step S120, the objective of the outer configuration model is to maximize the comprehensive benefits of the power grid, and the objective of the inner operation model is to minimize the operating costs of the thermal power units. Based on the cost model of the energy storage system, a two-layer optimization configuration model of the energy storage system is constructed, including:

[0109] Determine the objective function of the outer configuration model based on the net income of the energy storage system, the income from reducing wind and solar power abandonment, the operating income of the thermal power units and the income from reducing active power losses in the power grid; the net income of the energy storage system is determined based on the cost model of the energy storage system;

[0110] Taking power grid flow constraint and energy storage system capacity constraint as constraint conditions of the outer configuration model;

[0111] Minimizing the fuel cost and the start-up and shutdown cost of the thermal power unit is used as the objective function of the inner layer operation model;

[0112] The constraints of the inner layer operation model are power balance constraints of the power grid, output constraints of wind and solar power units, constraints of thermal power units and constraints of energy storage systems;

[0113] The two-layer optimization configuration model is constructed based on the objective function and constraint conditions of the outer configuration model and the objective function and constraint conditions of the inner operation model.

[0114] In this example implementation, the comprehensive benefit of the power grid can take into account the benefit of the energy storage system, the benefit of reducing the abandoned wind and solar power, the operating benefit of the thermal power units, and the benefit of reducing the active power network losses of the power grid. For example, the sum of the net benefit of the energy storage system, the benefit of reducing the abandoned wind and solar power, the operating benefit of the thermal power units, and the benefit of reducing the active power network losses of the power grid is taken as the comprehensive benefit of the power grid, and maximizing the comprehensive benefit of the power grid is taken as the objective function of the outer configuration model; the constraints of the outer configuration model may include conventional flow constraints, node voltage constraints, active / reactive power constraints, and the capacity of the energy storage system, and the outer optimization variables are the location and capacity of the energy storage system, so that the outer configuration model maximizes the comprehensive benefits of the power grid by configuring the location and capacity of the energy storage system. The objective function of the inner operation model is to minimize the operating cost of the thermal power unit. The operating cost of the thermal power unit includes fuel cost and start-stop cost. The constraints of the inner operation model may include power balance constraints, power generation output constraints, ramp constraints, spare capacity, and start-stop status of power generation equipment. The inner optimization variables may include the operation scheduling plans of the energy storage system, thermal power units, and new energy stations. For example, the inner optimization variables are the charging and discharging scheduling strategy of the energy storage system, the start-stop status and output of the thermal power unit, and the abandoned wind and solar power.

[0115] Exemplarily, the objective function of the outer configuration model is expressed as:

[0116] max C PS =C ESS +C REG +C TU +C N

[0117] Among them, C PS represents the comprehensive benefit of the power grid, C ESS represents the net benefit of the energy storage system, C REG represents the benefits of reducing wind and solar power abandonment, C TU represents the operating income of thermal power units, C N Represents the benefits of reducing active power losses in the power grid.

[0118] The net benefit of the energy storage system is expressed as:

[0119]

[0120] In the formula, σ TOU and σ pa are the time-of-use electricity price and the compensation price for peak load regulation service provided by the energy storage system, N WT is the number of wind farms, N PV is the number of photovoltaic stations, N ESS is the number of battery energy storage systems, P es,tis the charging or discharging power of the es-th energy storage system at time t, ΔP wc,t is the reduction in abandoned power of the wcth wind farm after the energy storage system is configured at time t, ΔP pv,t is the reduction in abandoned power of the pvth photovoltaic station after the energy storage system is configured at time t, Δt is the optimization step size, which can be 1 hour; T is the optimization period.

[0121] The expression for the benefit of reducing wind and solar power abandonment is:

[0122]

[0123] In the formula, σ REG the price of electricity generated by renewable energy;

[0124] The expression of the operating income of the thermal power unit is:

[0125] C TU =ΔC F +ΔC ON

[0126]

[0127] Where, ΔC F and ΔC ON are the reduction in fuel and start-up and shutdown costs of thermal power units after configuring the energy storage system, C F and C ON are the fuel cost and start-up and shutdown cost of thermal power units, N F is the number of thermal power units, s f,t is the start / stop state of the fth thermal power unit at time t. When the fth thermal power unit is in the start state at time t, s f,t =1, when the fth thermal power unit is in shutdown state at time t, s f,t =0;s f,t-1 is the start / stop status of the fth thermal power unit at time t-1; C f,t is the start-up and shutdown cost of the fth thermal power unit at time t, a f , b f and c f are the fuel cost coefficients of thermal power units, P f,t is the output of the f-th thermal power unit at time t.

[0128] The expression of the benefit of reducing the active power loss of the power grid is:

[0129]

[0130] In the formula, ΔP loss,t It is the reduction of active network loss of the power grid after the energy storage system is configured at time t.

