Hybrid Algorithm for DER Placement and Sizing in Power Grids
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Solution Overview
Problem
Current power system architectures face challenges in dynamically securing the grid against single failures due to the integration of Distributed Energy Resources (DERs), as existing systems fail to optimize the location and sizing of grid stabilizers, leading to potential power outages and high costs.
Innovation Solution
A hybrid algorithm combining data-driven and model-based approaches, utilizing Reinforcement Learning (RL) and Dynamic Security Optimization (DSO), is employed to determine the optimal location and sizing of new power generation or power regulating units, ensuring the power system's resilience against N−1 contingencies through a nonlinear simulation model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If new DERs are integrated into the power system to meet renewable energy targets, then sustainability and greenhouse gas reduction are improved, but dynamic security and system stability deteriorate
Solution Approach 1:
The system performs preliminary assessment and optimization of DER location and sizing before actual integration. The hybrid algorithm evaluates multiple scenarios and determines optimal configurations in advance, ensuring that dynamic security requirements are met before new DERs are connected to the grid.
Solution Approach 2:
The system uses simulation-based verification to assess the impact of DER integration on dynamic security. The feedback from simulation results is used to refine the optimization algorithm, allowing continuous improvement of system stability while increasing renewable energy integration.
2Reliability
If grid stabilizers are installed to improve dynamic security, then system stability during N-1 contingencies is improved, but CAPEX increases
Solution Approach 1:
The system optimizes the sizing parameters of grid stabilizers to achieve the minimum required capacity for N-1 security. By carefully adjusting the size and location parameters, the system reduces the total number of stabilizers needed while maintaining adequate dynamic security.
Solution Approach 2:
The hybrid algorithm identifies specific locations where grid stabilizers provide maximum benefit for dynamic security. By placing stabilizers at critical nodes rather than uniformly distributing them, the system achieves better security with fewer devices.
3Productivity
If existing optimization algorithms are used for DER allocation, then computational efficiency is improved, but dynamic security optimization is not achieved
Solution Approach 1:
The system merges heuristic methods with mathematical programming algorithms to create a hybrid optimization approach. This combination leverages the computational efficiency of heuristics for initial solution generation while using mathematical programming to ensure N-1 security constraints are satisfied.
Solution Approach 2:
The optimization framework combines multiple algorithmic approaches (heuristic, mathematical programming, simulation) into a composite methodology. Each component contributes its strengths to achieve both computational efficiency and dynamic security optimization.
Data Source
AI summary
A system determines a location and a size of a new power generation or power regulating unit within a current system architecture of a power system including a plurality of power generation units. The system comprises a controller including a processor and a memory, computer-readable logic code stored in the memory which, when executed by the processor, causes the controller to execute a hybrid algorithm as a combination of a data-driven algorithm and a model-based algorithm to determine an optimal location and size of the new power generation or power regulating unit. The data-driven algorithm encodes a location and a size information. The controller to enable the model-based algorithm to optimize performance of a selected location and size of the new power generation or power regulating unit, which is based on a linearized system or a nonlinear system to provide guidance for the data-driven algorithm to incorporate physical rules and verify a new system architecture.