Memory-based adaptive sliding mode load frequency control in interconnected power systems with energy storage

This paper presents a memory-based adaptive sliding mode load frequency control (LFC) strategy aimed at minimizing the impacts of exogenous power disturbances and parameter uncertainties on frequency deviations in interconnected power systems with energy storage. First, the dynamic model of the system is constructed by considering the participation of the energy storage system (ESS) in the conventional decentralized LFC model of a multiarea power system. A disturbance observer (DOB) is proposed to generate an online approximation of the lumped disturbance. In order to enhance the transient performance of the system and effectively mitigate the adverse effects of power fluctuations on grid frequency, a novel memory-based sliding surface is developed. This sliding surface incorporates the estimation of the lumped disturbance, as well as the past and present information of the state variables. The conservative assumption about the lumped disturbance is eased by considering the unknown upper bound of the disturbance and its first derivative. Based on the output of the proposed DOB, an adaptive continuous sliding mode controller with prescribed H performance index is introduced. This controller ensures that the sliding surface is reachable and guarantees asymptotic stability of the closed-loop system. The controller design utilizes strict linear matrix inequalities (LMIs) to achieve these objectives. Finally, the applicability and efficacy of the proposed scheme are verified through comparative simulation cases. Autho

Decentralized disturbance observer-based sliding mode load frequency control in multiarea interconnected power systems

The load frequency control (LFC) problem in interconnected multiarea power systems is facing more challenges due to increasing uncertainties caused by the penetration of intermittent renewable energy resources, random changes in load patterns, uncertainties in system parameters and unmodeled system dynamics, leading to a compromised reliability of power systems and increasing the risk of power outages. In responding to this problem, this paper proposes a decentralized disturbance observer-based sliding mode LFC scheme for multiarea interlinked power systems with external disturbances. First, a reduced power system order is constructed by lumping disturbances from tie-line power deviations, load variations and the output power from renewable energy resources. The disturbance observer is then designed to estimate the lumped disturbance, which is further utilized to construct a novel integral-based sliding surface. The necessary and sufficient conditions to determine the tuning parameters of the sliding surface are then formulated in terms of linear matrix inequalities (LMIs), thus guaranteeing that the resultant sliding mode dynamics meet the ${H_\infty }$ performance requirements. The sliding mode controller is then synthesized to drive the system trajectories onto the predesigned sliding surface in finite time in the presence of a lumped disturbance. From a practical perspective, the merit of the proposed control method is to minimize the impact of the lumped disturbance on the system frequency, which has not been considered to date in sliding mode LFC design. Numerical simulations are illustrated to validate the effectiveness of the proposed LFC strategy and verify its advantages over other approaches

Upgrade a Medium Size Enterprise Power System with Wind and Solar Sources: Design, Financial and Environmental Perspectives

Efficient generation and distribution are crucial for economic power production. In this paper we discuss the planning and design of upgrading a medium size enterprise power system by installing Distributed Energy Resources (DERs), with particular emphasis on economic viability and environmental benefits. The planning for this project considers both conventional grid and SmartGrid connections. Project planning, installation challenges and governmental support of renewable energy projects in Australia are discussed. It is found that upgrading a medium size enterprise power system with DERs can yield reasonable levels of energy cost savings and greenhouse gas mitigation with both conventional grid and SmartGrid connections, but that SmartGrid connection can deliver better outcomes

Fuzzy Interference System in Energy Demand Prediction

No abstract available

Fuzzy Inference System in Energy Demand Prediction

No abstract available

Superconducting Magnetic Energy Storage Unit for Damping Enhancement of a Wind Farm Generation System

A superconducting magnetic energy storage (SMES) system containsa high inducting coil and combines with power conversion system can act as a constant source of direct current. SMES unit connected to a power system is able to absorb and store both active and reactive power from this system and to inject these powers into the power system in the demand periods. These injected powers are controlled by changing both the duty cycle of the dc-dc chopper switches and its operation modes. This paper presents an efficient design based on an SMES unit controlled by the artificial neural network (ANN) to improve transient stability by regulating the dc link voltage and to damp the voltage and frequency fluctuations that are always associated with wind power generator. The authors propose interfacing the SMES between wind power farm and the power grid connected through the DC Link capacitor to rapidly stabilize the voltage and frequency fluctuations in the power system.The system behavior is tested with three different events for both voltage and frequency fluctuations of wind power generation with and without applying the SMES unit. The results show that both voltage and frequency stabilities are significantly increased when the SMES unit is applied in these three events

