New energy large-base economic optimization scheduling method and system considering multi-energy complementation

Abstract

The invention relates to the technical field of new energy, in particular to a new energy large-base economic optimization scheduling method and system considering multi-energy complementation. The method comprises the following steps: acquiring and processing operation data of each station in the new energy large base; constructing a multi-energy complementary model based on the processed operation data; on the premise that the scheduling target value is fully responded, the operation cost is minimized as the target, and an economical objective function is constructed; setting operation constraint conditions of the new energy large base; solving the economic objective function based on the operation constraint condition and an optimal scheduling algorithm to obtain an optimal scheduling scheme; and making an operation control strategy of each station based on the optimal scheduling scheme. According to the method, collaborative optimization of the new energy large base in multiple time scales and multiple space dimensions is realized, the new energy consumption rate and the system operation economy are remarkably improved, and a key technical support is provided for constructing a novel power system.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a method and system for optimizing the economic efficiency of a large-scale new energy base taking into account multi-energy complementarity. Background Art

[0002] Currently, the development of new energy bases, exemplified by the Shagohuang region, exhibits three key characteristics: First, installed capacity is growing by leaps and bounds, with clusters of tens of millions of kilowatts rapidly forming; second, integrated wind, solar, and storage development has become the mainstream model, enabling smooth output of new energy through the deployment of energy storage and flexible thermal power; and third, grid access requirements have significantly increased, meeting the dual needs of interprovincial power transmission and regional grid peak regulation. However, this power system, with its high penetration of new energy, faces significant challenges: the inherent intermittent, volatile, and anti-peaking characteristics of wind and solar power generation make it difficult for traditional dispatching models to balance the security and economics of power supply.

[0003] Traditional dispatch methods face the following technical bottlenecks: First, single-energy optimization models ignore the synergistic effects of wind, solar, hydro, thermal, and energy storage, resulting in redundant or insufficient system regulation capacity; second, deterministic dispatch strategies struggle to address wind and solar forecast errors, leading to high wind and solar curtailment rates; and third, a lack of an economically optimal decision-making mechanism prevents an effective balance between the cost of deep peak regulation from thermal power and the cost of energy storage deployment. These issues directly hinder the comprehensive benefits of new energy bases.

[0004] Therefore, optimizing the matching and scheduling of different energy sources, improving the stability of power output from new energy bases, achieving multi-energy complementarity and coordinated operation, and maximizing energy efficiency have become key issues that need to be addressed in the current new energy sector. To promote the healthy development of the new energy industry and build a safe, efficient, and clean modern energy system, breakthroughs in key technologies for multi-energy complementarity and coordinated control are urgently needed. Summary of the Invention

[0005] The purpose of the present invention is to solve at least one of the above technical problems and provide a method and system for economic optimization scheduling of large-scale new energy bases that takes into account multi-energy complementarity, which is suitable for the coordinated scheduling and optimization of multiple energy sources such as wind energy, solar energy, energy storage and thermal power.

[0006] The present invention achieves the above-mentioned purpose through the following technical solutions: A method for optimizing the economic performance of a large-scale new energy base considering multi-energy complementarity is provided, comprising: Obtain and process the operating data of each station in the new energy base; Based on the processed operating data, a multi-energy complementary model is constructed; Based on the premise of fully responding to the dispatch target value and minimizing the operating cost, an economic objective function is constructed; Setting operational constraints for large new energy bases; solving the economic objective function based on the operation constraints and an optimization scheduling algorithm, to obtain an optimal scheduling scheme; formulating operation control strategies for each station based on the optimal scheduling scheme.

[0007] Further, the operation data of each station in the new energy large base includes: real-time power generation of the new energy station, maximum power that can be increased / decreased, power prediction data, and meteorological data; installed capacity, real-time power generation, and maximum power that can be increased / decreased of the thermal power plant; installed capacity, battery SOC, and real-time power generation of the energy storage power station; grid load demand.

[0008] Further, a multi-energy complementary model is constructed, and factors considered include generation characteristics and energy storage characteristics of each station and volatility of grid load demand.

[0009] Further, the generation characteristics include wind speed-power curve and irradiance-power curve. The energy storage characteristics include energy storage charging and discharging efficiency, capacity limit, and SOC.

