Energy storage optimization scheduling method

Abstract

The embodiment of the invention discloses an energy storage optimization scheduling method. The method comprises the following steps: acquiring peak regulation basic data; taking the minimum pumped storage stabilizing net load fluctuation and the maximum pumped storage calling benefit as targets to construct an upper-layer scheduling model; on the basis of the upper-layer scheduling model, constructing a lower-layer scheduling model by taking the minimum total operation cost of the system as a target; and based on the peak regulation basic data and an MATLAB algorithm, solving the upper layer scheduling model and the lower layer scheduling model to obtain an optimal scheduling scheme. According to the optimal scheduling method provided by the invention, the regulation capacity is increased, the thermal power depth peak regulation pressure is relieved, new energy consumption is promoted, and the system economy improvement effect is optimal.

Description

Technical Field

[0001] This invention relates to the field of power grid peak shaving technology, and in particular to an energy storage optimization scheduling method. Background Technology

[0002] The increase in grid-connected wind and solar power capacity has made peak shaving more difficult, and how to meet the growing peak shaving demand of the system has become an important issue that urgently needs to be addressed.

[0003] Energy storage, with its time-shifting energy characteristics, is one of the important means of system peak shaving. Pumped storage is currently the most widely used and technologically mature energy storage technology. Some scholars have proposed a two-level optimization scheduling method and compared the peak shaving effects and economics of three different pumped storage call order strategies. Other scholars have designed a time-varying control method in systems containing pumped storage power plants and verified that this method can improve the scheduling accuracy of thermal power units; some scholars have optimized the coordinated operation of wind-storage systems with the goal of minimizing system wind curtailment; still others have solved the multi-objective problem of wind-storage systems and provided the optimal scheduling scheme. Although pumped storage can effectively alleviate system peak shaving pressure, its capacity development remains limited due to constraints such as site selection and other natural resources. Battery energy storage, as an energy storage technology with the second largest installed capacity after pumped storage, has developed rapidly in recent years. Some scholars have established a tiered optimization peak-shaving model for battery energy storage, analyzing the improvement and economic impact of adding battery energy storage on system peak shaving. Others have established a multi-objective optimization scheduling model for wind-solar-storage systems and solved it using a hybrid intelligent algorithm. Still others have improved the cost-benefit model of battery energy storage and analyzed the changes in various system costs and benefits after introducing battery energy storage for peak shaving. However, the above-mentioned peak-shaving methods all target the participation of a single type of energy storage in system peak shaving, resulting in poor peak-shaving effects. Summary of the Invention

[0004] Therefore, it is necessary to propose an energy storage optimization scheduling method to address the above problems.

[0005] An energy storage optimization scheduling method, the method comprising:

[0006] Acquire basic peak-shaving data; wherein, the basic peak-shaving data includes pumped storage energy peak-shaving data, thermal power unit peak-shaving data, battery energy storage peak-shaving data, and fuzzy quantities of wind power, photovoltaic power, and load;

[0007] With the goal of minimizing net load fluctuations and maximizing the efficiency of pumped storage deployment, an upper-level scheduling model is constructed. The constraints of the upper-level scheduling model include unit output constraints, single operating condition constraints, and reservoir water balance constraints.

[0008] Based on the upper-level scheduling model, a lower-level scheduling model is constructed with the goal of minimizing the total system operating cost. The total system operating cost includes the peak-shaving cost of thermal power, the operating cost of battery energy storage, the carbon emission cost, and the cost of wind and solar curtailment penalties. The constraints of the lower-level scheduling model include system power balance constraints, thermal power constraints, and positive and negative spinning reserve constraints.

[0009] Based on the aforementioned peak-shaving data and MATLAB algorithm, the upper-level scheduling model and the lower-level scheduling model are solved to obtain the optimal scheduling scheme.

[0010] Preferably, the method for obtaining the fuzzy quantities of wind power, photovoltaic power, and load specifically includes:

[0011] Using the day-ahead forecasts for wind power, solar power, and load as fuzzy quantities, the expressions are as follows:

[0012]

[0013] In the formula, This represents the fuzzy quantity of resource * at time t, where * represents wind power, solar power, or load. This represents the day-ahead forecast value of resource * at time t. This represents the proportional coefficient corresponding to the resource at time t.

[0014] Preferably, the expression for the upper-layer scheduling model is as follows:

[0015]

[0016] In the formula, S top This represents the upper-level scheduling model, where β represents the weight coefficient and TCC represents the total scheduling duration. This represents the day-ahead forecast value of resource * at time t. This indicates the output of the pumped storage power station at time t. This represents the predicted net load at time t. This indicates the operating efficiency of the pumped storage power station at time t. This represents the operating cost of the pumped storage power station at time t.

[0017] The expressions for the operating benefits and operating costs of pumped storage power stations are as follows:

[0018]

[0019] In the formula, I BH C represents the operational benefits of pumped storage power stations. BH C represents the operating cost of a pumped storage power station. price This indicates the on-grid electricity price during peak and off-peak periods. This represents the power generation capacity of the pumped storage power station at time t. C represents the pumping power of the pumped storage power station at time t, Δt represents the dispatching time interval, and C represents the pumping capacity of the pumped storage power station at time t. inv,BH C represents the total investment cost of a pumped storage power station. loss,BH ε represents the operating loss cost of a pumped storage power station. BH This represents the operating loss coefficient of the pumped storage power station. This indicates the power generation status of the pumped storage power station at time t. C represents the pumping operation status of the pumped storage power station at time t. BH,p C represents the unit power construction cost of a pumped storage power station. BH,e P represents the construction cost per unit capacity. BHN E represents the rated power of the pumped storage power station. BHN T represents the rated capacity of the pumped storage power station, r represents the discount rate, and T represents the present value. to This indicates the service life of the pumped storage power station project.

