A multi-type capacity application method and system for a virtual power plant

Abstract

The present invention relates to a multi-type capacity application method and system for a virtual power plant, which aims to improve the prediction accuracy of power demand and renewable energy output and the dispatching efficiency of the power system. The method first predicts power demand and renewable energy output respectively by training an LSTM model and an SVM model. The LSTM model is used to capture the long-term dependencies in time series data, while the SVM model accurately fits the changing trend of power resources. Then, the prediction results of the two are fused through a fuzzy logic algorithm to generate more accurate final prediction data. Based on these prediction data, a pre-trained deep Q network is used to generate an intelligent control strategy for the power system, optimize the charging and discharging operations of energy storage equipment, and achieve a balance between power supply and demand. Through this method, the stability and efficiency of the virtual power plant under various power resources and demand fluctuations can be effectively improved, ensuring the efficient operation of the power system and the optimal utilization of resources.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plants, and in particular relates to a multi-type capacity application method and system for a virtual power plant. Background Art

[0002] Today’s power grid is not very efficient because it is expensive and wasteful. Part of the problem is caused by daily fluctuations in load demand and the need to regulate the voltage and frequency of the grid.

[0003] When grid demand exceeds the capacity of baseload power plants, peaking plants are put into operation because the grid itself does not have enough energy storage, and sometimes spinning reserves are involved. When grid demand is low, electricity consumption is lower than the output of baseload power plants, so the unused energy will be wasted. In addition, the voltage and frequency regulation of the grid greatly increases the operating cost of the grid.

[0004] However, existing virtual power plants need to use multiple types of distributed resources, and it may be difficult to flexibly adjust the output or input of resources according to actual needs when scheduling resources of this capacity, which can easily affect the normal use of virtual power plant resources. Therefore, this application proposes a multi-type capacity mining system for virtual power plants. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a multi-type capacity application method and system for a virtual power plant.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] In one aspect, the present invention provides a method for applying multiple types of capacity of a virtual power plant, comprising the following steps:

[0008] Step S1: construct an LSTM model for predicting electricity demand and renewable energy output, and train the LSTM model;

[0009] Step S2: constructing an SVM model for predicting electricity demand and renewable energy output, and training the SVM model;

[0010] Step S3: obtaining current power resource data, inputting the current power resource data into the trained LSTM model and the SVM model respectively, obtaining first prediction data and second prediction data respectively, using fuzzy logic algorithm to assign weights, and obtaining final prediction data according to the weights, wherein the final prediction data includes power demand prediction value and renewable energy output prediction value;

[0011] Step S4: Using the pre-trained deep Q network, generate a power system control strategy through power demand forecast values, renewable energy output forecast values ​​and current power resource data.

[0012] Furthermore, the training of the LSTM model includes the following steps:

[0013] Obtain historical data on electricity demand and renewable energy;

[0014] Performing data normalization on the historical data of electricity demand and the historical data of renewable energy respectively;

[0015] The historical data of electricity demand and renewable energy are used as training data to train the LSTM model.

[0016] Furthermore, the loss function of the LSTM model is:

[0017] ,

[0018] ,

[0019] ,

[0020] ,

[0021] ,

[0022] ,

[0023] in, is the total loss function for LSTM model training, is the weight coefficient of the loss function, The loss function for LSTM model power demand prediction is: is the loss function for LSTM model renewable energy output prediction, is the LSTM model system stability loss function, N is the number of training samples, The electricity demand predicted by the LSTM model, is the actual power demand, Renewable energy output predicted by LSTM model, is the actual renewable energy output, f is a Gaussian function, are the error changes between the electricity demand forecast and the renewable energy output forecast at the i-th time step and the previous time step, respectively.

[0024] Furthermore, the training SVM model includes the following steps:

[0025] Obtain historical data on power resources, including distributed power source data, energy storage system data, controllable load data, electric vehicle data, and external environment data;

[0026] Obtain historical data on electricity demand and renewable energy;

[0027] Perform data normalization on the historical data of power resources, historical data of power demand and historical data of renewable energy respectively;

[0028] The historical data of power resources are used as input features, and the historical data of power demand and renewable energy are used as target variables to train the SVM model.