[0010] Further, the decision variables of the economic objective function include: generation cost of the new energy power station, generation cost of the thermal power plant and start-stop cost of the thermal power unit, charging and discharging cost of the energy storage power station and peak-valley price difference, grid power purchase cost, wind and light curtailment penalty cost, examination cost of the “Implementation Details of Grid-connected Operation Management of Power Plants”, and examination cost of the “Implementation Details of Auxiliary Service Management of Grid-connected Power Plants”.

[0011] Further, the operation constraints include power balance constraint, energy storage charging and discharging constraint, power station generation capacity constraint, and grid dispatching constraint.

[0012] Further, the optimization scheduling algorithm adopts a linear programming algorithm, a dynamic programming algorithm, or a genetic algorithm.

[0013] Further, the optimal scheduling scheme includes an optimal solution and multiple suboptimal solutions.

[0014] A new energy large base economic optimization scheduling system considering multi-energy complementarity includes: a data acquisition module configured to acquire operation data of each station in the new energy large base; a data processing module configured to clean, normalize, and store process data of the data acquisition module; a model construction module configured to construct a multi-energy complementary model based on output data of the data processing module; The objective function module is used to construct an economic objective function based on the premise of fully responding to the scheduling target value and minimizing the operating cost; Constraint module, used to set the operation constraints of the new energy base; An optimization solution module, configured to solve the economic objective function based on the operation constraints and the optimization scheduling algorithm to obtain an optimal scheduling solution; The scheduling implementation module is used to formulate the operation control strategy of each station based on the preferred scheduling plan.

[0015] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements any of the above-described methods for optimizing the economic efficiency of a large-scale new energy base taking into account multi-energy complementarity.

[0016] The present invention realizes the economic optimization scheduling of large-scale new energy bases by considering the complementary characteristics of multiple energy sources such as wind energy, solar energy, and energy storage, and has the following advantages: (1) Enhance grid stability: Through the coordinated interaction of multiple energy sources, give full play to the regulation capabilities of various power sources, the support capabilities of thermal power, and the flexible regulation capabilities of energy storage, realize the flexible operation of the power system, effectively deal with the volatility and uncertainty of renewable energy, and improve the stability of the grid; (2) Reduce operating costs: By optimizing scheduling, reduce power generation costs, energy storage costs, and grid power purchase costs, thereby improving economic efficiency; (3) Improve energy efficiency: Make full use of the complementary characteristics of wind and solar energy, reduce the phenomenon of wind and solar power abandonment, provide support for the large-scale access and consumption of renewable energy, and promote the rapid development of renewable energy; (4) High degree of automation: The system can collect data, optimize scheduling and implement plans in real time, reducing manual intervention; (5) Good social benefits: In line with the national energy development strategy, while achieving economic benefits, it is supported by economic optimization and scheduling technology to jointly promote national energy transformation and development, innovate energy services, and lead energy technology changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of the dispatching information and power flow of the Shagohuang New Energy Base according to one embodiment of the present invention; Figure 2 This is a flow chart of an economic optimization scheduling method for a large-scale new energy base considering multi-energy complementarity according to an embodiment of the present invention; Figure 3 This is a flow chart of a method for economic optimization scheduling of a large-scale new energy base considering multi-energy complementarity according to another embodiment of the present invention; Figure 4 This is a schematic diagram of the principle of an economic optimization scheduling system for a large-scale new energy base taking into account multi-energy complementarity in one embodiment of the present invention. DETAILED DESCRIPTION

[0018] The present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only intended to enable those skilled in the art to better understand and implement the present invention, rather than to imply any limitation on the scope of the present invention.

[0019] As used herein, the term "including" and variations thereof are to be interpreted as open-ended terms meaning "including, but not limited to." The term "based on" is to be interpreted as "based, at least in part, on." The terms "one embodiment" and "an embodiment" are to be interpreted as "at least one embodiment."

[0020] Figure 1 This is a schematic diagram of the dispatching information and power flow of the Shagohuang New Energy Base in one embodiment of the present invention. Figure 1 As shown, the Shagohuang New Energy Base includes: multiple wind farms, multiple photovoltaic power stations, thermal power plants and energy storage power stations; under the dispatching control of the power grid dispatching system, each station formulates a corresponding charging and discharging strategy according to the control instructions, and the electric energy of each station is boosted by the booster station and then aggregated and finally incorporated into the power grid system.

[0021] The present invention proposes a method and system for economic optimization scheduling of large-scale new energy bases that takes into account multi-energy complementarity. By integrating the complementary characteristics of multiple energy sources such as wind energy, solar energy, thermal power, and energy storage, various energy sources are optimally configured in time and space, further improving the efficiency of new energy utilization, promoting the consumption of renewable energy, and ensuring the safety and stability of the energy system.

