Power Flow Management Algorithms Research Articles

A non‐isolated bidirectional high gain integrated multiport converter for grid tied solar PV fed telecom load

AbstractA multiport converter (MPC) with a non‐isolated high gain bidirectional port is proposed for the grid integration of solar photovoltaic array (SPA) fed telecommunication load. The SPA along with a battery energy storage (BES) meets the power demand of the telecom DC load and the excess/deficit power is exchanged with AC grid. The MPC feeds the DC link of a voltage source converter for bidirectional operation with the AC grid. The small signal analysis of the converter shows that its operation is stable. The SPA, BES, and telecommunication load are rated for lower voltages, consecutively reducing the complexity with series‐connected SPA. The proposed MPC possesses the merits of high voltage gain, reduced inductor size, and reduced number of components. Moreover, a power flow management algorithm is devised for the proposed converter that regulates the DC voltage at the telecom load and ensures smooth power flow control among various ports. The MPC is able to operate at various modes by controlling the ports independently. The converter performance during steady state and dynamic operating conditions under various modes are analyzed with detailed simulation studies. An experimental prototype is developed and test results are demonstrated to prove the viability of the designed converter.

A SEPIC-based three-port converter system using a mode-specific power flow management control for solar energy harvesting

The growing demand of renewable energy sources in the energy sector has a considerable impact on the development of stand-alone solar photovoltaic (PV) systems. Three-port converter (TPC) topologies are widely used as a gateway between solar PV, energy storage, and loads. This paper proposes a three-port converter with buck and boost operating modes. The topology is SEPIC-based rather than the conventional buck-boost or Cuk circuit, hence eliminates the inverting output and reduce the input current ripple. The series capacitor in a SEPIC converter endows the TPC with inherent protection against short-circuit output. The TPC has four modes of operation: Dual Output (DO) mode, Dual Input (DI) mode, Single Input Single Output-1 (SISO-1) mode and Single Input Single Output-2 (SISO-2) mode. The power flow management algorithm employs a mode-specific voltage regulation strategy that distributes the duty cycle for each switch via time-sharing control scheme. Small signal model of the TPC is developed in each operating mode and based on this, voltage controller parameters are derived. When the TPC changes modes, the algorithm modifies the controller parameters to the mode-specific values. The proposed system is implemented by utilising the FPGA Cyclone-III comprising the Nios-II processor core with necessary hardware intellectual property (IP) forming a System on a Programmable Chip (SoPC). The system is evaluated in real time using a 120W solar PV as the energy source and lead-acid battery as the storage.

Power Flow Management With Q-Learning for a Grid Integrated Photovoltaic and Energy Storage System

This article develops a fuzzy Q-learning (FQL) approach-based power flow management algorithm for a single-phase grid-connected (GC) photovoltaic (PV) system with an energy storage unit (ESU). The FQL coordinates the PV power generation, which is based on the mission profile, the state of charge (SOC) of ESU, and the load profile such that a power balance is achieved in the network. Furthermore, the transition between standalone (SA) and GC modes during the power flow management is achieved using a proportional capacitor current feedback. While operating in the SA mode, the control action for the load voltage is achieved by an outer current loop. This combination of coordinated and transition control achieves power balance and smooth transition between the SA and GC modes. To verify the effectiveness of the proposed control strategy, a 4 – kWp PV system is operated along with an ESU in both SA and GC modes for a varying mission and load profile. The simulation and experimental results validated the multifunctional features of the proposed method.

Optimal Management for Megawatt Level Electric Vehicle Charging Stations With a Grid Interface Based on Modular Multilevel Converter

This paper proposes a strategy to manage an electric vehicle charging station (EVCSs) with a grid-side interface based on a Modular Multilevel Converter (MMC). The MMC topology is studied due to its potential for reducing the footprint and the use of active material in the internal distribution system by allowing for transformer-less connection to the medium voltage distribution grid. However, heterogeneous charging demands and arrival-departure profiles of the electric vehicles (EVs) could lead to significant loading unbalances among the MMC arms and among the modules of a single arm. Nevertheless, the current in the grid interface must be kept balanced and sinusoidal. Furthermore, the voltages of the modules of an arm must be balanced. This work combines a load management (LM) algorithm with a power flow management (PFM) algorithm to achieve the required characteristics of grid current and module voltages under the heterogeneity of the charging demand in MMC-based EVCSs. The PFM algorithm controls the circulating currents to compensate the phase-to-phase, arm-to-arm and intra-arm unbalances of the given loading. To minimize the additional losses resulting from active balancing by the PFM, the LM optimizes the charging schedules and allocations of incoming EVs into charging units in order to minimize phase-to-phase and arm-to-arm unbalances in the system. The performance of the proposed optimization-based LM is compared with a rule-based benchmark LM by simulating the daily operation of an example shopping mall parking with MMC-based grid interface. In scenarios with pronounced unbalance limitations, the optimization-based LM increases the supplied energy significantly. Real-time (RT) simulations demonstrate a balanced and sinusoidal grid current profile and balanced module voltages in MMC arms over the daily scenarios. These results indicate that the proposed strategy combining LM and PFM is applicable for real-world deployments.

