Shared Energy Storage Scheduling With MILP Cost Optimization
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Solution Overview
Problem
Existing energy systems face challenges such as low allocation rate of energy storage and high configuration cost, which hinder the optimization of power energy scheduling in energy storage sharing systems.
Innovation Solution
A power energy scheduling optimization method and system for a user-side energy storage sharing framework, which establishes an electricity cost model as an objective optimization function and uses a Mixed-Integer Linear Programming (MILP) algorithm to determine optimal power scheduling, thereby reducing user costs and electricity costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If energy storage sharing is implemented to improve allocation rate, then energy storage allocation rate is improved, but device complexity increases due to multiple users and coordinated management
Solution Approach 1:
The patent introduces a community energy management system as an intermediary platform that coordinates energy storage sharing among multiple users. This mediator handles the complex scheduling and coordination, allowing individual users to benefit from improved allocation rates without directly managing the system complexity themselves.
Solution Approach 2:
The patent transforms the energy storage scheduling problem into a mathematical optimization problem by changing parameters such as electricity prices, load demands, and storage capacities into quantifiable variables. This allows the use of Mixed-Integer Linear Programming (MILP) algorithms to optimize energy allocation while managing system complexity through computational methods.
2Loss of energy
If optimal power energy scheduling is implemented to reduce electricity costs, then electricity cost is reduced, but calculation complexity increases due to multiple variables and constraints
Solution Approach 1:
The patent replaces manual or heuristic scheduling methods with automated Mixed-Integer Linear Programming (MILP) algorithms. This substitution of computational mechanisms enables the system to handle multiple variables and constraints efficiently, finding optimal scheduling solutions that reduce electricity costs without requiring manual intervention in the complex calculations.
3Productivity
If capacity sharing is allowed between battery energy storage devices to improve resource utilization, then energy storage allocation rate is improved, but reliability constraints become more difficult to manage
Solution Approach 1:
The patent incorporates battery state of charge (SOC) constraints and reliability parameters as quantifiable variables in the MILP optimization model. By transforming reliability management into parameter-based constraints, the system can automatically ensure battery health and reliability while enabling capacity sharing to improve allocation rates.
Data Source
AI summary
The invention relates to a system for a user-side energy storage sharing framework. A power energy scheduling optimization method comprises: establishing a user load model, an energy storage battery model and a user-to-user capacity sharing model; with a minimum electricity cost of a whole community as an objective optimization function, resolving optimal scheduling based on the function using a MILP algorithm as an optimization technique for power energy scheduling of a user-side energy storage sharing framework; and performing power energy scheduling for users according to a resolved result. The invention determines a user cost model under the user-side energy storage sharing framework, establishes an electricity model of non-transferable loads and transferable loads and corresponding constraints, and obtains optimal values of adjustable variables to control electricity consumed by electric loads by resolving an optimal value of an objective function under the precondition of reducing user costs, thus further reducing electricity costs.