[0020] Preferably, the expression for the constraints of the upper-level scheduling model is as follows:

[0021]

[0022] In the formula, This indicates the power generation status of the j-th pumped-storage unit at time t. This indicates the pumping operation status of the j-th pumped storage unit at time t. This indicates the minimum output of the pumped-storage unit under power generation conditions. This represents the power generation capacity of the pumped storage power station at time t. This indicates the maximum output of the pumped-storage unit under power generation conditions. This indicates the pumping operation status of the j-th pumped storage unit at time t. This indicates the minimum output of the pumped storage unit under pumping conditions. This indicates the maximum output of the pumped storage unit under pumping conditions. J represents the pumping power of the pumped storage power station at time t, and J represents the number of pumped storage power stations. This indicates the power generation status of the pumped storage power station at time t. This indicates the pumping operation status of the pumped storage power station at time t. This represents the capacity of the upstream reservoir at time t. η represents the upstream reservoir capacity at time t-1. c η represents pumping efficiency. f Indicates power generation efficiency. This represents the minimum capacity of the upstream reservoir. This indicates the maximum capacity of the upstream reservoir. This represents the initial value of the upstream reservoir capacity. This indicates the final value of the upstream reservoir's capacity.

[0023] Preferably, the expression for the lower-level scheduling model is as follows:

[0024] S bottom =min[(C peak +C BE +C co2 )-(D wind +D pv )]

[0025] In the formula, S bottom Represents the lower-level scheduling model, C peak C represents the peak-shaving cost of thermal power units. BE C represents the operating cost of battery energy storage. co2 D represents the carbon emission cost of thermal power units. wind D represents the system's cost of wind curtailment. pv This represents the system's cost of wasting light.

[0026] Preferably, the expression for the peak-shaving cost of the thermal power unit is as follows:

[0027]

[0028] In the formula, C peak This represents the peak-shaving cost of thermal power units, where I represents the number of thermal power units and TCC represents the total dispatching time. Indicates coal consumption cost, This indicates the unit's loss cost. This indicates a deep adjustment in oil production costs. Indicates start-up and shutdown costs;

[0029] The expressions for coal consumption cost, unit loss cost, and deep-oil injection cost are specifically as follows:

[0030]

[0031] In the formula, a i b i c i These represent the first, second, and third coefficients of the energy consumption characteristic function of a thermal power unit, respectively. Let represent the active power output of the i-th thermal power unit at time t. C represents the active power output of the i-th thermal power unit at time t-1. coal N represents the coal price for the current season. f The number of rotor-induced cracking cycles is represented by τ, which represents the unit loss coefficient, and C is the number of cycles. unit,i This represents the actual purchase cost of the i-th generating unit. C represents the fuel consumption of the i-th thermal power unit during the deep-start oil injection phase at time t. oil This indicates the current quarter's oil price;

[0032] The expression for the start-up and shutdown costs is as follows:

[0033]

[0034] In the formula, and This indicates the operating status of the i-th thermal power unit at time t and time t-1. This represents the start-up cost of the i-th thermal power unit. Let represent the downtime cost of the i-th thermal power unit.

[0035] Preferably, the expressions for the battery energy storage operating cost and the carbon emission cost of the thermal power unit are as follows:

[0036]

[0037] In the formula, C inv,BE C represents the investment cost of battery energy storage. BE,P P represents the construction cost per unit power of battery energy storage. BEN C represents the rated energy storage capacity of the battery. BE,E E represents the construction cost per unit capacity of battery energy storage. BEN C represents the rated energy storage capacity of the battery. loss,BE The value represents the operating loss cost of battery energy storage, TCC represents the total scheduling time, and ε represents the total cost of battery energy storage operation. BE This represents the battery energy storage operation loss coefficient. This indicates the charging status of the battery at time t. This indicates the discharge time t of the battery's energy storage. C represents the battery's energy storage output at time t. BE T represents the total peak-shaving cost of battery energy storage, r represents the discount rate, and T represents the total peak-shaving cost of battery energy storage. B C indicates the service life of a battery energy storage power station project. co2 C represents the cost of carbon emissions. C This represents the trading price per unit of carbon emissions, where I represents the number of thermal power units, and K represents the price per unit of carbon emissions. C This indicates the carbon emissions per unit of thermal power unit. Let λ represent the active power output of the i-th thermal power unit at time t, and let λ represent the carbon emission quota of the thermal power unit.

[0038] Preferably, the expressions for the system's wind curtailment cost and system's solar curtailment cost are as follows:

[0039]

[0040] In the formula, D wind The system's cost of wind curtailment is represented by K, TCC represents the total scheduling time, and K represents the total cost of wind curtailment. wind This represents the wind curtailment penalty coefficient. This represents the predicted and dispatched wind power output at time t. D represents the actual wind power output at time t. pvK represents the system's cost of light curtailment. pv This represents the light-wasting penalty coefficient. This represents the photovoltaic power output predicted and scheduled at time t. This represents the actual power output of the photovoltaic system at time t.

