Small PV on the Electrical Distribution Network

Solving the Grid Managers Conundrum

Mike Green
Consulting and Design, M.G. lightning ltd, Israel

The electrical grid manager is responsible for coordinating between electrical energy production and consumption. Historically, this task has been challenging.  The advent of distributed generation of fluctuating solar energy has made this task all the more difficult. The dream of the environmentalist and the hue and cry of the solar advocate resonate with images of a grid supplied solely by renewable energy with many tens of percent supplied by distributed PV systems – production close to point of consumption.

Balancing the grid depends on knowing in advance the next day`s hourly amount of energy to be supplied by each power producer. The grid manager knows each day how much energy is expected to be produced each hour the next day, as well as how much reserve generation will be spinning, waiting to be engaged, warm and ready to engage, and cold but prepared to start. Solar PV energy is not a threat to the grid as long as it stays under 10% of total production. This is certainly not in line with the "Clean Dream" where high percentage of energy is produced by small residential roof mounted systems.

There exists today many solutions for predicting next day`s hourly production based on weather prediction and simulation on the PV plant. These solutions work for large power plants with homogenous design, high availability and most important, a large budget.

Whereas a neighborhood of small residential roof mounted systems easily reaches the installed power of an industrial or utility grade PV power station, the neighborhood is far from homogenous, availability is random and no budget exists.

So the forward looking image conscious modern Utility faces a difficult problem due to his inability to refuse solar energy on the one hand, yet is held to ever increasing power quality requirements on the other.

The answer then, is a software program or SaaS that can independently predict next day`s hourly yield for a small residential PV array, aggregate entire neighborhoods into a single virtual PV power plant, transmit this data to the grid manager, and cost nothing to the PV owner.

In our solution, we expect the smart grid topology to be able to manage the aggregated prediction, if not the aggregation itself. While the cost of the system would be paid by the homeowners voluntarily, in so far as the software will feed back to the homeowner as to the efficiency of the PV system, alerting as to a system that requires a service call, thereby simultaneously increasing availability for the grid manager and preserving expected revenues for the homeowner.

This machine learning software is up and running with a very high accuracy. An advanced failure prediction module is soon to be released that will increase the value of the tool in the homeowners eyes as it predicts future failures before they occur.

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