[0029] Furthermore, the distributed power data includes real-time power generation, power generation and power generation efficiency of solar power generation and wind power generation;

[0030] The energy storage system data includes the real-time storage capacity, energy storage efficiency, remaining storage time, charging power, discharging power, charging time, discharging time, health status, life prediction and maintenance records of the energy storage equipment;

[0031] The controllable load data includes real-time load demand and load fluctuation; the electric vehicle data includes real-time charging power, charging amount, charging time, health status, remaining life and maintenance records of the electric vehicle battery;

[0032] The external environment data includes temperature, humidity, wind speed, and sunshine intensity.

[0033] Furthermore, the loss function of the SVM model is:

[0034] ,

[0035] ,

[0036] ,

[0037] ,

[0038] ,

[0039] in, is the total loss function for SVM model training, is the loss function of the power demand prediction of the SVM model, is the loss function for renewable energy output prediction of the SVM model, is the stability loss function of the SVM model system, is the regularization term of SVM, is the weight coefficient of the total loss function of the SVM model, The power demand predicted by the SVM model, is the renewable energy output predicted by the SVM model, is the weight vector of the SVM model, is the regularization parameter.

[0040] Furthermore, the fuzzy logic algorithm is used to assign weights, and the final prediction data is obtained according to the weights. The calculation formula for the final prediction data is:

[0041] ,

[0042] ,

[0043] ,

[0044] in, is the weight of the first predicted data, is the weight of the second prediction data, is the predicted data predicted by LSTM at time j, is the predicted data predicted by SVM at time j, is the final forecast data at time j-1, is the final prediction data at time j.

[0045] Furthermore, the pre-trained deep Q network includes a state space, an action space, and a reward function. The state space includes the electric energy reserves in the current energy storage device, the charging and discharging power of the current energy storage device, the power demand forecast value, and the renewable energy output forecast value; the action space includes charging, discharging, and maintaining the current state.

[0046] Furthermore, the reward function is:

[0047] ,

[0048] in, is the reward function, is the predicted value of electricity demand at time t, is the predicted value of renewable energy output at time t, is the total power supply capacity of the system at time t, including renewable energy and energy storage system, is the charging power of the energy storage device at time t, is the discharge power of the energy storage device at time t, are the adjustment coefficients of the reward function respectively.

[0049] The present invention also provides a system based on the multi-type capacity application method of the virtual power plant, including a data acquisition and monitoring module, a prediction and evaluation module, an intelligent scheduling and optimization module, and an auxiliary service and market participation module;

[0050] The data acquisition and monitoring module is used to collect power resource data in real time and perform remote monitoring;

[0051] The prediction and evaluation module is used to predict power demand and renewable energy output based on the collected power resource data using a pre-trained LSTM model and SVM model;

[0052] The intelligent dispatching and optimization module is used to generate a power system control strategy based on the predicted power demand, renewable energy output, and power resource data using a pre-trained deep Q network, and to perform intelligent dispatching based on the generated power system control strategy to optimize the system's operating efficiency;

[0053] The auxiliary service and market participation module is used to provide auxiliary services and participate in electricity market transactions.

[0054] Compared with the prior art, the present invention has the following advantages:

[0055] (1) The present invention predicts power demand and renewable energy output by training LSTM model and SVM model respectively, and can realize efficient prediction and modeling of different types of power resources. LSTM model is good at capturing long-term dependencies in time series data and is suitable for predicting fluctuations in power demand and renewable energy; while SVM model improves prediction accuracy through precise fitting of support vector machine. The accuracy and robustness of prediction data are further improved by optimizing weight distribution through fuzzy logic algorithm.

