Microgrid Power Allocation for Self-Sufficiency and Grid Stability
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Solution Overview
Problem
Electrical microgrids and their interaction with the superordinate electrical grid face challenges in optimizing the utilization of power generating and storage entities, leading to inefficiencies and suboptimal use of resources.
Innovation Solution
A computer-implemented method for controlling microgrid components to maximize self-sufficiency, minimize energy transportation distances, and stabilize the frequency of the superordinate electrical grid by optimizing the allocation of resources and power exchange between the microgrid and the superordinate grid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If small generating facilities and storage facilities are operated by independently acting individuals, then operational simplicity is maintained, but resource utilization is not optimal
Solution Approach 1:
An optimization server acts as an intermediary between independently operating facilities and the microgrid controller. The server receives operational data from various facilities (photovoltaic installations, electric vehicles, storage facilities), performs centralized optimization calculations, and sends control commands back to the facilities. This mediator enables optimal resource utilization without requiring direct complex interactions between individual facility operators.
Solution Approach 2:
Each facility continues to operate autonomously based on its own needs and constraints, while the optimization server provides self-service optimization by automatically analyzing data from all facilities and generating coordinated control strategies. The system serves itself through automated optimization without requiring manual intervention from facility owners.
2Reliability
If the microgrid draws more power from the superordinate electrical grid, then frequency stability is easier to maintain, but self-sufficiency and cost increase
Solution Approach 1:
The optimization server performs preliminary calculations to determine optimal power exchange strategies before actual operation. It forecasts power generation from renewable sources, estimates consumption patterns, and pre-determines when the microgrid should draw from or feed into the superordinate grid. This advance planning enables the microgrid to maintain self-sufficiency while ensuring frequency stability through proactive power management.
Solution Approach 2:
The system continuously monitors actual power generation, consumption, and grid frequency, then feeds this information back to the optimization server. The server adjusts control commands in real-time based on deviations from planned operations, enabling the microgrid to maintain frequency stability while maximizing self-sufficiency through dynamic adaptation.
3Adaptability or versatility
If energy is transported over long distances within the microgrid, then flexibility in power distribution is improved, but transmission losses and congestion increase
Solution Approach 1:
The optimization server analyzes the microgrid's topology and load distribution to determine optimal local power exchange paths. Instead of allowing energy to travel through the entire microgrid network, the system identifies and enables direct local transactions between generating facilities and consuming loads in proximity to each other. This localized energy exchange maintains distribution flexibility while minimizing transmission distance and associated losses.
4Productivity
If photovoltaic installations produce energy during peak generation hours, then renewable energy utilization is maximized, but energy availability during peak demand periods is reduced
Solution Approach 1:
The optimization server schedules energy storage operations in advance based on forecasted photovoltaic generation and expected demand patterns. When photovoltaic installations generate excess energy during peak production hours, the system pre-arranges for this energy to be stored in storage facilities. Later, during peak demand periods when photovoltaic generation is low, the stored energy is automatically discharged, ensuring energy availability matches consumption needs.
Solution Approach 2:
The system ensures continuous useful action by maintaining a balanced energy flow throughout the day. Photovoltaic generation during daytime feeds into both immediate consumption and storage, while storage facilities provide continuous supply during evening peak demand. This continuous circulation of energy, orchestrated by the optimization server, eliminates gaps between generation and consumption, maximizing both renewable utilization and energy availability timing.
Data Source
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AI summary
A computer-implemented method (100) for controlling a plurality of components (1a-1f) in a microgrid (1, 22) for the distribution of electrical power, wherein the components (1a-1f) comprise sources and/or sinks for real and/or imaginary electrical currents, and/or pathways for electrical power, the microgrid (1, 22) is connected to a superordinate electrical grid (2), and the method (100) comprised the steps of: • providing (110) a first objective (3a) regarding operation of the microgrid (1, 22); • providing (120) a second objective (3b) regarding interaction of the microgrid (1, 22) with the superordinate electrical grid (2); • determining (130) an objective function (3) that quantifies performance of the first objective (3a) and/or of the second objective (3b), said objective function (3) being dependent on a set of optimization variables (4) comprising at least time programs of: ∘ actions to be performed by components (1a-1f) for operating the microgrid (1, 22) towards the first objective (3a); and ∘ shares of source and/or sink capacities of components (1a-1f) to be devoted to interaction of the microgrid (1, 22) with the superordinate electrical grid (2) towards the second objective (3b); and • optimizing (140) the optimization variables (4) towards the goal that the objective function (3) attains an extremum, said optimizing comprising simultaneous modification of at least one optimization variable (4) relating to said actions and at least one optimization variable (4) relating to said shares.