• Energy Research

As renewable energy (RE) penetration has a continuously increasing trend, the protection of RE integrated power systems is a critical issue. Recently, power networks developed for grid integration of solar energy (SE) have been designed with the help of multi-tapped lines to integrate small- and medium-sized SE plants and simultaneously supplying power to the loads. These tapped lines create protection challenges. This paper introduces an algorithm for the recognition of faults in the grid to which a solar photovoltaic (PV) system is integrated. A fault index (FI) was introduced to identify faults. This FI was calculated by multiplying the Wigner distribution (WD) index and Alienation (ALN) index. The WD-index was based on the energy density of the current signal evaluated using Wigner distribution function. The ALN-index was evaluated using sample-based alienation coefficients of the current signal. The performance of the algorithm was validated for various scenarios with different fault types at various locations, different fault incident angles, fault impedances, sampling frequencies, hybrid line consisting of overhead (OH) line and underground (UG) cable sections, different types of transformer windings and the presence of noise. Two phase faults with and without the involvement of ground were differentiated using the ground fault index based on the zero sequence current. This study was performed on the IEEE-13 nodes test network to which a solar PV plant with a capacity of 1 MW was integrated. The performance of the algorithm was also tested on the western part of utility grid in the Rajasthan State in India where solar PV energy integration is high. The performance of the algorithm was effectively established by comparing it with the discrete Wavelet transform (DWT), Wavelet packet transform (WPT) and Stockwell transform-based methods.

This paper proposes a new stochastic multi-objective optimal energy management model named SMO-OEM model for techno-economic operations of smart distribution network (SDN) under uncertainty. A typical SDN is integrated with various generating resources such WTs, PVs, DGs, BESS, and utility grid to meet ever increasing and uncertain energy demands. A scenario-based analysis is utilized for handling the uncertainty allied with renewable energy generation (PVs and WTs), load power demand, and utility grid prices. In the first phase of the proposed model, several initial scenarios are generated with respect to day-ahead forecasts of PVs, WTs, load demand, grid prices using Monte-Carlo simulations and subsequently reduced them to finalize the input test scenarios for next phase. Thereafter, in the second phase, two conflicting objectives i.e. expected total operational cost ( $EF_{TC}$ ), and the expected total active power loss ( $EF_{TPL}$ ) are optimized simultaneously. The proposed SMO-OEM model recommends the further reactive support acquired from WTs, PVs, and BESS along with a demand response program (DRP) for optimum SDN operations. The proposed model is applied to two distinct sized networks i.e. modified IEEE-33 and IEEE-69 bus distribution networks and examined for different uncertainty ranges of ±5%, ±10%, and ±20% with respect to a day-ahead forecasted uncertain variables. Three comprehensive case studies are presented for detailed model assessments and comparisons under different uncertainty ranges. It is found that significant reductions are achieved in both $EF_{TC}$ and $EF_{TPL}$ by the recommended supplementary reactive management through WTs, PVs, and BESS. Additionally, DRP scheme is also applied at few locations to offer peak load reductions by shifting them to other timings over a period of 24 hours which further reduces both objectives ( $EF_{TC}$ and $EF_{TP}$ ). These recommendations are found suitable to significantly improve the bus voltage profile as well as economic operations.

This paper proposes a new multi-objective stochastic market settlement model for a coupled energy and reactive power ancillary service, named as MOSMS-CERP model, for a prospective grid integration of wind energy. It is a comprehensive two-stage scenario-based stochastic optimization model to incorporate the system uncertainties associated with wind power generations along with load demands. In this multi-objective framework, two competing objective functions as the expected total payment functions for energy and reactive power ancillary services are simultaneously minimized while satisfying all the system operating constraints including technical as well as market driven constraints. An efficient hybrid payment strategy is designed for energy market as well as reactive power ancillary service market for creating a competitive and fair market auction. A pay-as-bid payment mechanism is adopted for all thermal generating units whereas a pay-at-MCP (Market Clearing Price) is adopted for all hydro-powered generating units and wind farms for the energy market settlement. However, the ancillary service market of reactive power is settled using uniform price payment mechanism. It is a unique market settlement model, which combines three significant complexities such as stochastic nature, multi-objective constrained optimization, and meta-heuristics. The proposed MOSMS-CERP model is examined by applying five well-known, recent, and advanced meta-heuristic methods such as Non-dominated Sorting Genetic Algorithms (NSGA-II & I-NSGA-III), Multi-Objective Keshtel Algorithm (MOKA), Hybrid Fuzzy Multi-Objective Evolutionary Algorithm (HFMOEA), and Multi-Objective Particle Swarm Optimization (MOPSO) for producing the sets of Pareto-optimal solutions. A fair statistical comparison is carryout for these five meta-heuristic methods based on No Free Launch Theorem. All simulations are conducted on a standard IEEE 24-bus RTS with a doubly fed induction generator (DFIG) based wind farm on a DIgSILENT Power Factory software platform. The obtained simulation results ensure the robustness and effectiveness of the proposed MOSMS-CERP model.

