Microgrid Operating Point Control Under Forecast Deviations
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Solution Overview
Problem
Microgrid control systems face challenges in maintaining optimal operating points due to inaccuracies in forecast variables, leading to potential power imbalances and non-optimal operations, as they rely on past and present data without adequate compensation for deviations in forecast values.
Innovation Solution
The method involves modifying operating point values for controllable assets based on asset-specific headroom and total power offsets, using a monotonically increasing function that considers the headroom of each asset and all assets collectively, allowing for adjustments to mitigate power imbalances without additional communication between the Power Management System (PMS) and Energy Management System (EMS).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If operating point values are determined based on forecast variable values, then the optimal operation of controllable assets is improved, but the reliability of power balance deteriorates when forecast variables are incorrect
Solution Approach 1:
The method pre-calculates compensation values based on forecast variable values before actual deviations occur. When forecast variables are incorrect, these pre-prepared compensation values enable immediate correction of operating point values, maintaining power balance without waiting for deviation detection. This proactive approach resolves the contradiction by preparing corrective actions in advance.
Solution Approach 2:
The method implements a feedback mechanism where actual values of forecast variables are continuously compared with forecast variable values. When deviations are detected, the system automatically adjusts operating point values using compensation values. This closed-loop feedback ensures reliability is maintained while preserving the benefits of forecast-based optimization.
2Reliability
If operating point values are adjusted to compensate for forecast deviations, then the robustness of power balance is improved, but the complexity of the control system increases
Solution Approach 1:
The Power Management System performs compensation calculations autonomously using locally available data including forecast variable values, actual values, and asset headroom information. The system self-adjusts operating point values without requiring additional communication with the Energy Management System, making the system self-sufficient and avoiding increased complexity from extra communication infrastructure.
Solution Approach 2:
The method adjusts operating point values by changing parameters based on calculated compensation values that consider asset-specific headroom and aggregate headroom. This parameter-based adjustment approach provides a systematic yet relatively simple mechanism to improve robustness without requiring complex control logic or additional system components.
3Adaptability or versatility
If asset-specific headroom is considered for compensation, then the adaptability of operating point adjustment is improved, but the computational complexity increases
Solution Approach 1:
The method applies local quality by considering asset-specific headroom for each controllable asset individually when calculating compensation values. Each asset's operating point is adjusted according to its own capabilities and constraints rather than applying a uniform adjustment. This localized approach improves adaptability while keeping computations manageable by focusing on individual asset characteristics.
Solution Approach 2:
The PMS selectively adjusts operating point values for assets where compensation is most beneficial, rather than uniformly adjusting all assets. By applying compensation actions partially to specific assets based on their headroom and deviation impact, the system achieves high adaptability without the computational burden of optimizing all assets simultaneously.
Data Source
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AI summary
A method of controlling a microgrid (10) comprises retrieving, by an energy management system, EMS (50), a forecast variable value for at least one forecast variable and executing, by the EMS (50), an optimization routine to determine an operating point vector for a plurality of controllable assets (11-14) that is optimal for the retrieved forecast variable value. The method comprises executing, by the EMS (50), the optimization routine to determine several additional operating point vectors for the plurality of controllable assets (11-14), the additional operating point vectors being determined to be optimal for modified forecast variable values that deviate from the retrieved forecast variable value. The method comprises providing both the operating point vector and the several additional operating point vectors to a power management system, PMS (40), of the microgrid (10).