[0131] The constraints of the outer configuration model include power grid flow constraints and energy storage system capacity constraints. Specifically, the expression of the power grid flow constraint is:

[0132]

[0133] Where i and j are node indices respectively, P fi and Q fi are the active power and reactive power of the thermal power unit at node i, respectively, P wi and Q wi are the active power and reactive power of the wind farm at node i, respectively, P pvi and Q pvi are the active power and reactive power of the photovoltaic station at node i, P li and Q li are the active power and reactive power of the load at node i, respectively, P esi is the charging and discharging power of the energy storage system at node i, P wci and P pvci are the abandoned wind power and abandoned optical power at node i, G ij and B ij are the conductance and susceptance between nodes i and j, respectively, δ ij is the phase angle between nodes i and j, U i and U j are the voltages of node i and node j respectively, and N is the number of nodes;

[0134] The expression of the energy storage system capacity constraint is:

[0135]

[0136] In the formula, S es,t is the charge and discharge capacity of the energy storage system at time t, P es,max and S es,max They are the maximum charging and discharging power and maximum charging and discharging capacity of the energy storage system respectively.

[0137] For example, for the "wind-solar-thermal-storage" power grid, the operating cost of renewable energy power generation is not considered, so the goal of the inner operation model is to minimize the total operating cost of traditional thermal power generating units. The objective function expression of the inner operation model is:

[0138] minC G =C F +C ON ;

[0139] In the formula, C G is the total operating cost of the thermal power unit, C F and C ONThey are the fuel cost and start-up and shutdown cost of thermal power units;

[0140] The constraints of the inner operation model include power grid power balance constraints, wind and solar power unit output constraints, thermal power unit constraints, and energy storage system constraints. Specifically, the expression of the power grid power balance constraint is:

[0141]

[0142] Where P f,t is the output of the fth thermal power unit at time t, P w,t and P wc,t are the output power and abandoned wind power of the w-th wind farm at time t, respectively, pv,t and P pvc,t are the output power and abandoned power of the pvth photovoltaic station at time t, P es,t is the charging and discharging power of the es-th energy storage system at time t, ∑P l,t is the total load power of the power grid at time t; N F is the number of thermal power units, N WT is the number of wind farms, N PV is the number of photovoltaic stations, N ESS is the number of battery energy storage systems.

[0143] The expression of wind and solar power unit output constraint is:

[0144]

[0145] Where P w,min and P w,max are the upper and lower limits of the output power of the wth wind turbine group, P pv,min and P pv,max They are respectively the upper and lower limits of the output power of the pvth photovoltaic unit.

[0146] The constraints of thermal power units can include output power constraints, ramp constraints, reserve capacity constraints and start-stop constraints, which are specifically expressed as:

[0147]

[0148] Where P f,min and P f,max are the upper and lower limits of the output active power of the f-th thermal power unit, Q f,t is the output reactive power of the fth thermal power unit at time t, Q f,min and Q f,max are the upper and lower limits of the output reactive power of the f-th thermal power unit; P f,t-1 is the output of the fth thermal power unit at time t-1, P f,r is the upper limit of the climbing capacity of the f-th thermal power unit, Pru and P rd The upper and lower limits of the rotating reserve power of the thermal power unit can be 5% of the total load, or 20% of the sum of wind power generation and photovoltaic power generation. and are the cumulative start-up and shutdown time of the fth thermal power unit at time t-1, and are the minimum start-up and shutdown time of the f-th thermal power unit respectively.

[0149] The energy storage system constraints include the charge and discharge state constraints of the energy storage system, the power constraints of the energy storage system, the capacity constraints of the energy storage system and the charge state constraints of the energy storage system.

[0150] The expression of the charge and discharge state constraint of the energy storage system is:

[0151] s c,t +s dis,t ≤1;

[0152] In the formula, s c,t represents the charging state of the energy storage system at time t, s dis,t represents the discharge state of the energy storage system at time t. When the energy storage system is in the charging state at time t, s c,t =1,s dis,t =0; when the energy storage system is in the discharge state at time t, s c,t =0,s dis,t =1, ensuring that the energy storage system cannot be charged and discharged at the same time.

[0153] The power constraint expression of the energy storage system is:

[0154] -P es,rate ≤P es,t ≤P es,rate ;

[0155] Where P es,t is the charging and discharging power of the es-th energy storage system at time t, P es,rate is the rated charging and discharging power of the esth energy storage system.

[0156] The expression of the capacity constraint of the energy storage system is:

[0157]

[0158] In the formula, S es,t is the charge and discharge capacity of the es-th energy storage system at time t, S es,rate is the rated charge and discharge capacity of the es-th energy storage system, S es,t-1 is the charge and discharge capacity of the es-th energy storage system at time t-1, and Δt is the optimization step length;

[0159] The expression of the state of charge constraint of the energy storage system is:

[0160] S es,t＝1 =S es,t＝T ;

[0161] In the formula, S es,t＝1 represents the charge and discharge capacity of the esth energy storage system at the beginning of the current optimization cycle, S es,t＝T It represents the charge and discharge capacity of the esth energy storage system at the end of the current optimization cycle, and T is the optimization cycle.

[0162] In the above embodiments, the outer configuration model optimizes the location and capacity of energy storage based on revenue, and the inner operation model guides the operation and dispatch of storage, fire, wind, and electricity based on the outer energy storage configuration and the composition of thermal power generation, so as to tap the economic benefits from the energy storage side and improve the peak-shaving capacity of energy storage in the power grid with a high proportion of new energy. At the same time, the charge and discharge state constraints, power constraints, capacity constraints, and charge state constraints of energy storage are considered to make the optimization results more in line with the actual situation and improve the dispatching accuracy of each unit operation.

