Investigations on Procurement of Ancillary Services in Renewable Integrated Deregulated Power System

Abstract

The electric utilities are changing their way of operation and business from vertically integrated arrangements to deregulated one. Earlier, the vertically integrated utilities performed all the functions involving generation, transmission, distribution, and were responsible for supplying the energy and service quality. Under deregulation, these electric utilities are segregated into three companies namely GENCOs, TRANSCOs, and DISCOMs that are specifically responsible for generation, transmission, and distribution, respectively. Such segregation is directed to enhance the competition and offer new choices and economic benefits to the consumers. Electricity is therefore being treated as a tradable commodity. The deregulation is confronted with two key challenges i.e. maintaining real-time supply-demand balance and transfer of bulk electricity through the desired path. The variability and intermittency of renewable wind and solar integration further complicate the power system operation in the deregulated environment. Such issues to an extent can be handled through congestion management and ancillary service procurement. Tackling network congestion is a vital concern for the System Operator (SO) in the deregulated market because it entails additional cost and poses a threat to power system security. The transmission network is also subjected to physical and operating constraints such as line limits and bus voltage magnitude, etc. in facilitating transactions. The network operation beyond these limits leads to a blackout. Therefore congestion management under deregulation is to relieve congestion by prioritizing the transactions and committing to such a schedule that would not overload the network. The Ancillary Services (AS) are the support services that are essential for supporting transactions while maintaining reliable operation and ensuring the required degree of quality and safety. The provision of ancillary services is another challenging task to be performed by the SO in the deregulated market environment. The SO acquires a central coordination role and is responsible for managing scheduling and transmission-related operating services. It has to ensure a required degree of quality and safety and provide corrective measures under contingent conditions. Therefore, the ancillary services which are beyond basic energy and power delivery services, such as scheduling and dispatch, frequency control, voltage control, operating reserves, etc. are vital for power system operation. In deregulated market environment, the AS are not an integral part of the electric supply, but are priced separately. The SO is responsible to purchase these services from AS providers and adequately remunerate them. Wind and solar are widely distributed and abundantly available Renewable Energy Sources (RES). The medium and high penetration of these RES is although economical and environmentally benign, their variability and intermittency are the challenges that greatly impact the system security. The integration of RES is thus increasing the requirement of ancillary services. Therefore, the uncertain behavior of these RES needs to be suitably addressed in the deregulated environment. In this research work, the different market models of the deregulated power system are analyzed and a congestion management problem is solved using Optimal Power Flow (OPF) and Available Transfer Capability (ATC) based methods. In both the methods, congestion is relieved by allocating Thyristor Controlled Series Compensator (TCSC) using a congestion rent contribution approach which is based on Locational Marginal Prices (LMP). The pool market model is considered in the OPF method and the bilateral contracts model is considered for the ATC method. The performance is investigated on different test systems for normal as well as contingency conditions comprising line outages and abrupt load variation. The Operating Reserve Ancillary Service (ORAS) procurement problem comprising demand response offers in wind incorporated deregulated system is investigated in the pool market to minimize the total Energy and Spinning Reserve (ESR) cost and emission. The stochastic nature of wind speed and wind power generation is represented by the Weibull Probability Density Function (PDF). The uncertainty in wind power generation is handled by enforcing imbalance penalties for any deviations from forecasted power output. This multi-objective problem is solved using a developed Pareto based Non-Dominated Sorting Multi-Objective Gravitational Search Algorithm (NS-MOGSA), which utilizes the non-dominated sorting concept and an external archive to store Pareto optimal solutions. The efficacy of the developed algorithm is validated by comparing its results with the results from Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The optimization investigations have been realized for different levels of wind uncertainty i.e. ±10%, ±20%, and ±30% in static as well as dynamic multi-objective framework.Reactive power support or Voltage Control Ancillary Services (VCAS) procurement problem is investigated to minimize cost and voltage deviation considering wind power generation uncertainties in a pool-based deregulated system. This problem is formulated as a dynamic bi-objective optimization problem and is solved using a developed Pareto-based Multi-Objective Artificial Electric Field Algorithm (MO-AEFA). The developed algorithm utilizes the non-dominated sorting principle and an external archive to store Pareto optimal solutions. The results obtained from MO-AEFA on different test systems are compared with MOPSO, Multi-Objective Gravitational Search Algorithm (MOGSA), and Multi-Objective Grey Wolf Optimizer (MOGWO) to validate the efficacy of the proposed approach. The analysis with wind integration is further investigated for different wind penetration levels. The investigation is extended to incorporate solar power generation along with wind generation in a multi-objective dynamic framework to procure VCAS in a deregulated environment. The profile of wind speed and solar irradiation is represented by Weibull PDF and Log-normal PDF, respectively. The uncertainty in power generation from wind and solar is addressed by imbalance penalties for over-estimation and under-estimation cases. This problem is investigated to co-optimize the total cost and voltage deviation by using the developed Hybrid Multi-Objective Swarm Assisted Electric Field Optimization (hMOSEFO) algorithm on different test systems. The performance of the proposed algorithm is compared with MOPSO and MO-AEFA. Furthermore, the performance of the model is also investigated for different solar irradiations to account for seasonal changes. The performance of developed multi-objective algorithms in this research work is also validated through statistical distance and diversity performance metrics. Moreover, to diminish the computational effort of these optimization algorithms in terms of memory and run time, a vectorized Newton power flow is used which exploits the sparsity of system admittance and Jacobian matrices.