Abstract

In order to reduce the impact of load power fluctuations on the power system and ensure the economic benefits of user-side energy storage operation, an optimization strategy of configuration and scheduling based on model predictive control for user-side energy storage is proposed in this study. Firstly, considering the cost and benefits of energy storage comprehensively, an energy storage configuration optimization model with the highest annualized net income as the goal is built to determine the parameters for configuring energy storage. Then, with the goal of maximizing the profit during the scheduling period, pre-month scheduling optimization model, day-ahead scheduling optimization model and intra-day scheduling optimization model are established. The goal of the pre-month scheduling optimization model is to determine the maximum monthly demand; part of the scheduling results in the day-ahead scheduling optimization model directly participate in the intra-day scheduling; the intra-day rolling optimization relies on the advantages of real-time feedback and closed-loop scheduling to smooth out power fluctuations caused by load forecast errors. Finally, the configuration and economic benefit of lithium iron phosphate batteries, lead-carbon batteries and sodium-sulfur batteries are analyzed and compared, and scheduling analysis is performed. The simulation results show that the proposed optimization method can cut peaks and fill valleys, ensure the economic benefits of users, and provide guidance for users to invest in energy storage.

Highlights

  • Energy storage can realize the migration of energy in time, and can adjust the change of electric load

  • 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 Model predictive control (MPC)

  • Figure shows the results of intra-day scheduling optimization

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Summary

Introduction

Energy storage can realize the migration of energy in time, and can adjust the change of electric load. 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. It analyzes the life model, cost model and revenue model of energy storage in detail, and builds an energy. 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

Life Model
Annual Installation Costs
Annual Operation and Maintenance Costs
Energy Storage Capacity Attenuation Costs
Regularization Function for Smoothing the Power Fluctuation of the Grid
Electricity Revenue
Reduced Average Annual Transformer Cost
Demand Response Benefit
Reliability Benefit
Annual Benefit from Government Subsidies
Objective Function
State-of-Charge Constraints
Demand Control Constraints and Restrictions on Preventing Power Backwards
Peak Clipping Constraints
Peak and Valley Constraints
Energy Storage Scheduling Model
Restrictions
Intra-Day Optimal Scheduling Strategy for Energy Storage Based on MPC
Schematic
Parameter Description
Case Analysis
Energy Storage Optimization Configuration Results
Energy
Optimization Results
Daily Scheduling Results
Conclusions

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