Network State-Based Algorithm Selection for Power Flow Management Using Machine Learning

This paper demonstrates that machine learning can be used to create effective algorithm selectors that select between power system control algorithms depending on the state of a network, achieving better performance than always using the same algorithm for every state. Also presented is a novel method for creating algorithm selectors that consider two objectives. The method is used to develop algorithm selectors for power flow management algorithms on versions of the IEEE 14- and 57-bus networks, and a network derived from a real distribution network. The selectors choose from within a diverse set of power flow management algorithms, including those based on constraint satisfaction, optimal power flow, power flow sensitivity factors, and linear programming. The network state-based algorithm selectors offer performance benefits over always using the same power flow management algorithm for every state, in terms of minimizing the number of overloads while also minimizing the curtailment applied to generators.

Economic Analysis of Real-Time Large-Scale PEVs Network Power Flow Control Algorithm With the Consideration of V2G Services

In this paper, the economic analysis of a real-time power flow control algorithm for charging a large-scale plug-in electric vehicles (PEVs) network is proposed. Such a system is in an urban area power system with optimized renewable energy resources in South Florida. In this model, the PEVs charging rates are controlled by a central aggregator through wireless communication. To limit the impact of the PEVs' charging to the utility grid, while optimizing the scheduling of vehicle-to-grid (V2G) frequency regulation services, a V2G intelligent power flow management algorithm based on fuzzy logic control was developed. The smart charging system will improve the power system stability and robustness while bringing benefits to the utility ac grid, the PEVs network, and its customers. An economic analysis model was built with the consideration of the battery degradation and capital cost. The proposed smart charging algorithm was tested with a 50 000 PEVs network model. The simulation results show the effectiveness and economic value of the proposed model.

Reducing Distributed Generator Curtailment Through Active Power Flow Management

This paper presents an evaluation of the main characteristics of two power flow management (PFM) methodologies against a traditional inter-trip approach typically used by distribution network operators. The two PFM algorithms were developed, by the authors, for real-time operation with an aim to implement them in distribution networks with growing penetrations of renewable DG. The first PFM approach is modelled as a constraint satisfaction problem (CSP), while the second is based on an optimal power flow (OPF) approach. These PFM algorithms are deployed on real substation hardware to simulate the monitoring and control of MV distribution network power flows through DG real power regulation. Multiple scenarios are presented to the closed-loop PFM test environment to demonstrate the algorithms ability of detecting and alleviating thermal overloads and recognizing when the constraint has passed. The main objective of this paper is the quantification of the resultant curtailment levels, for the two approaches, which are compared to that of a traditional inter-trip scheme for the same circuit overload duration. The results demonstrate that taking an active approach to managing power flows can significantly increase the output of DG units in a thermally constrained network.

Distribution Power Flow Management Utilizing an Online Optimal Power Flow Technique

This paper describes the current connection regime for distributed generation (DG) in the U.K. and presents a novel application of the optimal power flow (OPF) technique for automatic power flow management (PFM) to manage thermal constraints in distribution networks. OPF formulations have been used, in an offline mode, as a power system planning tool for several years. The novel implementation of OPF for “corrective” PFM in an online operational mode, for MV distribution networks, is presented and tested in this paper. The authors demonstrate, through simulations conducted on a commercially available substation computer, that such an application of OPF can represent first on, last off generator connection agreements that reflect the current principles of access in the U.K. Two case study networks, a 33 kV and an 11 kV, provide the basis for assessment of the OPF-based PFM algorithm in terms of computation time to arrive at a solution in the event of a network thermal excursion and the level of DG curtailment necessary to meet network thermal limits. Assessments are made and fully discussed of the suitability for an OPF-based approach for distribution network management within an online network control scheme including discussion of the important consideration of control robustness.

Power flow management algorithm for photovoltaic systems feeding DC/AC loads

In this paper, a power flow management algorithm for photovoltaic system feeding DC and AC loads is presented. PV being a major energy source enables the DC loads to be connected directly to the DC bus. A grid connected PV system involves one power source (PV array), one power sink (load) and two power sources/sink (utility and battery), and hence a power flow management system is required to balance the power flow among these sources. A unique control algorithm is developed for selecting the operating mode of the bidirectional converter by sensing the battery voltage. The bidirectional converter has three modes of operation namely, the rectifier mode, inverter mode and the voltage control mode. In both the rectifier and inverter modes of operation, the converter current is controlled by employing a hysteresis current controller and unity power factor is achieved in both the cases. In voltage control mode, the converter regulates the inverter voltage and supplies the reactive power to the grid. Detailed simulation studies are carried out on a single phase bidirectional converter in MATLAB/Simulink environment. The viability of the scheme has been ascertained by performing experimental studies on a laboratory prototype. The control strategy is digitally implemented on an Altera Cyclone II FPGA board and the algorithm is verified for different modes of operation by varying the load and the relevant results are presented.

System Integration and Power-Flow Management for a Series Hybrid Electric Vehicle Using Supercapacitors and Batteries

In this paper, system integration and power-flow management algorithms for a four-wheel-driven series hybrid electric vehicle (HEV) having multiple power sources composed of a diesel-engine-based generator, lead acid battery bank, and supercapacitor bank are presented. The super-capacitor is utilized as a short-term energy storage device to meet the dynamic performance of the vehicle, while the battery is utilized as a mid-term energy storage for the electric vehicle (EV) mode operation due to its higher energy density. The generator based on an interior permanent magnet machine (IPMM), run by a diesel engine, provides the average power for the normal operation of the vehicle. Thanks to the proposed power-flow management algorithm, each of the energy sources is controlled appropriately and also the dynamic performance of the vehicle has been improved. The proposed power-flow management algorithm has been experimentally verified with a full-scale prototype vehicle.