[0041] Preferably, the expression for the constraints of the lower-level scheduling model is as follows:

[0042]

[0043] In the formula, This represents the load forecast and dispatch output at time t, where I represents the number of thermal power units. Let represent the active power output of the i-th thermal power unit at time t. Let J represent the active power output of the i-th thermal power unit at time t-1, and let J represent the number of pumped storage power stations. This represents the output of pumped-storage unit j at time t. This represents the battery's energy storage output at time t. This represents the actual wind power output at time t. This represents the actual dispatch output of thermal power plants at time t. This indicates the operating status of the i-th thermal power unit at time t. This represents the minimum output value of thermal power unit i. V represents the maximum output value of thermal power unit i. g,i X represents the maximum climbing rate of thermal power unit i. i t-1 This indicates the operating status of the i-th thermal power unit at time t-1. This represents the continuous operating time of thermal power unit i within the time period t-1. This represents the continuous operating time of thermal power unit i within the time period t-1. This represents the minimum continuous operating time of thermal power unit i. This represents the continuous downtime of thermal power unit i during the time period t-1. This represents the minimum continuous downtime of thermal power unit i. and Let represent the fuzzy quantities of wind power, photovoltaic power, and load, respectively, and α represent the confidence level for satisfying the system spinning reserve constraint. This indicates the confidence level.

[0044] Preferably, the constraints of the lower-level scheduling model further include:

[0045]

[0046] In the formula, P BEN Indicates the rated power of battery energy storage. This represents the energy output of the i-th battery at time t. This represents the state of charge (SOC) of the i-th battery at time t. Indicates the maximum state of charge. This represents the minimum state of charge.

[0047] The embodiments of the present invention have the following beneficial effects:

[0048] The optimized scheduling method proposed in this invention increases the regulating capacity, which helps alleviate the pressure of deep peak shaving of thermal power, promotes the consumption of new energy, and has the best effect on improving the economic efficiency of the system. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] in:

[0051] Figure 1 Here is a flowchart of an energy storage optimization scheduling method in one embodiment;

[0052] Figure 2 Here is a diagram of the IEEE-39 node system architecture in one embodiment;

[0053] Figure 3 Here is a graph showing wind power, solar power, and load forecasting in one embodiment;

[0054] Figure 4 The following are the day-ahead scheduling results for each scheme in one embodiment: (a) is the scheduling result diagram for Scheme 1; (b) is the scheduling result diagram for Scheme 2; (c) is the scheduling result diagram for Scheme 3; (d) is the scheduling result diagram for the scheme in this embodiment.

[0055] Figure 5 This is a graph showing the total peak-shaving cost and net benefit under different battery storage capacities in one embodiment.

[0056] Figure 6 This is a graph showing the total peak-shaving cost and net benefit under different pumped storage capacities in one embodiment. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] This invention pertains to the application of energy storage systems in power systems, primarily involving a two-layer optimal scheduling method for combined thermal and energy storage peak shaving under uncertain source-load conditions. Existing research focuses on analyzing the participation of single-type energy storage in system peak shaving, lacking consideration of the impact of combined energy storage and thermal power. To better meet the system's peak shaving needs, this invention proposes an energy storage optimal scheduling method. First, fuzzy parameters are used to handle the uncertainties of wind, solar, and load factors. Then, considering the peak shaving characteristics and operating costs of thermal power and combined energy storage, a two-layer optimal scheduling model of thermal and energy storage is constructed. The upper layer aims to optimize the net load fluctuation and peak shaving benefits of pumped storage, while the lower layer aims to minimize the total system peak shaving cost. This yields the day-ahead scheduling plan for the thermal power and combined energy storage system, which is solved using a MATLAB algorithm. Finally, the effectiveness of the proposed method is verified based on the IEEE-39 node system.

[0059] The technical problem solved by this invention is to comprehensively consider the impact of combined energy storage and thermal power participation in system peak shaving, and propose a two-layer thermal-storage scheduling method that takes into account source-load uncertainty. Taking a combined energy storage system consisting of battery energy storage and pumped hydro storage as an example, this invention comprehensively considers the costs of deep peak shaving by thermal power, the investment costs of combined energy storage, and operating losses. It establishes a two-layer optimal scheduling model for joint thermal-storage peak shaving with fuzzy chance constraints, optimizing the output of thermal power units and combined energy storage at different time periods. A case study is conducted based on data from a typical day, and the impact of deep peak shaving by thermal power and combined energy storage on the system's peak shaving effect and economic benefits is discussed.

[0060] Figure 1 This is a flowchart of an energy storage optimization scheduling method in one embodiment. For example... Figure 1 As shown, the method specifically includes the following steps:

[0061] Step 202: Obtain basic peak-shaving data.

[0062] In some examples, the peak-shaving baseline data includes pumped storage peak-shaving baseline data, thermal power unit peak-shaving baseline data, battery energy storage peak-shaving baseline data, and fuzzy quantities for wind power, photovoltaics, and load.

[0063] For example, the basic data for pumped storage peak shaving includes the operating costs and benefits of pumped storage power stations, the predicted net charge, and the power output.

[0064] For example, the basic data for peak shaving of thermal power units includes the output of thermal power units, the coefficient of consumption characteristic function, the oil consumption during the deep-shaving oil injection stage, and the number of thermal power units.

[0065] For example, the basic data for battery energy storage peak shaving includes the rated power and rated capacity of battery energy storage, battery energy storage output, etc.