[0056] (2) The present invention conducts a comprehensive analysis of historical data on power demand, historical data on renewable energy, and data on multiple types of power resources (such as distributed power sources, energy storage systems, electric vehicles, etc.), and fuses the prediction results of the LSTM and SVM models through a fuzzy logic algorithm, thereby effectively reducing the errors that may occur in a single model. This multi-model fusion approach improves the accuracy and stability of power demand and renewable energy output forecasts, and can provide more reliable operating data support for virtual power plants.

[0057] (3) The present invention uses a deep Q network to automatically generate the control strategy of the power system. The deep Q network can autonomously learn and optimize the operation strategy of the power system, such as charging, discharging or maintaining the current state, based on the power demand forecast value, renewable energy output forecast value and current power resource status. Through reinforcement learning, the deep Q network can continuously adjust the control strategy to ensure the efficient use of power resources and the stable operation of the virtual power plant.

[0058] (4) The present invention introduces a system stability loss function in both the LSTM and SVM models, aiming to balance electricity demand with renewable energy output, reduce volatility in system operation, and thus ensure the stability and reliability of power supply. In addition, by optimizing the decision-making process of the deep Q network through the reward function, the power system can automatically adjust the charging and discharging operations of the energy storage equipment under dynamically changing demand and supply conditions, thereby improving the response speed and flexibility of the system.

[0059] (5) The present invention takes into account the energy flow balance and dynamic demand response of the power system in the design of the reward function of the deep Q network. By setting reasonable reward functions, such as energy flow balance rewards and dynamic demand response rewards, the system can make timely adjustments when the power demand fluctuates sharply or the output of renewable energy changes suddenly. For example, the system can adjust the charging and discharging power of the energy storage equipment in real time according to the difference between actual demand and predicted demand, or make up for the power gap through intelligent scheduling of electric vehicles when renewable energy supply is insufficient. This refined scheduling and response mechanism significantly improves the emergency response capability of the virtual power plant in the face of emergencies and ensures the continuity of power supply and the stability of the system.

[0060] (6) The multi-type capacity application method of the present invention can flexibly access a variety of power resource data, such as energy storage systems, distributed power generation, electric vehicle charging, etc., and therefore has good scalability. Whether it is for virtual power plants of different sizes or to cope with the challenges of future changes in power demand and renewable energy ratios, it can adapt to new application scenarios by adjusting model training and control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0063] Embodiment 1:

[0064] This embodiment provides a multi-type capacity application method for a virtual power plant, such as Figure 1 As shown, the following steps are included:

[0065] Step S1: constructing an LSTM model for predicting power demand and renewable energy output, and training the LSTM model; wherein training the LSTM model includes the following steps:

[0066] Obtain historical data on electricity demand and renewable energy;

[0067] Performing data normalization on the historical data of electricity demand and the historical data of renewable energy respectively;

[0068] The historical data of electricity demand and renewable energy are used as training data to train the LSTM model.

[0069] The loss function of the LSTM model is:

[0070] ,

[0071] ,

[0072] ,

[0073] ,

[0074] ,

[0075] ,

[0076] in, is the total loss function for LSTM model training, is the weight coefficient of the loss function, The loss function for LSTM model power demand prediction is: is the loss function for LSTM model renewable energy output prediction, is the LSTM model system stability loss function, N is the number of training samples, The electricity demand predicted by the LSTM model, is the actual power demand, Renewable energy output predicted by LSTM model, is the actual renewable energy output, f is a Gaussian function, are the error changes between the electricity demand forecast and the renewable energy output forecast at the i-th time step and the previous time step, respectively.

[0077] Step S2: constructing an SVM model for predicting power demand and renewable energy output, and training the SVM model; wherein training the SVM model includes the following steps:

[0078] Obtain historical data on power resources, including distributed power source data, energy storage system data, controllable load data, electric vehicle data, and external environment data;

[0079] Obtain historical data on electricity demand and renewable energy;

[0080] Perform data normalization on the historical data of power resources, historical data of power demand and historical data of renewable energy respectively;

[0081] The historical data of power resources are used as input features, and the historical data of power demand and renewable energy are used as target variables to train the SVM model.