[0163] In some example implementations, the step S130 of solving the two-layer optimization configuration model based on the obtained generation power forecast value and load power forecast value of each new energy station in the power grid in the future period, obtaining the optimization configuration information of the energy storage system in the future period and the operation scheduling plans of the energy storage system, thermal power unit and new energy station under the optimization configuration information, includes:

[0164] The improved beetle whisker search algorithm is used to solve the double-layer optimization configuration model, with the location and capacity of the energy storage system as the beetle's position, the comprehensive benefits of the power grid as the fitness function, and the outer configuration model is optimized based on the predicted power generation value and load power prediction value of each new energy station in the future period, to obtain the location and capacity of the energy storage system optimized in the current round;

[0165] The location and capacity of the energy storage system optimized in the current round are transferred to the inner operation model, and the solver is called to solve the operation scheduling plans of the energy storage system, thermal power units and new energy stations in the current round;

[0166] Transmitting the respective operation scheduling plans of the current round of energy storage systems, thermal power units and new energy stations to the outer configuration model for the next round of iterative calculations;

[0167] In each round of optimization process, the beetle position is updated based on chaotic perturbations.

[0168] In this example implementation, the beetle antennae search algorithm (BAS) is a bionic intelligent optimization algorithm inspired by the beetle foraging principle, which is based on the mechanism of beetles using two antennae to sense the smell of food. When the left antenna receives a stronger smell than the right antenna, the beetle will choose to fly to the left, thereby effectively determining the location of the food. In the BAS algorithm, since the beetle population is difficult to maintain during the optimization process, and a single antennae search is prone to fall into a local optimal solution, in order to solve this problem, this example uses chaotic perturbation to improve the BAS method. The specific process of solving the two-layer optimization configuration model based on the improved beetle antennae search algorithm is: 1) Initialize the beetle population, that is, the decision variable, and the beetle position corresponds to the decision variables to be solved, that is, the location and capacity of the energy storage system. 2) Calculate the fitness value of the beetle (the fitness function is the objective function of the outer configuration model, and the fitness value is the objective function value of the outer configuration model), and determine the optimal position BEST with the largest current fitness value X And the corresponding objective function value f(BEST X ). 3) The P corresponding to the beetle individual rate,es,i and S rate,es,i Pass it to the inner running model. 4) The inner running model calls the CPLEX solver to solve the start and stop status and output of the thermal power unit, the abandoned wind and solar power, the charging and discharging power of the energy storage, etc.; and pass the solution results to the outer configuration model. 5) The outer configuration model calculates the fitness value according to the objective function of the outer configuration model, and then updates the position of the beetle according to the search direction and step size. 6) Determine the target beetle that needs to be logistic chaotic optimization through similarity calculation, and perform chaotic perturbation on the target beetle. Update the beetle position by comparing the fitness value corresponding to the beetle position after chaotic perturbation to determine whether to update the target beetle position. 7) Determine whether the maximum number of iterations has been reached. If so, output the final beetle position and fitness value, otherwise go to step 3) to continue optimization. During the initialization of the beetle population, the beetle determines the direction of food by generating random vectors. The random direction is generated as follows:

[0169]

[0170] In the formula, is the beetle's foraging direction, rands(n,1) is a random function, and n represents the spatial dimension, that is, the number of independent variables in the optimization problem. Then, according to the direction vector, the left and right antennae of the beetle are defined as:

[0171]

[0172] In the formula, x lt and x rtThey represent the positions of the search areas on the left and right sides of the beetle at time t, respectively, t represents the position of the beetle at time t, and d0 is the distance between the left and right antennae of the beetle.

[0173] The fitness functions of the left and right antennae are defined as f(x lt ) and f(x rt ), the fitness function is the objective function of the outer configuration model, and then the position of the beetle at the next moment can be calculated based on these two functions. The specific calculation method is as follows:

[0174]

[0175] In the formula, x t+1 is the position of the beetle at time t+1, δ t represents the search step size at time t; sign(·) is a sign function. To search for the best solution, the search step size decreases as the number of iterations increases, as shown below:

[0176] δ t =λ·δ t-1 ;

[0177] Where λ is the attenuation coefficient of step size δ, δ t-1 Indicates the search step length at time t-1. In actual calculation, the length d0 between the left and right antennae changes with the step length, as shown in the following formula:

[0178] d0=δ t / c;

[0179] In the formula, c is a constant, generally ranging from 2 to 10.

[0180] For example, in the above chaotic disturbance process, the similarity is calculated by a similarity function, and then the target beetle that needs to be chaotically disturbed is found. The similarity formula of each beetle is first calculated as follows:

[0181]

[0182] In the formula, c i1 is the similarity of beetle i1, d(i1,j1) is the Euclidean distance from beetle i1 to j1, if i1 is the beetle with the best fitness, d min is the Euclidean distance of the beetle closest to i1, d max is the Euclidean distance of the beetle farthest from i1.

[0183] The population similarity is described as follows:

[0184]

[0185] In the formula, C is the population similarity, Np is the total number of beetle individuals in the population. The target beetles are selected based on the population similarity. For example, when the population similarity is greater than the similarity threshold, some beetles are randomly selected as target beetles.