[0022] Example 1 Figure 2 This is a flow chart of an economic optimization scheduling method for a large-scale new energy base considering multi-energy complementarity according to an embodiment of the present invention. Figure 2 As shown, according to one embodiment of the present invention, a method for optimizing the economic efficiency of a large-scale new energy base considering multi-energy complementarity includes the following steps: Step S102, obtaining and processing the operating data of each station in the large new energy base; Step S104: constructing a multi-energy complementary model based on the processed operating data; Step S106: constructing an economic objective function based on the premise of fully responding to the scheduling target value and minimizing the operating cost; Step S108, setting the operating constraints of the new energy base; Step S110, solving the economic objective function based on the operation constraints and the optimization scheduling algorithm to obtain the optimal scheduling solution; Step S112: Formulate an operation control strategy for each station based on the optimal scheduling plan.

[0023] This implementation proposes an economic optimization scheduling method for large-scale renewable energy bases that considers multi-energy complementarity. Through refined modeling and intelligent optimization algorithms, this approach aims to achieve efficient and economical scheduling of large-scale renewable energy bases in complex operating environments. This method, based on the core concept of multi-energy complementarity, fully integrates multiple energy sources, including wind power, photovoltaic power, thermal power, and energy storage, to construct an optimized scheduling framework encompassing the entire energy production, conversion, storage, and consumption chain. First, through a network of intelligent sensors deployed at each site in the large renewable energy base, the method collects real-time data on wind speed and direction from wind farms, irradiance and temperature from photovoltaic power plants, fuel supply and unit status from thermal power plants, and battery state of charge (SOC) and charge / discharge power from energy storage plants. Simultaneously, the method acquires external information, including grid-side load forecast curves, electricity price signals, and assessment indicators for the "two detailed rules." This multi-source, heterogeneous data undergoes preprocessing processes such as cleaning, calibration, and feature extraction. For example, Kalman filtering is used to suppress noise in meteorological data, and principal component analysis is used to reduce the dimensionality of high-dimensional data. This ultimately results in a high-quality structured dataset, providing a reliable data foundation for subsequent model construction. Based on this data processing, a multi-energy complementarity model is constructed, which deeply characterizes the dynamic characteristics of various energy sources. The renewable energy generation model, based on wind speed-power and irradiance-power curves, employs non-parametric modeling methods (such as support vector machines) to capture the randomness and volatility of wind and photovoltaic output, while also accounting for physical constraints such as wake effects and shadowing. A refined model of the thermal power unit is developed, taking into account boiler dynamics, turbine characteristics, and fuel costs. Constraints such as unit startup and shutdown costs and minimum operating time are quantified. The energy storage system model constructs a storage power station model that considers charge and discharge efficiency, capacity decay characteristics, and SOC status. A dynamic programming algorithm is used to optimize the energy storage charge and discharge strategy, achieving peak-valley arbitrage through "low charge, high discharge." A grid interaction model was established, taking into account network losses, line flow constraints, and node voltage safety, to achieve synergistic interaction between large-scale renewable energy bases and the larger power grid. With minimizing system operating costs as the core objective, an economic objective function was constructed, incorporating multidimensional cost items. The weight coefficients of each cost item were determined using the analytic hierarchy process (AHP), transforming the multi-objective optimization problem into a single-objective optimization problem to ensure the economic and regulatory compliance of the optimization results. To ensure the physical feasibility and system safety of the scheduling plan, multi-level operational constraints were set, and an optimization scheduling algorithm was used to solve the economic objective function. The final output was a set of optimal scheduling solutions, including the optimal solution and multiple suboptimal solutions, providing flexibility in decision-making. Finally, based on the optimal scheduling solution, a set of real-time control instructions was generated for each station. A feedback correction mechanism was established to dynamically adjust model parameters and control strategies by comparing actual operating data with the scheduling solution, forming a closed-loop control system of "optimization-execution-feedback-correction."

[0024] This invention realizes the coordinated optimization of large-scale new energy bases in multiple time scales (from seconds to days) and multiple spatial dimensions (from single machines to regional power grids), significantly improving the new energy absorption rate and system operation economy, and providing key technical support for the construction of new power systems.