[0082] The distributed power supply data includes the real-time power generation, power generation and power generation efficiency of solar power generation and wind power generation; the energy storage system data includes the real-time storage capacity, energy storage efficiency, remaining storage time, charging power, discharging power, charging time, discharging time, health status, life prediction and maintenance records of the energy storage equipment; the controllable load data includes real-time load demand and load fluctuation; the electric vehicle data includes the real-time charging power, charging capacity, charging time, health status, remaining life and maintenance records of the electric vehicle battery; the external environment data includes temperature, humidity, wind speed and sunshine intensity.

[0083] The loss function of the SVM model training process is:

[0084] ,

[0085] ,

[0086] ,

[0087] ,

[0088] ,

[0089] in, is the total loss function for SVM model training, is the loss function of the power demand prediction of the SVM model, is the loss function for renewable energy output prediction of the SVM model, is the stability loss function of the SVM model system, is the regularization term of SVM, is the weight coefficient of the total loss function of the SVM model, The power demand predicted by the SVM model, is the renewable energy output predicted by the SVM model, is the weight vector of the SVM model, is the regularization parameter.

[0090] Step S3: Obtain current power resource data, input the current power resource data into the trained LSTM model and SVM model respectively, obtain the first prediction data and the second prediction data respectively, use the fuzzy logic algorithm to assign weights, and obtain the final prediction data according to the weights. The final prediction data includes the power demand prediction value and the renewable energy output prediction value; wherein, the calculation formula of the final prediction data is:

[0091] ,

[0092] ,

[0093] ,

[0094] in, is the weight of the first predicted data, is the weight of the second prediction data, is the predicted data predicted by LSTM at time j, is the predicted data predicted by SVM at time j, is the final forecast data at time j-1, is the final prediction data at time j.

[0095] Step S4: Using the pre-trained deep Q network, generate a power system control strategy through power demand forecast values, renewable energy output forecast values ​​and current power resource data.

[0096] Among them, the pre-trained deep Q network includes state space, action space, and reward function. The state space includes the current energy reserve in the energy storage device, the charging and discharging power of the current energy storage device, the power demand forecast value, and the renewable energy output forecast value; the action space includes charging, discharging, and maintaining the current state.

[0097] The reward function is:

[0098] ,

[0099] in, is the reward function, is the predicted value of electricity demand at time t, is the predicted value of renewable energy output at time t, is the total power supply capacity of the system at time t, including renewable energy and energy storage system, is the charging power of the energy storage device at time t, is the discharge power of the energy storage device at time t, are the adjustment coefficients of the reward function respectively.

[0100] Embodiment 2:

[0101] The parts not mentioned in this embodiment are the same as those in Embodiment 1.

[0102] Example 1: Power demand and renewable energy forecasting and dispatch control of virtual power plants

[0103] In this embodiment, historical data on electricity demand and renewable energy are first collected, and the data covers electricity demand and solar and wind power generation in the past three years. These data will be normalized to ensure that the data are within the same dimensional range to avoid the influence of data of different magnitudes on the training effect of the model. Then, based on the normalized data, the LSTM (Long Short-Term Memory Network) model is used for training. The input of the LSTM model is historical electricity demand and renewable energy output data, and the output of the model is the electricity demand forecast value and renewable energy output forecast value for a period of time in the future. During the training process, a loss function including electricity demand forecast error, renewable energy output forecast error and system stability error is used to optimize the parameters of the LSTM model, so that the model can more accurately predict future electricity demand and renewable energy output.

[0104] At the same time, the collected power resource data also includes distributed power data (such as real-time power generation, power generation and power generation efficiency of solar energy and wind energy), energy storage system data (such as real-time storage capacity, charging power, discharge power, etc. of energy storage equipment), and electric vehicle data (such as charging power and charging capacity of electric vehicles, etc.). Using these data, the support vector machine (SVM) model is trained. The SVM model predicts the changing trends of power demand and renewable energy based on the historical data of power demand and renewable energy. In the training process of the SVM model, the loss function including power demand error, renewable energy error, stability error and regularization term is used to enhance the prediction accuracy and stability of the model.