[0186] Then, the chaotic sequence is generated by logistic mapping, and the mapping relationship is described as follows:

[0187]

[0188] Where, X k is the k-dimensional variable of the chaotic sequence X, that is, X k ∈[0,1], μ is the perturbation coefficient, μ∈(3.569,4).

[0189] Finally, whether to update the beetle position is determined by comparing the objective function value of the optimal beetle position in the objective function value domain obtained after chaotic disturbance.

[0190]

[0191] In the formula, BEST X is the current optimal beetle position, f(X t ) is the variable X at time t t The corresponding objective function value, f(X t-1 ) is the variable X at time t-1 t-1 The corresponding objective function value.

[0192] In some embodiments, after obtaining the optimized configuration information of the energy storage system in the future period and the operation scheduling plans of the energy storage system, the thermal power unit and the new energy station under the optimized configuration information, the method further includes:

[0193] Configure the energy storage system for the future period based on the optimized position and optimized capacity of the energy storage system;

[0194] Performing charging and discharging scheduling of the energy storage system configured in the corresponding future time period based on the operation scheduling plan of the energy storage system;

[0195] Based on the operation scheduling plan of the thermal power unit, the start / stop state and output of each thermal power unit in the corresponding future period are controlled;

[0196] Based on the optimized abandoned wind and solar power, operation scheduling of wind farms and photovoltaic farms in corresponding future time periods is performed.

[0197] In this example implementation, the optimized configuration information includes the optimized position and optimized capacity of the energy storage system, and the operation scheduling plan of the new energy station includes the optimized wind and solar power abandonment. The energy storage system is configured by optimizing the optimized position (such as node number) and optimized capacity of the energy storage system. The operation scheduling plan of the energy storage system and the thermal power unit obtained by optimizing the solution is used to control the charging and discharging strategy of the energy storage system and the start and stop status and output of each thermal power unit and the output of the wind farm and photovoltaic station in the future period (such as the next day), and the output of the wind farm and photovoltaic station is controlled based on the optimized wind and solar power abandonment.

[0198] The present invention is aimed at the "wind-solar-thermal-storage" power grid. On the basis of ensuring the safe and economical operation of the power grid under the power market environment, it proposes an optimization configuration strategy for the energy storage system of the sending-end power grid with a super-high proportion of new energy. The energy storage system can be further used to alleviate the peak-shaving pressure of the power grid and reduce the phenomenon of wind and solar abandonment. According to the cost model and the two-layer optimization configuration model of the battery energy storage system, a two-layer site selection and capacity optimization model for the battery energy storage system with coordinated configuration and control on the grid side is established, including an inner layer operation model and an outer layer configuration model. By establishing a mathematical cost model of the energy storage system that takes into account the investment cost and life cycle, the outer layer configuration model determines the location and capacity of the energy storage system with the goal of maximizing the comprehensive benefits of all parts of the power grid, and the inner layer operation model determines the charging and discharging power plan of the energy storage system with the goal of minimizing the operating cost of the thermal power units, which further improves the new energy absorption capacity, while realizing the optimization of the charging and discharging of the energy storage system and the peak-shaving of the power grid.

[0199] The present invention improves the absorption capacity of new energy power generation and reduces carbon emissions by rationally configuring the energy storage system in the sending-end power grid with a very high proportion of new energy. Through the complementary and coordinated control of the configuration of the energy storage system and the operation optimization of new energy and thermal power units, the cost-effectiveness and distributed configuration benefits of the energy storage system are further explored from the perspective of the sending-end power grid, and at the same time, the needs of power market transactions are met, the energy storage system is promoted to actively participate in power grid dispatching, and a better control strategy is provided for the energy storage system.

[0200] Embodiment 2:

[0201] Based on the same inventive concept, the present invention also provides an optimal configuration and dispatching system for a power grid energy storage system with a high proportion of new energy, the system comprising:

[0202] A cost model building module, used to build a cost model of the energy storage system associated with capacity optimization of the energy storage system by considering the investment cost and life cycle of the energy storage system;

[0203] An optimization model construction module is used to construct a double-layer optimization configuration model of the energy storage system based on the cost model of the energy storage system, taking maximizing the comprehensive benefits of the power grid as the goal of the outer configuration model and minimizing the operating cost of the thermal power unit as the goal of the inner operation model;

[0204] The optimization solution module is used to solve the double-layer optimization configuration model based on the obtained power generation power forecast value and load power forecast value of each new energy station in the power grid in the future period, and obtain the optimization configuration information of the energy storage system in the future period and the respective operation scheduling plans of the energy storage system, thermal power units and new energy stations under the optimization configuration information.

[0205] In a possible implementation, the energy storage system is a battery energy storage system, and the cost model building module includes:

[0206] A life cycle determination submodule, used to determine the equivalent life cycle of the battery energy storage system based on the charge and discharge depth of the battery energy storage system;

[0207] A unit cost determination submodule, used to determine the unit capacity cost and the unit power cost based on the equivalent life cycle, investment cost and operation and maintenance cost of the battery energy storage system;

[0208] The cost model construction submodule is used to construct a cost model of the battery energy storage system based on the unit capacity cost and the unit power cost.