An improved genetic algorithm based fractional open circuit voltage MPPT for solar PV systems

To extract the maximum power from solar PV, maximum power point tracking (MPPT) controllers are needed to operate the PV arrays at their maximum power point under varying environmental conditions. Fractional Open Circuit Voltage (FOCV) is a simple, cost-effective, and easy to implement MPPT technique. However, it suffers from the discontinuous power supply and low tracking efficiency. To overcome these drawbacks, a new hybrid MPPT technique based on the Genetic Algorithm (GA) and FOCV is proposed. The proposed technique is based on a single decision variable, reducing the complexity and convergence time of the algorithm. MATLAB/Simulink is used to test the robustness of the proposed technique under uniform and non-uniform irradiance conditions. The performance is compared to the Perturb & Observe, Incremental Conductance, and other hybrid MPPT techniques. Furthermore, the efficacy of the proposed technique is also assessed against a commercial PV system\u27s power output over one day. The results demonstrate that the proposed GA-FOCV technique improves the efficiency of the conventional FOCV method by almost 3%, exhibiting an average tracking efficiency of 99.96% and tracking speed of around 0.07 s with minimal steady-state oscillations. Additionally, the proposed technique can also efficiently track the global MPP under partial shading conditions and offers faster tracking speed, higher efficiency, and fewer oscillations than other hybrid MPPT techniques

Stability Analysis of an Autonomous Microgrid Operation Based on Particle Swarm Optimization

This paper presents the stability analysis for an inverter based Distributed Generation (DG) unit in an autonomous microgrid operation. The small-signal model of the controlled Voltage Source Inverter (VSI) system is developed in order to investigate the dynamic stability for the given operating point and under the proposed power controller. This model includes all the details of the proposed controller, while no switching actions are considered. System oscillatory modes and the sensitivity to the control parameters are the main performance indices which are considered, particularly when the microgrid is islanded or under the load change condition. In this work, the proposed power controller is composed of an inner current control loop and an outer power control loop, both based on a synchronous reference frame and conventional PI regulators. These controllers also utilize the Particle Swarm Optimization (PSO) for real-time self-tuning in order to improve the quality of the power supply. The complete small-signal model is linearized and used to define the system state matrix which is employed for eigenvalue analysis. The results prove that the stability analysis is fairly accurate and the controller offers reliable system\u27s operation

Tuning Fuzzy Systems to Achieve Economic Dispatch for Microgrids

In this paper, a Tuning Fuzzy System (TFS) is used to improve the energy demand forecasting for a medium-size microgrid. As a case study, the energy demand of the Joondalup Campus of Edith Cowan University (ECU) in Western Australia is modelled. The developed model is required to perform economic dispatch for the ECU microgrid in islanding mode. To achieve an active economic dispatch demand prediction model, actual load readings are considered. A fuzzy tuning mechanism is added to the prediction model to enhance the prediction accuracy based on actual load changes. The demand prediction is modelled by a Fuzzy Subtractive Clustering Method (FSCM) based Adaptive Neuro Fuzzy Inference System (ANFIS). Three years of historical load data which includes timing information is used to develop and verify the prediction model. The TFS is developed from the knowledge of the error between the actual and predicted demand values to tune the prediction output. The results show that the TFS can successfully tune the prediction values and reduce the error in the subsequent prediction iterations. Simulation results show that the proposed prediction model can be used for performing economic dispatch in the microgrid

PSO algorithm for an optimal power controller in a microgrid

This paper presents the Particle Swarm Optimization (PSO) algorithm to improve the quality of the power supply in a microgrid. This algorithm is proposed for a real-time selftuning method that used in a power controller for an inverter based Distributed Generation (DG) unit. In such system, the voltage and frequency are the main control objectives, particularly when the microgrid is islanded or during load change. In this work, the PSO algorithm is implemented to find the optimal controller parameters to satisfy the control objectives. The results show high performance of the applied PSO algorithm of regulating the microgrid voltage and frequency