[0105] After the prediction results are obtained, the fuzzy logic algorithm is used to fuse the prediction results of the LSTM and SVM models. Specifically, the fuzzy logic algorithm obtains the final power demand and renewable energy output prediction values ​​based on the weight distribution of the LSTM and SVM prediction data. The weight distribution rules are based on the error change of the model prediction and are adjusted through the fuzzy logic function to ensure that the final prediction results have high accuracy in different scenarios.

[0106] Based on these forecast data, the control strategy of the power system is further generated through the deep Q network (DQN). The deep Q network automatically generates a control strategy that adapts to the current power system needs through reinforcement learning by inputting the power demand forecast value, the renewable energy output forecast value and the current power resource status (such as the energy reserve of the energy storage device, the charging and discharging power, etc.), including the decision of charging, discharging or maintaining the current state of the energy storage device. The reward function optimizes the control strategy by comprehensively considering factors such as the degree of satisfaction of power demand, the utilization ratio of renewable energy and the operating efficiency of the energy storage device, so that the power system can achieve efficient and stable operation under dynamically changing demand and supply conditions.

[0107] Through the above steps, this embodiment successfully implements the power demand and renewable energy forecasting based on LSTM and SVM, and combines the fuzzy logic algorithm with the deep Q network control strategy to optimize the scheduling and control of the virtual power plant, thereby improving the utilization efficiency of power resources and the stability of the system.

[0108] The multi-type capacity mining method proposed in this embodiment can effectively cope with the challenges brought by the volatility of renewable energy in traditional power systems, optimize the dispatch of power resources, and improve the flexibility and regulation capabilities of the power grid, especially in areas with large fluctuations in power demand and a high proportion of renewable energy. It has important application value. Through the combination of deep learning and reinforcement learning, the system can achieve autonomous learning and optimization, and has strong adaptability and self-adjustment capabilities, providing new technical ideas and solutions for the intelligent and flexible dispatch of modern power systems.

[0109] This embodiment also provides a system based on the above-mentioned virtual power plant multi-type capacity mining method, including a data acquisition and monitoring module, a prediction and evaluation module, an intelligent scheduling and optimization module, and an auxiliary service and market participation module; the data acquisition and monitoring module is used to collect power resource data in real time and perform remote monitoring, and the prediction and evaluation module is used to predict power demand and renewable energy output based on the collected power resource data using a pre-trained LSTM model and SVM model; the intelligent scheduling and optimization module is used to generate a power system control strategy based on the predicted power demand and renewable energy output and power resource data using a pre-trained deep Q network, and perform intelligent scheduling based on the generated power system control strategy to optimize the system's operating efficiency; the auxiliary service and market participation module is used to provide auxiliary services and participate in power market transactions.

[0110] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0111] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A multi-type capacity application method for a virtual power plant, characterized in that: The following steps are involved: Step S1: construct an LSTM model for predicting electricity demand and renewable energy output, and train the LSTM model; The training LSTM model includes the following steps: Obtain historical data on electricity demand and renewable energy; Performing data normalization on the historical data of electricity demand and the historical data of renewable energy respectively; The historical data of electricity demand and renewable energy are used as training data to train the LSTM model; Step S2: constructing an SVM model for predicting electricity demand and renewable energy output, and training the SVM model; Step S3: obtaining current power resource data, inputting the current power resource data into the trained LSTM model and the SVM model respectively, obtaining first prediction data and second prediction data respectively, using fuzzy logic algorithm to assign weights, and obtaining final prediction data according to the weights, wherein the final prediction data includes power demand prediction value and renewable energy output prediction value; Step S4: Using the pre-trained deep Q network, generate a power system control strategy through power demand forecast values, renewable energy output forecast values ​​and current power resource data.

