Photovoltaic Energy Storage Control for Power Factor Correction
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Solution Overview
Problem
Photovoltaic energy systems face challenges in optimally controlling charge and discharge of stationary energy storage devices to minimize energy purchases from the grid, especially due to variable solar intensity caused by environmental factors like cloud shadows, leading to inefficiencies and increased costs.
Innovation Solution
A system comprising a photovoltaic energy field, a stationary energy storage device, an energy converter, and a controller that predicts campus load and energy generation, generates a cost function to minimize energy costs by determining the required reactive power and discharge rate, ensuring optimal power factor and energy balance, and applying constraints to manage energy storage and grid interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the battery charge and discharge is controlled to minimize energy purchases from the grid, then economic advantages are achieved, but the system complexity increases due to optimization algorithms and constraints
Solution Approach 1:
The controller performs predictive actions by forecasting campus load and photovoltaic generation ahead of time, then pre-determining optimal battery charge/discharge schedules based on these predictions and cost functions. This allows the system to minimize grid energy purchases in advance rather than reacting in real-time, reducing the computational complexity of real-time control decisions.
Solution Approach 2:
The system implements feedback mechanisms where the controller continuously monitors actual load and generation against predictions, and adjusts battery operations accordingly. The cost function and constraints are dynamically updated based on grid pricing signals and system state, creating a closed-loop control system that achieves economic optimization while managing complexity through structured feedback rather than overly complex open-loop algorithms.
2Reliability
If the energy converter supplies both AC power and reactive power, then the power factor is improved, but the converter capacity requirements increase
Solution Approach 1:
The system applies partial action by having the energy converter supply only the necessary amount of reactive power needed to achieve the target power factor, rather than providing excessive reactive power capacity. The controller calculates the precise reactive power requirement based on campus load characteristics and converter capacity constraints, optimizing the balance between power factor improvement and converter sizing.
Solution Approach 2:
The system dynamically adjusts operating parameters including the target power factor, charge/discharge rates, and reactive power output based on real-time conditions such as campus load, photovoltaic generation, and grid pricing. This allows the energy converter to operate efficiently within its capacity while achieving power factor correction goals under varying operational conditions.
3Productivity
If the discharge rate of AC power is increased to meet campus demand, then the energy storage utilization is improved, but the converter capacity constraints are violated when also providing reactive power
Solution Approach 1:
The system employs dynamic control where the controller continuously adjusts the discharge rate and reactive power output based on real-time campus load, photovoltaic generation, and converter capacity constraints. The optimization algorithm dynamically determines the optimal operating point that maximizes energy storage utilization while ensuring the energy converter does not exceed its apparent power capacity, adapting to changing conditions throughout the time horizon.
Solution Approach 2:
The energy converter is designed to perform multiple functions simultaneously: supplying active power to meet campus demand, providing reactive power for power factor correction, and operating within its capacity constraints. The controller coordinates these functions by solving an optimization problem that balances all requirements, making the converter a multi-functional device that handles both active and reactive power while respecting physical limitations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively reduces energy purchases from the grid by optimizing energy storage and release strategies, achieving economic savings and maintaining a desirable power factor, thereby enhancing the overall efficiency and cost-effectiveness of the photovoltaic energy system.
Implementation Method 1
Photovoltaic energy systems are used to convert solar energy into electricity using solar panels or other materials that exhibit the photovoltaic effect
Data Source
AI summary
An energy storage system includes a photovoltaic energy field, a stationary energy storage device, an energy converter, and a controller. The photovoltaic energy field converts solar energy into electrical energy and charges the stationary energy storage device with the electrical energy. The energy converter converts the electrical energy stored in the stationary energy storage device into AC power at a discharge rate and supplies a campus with the AC power at the discharge rate. The controller predicts a required load of the campus and an electrical generation of the photovoltaic energy field across a time horizon and optimizes a cost function subject to a set of constraints to determine a discharge rate of the AC power to achieve a desired power factor. At least one of the set of constraints applied to the cost function ensures that the energy converter can convert the electrical energy stored in the stationary energy storage device into AC power having the determined power factor and discharge rate.