[0209] In a possible implementation, the cost model of the battery energy storage system is expressed as:

[0210]

[0211] In the formula, C INV is the cost of the battery energy storage system, including investment and operation and maintenance costs, P rate,es,i and S rate,es,i are the rated charge and discharge power and rated charge and discharge capacity of the es-th battery energy storage system at node i, α ESS and β ESS are the annual unit power and capacity costs of each battery energy storage system, N ESS is the number of battery energy storage systems, c ESS and c OM are the unit investment and operation and maintenance costs of each battery energy storage system, T life is the equivalent life cycle of the battery energy storage system, μ is the cost ratio;

[0212] The expression of the equivalent life cycle of the battery energy storage system is:

[0213]

[0214] Where N M is the number of charge and discharge times, D cm is the cycle life of the mth charge and discharge process, DOD m is the charge and discharge depth corresponding to the mth charge and discharge process.

[0215] In a possible implementation, the optimization model building module includes:

[0216] The outer model construction submodule is used to determine the objective function of the outer configuration model based on the net income of the energy storage system, the income from reducing the abandoned wind and solar power, the operating income of the thermal power units and the income from reducing the active power loss of the power grid; the net income of the energy storage system is determined based on the cost model of the energy storage system; the power grid flow constraint and the energy storage system capacity constraint are the constraints of the outer configuration model;

[0217] The inner model construction submodule is used to minimize the fuel cost and start-stop cost of the thermal power unit as the objective function of the inner operation model; the power grid power balance constraint, wind and solar unit output constraint, thermal power unit constraint and energy storage system constraint are the constraints of the inner operation model;

[0218] The double-layer model construction submodule is used to construct the double-layer optimization configuration model based on the objective function and constraint conditions of the outer configuration model and the objective function and constraint conditions of the inner operation model.

[0219] In a possible implementation, the objective function of the outer configuration model is expressed as:

[0220] maxC PS =C ESS +C REG +C TU +C N

[0221] Among them, C PS represents the comprehensive benefit of the power grid, C ESS represents the net benefit of the energy storage system, C REG represents the benefits of reducing wind and solar power abandonment, C TU represents the operating income of thermal power units, C N Represents the benefits of reducing active power losses in the power grid;

[0222] The net benefit of the energy storage system is expressed as:

[0223]

[0224] In the formula, σ TOU and σ paare the time-of-use electricity price and the compensation price for peak load regulation service provided by the energy storage system, N WT is the number of wind farms, N PV is the number of photovoltaic stations, N ESS is the number of battery energy storage systems, P es,t is the charging or discharging power of the es-th energy storage system at time t, ΔP wc,t is the reduction in abandoned power of the wcth wind farm after the energy storage system is configured at time t, ΔP pv,t is the reduction in abandoned power of the pvth photovoltaic station after the energy storage system is configured at time t, Δt is the optimization step length, and T is the optimization period;

[0225] The expression for the benefit of reducing wind and solar power abandonment is:

[0226]

[0227] In the formula, σ REG the price of electricity generated by renewable energy;

[0228] The expression of the operating income of the thermal power unit is:

[0229] C TU =ΔC F +ΔC ON

[0230]

[0231] Where, ΔC F and ΔC ON are the reduction in fuel and start-up and shutdown costs of thermal power units after configuring the energy storage system, C F and C ON are the fuel cost and start-up and shutdown cost of thermal power units, N F is the number of thermal power units, s f,t is the start / stop status of the fth thermal power unit at time t, s f,t-1 is the start / stop status of the fth thermal power unit at time t-1, C f,t is the start-up and shutdown cost of the fth thermal power unit at time t, a f , b f and c f are the fuel cost coefficients of thermal power units, P f,t is the output of the fth thermal power unit at time t;

[0232] The expression of the benefit of reducing the active power loss of the power grid is:

[0233]

[0234] In the formula, ΔP loss,tis the reduction in active network loss of the power grid after the energy storage system is configured at time t;

[0235] The expression of the power grid flow constraint is:

[0236]

[0237] Where i and j are node indices respectively, P fi and Q fi are the active power and reactive power of the thermal power unit at node i, respectively, P wi and Q wi are the active power and reactive power of the wind farm at node i, respectively, P pvi and Q pvi are the active power and reactive power of the photovoltaic station at node i, P li and Q li are the active power and reactive power of the load at node i, respectively, P esi is the charging and discharging power of the energy storage system at node i, P wci and P pvci are the abandoned wind power and abandoned optical power at node i, G ij and B ij are the conductance and susceptance between nodes i and j, respectively, δ ij is the phase angle between nodes i and j, U i and U j are the voltages of node i and node j respectively, and N is the number of nodes;

[0238] The expression of the energy storage system capacity constraint is:

[0239]

[0240] In the formula, S es,t is the charge and discharge capacity of the energy storage system at time t, P es,max and S es,max They are the maximum charging and discharging power and maximum charging and discharging capacity of the energy storage system respectively.

[0241] In a possible implementation manner, the energy storage system constraints include a charge and discharge state constraint of the energy storage system, a power constraint of the energy storage system, a capacity constraint of the energy storage system, and a charge state constraint of the energy storage system.