The Soft Open Point (SOP) is a cutting-edge electronic apparatus consisting of voltage source converters linked by a DC capacitor, designed to connect neighbouring distribution feeders. The primary roles of SOPs include managing active power flow and offering reactive power assistance. By incorporating energy storage, this device gains the ability to regulate active power flow over time. This study proposes a framework that takes into account the capabilities of an energy storage-equipped SOP (ESOP) across temporal and spatial dimensions, along with demand side management (DSM) and coordinated dispatch of active and reactive power from solar PV and wind turbine generators. The aim is to optimise the operational costs of an active distribution network (ADN). To accurately depict demand, a voltage-sensitive exponential load model is employed. The suggested framework is formulated as a mixed integer nonlinear programming (MINLP) model, implemented on a modified IEEE 33 bus distribution network, and solved using the DICOPT solver in GAMS.

The rapid growth of grid integrated renewable energy (RE) sources resulted in development of the hybrid grids. Variable nature of RE generation resulted in problems related to the power quality (PQ), power system reliability, and adversely affects the protection relay operation. High penetration of RE to the utility grid is achieved using multi-tapped lines for integrating the wind and solar energy and also to supply loads. This created considerable challenges for power system protection. To overcome these challenges, an algorithm is introduced in this paper for providing protection to the hybrid grid with high RE penetration level. All types of fault were identified using a fault index (FI), which is based on both the voltage and current features. This FI is computed using element to element multiplication of current-based Wigner distribution index (WD-index) and voltage-based alienation index (ALN-index). Application of the algorithm is generalized by testing the algorithm for the recognition of faults during different scenarios such as fault at different locations on hybrid grid, different fault incident angles, fault impedances, sampling frequency, hybrid line consisting of overhead (OH) line and underground (UG) cable sections, and presence of noise. The algorithm is successfully tested for discriminating the switching events from the faulty events. Faults were classified using the number of faulty phases recognized using FI. A ground fault index (GFI) computed using the zero sequence current-based WD-index is also introduced for differentiating double phase and double phase to ground faults. The algorithm is validated using IEEE-13 nodes test network modelled as hybrid grid by integrating wind and solar energy plants. Performance of algorithm is effectively established by comparing with the discrete wavelet transform (DWT) and Stockwell transform based protection schemes.

This paper attempts to develop a multi-time period stochastic optimization model for economic operations of a typical microgrid by employing a scenario-based analysis approach to exploit various uncertainties associated with variable renewable energy (VRE) generations, electricity prices, and load demand. This stochastic model is aimed at generating the optimum schedules for various dispatchable generating resources such as micro-turbines, fuel cells, utility grid, energy storage devices as per the availability of the various VRE resources to meet the uncertain demand for a day-to-day microgrid operation. Further, a suitable scenario reduction approach named hybrid distance and similarity (HDS) approach is proposed to cater for two diverse objectives i.e., minimization of the Manhattan distance and maximization of the similarity index between an optimal scenario pair for generating a reduced scenario set by eliminating large redundant scenarios from its original large set. To verify the effectiveness of the proposed HDS, its performance is compared with three well developed distinct methods such as SBR (simultaneous backward reduction method), FFS (fast forward selection method), and SIMCOR (similarity-correlation method) on two different stochastic optimization problems including one real-life economic microgrid problem. All the competing scenario reduction methods are compared in terms of various performance indices i.e. OSDI (Output Sample Deviation Index), PSRI (Percentage Scenario Reduction Index), objective values, and computation time to verify their suitability and effectiveness on complex optimization problems. The proposed HDS method is found to be capable in achieving the lowest OSDI value of 5.68 at 98 % scenario reduction while compared to other competing methods i.e. 12.95 by SBR, 14.76 by FFS, and 16.32 by SIMCOR for the real-life microgrid problem. Moreover, the proposed HDS methods also outperforms the other three competing methods in terms of their objective function values after 98 % scenario reduction with a least computation time burden i.e. 87.6 %, 1.11 %, and 53 % less computing times are needed by HDS, FFS, and SIMCOR, respectively. These comprehensive simulation results reveal that the proposed HDS method is capable to generate high-quality scenarios, better approximation, superior stability, and with lower computation time burden as compared to the other three competing scenario reduction approaches.