A user-side source network load storage collaborative regulation method

Abstract

The application discloses a user-side source network load storage collaborative regulation method and system, takes net load, real-time electricity price and storage state of charge as input, realizes power distribution of distributed power supply, storage and power grid through four preset collaborative modes: when real-time electricity price is higher than high price threshold, storage discharges to cut peak; when real-time electricity price is lower than low price threshold, storage charges from power grid; when distributed power supply has surplus power, the surplus power is preferentially charged into storage; after storage is fully charged, surplus power is fed into power grid or abandoned. The application takes demand constraint as a priority, takes electricity meter electricity charge optimization as a secondary target, does not need load prediction, does not need to depend on external peak-valley time period table, and can be operated in a user-side local closed loop only by relying on real-time data, and simultaneously reduces user basic electricity charge and electricity meter electricity charge.

Description

Technical Field

[0001] This invention relates to the field of user-side energy storage system control technology, and in particular to a source-grid-load-storage coordinated control method and system that uses real-time electricity price and net load as decision inputs, requires no prediction, and performs local closed-loop execution. Background Technology

[0002] With the increasing adoption of distributed photovoltaic and wind power technologies by users, and the decreasing cost of energy storage systems, more and more industrial parks, factories, and commercial complexes are beginning to build integrated "source-grid-load-storage" systems. However, existing technologies have the following problems; 1. Prediction-dependent solutions: Existing energy storage dispatch systems generally rely on load forecasting, photovoltaic output forecasting, and electricity price forecasting. Forecasting errors lead to decision-making biases, resulting in high system complexity and maintenance costs. 2. Cloud platform dependent solution: The existing system needs to upload data to the cloud platform for centralized computing and optimization, which relies on network communication and has latency and reliability issues; 3. Single control object: Existing solutions mostly focus on the charging and discharging optimization of energy storage itself, or only control adjustable loads, without achieving coordinated optimization of power source, grid, load, and energy storage; 4. Arbitrage-oriented solutions: Existing energy storage systems aim at independent arbitrage by "buying low and selling high," which is detached from the actual electricity demand of users. They require virtual power plant or load aggregator qualifications and are not suitable for ordinary users. 5. Time-of-use schedule-dependent solutions: Existing solutions typically rely on fixed peak-valley time-of-use schedules published by the power supply bureau as the basis for judgment. When electricity pricing policies change or users enter the electricity spot market, the fixed time-of-use schedules become invalid, and the system needs to be manually reconfigured, resulting in poor adaptability. Therefore, a simple, reliable, unpredictable, and locally autonomous source-grid-load-storage coordinated control method is needed, centered on user electricity load, utilizing real-time data for immediate response, to achieve unattended operation and cost optimization. Summary of the Invention

[0003] This invention provides a user-side source-grid-load-storage coordinated regulation method and system to solve the above-mentioned technical problems. Technical solution

[0004] A user-side source-grid-load-storage coordinated regulation method, characterized in that it includes: (1) Data collection steps: Real-time data collection of user-side power load, distributed power output, grid-side real-time electricity price, and energy storage system charge status; (2) Calculation steps: Calculate the net load based on the difference between the power of the electrical load and the output of the distributed power source; (3) Decision-making steps: Using the net load, real-time electricity price and state of charge as inputs, determine the power allocation between distributed power sources, energy storage systems and the power grid according to preset coordination rules; the coordination rules aim to optimize the comprehensive electricity cost on the user side, and are achieved by reasonably allocating the output of distributed power sources, the charging and discharging of energy storage and the power taken from the power grid. (4) Execution steps: The power distribution is output to the energy storage converter and the distributed power inverter for execution.

[0005] Furthermore, the collaborative rules include a first mode: When the output of the distributed power source is less than the electricity load, the real-time electricity price is higher than the high price threshold, and the state of charge of the energy storage system is higher than the discharge lower limit, the energy storage system is controlled to discharge in order to reduce the power drawn from the grid.