2. The multi-type capacity application method of a virtual power plant according to claim 1, characterized in that: The loss function of the LSTM model is: ， ， ， ， ， ， in, is the total loss function for LSTM model training, is the weight coefficient of the loss function, The loss function for the LSTM model power demand prediction is: is the loss function for LSTM model renewable energy output prediction, is the LSTM model system stability loss function, N is the number of training samples, The electricity demand predicted by the LSTM model, is the actual power demand, Renewable energy output predicted by LSTM model, is the actual renewable energy output, f is a Gaussian function, Respectively i The error change between the power demand forecast and renewable energy output forecast at a time step and the previous time step.

3. The multi-type capacity application method of a virtual power plant according to claim 1, characterized in that: The training SVM model comprises the following steps: Obtain historical data on power resources, including distributed power source data, energy storage system data, controllable load data, electric vehicle data, and external environment data; Obtain historical data on electricity demand and renewable energy; Perform data normalization on the historical data of power resources, historical data of power demand and historical data of renewable energy respectively; The historical data of power resources are used as input features, and the historical data of power demand and renewable energy are used as target variables to train the SVM model.

4. The multi-type capacity application method of a virtual power plant according to claim 3 is characterized in that: The distributed power data includes real-time power generation, power generation and power generation efficiency of solar power generation and wind power generation; The energy storage system data includes the real-time storage capacity, energy storage efficiency, remaining storage time, charging power, discharging power, charging time, discharging time, health status, life prediction and maintenance records of the energy storage equipment; The controllable load data includes real-time load demand and load fluctuation; the electric vehicle data includes real-time charging power, charging amount, charging time, health status, remaining life and maintenance records of the electric vehicle battery; The external environment data includes temperature, humidity, wind speed, and sunshine intensity.

5. The multi-type capacity application method of a virtual power plant according to claim 1, characterized in that: The loss function of the SVM model is: ， ， ， ， ， in, is the total loss function for SVM model training, is the loss function of the power demand prediction of the SVM model, is the loss function for renewable energy output prediction of the SVM model, is the stability loss function of the SVM model system, is the regularization term of SVM, is the weight coefficient of the total loss function of the SVM model, The power demand predicted by the SVM model, is the renewable energy output predicted by the SVM model, is the weight vector of the SVM model, is the regularization parameter.

6. The multi-type capacity application method of a virtual power plant according to claim 1, characterized in that: The fuzzy logic algorithm is used to assign weights, and the final prediction data is obtained according to the weights. The calculation formula for the final prediction data is: ， ， ， in, is the weight of the first prediction data, is the weight of the second prediction data, is the predicted data predicted by LSTM at time j, is the predicted data predicted by SVM at time j, is the final forecast data at time j-1, is the final prediction data at time j.

7. The multi-type capacity application method of a virtual power plant according to claim 1, characterized in that: The pre-trained deep Q network includes a state space, an action space, and a reward function. The state space includes the electric energy reserve in the current energy storage device, the charging and discharging power of the current energy storage device, the power demand forecast value, and the renewable energy output forecast value; the action space includes charging, discharging, and maintaining the current state.

8. The multi-type capacity application method of a virtual power plant according to claim 7, characterized in that: The reward function is: ， in, is the reward function, is the predicted value of electricity demand at time t, is the predicted value of renewable energy output at time t, is the total power supply capacity of the system at time t, including renewable energy and energy storage system, is the charging power of the energy storage device at time t, is the discharge power of the energy storage device at time t, are the adjustment coefficients of the reward function respectively.

9. A system based on the multi-type capacity application method of a virtual power plant according to any one of claims 1 to 8, characterized in that: It includes data collection and monitoring module, prediction and evaluation module, intelligent scheduling and optimization module, auxiliary service and market participation module; The data acquisition and monitoring module is used to collect power resource data in real time and perform remote monitoring; The prediction and evaluation module is used to predict power demand and renewable energy output based on the collected power resource data using a pre-trained LSTM model and SVM model; The intelligent dispatching and optimization module is used to generate a power system control strategy based on the predicted power demand, renewable energy output, and power resource data using a pre-trained deep Q network, and to perform intelligent dispatching based on the generated power system control strategy to optimize the system's operating efficiency; The auxiliary service and market participation module is used to provide auxiliary services and participate in electricity market transactions.

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