[0066] In some examples, the methods for obtaining fuzzy quantities of wind power, solar power, and load specifically include:

[0067] Addressing Source-Load Uncertainty: Due to the inherent uncertainties in both renewable energy sources and load, day-ahead forecasts may contain errors. To ensure the safe and stable operation of the power system, power departments typically need to consider these uncertainties when scheduling day-ahead dispatch plans. This embodiment employs a fuzzy chance constraint method to address the uncertainty problem, using trapezoidal fuzzy parameters to handle the uncertainties of wind, solar, and load.

[0068] Using the day-ahead forecasts for wind power, solar power, and load as fuzzy parameters, the expressions are as follows:

[0069]

[0070] In the formula, This represents the fuzzy quantity of resource * at time t, where * represents wind power, solar power, or load. This represents the day-ahead forecast values ​​for wind power, solar power, and load at time t. This represents the proportional coefficient corresponding to resources at time t, and its value is generally determined by historical data.

[0071] Step 204: Construct an upper-level scheduling model with the goal of minimizing net load fluctuations and maximizing the benefits of pumped storage deployment.

[0072] Constructing an upper-level scheduling model: The upper-level scheduling uses pumped storage to smooth net load fluctuations, while also considering the peak-shaving effect and operational economy of pumped storage. The optimization objective is to minimize net load fluctuations and maximize the efficiency of pumped storage deployment, thereby obtaining the pumped storage start-up, shutdown, and output plans. This stage aims to optimize the improvement of net load volatility and maximize the efficiency of pumped storage deployment, and its expression is:

[0073]

[0074] In the formula, S top This represents the upper-level scheduling model, β represents the weight coefficient (in this embodiment, β is 0.5), and TCC represents the total scheduling duration. This represents the day-ahead forecast value of resource * at time t. This indicates the output of the pumped storage power station at time t. Represents power generation operating conditions. (Representing pumping conditions) This represents the predicted net load at time t. This indicates the operating efficiency of the pumped storage power station at time t. This represents the operating cost of the pumped storage power station at time t.

[0075] During operation, pumped storage primarily generates electricity. This embodiment also considers investment costs and operating losses. Investment costs are mainly related to the rated power and capacity of the pumped storage power station, while operating losses are mainly related to the operating power of the pumped storage station. Therefore, the electricity benefit I of pumped storage can be obtained. BH and operating costs C BH Specifically:

[0076]

[0077] In the formula, I BH C represents the operational benefits of pumped storage power stations. BH C represents the operating cost of a pumped storage power station. price This indicates the on-grid electricity price during peak and off-peak periods. This represents the power generation capacity of the pumped storage power station at time t. C represents the pumping power of the pumped storage power station at time t, Δt represents the dispatching time interval, and C represents the pumping capacity of the pumped storage power station at time t. inv,BH C represents the total investment cost of a pumped storage power station. loss,BH ε represents the operating loss cost of a pumped storage power station. BH This represents the operating loss coefficient of the pumped storage power station. This indicates the power generation status of the pumped storage power station at time t. This indicates the pumping operation status of the pumped storage power station at time t. and (Take 1 to represent power generation and pumping conditions respectively, and 0 to represent static conditions), C BH,p C represents the unit power construction cost of a pumped storage power station. BH,e P represents the construction cost per unit capacity. BHN E represents the rated power of a pumped storage power station (in MW·h). BHN T represents the rated capacity of the pumped storage power station (in MW·h), and r represents the discount rate, which is taken as 8% in this embodiment. to This indicates the service life of the pumped storage power station project; in this example, it is taken as 40 years.

[0078] The upper-level dispatch constraints are mainly pumped-storage power station constraints. The constraints of the upper-level dispatch model include unit output constraints, single-condition constraints, and reservoir water balance constraints, specifically:

[0079]

[0080] In the formula, This indicates the power generation status of the j-th pumped-storage unit at time t. This indicates the pumping operation status of the j-th pumped storage unit at time t (a value of 1 represents the unit being in both power generation and pumping operation, while a value of 0 represents the stationary operation). This indicates the minimum output of the pumped-storage unit under power generation conditions. This represents the power generation capacity of the pumped storage power station at time t. This indicates the maximum output of the pumped-storage unit under power generation conditions. This indicates the pumping operation status of the j-th pumped storage unit at time t. This indicates the minimum output of the pumped storage unit under pumping conditions. This indicates the maximum output of the pumped storage unit under pumping conditions. J represents the pumping power of the pumped storage power station at time t, and J represents the number of pumped storage power stations. This indicates the power generation status of the pumped storage power station at time t. This indicates the pumping operation status of the pumped storage power station at time t. This represents the capacity of the upstream reservoir at time t. η represents the upstream reservoir capacity at time t-1. c η represents pumping efficiency. f Indicates power generation efficiency. This represents the minimum capacity of the upstream reservoir. This indicates the maximum capacity of the upstream reservoir. This represents the initial value of the upstream reservoir capacity. This indicates the final value of the upstream reservoir's capacity.

[0081] Step 206: Based on the upper-level scheduling model, construct the lower-level scheduling model with the goal of minimizing the total system operating cost.