[0006] Furthermore, the collaborative rules include a second modality: When the output of the distributed power source is less than the power load, the real-time electricity price is lower than the low price threshold, and the state of charge of the energy storage system is lower than the charging upper limit, the energy storage system is controlled to draw power from the grid to charge in order to store low-priced electricity.

[0007] Furthermore, the coordination rules include a third modality: When the output of the distributed power source is greater than the power load and the state of charge of the energy storage system is lower than the charging limit, the energy storage system is controlled to charge and absorb the surplus power of the distributed power source.

[0008] Furthermore, the collaborative rules include a fourth modality: When the output of the distributed power source exceeds the power load and the state of charge of the energy storage system has reached the charging limit, the surplus power of the distributed power source is preferentially fed into the grid; if the grid does not allow power feeding, the power is abandoned or transferred to other loads.

[0009] Furthermore, the coordination rules also include: When the output of the distributed power source is less than the electricity load and the real-time electricity price is between the low price threshold and the high price threshold, the energy storage system remains idle and is directly powered by the grid.

[0010] Furthermore, the high-price threshold and the low-price threshold are user-defined fixed values ​​or dynamic values ​​automatically calculated by the system based on historical electricity price data.

[0011] Furthermore, the coordination rule prioritizes limiting the power taken from the grid to not exceed a preset demand threshold; when the power taken from the grid approaches the demand threshold, the energy storage system prioritizes the discharge action, which has a higher priority than any charging action.

[0012] Furthermore, the real-time electricity price originates from the grid side, the power trading center, or the aggregator, and includes at least one of time-of-use pricing, dynamic pricing, or fixed pricing; the method further includes: optionally using ancillary service signals issued by the aggregator as input to the preset coordination rules to participate in local power allocation decisions.

[0013] Furthermore, the energy storage converter serves as a power controllable interface between the energy storage system and the power grid, and the distributed power inverter serves as a power controllable interface between the distributed power source and the power grid.

[0014] A user-side source-grid-load-storage coordinated control system is characterized in that it includes: a data acquisition unit, a net load calculation unit, a coordinated decision-making unit, and an execution drive unit for executing the above method. Beneficial effects

[0015] No forecasting required: This invention makes decisions based on real-time electricity prices and real-time net load, without the need to predict future load, future photovoltaic output, or future electricity prices, thus avoiding decision-making biases caused by forecasting errors. The system is simple and reliable.

[0016] Local autonomy: All data collection, calculation, decision-making, and execution steps are completed locally on the user side, without relying on external cloud platforms or remote scheduling commands, resulting in fast response speed and high reliability.

[0017] Load-centric approach: This invention takes user electricity load as the core of decision-making, prioritizes distributed power sources to meet load requirements, coordinates and regulates energy storage systems, and uses the power grid as a supplement to achieve coordinated optimization of power sources, grid, load, and storage.

[0018] Cost optimization: By automatically switching between multiple modes such as high-price discharge, low-price charging, surplus power consumption, and demand shaving, the overall electricity cost on the user side is optimized by reducing both electricity consumption fees and basic electricity fees.

[0019] Unattended operation: The four modes and static state automatically switch according to real-time data, without the need for manual intervention, realizing intelligent unattended scheduling.

[0020] Highly adaptable: This invention makes decisions based on real-time electricity prices rather than fixed time slots. When electricity price policies change or users enter the electricity spot market, the system automatically adapts without the need for manual reconfiguration.

[0021] Revenue scalability: This invention supports users to selectively participate in the ancillary services market, and while maintaining local autonomy, obtain additional revenue through peak shaving, frequency regulation, green electricity trading, etc., thereby further reducing the overall cost of electricity. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the user-side source-grid-load-storage coordinated regulation method of the present invention.