[0025] According to one embodiment of the present invention, the operating data of each station in the new energy base includes: Real-time power generation capacity, maximum power increase / decrease, power forecast data, and meteorological data of new energy stations; The installed capacity, real-time power generation, and maximum power that can be increased or decreased of the thermal power plant; The installed capacity, battery SOC, and real-time power generation of the energy storage power station; Grid load demand.

[0026] Operating data from each station within a large-scale new energy base is the core foundation for building an economically optimized dispatch model. Its data dimensions and processing logic provide a deep understanding of the dynamic characteristics of a multi-energy complementary system. In this implementation, operating data includes data from new energy stations, thermal power plants, energy storage power stations, and grid load demand. This data is collected in real time by intelligent terminals deployed at wind and photovoltaic power plants, collecting meteorological data such as wind speed, direction, irradiance, and ambient temperature. This data is then combined with the unit status monitoring system to obtain real-time generated power and maximum power that can be increased or decreased. Meteorological data is processed through spatiotemporal alignment, and a neural network model (such as LSTM) is used for ultra-short-term power forecasting, generating power forecast curves for the next 15 minutes to 4 hours. The maximum power that can be increased or decreased is dynamically adjusted to account for constraints such as wake effects, shadowing, and equipment failure rates, ensuring the executability of dispatch instructions. Thermal power plant data includes installed capacity, real-time generated power, and maximum power that can be increased or decreased. Installed capacity, a static parameter, defines the output boundaries of thermal power units. Real-time generated power is uploaded in real time via the DCS (distributed control system), reflecting the unit's current load level. Maximum power that can be increased or decreased is calculated based on dynamic models such as boiler thermal inertia and turbine regulation characteristics, taking into account constraints such as unit ramp rate and minimum operating time to avoid life loss and increased costs due to frequent starts and stops. Energy storage power station data includes installed capacity, battery SOC, and real-time generated power. Installed capacity determines the energy regulation capability of the energy storage system. Battery SOC is monitored in real time by a high-precision BMS (battery management system), using a Kalman filter algorithm for state estimation to avoid overcharging and over-discharging. Real-time generated power is combined with charge and discharge efficiency curves for bidirectional correction, considering the impact of battery aging on the available energy storage capacity to ensure the long-term economic efficiency of the scheduling strategy. Grid load demand data includes total load power, load curves, and load forecast error distribution. Real-time user-side load data is obtained through AMI (Advanced Metering Infrastructure), and a load forecast model is constructed based on historical load curves and meteorological factors (such as temperature and humidity) to generate load demand scenarios for the next 24 hours to 7 days. Taking load uncertainty into account, a probability density function is used to describe load forecast errors, providing risk quantification input for the scheduling model.

[0027] After standardized processing, operational data is fed into a multi-energy complementary model for joint optimization. New energy station data characterizes the volatility and randomness of renewable energy, thermal power plant data provides flexibility support, energy storage power station data enables energy temporal and spatial translation, and grid load demand data defines scheduling objectives. Through data fusion and feature extraction, a multi-objective optimization model is constructed that incorporates power balance constraints, equipment capacity constraints, and grid security constraints. An optimized scheduling algorithm is employed to solve the problem and generate a scheduling instruction set that balances economic efficiency and safety.

[0028] The application realizes comprehensive perception and dynamic optimization of the operation state of a new energy large base by constructing an operation data system, and provides data-driven decision support for economic scheduling of a multi-energy complementary system.

[0029] According to an embodiment of the application, a multi-energy complementary model is constructed, and factors considered include power generation characteristics and energy storage characteristics of each station, and fluctuation of power grid load demand.

[0030] Preferably, the power generation characteristics include a wind speed-power curve and an irradiance-power curve. The energy storage characteristics include energy storage charging and discharging efficiency, capacity limitation, and SOC.

[0031] In this embodiment, the technical solution for constructing the multi-energy complementary model realizes accurate characterization and collaborative optimization of the operation characteristics of a new energy large base by deeply fusing multi-energy form dynamic characteristics and power grid load interaction rules. The construction process of the multi-energy complementary model includes power generation characteristic modeling, energy storage characteristic modeling, and power grid load demand modeling.