[0082] Constructing a lower-level scheduling model: The upper-level scheduling yields the pumped storage scheduling plan, which, combined with the net load curve, generates the equivalent load curve and is then passed down to the lower level. The lower-level scheduling involves peak shaving by thermal power units and battery storage, with the goal of minimizing the total system operating cost. This leads to the solution of the output plans for thermal power units and battery storage. This stage aims to minimize the total system operating cost. The objective function considers the peak shaving cost of thermal power, the operating cost of battery storage, carbon emission costs, and the cost of wind and solar curtailment penalties. Its expression is:

[0083] S bottom =min[(C peak +C BE +C co2 )-(D wind +D pv )]

[0084] In the formula, S bottom Represents the lower-level scheduling model, C peak C represents the peak-shaving cost of thermal power units. BE C represents the operating cost of battery energy storage. co2 D represents the carbon emission cost of thermal power units. wind D represents the system's cost of wind curtailment. pv This represents the system's cost of wasting light.

[0085] Among them, the peak shaving cost of thermal power plants considers the costs of deep peak shaving and start-up and shutdown. The costs of deep peak shaving of thermal power units mainly include three aspects: coal consumption cost, unit loss cost, and deep peak shaving oil injection cost. Start-up and shutdown costs are related to the operating status of thermal power units and include the start-up cost and shutdown cost of thermal power units. The total peak shaving cost of thermal power plants can be expressed as:

[0086]

[0087] In the formula, C peak This represents the peak-shaving cost of thermal power units, where I represents the number of thermal power units and TCC represents the total dispatching time. Indicates coal consumption cost, This indicates the unit's loss cost. This indicates a deep adjustment in oil production costs. Indicates start-up and shutdown costs;

[0088] The expressions for coal consumption cost, unit loss cost, and deep-oil injection cost are as follows:

[0089]

[0090] In the formula, a i b i c i These represent the first, second, and third coefficients of the thermal power unit's consumption characteristic function, respectively, and their values ​​are related to the unit type, boiler model, and coal quality. Let represent the active power output of the i-th thermal power unit at time t. C represents the active power output of the i-th thermal power unit at time t-1. coal N represents the coal price for the current season. f The number of rotor-induced cracking cycles is represented by τ, which represents the unit loss coefficient, and C is the number of cycles. unit,i This represents the actual purchase cost of the i-th generating unit. C represents the fuel consumption of the i-th thermal power unit during the deep-start oil injection phase at time t. oil This indicates the current quarter's oil price;

[0091] The expression for start-up and shutdown costs is as follows:

[0092]

[0093] In the formula, and This indicates the operating status of the i-th thermal power unit at time t and time t-1. Set 1 to indicate the power-on state. Setting it to 0 indicates a stopped state. This represents the start-up cost of the i-th thermal power unit. Let represent the downtime cost of the i-th thermal power unit.

[0094] The cost of battery energy storage is similar to that of pumped hydro storage. Considering investment costs and operating losses, the total peak-shaving cost and carbon emission cost of energy storage can be expressed as follows:

[0095]

[0096] In the formula, C inv,BE C represents the investment cost of battery energy storage. BE,P P represents the construction cost per unit power of battery energy storage. BEN C represents the rated energy storage capacity of the battery. BE,E E represents the construction cost per unit capacity of battery energy storage. BEN C represents the rated energy storage capacity of the battery. loss,BE The value represents the operating loss cost of battery energy storage, TCC represents the total scheduling time, and ε represents the total cost of battery energy storage operation. BE This represents the battery energy storage operation loss coefficient. This indicates the charging status of the battery at time t. This indicates the discharge time t of the battery's energy storage. and A value of 1 indicates both the discharge and charging states, while a value of 0 indicates the idle state. C represents the battery's energy storage output at time t. BE T represents the total peak-shaving cost of battery energy storage, r represents the discount rate, and T represents the total peak-shaving cost of battery energy storage. B This indicates the service life of the battery energy storage power station project; in this example, it is taken as 10 years. co2 C represents the cost of carbon emissions. C This represents the trading price per unit of carbon emissions, where I represents the number of thermal power units, and K represents the price per unit of carbon emissions. C This indicates the carbon emissions per unit of thermal power unit. Let λ represent the active power output of the i-th thermal power unit at time t, and let λ represent the carbon emission quota of the thermal power unit.

[0097] The penalty cost for wind and solar power curtailment can be expressed as:

[0098]

[0099] In the formula, D wind The system's cost of wind curtailment is represented by K, TCC represents the total scheduling time, and K represents the total cost of wind curtailment. wind This represents the wind curtailment penalty coefficient. This represents the predicted and dispatched wind power output at time t. D represents the actual wind power output at time t. pv K represents the system's cost of light curtailment. pv This represents the light-wasting penalty coefficient. This represents the photovoltaic power output predicted and scheduled at time t. This represents the actual power output of the photovoltaic system at time t.