[0023] Figure 1 illustrate: This flowchart illustrates the complete execution process of the user-side source-grid-load-storage coordinated regulation method, including four steps: Step 1 (Data Acquisition): Real-time data acquisition of user-side electricity load power, distributed power generation output (photovoltaics, wind power, etc.), grid-side real-time electricity price, and energy storage system state of charge (SOC). Step 2 (Calculation Step): Calculate the net load based on the difference between the electrical load power and the distributed power output. Net Load = Load Power - Photovoltaic Output - Wind Power Output; Step 3 (Decision-Making Step): Using net load, real-time electricity price, and state of charge as inputs, determine the power allocation among distributed power sources, energy storage systems, and the grid according to preset coordination rules. The coordination rules aim to optimize the overall electricity cost on the user side. The cooperative rules include four modes and one static state: First mode (high-price discharge): Net load > 0, real-time electricity price > high-price threshold, SOC > discharge lower limit → energy storage discharge, reducing grid power consumption; Second mode (low-price charging): Net load > 0, real-time electricity price < low-price threshold, SOC < charging limit → energy storage charging, storing low-price electricity; Third mode (residual power charging): Net load < 0, SOC < charging limit → energy storage charging, absorbing residual power from distributed power sources; Fourth Mode (Residual Power Handling): Net load < 0, SOC ≥ charging limit → Prioritize feeding residual power back to the grid; otherwise, abandon or transfer the power. Quiet state: Net load > 0, low price threshold < real-time electricity price < high price threshold → Energy storage idle, direct grid supply; Demand priority constraint (through all modes): When the power taken from the grid approaches the preset demand threshold, the energy storage will be discharged first to reduce peaks, with a higher priority than any charging action. Step 4 (Execution Step): Power distribution output to the energy storage converter and distributed power inverter is executed.

[0024] System architecture: includes a data acquisition unit, a net load calculation unit, a collaborative decision-making unit, and an execution drive unit.

[0025] Execution in a loop: The above steps are repeated in real time to form a local closed loop, without the need for an external cloud platform or remote scheduling instructions. Detailed Implementation

[0026] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0027] Threshold setting method** This invention uses two electricity price values, "high price threshold" and "low price threshold," as the judgment criteria, and does not rely on a fixed peak-valley time table. (a) Fixed threshold method** Users manually set high-price and low-price thresholds according to local electricity pricing policies. The high-price threshold is used to determine whether the electricity price is too high—discharging is triggered when the real-time electricity price is higher than this value; the low-price threshold is used to determine whether the electricity price is too low—charging is triggered when the real-time electricity price is lower than this value. The area between the two thresholds is a resting zone, where energy storage does not operate. (ii) Dynamic threshold method The system automatically collects historical electricity price data from the past N days (e.g., 30 days), automatically calculates and generates high-price and low-price thresholds using certain statistical quantiles (e.g., using the 70th percentile as the high-price threshold and the 30th percentile as the low-price threshold), and updates it regularly (e.g., once a day). When changes in the supply and demand relationship in the electricity market cause the electricity price curve to invert (e.g., due to a surge in renewable energy generation leading to lower daytime electricity prices than nighttime prices), the system automatically adapts through the aforementioned dynamic threshold update mechanism—high-price and low-price thresholds are recalculated and generated based on rolling historical electricity price data, without requiring manual adjustment or switching of strategy modes. The decision-making logic of this invention is always based on the comparison between real-time electricity prices and dynamic thresholds, regardless of the specific shape of the electricity price curve. Therefore, it is naturally adaptable to various electricity price scenarios, including but not limited to traditional time-of-use pricing, spot market price fluctuations, and electricity price curve inversions.

[0028] Example 1 Scenario: An industrial park is equipped with a 2MW photovoltaic power generation system, a 4MWh energy storage system, and a 30kW wind power generation system. The park's electricity load is approximately 1500kW, the transformer capacity is 2000kVA, and time-of-use pricing is applied (1.2 yuan / kWh during peak hours and 0.4 yuan / kWh during off-peak hours). Step 1: Real-time data collection. Electricity load power: 1500kW, photovoltaic output: 1200kW, wind power output: 20kW, real-time electricity price: 1.2 yuan / kWh (peak hours), energy storage SOC: 60%; Step 2: Calculate the net load. Net load = 1500 - 1200 - 20 = 280kW; Step 3: Decision. Net load > 0, real-time electricity price 1.2 yuan > high price threshold (assume 1.0 yuan), SOC 60% > discharge lower limit (assume 20%). Trigger first mode: energy storage discharge; Step 4: Execute. Energy storage discharge power = min(280kW, maximum energy storage discharge power, demand threshold margin). Grid power draw is reduced from 280kW to 0kW, saving on peak electricity costs.

