Closed-Loop Tuning of Cascade Controller for Load Frequency Control of Multi-Area Distributed Generation Resources Optimized by ASOS Algorithm

This paper provides closed loop tuning of cascaded-tilted integral derivative controller (CC-TID) for load frequency control (LFC) of micro grid system. A micro grid system is the arrangement of distributed generation resources such as wind turbine generator (WTG), fuel cell (FC), aqua electrolyser (AE), diesel engine generator (DEG) and battery energy storage system (BESS). Different controllers such as proportional integral derivative (PID), two degree of freedom (2DOFPID), three degree of freedom (3DOFPID) and tilted integral derivative (TID) are used not only to sustain the disparity between real power generation and load demand but also accomplish zero steady state error to enrich the frequency and tie power regulations. The anticipated controller encompasses both the value of cascade (CC) and fractional order (FO) controls for better elimination of system instabilities. In the proposed CC-3DOFPID-TID controller, TID controller is castoff as a slave controller and 3DOFPID controller aided the role of dominant controller. The controlled parameters are optimized by adaptive symbiotic organism search (ASOS) algorithm for keen results of difficulties in LFC. To persist in ecosystem, symbiotic relations are predictable by organism through imitators. Further the dynamic behaviours of controller optimized by ASOS, teaching learning based optimization (TLBO) and differential evolution particle swarm optimization (DEPSO) are compared by extensive simulations in MATLAB/SIMULINK. Moreover the supremacy of proposed controller is performed through system dynamics comparison among PID, 2DOFPID, 3DOF-PID and CC-3DOFPID-TID controllers. Finally sensitivity of proposed controller has proven though random load perturbation

Fuel-constrained joint heat and power dynamic economic environmental dispatch

The economical use of available fuel for producing electricity has been a very important challenge for power companies due to the continuously declining supply of fossil fuels. FCJHPDEED (fuel-constrained joint heat and power dynamic economic environmental dispatch) and JHPDEED (joint heat and power dynamic economic environmental dispatch) with DSM (demand-side management) integrating solar PV plants, WTGs (wind turbine generators), and PHS (pumped hydro storage) plants have been presented. Using SPEA 2 (strength Pareto evolutionary algorithm 2) and NSGA-II (non-dominated sorting genetic algorithm-II), FCJHPDEED and JHPDEED have been solved. It is seen that the results obtained without fuel constraints are more optimal than the results obtained with fuel constraints. The joint heat and power dynamic economic dispatch cost obtained with fuel constraints is approximately 2.14% more than the cost obtained without fuel constraints and joint heat and power dynamic emission dispatch, and the emission obtained with fuel constraints is approximately 6.7% more than the emission obtained without fuel constraints

Dynamic optimal power flow for multi‐operator renewable energy‐based virtual power plants

Abstract Recently, infiltration of distributed energy resources (DERs) is augmented considerably to upsurge network flexibility, better economic indicator, and reduced power loss. But integration of different DERs may cause challenges in power grid. To overwhelmed these challenges and obtain maximum advantage of DERs, virtual power plant's concept has been emerged. Virtual power plants (VPPs) has the capacity to partake in electricity market and rivalry of VPPs to achieve more profit, deregulated multi‐operator markets are developed. This paper suggests dynamic optimum power flow (DOPF) for multi‐operator VPPs considering demand side management (DSM) and uncertainty of renewable energy sources. VPPs with different proprietorships are interconnected with each other by tie lines. Each VPP has small hydro power plants (SHPPs), solar PV plants (SPVPs), wind turbine generators (WTGs), bioenergy power plant (BPPs), and plug‐in electric vehicles (PEVs). VPP 1 comprises IEEE 33‐bus system, VPP 2 comprises 15‐bus system, and VPP 3 comprises IEEE 69‐bus system. Bottlenose dolphin optimizer (BDO), HPSO‐TVAC, and GWO have been applied to solve DOPF problem and maximize the net profit of multi‐operator VPPs

Scenario-Based Fuel Constrained Short-Term Hydrothermal Scheduling

For electric power companies, the economic utilization of existing fossil fuel is currently a primary issue because of the diminishing supply of fossil fuel. Thermal power plants have fuel limitations and contractual restrictions that must be adhered to. As a result, the scenario-based fuel-constrained short-term hydrothermal scheduling problem with renewable energy sources is presented in this paper. The elephant clan optimization (ECO) approach is offered for short-term hydro-thermal scheduling (STHTS) with thermal generators, cascaded hydro, solar PV plants, wind turbine generators (WTG), and pumped storage hydro (PSH) with and without demand side management (DSM) for various scenarios. On a typical test system, the suggested approach is shown to be successful. An analysis of the typical test system’s numerical results is compared to those produced via the self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) and grey wolf algorithms (GWO). The comparison shows that the suggested ECO is capable of providing a better solution