[0032] For a wind farm, the power generation characteristic modeling adopts a combination of a physical model and a data-driven model to construct a wind speed-power curve. The physical model is based on the Betz limit theory and considers parameters such as air density, pitch angle, and wake effect to establish a functional relationship between wind energy capture efficiency and wind speed. The data-driven model uses historical operation data to capture the deviation between actual output and theoretical curve of the wind turbine through support vector machines (SVM) or deep learning networks (such as CNN-LSTM), and corrects the model mismatch caused by factors such as blade fouling and sensor errors. For a photovoltaic power station, when establishing an irradiance-power curve, correction factors such as ambient temperature, component attenuation coefficient, and shadow blocking ratio are introduced, and a single diode equivalent circuit model is used to fuse real-time weather data to dynamically calculate the maximum power point tracking (MPPT) efficiency, ensuring that the power prediction accuracy reaches more than 95%.

[0033] The energy storage characteristic modeling includes charging and discharging efficiency, capacity limitation, and SOC state. The charging and discharging efficiency adopts a dynamic efficiency curve instead of a fixed value, considering the influence of charging and discharging power, SOC state, and battery temperature on Coulomb efficiency. The capacity limitation is based on a battery aging model, introduces dual constraints of calendar life and cycle life, statistically counts the number of SOC change cycles through the rain flow counting method, and dynamically corrects the available capacity. The SOC state uses Kalman filtering and ampere-hour integration method to estimate the SOC, combines a battery equivalent circuit model (such as Thevenin model), and controls the SOC error within ±2% to provide accurate state perception for charging and discharging decision-making.

[0034] The power grid load demand modeling adopts a hierarchical architecture, quantifies the influence of load prediction error on the dispatching result according to short-term fluctuations, medium and long-term trends of the load, and the like, in combination with typical scenarios. The short-term fluctuations are based on historical load data, an autoregressive integrated moving average (ARIMA) model is constructed to capture daily periodic load fluctuations, and the prediction error is controlled within 3%. The medium and long-term trends are combined with macroeconomic indicators (such as GDP growth rate), climate prediction data (such as summer cooling load), and a Prophet time series model is used to predict the monthly load trend.

[0035] The multi-energy complementary model adopts the strategies of new energy priority consumption, energy storage-load linkage and frequency modulation of fire storage; under the premise of meeting the safety constraints of the power grid, the maximum consumption space of new energy is provided by deep peak shaving of thermal power units and rapid response of energy storage systems; according to the time-of-use price curve, the energy storage system is charged during the low valley price period and discharged during the peak price period, and combined with the load prediction data, the peak-valley price difference arbitrage is realized; when the frequency deviation caused by the sudden drop of new energy output exceeds the set threshold, the energy storage system responds within the preset time to jointly maintain the stability of the power grid frequency.

[0036] The multi-energy complementary model of the present application realizes the high coupling and global optimization of multi-energy flow in a large new energy base, significantly improves the economy and flexibility of system operation, and provides key technical support for the construction of new power systems.

[0037] According to an embodiment of the present application, the decision variables of the economic objective function include: The new energy power station generation cost, the thermal power plant generation cost and the thermal power unit start-stop cost, the energy storage power station charging and discharging cost and the peak-valley price difference, the power grid purchase cost, the wind and light abandonment penalty cost, the examination cost of the “Implementation Rules for Grid-connected Operation Management of Power Plants” and the examination cost of the “Implementation Rules for Management of Auxiliary Services of Grid-connected Power Plants”.

[0038] Preferably, the operation constraints include: power balance constraints, energy storage charging and discharging constraints, power station generation capacity constraints, and power grid dispatching constraints.

[0039] In this embodiment, by constructing a multi-dimensional cost objective function and a rigid constraint system, the economic-safety-environment multi-dimensional target collaborative optimization of a complex energy system is realized.

[0040] The objective function is guided by minimizing lifecycle costs, integrating multiple decision variables to form a quantitative optimization model. The LCOE (levelized cost of electricity) model is used for renewable energy generation costs, incorporating wind turbine / PV module depreciation, operation and maintenance costs, and dynamic efficiency degradation. The comprehensive cost of thermal power generation incorporates fuel costs, startup and shutdown costs, and environmental taxes. Energy storage system costs include charging and discharging losses and peak-valley price arbitrage profits. The charging and discharging cycle is optimized using a dynamic programming algorithm. A joint optimization model for real-time price (RTP) and day-ahead price (DAP) is developed for grid interaction costs, factoring in reserve capacity costs and demand response subsidies. Curtailment penalties for wind and solar power use the opportunity cost method, multiplying the amount of curtailed electricity by the theoretical price and environmental value. The "two detailed rules" cost assessment converts indicators such as AGC response deviation and primary frequency regulation pass rate into economic costs, enabling early intervention through compliance prediction models (such as LSTM networks).