[0242] In a possible implementation, the objective function expression of the inner layer operation model is:

[0243] minC G =C F +C ON ;

[0244] In the formula, CG is the total operating cost of the thermal power unit, C F and C ON They are the fuel cost and start-up and shutdown cost of thermal power units;

[0245] The expression of the power grid power balance constraint is:

[0246]

[0247] Where P f,t is the output of the fth thermal power unit at time t, P w,t and P wc,t are the output power and abandoned wind power of the w-th wind farm at time t, respectively, pv,t and P pvc,t are the output power and abandoned power of the pvth photovoltaic station at time t, P es,t is the charging and discharging power of the es-th energy storage system at time t, ∑P l,t is the total load power of the power grid at time t; N F is the number of thermal power units, N WT is the number of wind farms, N PV is the number of photovoltaic stations, N ESS is the number of battery energy storage systems;

[0248] The expression of the charge and discharge state constraint of the energy storage system is:

[0249] s c,t +s dis,t ≤1;

[0250] In the formula, s c,t represents the charging state of the energy storage system at time t, s dis,t represents the discharge state of the energy storage system at time t. When the energy storage system is in the charging state at time t, s c,t =1,s dis,t =0; when the energy storage system is in the discharge state at time t, s c,t =0,s dis,t =1;

[0251] The power constraint expression of the energy storage system is:

[0252] -P es,rate ≤P es,t ≤P es,rate ;

[0253] Where P es,t is the charging and discharging power of the es-th energy storage system at time t, P es,rate is the rated charge and discharge power of the es-th energy storage system;

[0254] The expression of the capacity constraint of the energy storage system is:

[0255]

[0256] In the formula, S es,t is the charge and discharge capacity of the es-th energy storage system at time t, S es,rate is the rated charge and discharge capacity of the es-th energy storage system, S es,t-1 is the charge and discharge capacity of the es-th energy storage system at time t-1, and Δt is the optimization step length;

[0257] The expression of the state of charge constraint of the energy storage system is:

[0258] S es,t＝1 =S es,t＝T ;

[0259] In the formula, S es,t＝1 represents the charge and discharge capacity of the esth energy storage system at the beginning of the current optimization cycle, S es,t＝T It represents the charge and discharge capacity of the esth energy storage system at the end of the current optimization cycle, and T is the optimization cycle.

[0260] In a possible implementation, the optimization solution module is specifically used to:

[0261] The improved beetle whisker search algorithm is used to solve the double-layer optimization configuration model, with the location and capacity of the energy storage system as the beetle's position, the comprehensive benefits of the power grid as the fitness function, and the outer configuration model is optimized based on the predicted power generation value and load power prediction value of each new energy station in the future period, to obtain the location and capacity of the energy storage system optimized in the current round;

[0262] The location and capacity of the energy storage system optimized in the current round are transferred to the inner operation model, and the solver is called to solve the operation scheduling plans of the energy storage system, thermal power units and new energy stations in the current round;

[0263] Transmitting the respective operation scheduling plans of the current round of energy storage systems, thermal power units and new energy stations to the outer configuration model for the next round of iterative calculations;

[0264] In each round of optimization process, the beetle position is updated based on chaotic perturbations.

[0265] In a possible implementation, the optimized configuration information includes an optimized position and an optimized capacity of the energy storage system, the operation scheduling plan of the new energy station includes an optimized abandoned wind and solar power, and the configuration scheduling system further includes: a control scheduling module, the control scheduling module is used to:

[0266] Configure the energy storage system for the future period based on the optimized position and optimized capacity of the energy storage system;

[0267] Performing charging and discharging scheduling of the energy storage system configured in the corresponding future time period based on the operation scheduling plan of the energy storage system;

[0268] Based on the operation scheduling plan of the thermal power unit, the start / stop state and output of each thermal power unit in the corresponding future period are controlled;

[0269] Based on the optimized abandoned wind and solar power, operation scheduling of wind farms and photovoltaic farms in corresponding future time periods is performed.

[0270] Embodiment 3:

[0271] like Figure 2 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0272] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the method for optimizing configuration and scheduling of a power grid energy storage system with a super-high proportion of new energy in the above embodiment.

[0273] Example 4

[0274] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It is understandable that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of the method for optimizing the configuration and scheduling of a power grid energy storage system with a super-high proportion of new energy in the above embodiment.

[0275] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0276] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0277] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0278] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0279] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims to be approved.

Claims

1. A method for optimizing configuration and scheduling of a power grid energy storage system with a high proportion of new energy, characterized in that: include: Considering the investment cost and life cycle of the energy storage system, a cost model of the energy storage system associated with the capacity optimization of the energy storage system is constructed; Taking maximizing the comprehensive benefits of the power grid as the goal of the outer configuration model and minimizing the operating costs of the thermal power units as the goal of the inner operation model, a two-layer optimization configuration model of the energy storage system is constructed based on the cost model of the energy storage system; The two-layer optimization configuration model is solved based on the obtained predicted power generation power and load power of each new energy station in the power grid in the future period, and the optimized configuration information of the energy storage system in the future period and the respective operation scheduling plans of the energy storage system, thermal power units and new energy stations under the optimized configuration information are obtained.

