Model Predictive Controller for Battery Energy Storage Peak Shaving

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Solution Overview

Problem

Conventional energy storage systems struggle to optimize PV utilization and charging/discharging functions, leading to inefficiencies in peak shaving, battery degradation, and high demand charge costs due to opposing natures of DC management and PV utilization.

Innovation Solution

A computer-implemented method using a model predictive controller (MPC) to determine optimal charging/discharging profiles based on historical data, real-time PV/load profiles, and grid feed-in limitations, ensuring continuous optimal operations and overriding profiles for high excess PV generation or peak shaving events.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Object-affected harmful factors

If the BESS is fully charged to maximize peak shaving capability, then demand charge costs are minimized, but PV utilization is reduced because the battery cannot store excess PV generation

Engineering Contradiction:

Improvedemand charge costsVSAvoidPV utilization

Core Design Contradiction:

Object-affected harmful factorsVSProductivity

Solution Approach 1:

The system dynamically adjusts the charging/discharging strategy based on real-time conditions. The MPC controller continuously optimizes the BESS operation mode (charging, discharging, or idle) by considering forecasted PV generation, load demand, and price signals, allowing the system to adapt between peak shaving and PV storage modes as conditions change

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes operational parameters (charging/discharging power levels, state of charge targets) based on varying conditions. The MPC controller adjusts these parameters in real-time to optimize both demand charge management and PV utilization, transitioning between different operational states as needed

Inventive Principle:

Principle #35Parameter changes

2Object-affected harmful factors

If conventional rule-based controllers discharge the battery when grid power is high, then demand charge costs are reduced, but excess PV production cannot be captured and curtailment occurs

Engineering Contradiction:

Improvedemand charge costsVSAvoidexcess PV generation curtailment

Core Design Contradiction:

Object-affected harmful factorsVSLoss of energy

Solution Approach 1:

The MPC controller uses feedback from real-time measurements of PV generation, load demand, and BESS state of charge, combined with forecasts, to continuously adjust the charging/discharging decisions. This closed-loop control enables the system to capture excess PV when available while still providing peak shaving when needed, avoiding the open-loop limitations of rule-based controllers

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary charging during periods of high PV generation and low demand, before peak demand periods occur. By proactively storing energy when conditions are favorable, the system prepares for upcoming peak periods without causing curtailment, as the MPC optimizer balances storage needs against PV utilization goals

Inventive Principle:

Principle #10Preliminary action

3Reliability

If the BESS maintains high average annual state of charge, then peak shaving reliability is improved, but battery lifetime is reduced due to increased degradation

Engineering Contradiction:

Improvepeak shaving reliabilityVSAvoidbattery lifetime

Core Design Contradiction:

ReliabilityVSDuration of action of stationary object

Solution Approach 1:

The system dynamically adjusts the state of charge parameters based on operational needs and battery health considerations. The MPC controller optimizes charge/discharge cycles to achieve necessary peak shaving reliability while minimizing unnecessary cycling and maintaining SOC within ranges that reduce degradation, rather than maintaining consistently high SOC

Inventive Principle:

Principle #35Parameter changes

4Productivity

If delayed charging algorithms increase SOC every 15 minutes linearly, then PV utilization is improved, but insufficient energy is stored for early peak shaving events

Engineering Contradiction:

ImprovePV utilizationVSAvoidenergy stored in battery

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The system dynamically adjusts the charging rate and timing based on forecasted peak events and PV generation patterns. Rather than following a fixed linear charging schedule, the MPC controller intensifies charging before predicted peak periods and adjusts the state of charge trajectory to ensure sufficient energy is stored for early peak shaving events while still maximizing PV utilization

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS10634725B2System and method for model predictive energy storage system control

Publication Date: 2020.04.28 NEC CORP

AI summary

Systems and methods for controlling Battery Energy Storage Systems (BESSs), including determining historical minimum state of charge (SOC) for peak shaving of a previous day based on historical photovoltaic (PV)/load profiles, historical demand charge thresholds (DCT), and battery capacity of the BESSs. A minimum SOC for successful peak shaving of a next day is estimated by generating a weighted average based on the historical minimum SOC, and optimal charging/discharging profiles for predetermined intervals are generated based on estimated PV/load profiles for a next selected time period and grid feed-in limitations. Continuous optimal charging/discharging functions are provided for the one or more BESSs using a real-time controller configured for overriding the optimal charging/discharging profiles when at least one of a high excess PV generation, a peak shaving event, or a feed-in limit violation is detected.