Simulation of Blockchain based Power Trading with Solar Power Prediction in Prosumer Consortium Model

Prosumer consortium energy transactive models can be one of the solutions for energy costs, increasing performance and for providing reliable electricity utilizing distributed power generation, to a local group or community, like a university. This research study demonstrates the simulation of blockchain based power trading, supplemented by the solar power prediction using MLFF neural network training in two prosumer nodes. This study can be the initial step in the implementation of a power trading market model based on a decentralized blockchain system, with distributed generations in a university grid system. This system can balance the electricity demand and supply within the institute, enable secure and rapid transactions, and the local market system can be reinforced by forecasting solar generation. The performance of the MLFF training can predict almost 90% accuracy of the model as short term ahead forecasting. Because of it, the prosumer bodies can complete the decision making before trading to their benefit

Optimal congestion management in an electricity market using particle swarm optimization with time-varying acceleration coefficients

AbstractThis paper proposes an optimal congestion management approach in a deregulated electricity market using particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC). Initially, the values of generator sensitivity are used to select redispatched generators. PSO-TVAC is used to determine the minimum redispatch cost. Test results on IEEE 30-bus and 118-bus systems indicate that the PSO-TVAC approach could provide a lower rescheduling cost solution compared to classical particle swarm optimization and particle swarm optimization with time-varying inertia weight

Levenberg-Marquardt Recurrent Networks for Long-Term Electricity Peak Load Forecasting

 Increasing electricity demand in Java-Madura-Bali, Indonesia, must be addressed appropriately to avoid blackout by determining accurate peak load forecasting. Econometric approach may not be sufficient to handle this problem due to limitation in modelling nonlinear interaction of factors involved. To overcome this problem, Elman and Jordan Recurrent Neural Network based on Levenberg-Marquardt learning algorithm is proposed to forecast annual peak load of Java-Madura-Bali interconnection for 2009-2011. Actual historical regional data which consists of economic, electricity statistic and weather during 1995-2008 are applied as inputs. The networks structure is firstly justified using true historical data of 1995-2005 to forecast peak load of 2006-2008. Afterwards, peak load forecasting of 2009-2011 is conducted subsequently using actual historical data of 1995-2008. Overall, the proposed networks shown better performance compared to that obtained by Levenberg-Marquardt-Feedforward network, Double-log Multiple Regression, and with projection by PLN for 2006-2010

Optimal placement of FACTS controllers for maximising system loadability by PSO

In this paper, a multi objective-based method has been suggested to enhance the power system loadability with optimal placement of flexible AC transmission system (FACTS) controllers using particle swarm optimisation (PSO) technique. The objective function is to maximise the system loadability subjected to maintaining the system security, integrity, and stability margins within limits by obtaining the optimal location, installation costs, and control settings of the FACTS controllers. The various FACTS controllers, i.e., static var compensator (SVC), thyristor controlled series compensator (TCSC), and unified power flow controller (UPFC), have been considered in this study. The effectiveness of the proposed methodology has been investigated on the standard IEEE 14-bus, 30-bus, and practical Java-Bali 24-bus Indonesian system and the results are compared with the method suggested in the literatures. Moreover, the results obtained by PSO have also been compared with other evolutionary approach, viz., genetic algorithm (GA)

Low Carbon Energy Symbiosis for Sustainability: Review of Shared Value-based Policy Metabolism to Enhance the Implementability of the Sustainable Development Goals in Asia

The low energy symbiosis for development metabolism is reviewed for its potential to enhance the implementability of the Sustainable Development Goals. Metabolism is the carrying capacity limit of rural-urban or rurban eco-systems, that is self-replenishable through endurability drawn from metabolic processes. This research paper probes the symbiotic common-ground for sustainability for the shared value-based policy metabolism, deployed on emerging Asia. The unified motivation would be to co-implement quantum innovations and adaptations on governance mechanisms to usher pathways on symbiosis for sustainability. Intended outcomes are budgeting social metabolism, symbiotic scale-up that would attain efficiency and practicality. An important destination is trust renaissance developed on common-ground challenges facing the aspirational low carbon Energy-Asia. This conceptual paper posits a dual aimed methodology. (i) Where low carbon Energy-Asia would like to be for symbiotic common-ground for sustainability through trust renaissance and, (ii) what shared value policy trajectory should be plugged-in for healthy metabolism into their symbiotic development strategy. The unified motivation would be to co-implement quantum innovations and adaptations on governance mechanisms to usher pathways on symbiosis for sustainability. Keywords: Symbiosis for Sustainability, Low Carbon Energy-Asia, Shared Value-based Policy Metabolism, Trust Renaissance, Water – Waste – Energy Metabolism JEL Classifications: Q01, O35, R580 DOI: https://doi.org/10.32479/ijeep.723

Levenberg-Marquardt Recurrent Networks for Long- Term Electricity Peak Load Forecasting

Abstract Increasing electricity demand in Java-Madura-Bali, Indonesia, mus

Optimal Placement of A Series FACTS ControllerinJava-Bali 24-bus Indonesian System for Maximizing System Loadability by Evolutionary Optimization Technique

In this paper, a series FACTS controllernamely Thyristor Controller Series Compensator (TCSC) has been suggested to enhance the power system loadability.The location of the controllerand the setting of their control parameters are optimized by one type of Evolutionary Optimization Technique to improve the performance of the power network. The objective functions are to maximize the system loadability whereas maintaining system security and stability margins, e.g., small signal stability, voltage stability index, and line stability factor within limits by considering the investment costs of the controllerand minimizing active power loss of the system. The series FACTS controllerismodeled and incorporated in the Newton Raphson power flow problem. The effectiveness of the proposed methodology has been investigated on a practical Java-Bali 24-bus Indonesian grid syste

Development of PSO Based Control Algorithms for Maximizing Wind Energy Penetration

In this paper, new methodologies have been proposed for attaining the maximum safe instantaneous wind energy penetration. Various types of control algorithms namely, load increase, generation displacement and the combined load increase and generation displacement have been developed to obtain the maximum penetration. Wind Turbine used is DFIG and dynamic model of the system by considering Turbine governor (TG), Automatic voltage regulator (AVR) have been considered. Grid stability at high penetration level is obtained by conducting eigenvalue analysis of the complete power system grid. All the control algorithms are powered by Particle Swarm Optimization Algorithm (PSO) which adjusts the grid parameters for achieving maximum wind penetration. The developed algorithms have been tested with 25-bus, 220kV practical system. The results have shown the maximum safe instantaneous wind energy penetration limit possible by various methodologies proposed