2. The method according to claim 1, characterized in that The energy storage system is a battery energy storage system, and the cost model of the energy storage system associated with the capacity optimization of the energy storage system is constructed by considering the investment cost and life cycle of the energy storage system, including: Determining an equivalent life cycle of the battery energy storage system based on the charge and discharge depth of the battery energy storage system; Determine the unit capacity cost and unit power cost based on the equivalent life cycle, investment cost and operation and maintenance cost of the battery energy storage system; A cost model of the battery energy storage system is constructed based on the unit capacity cost and the unit power cost.

3. The method according to claim 2, characterized in that The cost model of the battery energy storage system is expressed as: In the formula, C INV is the cost of the battery energy storage system, P rate,es,i and S rate,es,i are the rated charge and discharge power and rated charge and discharge capacity of the es-th battery energy storage system at node i, α ESS and β ESS are the annual unit power and capacity costs of each battery energy storage system, N ESS is the number of battery energy storage systems, c ESS and c OM are the unit investment and operation and maintenance costs of each battery energy storage system, T life is the equivalent life cycle of the battery energy storage system, μ is the cost ratio; The expression of the equivalent life cycle of the battery energy storage system is: Where N M is the number of charge and discharge times, D cm is the cycle life of the mth charge and discharge process, DOD m is the charge and discharge depth corresponding to the mth charge and discharge process.

4. The method according to claim 1 or 2, characterized in that: The outer configuration model is targeted at maximizing the comprehensive benefits of the power grid, and the inner operation model is targeted at minimizing the operating costs of thermal power units. Based on the cost model of the energy storage system, a two-layer optimization configuration model of the energy storage system is constructed, including: Determine the objective function of the outer configuration model based on the net income of the energy storage system, the income from reducing wind and solar power abandonment, the operating income of the thermal power units and the income from reducing active power losses in the power grid; the net income of the energy storage system is determined based on the cost model of the energy storage system; Taking power grid flow constraint and energy storage system capacity constraint as constraint conditions of the outer configuration model; Minimizing the fuel cost and the start-up and shutdown cost of the thermal power unit is used as the objective function of the inner layer operation model; The constraints of the inner layer operation model are power balance constraints of the power grid, output constraints of wind and solar power units, constraints of thermal power units and constraints of energy storage systems; The two-layer optimization configuration model is constructed based on the objective function and constraint conditions of the outer configuration model and the objective function and constraint conditions of the inner operation model.

5. The method according to claim 4, characterized in that The objective function of the outer configuration model is expressed as: maxC PS =C ESS +C REG +C TU +C N Among them, C PS represents the comprehensive benefit of the power grid, C ESS represents the net benefit of the energy storage system, C REG represents the benefits of reducing wind and solar power curtailment, C TU represents the operating income of thermal power units, C N Represents the benefits of reducing active power losses in the power grid; The net benefit of the energy storage system is expressed as: In the formula, σ TOU and σ pa are the time-of-use electricity price and the compensation price for peak load regulation service provided by the energy storage system, N WT is the number of wind farms, N PV is the number of photovoltaic stations, N ESS is the number of battery energy storage systems, P es,t is the charging or discharging power of the es-th energy storage system at time t, ΔP wc,t is the reduction in abandoned power of the wcth wind farm after the energy storage system is configured at time t, ΔP pv,t is the reduction in abandoned power of the pvth photovoltaic station after the energy storage system is configured at time t, Δt is the optimization step length, and T is the optimization period; C INV The cost of battery energy storage systems; The expression for the benefit of reducing wind and solar power abandonment is: In the formula, σ REG the price of electricity generated by renewable energy; The expression of the operating income of the thermal power unit is: C TU =ΔC F +ΔC ON Where, ΔC F and ΔC ON are the reduction in fuel and start-up and shutdown costs of thermal power units after configuring the energy storage system, C F and C ON are the fuel cost and start-up and shutdown cost of thermal power units, N F is the number of thermal power units, s f,t is the start / stop status of the fth thermal power unit at time t, s f,t-1 is the start / stop status of the fth thermal power unit at time t-1, C f,t is the start-up and shutdown cost of the fth thermal power unit at time t, a f 、b f and c f are the fuel cost coefficients of thermal power units, P f,t is the output of the fth thermal power unit at time t; The expression of the benefit of reducing the active power loss of the power grid is: Where ΔP loss,t is the reduction in active network loss of the power grid after the energy storage system is configured at time t; The expression of the power grid flow constraint is: Where i and j are node indices respectively, P fi and Q fi are the active power and reactive power of the thermal power unit at node i, respectively, P wi and Q wi are the active power and reactive power of the wind farm at node i, respectively, P pvi and Q pvi are the active power and reactive power of the photovoltaic station at node i, P li and Q li are the active power and reactive power of the load at node i, respectively, P esi is the charging and discharging power of the energy storage system at node i, P wci and P pvci are the abandoned wind power and abandoned optical power at node i, G ij and B ij are the conductance and susceptance between nodes i and j, respectively, δ ij is the phase angle between nodes i and j, U i and U j are the voltages of node i and node j respectively, and N is the number of nodes; The expression of the energy storage system capacity constraint is: In the formula, S es,t is the charge and discharge capacity of the energy storage system at time t, P es,max and S es,max They are the maximum charge and discharge power and maximum charge and discharge capacity of the energy storage system respectively.

