• Energy Research

This paper recommends chaotic fast convergence evolutionary programming (CFCEP) for solving real-world dynamic economic dispatch (DED) with demand-side management (DSM) incorporating renewable energy sources and pumped-storage hydroelectric unit. Here, solar–wind–thermal energy system has been considered taking into account pumped-storage hydroelectric unit and uncertainty of solar and wind energy sources. DSM programs reduce cost and boost up power system security. To investigate the upshot of DSM, the DED problem is solved with and without DSM. In the recommended technique, chaotic sequences have been applied for acquiring the dynamic scaling factor setting in FCEP. The efficiency of the recommended technique is revealed on two test systems. Simulation outcomes of the suggested technique have been matched against those acquired by fast convergence evolutionary programming (FCEP), colonial competitive differential evolution and heterogeneous strategy particle swarm optimization. It has been observed from the comparison that the recommended CFCEP technique has the ability to give better-quality solution.

AbstractRecently, 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.

Abstract Due to gradually diminution of fossil fuel, the cost-effective utilization of available fuel for power generation has turn out to be a vital concern of electric power utilities. Thermal power plants have to operate within their fuel confines and contractual constraints. This work suggests social group entropy optimization (SGEO) technique to solve short-term generation scheduling of a power system consisting of fuel constrained thermal generating units, cascaded hydro power plants, solar PV plants, wind turbine generators and pumped storage hydro (PSH) plants with demand side management (DSM). Simulation results of the test system have been compared with those acquired by self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), fast convergence evolutionary programming (FCEP) and differential evolution (DE). Numerical results show that fuel consumption can be adequately controlled for fulfilling constraints imposed by suppliers and total cost with fuel constraints is more than the cost without fuel constraints. It has been also observed from the comparison that the suggested SGEO has the ability to bestow with superior-quality solution.