[0029] Example 2 Scenario: Mid-morning off-peak hours, load 800kW, PV 0kW, wind 10kW, real-time electricity price 0.4 yuan / kWh, energy storage SOC 30% Step 1: Real-time data collection. Electricity load power: 800kW; Solar power output: 0kW; Wind power output: 10kW; Real-time electricity price: 0.4 yuan / kWh (off-peak); Energy storage SOC: 30%. Step 2: Calculate the net load. Net load = 800 - 0 - 10 = 790kW; Step 3: Decision. Net load > 0, real-time electricity price 0.4 yuan < low price threshold (assume 0.5 yuan), SOC 30% < charging limit (assume 90%). Trigger the second mode: energy storage charging; Step 4: Execution. Energy storage charging power = preset smooth charging power (e.g., 0.3C = 1200kW), limited by transformer capacity. The grid supply of 800kW meets the load, while simultaneously supplying 1200kW to charge the energy storage.

[0030] Example 3 Scenario: At midday, solar power generation is at its peak, with a load of 1000kW, solar power at 1500kW, wind power at 5kW, real-time electricity price at 0.8 yuan / kWh (average), and energy storage SOC at 80%. Step 1: Real-time data collection. Electricity load power: 1000kW; photovoltaic output: 1500kW; wind power output: 5kW; real-time electricity price: 0.8 yuan / kWh; energy storage SOC: 80%. Step 2: Calculate the net load. Net load = 1000 - 1500 - 5 = -505kW; Step 3: Decision. Net load < 0 (power supply greater than load), SOC 80% < charging limit 90%. Trigger the third mode: residual power charging; Step 4: Execution. Energy storage charging power = min(505kW, power corresponding to the remaining charging space of energy storage). 1000kW of photovoltaic power supplies the load, and the surplus power of 505kW charges the energy storage. 0kW of power is drawn from the grid, achieving 100% green electricity consumption.

[0031] Example 4 Scenario: Same as Example 3, but the energy storage SOC has reached the 90% upper limit: Step 3: Decision. Net load < 0, SOC ≥ 90% (charging limit reached). Trigger the fourth mode: residual power handling.

[0032] Step 4: Execution. Prioritize feeding surplus power into the grid; if the grid does not allow power feeding, then either discard the power or transfer it to other loads (such as charging piles, heat pumps, etc.).

[0033] Example 5 Scenario: A sudden increase in load, with net load rising from 500kW to 2000kW, approaching the demand threshold of 1800kW; Demand priority constraint triggering: Regardless of the current mode, the energy storage system prioritizes discharging to reduce the grid power intake to below 1800kW. Discharging has higher priority than any charging action (including off-peak charging in the second mode).

[0034] Example 6 Scenario: Users selectively participate in the ancillary services market; In actual operation, ancillary service signals are obtained in advance through the day-ahead trading mechanism of the electricity market, including the call period, service type, and compensation price information. The system conducts a daily revenue assessment, comparing the expected revenue from participating in ancillary services with the daily arbitrage revenue under the current four modes: if the ancillary service revenue is higher than the daily arbitrage revenue and energy storage has available capacity, then selective participation is allowed during the corresponding time period; otherwise, the original mode is operated. The response to ancillary service signals always prioritizes not weakening the local load power supply capacity. The ancillary service signals include, but are not limited to, one or more of the following: peak shaving ancillary service signals, frequency regulation ancillary service signals, green electricity trading signals, or carbon trading signals. Users can selectively participate in one or more of these signals based on their own configuration and qualifications to obtain corresponding benefits. It should be noted that this auxiliary service signal is only an optional input reference and only takes effect when the user selects to enable it. For users who do not participate in the auxiliary service, the system still operates normally according to the four preset collaborative modes, without relying on any external platform or external commands. The auxiliary service function does not affect the independent operation capability of this invention; it is merely a way to enhance benefits while maintaining local autonomy.

