1. Introduction
Energy storage can realize the migration of energy in time, and then can adjust the change of electric load. Therefore, it is widely used in smoothing the load power curve, cutting peaks and filling valleys as well as reducing load peaks [
1,
2,
3,
4,
5,
6]. China has also issued corresponding policies to encourage the development of energy storage on the user side, and pointed out that the peak-to-valley difference in electricity prices is expected to be further widened in the future, and the room for users to benefit from additional energy storage will be further expanded. Therefore, the research on the optimal configuration of user-side energy storage has certain practical significance. At the same time, in order to give full play to the advantages of energy storage, efficient scheduling strategies need to be adopted to ensure optimal operation of energy storage and reduce user electricity costs.
There have been some studies on user-side energy storage configuration, but there are few related papers on demand response. Refs. [
7,
8,
9,
10] established a capacity market model considering demand response, but failed to conduct in-depth research on energy storage configuration. Ref. [
11] established an energy storage investment model under the full life cycle, but did not consider demand response. Ref. [
12] established an energy storage operation model, but only considered the peak-to-valley spread arbitrage in the income. Ref. [
13] studied energy storage configuration, but did not consider peak shaving revenue in the revenue. Refs. [
14,
15] established an energy storage system model with the maximum net present value as the objective function, but did not fully consider the benefits of energy storage. Ref. [
16] established an energy storage planning and scheduling model, but did not consider the demand response benefits. Ref. [
17] proposed an optimization method for energy storage configuration, but only the two-stage electricity revenue was considered in the objective function. Ref. [
18] proposed an energy storage configuration model considering the capacity market, but its research focused on foreign capacity markets, which is quite different from China’s market environment. Refs. [
19,
20] established an energy storage configuration model considering demand management, Refs. [
21,
22] established an energy storage configuration model with the goal of maximizing net income, and Ref. [
23] established a life cycle energy storage model. But none of its benefits involve demand response. The current energy storage configuration model does not fully consider the relevant technical parameters and performance characteristics of energy storage.
Energy storage is mainly involved in energy scheduling as one of the multiple devices in the integrated energy system. Refs. [
24,
25] optimized energy dispatch for systems including heat storage devices, combined heat and power (CHP) and wind turbines; Ref. [
26] established a day-ahead optimal dispatch model for electric–heat–gas coupled systems; Ref. [
27] carried out the optimal dispatching of cold, heat and electricity based on energy hub; Ref. [
28] carried out coordinated dispatching of systems, including electric energy storage, thermal energy storage, electric heat pump and CHP equipment. In [
29], based on the virtual energy storage system, the optimal dispatching of cold, heat and electricity is carried out. The above-mentioned optimal scheduling is carried out based on accurate load forecasting, but in the actual operation of the system, there is a deviation between the forecasted load and the actual load, which is not conducive to the operation of the system. In order to solve the problem of uncertainty in forecasting load, interval planning [
30,
31], stochastic planning [
32], robust planning [
33,
34], etc. have been widely used. The above methods make scheduling plans for multiple periods in advance, which cannot meet the requirements of online and real-time optimized operation of the system.
Model predictive control (MPC) designs future scheduling plans based on system predictive data and actual system status, and at the same time continuously updates the actual status of the system over time, and performs rolling forward optimization. Only the plan value of the first time period is executed each time, and the control performance is good. Ref. [
35] combined MPC method and demand response mechanism to carry out intra-day rolling optimization dispatch of microgrid; Ref. [
36] used the MPC method to optimize the dispatch of the blockchain-based microgrid power market; Ref. [
37] used the MPC method to perform multi-time-scale scheduling on a microgrid with multiple buildings, which can reduce system costs and stabilize tie-line power fluctuations; Ref. [
38] based on the MPC method and consistency theory, the proposed distributed optimal dispatching can solve the problem of solving microgrid clusters; Ref. [
39] used the MPC method to regulate the building microgrid system including virtual energy storage, which was robust in uncertain scenarios such as renewable energy output and load forecasting; Ref. [
40] used the MPC method to optimize the energy system of the park. The above-mentioned literature uses the MPC method to enhance the robustness of the system, but the above-mentioned systems are all scheduling plans given when the model has an optimal solution, and no solution is given when the model has no optimal solution.