6. The method according to claim 4, characterized in that The energy storage system constraints include the charge and discharge state constraints of the energy storage system, the power constraints of the energy storage system, the capacity constraints of the energy storage system and the charge state constraints of the energy storage system.

7. The method according to claim 6, characterized in that The objective function expression of the inner layer operation model is: my C G =C F +C ON ; In the formula, C G is the total operating cost of the thermal power unit, C F and C ON They are the fuel cost and start-up and shutdown cost of thermal power units; The expression of the power grid power balance constraint is: Where P f,t is the output of the fth thermal power unit at time t, P w,t and P wc,t are the output power and abandoned wind power of the w-th wind farm at time t, respectively, pv,t and P pvc,t are the output power and abandoned power of the pvth photovoltaic station at time t, P es,t is the charging and discharging power of the es-th energy storage system at time t, ∑P l,t is the total load power of the power grid at time t; N F is the number of thermal power units, N WT is the number of wind farms, N PV is the number of photovoltaic stations, N ESS is the number of battery energy storage systems; The expression of the charge and discharge state constraint of the energy storage system is: s c,t +s dis,t ≤1; In the formula, s c,t represents the charging state of the energy storage system at time t, s dis,t represents the discharge state of the energy storage system at time t. When the energy storage system is in the charging state at time t, s c,t =1,s dis,t =0; when the energy storage system is in the discharge state at time t, s c,t =0,s dis,t =1; The power constraint expression of the energy storage system is: -P es,rate ≤P es,t ≤P es,rate ; Where P es,t is the charging and discharging power of the es-th energy storage system at time t, P es,rate is the rated charge and discharge power of the es-th energy storage system; The expression of the capacity constraint of the energy storage system is: In the formula, S es,t is the charge and discharge capacity of the es-th energy storage system at time t, S es,rate is the rated charge and discharge capacity of the es-th energy storage system, S es,t-1 is the charge and discharge capacity of the es-th energy storage system at time t-1, and Δt is the optimization step length; The expression of the state of charge constraint of the energy storage system is: S es,t＝1 =S es,t＝T ; In the formula, S es,t＝1 represents the charge and discharge capacity of the esth energy storage system at the beginning of the current optimization cycle, S es,t＝T It represents the charge and discharge capacity of the esth energy storage system at the end of the current optimization cycle, and T is the optimization cycle.

8. The method according to claim 1, characterized in that The two-layer optimization configuration model is solved based on the obtained power generation power forecast value and load power forecast value of each new energy station in the power grid in the future period, and the optimization configuration information of the energy storage system in the future period and the operation scheduling plan of the energy storage system, thermal power unit and new energy station under the optimization configuration information are obtained, including: The improved beetle whisker search algorithm is used to solve the double-layer optimization configuration model, with the location and capacity of the energy storage system as the beetle's position, the comprehensive benefits of the power grid as the fitness function, and the outer configuration model is optimized based on the predicted power generation value and load power prediction value of each new energy station in the future period, to obtain the location and capacity of the energy storage system optimized in the current round; The location and capacity of the energy storage system optimized in the current round are transferred to the inner operation model, and the solver is called to solve the operation scheduling plans of the energy storage system, thermal power units and new energy stations in the current round; Transmitting the respective operation scheduling plans of the current round of energy storage systems, thermal power units and new energy stations to the outer configuration model for the next round of iterative calculations; In each round of optimization process, the beetle position is updated based on chaotic perturbations.

9. The method according to claim 1, characterized in that: The optimized configuration information includes the optimized position and optimized capacity of the energy storage system, and the operation scheduling plan of the new energy station includes the optimized abandoned wind and solar power. After obtaining the optimized configuration information of the energy storage system in the future period and the operation scheduling plans of the energy storage system, thermal power unit and new energy station under the optimized configuration information, the method further includes: Configure the energy storage system for the future period based on the optimized position and optimized capacity of the energy storage system; Performing charging and discharging scheduling of the energy storage system configured in the corresponding future time period based on the operation scheduling plan of the energy storage system; Based on the operation scheduling plan of the thermal power unit, the start / stop state and output of each thermal power unit in the corresponding future period are controlled; Based on the optimized abandoned wind and solar power, operation scheduling of wind farms and photovoltaic farms in corresponding future time periods is performed.

10. An optimized configuration and dispatching system for a power grid energy storage system with a high proportion of new energy, characterized in that: include: A cost model building module, used to build a cost model of the energy storage system associated with capacity optimization of the energy storage system by considering the investment cost and life cycle of the energy storage system; An optimization model construction module is used to construct a double-layer optimization configuration model of the energy storage system based on the cost model of the energy storage system, taking maximizing the comprehensive benefits of the power grid as the goal of the outer configuration model and minimizing the operating cost of the thermal power unit as the goal of the inner operation model; The optimization solution module is used to solve the double-layer optimization configuration model based on the obtained power generation power forecast value and load power forecast value of each new energy station in the power grid in the future period, and obtain the optimization configuration information of the energy storage system in the future period and the respective operation scheduling plans of the energy storage system, thermal power units and new energy stations under the optimization configuration information.

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