[0100] Lower-level scheduling constraints include system power balance constraints, thermal power constraints, and positive and negative spinning reserve constraints. Among these, thermal power constraints mainly include unit output constraints, ramp-up constraints, and minimum start-up / shutdown time constraints, which can be expressed as:

[0101]

[0102] In the formula, This represents the load forecast and dispatch output at time t, where I represents the number of thermal power units. Let represent the active power output of the i-th thermal power unit at time t. Let J represent the active power output of the i-th thermal power unit at time t-1, and let J represent the number of pumped storage power stations. This represents the output of pumped-storage unit j at time t. This represents the battery's energy storage output at time t. This represents the actual wind power output at time t. This represents the actual dispatch output of thermal power plants at time t. This indicates the operating status of the i-th thermal power unit at time t. This represents the minimum output value of thermal power unit i. V represents the maximum output value of thermal power unit i. g,i This represents the maximum climbing rate of thermal power unit i. This indicates the operating status of the i-th thermal power unit at time t-1. This represents the continuous operating time of thermal power unit i within the time period t-1. This represents the continuous operating time of thermal power unit i within the time period t-1. This represents the minimum continuous operating time of thermal power unit i. This represents the continuous downtime of thermal power unit i during the time period t-1. This represents the minimum continuous downtime of thermal power unit i. and Let represent the fuzzy quantities for wind power, photovoltaic power, and load, respectively. α represents the confidence level for satisfying the system spinning reserve constraint, which is set to 0.95 in this embodiment. □{·} represents the confidence level expression.

[0103] Battery energy storage constraints mainly include charge / discharge power constraints and state of charge constraints. The expressions are as follows:

[0104]

[0105] In the formula, P BEN Indicates the rated power of battery energy storage. This represents the energy output of the i-th battery at time t. This represents the state of charge (SOC) of the i-th battery at time t. Indicates the maximum state of charge. This represents the minimum state of charge.

[0106] Step 208: Based on the peak shaving data and MATLAB algorithm, solve the upper-level scheduling model and the lower-level scheduling model to obtain the optimal scheduling scheme.

[0107] Solve the two-level scheduling model using MATLAB's built-in solver or optimization algorithm.

[0108] To verify the feasibility of this embodiment, a reference case analysis was conducted using the IEEE-39 node system.

[0109] Figure 2 This is a system architecture diagram of the IEEE-39 node in one embodiment. The access locations of each unit are as follows. Figure 2 As shown. The wind farm and photovoltaic power station have capacities of 600MW and 300MW respectively; the pumped storage power station has a capacity of 200MW (including 4 pumped storage units); among them, only the thermal power units of 300MW or more (G1, G2) have deep peak-shaving capabilities. The minimum output during the conventional peak-shaving phase of thermal power, the minimum output during the deep peak-shaving phase without oil injection, and the minimum output during the oil injection phase are set to 60%, 45%, and 30% of the maximum output respectively. The remaining thermal power units (G3-G6) only participate in conventional peak-shaving; the dispatch cycle is 24h, and the time scale is 1h.

[0110] Figure 3 This is a wind power, solar power, and load forecast curve diagram in one embodiment. The system selects data from a typical day as the basis for the study; the original load and wind / solar forecast curves are shown below. Figure 3 Membership parameters are shown in Table 1.

[0111] Table 1 Gradient fuzzy membership parameters

[0112]

[0113] To demonstrate the role of coordinated operation of composite energy storage in enhancing the system's peak-shaving capacity and economic efficiency, the following three scheduling schemes are set up for comparative analysis.

[0114] (1) Option 1: Composite energy storage does not participate in peak shaving, only thermal power units participate in system peak shaving, with the goal of minimizing the total system peak shaving cost;

[0115] (2) Option 2: Only includes pumped storage power stations, adopts a two-level dispatch scheme, and only thermal power units participate in system peak shaving in the lower level dispatch;

[0116] (3) Option 3: Includes composite energy storage and adopts a two-level dispatch scheme, with thermal power units only performing conventional peak shaving;

[0117] (4) This embodiment includes composite energy storage and adopts a two-layer scheduling scheme.

[0118] Figure 4The following diagram shows the day-ahead scheduling results for each scheme in one embodiment. (a) shows the scheduling results for Scheme 1; (b) shows the scheduling results for Scheme 2; (c) shows the scheduling results for Scheme 3; and (d) shows the scheduling results for the scheme in this embodiment. The MATLAB algorithm is used for solving the problem. The system optimization scheduling results and peak-shaving performance indicators for each scheme are as follows: Figure 4 As shown in Table 2.

[0119] Table 2 System peak-shaving indicators under each scheme

[0120]

[0121] Configuring larger capacity energy storage devices can better mitigate fluctuations in renewable energy and improve the spinning reserve margin of thermal power units; however, the operating costs of energy storage will also increase accordingly. Excessive energy storage capacity not only fails to generate additional electricity revenue but also reduces the system's peak-shaving efficiency. Therefore, this embodiment, based on a two-layer dispatch scheme incorporating composite energy storage, compares the impact of battery energy storage and pumped hydro storage of different capacities on the system's economics while keeping other system parameters constant.

[0122] Figure 5 This is a graph showing the total peak-shaving cost and net benefit under different battery storage capacities in one embodiment. Figure 5 It is evident that the operating cost of battery energy storage exceeds its operating benefits. Although the resulting losses increase with capacity, it also reduces the output fluctuations and start-stop frequency of thermal power units, lowering the peak-shaving costs of thermal power. Therefore, when a certain capacity of battery energy storage is configured, the peak-shaving cost savings for the system can offset the resulting losses. When the capacity reaches 150MW or higher, the operating cost of battery energy storage increases, and the resulting losses exceed the peak-shaving cost savings for the system, leading to a decrease in the system's economic efficiency.