In summary, fully considering the cost and benefits of energy storage and the impact of the uncertainty of load forecast power on the energy scheduling of user systems with additional energy storage, this paper builds a user-side energy storage configuration optimization model that participates in demand response, and proposes an optimization strategy for user-side energy storage scheduling based on MPC. First, it analyzes the life model, cost model and revenue model of energy storage in detail, and builds an energy storage configuration model to solve the rated capacity and rated power of the additional energy storage. Second, under the two-part electricity price mechanism, based on the pre-month load forecast data, a pre-month optimization model with the maximum monthly income as the goal is built to determine the maximum monthly demand. Third, based on the monthly maximum demand value and day-ahead load forecast data, a day-ahead optimization model with the goal of maximizing daily income is built to obtain the charge and discharge power of the energy storage at each moment in the day before. Fourth, based on the results of the above model and the actual load data, the feedback correction is performed, and the energy storage output is optimized using the MPC-based rolling optimization scheduling strategy. Finally, the effectiveness and rationality of the proposed method are verified through simulation. Through the configuration of energy storage, peak shaving and valley filling are realized, the peak load is reduced, the smooth operation of the power grid is ensured and certain economic benefits are brought to users.
5. Intra-Day Optimal Scheduling Strategy for Energy Storage Based on MPC
MPC is a model-based finite time-domain closed-loop control method, including model prediction, rolling optimization and feedback correction [
41,
42,
43]. Feedback correction only adjusts the scheduling plan for the next time period. Model predictive control can correct the uncertainty problem caused by disturbance in time [
43], and improve the actual control performance of the system.
The forecast time domain refers to the length of time for load forecasting, and the control time domain refers to the length of time to execute the results of energy storage scheduling. The basic working principle of MPC is shown in
Figure 1 [
40]. Its core idea is: at the initial time
t0, based on the load forecast value in the predicted time domain, the model is optimized to obtain the scheduling plan in the entire time domain, and only execute the planned value of the first time interval (control time domain). In the next optimal scheduling, based on the latest actual value fed back by the system, the forecast time domain is shifted back by a time interval Δ
t, and the model is optimized and solved to obtain the scheduling plan in the forecast time domain, and only the plan value of the first time interval (control time domain) is executed. In such a rolling operation, the prediction time domain is compressed to the end of the scheduling period as time passes, and the control time domain follows the prediction time domain to move backwards continuously until the scheduling plan of the entire scheduling period is completed.
Energy storage intra-day optimization scheduling strategy includes energy storage day-ahead optimization operation and MPC-based intra-day rolling optimization operation.
Figure 2 is a flow chart of energy storage intra-day optimization scheduling strategy. The steps are as follows.
The first step is to obtain the optimal scheduling situation of the energy storage day-ahead for the day to be scheduled based on the day-ahead load forecast data.
In the second step, starting from a time point of 0 o’clock, during the low electricity price period, the charging result of energy storage in the day-ahead optimal scheduling model is adopted. After entering the non-low electricity price period, the energy storage begins to discharge. The optimal scheduling result for the whole period is calculated with the maximum daily income as the goal, by using the known actual load data before time t and load forecast data at time t and after; only the energy storage power value at time t for scheduling is executed.
In the third step, at time t + 1, based on the determined energy storage operating power and load actual data at time t and before, and load forecast data at time t + 1 and after, the optimal scheduling is performed again. If the model has an optimal solution, only the energy storage power value at time t + 1 is executed; if the model does not have an optimal solution, the load forecast data is used at time t to optimize scheduling with the goal of maximizing daily income, and only the energy storage power value at time t + 1 is executed.
In the fourth step, it is judged whether the time t to be scheduled is greater than the number of samples per day of 96. If it is not greater, the optimization operation is continued; if it is greater, the optimization of the scheduling day ends.