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ENERGY DISTATCH & OPTIMIZATION

In the context of multiple constraints,
Make every charge more valuable.

Within the boundaries of grid requirements, load forecasting, renewable energy, equipment health and safety, a uniform arrangement is made for when and at what power to charge, when to discharge, and how power to operate, and to continuously amend the plan by rolling optimization.

It's physical. The economy is the best. Scrollable fixes It's retroactive.
PV
Photovoltaic power generation
LOAD
Industrial load
GRID
Network / transformer
¥
Market / Price Signal

Dispatch optimization platform

  • Projections
  • Optimization
  • Planned
  • Implementation
Schedule cycle 48 h 15 minute resolution
Available Energy 86.4 MWh 95% SOC cap
Peak power ceiling 40.0 MW Full/ Unlimited
Stand-by target. 20.0 MW Two hours.
Optimizing Benefits (Today) ¥286,540 +18.7%
Planned Status Published OD-240519-01

01 Enter a panorama: unify all constraints and drivers on the time axis

Load forecast

MW

Renewable energy

MW

Time price of electricity

¥ / MWh

Network requirements

MW

Stand-by target.

MW

Run Status

Status
00:00 06:00 12:00 18:00 24:00
Load projection: The provision of future changes in industrial loads is an important input for peaking, demand control and energy balance.

02 Physical feasibility model: hard bound boundaries of equipment

SOC
SOC/ Energy Window 10% – 90%
P
Yes, no, no, no. ±40 MW / 10 MW·min⁻¹
η
Efficiency and energy consumption models Charge efficiency / Auxiliary energy consumption
T
Thermal management constraints Temperature window and cooling state
HA
Availability and maintenance Stopping equipment is not allowed into the schedule
SOC
Energy boundary SOC upper / lower limit
PQ
Power boundary P/ Q / Mount
EFF
Efficiency boundaries Full/ Efficient
TEMP
Temperature boundary Environment and equipment temperature
AVL
Availability boundary Pool of available equipment

• From the original projection to the plan to be implemented, it must first pass through the boundable realm

Optimization results enter release only when device, safety, grid, and operating boundaries are all satisfied.

· Initial projections

Load / Optical / Price scene

· Target function

Minimize Cost + Maximize Combined Value

· Containable areas

SOC / Power / Temperature / Network Boundary

04. Candidatures

Generate multiple candidate paths for binding

• Optimal implementation plan

Economic objectives + risk constraints
Load forecast Economic objectives SOC Window Availability of equipment Network constraints Final plan

04 Baseline VS Optimization: 24-hour plan and revenue dismantling

Schedule plan (power MW / SOC)

Net side power (MW)

Proceeds dismantled

Peak-valley arbitrage revenue +132,660
Demand / volume gain +93,240
Photovoltaic gains +48,530
Income from spare capacity +34,650
Battery degradation costs -22,540
Net gains today ¥286,540

05 Rolling optimization: responding to projected deviations and equipment events

Event Trigger 14:30

  • Load forecast up +18 MW
  • Team #12, malfunction not available
  • +45 ¥/ MWh

Before updating. Plan V1

Update binding feasible fields

Unit usability update: 23/24 Available

Update . Plan V2

Policy down.

Planned version V2
EMS State Published
Networking Pass.
Equipment available 23
Unusable equipment 1

06 Multi-objective priority: security and commitment priority, followed by economic goals

First Priority
Safety and netting constraints
Second Priority
Availability and service commitments
Third Priority
Economic optimization target

Operational intervention and role approval

Standby level 20 MW
Power ceiling 40 MW
SOC Bottom 15%
Strategy preference Gain / Risk balance
Dispatchers
Confirmed
Duty manager.
Approved
Head of the plant
Approved

• Optimization of results must be implemented and explained

Each dispatch plan revision retains input version, constraint version, solver, approval, and execution targets.

Planned identity

Plan ID OD-240519-02
Date 2024-05-19
Interval 15 min
Organisation UTC+8

Input Version

Load v3.2
Photovolt v2.8
Electricity v4.1
Network requirements v1.6

Constraints and models

Device model v3.0
Protection boundaries v2.1
Run constraints v1.3
Solver Gurobi 11.0

Optimizing objectives

Main objective Minimize Cost
Sub-objective Maximizing benefits
Weight Version MILP / Pareto

Approval records

Dispatchers 14:22
Duty manager. 14:25
Head of the plant 14:27
Planned Status Published Execute V2

08 Implementing evidence: planned VS actual and deviation attributable

Power

SOC(%)

Deviation attribute (energy MWh)

Validation principles: The “plan through optimizer” does not mean “implementation has proved to be effective”. Actual implementation deviations must be attributed to projected errors, equipment availability, grid directives, delays in implementation and other factors of impact.

09 Site laboratories: value of different strategies versus risk

Policy Total cost (¥) Net income (¥) Photovoltage rate Standby satisfaction rate Day target deviation Volatility of income Lu's great.
Conservative policy 1,354,210 168,320 82.1% 100% 0.68 12,450 91 / 100
Balance strategy (recommended) 1,180,540 286,540 92.7% 100% 1.12 8,730 95 / 100
Value Maximising Policy 1,052,880 415,230 97.6% 98.1% 1.68 14,820 88 / 100
Balance policy: The currently released plan balances safety, reserve adequacy, and economic return.

10 Governance and Rollback: Controllable release and security pocket Bottom

01 Plan approval Multiplay approval
02 Models and Parameters Controlled Version retroactive
03 Release marks Policy and Implementation Version
04 Run deviation monitoring Planned versus actual
05 Strategy Roll Back Stabilization plan
06 Collapse Clock Optimizing models for continuous improvement

11 Boundaries and responsibilities: Distinction of functions, Convergence

Dispatch optimization platform

  • Forecast and scene input
  • Binding Modelling
  • Optimizing solver
  • Plan Version Management
  • Manual approval and interpretation
Core scope of the page
↔

EMS Real-Time Management System

  • Implementation plan and real-time control
  • Device status feedback
  • Grid Response.
  • Safety interlock
  • Results returned
Optimizers are not allowed to bypass direct control of EMS safety boundaries
↔

Performance Analysis Platform

  • Analysis of planned and actual deviations
  • Validation of efficiency and benefits
  • Forecast error analysis
  • Model impact evaluation
  • Long-term strategy improvements
Implementation of re-entry data into analysis and model improvement loops
Ultimate Principle: The goal of energy optimization is not the greatest single gain, but rather the greatest benefit. Physical feasibility, safe control, service commitment met, performance validated In this context, the value of the system throughout its life cycle is continuously increased.