Battery Scheduling for VPP Demand Response and Power Cost Control
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Solution Overview
Problem
Conventional energy storage systems struggle to optimally respond to demand response requests from virtual power plants, leading to inefficiencies in power consumption and increased costs, as they are not optimized for maximizing profits through demand response and minimizing power purchase costs.
Innovation Solution
An energy management apparatus and method that includes a processor and memory to collect and analyze power generation and consumption data, establish an operation schedule to minimize grid power costs, and adjust the schedule in response to demand response signals, using objective functions and constraints to optimize battery charging and discharging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional energy storage systems control charging and discharging according to sunlight conditions and load conditions only, then the system operates autonomously based on local conditions, but the system cannot properly respond to demand response requests from virtual power plants
Solution Approach 1:
The energy management apparatus integrates multiple control functions into a single system that can handle both autonomous local optimization (based on sunlight and load conditions) and virtual power plant demand response requests. The system universally processes different types of control signals and adjusts battery operations accordingly, making it adaptable to multiple operational modes without requiring separate control systems.
Solution Approach 2:
The energy management apparatus acts as an intermediary between the battery system and external control signals (both local conditions and VPP requests). It receives various inputs including sunlight information, load conditions, and demand response requests, processes them through optimization algorithms, and generates appropriate charging/discharging control signals. This intermediary function resolves the contradiction by coordinating multiple requirements through a centralized control mechanism.
2Loss of energy
If conventional energy storage systems operate to maximize internal power generation or minimize external power consumption, then the system optimizes local energy efficiency, but the system is not optimized in terms of power purchase cost
Solution Approach 1:
The system dynamically changes operational parameters (charging/discharging rates, timing, and duration) based on real-time conditions including power prices, sunlight availability, and load demands. The energy management apparatus calculates optimal parameters by considering both local energy efficiency and power purchase costs, adjusting battery operations to charge during low-cost periods and discharge during high-cost periods, thereby minimizing overall power purchase costs while maintaining local optimization.
Solution Approach 2:
The operation control system transitions from static local optimization to dynamic multi-objective optimization. The energy management apparatus continuously adjusts charging and discharging strategies based on changing conditions such as power prices, weather forecasts, and demand response requests. This dynamic approach allows the system to balance local energy efficiency with cost minimization by adapting operations in real-time rather than following fixed rules.
3Reliability
If demand response resources reduce power consumption according to virtual power plant requests, then the power grid stability is improved, but the operation profits are not maximized due to suboptimal energy storage control
Solution Approach 1:
The energy management apparatus implements feedback mechanisms that monitor both grid conditions (demand response requests, power prices) and battery state (charge level, temperature, health). Based on this feedback, the system continuously adjusts charging/discharging operations to maximize profits while meeting grid stability requirements. The feedback loop ensures that the system responds appropriately to VPP requests while optimizing economic outcomes through real-time decision-making.
Solution Approach 2:
The system performs preliminary charging or discharging actions in anticipation of future demand response requests or high-power-price periods. The energy management apparatus forecasts future conditions and pre-adjusts battery state to maximize profit opportunities. For example, it may charge the battery during low-price periods before expected demand response events, ensuring the battery is ready to provide grid support services when needed while having minimized purchase costs during charging.
Data Source
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AI summary
An energy storage system according to embodiments of the present invention may be configured in a demand response resource which is associated with a virtual power plant (VPP) system. The energy storage system may include: a power generation device for producing electric power; a battery for storing power; an energy management apparatus configured to monitor a power generation state and a power consumption state and to establish an operation schedule for the battery; and a power converter configured to control charging/discharging operation of the battery according to the established operation schedule.