Virtual Power Plant Control Algorithm for Renewable Integration
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Solution Overview
Problem
Current aggregated virtual power plant (VPP) control techniques fail to adequately adjust energy use, energy production, and energy curtailment in systems that include both supply and prosumer sides, leading to inefficiencies in renewable energy integration and demand response.
Innovation Solution
A method involving a VPP controller server that receives control variable values, inputs them into an objective algorithm to increase renewable energy production and reduce utility energy generation, adjusting energy loads and production of prosumers, and generating a DR event schedule to communicate control signals for energy management across the VPP.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current aggregated VPP control techniques are used, then system operation is simplified, but renewable energy integration is insufficient and energy production/curtailment adjustment is inadequate
Solution Approach 1:
The control system is segmented into multiple independent optimization modules: energy production adjustment module, energy load adjustment module, and energy curtailment adjustment module. Each module handles specific aspects of VPP control independently, allowing the system to manage complex renewable energy integration without requiring a monolithic complex control architecture.
Solution Approach 2:
The control system dynamically adjusts energy production, loads, and curtailment based on real-time conditions. The optimization algorithm continuously receives control variable values from previous time intervals and generates updated DR event schedules, enabling the system to adapt to changing renewable energy availability and grid conditions without fixed rigid control structures.
2Adaptability or versatility
If traditional energy generation is reduced, then renewable energy contribution increases, but energy supply reliability may deteriorate
Solution Approach 1:
The control system implements feedback mechanisms by receiving control variable values from previous time intervals and using this historical data to inform current optimization decisions. The objective algorithm continuously monitors and adjusts energy production, loads, and curtailment based on actual system performance, ensuring reliability is maintained while increasing renewable energy contribution.
Solution Approach 2:
The system changes operational parameters dynamically by adjusting energy production levels, load demands, and curtailment amounts based on real-time conditions. The optimization algorithm modifies these parameters to balance renewable energy integration with reliability requirements, transitioning the system from static to adaptive parameter management.
3Productivity
If energy loads and production are adjusted frequently, then renewable energy integration is optimized, but system stability may be compromised
Solution Approach 1:
The control system performs preliminary optimization calculations by receiving control variable values from previous time intervals before implementing adjustments. The objective algorithm pre-computes optimal energy production, load, and curtailment adjustments, allowing the system to make coordinated changes that maintain stability while optimizing renewable energy integration.
Solution Approach 2:
The system merges three adjustment operations (energy production adjustment, energy load adjustment, and energy curtailment adjustment) into a unified optimization process. By coordinating these adjustments simultaneously through the objective algorithm, the system achieves high renewable energy integration efficiency while maintaining overall stability through balanced, coordinated changes rather than isolated frequent adjustments.
Data Source
AI summary
A method of aggregated virtual power plant (VPP) control includes receiving control variable values. The control variables are received for control variables related to energy production and loads of devices electrically coupled to an electrical grid and communicatively coupled to the VPP controller server. The method includes inputting the control variable values into an objective algorithm. The method may include executing the objective algorithm. Executing the objective algorithm includes adjusting energy loads and energy production of prosumers, adjusting an energy amount supplied from a supply side for multiple time intervals, and adjusting curtailment of the energy loads in the prosumers based thereon. The method includes generating a VPP DR event schedule and communicating it to VPP client servers. The VPP DR event schedule includes control signals that are configured to affect an operating condition of the devices that are controlled by the VPP client servers.