[0041] Power balance constraints require that at any given moment, renewable energy output + thermal power output + energy storage charging and discharging power = load demand ± grid losses. A DC power flow model is used to calculate the grid loss coefficient. Energy storage charging and discharging constraints establish a safe state-of-charge (SOC) range, charge and discharge power limits, and cycle life constraints. For power generation capacity constraints, renewable energy sites consider the maximum power that can be increased or decreased, the minimum technical output of thermal power units, and the energy conservation law of the energy storage system. Grid dispatch constraints include cross-section power flow limits, voltage compliance ranges, and frequency deviation constraints. Safety checks ensure fault ride-through capability.

[0042] The present invention provides a full-factor, full-cycle economic optimization tool for large-scale new energy bases, significantly reduces operating costs while ensuring the safety of the power grid, and provides key technical support for the construction of new power systems.

[0043] According to one embodiment of the present invention, the optimization scheduling algorithm adopts a linear programming algorithm, a dynamic programming algorithm or a genetic algorithm.

[0044] Preferably, the optimal scheduling solution includes: an optimal solution and multiple suboptimal solutions.

[0045] In this implementation, an optimization scheduling algorithm is employed to meet the multi-timescale and multi-dimensional optimization requirements of large-scale renewable energy bases. The optimization scheduling algorithm is selected based on the scale of the energy base, the complexity of the network architecture, and the computing resources available. It can include linear programming, dynamic programming, or genetic algorithms. For example, a three-tiered algorithm architecture is employed. The basic algorithm layer integrates three fundamental solvers: linear programming (LP), dynamic programming (DP), and genetic algorithm (GA). LP is used to solve continuous-variable linear optimization problems (such as the economic load dispatch of thermal power units), DP is suitable for optimizing multi-stage energy storage charging and discharging strategies, and GA excels at solving nonlinear mixed-integer problems (such as optimizing unit start-stop combinations). The algorithm fusion layer develops a hybrid algorithm engine, for example, employing an LP-GA collaborative mechanism. LP is first used to quickly generate an initial feasible solution, followed by a global search using the GA. This, combined with simulated annealing operators, avoids local optima and improves solution efficiency. The constraint processing layer uses the large-M method for linearization of hard constraints such as power balance and ramp rate. For soft constraints such as the SOC safety range, slack variables and penalty functions are introduced to quantitatively control the degree of constraint violations.

[0046] By optimizing the scheduling algorithm to obtain the optimal solution and multiple suboptimal solutions, combined with the actual scenario requirements, the corresponding optimized scheduling plan is selected to formulate the operation control strategy of each station.

[0047] This invention provides an efficient and robust optimization and scheduling tool for large-scale new energy bases. While ensuring the safe and stable operation of the power grid, it significantly improves the economic efficiency of system operation and provides quantitative decision-making support for complex energy management under new power systems.

[0048] Example 2 Figure 3 This is a flow chart of an economic optimization scheduling method for a large-scale new energy base considering multi-energy complementarity in another embodiment of the present invention. Figure 3 As shown, according to one embodiment of the present invention, a method for optimizing the economic efficiency of a large-scale new energy base considering multi-energy complementarity includes the following steps: Step S201, data collection and processing; Establish communication links with various power generation stations in large new energy bases, and collect real-time operating data of wind farms, photovoltaic power stations, thermal power plants, energy storage power stations and other stations in large new energy bases through front-end servers; including: Real-time power generation of new energy stations, maximum power that can be increased / decreased, power forecast data, wind speed, irradiance and other meteorological data; Thermal power plant installed capacity, real-time power generation, and maximum power that can be increased or decreased; Energy storage power station installed capacity, battery SOC, and real-time power generation; Grid load demand and other data.

[0049] The collected data is pre-processed, including data cleaning, normalization and storage, and the input data required for the multi-energy complementary model is generated.

[0050] Step S202, multi-energy complementary model construction; Based on the collected data, a multi-energy complementary mathematical model of different power generation units such as wind farms, photovoltaic power stations, thermal power plants and energy storage power stations is constructed. The power generation characteristics of each energy are considered, including wind speed-power curve, irradiance-power curve, energy storage charging and discharging efficiency, capacity limit, SOC characteristics and load demand volatility.

[0051] Step S203, economic objective function establishment; Under the premise of fully responding to the dispatch target value, the economic objective function is established to minimize the operation cost. The objective function includes: Wind farm and photovoltaic power station generation cost; Thermal power plant generation cost and unit start-stop cost; Charging and discharging cost of energy storage device and peak-valley price difference; Grid purchase cost; Penalty cost of abandoned wind and light, etc.

