Stability-aware social welfare optimization for renewable power dispatch with virtual synchronous generators

Abstract

In the last few decades, there has been a considerable increase in the implementation of renewable generation technologies within power systems, owing to the fact that there is a need to provide sustainable and environment- friendly options for generating electricity, as opposed to the traditional modes. However, the unpredictable nature of the renewable generation makes it necessary to implement proper demand management techniques. Demand response-based rescheduling algorithms such as Social Welfare Maximisation are thus gaining attention for mitigating this problem in the dispatch planning stage of power system. However, for very short or real time equilibrium system has to rely on inherent inertia. Conventional thermal power plants generate considerable rotational inertia using synchronous machines; therefore, they can deal with real-time disturbances because of load fluctuations, generation variations, and faults in the electrical system. To address these challenges of rich Renewable power systems, the Virtual Synchronous Generator’s theories has proven to be a potential solution. The virtual inertia and damping parameters are software-defined, but constrained by converter power ratings and control loop bandwidth. Also, excessively high virtual inertia settings can cause slow dynamic response or instability under fast disturbances. This paper revisits the VSG approach and intended to extend the range of controllability of VSGs by additional closed loop control circuit, enhancing the inherent software inertia adaptively, and thereby improving transient stability of VSG augmented renewable systems. Further, the renewable model thus developed with VSG was used in a benchmark IEEE 30 bus system with demand response based Social Welfare allied optimisation algorithm which demonstrated improvement of the values of Critical Clearing Time during worst possible contingencies and electrical faults of the system. The DEQPSO algorithm, based on the combination of the Differential Evolution (DE) algorithm and the Quantum Particle Swarm Optimisation (QPSO) algorithm, is ideal for solving the nonlinear optimization problem investigated in this research, where not only static operating constraints but also transient stability constraints are involved. In this respect, the current research aims to incorporate renewable energy sources with the VSG controller in the power system in order to improve its transient stability and reliability.