[0123] Figure 6 This is a graph showing the total peak-shaving cost and net benefit under different pumped storage capacities in one embodiment. Figure 6 It is evident that as the scale of pumped storage increases, the increased pumping power enhances the absorption capacity of new energy sources while simultaneously reducing peak-hour thermal power output, thus continuously decreasing the overall peak-shaving cost of the system. With the increase in pumped storage capacity, the increased electricity revenue exceeds the increase in operating costs, resulting in a continuous increase in net profit. When the pumped storage capacity reaches a certain level, the system's demand for it becomes saturated, and the system's wind and solar curtailment rate, carbon emissions, and overall peak-shaving cost tend to stabilize. However, the operating cost of pumped storage continues to increase, leading to a decrease in net profit.

[0124] Table 3 compares the system peak-shaving costs under each scheme. Table 4 compares the system benefits under each scheme. It is evident that this embodiment achieves the best improvement in system economics.

[0125] Table 3 Comparison of System Peak Shaving Costs for Each Scheme

[0126]

[0127] Table 4 Comparison of System Benefits under Different Schemes

[0128]

[0129]

[0130] In the context of new power systems, to further investigate the impact of composite energy storage and deep peak shaving by thermal power on the system's peak-shaving capacity and economic efficiency, this embodiment proposes a two-tiered thermal-storage dispatch method considering source-load uncertainty. Comparing various dispatch schemes, the composite energy storage-participation peak-shaving scheme demonstrates the best peak-shaving effect. Through two-tiered optimized dispatch, the peak-shaving pressure of thermal power is significantly alleviated, wind curtailment is significantly reduced, and the utilization rate of composite energy storage is also improved. This embodiment increases the system's regulation capacity, helps alleviate the pressure of deep peak shaving by thermal power, promotes the consumption of new energy sources, and achieves the best improvement in system economic efficiency.

[0131] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0132] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0133] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for optimizing energy storage scheduling, characterized in that: The method comprises: Obtain peak-shaving basic data; wherein the peak-shaving basic data includes pumped storage peak-shaving basic data, thermal power unit peak-shaving basic data, battery energy storage peak-shaving basic data, and fuzzy quantities of wind power, photovoltaic power, and load; An upper-level dispatching model is constructed with the goal of minimizing the net load fluctuation of pumped storage and maximizing the benefit of pumped storage. The constraints of the upper-level dispatching model include unit output constraints, single operating condition constraints and reservoir water balance constraints. On the basis of the upper-level dispatching model, a lower-level dispatching model is constructed with the goal of minimizing the total system operating cost; wherein the total system operating cost includes the peak-shaving cost of thermal power, the operating cost of battery energy storage, the carbon emission cost, and the penalty cost of wind and solar power abandonment; and the constraints of the lower-level dispatching model include system power balance constraints, thermal power constraints, and positive and negative spinning reserve constraints; Based on the peak-shaving basic data and the MATLAB algorithm, the upper-level scheduling model and the lower-level scheduling model are solved to obtain the optimal scheduling solution.

2. The energy storage optimization scheduling method according to claim 1 is characterized in that: The method for obtaining the fuzzy quantities of wind power, photovoltaic power and load specifically includes: The day-ahead forecast results of wind power, photovoltaic power and load are used as fuzzy quantities, respectively, and the expressions are as follows: In the formula, represents the fuzzy quantity of resource* at time t, where * represents wind power, photovoltaic power or load, represents the day-ahead forecast value of resource* at time t, Indicates the proportional coefficient corresponding to resource* at time t.

3. The energy storage optimization scheduling method according to claim 1 is characterized in that: The expression of the upper-layer scheduling model is specifically: In the formula, S top represents the upper-layer scheduling model, β represents the weight coefficient, TCC represents the total scheduling duration, represents the day-ahead forecast value of resource* at time t, represents the output of the pumped storage power station at time t, represents the predicted net load value at time t, represents the operating benefit of the pumped storage power station at time t, represents the operating cost of the pumped storage power station at time t; Among them, the expressions of the operating benefits and operating costs of the pumped storage power station are as follows: In the formula, I BH represents the operating efficiency of the pumped storage power station, C BH represents the operating cost of the pumped storage power station, C price Indicates the on-grid electricity price during the peak and valley periods. represents the power generation of the pumped storage power station at time t, represents the pumped power of the pumped storage power station at time t, Δt represents the dispatching time interval, C inv,BH represents the total investment cost of the pumped storage power station, C loss,BH represents the operating loss cost of the pumped storage power station, ε BH represents the operating loss coefficient of the pumped storage power station, represents the power generation condition of the pumped storage power station at time t, represents the pumping condition of the pumped storage power station at time t, C BH,p represents the unit power construction cost of the pumped storage power station, C BH,e represents the unit capacity construction cost, P BHN Indicates the rated power of the pumped storage power station, E BHN represents the rated capacity of the pumped storage power station, r represents the discount rate, T to Indicates the service life of the pumped-storage power station project.

4. The energy storage optimization scheduling method according to claim 1, characterized in that: The expression of the constraint condition of the upper-layer scheduling model is specifically: In the formula, represents the generating condition of the jth pumped storage unit at time t, represents the pumping condition of the jth pumped storage unit at time t, Indicates the minimum output of the pumped storage unit under power generation conditions, represents the power generation of the pumped storage power station at time t, Indicates the maximum output of the pumped storage unit under power generation conditions, represents the pumping condition of the jth pumped storage unit at time t, Indicates the minimum output of the pumped storage unit under pumping conditions, Indicates the maximum output of the pumped storage unit under pumping conditions, represents the pumping power of the pumped storage power station at time t, J represents the number of pumped storage power stations, represents the power generation condition of the pumped storage power station at time t, represents the pumping condition of the pumped storage power station at time t, represents the upstream reservoir capacity at time t, represents the upstream reservoir capacity at time t-1, η c represents the pumping efficiency, η f represents the power generation efficiency, represents the minimum capacity of the upstream reservoir, represents the maximum capacity of the upstream reservoir, represents the initial value of the upstream reservoir capacity, Indicates the end value of the upstream reservoir capacity.