[0052] Step S204, constraint condition setting; The system operation constraints are set, including: Power balance constraint: the generated power meets the dispatch target value response demand; Energy storage charging and discharging constraint: battery charging and discharging power and capacity limit; Power station generation capacity constraint: maximum incremental and decremental power of photovoltaic power station and wind farm, installed capacity and maximum incremental and decremental power of thermal power unit; Grid dispatching constraint: grid dispatching requirements and limitations.

[0053] Step S205, optimization solution; Optimization algorithms (linear programming, dynamic programming, genetic algorithm) are used to solve the objective function to obtain the optimal dispatching scheme.

[0054] The optimization algorithm is selected according to the scale of the energy base, the complexity of the network architecture and the computing resources.

[0055] Step S206, dispatching scheme implementation.

[0056] According to the results of the optimization solution, the energy storage charging and discharging strategy, the wind and light generation strategy, and the thermal power generation strategy are formulated to generate the new energy day-ahead generation plan, the energy storage charging and discharging plan, and the thermal power generation plan. Through the automatic control system, the economic optimal dispatching scheme is implemented according to the generation plan and the energy storage charging and discharging plan of the optimization dispatching result.

[0057] The application realizes the economic optimal scheduling scheme of a new energy large base by integrating the complementary characteristics of wind energy, solar energy, thermal power, energy storage and other multiple energies, can effectively reduce the operation cost, improve the energy utilization efficiency, enhance the stability of the power grid, and has a wide application prospect.

[0058] Embodiment three Figure 4 The figure is a schematic diagram of the principle of the economic optimal scheduling system of a new energy large base considering multi-energy complementation according to an embodiment of the application. Figure 4 As shown in the figure, according to an embodiment of the application, an economic optimal scheduling system of a new energy large base considering multi-energy complementation comprises: a data acquisition module for acquiring operation data of each station in the new energy large base; a data processing module for cleaning, normalizing and storing the data of the data acquisition module; a model construction module for constructing a multi-energy complementation model based on the output data of the data processing module; a target function module for constructing an economic target function with the premise of fully responding to the scheduling target value and the goal of minimizing the operation cost; a constraint condition module for setting the operation constraint conditions of the new energy large base; an optimal solution module for solving the economic target function based on the operation constraint conditions and the optimal scheduling algorithm to obtain an optimal scheduling scheme; a scheduling implementation module for formulating the operation control strategy of each station based on the optimal scheduling scheme.

[0059] This implementation proposes an economic optimization and scheduling system for large-scale renewable energy bases that considers multi-energy complementarity. The system comprises a data acquisition module, a data processing module, a model building module, an objective function module, a constraint module, an optimization solution module, and a scheduling implementation module. The data acquisition module collects real-time data such as photovoltaic power station operation data and power forecast data, wind farm site operation data and power forecast data, thermal power plant site operation data, energy storage power station operation data and battery status data, meteorological data, and load demand data. The data processing module cleans, normalizes, and stores the collected data to generate the input data required for the multi-energy complementarity model. The model building module constructs a mathematical model for multi-energy complementarity, including wind farms, photovoltaic power stations, thermal power plants, and energy storage power stations, taking into account the generation and storage characteristics of each energy source. The objective function module establishes an economic objective function that includes power generation costs, energy storage charging and discharging costs and peak-valley price differences, grid power purchase costs, penalty costs for wind and solar curtailment, and the costs of two detailed assessments. The constraint module sets the system's operating constraints, including power balance constraints, energy storage charging and discharging constraints, power plant generation capacity constraints, and grid dispatch constraints. The optimization solution module uses an optimization algorithm to solve the objective function and determine the optimal dispatch solution. Based on the optimization solution results, the dispatch implementation module formulates energy storage charging and discharging strategies, wind and solar power generation strategies, and thermal power generation strategies, generating a day-ahead renewable energy generation plan, energy storage charging and discharging plans, and thermal power generation plans. The automated control system implements the most economically efficient dispatch solution based on the power generation plan and energy storage charging and discharging plans.

[0060] Through the deep integration of data-driven and intelligent optimization, this invention realizes the efficient coordinated operation of multi-energy systems and the optimal decision-making of all costs; through the modular architecture design, the system significantly improves the operating economy, safety and flexibility of large new energy bases.

