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Construction 4.0 case studies
Published in Anil Sawhney, Mike Riley, Javier Irizarry, Construction 4.0, 2020
Cristina Toca Pérez, Dayana Bastos Costa, Mike Farragher
WikiHouse’s development so far suggests that open-source architecture will have significant economic, social and environmental effects, and is therefore likely to disrupt the construction industry. The most advanced technology to date, Wren, has already been used to realize constructed projects in the United Kingdom and abroad, including the double-story WikiFarmHouse. The case study demonstrates that a high-performance, adaptable WikiHouse can be achieved for £800 per m2, about half the cost of a typical site-built home of similar size. In addition to economic advantages, owners also benefit from having greater control over their building because they can make future modifications more easily.
Roth-Erev Reinforcement Learning Approach for Smart Generator Bidding towards Long Term Electricity Market Operation Using Agent Based Dynamic Modeling
Published in Electric Power Components and Systems, 2020
Kiran Purushothaman, Vijaya Chandrakala
One of the important specialities of AMES tool is its capability to perform the learning algorithm in GenCo’s. The supply offer selected by the GenCo will depend on past rewards and the learner module actions. This tool permits modeling and analyzing both wholesale and retail power markets having large number of buses and traders in an effective manner. The AMES Graphical User Interface (GUI) is used for design, analysis and revision of various test cases. The AMES is developed in Java language to enable readability and usage. Its open source architecture permits the user to modify the code in order to suit their needs. The various open source tools which are used for the development of AMES includes Java Development Kit (JDK), Java Integrated Development Environment (IDE), Java Chart Library, Java Agent Based toolkit, Java DC Optimal Power Flow Solver (DCOPFJ) etc. AMES software supports with DCOPF which is used for LMP simulation solved using linear programming (LP) as; it proves the ability of application towards robustness and speed. In optimization problem; the Lagrangian function (L) for optimal load dispatch is given by Eq. (1), where; ‘n’ is number of Generators, Ci(Pgi) is cost curve of generator located at bus ‘i’, ‘N’ is number of buses, Pgi and Pdi are bus ‘i’ power generation and demand, ‘λ’ is Lagrangian multiplier with respect to equality constraint and ‘μ’ is column vector containing Lagrangian multiplier of in-equality constraints, ‘SF’ is Sensitivity matrix relating Power injections and line flows, ‘Pg’ and ‘Pd’ are Column vector containing Power generation and demand at all buses. At bus ‘i’ LMP is evaluated by taking the partial derivative of Lagrangian with respect to the demand. Therefore, LMP is calculated using Eq. (2); where ‘M’ is total number of transmission lines. The LMP obtained has two components; the first term refers to as the energy component which illustrates the inequality constraint that corresponds to line power flows. LMP will remain same at all the buses if there is no congestion due to constraint violation. But; different values of LMP at all the buses indicate the presence of congestion in lines due to constraint violation. Therefore, in this work the structure of wholesale market is created on a standard bus system using AMES and LMP with GenCo commitment based on day-ahead market operation.