5. The energy storage optimization scheduling method according to claim 1, characterized in that: The expression of the lower-layer scheduling model is specifically: S bottom =min[(C peak +C BE +C co2 )-(D wind +D pv )] In the formula, S bottom represents the lower-level scheduling model, C peak represents the peak load regulation cost of thermal power units, C BE represents the battery energy storage operating cost, C co2 represents the carbon emission cost of thermal power units, D wind represents the system wind curtailment cost, D pv Indicates the system abandoned light cost.

6. The energy storage optimization scheduling method according to claim 5 is characterized in that: The expression of the peak load regulation cost of the thermal power unit is specifically: In the formula, C peak represents the peak load regulation cost of thermal power units, I represents the number of thermal power units, TCC represents the total dispatching time, represents the coal consumption cost, represents the unit loss cost, Indicates the cost of deep adjustment of oil input, represents the start-stop cost; The expressions of the coal consumption cost, unit loss cost and deep adjustment oil input cost are specifically as follows: In the formula, a i , b i 、c i They represent the first, second and third coefficients of the thermal power unit consumption characteristic function, represents the active power output of the i-th thermal power unit at time t, represents the active power output of the i-th thermal power unit at time t-1, C coal represents the coal price of the season, N f represents the number of rotor cracking cycles, τ represents the unit loss coefficient, C unit,i represents the actual purchase cost of the i-th unit, represents the fuel consumption of the i-th thermal power unit during the deep adjustment and oil injection stage at time t, C oil represents the oil price of the season; The expression of the start-stop cost is specifically: In the formula, and Indicates the operating status of the i-th thermal power unit at time t and time t-1, represents the startup cost of the i-th thermal power unit, represents the shutdown cost of the i-th thermal power unit.

7. The energy storage optimization scheduling method according to claim 5, characterized in that: The expressions for the battery energy storage operating cost and the carbon emission cost of the thermal power unit are specifically: In the formula, C inv,BE represents the battery energy storage investment cost, C BE,P represents the unit power construction cost of battery energy storage, P BEN Indicates the rated power of battery energy storage, C BE,E represents the construction cost per unit capacity of battery energy storage, E BEN Indicates the rated capacity of battery energy storage, C loss,BE represents the battery energy storage operation loss cost, TCC represents the total dispatch time, ε BE represents the battery energy storage operation loss coefficient, Indicates the battery energy storage charging mark at time t. Indicates the discharge mark of the battery energy storage at time t. represents the battery energy output at time t, C BE represents the total peak-shaving cost of battery energy storage, r represents the discount rate, T B Indicates the service life of the battery energy storage power station project, C co2 represents the carbon emission cost, C C represents the trading price per unit of carbon emissions, I represents the number of thermal power units, K C represents the unit carbon emission of thermal power units, represents the active output of the i-th thermal power unit at time t, and λ represents the carbon emission quota of the thermal power unit.

8. The energy storage optimization scheduling method according to claim 5, characterized in that: The expressions of the system wind curtailment cost and the system solar curtailment cost are specifically: Where D wind represents the system wind curtailment cost, TCC represents the total dispatching time, K wind represents the wind abandonment penalty coefficient, represents the predicted dispatch output of wind power at time t, represents the actual dispatch output of wind power at time t, D pv represents the system abandoned light cost, K pv represents the light abandonment penalty coefficient, represents the photovoltaic forecast dispatch output at time t, Indicates the actual dispatching output of photovoltaic power at time t.

9. The energy storage optimization scheduling method according to claim 1, characterized in that: The expression of the constraint condition of the lower-layer scheduling model is specifically: In the formula, represents the load forecast dispatch output at time t, I represents the number of thermal power units, represents the active power output of the i-th thermal power unit at time t, represents the active power output of the i-th thermal power unit at time t-1, J represents the number of pumped storage power stations, represents the output of pumped storage unit j at time t, Indicates the battery energy output at time t, represents the actual dispatch output of wind power at time t, represents the actual dispatch output of thermal power at time t, represents the operating status of the i-th thermal power unit at time t, represents the minimum output value of thermal power unit i, Represents the maximum output value of thermal power unit i, V g,i Indicates the maximum ramp rate of thermal power unit i, represents the operating status of the i-th thermal power unit at time t-1, represents the continuous operation time of thermal power unit i in the period t-1, represents the continuous operation time of thermal power unit i in the period t-1, Indicates the minimum continuous operation time of thermal power unit i, represents the continuous downtime of thermal power unit i in the period t-1, represents the minimum continuous downtime of thermal power unit i, and They represent the fuzzy quantities of wind power, photovoltaic power and load respectively, α represents the confidence level of satisfying the system spinning reserve constraint, Represents the confidence level expression.

10. The energy storage optimization scheduling method according to claim 1, characterized in that: The constraints of the lower scheduling model also include: Where P BEN Indicates the rated power of battery energy storage, represents the energy storage output of the ith battery at time t, Indicates the state of charge of the i-th battery at time t, Indicates the maximum state of charge, Indicates the minimum state of charge.

Images