[0061] According to one embodiment of the present invention, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements any economic optimization scheduling method for a large new energy base considering multi-energy complementarity of the present invention.

[0062] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system and medium can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0063] The above description is merely that of the preferred embodiments of the application and of the principles thereof. It will be appreciated that those skilled in the art will be able to devise numerous alternative arrangements based on the principles described herein without departing from the scope of the application as defined by the appended claims, and their equivalents. For example, the features recited in the specification of the application but not in the claims of the application, or in the claims of the application but not in the specification, can be combined with features of the claims of the application or of the specification of the application, respectively, to form alternative embodiments of the application.

[0064] It should be understood that the size of the serial number of the steps in the summary of the application and in the embodiments of the application does not absolutely mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the application.

Claims

1. A method for optimizing the economic performance of a large-scale new energy base considering multi-energy complementarity, characterized in that: include: Obtain and process the operating data of each station in the new energy base; Based on the processed operating data, a multi-energy complementary model is constructed; Based on the premise of fully responding to the dispatch target value and minimizing the operating cost, an economic objective function is constructed; Setting operational constraints for large new energy bases; Solving the economic objective function based on the operating constraints and the optimization scheduling algorithm to obtain an optimal scheduling solution; An operation control strategy for each station is formulated based on the preferred scheduling plan.

2. The method for economic optimization and scheduling of large-scale new energy bases considering multi-energy complementarity according to claim 1 is characterized in that: Operational data of each station in the new energy base, including: Real-time power generation capacity, maximum power increase / decrease, power forecast data, and meteorological data of new energy stations; The installed capacity, real-time power generation, and maximum power that can be increased or decreased of the thermal power plant; The installed capacity, battery SOC, and real-time power generation of the energy storage power station; Grid load demand.

3. The method for economic optimization and scheduling of large-scale new energy bases considering multi-energy complementarity according to claim 1 is characterized in that: When constructing a multi-energy complementary model, factors to be considered include: the power generation characteristics and energy storage characteristics of each site, and the volatility of grid load demand.

4. The method for economic optimization and scheduling of large-scale new energy bases considering multi-energy complementarity according to claim 3 is characterized by: Power generation characteristics include wind speed-power curve and irradiance-power curve; Energy storage characteristics include energy storage charging and discharging efficiency, capacity limitation, and SOC.

5. The method for economic optimization and scheduling of large-scale new energy bases considering multi-energy complementarity according to claim 1 is characterized in that: The decision variables of the economic objective function include: The power generation costs of new energy power stations, the power generation costs of thermal power plants and the start-up and shutdown costs of thermal power units, the charging and discharging costs and peak-valley price differences of energy storage power stations, the power purchase costs of the power grid, the penalty costs for curtailment of wind and solar power, the assessment costs of the "Implementation Rules for the Management of Grid-Connected Operation of Power Plants" and the assessment costs of the "Implementation Rules for the Management of Auxiliary Services of Grid-Connected Power Plants".

6. The method for economic optimization and scheduling of large-scale new energy bases considering multi-energy complementarity according to claim 1 is characterized in that: The operating constraints include: power balance constraints, energy storage charging and discharging constraints, power station power generation capacity constraints, and grid dispatch constraints.

7. The method for economic optimization and scheduling of large-scale new energy bases considering multi-energy complementarity according to claim 1 is characterized by: The optimization scheduling algorithm adopts a linear programming algorithm, a dynamic programming algorithm or a genetic algorithm.

8. The method for economic optimization and scheduling of large-scale new energy bases considering multi-energy complementarity according to claim 1 is characterized in that: The preferred scheduling solution includes: an optimal solution and multiple suboptimal solutions.

9. A new energy large-scale base economic optimization scheduling system considering multi-energy complementarity, characterized by: include: Data acquisition module, used to obtain the operating data of each station in the new energy base; A data processing module, used for cleaning, normalizing and storing the data from the data acquisition module; A model building module, configured to build a multi-energy complementary model based on the output data of the data processing module; The objective function module is used to construct an economic objective function based on the premise of fully responding to the scheduling target value and minimizing the operating cost; Constraint module, used to set the operating constraints of the new energy base; An optimization solution module, configured to solve the economic objective function based on the operation constraints and the optimization scheduling algorithm to obtain an optimal scheduling solution; The scheduling implementation module is used to formulate the operation control strategy of each station based on the preferred scheduling plan.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the economic optimization scheduling method for a large new energy base considering multi-energy complementarity as described in any one of claims 1-8.

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