16 research outputs found
ZCS redux
Learning classifier systems traditionally use genetic algorithms to facilitate rule discovery, where rule fitness is payoff based. Current research has shifted to the use of accuracy-based fitness. This paper re-examines the use of a particular payoff-based learning classifier system - ZCS. By using simple difference equation models of ZCS, we show that this system is capable of optimal performance subject to appropriate parameter settings. This is demonstrated for both single- and multistep tasks. Optimal performance of ZCS in well-known, multistep maze tasks is then presented to support the findings from the models
Architecting system of systems: artificial life analysis of financial market behavior
This research study focuses on developing a framework that can be utilized by system architects to understand the emergent behavior of system architectures. The objective is to design a framework that is modular and flexible in providing different ways of modeling sub-systems of System of Systems. At the same time, the framework should capture the adaptive behavior of the system since evolution is one of the key characteristics of System of Systems. Another objective is to design the framework so that humans can be incorporated into the analysis. The framework should help system architects understand the behavior as well as promoters or inhibitors of change in human systems. Computational intelligence tools have been successfully used in analysis of Complex Adaptive Systems. Since a System of Systems is a collection of Complex Adaptive Systems, a framework utilizing combination of these tools can be developed. Financial markets are selected to demonstrate the various architectures developed from the analysis framework --Introduction, page 3
Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems
Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems
Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts-Volume II
The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications, such as hybrid and microgrid power systems based on the Energy Internet, Blockchain technology, and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above
PV Charging and Storage for Electric Vehicles
Electric vehicles are only ‘green’ as long as the source of electricity is ‘green’ as well. At the same time, renewable power production suffers from diurnal and seasonal variations, creating the need for energy storage technology. Moreover, overloading and voltage problems are expected in the distributed network due to the high penetration of distributed generation and increased power demand from the charging of electric vehicles. The energy and mobility transition hence calls for novel technological innovations in the field of sustainable electric mobility powered from renewable energy. This Special Issue focuses on recent advances in technology for PV charging and storage for electric vehicles
Electricity Market Participation and Investment Planning Frameworks for Energy Storage Systems
The recent trend of increasing share of renewable energy sources (RES) in the generation mix has necessitated new operational and planning studies because of the high degree of uncertainty and variability of these sources. RES such as solar photovoltaic and wind generation are not dispatchable, and when there is excess energy supply during off-peak hours, RES curtailment is required to maintain the demand-supply balance. Furthermore, RES are intermittent resources which have introduced new challenges to the provision of ancillary services that are critical to maintaining the operational reliability of power systems. Energy storage systems (ESS) play a pivotal role in facilitating the integration of RES to mitigate the aforementioned issues. Therefore, there is a growing interest in recent years to examine the potential of ESS in the future electricity grids. This research focuses on developing market participation and investment planning frameworks for ESS considering different ownership structures. First, a novel stochastic planning framework is proposed to determine the optimal battery energy storage system (BESS) capacity and year of installation in an isolated microgrid using a novel representation of the BESS energy diagram. A decomposition-based approach is proposed to solve the problem of stochastic planning of BESS under uncertainty. The optimal decisions minimize the net present value of total expected costs over a multi-year horizon considering optimal BESS operation using a novel matrix representing BESS energy capacity degradation. The proposed approach is solved in two stages as mixed integer linear programming (MILP) problems; the optimal ratings of the BESS are determined in the first stage, while the optimal installation year is determined in the second stage. Extensive studies considering four types of BESS technologies for deterministic, Monte Carlo Simulations, and stochastic cases are presented to demonstrate the effectiveness of the proposed approach. The thesis further studies the investment decisions on BESS installations by a third-party investor in a microgrid. The optimal BESS power rating, energy capacity, and the year of installation are determined while maximizing the investor's profit and simultaneously minimizing the microgrid operational cost. The multi-objective problem is solved using a goal programming approach with a weight assigned to each objective. The BESS is modeled to participate in energy arbitrage and provision of operating reserves to the microgrid, considering its performance parameters and capacity degradation over the planning horizon. Finally, in the third problem addressed in the thesis in the context of electricity markets, the non-strategic and strategic participation of a pumped hydro energy storage (PHES) facility in day-ahead energy and performance-based regulation (PBR) markets, which includes regulation capacity and mileage, are examined. The PHES is modeled with the capability of operating in hydraulic short-circuit (HSC) mode with detailed representation of its operational constraints, and integrated with an energy-cum-PBR market clearing model. For its strategic participation, a bi-level market framework is proposed to determine the optimal offers and bids of the PHES that maximize its profit. The operation of PHES is modeled at the upper level, while the market clearing is modeled in the lower level problem. The bi-level problem is formulated as a mathematical programming with equilibrium constraints (MPEC) model, which is linearized and solved as an MILP problem. Several case studies are carried out to demonstrate the impact of PHES' non-strategic and strategic operations on market outcomes. Furthermore, stochastic case studies are conducted to determine the PHES strategies considering the uncertainty of the net demand and rivals' price and quantity offers
Financial viability and competitive balance in English football
Imperial Users onl