[0035] Industrial applicability The method of this invention has clear logic and simple calculations, and can be deployed on low-cost edge computing devices (such as industrial gateways, industrial control computers, etc.). It is suitable for various industrial and commercial users and park users with distributed power and energy storage systems. This invention does not rely on virtual power plant qualifications or load forecasting models, and can operate solely on local real-time data, thus possessing broad industrial practical value and market promotion prospects.

Claims

1. A user-side source-grid-load-storage coordinated regulation method, characterized in that, include: (1) Data collection steps: Real-time data collection of user-side power load, distributed power output, grid-side real-time electricity price, and energy storage system charge status; (2) Calculation steps: Calculate the net load based on the difference between the power of the electrical load and the output of the distributed power source; (3) Decision-making steps: Using the net load, real-time electricity price and state of charge as inputs, determine the power allocation between distributed power sources, energy storage systems and the power grid according to preset coordination rules; (4) Execution steps: The power distribution is output to the energy storage converter and the distributed power inverter for execution.

2. The method according to claim 1, characterized in that, The coordination rules include a first mode: When the output of the distributed power source is less than the electricity load, the real-time electricity price is higher than the high price threshold, and the state of charge of the energy storage system is higher than the discharge lower limit, the energy storage system is controlled to discharge in order to reduce the power drawn from the grid.

3. The method according to claim 1, characterized in that, The coordination rules include a second modality: When the output of the distributed power source is less than the power load, the real-time electricity price is lower than the low price threshold, and the state of charge of the energy storage system is lower than the charging upper limit, the energy storage system is controlled to draw power from the grid to charge in order to store low-priced electricity.

4. The method according to claim 1, characterized in that, The coordination rules include a third modality: When the output of the distributed power source is greater than the power load and the state of charge of the energy storage system is lower than the charging limit, the energy storage system is controlled to charge and absorb the surplus power of the distributed power source.

5. The method according to claim 1, characterized in that, The coordination rules include a fourth modality: When the output of the distributed power source exceeds the power load and the state of charge of the energy storage system has reached the charging limit, the surplus power of the distributed power source is preferentially fed into the grid; if the grid does not allow power feeding, the power is abandoned or transferred to other loads.

6. The method according to claim 1, characterized in that, The coordination rules also include: When the output of the distributed power source is less than the electricity load and the real-time electricity price is between the low price threshold and the high price threshold, the energy storage system remains idle and is directly powered by the grid.

7. The method according to claim 1, characterized in that, The high-price threshold and low-price threshold are user-defined fixed values ​​or dynamic values ​​automatically calculated by the system based on historical electricity price data.

8. The method according to claim 1, characterized in that, The coordination rule prioritizes limiting the power taken from the grid to a preset demand threshold. When the power taken from the grid approaches the demand threshold, the energy storage system prioritizes the discharge action, which has a higher priority than any charging action.

9. The method according to claim 1, characterized in that, The real-time electricity price originates from the grid side, the power trading center, or the aggregator, and includes at least one of time-of-use pricing, dynamic pricing, or fixed pricing; the method further includes: optionally using ancillary service signals issued by the aggregator as input to the preset coordination rules to participate in local power allocation decisions.

10. The method according to claim 1, characterized in that, The energy storage converter is a power controllable interface between the energy storage system and the power grid, and the distributed power inverter is a power controllable interface between the distributed power source and the power grid.

11. A user-side source-grid-load-storage coordinated control system, characterized in that, For performing the method according to any one of claims 1-10, comprising: The data acquisition unit is used to acquire load power, distributed power output, real-time electricity price, and state of charge in real time. A net load calculation unit is used to calculate the net load based on the difference between the load power and the output of the distributed power source; The collaborative decision-making unit is used to determine power allocation according to preset collaborative rules, taking the net load, real-time electricity price and state of charge as inputs. The execution drive unit is used to output the power distribution to the energy storage converter and the distributed power inverter.

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